From 48f2ef0898137f6eabc8db62cbd8ad91011a584d Mon Sep 17 00:00:00 2001 From: andrei Date: Tue, 10 Feb 2026 17:36:35 +0100 Subject: [PATCH 001/205] bugfix: snapshots configuration --- local_dev/config_local_headstart.ini | 2 ++ server/preprocessing/conf/config.ini | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/local_dev/config_local_headstart.ini b/local_dev/config_local_headstart.ini index d1d7c8b11..7c5b58101 100644 --- a/local_dev/config_local_headstart.ini +++ b/local_dev/config_local_headstart.ini @@ -35,6 +35,8 @@ snapshot_php = "headstart/server/services/snapshot/headstart_snapshot.php" snapshot_local_protocol = "http://" # Size of thumbnail width snapshot_width = "1200px" +# chrome executable path optional, only needed if puppeteer cannot find chrome automatically +chrome_executable_path = "/path/to/chrome" [connection] # PostgreSQL database diff --git a/server/preprocessing/conf/config.ini b/server/preprocessing/conf/config.ini index 56de127b3..a4d503026 100644 --- a/server/preprocessing/conf/config.ini +++ b/server/preprocessing/conf/config.ini @@ -32,7 +32,7 @@ storage_path = "/path/to/storage/" snapshot_php = "server/services/snapshot/headstart_snapshot.php" # snapshot_local_protocol fallback for non-server environments snapshot_local_protocol = "http://" -# chrome executable path (optional, only needed if puppeteer cannot find chrome automatically) +# chrome executable path optional, only needed if puppeteer cannot find chrome automatically chrome_executable_path = "/path/to/chrome" From 1419b0370bdbe3fee24766bca33f4172397df3c7 Mon Sep 17 00:00:00 2001 From: andrei Date: Wed, 11 Feb 2026 09:17:35 +0100 Subject: [PATCH 002/205] feat: usage of dccollection --- server/preprocessing/other-scripts/base.R | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/server/preprocessing/other-scripts/base.R b/server/preprocessing/other-scripts/base.R index 01619adba..a9f609400 100644 --- a/server/preprocessing/other-scripts/base.R +++ b/server/preprocessing/other-scripts/base.R @@ -58,7 +58,7 @@ get_papers <- function(query, params, document_types = paste("dctypenorm:", "(", paste(params$document_types, collapse=" OR "), ")", sep="") sortby_string = ifelse(params$sorting == "most-recent", "dcyear desc", "") - return_fields <- "dcdocid,dctitle,dcdescription,dcsource,dcdate,dcsubject,dccreator,dclink,dcoa,dcidentifier,dcrelation,dctype,dctypenorm,dcprovider,dclang,dclanguage,dccoverage" + return_fields <- "dcdocid,dctitle,dcdescription,dcsource,dcdate,dcsubject,dccreator,dclink,dcoa,dcidentifier,dcrelation,dctype,dctypenorm,dcprovider,dclang,dclanguage,dccoverage,dccollection" if (!is.null(exact_query) && exact_query != '') { base_query <- paste(paste0("(",exact_query,")"), document_types, collapse=" ") @@ -281,6 +281,7 @@ etl <- function(res, repo, non_public) { metadata$language = check_metadata(res$dclanguage) metadata$content_provider = check_metadata(res$dcprovider) metadata$coverage = check_metadata(res$dccoverage) + metadata$collection = check_metadata(res$dccollection) if(repo=="fttriple" && non_public==TRUE) { metadata$content_provider <- "GoTriple" } @@ -406,5 +407,6 @@ fieldmapper <- list( "lang"="dclang", "language"="dclanguage", "content_provider"="dcprovider", - "coverage"="dccoverage" + "coverage"="dccoverage", + "collection"="dccollection" ) \ No newline at end of file From 6fd83b82812096d309baf4d21fbeaa99dcb41713 Mon Sep 17 00:00:00 2001 From: andrei Date: Wed, 11 Feb 2026 10:33:30 +0100 Subject: [PATCH 003/205] refactor: usage of is_anchor flag instead of keep and is_latest --- server/workers/base/src/base.py | 10 ++++---- server/workers/base/tests/unit/test_base.py | 5 ++-- server/workers/common/common/deduplication.py | 24 +++++++------------ 3 files changed, 14 insertions(+), 25 deletions(-) diff --git a/server/workers/base/src/base.py b/server/workers/base/src/base.py index a9b23c7f4..56b275b43 100644 --- a/server/workers/base/src/base.py +++ b/server/workers/base/src/base.py @@ -235,11 +235,10 @@ def handle_contentproviders(self, request_id, params): def filter_duplicates(df): df.drop_duplicates("id", inplace=True, keep="first") - df["is_latest"] = True + df["is_anchor"] = False df["doi_duplicate"] = False df["has_relations"] = False df["link_duplicate"] = False - df["keep"] = False df["duplicates"] = df.apply( lambda x: ",".join([x["id"], x["duplicates"]]) if len(x["duplicates"].split(",")) >= 1 @@ -266,17 +265,16 @@ def filter_duplicates(df): non_datasets = df.loc[df.index.difference(pure_datasets.index)] non_datasets = prioritize_OA_and_latest(non_datasets, dupind) pure_datasets = mark_latest_doi(pure_datasets, dupind) - filtered_non_datasets = non_datasets[non_datasets.is_latest == True] + filtered_non_datasets = non_datasets[non_datasets.is_anchor == True] filtered_datasets = pure_datasets[ - (pure_datasets.keep == True) | (pure_datasets.is_duplicate == False) + (pure_datasets.is_anchor == True) | (pure_datasets.is_duplicate == False) ] filtered = pd.concat([filtered_non_datasets, filtered_datasets]) filtered.sort_index(inplace=True) for c in [ "doi_duplicate", "link_duplicate", - "is_latest", - "keep", + "is_anchor", "duplicates", "doi_version", "unversioned_doi", diff --git a/server/workers/base/tests/unit/test_base.py b/server/workers/base/tests/unit/test_base.py index 675572a70..f77ce257e 100644 --- a/server/workers/base/tests/unit/test_base.py +++ b/server/workers/base/tests/unit/test_base.py @@ -193,8 +193,7 @@ def test_filter_duplicates(): # Add extra columns that filter_duplicates is supposed to drop. df["doi_duplicate"] = False df["link_duplicate"] = False - df["is_latest"] = True - df["keep"] = False + df["is_anchor"] = False df["doi_version"] = ["v1", "v1", "v2"] df["unversioned_doi"] = ["doi1", "doi1", "doi2"] df["publisher_doi"] = ["pub1", "pub1", "pub2"] @@ -203,7 +202,7 @@ def test_filter_duplicates(): filtered = filter_duplicates(df.copy()) # Verify that the dropped columns are not present. for col in [ - "doi_duplicate", "link_duplicate", "is_latest", "keep", + "doi_duplicate", "link_duplicate", "is_anchor", "doi_version", "unversioned_doi", "publisher_doi", "has_relations" ]: assert col not in filtered.columns diff --git a/server/workers/common/common/deduplication.py b/server/workers/common/common/deduplication.py index 84d5965cc..6838003d5 100644 --- a/server/workers/common/common/deduplication.py +++ b/server/workers/common/common/deduplication.py @@ -72,16 +72,13 @@ def remove_textual_duplicates_from_different_sources(df, dupind): if len(idx) > 1: tmp = df.loc[idx] df.loc[tmp.index, "is_duplicate"] = True - df.loc[tmp.index, "is_latest"] = False + df.loc[tmp.index, "is_anchor"] = False publisher_dois = list(filter(None, tmp.publisher_doi.unique().tolist())) if len(publisher_dois) > 0: # keep entry with doi - df.loc[idx, "keep"] = False - df.loc[tmp[tmp.publisher_doi!=""].index, "is_latest"] = True - df.loc[tmp[tmp.publisher_doi!=""].index, "keep"] = True + df.loc[tmp[tmp.publisher_doi!=""].index, "is_anchor"] = True else: - df.loc[tmp.sort_values(["doi", "year"], ascending=[False, False]).head(1).index, "is_latest"] = True - df.loc[tmp.sort_values(["doi", "year"], ascending=[False, False]).head(1).index, "keep"] = True + df.loc[tmp.sort_values(["doi", "year"], ascending=[False, False]).head(1).index, "is_anchor"] = True return df def mark_latest_doi(df, dupind): @@ -91,14 +88,12 @@ def mark_latest_doi(df, dupind): for udoi in list(filter(None, tmp.unversioned_doi.unique().tolist())): tmp2 = tmp[tmp.unversioned_doi == udoi] if len(tmp2) > 0: - df.loc[tmp2.index, "is_latest"] = False - df.loc[tmp2.index, "keep"] = False + df.loc[tmp2.index, "is_anchor"] = False versions = tmp2.id latest = tmp2.sort_values("doi_version", ascending=False).head(1).id v = [{"versions": versions.values.tolist(), "latest": latest.values.tolist()}]*len(tmp2) df.loc[versions.index, "versions"] = v - df.loc[latest.index, "is_latest"] = True - df.loc[latest.index, "keep"] = True + df.loc[latest.index, "is_anchor"] = True return df def prioritize_OA_and_latest(df, dupind): @@ -106,14 +101,11 @@ def prioritize_OA_and_latest(df, dupind): idx = df.index.intersection(idx) if len(idx) > 1: tmp = df.loc[idx] - df.loc[idx, "keep"] = False - df.loc[idx, "is_latest"] = False + df.loc[idx, "is_anchor"] = False if len(tmp[tmp.oa_state=="1"]) > 0: - df.loc[tmp[tmp.oa_state=="1"].sort_values("year", ascending=False).head(1).index, "keep"] = True - df.loc[tmp[tmp.oa_state=="1"].sort_values("year", ascending=False).head(1).index, "is_latest"] = True + df.loc[tmp[tmp.oa_state=="1"].sort_values("year", ascending=False).head(1).index, "is_anchor"] = True else: - df.loc[tmp.sort_values("year", ascending=False).head(1).index, "keep"] = True - df.loc[tmp.sort_values("year", ascending=False).head(1).index, "is_latest"] = True + df.loc[tmp.sort_values("year", ascending=False).head(1).index, "is_anchor"] = True return df def mark_duplicates(metadata): From f16c5fbc649f9582bc1e15048bf6cf888a4605d3 Mon Sep 17 00:00:00 2001 From: andrei Date: Wed, 11 Feb 2026 10:51:13 +0100 Subject: [PATCH 004/205] refactor: filtering non_datasets and pure_datasets the same way --- server/workers/base/src/base.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/server/workers/base/src/base.py b/server/workers/base/src/base.py index 56b275b43..114832281 100644 --- a/server/workers/base/src/base.py +++ b/server/workers/base/src/base.py @@ -265,12 +265,15 @@ def filter_duplicates(df): non_datasets = df.loc[df.index.difference(pure_datasets.index)] non_datasets = prioritize_OA_and_latest(non_datasets, dupind) pure_datasets = mark_latest_doi(pure_datasets, dupind) + + pure_datasets_condition_mask = (pure_datasets.is_anchor == True) | (pure_datasets.is_duplicate == False) + pure_datasets.loc[pure_datasets_condition_mask, "is_anchor"] = True + filtered_non_datasets = non_datasets[non_datasets.is_anchor == True] - filtered_datasets = pure_datasets[ - (pure_datasets.is_anchor == True) | (pure_datasets.is_duplicate == False) - ] + filtered_datasets = pure_datasets[pure_datasets.is_anchor == True] filtered = pd.concat([filtered_non_datasets, filtered_datasets]) filtered.sort_index(inplace=True) + for c in [ "doi_duplicate", "link_duplicate", @@ -284,6 +287,7 @@ def filter_duplicates(df): ]: if c in filtered.columns: filtered.drop(c, axis=1, inplace=True) + return filtered From 8b3d651b2085a6d9b165485ee3d868846ad1c3d3 Mon Sep 17 00:00:00 2001 From: andrei Date: Wed, 11 Feb 2026 14:03:02 +0100 Subject: [PATCH 005/205] feat: prioritize entries using doi and data provider --- server/workers/base/src/base.py | 2 + server/workers/common/common/deduplication.py | 52 ++++++++++++++++++- 2 files changed, 53 insertions(+), 1 deletion(-) diff --git a/server/workers/base/src/base.py b/server/workers/base/src/base.py index 114832281..e3ae7364f 100644 --- a/server/workers/base/src/base.py +++ b/server/workers/base/src/base.py @@ -17,6 +17,7 @@ remove_textual_duplicates_from_different_sources, mark_latest_doi, prioritize_OA_and_latest, + prioritize_doi_and_provider, ) import re @@ -264,6 +265,7 @@ def filter_duplicates(df): pure_datasets = df[df.typenorm == "7"] non_datasets = df.loc[df.index.difference(pure_datasets.index)] non_datasets = prioritize_OA_and_latest(non_datasets, dupind) + non_datasets = prioritize_doi_and_provider(non_datasets, dupind) pure_datasets = mark_latest_doi(pure_datasets, dupind) pure_datasets_condition_mask = (pure_datasets.is_anchor == True) | (pure_datasets.is_duplicate == False) diff --git a/server/workers/common/common/deduplication.py b/server/workers/common/common/deduplication.py index 6838003d5..10d618163 100644 --- a/server/workers/common/common/deduplication.py +++ b/server/workers/common/common/deduplication.py @@ -169,4 +169,54 @@ def compute_lv_matrix(titles, n): dist = Levenshtein.distance(titles[i], titles[j]) distance_matrix[i, j] = dist distance_matrix[j, i] = dist # Symmetric matrix - return distance_matrix \ No newline at end of file + return distance_matrix + +def prioritize_doi_and_provider(df, dupind): + for _, idx in dupind.items(): + idx = df.index.intersection(idx) + + if len(idx) <= 1: + continue + + tmp = df.loc[idx].copy() + + has_doi_and_collection = ( + tmp.doi.notna() & + (tmp.doi != "") & + tmp.collection.notna() & + (tmp.collection != "") + ) + + candidates = tmp[has_doi_and_collection] + + if len(candidates) == 0: + continue + + candidates = candidates.copy() + candidates["provider_priority"] = candidates.collection.map(get_provider_priority) + + max_priority = candidates["provider_priority"].max() + highest_priority_candidates = candidates[candidates["provider_priority"] == max_priority] + + if len(highest_priority_candidates) > 0: + anchor_idx = highest_priority_candidates.index[0] + df.loc[anchor_idx, "is_anchor"] = True + + other_idx = idx.difference([anchor_idx]) + df.loc[other_idx, "is_anchor"] = False + + return df + +def get_provider_priority(provider): + is_provider_not_available = pd.isna(provider) or provider == "" + if is_provider_not_available: + return -1 + + formatted_provider = str(provider).lower() + + if "ftdatacite" in formatted_provider: + return 1 + elif "cr" in formatted_provider: + return 2 + else: + return 0 \ No newline at end of file From 78ee23c8d14b3a6e9b1ac876870337961f8a1fb9 Mon Sep 17 00:00:00 2001 From: andrei Date: Wed, 11 Feb 2026 15:54:48 +0100 Subject: [PATCH 006/205] refactor: usage of doi and provider prioritization only for ORCID --- server/workers/base/src/base.py | 15 ++++++++++++--- server/workers/orcid/src/orcid_service.py | 1 + 2 files changed, 13 insertions(+), 3 deletions(-) diff --git a/server/workers/base/src/base.py b/server/workers/base/src/base.py index e3ae7364f..04cf76dd9 100644 --- a/server/workers/base/src/base.py +++ b/server/workers/base/src/base.py @@ -2,6 +2,7 @@ import json import subprocess import pandas as pd +import logging from common.r_wrapper import RWrapper from common.deduplication import ( find_version_in_doi, @@ -29,6 +30,7 @@ import sys from common.rate_limiter import RateLimiter +logger = logging.getLogger(__name__) class BaseClient(RWrapper): def __init__(self, *args): @@ -50,7 +52,10 @@ def next_item(self): message = json.loads(message.decode("utf-8")) request_id = message.get("id") params = self.add_default_params(message.get("params")) + original_service = params.get("original_service") params["service"] = "base" + if original_service: + params["original_service"] = original_service endpoint = message.get("endpoint") self.logger.debug(f"Request ID: {request_id}, Params: {params}, Endpoint: {endpoint}") return request_id, params, endpoint @@ -58,6 +63,7 @@ def next_item(self): def execute_search(self, params): q = params.get("q") service = params.get("service") + original_service = params.get("original_service", service) data = {} data["params"] = params cmd = [self.command, self.runner, self.wd, q, service] @@ -80,7 +86,7 @@ def execute_search(self, params): else: metadata = pd.DataFrame(raw_metadata) metadata = self.sanitize_metadata(metadata) - metadata = filter_duplicates(metadata) + metadata = filter_duplicates(metadata, original_service) metadata = pd.concat( [metadata, parse_annotations_for_all(metadata, "subject_orig")], axis=1, @@ -234,7 +240,7 @@ def handle_contentproviders(self, request_id, params): pattern_annotations = re.compile(r"([A-Za-z]+:[\w'\- ]+);?") -def filter_duplicates(df): +def filter_duplicates(df, service): df.drop_duplicates("id", inplace=True, keep="first") df["is_anchor"] = False df["doi_duplicate"] = False @@ -262,10 +268,13 @@ def filter_duplicates(df): df = remove_textual_duplicates_from_different_sources(df, dupind) df = add_false_negatives(df) df = mark_latest_doi(df, dupind) + pure_datasets = df[df.typenorm == "7"] non_datasets = df.loc[df.index.difference(pure_datasets.index)] + non_datasets = prioritize_OA_and_latest(non_datasets, dupind) - non_datasets = prioritize_doi_and_provider(non_datasets, dupind) + if service == "orcid": + non_datasets = prioritize_doi_and_provider(non_datasets, dupind) pure_datasets = mark_latest_doi(pure_datasets, dupind) pure_datasets_condition_mask = (pure_datasets.is_anchor == True) | (pure_datasets.is_duplicate == False) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 402b5b6ee..d9b728289 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -177,6 +177,7 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D 'today': '2024-10-21', 'unique_id': 'abf2625e2d84eb4367fb443e2cb6f4a1', 'service': 'base', + 'original_service': 'orcid', 'embed': 'false', 'vis_id': 'abf2625e2d84eb4367fb443e2cb6f4a1', 'limit': 120, From 45b483c85ffd99c0ba99031ac4d73798295b858e Mon Sep 17 00:00:00 2001 From: andrei Date: Wed, 11 Feb 2026 17:21:47 +0100 Subject: [PATCH 007/205] feat: subject_orig replacement --- server/workers/base/src/base.py | 6 +- server/workers/common/common/enrichment.py | 90 ++++++++++++++++++++++ 2 files changed, 95 insertions(+), 1 deletion(-) create mode 100644 server/workers/common/common/enrichment.py diff --git a/server/workers/base/src/base.py b/server/workers/base/src/base.py index 04cf76dd9..ab0c7b5f2 100644 --- a/server/workers/base/src/base.py +++ b/server/workers/base/src/base.py @@ -20,7 +20,7 @@ prioritize_OA_and_latest, prioritize_doi_and_provider, ) - +from common.enrichment import enrich_anchor_using_duplicates import re import time from parsers import improved_df_parsing @@ -280,6 +280,10 @@ def filter_duplicates(df, service): pure_datasets_condition_mask = (pure_datasets.is_anchor == True) | (pure_datasets.is_duplicate == False) pure_datasets.loc[pure_datasets_condition_mask, "is_anchor"] = True + if service == "orcid": + non_datasets = enrich_anchor_using_duplicates(non_datasets, dupind) + pure_datasets = enrich_anchor_using_duplicates(pure_datasets, dupind) + filtered_non_datasets = non_datasets[non_datasets.is_anchor == True] filtered_datasets = pure_datasets[pure_datasets.is_anchor == True] filtered = pd.concat([filtered_non_datasets, filtered_datasets]) diff --git a/server/workers/common/common/enrichment.py b/server/workers/common/common/enrichment.py new file mode 100644 index 000000000..7327f36b7 --- /dev/null +++ b/server/workers/common/common/enrichment.py @@ -0,0 +1,90 @@ +import pandas as pd + +def enrich_anchor_using_duplicates(df, dupind): + """ + Enriches anchor elements using data from duplicates in their groups. + + The function finds anchor elements (is_anchor=True) in duplicate groups and improves + their properties by copying the best values from duplicates in the group or merging + initial values with values from duplicates. + + List of improvements: + - subject_orig: replaced from a duplicate with the highest number of keywords + + Args: + df: DataFrame with metadata, containing the column is_anchor + dupind: Series with indices of duplicates for each id + + Returns: + DataFrame with improved anchor properties + """ + for _, idx in dupind.items(): + idx = df.index.intersection(idx) + + if len(idx) <= 1: + continue + + group_data = df.loc[idx] + + anchor_mask = group_data.is_anchor == True + anchors = group_data[anchor_mask] + + if len(anchors) > 0: + anchor = anchors.iloc[0] + anchor_idx = anchor.name + + df = replace_subjects(df, anchor_idx, idx) + + return df + +def replace_subjects(df, anchor_idx, group_indices): + """ + Replaces the subject_orig field of the anchor element with the best + value from the group of duplicates by the number of keywords. + Also updates the subject field for consistency. + + Finds the element with the highest number of keywords in the subject_orig field + (keywords separated by ";") and replaces the subject_orig of the anchor element. + Also updates the subject field for consistency. + + Args: + df: DataFrame with metadata + anchor_idx: Index of the anchor element in the DataFrame + group_indices: Indices of all elements in the group (including the anchor) + + Returns: + DataFrame with the updated subject_orig of the anchor element + """ + group_data = df.loc[group_indices] + + if 'subject_orig' not in group_data.columns: + return df + + best_subject_orig = None + best_count = 0 + best_idx = None + + for idx in group_indices: + subject_orig_value = group_data.loc[idx, 'subject_orig'] + + is_empty = pd.isna(subject_orig_value) or subject_orig_value == '' + if is_empty: + continue + + keywords = [kw.strip() for kw in str(subject_orig_value).split(';') if kw.strip()] + keyword_count = len(keywords) + if keyword_count > best_count: + best_count = keyword_count + best_subject_orig = subject_orig_value + best_idx = idx + + if best_subject_orig is not None and best_idx is not None: + current_subject_orig_in_anchor = df.loc[anchor_idx, 'subject_orig'] + + if pd.isna(current_subject_orig_in_anchor) or str(current_subject_orig_in_anchor) != str(best_subject_orig): + df.loc[anchor_idx, 'subject_orig'] = best_subject_orig + + if 'subject' in df.columns: + df.loc[anchor_idx, 'subject'] = best_subject_orig + + return df \ No newline at end of file From 84db5105a46c12a1f0f6db6c1d7e97fbc00c1c95 Mon Sep 17 00:00:00 2001 From: andrei Date: Wed, 11 Feb 2026 17:51:11 +0100 Subject: [PATCH 008/205] feat: paper_abstract replacement --- server/workers/common/common/enrichment.py | 51 ++++++++++++++++++++-- 1 file changed, 48 insertions(+), 3 deletions(-) diff --git a/server/workers/common/common/enrichment.py b/server/workers/common/common/enrichment.py index 7327f36b7..4fa03a407 100644 --- a/server/workers/common/common/enrichment.py +++ b/server/workers/common/common/enrichment.py @@ -10,6 +10,7 @@ def enrich_anchor_using_duplicates(df, dupind): List of improvements: - subject_orig: replaced from a duplicate with the highest number of keywords + - paper_abstract: replaced from a duplicate with the longest description Args: df: DataFrame with metadata, containing the column is_anchor @@ -34,6 +35,7 @@ def enrich_anchor_using_duplicates(df, dupind): anchor_idx = anchor.name df = replace_subjects(df, anchor_idx, idx) + df = replace_paper_abstract(df, anchor_idx, idx) return df @@ -62,7 +64,6 @@ def replace_subjects(df, anchor_idx, group_indices): best_subject_orig = None best_count = 0 - best_idx = None for idx in group_indices: subject_orig_value = group_data.loc[idx, 'subject_orig'] @@ -76,9 +77,8 @@ def replace_subjects(df, anchor_idx, group_indices): if keyword_count > best_count: best_count = keyword_count best_subject_orig = subject_orig_value - best_idx = idx - if best_subject_orig is not None and best_idx is not None: + if best_subject_orig is not None: current_subject_orig_in_anchor = df.loc[anchor_idx, 'subject_orig'] if pd.isna(current_subject_orig_in_anchor) or str(current_subject_orig_in_anchor) != str(best_subject_orig): @@ -87,4 +87,49 @@ def replace_subjects(df, anchor_idx, group_indices): if 'subject' in df.columns: df.loc[anchor_idx, 'subject'] = best_subject_orig + return df + +def replace_paper_abstract(df, anchor_idx, group_indices): + """ + Replaces the paper_abstract field of the anchor element with + the best value from the group of duplicates. + + Finds the element with the longest description in the paper_abstract field + and replaces the paper_abstract of the anchor element with this variant. + + Args: + df: DataFrame with metadata + anchor_idx: Index of the anchor element in the DataFrame + group_indices: Indices of all elements in the group (including the anchor) + + Returns: + DataFrame with the updated paper_abstract of the anchor element + """ + group_data = df.loc[group_indices] + + if 'paper_abstract' not in group_data.columns: + return df + + best_paper_abstract = None + best_length = 0 + + for idx in group_indices: + paper_abstract_value = group_data.loc[idx, 'paper_abstract'] + + if pd.isna(paper_abstract_value) or paper_abstract_value == '': + continue + + paper_abstract_str = str(paper_abstract_value) + abstract_length = len(paper_abstract_str) + + if abstract_length > best_length: + best_length = abstract_length + best_paper_abstract = paper_abstract_value + + if best_paper_abstract is not None: + current_paper_abstract_in_anchor = df.loc[anchor_idx, 'paper_abstract'] + + if pd.isna(current_paper_abstract_in_anchor) or str(current_paper_abstract_in_anchor) != str(best_paper_abstract): + df.loc[anchor_idx, 'paper_abstract'] = best_paper_abstract + return df \ No newline at end of file From 26541d02ac03eea90d23031058905e75b48251da Mon Sep 17 00:00:00 2001 From: andrei Date: Thu, 12 Feb 2026 09:48:48 +0100 Subject: [PATCH 009/205] feat: subject replacement --- server/workers/common/common/enrichment.py | 67 +++++++++++++--------- 1 file changed, 40 insertions(+), 27 deletions(-) diff --git a/server/workers/common/common/enrichment.py b/server/workers/common/common/enrichment.py index 4fa03a407..c2e4bf090 100644 --- a/server/workers/common/common/enrichment.py +++ b/server/workers/common/common/enrichment.py @@ -41,13 +41,12 @@ def enrich_anchor_using_duplicates(df, dupind): def replace_subjects(df, anchor_idx, group_indices): """ - Replaces the subject_orig field of the anchor element with the best - value from the group of duplicates by the number of keywords. - Also updates the subject field for consistency. + Replaces subject_orig and subject fields of the anchor element with the best + values from the group of duplicates by the number of keywords. - Finds the element with the highest number of keywords in the subject_orig field - (keywords separated by ";") and replaces the subject_orig of the anchor element. - Also updates the subject field for consistency. + Finds the element with the highest number of keywords in each field + (keywords separated by ";") and replaces the corresponding field of the anchor element. + Each field is searched independently to find the best value. Args: df: DataFrame with metadata @@ -55,37 +54,51 @@ def replace_subjects(df, anchor_idx, group_indices): group_indices: Indices of all elements in the group (including the anchor) Returns: - DataFrame with the updated subject_orig of the anchor element + DataFrame with the updated subject_orig and subject of the anchor element """ group_data = df.loc[group_indices] - if 'subject_orig' not in group_data.columns: + has_subject_orig = 'subject_orig' in group_data.columns + has_subject = 'subject' in group_data.columns + + if not has_subject_orig and not has_subject: return df best_subject_orig = None - best_count = 0 + best_subject_orig_count = 0 + best_subject = None + best_subject_count = 0 for idx in group_indices: - subject_orig_value = group_data.loc[idx, 'subject_orig'] - - is_empty = pd.isna(subject_orig_value) or subject_orig_value == '' - if is_empty: - continue - - keywords = [kw.strip() for kw in str(subject_orig_value).split(';') if kw.strip()] - keyword_count = len(keywords) - if keyword_count > best_count: - best_count = keyword_count - best_subject_orig = subject_orig_value - - if best_subject_orig is not None: - current_subject_orig_in_anchor = df.loc[anchor_idx, 'subject_orig'] - - if pd.isna(current_subject_orig_in_anchor) or str(current_subject_orig_in_anchor) != str(best_subject_orig): + if has_subject_orig: + subject_orig_value = group_data.loc[idx, 'subject_orig'] + if not (pd.isna(subject_orig_value) or subject_orig_value == ''): + keywords = [kw.strip() for kw in str(subject_orig_value).split(';') if kw.strip()] + keyword_count = len(keywords) + if keyword_count > best_subject_orig_count: + best_subject_orig_count = keyword_count + best_subject_orig = subject_orig_value + + if has_subject: + subject_value = group_data.loc[idx, 'subject'] + if not (pd.isna(subject_value) or subject_value == ''): + keywords = [kw.strip() for kw in str(subject_value).split(';') if kw.strip()] + keyword_count = len(keywords) + if keyword_count > best_subject_count: + best_subject_count = keyword_count + best_subject = subject_value + + is_best_subject_orig = best_subject_orig is not None + if is_best_subject_orig: + current_subject_orig = df.loc[anchor_idx, 'subject_orig'] + if pd.isna(current_subject_orig) or str(current_subject_orig) != str(best_subject_orig): df.loc[anchor_idx, 'subject_orig'] = best_subject_orig - if 'subject' in df.columns: - df.loc[anchor_idx, 'subject'] = best_subject_orig + is_best_subject = best_subject is not None + if is_best_subject: + current_subject = df.loc[anchor_idx, 'subject'] + if pd.isna(current_subject) or str(current_subject) != str(best_subject): + df.loc[anchor_idx, 'subject'] = best_subject return df From b9219f449fd6f8c319bf6cf31b709da047bce535 Mon Sep 17 00:00:00 2001 From: andrei Date: Thu, 12 Feb 2026 10:26:16 +0100 Subject: [PATCH 010/205] refactor: group all enrichments in one cycle --- server/workers/common/common/enrichment.py | 191 ++++++++------------- 1 file changed, 74 insertions(+), 117 deletions(-) diff --git a/server/workers/common/common/enrichment.py b/server/workers/common/common/enrichment.py index c2e4bf090..c27ca97f5 100644 --- a/server/workers/common/common/enrichment.py +++ b/server/workers/common/common/enrichment.py @@ -5,12 +5,13 @@ def enrich_anchor_using_duplicates(df, dupind): Enriches anchor elements using data from duplicates in their groups. The function finds anchor elements (is_anchor=True) in duplicate groups and improves - their properties by copying the best values from duplicates in the group or merging - initial values with values from duplicates. + their properties by copying the best values from duplicates in the group. + All improvements are done in a single pass through the group for efficiency. List of improvements: - - subject_orig: replaced from a duplicate with the highest number of keywords - - paper_abstract: replaced from a duplicate with the longest description + - subject_orig: replaced with value having the highest number of keywords + - subject: replaced with value having the highest number of keywords + - paper_abstract: replaced with the longest description Args: df: DataFrame with metadata, containing the column is_anchor @@ -19,10 +20,19 @@ def enrich_anchor_using_duplicates(df, dupind): Returns: DataFrame with improved anchor properties """ + has_subject_orig = 'subject_orig' in df.columns + has_subject = 'subject' in df.columns + has_paper_abstract = 'paper_abstract' in df.columns + + is_all_columns_are_missing = not has_subject_orig and not has_subject and not has_paper_abstract + if is_all_columns_are_missing: + return df + for _, idx in dupind.items(): idx = df.index.intersection(idx) - if len(idx) <= 1: + is_group_has_only_one_element = len(idx) <= 1 + if is_group_has_only_one_element: continue group_data = df.loc[idx] @@ -30,119 +40,66 @@ def enrich_anchor_using_duplicates(df, dupind): anchor_mask = group_data.is_anchor == True anchors = group_data[anchor_mask] - if len(anchors) > 0: - anchor = anchors.iloc[0] - anchor_idx = anchor.name - - df = replace_subjects(df, anchor_idx, idx) - df = replace_paper_abstract(df, anchor_idx, idx) - - return df - -def replace_subjects(df, anchor_idx, group_indices): - """ - Replaces subject_orig and subject fields of the anchor element with the best - values from the group of duplicates by the number of keywords. - - Finds the element with the highest number of keywords in each field - (keywords separated by ";") and replaces the corresponding field of the anchor element. - Each field is searched independently to find the best value. - - Args: - df: DataFrame with metadata - anchor_idx: Index of the anchor element in the DataFrame - group_indices: Indices of all elements in the group (including the anchor) - - Returns: - DataFrame with the updated subject_orig and subject of the anchor element - """ - group_data = df.loc[group_indices] - - has_subject_orig = 'subject_orig' in group_data.columns - has_subject = 'subject' in group_data.columns - - if not has_subject_orig and not has_subject: - return df - - best_subject_orig = None - best_subject_orig_count = 0 - best_subject = None - best_subject_count = 0 - - for idx in group_indices: - if has_subject_orig: - subject_orig_value = group_data.loc[idx, 'subject_orig'] - if not (pd.isna(subject_orig_value) or subject_orig_value == ''): - keywords = [kw.strip() for kw in str(subject_orig_value).split(';') if kw.strip()] - keyword_count = len(keywords) - if keyword_count > best_subject_orig_count: - best_subject_orig_count = keyword_count - best_subject_orig = subject_orig_value - - if has_subject: - subject_value = group_data.loc[idx, 'subject'] - if not (pd.isna(subject_value) or subject_value == ''): - keywords = [kw.strip() for kw in str(subject_value).split(';') if kw.strip()] - keyword_count = len(keywords) - if keyword_count > best_subject_count: - best_subject_count = keyword_count - best_subject = subject_value - - is_best_subject_orig = best_subject_orig is not None - if is_best_subject_orig: - current_subject_orig = df.loc[anchor_idx, 'subject_orig'] - if pd.isna(current_subject_orig) or str(current_subject_orig) != str(best_subject_orig): - df.loc[anchor_idx, 'subject_orig'] = best_subject_orig - - is_best_subject = best_subject is not None - if is_best_subject: - current_subject = df.loc[anchor_idx, 'subject'] - if pd.isna(current_subject) or str(current_subject) != str(best_subject): - df.loc[anchor_idx, 'subject'] = best_subject - - return df - -def replace_paper_abstract(df, anchor_idx, group_indices): - """ - Replaces the paper_abstract field of the anchor element with - the best value from the group of duplicates. - - Finds the element with the longest description in the paper_abstract field - and replaces the paper_abstract of the anchor element with this variant. - - Args: - df: DataFrame with metadata - anchor_idx: Index of the anchor element in the DataFrame - group_indices: Indices of all elements in the group (including the anchor) - - Returns: - DataFrame with the updated paper_abstract of the anchor element - """ - group_data = df.loc[group_indices] - - if 'paper_abstract' not in group_data.columns: - return df - - best_paper_abstract = None - best_length = 0 - - for idx in group_indices: - paper_abstract_value = group_data.loc[idx, 'paper_abstract'] - - if pd.isna(paper_abstract_value) or paper_abstract_value == '': + is_no_anchors = len(anchors) == 0 + if is_no_anchors: continue - paper_abstract_str = str(paper_abstract_value) - abstract_length = len(paper_abstract_str) - - if abstract_length > best_length: - best_length = abstract_length - best_paper_abstract = paper_abstract_value - - if best_paper_abstract is not None: - current_paper_abstract_in_anchor = df.loc[anchor_idx, 'paper_abstract'] - - if pd.isna(current_paper_abstract_in_anchor) or str(current_paper_abstract_in_anchor) != str(best_paper_abstract): - df.loc[anchor_idx, 'paper_abstract'] = best_paper_abstract + anchor = anchors.iloc[0] + anchor_idx = anchor.name + + best_subject_orig = None + best_subject_orig_count = 0 + best_subject = None + best_subject_count = 0 + best_paper_abstract = None + best_paper_abstract_length = 0 + + for element_idx in idx: + if has_subject_orig: + subject_orig_value = group_data.loc[element_idx, 'subject_orig'] + is_subject_orig_value_is_missing = pd.isna(subject_orig_value) or subject_orig_value == '' + if not is_subject_orig_value_is_missing: + keywords = [kw.strip() for kw in str(subject_orig_value).split(';') if kw.strip()] + keyword_count = len(keywords) + if keyword_count > best_subject_orig_count: + best_subject_orig_count = keyword_count + best_subject_orig = subject_orig_value + + if has_subject: + subject_value = group_data.loc[element_idx, 'subject'] + is_subject_value_is_missing = pd.isna(subject_value) or subject_value == '' + if not is_subject_value_is_missing: + keywords = [kw.strip() for kw in str(subject_value).split(';') if kw.strip()] + keyword_count = len(keywords) + if keyword_count > best_subject_count: + best_subject_count = keyword_count + best_subject = subject_value + + if has_paper_abstract: + paper_abstract_value = group_data.loc[element_idx, 'paper_abstract'] + is_paper_abstract_value_is_missing = pd.isna(paper_abstract_value) or paper_abstract_value == '' + if not is_paper_abstract_value_is_missing: + abstract_length = len(str(paper_abstract_value)) + if abstract_length > best_paper_abstract_length: + best_paper_abstract_length = abstract_length + best_paper_abstract = paper_abstract_value + + is_better_subject_orig_presented = best_subject_orig is not None + if is_better_subject_orig_presented: + current = df.loc[anchor_idx, 'subject_orig'] + if pd.isna(current) or str(current) != str(best_subject_orig): + df.loc[anchor_idx, 'subject_orig'] = best_subject_orig + + is_better_subject_presented = best_subject is not None + if is_better_subject_presented: + current = df.loc[anchor_idx, 'subject'] + if pd.isna(current) or str(current) != str(best_subject): + df.loc[anchor_idx, 'subject'] = best_subject + + is_better_paper_abstract_presented = best_paper_abstract is not None + if is_better_paper_abstract_presented: + current = df.loc[anchor_idx, 'paper_abstract'] + if pd.isna(current) or str(current) != str(best_paper_abstract): + df.loc[anchor_idx, 'paper_abstract'] = best_paper_abstract return df \ No newline at end of file From 814be7648b01b44bbf101ee4f9ed03061521c454 Mon Sep 17 00:00:00 2001 From: andrei Date: Thu, 12 Feb 2026 10:53:42 +0100 Subject: [PATCH 011/205] feat: native merging strategy for subject and subject_orig --- server/workers/common/common/enrichment.py | 86 +++++++++++++++------- 1 file changed, 58 insertions(+), 28 deletions(-) diff --git a/server/workers/common/common/enrichment.py b/server/workers/common/common/enrichment.py index c27ca97f5..ad4f85597 100644 --- a/server/workers/common/common/enrichment.py +++ b/server/workers/common/common/enrichment.py @@ -1,6 +1,9 @@ import pandas as pd -def enrich_anchor_using_duplicates(df, dupind): +STRATEGY_REPLACE = 'replace' +STRATEGY_MERGE = 'merge' + +def enrich_anchor_using_duplicates(df, dupind, subject_strategy=STRATEGY_MERGE): """ Enriches anchor elements using data from duplicates in their groups. @@ -9,13 +12,16 @@ def enrich_anchor_using_duplicates(df, dupind): All improvements are done in a single pass through the group for efficiency. List of improvements: - - subject_orig: replaced with value having the highest number of keywords - - subject: replaced with value having the highest number of keywords + - subject_orig: processed according to subject_strategy + - subject: processed according to subject_strategy - paper_abstract: replaced with the longest description Args: df: DataFrame with metadata, containing the column is_anchor dupind: Series with indices of duplicates for each id + subject_strategy: Strategy for processing keywords ('replace' or 'merge') + - 'replace': select the value with the highest number of keywords + - 'merge': combine all keywords from the group, removing duplicates Returns: DataFrame with improved anchor properties @@ -28,6 +34,8 @@ def enrich_anchor_using_duplicates(df, dupind): if is_all_columns_are_missing: return df + is_use_merge_strategy = subject_strategy == STRATEGY_MERGE + for _, idx in dupind.items(): idx = df.index.intersection(idx) @@ -47,54 +55,76 @@ def enrich_anchor_using_duplicates(df, dupind): anchor = anchors.iloc[0] anchor_idx = anchor.name + all_subject_orig_keywords = set() + all_subject_keywords = set() + best_subject_orig = None best_subject_orig_count = 0 best_subject = None best_subject_count = 0 + best_paper_abstract = None best_paper_abstract_length = 0 for element_idx in idx: if has_subject_orig: subject_orig_value = group_data.loc[element_idx, 'subject_orig'] - is_subject_orig_value_is_missing = pd.isna(subject_orig_value) or subject_orig_value == '' - if not is_subject_orig_value_is_missing: + is_not_empty = not (pd.isna(subject_orig_value) or subject_orig_value == '') + if is_not_empty: keywords = [kw.strip() for kw in str(subject_orig_value).split(';') if kw.strip()] - keyword_count = len(keywords) - if keyword_count > best_subject_orig_count: - best_subject_orig_count = keyword_count - best_subject_orig = subject_orig_value + if is_use_merge_strategy: + all_subject_orig_keywords.update(keywords) + else: + keyword_count = len(keywords) + if keyword_count > best_subject_orig_count: + best_subject_orig_count = keyword_count + best_subject_orig = subject_orig_value if has_subject: subject_value = group_data.loc[element_idx, 'subject'] - is_subject_value_is_missing = pd.isna(subject_value) or subject_value == '' - if not is_subject_value_is_missing: + is_not_empty = not (pd.isna(subject_value) or subject_value == '') + if is_not_empty: keywords = [kw.strip() for kw in str(subject_value).split(';') if kw.strip()] - keyword_count = len(keywords) - if keyword_count > best_subject_count: - best_subject_count = keyword_count - best_subject = subject_value + if is_use_merge_strategy: + all_subject_keywords.update(keywords) + else: + keyword_count = len(keywords) + if keyword_count > best_subject_count: + best_subject_count = keyword_count + best_subject = subject_value if has_paper_abstract: paper_abstract_value = group_data.loc[element_idx, 'paper_abstract'] - is_paper_abstract_value_is_missing = pd.isna(paper_abstract_value) or paper_abstract_value == '' - if not is_paper_abstract_value_is_missing: + is_not_empty = not (pd.isna(paper_abstract_value) or paper_abstract_value == '') + if is_not_empty: abstract_length = len(str(paper_abstract_value)) if abstract_length > best_paper_abstract_length: best_paper_abstract_length = abstract_length best_paper_abstract = paper_abstract_value - is_better_subject_orig_presented = best_subject_orig is not None - if is_better_subject_orig_presented: - current = df.loc[anchor_idx, 'subject_orig'] - if pd.isna(current) or str(current) != str(best_subject_orig): - df.loc[anchor_idx, 'subject_orig'] = best_subject_orig - - is_better_subject_presented = best_subject is not None - if is_better_subject_presented: - current = df.loc[anchor_idx, 'subject'] - if pd.isna(current) or str(current) != str(best_subject): - df.loc[anchor_idx, 'subject'] = best_subject + if has_subject_orig: + if is_use_merge_strategy: + if all_subject_orig_keywords: + merged_value = '; '.join(sorted(all_subject_orig_keywords)) + df.loc[anchor_idx, 'subject_orig'] = merged_value + else: + is_better_subject_orig_presented = best_subject_orig is not None + if is_better_subject_orig_presented: + current = df.loc[anchor_idx, 'subject_orig'] + if pd.isna(current) or str(current) != str(best_subject_orig): + df.loc[anchor_idx, 'subject_orig'] = best_subject_orig + + if has_subject: + if is_use_merge_strategy: + if all_subject_keywords: + merged_value = '; '.join(sorted(all_subject_keywords)) + df.loc[anchor_idx, 'subject'] = merged_value + else: + is_better_subject_presented = best_subject is not None + if is_better_subject_presented: + current = df.loc[anchor_idx, 'subject'] + if pd.isna(current) or str(current) != str(best_subject): + df.loc[anchor_idx, 'subject'] = best_subject is_better_paper_abstract_presented = best_paper_abstract is not None if is_better_paper_abstract_presented: From c4ad1fe14a76ff51a4ae637b5722ca99a1aa6b04 Mon Sep 17 00:00:00 2001 From: andrei Date: Thu, 12 Feb 2026 14:43:51 +0100 Subject: [PATCH 012/205] bugfix: dates in the request to base --- server/workers/orcid/src/orcid_service.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index d9b728289..9260df0cf 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -16,6 +16,7 @@ from model import AuthorInfo, SuccessResult, ErrorResult from dataclasses import asdict import time +from datetime import datetime def remove_doi_prefix(doi): if pd.isna(doi) or doi == '': # Handle NaN, None, or empty strings @@ -171,10 +172,10 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D 'document_types': ['4', '11', '111', '13', '16', '7', '5', '12', '121', '122', '17', '19', '3', '52', '2', 'F', '1A', '14', '15', '6', '51', '1', '18', '181', '183', '182'], 'min_descsize': '0', 'from': '1665-01-01', - 'to': '2024-10-21', + 'to': datetime.now().strftime('%Y-%m-%d'), # ? is that a good idea to pass here empty query? 'q': '', - 'today': '2024-10-21', + 'today': datetime.now().strftime('%Y-%m-%d'), 'unique_id': 'abf2625e2d84eb4367fb443e2cb6f4a1', 'service': 'base', 'original_service': 'orcid', From 4cc4681ee7764d7b8a3c861bdab042e57e5d8a82 Mon Sep 17 00:00:00 2001 From: andrei Date: Thu, 12 Feb 2026 14:44:34 +0100 Subject: [PATCH 013/205] bugfix: limit in the request to base --- server/workers/orcid/src/orcid_service.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 9260df0cf..dca6157c9 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -181,7 +181,7 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D 'original_service': 'orcid', 'embed': 'false', 'vis_id': 'abf2625e2d84eb4367fb443e2cb6f4a1', - 'limit': 120, + 'limit': 240, 'list_size': 100, 'deduplicate_base': 'false' }, From f4ae820f729bce91871865fe280d6f50aabde451 Mon Sep 17 00:00:00 2001 From: andrei Date: Thu, 12 Feb 2026 15:19:31 +0100 Subject: [PATCH 014/205] feat: merging keywords with usage of RapidFuzz library --- server/workers/base/requirements.txt | 1 + server/workers/common/common/enrichment.py | 52 ++++++++++++++++++++-- 2 files changed, 50 insertions(+), 3 deletions(-) diff --git a/server/workers/base/requirements.txt b/server/workers/base/requirements.txt index d461b1714..8dbed7936 100644 --- a/server/workers/base/requirements.txt +++ b/server/workers/base/requirements.txt @@ -7,6 +7,7 @@ importlib-metadata==4.8.3 keyring==10.6.0 keyrings.alt==3.0 Levenshtein==0.21.1 +rapidfuzz==3.9.7 numpy==1.19.5 packaging==21.3 pandas==1.1.5 diff --git a/server/workers/common/common/enrichment.py b/server/workers/common/common/enrichment.py index ad4f85597..2df12f3a5 100644 --- a/server/workers/common/common/enrichment.py +++ b/server/workers/common/common/enrichment.py @@ -1,8 +1,11 @@ import pandas as pd +from rapidfuzz import fuzz STRATEGY_REPLACE = 'replace' STRATEGY_MERGE = 'merge' +KEYWORD_SIMILARITY_THRESHOLD = 85 + def enrich_anchor_using_duplicates(df, dupind, subject_strategy=STRATEGY_MERGE): """ Enriches anchor elements using data from duplicates in their groups. @@ -105,7 +108,8 @@ def enrich_anchor_using_duplicates(df, dupind, subject_strategy=STRATEGY_MERGE): if has_subject_orig: if is_use_merge_strategy: if all_subject_orig_keywords: - merged_value = '; '.join(sorted(all_subject_orig_keywords)) + unique_keywords = deduplicate_keywords(all_subject_orig_keywords) + merged_value = '; '.join(sorted(unique_keywords)) df.loc[anchor_idx, 'subject_orig'] = merged_value else: is_better_subject_orig_presented = best_subject_orig is not None @@ -117,7 +121,8 @@ def enrich_anchor_using_duplicates(df, dupind, subject_strategy=STRATEGY_MERGE): if has_subject: if is_use_merge_strategy: if all_subject_keywords: - merged_value = '; '.join(sorted(all_subject_keywords)) + unique_keywords = deduplicate_keywords(all_subject_keywords) + merged_value = '; '.join(sorted(unique_keywords)) df.loc[anchor_idx, 'subject'] = merged_value else: is_better_subject_presented = best_subject is not None @@ -132,4 +137,45 @@ def enrich_anchor_using_duplicates(df, dupind, subject_strategy=STRATEGY_MERGE): if pd.isna(current) or str(current) != str(best_paper_abstract): df.loc[anchor_idx, 'paper_abstract'] = best_paper_abstract - return df \ No newline at end of file + return df + +def deduplicate_keywords(keywords, similarity_threshold=KEYWORD_SIMILARITY_THRESHOLD): + """ + Removes similar keywords from the list, leaving only unique. + + Uses RapidFuzz for fuzzy string comparison. If two keywords + are similar more than threshold%, the longer variant is kept. + + Examples of duplicates that will be recognized: + - "ME CFS", "ME/CFS", "ME-CFS" + - "chronic fatigue", "Chronic Fatigue" + + Args: + keywords: Set or list of keywords + similarity_threshold: Threshold for similarity (0-100), above which words are considered duplicates + + Returns: + List of unique keywords + """ + if not keywords: + return [] + + keywords_list = list(keywords) + unique_keywords = [] + + for keyword in keywords_list: + is_duplicate = False + + for i, existing in enumerate(unique_keywords): + similarity = fuzz.token_sort_ratio(keyword.lower(), existing.lower()) + + is_similar = similarity >= similarity_threshold + if is_similar: + is_duplicate = True + if len(keyword) > len(existing): + unique_keywords[i] = keyword + + if not is_duplicate: + unique_keywords.append(keyword) + + return unique_keywords \ No newline at end of file From 8fb46f06d4b1c96b8a17b4197c78c2d69c9b75b3 Mon Sep 17 00:00:00 2001 From: andrei Date: Thu, 12 Feb 2026 16:45:57 +0100 Subject: [PATCH 015/205] feat: finding best OA status --- server/workers/common/common/enrichment.py | 29 +++++++++++++++++++++- 1 file changed, 28 insertions(+), 1 deletion(-) diff --git a/server/workers/common/common/enrichment.py b/server/workers/common/common/enrichment.py index 2df12f3a5..eb80b391c 100644 --- a/server/workers/common/common/enrichment.py +++ b/server/workers/common/common/enrichment.py @@ -6,6 +6,12 @@ KEYWORD_SIMILARITY_THRESHOLD = 85 +OA_STATE_PRIORITY = { + "1": 0, # yes + "0": 1, # no + "2": 2, # unknown +} + def enrich_anchor_using_duplicates(df, dupind, subject_strategy=STRATEGY_MERGE): """ Enriches anchor elements using data from duplicates in their groups. @@ -18,6 +24,7 @@ def enrich_anchor_using_duplicates(df, dupind, subject_strategy=STRATEGY_MERGE): - subject_orig: processed according to subject_strategy - subject: processed according to subject_strategy - paper_abstract: replaced with the longest description + - oa_state: replaced with the highest priority status (yes > no > unknown) Args: df: DataFrame with metadata, containing the column is_anchor @@ -32,8 +39,9 @@ def enrich_anchor_using_duplicates(df, dupind, subject_strategy=STRATEGY_MERGE): has_subject_orig = 'subject_orig' in df.columns has_subject = 'subject' in df.columns has_paper_abstract = 'paper_abstract' in df.columns + has_oa_state = 'oa_state' in df.columns - is_all_columns_are_missing = not has_subject_orig and not has_subject and not has_paper_abstract + is_all_columns_are_missing = not has_subject_orig and not has_subject and not has_paper_abstract and not has_oa_state if is_all_columns_are_missing: return df @@ -69,6 +77,9 @@ def enrich_anchor_using_duplicates(df, dupind, subject_strategy=STRATEGY_MERGE): best_paper_abstract = None best_paper_abstract_length = 0 + best_oa_state = None + best_oa_state_priority = float('inf') + for element_idx in idx: if has_subject_orig: subject_orig_value = group_data.loc[element_idx, 'subject_orig'] @@ -105,6 +116,16 @@ def enrich_anchor_using_duplicates(df, dupind, subject_strategy=STRATEGY_MERGE): best_paper_abstract_length = abstract_length best_paper_abstract = paper_abstract_value + if has_oa_state: + oa_state_value = group_data.loc[element_idx, 'oa_state'] + is_not_empty = not pd.isna(oa_state_value) + if is_not_empty: + oa_state_str = str(oa_state_value) + priority = OA_STATE_PRIORITY.get(oa_state_str, float('inf')) + if priority < best_oa_state_priority: + best_oa_state_priority = priority + best_oa_state = oa_state_value + if has_subject_orig: if is_use_merge_strategy: if all_subject_orig_keywords: @@ -137,6 +158,12 @@ def enrich_anchor_using_duplicates(df, dupind, subject_strategy=STRATEGY_MERGE): if pd.isna(current) or str(current) != str(best_paper_abstract): df.loc[anchor_idx, 'paper_abstract'] = best_paper_abstract + is_better_oa_state_presented = best_oa_state is not None + if is_better_oa_state_presented: + current = df.loc[anchor_idx, 'oa_state'] + if pd.isna(current) or str(current) != str(best_oa_state): + df.loc[anchor_idx, 'oa_state'] = best_oa_state + return df def deduplicate_keywords(keywords, similarity_threshold=KEYWORD_SIMILARITY_THRESHOLD): From d0a099f7589e7339674acc318cd2026bd1531d27 Mon Sep 17 00:00:00 2001 From: andrei Date: Fri, 13 Feb 2026 13:10:14 +0100 Subject: [PATCH 016/205] feat: link merging --- server/workers/common/common/enrichment.py | 78 +++++++++++++++++++++- 1 file changed, 76 insertions(+), 2 deletions(-) diff --git a/server/workers/common/common/enrichment.py b/server/workers/common/common/enrichment.py index eb80b391c..6c5d4dd56 100644 --- a/server/workers/common/common/enrichment.py +++ b/server/workers/common/common/enrichment.py @@ -1,5 +1,6 @@ import pandas as pd from rapidfuzz import fuzz +from urllib.parse import urlparse STRATEGY_REPLACE = 'replace' STRATEGY_MERGE = 'merge' @@ -25,6 +26,7 @@ def enrich_anchor_using_duplicates(df, dupind, subject_strategy=STRATEGY_MERGE): - subject: processed according to subject_strategy - paper_abstract: replaced with the longest description - oa_state: replaced with the highest priority status (yes > no > unknown) + - link: merged from all duplicates, duplicates links are removed (https > http) Args: df: DataFrame with metadata, containing the column is_anchor @@ -40,8 +42,9 @@ def enrich_anchor_using_duplicates(df, dupind, subject_strategy=STRATEGY_MERGE): has_subject = 'subject' in df.columns has_paper_abstract = 'paper_abstract' in df.columns has_oa_state = 'oa_state' in df.columns + has_link = 'link' in df.columns - is_all_columns_are_missing = not has_subject_orig and not has_subject and not has_paper_abstract and not has_oa_state + is_all_columns_are_missing = not has_subject_orig and not has_subject and not has_paper_abstract and not has_oa_state and not has_link if is_all_columns_are_missing: return df @@ -80,6 +83,8 @@ def enrich_anchor_using_duplicates(df, dupind, subject_strategy=STRATEGY_MERGE): best_oa_state = None best_oa_state_priority = float('inf') + all_links = set() + for element_idx in idx: if has_subject_orig: subject_orig_value = group_data.loc[element_idx, 'subject_orig'] @@ -126,6 +131,13 @@ def enrich_anchor_using_duplicates(df, dupind, subject_strategy=STRATEGY_MERGE): best_oa_state_priority = priority best_oa_state = oa_state_value + if has_link: + link_value = group_data.loc[element_idx, 'link'] + is_not_empty = not (pd.isna(link_value) or link_value == '') + if is_not_empty: + links = [link.strip() for link in str(link_value).split(';') if link.strip()] + all_links.update(links) + if has_subject_orig: if is_use_merge_strategy: if all_subject_orig_keywords: @@ -164,6 +176,13 @@ def enrich_anchor_using_duplicates(df, dupind, subject_strategy=STRATEGY_MERGE): if pd.isna(current) or str(current) != str(best_oa_state): df.loc[anchor_idx, 'oa_state'] = best_oa_state + if has_link: + if all_links: + unique_links = deduplicate_links(all_links) + if unique_links: + merged_links = '; '.join(sorted(unique_links)) + df.loc[anchor_idx, 'link'] = merged_links + return df def deduplicate_keywords(keywords, similarity_threshold=KEYWORD_SIMILARITY_THRESHOLD): @@ -205,4 +224,59 @@ def deduplicate_keywords(keywords, similarity_threshold=KEYWORD_SIMILARITY_THRES if not is_duplicate: unique_keywords.append(keyword) - return unique_keywords \ No newline at end of file + return unique_keywords + +def deduplicate_links(links): + """ + Removes duplicates links from the list, considering the difference in protocols. + + If the same link appears with http and https, the https version is kept. + Other duplicates are also removed. + + Args: + links: List or set of links + + Returns: + List of unique links (https versions are preferred) + """ + if not links: + return [] + + normalized_to_link = {} + invalid_urls = set() + + for link in links: + link_str = str(link).strip() + if not link_str: + continue + + try: + parsed = urlparse(link_str) + protocol = parsed.scheme.lower() + + if not protocol: + if link_str.startswith('//'): + link_str = 'http:' + link_str + parsed = urlparse(link_str) + protocol = parsed.scheme.lower() + else: + invalid_urls.add(link_str) + continue + + normalized = f"{parsed.netloc}{parsed.path}{parsed.params}{parsed.query}{parsed.fragment}" + + if normalized in normalized_to_link: + existing_link = normalized_to_link[normalized] + existing_protocol = urlparse(existing_link).scheme.lower() + + if protocol == 'https' and existing_protocol == 'http': + normalized_to_link[normalized] = link_str + elif protocol == 'http' and existing_protocol == 'https': + continue + else: + normalized_to_link[normalized] = link_str + except Exception: + invalid_urls.add(link_str) + + result = list(normalized_to_link.values()) + list(invalid_urls) + return result \ No newline at end of file From 4556aff0094db35a283663169ea8f9775bfa729f Mon Sep 17 00:00:00 2001 From: andrei Date: Fri, 13 Feb 2026 13:29:26 +0100 Subject: [PATCH 017/205] refactor: code clean up --- server/workers/common/common/enrichment.py | 267 ++++++++++++++------- 1 file changed, 177 insertions(+), 90 deletions(-) diff --git a/server/workers/common/common/enrichment.py b/server/workers/common/common/enrichment.py index 6c5d4dd56..7e1876f56 100644 --- a/server/workers/common/common/enrichment.py +++ b/server/workers/common/common/enrichment.py @@ -69,122 +69,209 @@ def enrich_anchor_using_duplicates(df, dupind, subject_strategy=STRATEGY_MERGE): anchor = anchors.iloc[0] anchor_idx = anchor.name - all_subject_orig_keywords = set() - all_subject_keywords = set() - - best_subject_orig = None - best_subject_orig_count = 0 - best_subject = None - best_subject_count = 0 - - best_paper_abstract = None - best_paper_abstract_length = 0 - - best_oa_state = None - best_oa_state_priority = float('inf') - + subject_orig_acc = {'all_keywords': set(), 'best_value': None, 'best_count': 0} + subject_acc = {'all_keywords': set(), 'best_value': None, 'best_count': 0} + paper_abstract_acc = {'best_value': None, 'best_length': 0} + oa_state_acc = {'best_value': None, 'best_priority': float('inf')} all_links = set() for element_idx in idx: if has_subject_orig: subject_orig_value = group_data.loc[element_idx, 'subject_orig'] - is_not_empty = not (pd.isna(subject_orig_value) or subject_orig_value == '') - if is_not_empty: - keywords = [kw.strip() for kw in str(subject_orig_value).split(';') if kw.strip()] - if is_use_merge_strategy: - all_subject_orig_keywords.update(keywords) - else: - keyword_count = len(keywords) - if keyword_count > best_subject_orig_count: - best_subject_orig_count = keyword_count - best_subject_orig = subject_orig_value + process_subject_orig_element(subject_orig_value, subject_orig_acc, is_use_merge_strategy) if has_subject: subject_value = group_data.loc[element_idx, 'subject'] - is_not_empty = not (pd.isna(subject_value) or subject_value == '') - if is_not_empty: - keywords = [kw.strip() for kw in str(subject_value).split(';') if kw.strip()] - if is_use_merge_strategy: - all_subject_keywords.update(keywords) - else: - keyword_count = len(keywords) - if keyword_count > best_subject_count: - best_subject_count = keyword_count - best_subject = subject_value + process_subject_element(subject_value, subject_acc, is_use_merge_strategy) if has_paper_abstract: paper_abstract_value = group_data.loc[element_idx, 'paper_abstract'] - is_not_empty = not (pd.isna(paper_abstract_value) or paper_abstract_value == '') - if is_not_empty: - abstract_length = len(str(paper_abstract_value)) - if abstract_length > best_paper_abstract_length: - best_paper_abstract_length = abstract_length - best_paper_abstract = paper_abstract_value + process_paper_abstract_element(paper_abstract_value, paper_abstract_acc) if has_oa_state: oa_state_value = group_data.loc[element_idx, 'oa_state'] - is_not_empty = not pd.isna(oa_state_value) - if is_not_empty: - oa_state_str = str(oa_state_value) - priority = OA_STATE_PRIORITY.get(oa_state_str, float('inf')) - if priority < best_oa_state_priority: - best_oa_state_priority = priority - best_oa_state = oa_state_value + process_oa_state_element(oa_state_value, oa_state_acc) if has_link: link_value = group_data.loc[element_idx, 'link'] - is_not_empty = not (pd.isna(link_value) or link_value == '') - if is_not_empty: - links = [link.strip() for link in str(link_value).split(';') if link.strip()] - all_links.update(links) + process_link_element(link_value, all_links) if has_subject_orig: - if is_use_merge_strategy: - if all_subject_orig_keywords: - unique_keywords = deduplicate_keywords(all_subject_orig_keywords) - merged_value = '; '.join(sorted(unique_keywords)) - df.loc[anchor_idx, 'subject_orig'] = merged_value - else: - is_better_subject_orig_presented = best_subject_orig is not None - if is_better_subject_orig_presented: - current = df.loc[anchor_idx, 'subject_orig'] - if pd.isna(current) or str(current) != str(best_subject_orig): - df.loc[anchor_idx, 'subject_orig'] = best_subject_orig + apply_subject_improvements(df, anchor_idx, subject_orig_acc, is_use_merge_strategy, 'subject_orig') if has_subject: - if is_use_merge_strategy: - if all_subject_keywords: - unique_keywords = deduplicate_keywords(all_subject_keywords) - merged_value = '; '.join(sorted(unique_keywords)) - df.loc[anchor_idx, 'subject'] = merged_value - else: - is_better_subject_presented = best_subject is not None - if is_better_subject_presented: - current = df.loc[anchor_idx, 'subject'] - if pd.isna(current) or str(current) != str(best_subject): - df.loc[anchor_idx, 'subject'] = best_subject - - is_better_paper_abstract_presented = best_paper_abstract is not None - if is_better_paper_abstract_presented: - current = df.loc[anchor_idx, 'paper_abstract'] - if pd.isna(current) or str(current) != str(best_paper_abstract): - df.loc[anchor_idx, 'paper_abstract'] = best_paper_abstract - - is_better_oa_state_presented = best_oa_state is not None - if is_better_oa_state_presented: - current = df.loc[anchor_idx, 'oa_state'] - if pd.isna(current) or str(current) != str(best_oa_state): - df.loc[anchor_idx, 'oa_state'] = best_oa_state + apply_subject_improvements(df, anchor_idx, subject_acc, is_use_merge_strategy, 'subject') + + if has_paper_abstract: + apply_paper_abstract_improvements(df, anchor_idx, paper_abstract_acc) + + if has_oa_state: + apply_oa_state_improvements(df, anchor_idx, oa_state_acc) if has_link: - if all_links: - unique_links = deduplicate_links(all_links) - if unique_links: - merged_links = '; '.join(sorted(unique_links)) - df.loc[anchor_idx, 'link'] = merged_links + apply_link_improvements(df, anchor_idx, all_links) return df +def process_subject_orig_element(value, accumulator, use_merge_strategy): + """ + Processes the subject_orig value for one element of the group. + + Args: + value: The subject_orig value from the element + accumulator: Dictionary with accumulative data + use_merge_strategy: If True, uses the merge strategy, otherwise replace + """ + is_not_empty = not (pd.isna(value) or value == '') + if not is_not_empty: + return + + keywords = [kw.strip() for kw in str(value).split(';') if kw.strip()] + if use_merge_strategy: + accumulator['all_keywords'].update(keywords) + else: + keyword_count = len(keywords) + if keyword_count > accumulator['best_count']: + accumulator['best_count'] = keyword_count + accumulator['best_value'] = value + +def process_subject_element(value, accumulator, use_merge_strategy): + """ + Processes the subject value for one element of the group. + + Args: + value: The subject value from the element + accumulator: Dictionary with accumulative data + use_merge_strategy: If True, uses the merge strategy, otherwise replace + """ + is_not_empty = not (pd.isna(value) or value == '') + if not is_not_empty: + return + + keywords = [kw.strip() for kw in str(value).split(';') if kw.strip()] + if use_merge_strategy: + accumulator['all_keywords'].update(keywords) + else: + keyword_count = len(keywords) + if keyword_count > accumulator['best_count']: + accumulator['best_count'] = keyword_count + accumulator['best_value'] = value + +def process_paper_abstract_element(value, accumulator): + """ + Processes the paper_abstract value for one element of the group. + + Args: + value: The paper_abstract value from the element + accumulator: Dictionary with accumulative data + """ + is_not_empty = not (pd.isna(value) or value == '') + if not is_not_empty: + return + + abstract_length = len(str(value)) + if abstract_length > accumulator['best_length']: + accumulator['best_length'] = abstract_length + accumulator['best_value'] = value + +def process_oa_state_element(value, accumulator): + """ + Processes the oa_state value for one element of the group. + + Args: + value: The oa_state value from the element + accumulator: Dictionary with accumulative data + """ + is_not_empty = not pd.isna(value) + if not is_not_empty: + return + + oa_state_str = str(value) + priority = OA_STATE_PRIORITY.get(oa_state_str, float('inf')) + if priority < accumulator['best_priority']: + accumulator['best_priority'] = priority + accumulator['best_value'] = value + +def process_link_element(value, accumulator): + """ + Processes the link value for one element of the group. + + Args: + value: The link value from the element + accumulator: Set to collect all links + """ + is_not_empty = not (pd.isna(value) or value == '') + if not is_not_empty: + return + + links = [link.strip() for link in str(value).split(';') if link.strip()] + accumulator.update(links) + +def apply_subject_improvements(df, anchor_idx, accumulator, use_merge_strategy, column_name): + """ + Applies improvements for subject or subject_orig to the anchor element. + + Args: + df: DataFrame with data + anchor_idx: Index of the anchor element + accumulator: Dictionary with accumulative data + use_merge_strategy: If True, uses the merge strategy, otherwise replace + column_name: Column name ('subject' or 'subject_orig') + """ + if use_merge_strategy: + if accumulator['all_keywords']: + unique_keywords = deduplicate_keywords(accumulator['all_keywords']) + merged_value = '; '.join(sorted(unique_keywords)) + df.loc[anchor_idx, column_name] = merged_value + else: + if accumulator['best_value'] is not None: + current = df.loc[anchor_idx, column_name] + if pd.isna(current) or str(current) != str(accumulator['best_value']): + df.loc[anchor_idx, column_name] = accumulator['best_value'] + +def apply_paper_abstract_improvements(df, anchor_idx, accumulator): + """ + Applies improvements for paper_abstract to the anchor element. + + Args: + df: DataFrame with data + anchor_idx: Index of the anchor element + accumulator: Dictionary with accumulative data + """ + if accumulator['best_value'] is not None: + current = df.loc[anchor_idx, 'paper_abstract'] + if pd.isna(current) or str(current) != str(accumulator['best_value']): + df.loc[anchor_idx, 'paper_abstract'] = accumulator['best_value'] + +def apply_oa_state_improvements(df, anchor_idx, accumulator): + """ + Applies improvements for oa_state to the anchor element. + + Args: + df: DataFrame with data + anchor_idx: Index of the anchor element + accumulator: Dictionary with accumulative data + """ + if accumulator['best_value'] is not None: + current = df.loc[anchor_idx, 'oa_state'] + if pd.isna(current) or str(current) != str(accumulator['best_value']): + df.loc[anchor_idx, 'oa_state'] = accumulator['best_value'] + +def apply_link_improvements(df, anchor_idx, all_links): + """ + Applies improvements for link to the anchor element. + + Args: + df: DataFrame with data + anchor_idx: Index of the anchor element + all_links: Set of all collected links + """ + if all_links: + unique_links = deduplicate_links(all_links) + if unique_links: + merged_links = '; '.join(sorted(unique_links)) + df.loc[anchor_idx, 'link'] = merged_links + def deduplicate_keywords(keywords, similarity_threshold=KEYWORD_SIMILARITY_THRESHOLD): """ Removes similar keywords from the list, leaving only unique. From ef58c5751f6e1e930a014df53e522c35d82554dd Mon Sep 17 00:00:00 2001 From: andrei Date: Fri, 13 Feb 2026 13:37:58 +0100 Subject: [PATCH 018/205] refactor: deduplication functions moved to deduplication.py --- server/workers/common/common/deduplication.py | 100 ++++++++++++++++- server/workers/common/common/enrichment.py | 101 +----------------- 2 files changed, 101 insertions(+), 100 deletions(-) diff --git a/server/workers/common/common/deduplication.py b/server/workers/common/common/deduplication.py index 10d618163..00f03d349 100644 --- a/server/workers/common/common/deduplication.py +++ b/server/workers/common/common/deduplication.py @@ -2,6 +2,8 @@ import numpy as np import pandas as pd import Levenshtein +from rapidfuzz import fuzz +from urllib.parse import urlparse pattern_doi = re.compile(r"\.v(\d)+$") @@ -219,4 +221,100 @@ def get_provider_priority(provider): elif "cr" in formatted_provider: return 2 else: - return 0 \ No newline at end of file + return 0 + +def deduplicate_keywords(keywords, similarity_threshold): + """ + Removes similar keywords from the list, leaving only unique. + + Uses RapidFuzz for fuzzy string comparison. If two keywords + are similar more than threshold%, the longer variant is kept. + + Examples of duplicates that will be recognized: + - "ME CFS", "ME/CFS", "ME-CFS" + - "chronic fatigue", "Chronic Fatigue" + + Args: + keywords: Set or list of keywords + similarity_threshold: Threshold for similarity (0-100), above which words are considered duplicates + + Returns: + List of unique keywords + """ + if not keywords: + return [] + + keywords_list = list(keywords) + unique_keywords = [] + + for keyword in keywords_list: + is_duplicate = False + + for i, existing in enumerate(unique_keywords): + similarity = fuzz.token_sort_ratio(keyword.lower(), existing.lower()) + + is_similar = similarity >= similarity_threshold + if is_similar: + is_duplicate = True + if len(keyword) > len(existing): + unique_keywords[i] = keyword + + if not is_duplicate: + unique_keywords.append(keyword) + + return unique_keywords + +def deduplicate_links(links): + """ + Removes duplicates links from the list, considering the difference in protocols. + + If the same link appears with http and https, the https version is kept. + Other duplicates are also removed. + + Args: + links: List or set of links + + Returns: + List of unique links (https versions are preferred) + """ + if not links: + return [] + + normalized_to_link = {} + invalid_urls = set() + + for link in links: + link_str = str(link).strip() + if not link_str: + continue + + try: + parsed = urlparse(link_str) + protocol = parsed.scheme.lower() + + if not protocol: + if link_str.startswith('//'): + link_str = 'http:' + link_str + parsed = urlparse(link_str) + protocol = parsed.scheme.lower() + else: + invalid_urls.add(link_str) + continue + + normalized = f"{parsed.netloc}{parsed.path}{parsed.params}{parsed.query}{parsed.fragment}" + + if normalized in normalized_to_link: + existing_link = normalized_to_link[normalized] + existing_protocol = urlparse(existing_link).scheme.lower() + + if protocol == 'https' and existing_protocol == 'http': + normalized_to_link[normalized] = link_str + elif protocol == 'http' and existing_protocol == 'https': + continue + else: + normalized_to_link[normalized] = link_str + except Exception: + invalid_urls.add(link_str) + + result = list(normalized_to_link.values()) + list(invalid_urls) + return result \ No newline at end of file diff --git a/server/workers/common/common/enrichment.py b/server/workers/common/common/enrichment.py index 7e1876f56..6463d7dcd 100644 --- a/server/workers/common/common/enrichment.py +++ b/server/workers/common/common/enrichment.py @@ -1,6 +1,5 @@ import pandas as pd -from rapidfuzz import fuzz -from urllib.parse import urlparse +from common.deduplication import deduplicate_keywords, deduplicate_links STRATEGY_REPLACE = 'replace' STRATEGY_MERGE = 'merge' @@ -220,7 +219,7 @@ def apply_subject_improvements(df, anchor_idx, accumulator, use_merge_strategy, """ if use_merge_strategy: if accumulator['all_keywords']: - unique_keywords = deduplicate_keywords(accumulator['all_keywords']) + unique_keywords = deduplicate_keywords(accumulator['all_keywords'], KEYWORD_SIMILARITY_THRESHOLD) merged_value = '; '.join(sorted(unique_keywords)) df.loc[anchor_idx, column_name] = merged_value else: @@ -271,99 +270,3 @@ def apply_link_improvements(df, anchor_idx, all_links): if unique_links: merged_links = '; '.join(sorted(unique_links)) df.loc[anchor_idx, 'link'] = merged_links - -def deduplicate_keywords(keywords, similarity_threshold=KEYWORD_SIMILARITY_THRESHOLD): - """ - Removes similar keywords from the list, leaving only unique. - - Uses RapidFuzz for fuzzy string comparison. If two keywords - are similar more than threshold%, the longer variant is kept. - - Examples of duplicates that will be recognized: - - "ME CFS", "ME/CFS", "ME-CFS" - - "chronic fatigue", "Chronic Fatigue" - - Args: - keywords: Set or list of keywords - similarity_threshold: Threshold for similarity (0-100), above which words are considered duplicates - - Returns: - List of unique keywords - """ - if not keywords: - return [] - - keywords_list = list(keywords) - unique_keywords = [] - - for keyword in keywords_list: - is_duplicate = False - - for i, existing in enumerate(unique_keywords): - similarity = fuzz.token_sort_ratio(keyword.lower(), existing.lower()) - - is_similar = similarity >= similarity_threshold - if is_similar: - is_duplicate = True - if len(keyword) > len(existing): - unique_keywords[i] = keyword - - if not is_duplicate: - unique_keywords.append(keyword) - - return unique_keywords - -def deduplicate_links(links): - """ - Removes duplicates links from the list, considering the difference in protocols. - - If the same link appears with http and https, the https version is kept. - Other duplicates are also removed. - - Args: - links: List or set of links - - Returns: - List of unique links (https versions are preferred) - """ - if not links: - return [] - - normalized_to_link = {} - invalid_urls = set() - - for link in links: - link_str = str(link).strip() - if not link_str: - continue - - try: - parsed = urlparse(link_str) - protocol = parsed.scheme.lower() - - if not protocol: - if link_str.startswith('//'): - link_str = 'http:' + link_str - parsed = urlparse(link_str) - protocol = parsed.scheme.lower() - else: - invalid_urls.add(link_str) - continue - - normalized = f"{parsed.netloc}{parsed.path}{parsed.params}{parsed.query}{parsed.fragment}" - - if normalized in normalized_to_link: - existing_link = normalized_to_link[normalized] - existing_protocol = urlparse(existing_link).scheme.lower() - - if protocol == 'https' and existing_protocol == 'http': - normalized_to_link[normalized] = link_str - elif protocol == 'http' and existing_protocol == 'https': - continue - else: - normalized_to_link[normalized] = link_str - except Exception: - invalid_urls.add(link_str) - - result = list(normalized_to_link.values()) + list(invalid_urls) - return result \ No newline at end of file From c380e29394315011c6c951371fc3b49b47c4d845 Mon Sep 17 00:00:00 2001 From: andrei Date: Fri, 13 Feb 2026 15:36:09 +0100 Subject: [PATCH 019/205] refactor: subject strategy from envs --- docker-compose.yml | 1 + server/workers/common/common/enrichment.py | 5 ++++- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/docker-compose.yml b/docker-compose.yml index 16336b40d..c941f95fb 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -161,6 +161,7 @@ services: RENV_PATHS_CACHE: /renv/cache PYTHONIOENCODING: "utf-8" R_BASE_APIKEY: "${R_BASE_APIKEY}" + ENRICHMENT_STRATEGY_FOR_SUBJECT: "${ENRICHMENT_STRATEGY_FOR_SUBJECT}" restart: unless-stopped volumes: - ./local_dev/renv/cache:/renv/cache diff --git a/server/workers/common/common/enrichment.py b/server/workers/common/common/enrichment.py index 6463d7dcd..df58da470 100644 --- a/server/workers/common/common/enrichment.py +++ b/server/workers/common/common/enrichment.py @@ -1,6 +1,9 @@ +import os import pandas as pd from common.deduplication import deduplicate_keywords, deduplicate_links +ENRICHMENT_STRATEGY_FOR_SUBJECT = os.getenv("ENRICHMENT_STRATEGY_FOR_SUBJECT") + STRATEGY_REPLACE = 'replace' STRATEGY_MERGE = 'merge' @@ -12,7 +15,7 @@ "2": 2, # unknown } -def enrich_anchor_using_duplicates(df, dupind, subject_strategy=STRATEGY_MERGE): +def enrich_anchor_using_duplicates(df, dupind, subject_strategy=ENRICHMENT_STRATEGY_FOR_SUBJECT): """ Enriches anchor elements using data from duplicates in their groups. From f073cfa63a3f3e149eb384b401e6c616413ba1c0 Mon Sep 17 00:00:00 2001 From: andrei Date: Mon, 16 Feb 2026 09:28:44 +0100 Subject: [PATCH 020/205] bugfix: some entries are not enriched --- server/workers/orcid/src/orcid_service.py | 26 +++++++++++++++++------ 1 file changed, 20 insertions(+), 6 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index dca6157c9..a8c75f897 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -148,8 +148,7 @@ def log_dataframe(self, df: pd.DataFrame, params: Dict[str, str], name: str, ): def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.DataFrame: orcid = params.get('orcid') - - batch_size = 25 + batch_size = 10 batches = [dois[i:i + batch_size] for i in range(0, len(dois), batch_size)] base_metadata = pd.DataFrame(dtype=object) @@ -202,9 +201,15 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D base_response: str = get_nested_value(result, ["input_data", "metadata"], '[]') # type: ignore + batch_df = pd.DataFrame(json.loads(base_response)) + if len(batch_df) < len(batch): + self.logger.warning( + f"BASE response shortfall: requested {len(batch)} DOIs, received {len(batch_df)} rows (limit or missing records?)" + ) + base_metadata = pd.concat([ - base_metadata, - pd.DataFrame(json.loads(base_response) ) + base_metadata, + batch_df ], ignore_index=True) timing_df = pd.DataFrame(timing_data) @@ -214,10 +219,10 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D timing_df.to_csv(f'{folder}/stat_base_requests.csv', index=False) return base_metadata - + def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFrame) -> pd.DataFrame: self.logger.debug(f"Enriching metadata with base for ORCID {params.get('orcid')}") - + original_columns = metadata.columns.to_list() required_fields = ['id', 'identifier', 'relevance', 'relation', 'title', 'subtitle', 'doi', 'paper_abstract', 'link', 'subject', 'oa_state', 'subject_orig', 'published_in', @@ -249,6 +254,15 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra self.logger.debug(f"Dois to search in base: {dois}") base_metadata = self.request_base_metadata(dois, params) + received = len(base_metadata) + requested = len(dois) + if received < requested: + self.logger.warning( + f"BASE enrichment shortfall: requested {requested} DOIs, received {received} rows ({requested - received} missing)" + ) + else: + self.logger.debug(f"BASE response: {received} rows for {requested} requested DOIs") + base_metadata = base_metadata.reindex(columns=required_fields) base_metadata.loc[:, 'doi'] = base_metadata['doi'].apply(remove_doi_prefix) From e1207ad1c30142b595e7272449e3b7ebb38e904f Mon Sep 17 00:00:00 2001 From: andrei Date: Mon, 16 Feb 2026 12:27:30 +0100 Subject: [PATCH 021/205] bugfix: mark the only entry in a group as is_anchor --- server/workers/base/src/base.py | 1 + 1 file changed, 1 insertion(+) diff --git a/server/workers/base/src/base.py b/server/workers/base/src/base.py index ab0c7b5f2..d9277d19b 100644 --- a/server/workers/base/src/base.py +++ b/server/workers/base/src/base.py @@ -268,6 +268,7 @@ def filter_duplicates(df, service): df = remove_textual_duplicates_from_different_sources(df, dupind) df = add_false_negatives(df) df = mark_latest_doi(df, dupind) + df.loc[df[~df.is_duplicate].index, "is_anchor"] = True pure_datasets = df[df.typenorm == "7"] non_datasets = df.loc[df.index.difference(pure_datasets.index)] From a90ec0fc1f098b83cf4200fd6884ee96615dcdce Mon Sep 17 00:00:00 2001 From: andrei Date: Tue, 17 Feb 2026 15:49:17 +0100 Subject: [PATCH 022/205] feat: DOIs preparation before the BASE request --- server/workers/orcid/src/orcid_service.py | 43 +++++++++++++++++++++-- 1 file changed, 41 insertions(+), 2 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index a8c75f897..5e1b20720 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -220,6 +220,45 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D return base_metadata + def _prepare_dois_for_base_query(self, dois: List[str]) -> List[str]: + """ + Prepare DOI list for BASE query by adding lowercase variants for DOIs containing uppercase letters. + + For each DOI that contains uppercase letters, this function adds a lowercase version + to ensure case-insensitive matching in BASE search. + + Parameters: + - dois: List of original DOIs from ORCID + + Returns: + - List of DOIs including originals and lowercase variants (without duplicates) + """ + self.logger.debug(f"Original DOIs from ORCID: {dois}") # TODO: remove after implementation and testing + + dois_for_base_query = [] + doi_mapping = {} + + for doi in dois: + dois_for_base_query.append(doi) + + if doi != doi.lower(): + lowercase_doi = doi.lower() + dois_for_base_query.append(lowercase_doi) + + if lowercase_doi not in doi_mapping: + doi_mapping[lowercase_doi] = [] + + doi_mapping[lowercase_doi].append(doi) + self.logger.debug(f"Added lowercase version for DOI: '{doi}' -> '{lowercase_doi}'") # TODO: remove after implementation and testing + + dois_for_base_query = list(dict.fromkeys(dois_for_base_query)) + + # TODO: remove after implementation and testing + self.logger.info(f"DOIs to search in BASE (original + lowercase variants): {dois_for_base_query}") + self.logger.info(f"Total DOIs for BASE query: {len(dois_for_base_query)} (original: {len(dois)}, added lowercase variants: {len(dois_for_base_query) - len(dois)})") + + return dois_for_base_query + def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFrame) -> pd.DataFrame: self.logger.debug(f"Enriching metadata with base for ORCID {params.get('orcid')}") @@ -251,9 +290,9 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra raw_dois = metadata["doi"].tolist() dois = [doi for doi in raw_dois if doi and pd.notna(doi)] - self.logger.debug(f"Dois to search in base: {dois}") + dois_for_base_query = self._prepare_dois_for_base_query(dois) - base_metadata = self.request_base_metadata(dois, params) + base_metadata = self.request_base_metadata(dois_for_base_query, params) received = len(base_metadata) requested = len(dois) if received < requested: From 9459892c8b2ed5fd59e86be5a7f9c6009f0af50c Mon Sep 17 00:00:00 2001 From: andrei Date: Tue, 17 Feb 2026 17:15:03 +0100 Subject: [PATCH 023/205] feat: mapping DOIs back to initial case --- server/workers/orcid/src/orcid_service.py | 59 ++++++++++++++++++++--- 1 file changed, 51 insertions(+), 8 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 5e1b20720..9f9809d84 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -220,7 +220,7 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D return base_metadata - def _prepare_dois_for_base_query(self, dois: List[str]) -> List[str]: + def _prepare_dois_for_base_query(self, dois: List[str]) -> Tuple[List[str], Dict[str, List[str]]]: """ Prepare DOI list for BASE query by adding lowercase variants for DOIs containing uppercase letters. @@ -231,9 +231,10 @@ def _prepare_dois_for_base_query(self, dois: List[str]) -> List[str]: - dois: List of original DOIs from ORCID Returns: - - List of DOIs including originals and lowercase variants (without duplicates) + - Tuple of (list of DOIs including originals and lowercase variants, mapping from lowercase DOI to original DOIs) """ - self.logger.debug(f"Original DOIs from ORCID: {dois}") # TODO: remove after implementation and testing + # TODO: remove after implementation and testing + self.logger.debug(f"Original DOIs from ORCID: {dois}") dois_for_base_query = [] doi_mapping = {} @@ -249,15 +250,54 @@ def _prepare_dois_for_base_query(self, dois: List[str]) -> List[str]: doi_mapping[lowercase_doi] = [] doi_mapping[lowercase_doi].append(doi) - self.logger.debug(f"Added lowercase version for DOI: '{doi}' -> '{lowercase_doi}'") # TODO: remove after implementation and testing + # TODO: remove after implementation and testing + self.logger.debug(f"Added lowercase version for DOI: '{doi}' -> '{lowercase_doi}'") dois_for_base_query = list(dict.fromkeys(dois_for_base_query)) # TODO: remove after implementation and testing - self.logger.info(f"DOIs to search in BASE (original + lowercase variants): {dois_for_base_query}") - self.logger.info(f"Total DOIs for BASE query: {len(dois_for_base_query)} (original: {len(dois)}, added lowercase variants: {len(dois_for_base_query) - len(dois)})") + self.logger.debug(f"DOIs to search in BASE (original + lowercase variants): {dois_for_base_query}") + self.logger.debug(f"Total DOIs for BASE query: {len(dois_for_base_query)} (original: {len(dois)}, added lowercase variants: {len(dois_for_base_query) - len(dois)})") + + return dois_for_base_query, doi_mapping + + def _normalize_base_results_to_original_dois( + self, + base_metadata: pd.DataFrame, + doi_mapping: Dict[str, List[str]] + ) -> pd.DataFrame: + """ + Normalize DOI values in BASE results to match original DOIs from ORCID. + + If BASE returns results with lowercase DOI variants, this function maps them back + to the original DOI format from ORCID to ensure proper merging. + + Parameters: + - base_metadata: DataFrame with results from BASE + - doi_mapping: Mapping from lowercase DOI to list of original DOIs + + Returns: + - DataFrame with normalized DOI values + """ + if base_metadata.empty: + return base_metadata + + def normalize_doi(doi_value): + if pd.isna(doi_value) or doi_value == '': + return doi_value + + if doi_value in doi_mapping: + original_dois_for_variant = doi_mapping[doi_value] + # TODO: remove after implementation and testing + self.logger.debug(f"Normalized DOI from BASE: '{doi_value}' -> '{original_dois_for_variant}'") + return original_dois_for_variant[0] + + return doi_value - return dois_for_base_query + base_metadata = base_metadata.copy() + base_metadata.loc[:, 'doi'] = base_metadata['doi'].apply(normalize_doi) + + return base_metadata def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFrame) -> pd.DataFrame: self.logger.debug(f"Enriching metadata with base for ORCID {params.get('orcid')}") @@ -290,7 +330,7 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra raw_dois = metadata["doi"].tolist() dois = [doi for doi in raw_dois if doi and pd.notna(doi)] - dois_for_base_query = self._prepare_dois_for_base_query(dois) + dois_for_base_query, doi_mapping = self._prepare_dois_for_base_query(dois) base_metadata = self.request_base_metadata(dois_for_base_query, params) received = len(base_metadata) @@ -307,6 +347,9 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra # Remove rows where 'doi' is pd.NaN base_metadata = base_metadata[pd.notna(base_metadata['doi'])] + + base_metadata = self._normalize_base_results_to_original_dois(base_metadata, doi_mapping) + base_metadata = base_metadata[base_metadata['doi'].isin(dois)] base_metadata = base_metadata.drop_duplicates(subset='doi', keep='first') From 4dd20faa92f4165728209ae7e8393fa1d2b8c2b2 Mon Sep 17 00:00:00 2001 From: chreman Date: Wed, 18 Feb 2026 00:40:51 +0100 Subject: [PATCH 024/205] doi metadata bugfix --- server/preprocessing/other-scripts/base.R | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/server/preprocessing/other-scripts/base.R b/server/preprocessing/other-scripts/base.R index a9f609400..eeb9db88c 100644 --- a/server/preprocessing/other-scripts/base.R +++ b/server/preprocessing/other-scripts/base.R @@ -58,7 +58,7 @@ get_papers <- function(query, params, document_types = paste("dctypenorm:", "(", paste(params$document_types, collapse=" OR "), ")", sep="") sortby_string = ifelse(params$sorting == "most-recent", "dcyear desc", "") - return_fields <- "dcdocid,dctitle,dcdescription,dcsource,dcdate,dcsubject,dccreator,dclink,dcoa,dcidentifier,dcrelation,dctype,dctypenorm,dcprovider,dclang,dclanguage,dccoverage,dccollection" + return_fields <- "dcdocid,dctitle,dcdescription,dcsource,dcdate,dcsubject,dccreator,dclink,dcoa,dcidentifier,dcrelation,dctype,dctypenorm,dcprovider,dclang,dclanguage,dccoverage,dccollection,dcdoi" if (!is.null(exact_query) && exact_query != '') { base_query <- paste(paste0("(",exact_query,")"), document_types, collapse=" ") @@ -276,7 +276,7 @@ etl <- function(res, repo, non_public) { metadata$resulttype = lapply(res$dctypenorm, decode_dctypenorm) metadata$type = check_metadata(res$dctype) metadata$typenorm = check_metadata(res$dctypenorm) - metadata$doi = unlist(lapply(metadata$link, find_dois)) + metadata$doi = check_metadata(res$dcdoi) metadata$lang = check_metadata(res$dclang) metadata$language = check_metadata(res$dclanguage) metadata$content_provider = check_metadata(res$dcprovider) From 692d4ec4cbf1cc49795aeb450bd8192218d83bb3 Mon Sep 17 00:00:00 2001 From: chreman Date: Wed, 18 Feb 2026 11:13:15 +0100 Subject: [PATCH 025/205] updated query to include docs without date --- server/workers/orcid/src/orcid_service.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 9f9809d84..b7e427c94 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -182,7 +182,8 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D 'vis_id': 'abf2625e2d84eb4367fb443e2cb6f4a1', 'limit': 240, 'list_size': 100, - 'deduplicate_base': 'false' + 'deduplicate_base': 'false', + 'exclude_date_filters': 'true' }, "endpoint": "search" } From dbd95dc40cd71dc8fb4e3c183055524e467e25f6 Mon Sep 17 00:00:00 2001 From: chreman Date: Wed, 18 Feb 2026 11:18:44 +0100 Subject: [PATCH 026/205] increased limits and size of possible return set --- server/workers/orcid/src/orcid_service.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index b7e427c94..aa464eadd 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -180,8 +180,8 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D 'original_service': 'orcid', 'embed': 'false', 'vis_id': 'abf2625e2d84eb4367fb443e2cb6f4a1', - 'limit': 240, - 'list_size': 100, + 'limit': 360, + 'list_size': 360, 'deduplicate_base': 'false', 'exclude_date_filters': 'true' }, From 308a6c589089f94753ec767456199517960b5ab1 Mon Sep 17 00:00:00 2001 From: chreman Date: Wed, 18 Feb 2026 12:50:53 +0100 Subject: [PATCH 027/205] work-in-progress --- server/preprocessing/other-scripts/base.R | 7 +++++++ server/workers/orcid/src/orcid_service.py | 4 ++-- 2 files changed, 9 insertions(+), 2 deletions(-) diff --git a/server/preprocessing/other-scripts/base.R b/server/preprocessing/other-scripts/base.R index eeb9db88c..e0297e5af 100644 --- a/server/preprocessing/other-scripts/base.R +++ b/server/preprocessing/other-scripts/base.R @@ -277,6 +277,7 @@ etl <- function(res, repo, non_public) { metadata$type = check_metadata(res$dctype) metadata$typenorm = check_metadata(res$dctypenorm) metadata$doi = check_metadata(res$dcdoi) + metadata$doi = check_metadata(lapply(metadata$doi, clean_doi_keep_first)) metadata$lang = check_metadata(res$dclang) metadata$language = check_metadata(res$dclanguage) metadata$content_provider = check_metadata(res$dcprovider) @@ -350,6 +351,12 @@ find_dois <- function(link) { return(doi) } +clean_doi_keep_first <- function(doi_string) { + dois <- strsplit(doi_string, ";")[[1]] + result <- trimws(dois[1]) + return(result) +} + decode_dctypenorm <- function(dctypestring) { typecodes <- strsplit(dctypestring, "; ") diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index aa464eadd..a4519b25e 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -175,11 +175,11 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D # ? is that a good idea to pass here empty query? 'q': '', 'today': datetime.now().strftime('%Y-%m-%d'), - 'unique_id': 'abf2625e2d84eb4367fb443e2cb6f4a1', + 'unique_id': request_id, 'service': 'base', 'original_service': 'orcid', 'embed': 'false', - 'vis_id': 'abf2625e2d84eb4367fb443e2cb6f4a1', + 'vis_id': request_id, 'limit': 360, 'list_size': 360, 'deduplicate_base': 'false', From 2ff5c377516cd505c9b722c2033f8dbe88b038cf Mon Sep 17 00:00:00 2001 From: chreman Date: Wed, 18 Feb 2026 16:29:26 +0100 Subject: [PATCH 028/205] wip for merging with multiple DOIs --- server/preprocessing/other-scripts/base.R | 17 +++++++++++------ server/workers/orcid/src/orcid_service.py | 8 ++++++++ 2 files changed, 19 insertions(+), 6 deletions(-) diff --git a/server/preprocessing/other-scripts/base.R b/server/preprocessing/other-scripts/base.R index e0297e5af..8ca34e393 100644 --- a/server/preprocessing/other-scripts/base.R +++ b/server/preprocessing/other-scripts/base.R @@ -275,9 +275,10 @@ etl <- function(res, repo, non_public) { metadata$relevance = c(nrow(metadata):1) metadata$resulttype = lapply(res$dctypenorm, decode_dctypenorm) metadata$type = check_metadata(res$dctype) - metadata$typenorm = check_metadata(res$dctypenorm) - metadata$doi = check_metadata(res$dcdoi) - metadata$doi = check_metadata(lapply(metadata$doi, clean_doi_keep_first)) + metadata$typenorm = check_metadata(res$dctypenorm) + metadata$doi = unlist(lapply(metadata$link, find_dois)) + metadata$doi_merge = check_metadata(res$dcdoi) + metadata$doi_merge = check_metadata(lapply(metadata$doi_merge, prepare_dois)) # "10.17169/refubium-48053; 10.1371/journal.pone.0311918" metadata$lang = check_metadata(res$dclang) metadata$language = check_metadata(res$dclanguage) metadata$content_provider = check_metadata(res$dcprovider) @@ -351,10 +352,14 @@ find_dois <- function(link) { return(doi) } -clean_doi_keep_first <- function(doi_string) { +prepare_dois <- function(doi_string) { + # TODO: Check if it returns "https://doi.org/10.17169/refubium-48053; https://doi.org/10.1371/journal.pone.0311918" dois <- strsplit(doi_string, ";")[[1]] - result <- trimws(dois[1]) - return(result) + dois <- trimws(dois) + dois <- dois[!is.na(dois) & nchar(dois) > 0] + if (length(dois) == 0) return("") + result <- paste0("https://doi.org/", dois) + return(paste(result, collapse = "; ")) } diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index a4519b25e..faa62cbe5 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -334,6 +334,14 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra dois_for_base_query, doi_mapping = self._prepare_dois_for_base_query(dois) base_metadata = self.request_base_metadata(dois_for_base_query, params) + # dataframe + # paper, doi= "10.17169/refubium-48053; 10.1371/journal.pone.0311918" + # 1. step: split on "; " -> ["10.17169/refubium-48053", "10.1371/journal.pone.0311918"] + # use pandas explode to create new rows for each DOI variant, + # then we can merge on the 'doi' column with the original metadata + # paper identical metadata except doi 1: "10.17169/refubium-48053" + # paper identical metadata except doi 2: "10.1371/journal.pone.0311918" + # after that we can apply the merge, but for the base_metadata is has to use the doi_merge field, not doi received = len(base_metadata) requested = len(dois) if received < requested: From cf93efbe59b597de25a6a759ae1e74c2fb5ee3a0 Mon Sep 17 00:00:00 2001 From: andrei Date: Wed, 18 Feb 2026 16:48:36 +0100 Subject: [PATCH 029/205] feat: preparing merged version of DOIs --- server/preprocessing/other-scripts/base.R | 20 +++++++++++++------- 1 file changed, 13 insertions(+), 7 deletions(-) diff --git a/server/preprocessing/other-scripts/base.R b/server/preprocessing/other-scripts/base.R index 8ca34e393..d4e83faff 100644 --- a/server/preprocessing/other-scripts/base.R +++ b/server/preprocessing/other-scripts/base.R @@ -277,8 +277,8 @@ etl <- function(res, repo, non_public) { metadata$type = check_metadata(res$dctype) metadata$typenorm = check_metadata(res$dctypenorm) metadata$doi = unlist(lapply(metadata$link, find_dois)) - metadata$doi_merge = check_metadata(res$dcdoi) - metadata$doi_merge = check_metadata(lapply(metadata$doi_merge, prepare_dois)) # "10.17169/refubium-48053; 10.1371/journal.pone.0311918" + metadata$merged_dois = check_metadata(res$dcdoi) + metadata$merged_dois = check_metadata(lapply(metadata$merged_dois, normalize_dois)) metadata$lang = check_metadata(res$dclang) metadata$language = check_metadata(res$dclanguage) metadata$content_provider = check_metadata(res$dcprovider) @@ -352,14 +352,20 @@ find_dois <- function(link) { return(doi) } -prepare_dois <- function(doi_string) { - # TODO: Check if it returns "https://doi.org/10.17169/refubium-48053; https://doi.org/10.1371/journal.pone.0311918" +normalize_dois <- function(doi_string) { dois <- strsplit(doi_string, ";")[[1]] dois <- trimws(dois) dois <- dois[!is.na(dois) & nchar(dois) > 0] - if (length(dois) == 0) return("") - result <- paste0("https://doi.org/", dois) - return(paste(result, collapse = "; ")) + + if (length(dois) == 0) { + return("") + } + + dois_cleaned <- gsub("^https?://(dx\\.)?doi\\.org/", "", dois, ignore.case = TRUE) + result <- paste0("https://doi.org/", dois_cleaned) + final_result <- paste(result, collapse = "; ") + + return(final_result) } From 0c66fc432121038b0bb6e1964c98f9cc0421710e Mon Sep 17 00:00:00 2001 From: andrei Date: Wed, 18 Feb 2026 17:38:01 +0100 Subject: [PATCH 030/205] feat: explode merged DOIs --- server/workers/orcid/src/orcid_service.py | 66 +++++++++++++++++++---- 1 file changed, 57 insertions(+), 9 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index faa62cbe5..7db6c04af 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -300,6 +300,61 @@ def normalize_doi(doi_value): return base_metadata + def _explode_merged_dois(self, base_metadata: pd.DataFrame) -> pd.DataFrame: + """ + Explode merged_dois field to create separate rows for each DOI variant. + + If base_metadata contains a 'merged_dois' field with multiple DOIs separated by '; ', + this function creates separate rows for each DOI, allowing matching with ORCID metadata + by any of those DOIs. + + Parameters: + - base_metadata: DataFrame with BASE metadata, potentially containing 'merged_dois' field + + Returns: + - DataFrame with exploded rows where each row has a single DOI in the 'doi' column + """ + # Process merged_dois: explode to create separate rows for each DOI variant + # This allows us to match BASE records with multiple DOIs to ORCID records by any of those DOIs + if 'merged_dois' in base_metadata.columns: + # Split merged_dois by "; " and create a list of DOIs for each row + base_metadata['merged_dois_list'] = base_metadata['merged_dois'].apply( + lambda x: [remove_doi_prefix(doi.strip()) for doi in str(x).split('; ') if doi.strip()] + if pd.notna(x) and str(x).strip() else [] + ) + + # Separate rows with merged_dois and without + rows_with_merged_dois = base_metadata[base_metadata['merged_dois_list'].apply(lambda x: len(x) > 0)] + rows_without_merged_dois = base_metadata[base_metadata['merged_dois_list'].apply(lambda x: len(x) == 0)] + + # Explode rows with merged_dois to create separate rows for each DOI variant + # This creates multiple rows with identical metadata except for the DOI + if len(rows_with_merged_dois) > 0: + base_metadata_exploded = rows_with_merged_dois.explode('merged_dois_list', ignore_index=True) + + # Use merged_dois_list as the DOI for merging + base_metadata_exploded['doi'] = base_metadata_exploded['merged_dois_list'] + + # Drop temporary column + base_metadata_exploded = base_metadata_exploded.drop(columns=['merged_dois_list']) + + # Process regular doi column for rows without merged_dois + if len(rows_without_merged_dois) > 0: + rows_without_merged_dois = rows_without_merged_dois.drop(columns=['merged_dois_list']) + rows_without_merged_dois.loc[:, 'doi'] = rows_without_merged_dois['doi'].apply(remove_doi_prefix) + base_metadata = pd.concat([base_metadata_exploded, rows_without_merged_dois], ignore_index=True) + else: + base_metadata = base_metadata_exploded + else: + # No rows with merged_dois, process regular doi column + base_metadata = rows_without_merged_dois.drop(columns=['merged_dois_list']) + base_metadata.loc[:, 'doi'] = base_metadata['doi'].apply(remove_doi_prefix) + else: + # No merged_dois column, process regular doi column + base_metadata.loc[:, 'doi'] = base_metadata['doi'].apply(remove_doi_prefix) + + return base_metadata + def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFrame) -> pd.DataFrame: self.logger.debug(f"Enriching metadata with base for ORCID {params.get('orcid')}") @@ -342,17 +397,10 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra # paper identical metadata except doi 1: "10.17169/refubium-48053" # paper identical metadata except doi 2: "10.1371/journal.pone.0311918" # after that we can apply the merge, but for the base_metadata is has to use the doi_merge field, not doi - received = len(base_metadata) - requested = len(dois) - if received < requested: - self.logger.warning( - f"BASE enrichment shortfall: requested {requested} DOIs, received {received} rows ({requested - received} missing)" - ) - else: - self.logger.debug(f"BASE response: {received} rows for {requested} requested DOIs") base_metadata = base_metadata.reindex(columns=required_fields) - base_metadata.loc[:, 'doi'] = base_metadata['doi'].apply(remove_doi_prefix) + + base_metadata = self._explode_merged_dois(base_metadata) # Remove rows where 'doi' is pd.NaN base_metadata = base_metadata[pd.notna(base_metadata['doi'])] From 231265d27198a7d1aaea33ed69da70c34988d9c0 Mon Sep 17 00:00:00 2001 From: andrei Date: Wed, 18 Feb 2026 17:44:25 +0100 Subject: [PATCH 031/205] refactor: simplier explode function --- server/workers/orcid/src/orcid_service.py | 35 ++++++++--------------- 1 file changed, 12 insertions(+), 23 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 7db6c04af..5adf98b39 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -318,37 +318,26 @@ def _explode_merged_dois(self, base_metadata: pd.DataFrame) -> pd.DataFrame: # This allows us to match BASE records with multiple DOIs to ORCID records by any of those DOIs if 'merged_dois' in base_metadata.columns: # Split merged_dois by "; " and create a list of DOIs for each row + # If merged_dois is empty/NaN, create empty list; otherwise split and process each DOI base_metadata['merged_dois_list'] = base_metadata['merged_dois'].apply( lambda x: [remove_doi_prefix(doi.strip()) for doi in str(x).split('; ') if doi.strip()] if pd.notna(x) and str(x).strip() else [] ) - # Separate rows with merged_dois and without - rows_with_merged_dois = base_metadata[base_metadata['merged_dois_list'].apply(lambda x: len(x) > 0)] - rows_without_merged_dois = base_metadata[base_metadata['merged_dois_list'].apply(lambda x: len(x) == 0)] + # Use explode to create separate rows for each DOI in merged_dois_list + # Rows with empty lists will remain as single rows + base_metadata = base_metadata.explode('merged_dois_list', ignore_index=True) - # Explode rows with merged_dois to create separate rows for each DOI variant - # This creates multiple rows with identical metadata except for the DOI - if len(rows_with_merged_dois) > 0: - base_metadata_exploded = rows_with_merged_dois.explode('merged_dois_list', ignore_index=True) + # For rows where merged_dois_list is not empty, use it as the DOI + # For rows where merged_dois_list is empty/NaN, use the regular doi column + mask_has_merged_doi = pd.notna(base_metadata['merged_dois_list']) & (base_metadata['merged_dois_list'] != '') + base_metadata.loc[mask_has_merged_doi, 'doi'] = base_metadata.loc[mask_has_merged_doi, 'merged_dois_list'] - # Use merged_dois_list as the DOI for merging - base_metadata_exploded['doi'] = base_metadata_exploded['merged_dois_list'] + # Process regular doi column for rows without merged_dois + base_metadata.loc[~mask_has_merged_doi, 'doi'] = base_metadata.loc[~mask_has_merged_doi, 'doi'].apply(remove_doi_prefix) - # Drop temporary column - base_metadata_exploded = base_metadata_exploded.drop(columns=['merged_dois_list']) - - # Process regular doi column for rows without merged_dois - if len(rows_without_merged_dois) > 0: - rows_without_merged_dois = rows_without_merged_dois.drop(columns=['merged_dois_list']) - rows_without_merged_dois.loc[:, 'doi'] = rows_without_merged_dois['doi'].apply(remove_doi_prefix) - base_metadata = pd.concat([base_metadata_exploded, rows_without_merged_dois], ignore_index=True) - else: - base_metadata = base_metadata_exploded - else: - # No rows with merged_dois, process regular doi column - base_metadata = rows_without_merged_dois.drop(columns=['merged_dois_list']) - base_metadata.loc[:, 'doi'] = base_metadata['doi'].apply(remove_doi_prefix) + # Drop temporary column + base_metadata = base_metadata.drop(columns=['merged_dois_list']) else: # No merged_dois column, process regular doi column base_metadata.loc[:, 'doi'] = base_metadata['doi'].apply(remove_doi_prefix) From 47e24d9a2d8f317165fe1b22fbcaf67cdffd6c31 Mon Sep 17 00:00:00 2001 From: andrei Date: Wed, 18 Feb 2026 18:35:58 +0100 Subject: [PATCH 032/205] feat: DOIs versions mapping --- server/workers/orcid/src/orcid_service.py | 72 ++++++++++++++++++----- 1 file changed, 57 insertions(+), 15 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 5adf98b39..fcb7223d1 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -1,4 +1,5 @@ import logging +import re import json import pandas as pd import os @@ -234,9 +235,6 @@ def _prepare_dois_for_base_query(self, dois: List[str]) -> Tuple[List[str], Dict Returns: - Tuple of (list of DOIs including originals and lowercase variants, mapping from lowercase DOI to original DOIs) """ - # TODO: remove after implementation and testing - self.logger.debug(f"Original DOIs from ORCID: {dois}") - dois_for_base_query = [] doi_mapping = {} @@ -251,15 +249,9 @@ def _prepare_dois_for_base_query(self, dois: List[str]) -> Tuple[List[str], Dict doi_mapping[lowercase_doi] = [] doi_mapping[lowercase_doi].append(doi) - # TODO: remove after implementation and testing - self.logger.debug(f"Added lowercase version for DOI: '{doi}' -> '{lowercase_doi}'") dois_for_base_query = list(dict.fromkeys(dois_for_base_query)) - # TODO: remove after implementation and testing - self.logger.debug(f"DOIs to search in BASE (original + lowercase variants): {dois_for_base_query}") - self.logger.debug(f"Total DOIs for BASE query: {len(dois_for_base_query)} (original: {len(dois)}, added lowercase variants: {len(dois_for_base_query) - len(dois)})") - return dois_for_base_query, doi_mapping def _normalize_base_results_to_original_dois( @@ -289,8 +281,6 @@ def normalize_doi(doi_value): if doi_value in doi_mapping: original_dois_for_variant = doi_mapping[doi_value] - # TODO: remove after implementation and testing - self.logger.debug(f"Normalized DOI from BASE: '{doi_value}' -> '{original_dois_for_variant}'") return original_dois_for_variant[0] return doi_value @@ -300,6 +290,61 @@ def normalize_doi(doi_value): return base_metadata + def _match_dois_by_version( + self, + base_metadata: pd.DataFrame, + original_dois: List[str], + ) -> pd.DataFrame: + """ + Match BASE results that have versioned DOIs (e.g. .v1, .v2) to original DOIs without version. + + If BASE returned a DOI with a version suffix but the original ORCID DOI is without version, + this function updates the base_metadata 'doi' column so that those rows match the original + DOI for merging. + + Parameters: + - base_metadata: DataFrame with 'doi' column (after explode and normalize) + - original_dois: List of original DOIs from ORCID + + Returns: + - DataFrame with 'doi' updated where versioned variants were matched to original DOIs + """ + pattern_doi_version = re.compile(r"\.v(\d)+$") + + def get_unversioned_doi(doi_str): + if pd.isna(doi_str) or doi_str == '': + return None + return pattern_doi_version.sub("", str(doi_str)) + + dois_received = base_metadata['doi'].unique().tolist() + base_unversioned_to_versioned = {} + for doi_from_base in dois_received: + unversioned = get_unversioned_doi(doi_from_base) + if unversioned and unversioned != doi_from_base: + if unversioned not in base_unversioned_to_versioned: + base_unversioned_to_versioned[unversioned] = [] + base_unversioned_to_versioned[unversioned].append(doi_from_base) + + dois_lost = [doi for doi in original_dois if doi not in dois_received] + dois_lost_with_versions = [] + for lost_doi in dois_lost: + unversioned_lost = get_unversioned_doi(lost_doi) + if unversioned_lost in base_unversioned_to_versioned: + dois_lost_with_versions.append({ + 'original': lost_doi, + 'versioned_variants_found': base_unversioned_to_versioned[unversioned_lost], + }) + + base_metadata = base_metadata.copy() + for lost_doi_info in dois_lost_with_versions: + original_doi = lost_doi_info['original'] + versioned_variants = lost_doi_info['versioned_variants_found'] + versioned_mask = base_metadata['doi'].isin(versioned_variants) + if versioned_mask.any(): + base_metadata.loc[versioned_mask, 'doi'] = original_doi + + return base_metadata + def _explode_merged_dois(self, base_metadata: pd.DataFrame) -> pd.DataFrame: """ Explode merged_dois field to create separate rows for each DOI variant. @@ -395,6 +440,7 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra base_metadata = base_metadata[pd.notna(base_metadata['doi'])] base_metadata = self._normalize_base_results_to_original_dois(base_metadata, doi_mapping) + base_metadata = self._match_dois_by_version(base_metadata, dois) base_metadata = base_metadata[base_metadata['doi'].isin(dois)] base_metadata = base_metadata.drop_duplicates(subset='doi', keep='first') @@ -448,8 +494,6 @@ def custom_merge_link_oa_state(row): lambda row: custom_merge(row['relation'], row['relation_base']), axis=1 ) - self.logger.debug('assigned some fields') - # Apply custom logic for link and oa_state and assign results link_oa_state_values = enriched_metadata.apply(custom_merge_link_oa_state, axis=1) enriched_metadata['link'], enriched_metadata['oa_state'] = zip(*link_oa_state_values) @@ -460,8 +504,6 @@ def custom_merge_link_oa_state(row): #self.log_dataframe(enriched_metadata, params, '_enriched') # TEMPORAL - self.logger.debug(f"Enriched metadata using base for ORCID {params.get('orcid')}: {enriched_metadata[['id', 'link', 'oa_state']].head()}") - # temporal solution, for some reason if we have some undefined data, dataprocessing is failing enriched_metadata = enriched_metadata.reindex(columns=list(set(original_columns + ['oa_state', 'subject', 'subject_orig', 'paper_abstract', 'link', 'relation']))) From 98d2c7f7f3b8ca7134418d880aea71de48522d2e Mon Sep 17 00:00:00 2001 From: chreman Date: Wed, 18 Feb 2026 22:35:07 +0100 Subject: [PATCH 033/205] add timing logs --- server/workers/orcid/src/worker.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/server/workers/orcid/src/worker.py b/server/workers/orcid/src/worker.py index 05ac6b2e7..812eb54fc 100644 --- a/server/workers/orcid/src/worker.py +++ b/server/workers/orcid/src/worker.py @@ -50,6 +50,7 @@ def run(self) -> None: def handle_search(self, request_id: str, params: Dict[str, str]) -> None: try: + start_time = time.time() self.logger.debug(f"Handle search with request_id: {request_id} and params {params}") res = self.data_retriever.execute_search(params) @@ -62,6 +63,8 @@ def handle_search(self, request_id: str, params: Dict[str, str]) -> None: self.redis_store.rpush("input_data", json.dumps(res).encode("utf8")) queue_length = self.redis_store.llen("input_data") self.logger.debug(f"Queue length: input_data {queue_length} {request_id}") + end_time = time.time() + self.logger.debug(f"ORCID {params.get('orcid')} Time taken: {end_time - start_time:.2f}") except Exception as e: self.logger.exception("Exception during data retrieval.") self.logger.error(params) From 1eefbf45af82db4b954362d2fd2edb3a56be785e Mon Sep 17 00:00:00 2001 From: chreman Date: Wed, 18 Feb 2026 22:58:53 +0100 Subject: [PATCH 034/205] bugfix for column handling and OA state merging --- server/workers/orcid/src/orcid_service.py | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index fcb7223d1..7ef7becb5 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -173,7 +173,6 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D 'min_descsize': '0', 'from': '1665-01-01', 'to': datetime.now().strftime('%Y-%m-%d'), - # ? is that a good idea to pass here empty query? 'q': '', 'today': datetime.now().strftime('%Y-%m-%d'), 'unique_id': request_id, @@ -400,6 +399,7 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra 'relations', 'annotations', 'repo', 'source', 'volume', 'issue', 'page', 'issn', 'citation_count', 'cited_by_wikipedia_count', 'cited_by_msm_count', 'cited_by_policies_count', 'cited_by_patents_count', 'cited_by_accounts_count', 'cited_by_fbwalls_count', + 'merged_dois', 'cited_by_feeds_count', 'cited_by_gplus_count', 'cited_by_rdts_count', @@ -421,7 +421,6 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra dois = [doi for doi in raw_dois if doi and pd.notna(doi)] dois_for_base_query, doi_mapping = self._prepare_dois_for_base_query(dois) - base_metadata = self.request_base_metadata(dois_for_base_query, params) # dataframe # paper, doi= "10.17169/refubium-48053; 10.1371/journal.pone.0311918" @@ -434,7 +433,12 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra base_metadata = base_metadata.reindex(columns=required_fields) - base_metadata = self._explode_merged_dois(base_metadata) + #base_metadata = self._explode_merged_dois(base_metadata) + base_metadata = base_metadata.explode('merged_dois', ignore_index=True) if 'merged_dois' in base_metadata.columns else base_metadata + # replace doi with merged_dois if merged_dois is not empty, otherwise keep doi + if 'merged_dois' in base_metadata.columns: + base_metadata.loc[base_metadata['merged_dois'].notna() & (base_metadata['merged_dois'] != ''), 'doi'] = base_metadata.loc[base_metadata['merged_dois'].notna() & (base_metadata['merged_dois'] != ''), 'merged_dois'] + base_metadata.loc[:, 'doi'] = base_metadata['doi'].apply(remove_doi_prefix) # Remove rows where 'doi' is pd.NaN base_metadata = base_metadata[pd.notna(base_metadata['doi'])] @@ -445,10 +449,6 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra base_metadata = base_metadata[base_metadata['doi'].isin(dois)] base_metadata = base_metadata.drop_duplicates(subset='doi', keep='first') - # TEMPORAL - # self.log_dataframe(base_metadata, params, '_base') - # TEMPORAL - # Select and rename relevant fields from base_metadata, including subject_orig fields_to_merge = { 'oa_state': 'oa_state_base', @@ -476,7 +476,8 @@ def custom_merge(existing_value, new_value): def custom_merge_link_oa_state(row): existing_link, existing_oa_state = row['link'], row['oa_state'] - new_link, new_oa_state = row.get('link_base', None), row.get('oa_state_base', None) + new_link, oa_state_base = row.get('link_base', None), row.get('oa_state_base', None) + new_oa_state = "1" if oa_state_base == "1" else existing_oa_state # If BASE indicates OA, set oa_state to 1, otherwise keep original if pd.isna(existing_link) and pd.notna(new_link): return new_link, new_oa_state return existing_link, existing_oa_state From 4889ec1e3395b76fc1d6f61b71f849330ba36c3f Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 19 Feb 2026 00:21:06 +0100 Subject: [PATCH 035/205] improved doi replacement --- server/workers/orcid/src/orcid_service.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 7ef7becb5..836150c6c 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -434,10 +434,12 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra base_metadata = base_metadata.reindex(columns=required_fields) #base_metadata = self._explode_merged_dois(base_metadata) - base_metadata = base_metadata.explode('merged_dois', ignore_index=True) if 'merged_dois' in base_metadata.columns else base_metadata + base_metadata['merged_dois'] = base_metadata['merged_dois'].apply(lambda x: x[0] if isinstance(x, list) and len(x) > 0 else x) + base_metadata['merged_dois'] = base_metadata['merged_dois'].apply(lambda x: x.split(';') if isinstance(x, str) else []) + base_metadata['merged_dois'] = base_metadata['merged_dois'].apply(lambda x: [x.strip() for x in x] if isinstance(x, list) else x) + base_metadata = base_metadata.explode('merged_dois', ignore_index=True) # replace doi with merged_dois if merged_dois is not empty, otherwise keep doi - if 'merged_dois' in base_metadata.columns: - base_metadata.loc[base_metadata['merged_dois'].notna() & (base_metadata['merged_dois'] != ''), 'doi'] = base_metadata.loc[base_metadata['merged_dois'].notna() & (base_metadata['merged_dois'] != ''), 'merged_dois'] + base_metadata.loc[base_metadata['merged_dois'].notna() & (base_metadata['merged_dois'] != ''), 'doi'] = base_metadata.loc[base_metadata['merged_dois'].notna() & (base_metadata['merged_dois'] != ''), 'merged_dois'] base_metadata.loc[:, 'doi'] = base_metadata['doi'].apply(remove_doi_prefix) # Remove rows where 'doi' is pd.NaN From 9a09e40535904dc3e88d31fd70e9aa42bf99ad37 Mon Sep 17 00:00:00 2001 From: andrei Date: Fri, 20 Feb 2026 15:37:07 +0100 Subject: [PATCH 036/205] refactor: log that shows amount of not recieved DOIs from BASE --- server/workers/orcid/src/orcid_service.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 836150c6c..ad609a0b5 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -203,10 +203,7 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D base_response: str = get_nested_value(result, ["input_data", "metadata"], '[]') # type: ignore batch_df = pd.DataFrame(json.loads(base_response)) - if len(batch_df) < len(batch): - self.logger.warning( - f"BASE response shortfall: requested {len(batch)} DOIs, received {len(batch_df)} rows (limit or missing records?)" - ) + self._log_is_base_response_missing_dois(batch, batch_df) base_metadata = pd.concat([ base_metadata, @@ -679,4 +676,11 @@ def _handle_insufficient_results(self, params: Dict[str, str], orcid_id: str) -> def _handle_error(self, params: Dict[str, str], reason: str, exception: Exception) -> ErrorResult: self.logger.debug(f"Error processing ORCID: {exception}. Params: {params}") self.logger.debug(exception.__traceback__) - return {"status": "error", "reason": [reason]} \ No newline at end of file + return {"status": "error", "reason": [reason]} + + def _log_is_base_response_missing_dois(self, batch: List[str], batch_df: pd.DataFrame): + is_some_dois_missing = len(batch_df) < len(batch) + if is_some_dois_missing: + self.logger.debug( + f"BASE response statistics: requested {len(batch)} DOIs, received {len(batch_df)} rows" + ) From 8d67706d60600e396e567a4a3c8f28623a8bff96 Mon Sep 17 00:00:00 2001 From: chreman Date: Mon, 16 Feb 2026 20:56:56 +0100 Subject: [PATCH 037/205] added waiting time analysis script --- local_dev/tools/analytics/README.md | 32 ++++++++ local_dev/tools/analytics/compare_logs.py | 93 +++++++++++++++++++++++ 2 files changed, 125 insertions(+) create mode 100644 local_dev/tools/analytics/README.md create mode 100644 local_dev/tools/analytics/compare_logs.py diff --git a/local_dev/tools/analytics/README.md b/local_dev/tools/analytics/README.md new file mode 100644 index 000000000..b53fea4f1 --- /dev/null +++ b/local_dev/tools/analytics/README.md @@ -0,0 +1,32 @@ +# Log Performance Comparison + +Compare "Time taken" statistics between two Headstart log files (e.g. before and after a change). + +## Setup + +Copy the Headstart log file out of the Docker container into this folder, in this case for the BASE API: + +```bash +docker cp dev-base-1:/var/log/headstart/headstart.log ./headstart-before.log +``` + +After making your changes, collect a second log file, in this case for the BASE API: + +```bash +docker cp dev-base-1:/var/log/headstart/headstart.log ./headstart-after.log +``` + +## Usage + +```bash +python3 compare_logs.py headstart-before.log headstart-after.log +``` + +Both arguments are optional and default to `headstart-before.log` and `headstart-after.log`. + +## Output + +The script extracts all `Time taken:` log entries, groups them by source (`api.base`, `api.pubmed`, `api.openaire`, `vis`, `metrics`), and reports per group and overall: + +- Count, Min, Median, Mean, Max +- Change in median and mean (absolute and percentage) diff --git a/local_dev/tools/analytics/compare_logs.py b/local_dev/tools/analytics/compare_logs.py new file mode 100644 index 000000000..df78e530e --- /dev/null +++ b/local_dev/tools/analytics/compare_logs.py @@ -0,0 +1,93 @@ +#!/usr/bin/env python3 +"""Compare 'Time taken' entries between two Headstart log files.""" + +import re +import sys +import statistics +from collections import defaultdict + +PATTERN = re.compile( + r"^\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2} INFO:(\S+):vis_id: \S+ Time taken: ([\d.]+)" +) + + +def parse_log(filepath): + """Extract Time taken values grouped by log source.""" + groups = defaultdict(list) + with open(filepath) as f: + for line in f: + m = PATTERN.match(line) + if m: + source = m.group(1) + value = float(m.group(2)) + groups[source].append(value) + return groups + + +def print_stats(label, values): + """Print descriptive statistics for a list of values.""" + values_sorted = sorted(values) + n = len(values_sorted) + mean = statistics.mean(values_sorted) + median = statistics.median(values_sorted) + print(f" {label}:") + print(f" Count: {n}") + print(f" Min: {values_sorted[0]:.3f}s") + print(f" Median: {median:.3f}s") + print(f" Mean: {mean:.3f}s") + print(f" Max: {values_sorted[-1]:.3f}s") + return {"count": n, "min": values_sorted[0], "median": median, "mean": mean, "max": values_sorted[-1]} + + +def main(): + before_file = sys.argv[1] if len(sys.argv) > 1 else "headstart-before.log" + after_file = sys.argv[2] if len(sys.argv) > 2 else "headstart-after.log" + + before = parse_log(before_file) + after = parse_log(after_file) + + all_sources = sorted(set(list(before.keys()) + list(after.keys()))) + + # Also compute totals across all sources + before_all = [] + after_all = [] + for src in all_sources: + before_all.extend(before.get(src, [])) + after_all.extend(after.get(src, [])) + + print("=" * 60) + print("BEFORE vs AFTER comparison") + print("=" * 60) + + # Per-source stats + for src in all_sources: + print(f"\n--- {src} ---") + b_stats = a_stats = None + if src in before: + b_stats = print_stats("Before", before[src]) + else: + print(" Before: (no data)") + if src in after: + a_stats = print_stats("After", after[src]) + else: + print(" After: (no data)") + + if b_stats and a_stats: + print(f" Change in median: {a_stats['median'] - b_stats['median']:+.3f}s ({(a_stats['median'] / b_stats['median'] - 1) * 100:+.1f}%)") + print(f" Change in mean: {a_stats['mean'] - b_stats['mean']:+.3f}s ({(a_stats['mean'] / b_stats['mean'] - 1) * 100:+.1f}%)") + + # Overall stats + print(f"\n{'=' * 60}") + print("ALL SOURCES COMBINED") + print("=" * 60) + if before_all: + b_total = print_stats("Before", before_all) + if after_all: + a_total = print_stats("After", after_all) + if before_all and after_all: + print(f" Change in median: {a_total['median'] - b_total['median']:+.3f}s ({(a_total['median'] / b_total['median'] - 1) * 100:+.1f}%)") + print(f" Change in mean: {a_total['mean'] - b_total['mean']:+.3f}s ({(a_total['mean'] / b_total['mean'] - 1) * 100:+.1f}%)") + + +if __name__ == "__main__": + main() From 9857010e9f18388f8b326a950cdc037b25fb55a0 Mon Sep 17 00:00:00 2001 From: chreman Date: Wed, 18 Feb 2026 23:04:42 +0100 Subject: [PATCH 038/205] added new run time evaluation script --- .../tools/analytics/compare_orcid_logs.py | 97 +++++++++++++++++++ 1 file changed, 97 insertions(+) create mode 100644 local_dev/tools/analytics/compare_orcid_logs.py diff --git a/local_dev/tools/analytics/compare_orcid_logs.py b/local_dev/tools/analytics/compare_orcid_logs.py new file mode 100644 index 000000000..315cc6c14 --- /dev/null +++ b/local_dev/tools/analytics/compare_orcid_logs.py @@ -0,0 +1,97 @@ +#!/usr/bin/env python3 +"""Compare 'Time taken' entries between two ORCID worker log files.""" + +import re +import sys +import statistics +from collections import defaultdict + +PATTERN = re.compile( + r"^\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2} DEBUG\s+ORCID (\S+) Time taken: ([\d.]+)" +) + + +def parse_log(filepath): + """Extract Time taken values grouped by ORCID ID.""" + groups = defaultdict(list) + with open(filepath) as f: + for line in f: + m = PATTERN.match(line) + if m: + orcid_id = m.group(1) + value = float(m.group(2)) + groups[orcid_id].append(value) + return groups + + +def print_stats(label, values): + """Print descriptive statistics for a list of values.""" + values_sorted = sorted(values) + n = len(values_sorted) + mean = statistics.mean(values_sorted) + median = statistics.median(values_sorted) + print(f" {label}:") + print(f" Count: {n}") + print(f" Min: {values_sorted[0]:.3f}s") + print(f" Median: {median:.3f}s") + print(f" Mean: {mean:.3f}s") + print(f" Max: {values_sorted[-1]:.3f}s") + return {"count": n, "min": values_sorted[0], "median": median, "mean": mean, "max": values_sorted[-1]} + + +def main(): + before_file = sys.argv[1] if len(sys.argv) > 1 else "orcid-before.log" + after_file = sys.argv[2] if len(sys.argv) > 2 else "orcid-after.log" + + before = parse_log(before_file) + after = parse_log(after_file) + + all_orcids = sorted(set(list(before.keys()) + list(after.keys()))) + + before_all = [] + after_all = [] + for orcid in all_orcids: + before_all.extend(before.get(orcid, [])) + after_all.extend(after.get(orcid, [])) + + print("=" * 60) + print("BEFORE vs AFTER comparison (ORCID worker)") + print("=" * 60) + + # Per-ORCID stats + for orcid in all_orcids: + print(f"\n--- {orcid} ---") + b_stats = a_stats = None + if orcid in before: + b_stats = print_stats("Before", before[orcid]) + else: + print(" Before: (no data)") + if orcid in after: + a_stats = print_stats("After", after[orcid]) + else: + print(" After: (no data)") + + if b_stats and a_stats: + print(f" Change in median: {a_stats['median'] - b_stats['median']:+.3f}s ({(a_stats['median'] / b_stats['median'] - 1) * 100:+.1f}%)") + print(f" Change in mean: {a_stats['mean'] - b_stats['mean']:+.3f}s ({(a_stats['mean'] / b_stats['mean'] - 1) * 100:+.1f}%)") + + # Overall stats + print(f"\n{'=' * 60}") + print("ALL ORCID IDs COMBINED") + print("=" * 60) + b_total = a_total = None + if before_all: + b_total = print_stats("Before", before_all) + else: + print(" Before: (no data)") + if after_all: + a_total = print_stats("After", after_all) + else: + print(" After: (no data)") + if b_total and a_total: + print(f" Change in median: {a_total['median'] - b_total['median']:+.3f}s ({(a_total['median'] / b_total['median'] - 1) * 100:+.1f}%)") + print(f" Change in mean: {a_total['mean'] - b_total['mean']:+.3f}s ({(a_total['mean'] / b_total['mean'] - 1) * 100:+.1f}%)") + + +if __name__ == "__main__": + main() From 68af5f0fbb2decf38111edd5b96868b94c72101c Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 19 Feb 2026 13:49:20 +0100 Subject: [PATCH 039/205] README update --- local_dev/tools/analytics/README.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/local_dev/tools/analytics/README.md b/local_dev/tools/analytics/README.md index b53fea4f1..2ef32ce57 100644 --- a/local_dev/tools/analytics/README.md +++ b/local_dev/tools/analytics/README.md @@ -4,29 +4,29 @@ Compare "Time taken" statistics between two Headstart log files (e.g. before and ## Setup -Copy the Headstart log file out of the Docker container into this folder, in this case for the BASE API: +Copy the Headstart log file out of the Docker container into this folder, in this case for the ORCID API: ```bash -docker cp dev-base-1:/var/log/headstart/headstart.log ./headstart-before.log +docker cp dev-orcid-1:/var/log/headstart/headstart.log ./orcid-before.log ``` -After making your changes, collect a second log file, in this case for the BASE API: +After making your changes, collect a second log file, in this case for the ORCID API: ```bash -docker cp dev-base-1:/var/log/headstart/headstart.log ./headstart-after.log +docker cp dev-orcid-1:/var/log/headstart/headstart.log ./orcid-after.log ``` ## Usage ```bash -python3 compare_logs.py headstart-before.log headstart-after.log +python3 compare_orcid_logs.py orcid-before.log orcid-after.log ``` -Both arguments are optional and default to `headstart-before.log` and `headstart-after.log`. +Both arguments are optional and default to `orcid-before.log` and `orcid-after.log`. ## Output -The script extracts all `Time taken:` log entries, groups them by source (`api.base`, `api.pubmed`, `api.openaire`, `vis`, `metrics`), and reports per group and overall: +The script extracts all `Time taken:` log entries and reports descriptive statistics: - Count, Min, Median, Mean, Max - Change in median and mean (absolute and percentage) From 5d4f9fbe580e77a0a6bdbb21d8bfb2f417065ba6 Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 19 Feb 2026 19:07:49 +0100 Subject: [PATCH 040/205] datatype bugfix for oa_state enrichment; code & logging cleanup --- server/workers/orcid/src/orcid_service.py | 94 ++++++++--------------- 1 file changed, 31 insertions(+), 63 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index ad609a0b5..24855a1cb 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -131,7 +131,7 @@ def enrich_metadata(self, params: Dict[str, str], metadata: pd.DataFrame) -> pd. return metadata - def log_dataframe(self, df: pd.DataFrame, params: Dict[str, str], name: str, ): + def _log_dataframe(self, df: pd.DataFrame, params: Dict[str, str], name: str, ): orcid = params.get('orcid') columns_to_print = ['id', 'title', 'doi', 'paper_abstract', 'link', 'subject', 'subject_orig', 'oa_state'] @@ -145,7 +145,10 @@ def log_dataframe(self, df: pd.DataFrame, params: Dict[str, str], name: str, ): if not os.path.exists(folder): os.makedirs(folder) file_path = f"{folder}/{name}.csv" - transformed.sort_values(by='doi', ascending=True).to_csv(file_path, index=False) + transformed.to_csv(file_path, index=False) + # log a small file with the datatypes of each column, to understand if there are some issues with the data + dtypes_path = f"{folder}/{name}_dtypes.csv" + pd.DataFrame(transformed.dtypes, columns=['dtype']).to_csv(dtypes_path) def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.DataFrame: orcid = params.get('orcid') @@ -210,12 +213,14 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D batch_df ], ignore_index=True) - timing_df = pd.DataFrame(timing_data) - folder = f'./output/{orcid}' - if not os.path.exists(folder): - os.makedirs(folder) - timing_df.to_csv(f'{folder}/stat_base_requests.csv', index=False) + if self.logger.isEnabledFor(logging.DEBUG): + timing_df = pd.DataFrame(timing_data) + folder = f'./output/{orcid}' + if not os.path.exists(folder): + os.makedirs(folder) + timing_df.to_csv(f'{folder}/stat_base_requests.csv', index=False) + base_metadata["oa_state"] = base_metadata["oa_state"].fillna("0").astype(int) return base_metadata def _prepare_dois_for_base_query(self, dois: List[str]) -> Tuple[List[str], Dict[str, List[str]]]: @@ -341,50 +346,6 @@ def get_unversioned_doi(doi_str): return base_metadata - def _explode_merged_dois(self, base_metadata: pd.DataFrame) -> pd.DataFrame: - """ - Explode merged_dois field to create separate rows for each DOI variant. - - If base_metadata contains a 'merged_dois' field with multiple DOIs separated by '; ', - this function creates separate rows for each DOI, allowing matching with ORCID metadata - by any of those DOIs. - - Parameters: - - base_metadata: DataFrame with BASE metadata, potentially containing 'merged_dois' field - - Returns: - - DataFrame with exploded rows where each row has a single DOI in the 'doi' column - """ - # Process merged_dois: explode to create separate rows for each DOI variant - # This allows us to match BASE records with multiple DOIs to ORCID records by any of those DOIs - if 'merged_dois' in base_metadata.columns: - # Split merged_dois by "; " and create a list of DOIs for each row - # If merged_dois is empty/NaN, create empty list; otherwise split and process each DOI - base_metadata['merged_dois_list'] = base_metadata['merged_dois'].apply( - lambda x: [remove_doi_prefix(doi.strip()) for doi in str(x).split('; ') if doi.strip()] - if pd.notna(x) and str(x).strip() else [] - ) - - # Use explode to create separate rows for each DOI in merged_dois_list - # Rows with empty lists will remain as single rows - base_metadata = base_metadata.explode('merged_dois_list', ignore_index=True) - - # For rows where merged_dois_list is not empty, use it as the DOI - # For rows where merged_dois_list is empty/NaN, use the regular doi column - mask_has_merged_doi = pd.notna(base_metadata['merged_dois_list']) & (base_metadata['merged_dois_list'] != '') - base_metadata.loc[mask_has_merged_doi, 'doi'] = base_metadata.loc[mask_has_merged_doi, 'merged_dois_list'] - - # Process regular doi column for rows without merged_dois - base_metadata.loc[~mask_has_merged_doi, 'doi'] = base_metadata.loc[~mask_has_merged_doi, 'doi'].apply(remove_doi_prefix) - - # Drop temporary column - base_metadata = base_metadata.drop(columns=['merged_dois_list']) - else: - # No merged_dois column, process regular doi column - base_metadata.loc[:, 'doi'] = base_metadata['doi'].apply(remove_doi_prefix) - - return base_metadata - def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFrame) -> pd.DataFrame: self.logger.debug(f"Enriching metadata with base for ORCID {params.get('orcid')}") @@ -410,15 +371,19 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra self.logger.debug('metadata reindexed') - # TEMPORAL - #self.log_dataframe(metadata, params, '_original') - # TEMPORAL + # run only if loglevel is debug, otherwise it is too expensive and we don't want it on production + if self.logger.isEnabledFor(logging.DEBUG): + self._log_dataframe(metadata, params, '_original') raw_dois = metadata["doi"].tolist() dois = [doi for doi in raw_dois if doi and pd.notna(doi)] dois_for_base_query, doi_mapping = self._prepare_dois_for_base_query(dois) base_metadata = self.request_base_metadata(dois_for_base_query, params) + + if self.logger.isEnabledFor(logging.DEBUG): + self._log_dataframe(base_metadata, params, 'base_metadata') + # dataframe # paper, doi= "10.17169/refubium-48053; 10.1371/journal.pone.0311918" # 1. step: split on "; " -> ["10.17169/refubium-48053", "10.1371/journal.pone.0311918"] @@ -430,7 +395,6 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra base_metadata = base_metadata.reindex(columns=required_fields) - #base_metadata = self._explode_merged_dois(base_metadata) base_metadata['merged_dois'] = base_metadata['merged_dois'].apply(lambda x: x[0] if isinstance(x, list) and len(x) > 0 else x) base_metadata['merged_dois'] = base_metadata['merged_dois'].apply(lambda x: x.split(';') if isinstance(x, str) else []) base_metadata['merged_dois'] = base_metadata['merged_dois'].apply(lambda x: [x.strip() for x in x] if isinstance(x, list) else x) @@ -475,11 +439,17 @@ def custom_merge(existing_value, new_value): def custom_merge_link_oa_state(row): existing_link, existing_oa_state = row['link'], row['oa_state'] - new_link, oa_state_base = row.get('link_base', None), row.get('oa_state_base', None) - new_oa_state = "1" if oa_state_base == "1" else existing_oa_state # If BASE indicates OA, set oa_state to 1, otherwise keep original + new_link, new_oa_state = row.get('link_base', None), row.get('oa_state_base', None) + # set final oa_state to 1 if either existing or new oa_state is 1, otherwise keep existing oa_state + if pd.notna(existing_oa_state) and existing_oa_state == 1: + final_oa_state = 1 + elif pd.notna(new_oa_state) and new_oa_state == 1: + final_oa_state = 1 + else: + final_oa_state = existing_oa_state if pd.isna(existing_link) and pd.notna(new_link): - return new_link, new_oa_state - return existing_link, existing_oa_state + return new_link, final_oa_state + return existing_link, final_oa_state enriched_metadata['paper_abstract'] = enriched_metadata.apply( lambda row: custom_merge(row['paper_abstract'], row['paper_abstract_base']), axis=1 @@ -500,9 +470,8 @@ def custom_merge_link_oa_state(row): enriched_metadata.drop(columns=['paper_abstract_base', 'subject_orig_base', 'subject_base', 'oa_state_base', 'link_base', 'relation_base'], inplace=True) - # TEMPORAL - #self.log_dataframe(enriched_metadata, params, '_enriched') - # TEMPORAL + if self.logger.isEnabledFor(logging.DEBUG): + self._log_dataframe(enriched_metadata, params, '_enriched') # temporal solution, for some reason if we have some undefined data, dataprocessing is failing enriched_metadata = enriched_metadata.reindex(columns=list(set(original_columns + ['oa_state', 'subject', 'subject_orig', 'paper_abstract', 'link', 'relation']))) @@ -564,7 +533,6 @@ def enrich_author_info(self, author_info: AuthorInfo, metadata: pd.DataFrame, pa ) # Calculate h-index - self.logger.debug('citation counts', citation_counts) h_index = 0 for i, citation in enumerate(citation_counts, start=1): if citation >= i: From 228ebb8be38db602f4522972575d1e797dccaf25 Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 19 Feb 2026 19:53:59 +0100 Subject: [PATCH 041/205] bugfix in unstable merge logic for oa_state --- server/workers/orcid/src/orcid_service.py | 46 ++++++++++------------- 1 file changed, 20 insertions(+), 26 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 24855a1cb..d9c7f0c84 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -134,7 +134,10 @@ def enrich_metadata(self, params: Dict[str, str], metadata: pd.DataFrame) -> pd. def _log_dataframe(self, df: pd.DataFrame, params: Dict[str, str], name: str, ): orcid = params.get('orcid') - columns_to_print = ['id', 'title', 'doi', 'paper_abstract', 'link', 'subject', 'subject_orig', 'oa_state'] + columns_to_print = ['id', 'title', 'doi', 'merged_dois', 'paper_abstract', 'link', 'subject', 'subject_orig', 'oa_state'] + + available_columns = df.columns.tolist() + columns_to_print = [col for col in columns_to_print if col in available_columns] transformed = df.copy().reindex(columns=columns_to_print) @@ -146,9 +149,6 @@ def _log_dataframe(self, df: pd.DataFrame, params: Dict[str, str], name: str, ): os.makedirs(folder) file_path = f"{folder}/{name}.csv" transformed.to_csv(file_path, index=False) - # log a small file with the datatypes of each column, to understand if there are some issues with the data - dtypes_path = f"{folder}/{name}_dtypes.csv" - pd.DataFrame(transformed.dtypes, columns=['dtype']).to_csv(dtypes_path) def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.DataFrame: orcid = params.get('orcid') @@ -220,7 +220,7 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D os.makedirs(folder) timing_df.to_csv(f'{folder}/stat_base_requests.csv', index=False) - base_metadata["oa_state"] = base_metadata["oa_state"].fillna("0").astype(int) + base_metadata["oa_state"] = base_metadata["oa_state"].fillna("2").astype(int) return base_metadata def _prepare_dois_for_base_query(self, dois: List[str]) -> Tuple[List[str], Dict[str, List[str]]]: @@ -381,9 +381,6 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra dois_for_base_query, doi_mapping = self._prepare_dois_for_base_query(dois) base_metadata = self.request_base_metadata(dois_for_base_query, params) - if self.logger.isEnabledFor(logging.DEBUG): - self._log_dataframe(base_metadata, params, 'base_metadata') - # dataframe # paper, doi= "10.17169/refubium-48053; 10.1371/journal.pone.0311918" # 1. step: split on "; " -> ["10.17169/refubium-48053", "10.1371/journal.pone.0311918"] @@ -410,7 +407,12 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra base_metadata = self._match_dois_by_version(base_metadata, dois) base_metadata = base_metadata[base_metadata['doi'].isin(dois)] - base_metadata = base_metadata.drop_duplicates(subset='doi', keep='first') + # Sort ascending so oa_state=1 (open access) rows come before oa_state=2, + # ensuring the most open record is kept when deduplicating by DOI. + base_metadata = base_metadata.sort_values(by='oa_state', ascending=True).drop_duplicates(subset='doi', keep='first') + + if self.logger.isEnabledFor(logging.DEBUG): + self._log_dataframe(base_metadata, params, 'base_metadata') # Select and rename relevant fields from base_metadata, including subject_orig fields_to_merge = { @@ -437,19 +439,15 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra def custom_merge(existing_value, new_value): return existing_value if pd.notnull(existing_value) and existing_value else new_value - def custom_merge_link_oa_state(row): - existing_link, existing_oa_state = row['link'], row['oa_state'] - new_link, new_oa_state = row.get('link_base', None), row.get('oa_state_base', None) - # set final oa_state to 1 if either existing or new oa_state is 1, otherwise keep existing oa_state - if pd.notna(existing_oa_state) and existing_oa_state == 1: - final_oa_state = 1 - elif pd.notna(new_oa_state) and new_oa_state == 1: - final_oa_state = 1 - else: - final_oa_state = existing_oa_state - if pd.isna(existing_link) and pd.notna(new_link): - return new_link, final_oa_state - return existing_link, final_oa_state + enriched_metadata['oa_state'] = enriched_metadata.apply( + lambda row: 1 if (pd.notna(row['oa_state']) and row['oa_state'] == 1) + or (pd.notna(row.get('oa_state_base', None)) and row.get('oa_state_base', None) == 1) + else row['oa_state'], axis=1 + ) + + enriched_metadata['link'] = enriched_metadata.apply( + lambda row: custom_merge(row['link'], row['link_base']), axis=1 + ) enriched_metadata['paper_abstract'] = enriched_metadata.apply( lambda row: custom_merge(row['paper_abstract'], row['paper_abstract_base']), axis=1 @@ -464,10 +462,6 @@ def custom_merge_link_oa_state(row): lambda row: custom_merge(row['relation'], row['relation_base']), axis=1 ) - # Apply custom logic for link and oa_state and assign results - link_oa_state_values = enriched_metadata.apply(custom_merge_link_oa_state, axis=1) - enriched_metadata['link'], enriched_metadata['oa_state'] = zip(*link_oa_state_values) - enriched_metadata.drop(columns=['paper_abstract_base', 'subject_orig_base', 'subject_base', 'oa_state_base', 'link_base', 'relation_base'], inplace=True) if self.logger.isEnabledFor(logging.DEBUG): From 705d06e677916edb19badfd282765214586004f7 Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 19 Feb 2026 20:34:37 +0100 Subject: [PATCH 042/205] tweaking batch size --- server/workers/orcid/src/orcid_service.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index d9c7f0c84..df353a705 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -152,7 +152,7 @@ def _log_dataframe(self, df: pd.DataFrame, params: Dict[str, str], name: str, ): def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.DataFrame: orcid = params.get('orcid') - batch_size = 10 + batch_size = 15 batches = [dois[i:i + batch_size] for i in range(0, len(dois), batch_size)] base_metadata = pd.DataFrame(dtype=object) @@ -379,6 +379,8 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra dois = [doi for doi in raw_dois if doi and pd.notna(doi)] dois_for_base_query, doi_mapping = self._prepare_dois_for_base_query(dois) + self.logger.debug(f"Prepared DOIs for BASE query: {dois_for_base_query}") + self.logger.debug(f"DOI mapping for normalization: {doi_mapping}") base_metadata = self.request_base_metadata(dois_for_base_query, params) # dataframe From 76f4ba9d8b13b422a88ef2b042c75ce4662dd4c7 Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 19 Feb 2026 22:57:44 +0100 Subject: [PATCH 043/205] further query simplification --- server/preprocessing/other-scripts/base.R | 5 +++++ server/workers/orcid/src/orcid_service.py | 6 +++++- 2 files changed, 10 insertions(+), 1 deletion(-) diff --git a/server/preprocessing/other-scripts/base.R b/server/preprocessing/other-scripts/base.R index d4e83faff..5a7ab46ef 100644 --- a/server/preprocessing/other-scripts/base.R +++ b/server/preprocessing/other-scripts/base.R @@ -91,6 +91,11 @@ get_papers <- function(query, params, base_query <- paste(base_query, q_advanced) } + if (!is.null(params$q_advanced_only) + && (params$q_advanced_only == TRUE || params$q_advanced_only == "true")) { + base_query <- q_advanced + } + min_descsize <- if (is.null(params$min_descsize)) 300 else params$min_descsize filter <- I(paste0('descsize:[', min_descsize, '%20TO%20*]')) limit <- params$limit diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index df353a705..a7bc938f0 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -186,7 +186,8 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D 'limit': 360, 'list_size': 360, 'deduplicate_base': 'false', - 'exclude_date_filters': 'true' + 'exclude_date_filters': 'true', + 'q_advanced_only': 'true' }, "endpoint": "search" } @@ -383,6 +384,9 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra self.logger.debug(f"DOI mapping for normalization: {doi_mapping}") base_metadata = self.request_base_metadata(dois_for_base_query, params) + if self.logger.isEnabledFor(logging.DEBUG): + self._log_dataframe(base_metadata, params, 'base_metadata_raw') + # dataframe # paper, doi= "10.17169/refubium-48053; 10.1371/journal.pone.0311918" # 1. step: split on "; " -> ["10.17169/refubium-48053", "10.1371/journal.pone.0311918"] From b77e5b58ab8bd282820b66993c082bb5f3f296bc Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 20 Feb 2026 13:32:16 +0100 Subject: [PATCH 044/205] reconcile code divergence --- server/workers/orcid/src/orcid_service.py | 71 +++++++++++++++++++---- 1 file changed, 61 insertions(+), 10 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index a7bc938f0..ffa1b33a5 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -161,6 +161,7 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D for batch in batches: start_time = time.time() q_advanced = " OR ".join([f"dcdoi:{doi}" for doi in batch if doi]) + q_advanced = " OR ".join([f"dcdoi:{doi}" for doi in batch if doi]) request_id = str(uuid.uuid4()) @@ -186,8 +187,7 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D 'limit': 360, 'list_size': 360, 'deduplicate_base': 'false', - 'exclude_date_filters': 'true', - 'q_advanced_only': 'true' + 'exclude_date_filters': 'true' }, "endpoint": "search" } @@ -205,9 +205,11 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D }) base_response: str = get_nested_value(result, ["input_data", "metadata"], '[]') # type: ignore - batch_df = pd.DataFrame(json.loads(base_response)) - self._log_is_base_response_missing_dois(batch, batch_df) + if len(batch_df) < len(batch): + self.logger.warning( + f"BASE response shortfall: requested {len(batch)} DOIs, received {len(batch_df)} rows (limit or missing records?)" + ) base_metadata = pd.concat([ base_metadata, @@ -221,7 +223,6 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D os.makedirs(folder) timing_df.to_csv(f'{folder}/stat_base_requests.csv', index=False) - base_metadata["oa_state"] = base_metadata["oa_state"].fillna("2").astype(int) return base_metadata def _prepare_dois_for_base_query(self, dois: List[str]) -> Tuple[List[str], Dict[str, List[str]]]: @@ -347,6 +348,50 @@ def get_unversioned_doi(doi_str): return base_metadata + def _explode_merged_dois(self, base_metadata: pd.DataFrame) -> pd.DataFrame: + """ + Explode merged_dois field to create separate rows for each DOI variant. + + If base_metadata contains a 'merged_dois' field with multiple DOIs separated by '; ', + this function creates separate rows for each DOI, allowing matching with ORCID metadata + by any of those DOIs. + + Parameters: + - base_metadata: DataFrame with BASE metadata, potentially containing 'merged_dois' field + + Returns: + - DataFrame with exploded rows where each row has a single DOI in the 'doi' column + """ + # Process merged_dois: explode to create separate rows for each DOI variant + # This allows us to match BASE records with multiple DOIs to ORCID records by any of those DOIs + if 'merged_dois' in base_metadata.columns: + # Split merged_dois by "; " and create a list of DOIs for each row + # If merged_dois is empty/NaN, create empty list; otherwise split and process each DOI + base_metadata['merged_dois_list'] = base_metadata['merged_dois'].apply( + lambda x: [remove_doi_prefix(doi.strip()) for doi in str(x).split('; ') if doi.strip()] + if pd.notna(x) and str(x).strip() else [] + ) + + # Use explode to create separate rows for each DOI in merged_dois_list + # Rows with empty lists will remain as single rows + base_metadata = base_metadata.explode('merged_dois_list', ignore_index=True) + + # For rows where merged_dois_list is not empty, use it as the DOI + # For rows where merged_dois_list is empty/NaN, use the regular doi column + mask_has_merged_doi = pd.notna(base_metadata['merged_dois_list']) & (base_metadata['merged_dois_list'] != '') + base_metadata.loc[mask_has_merged_doi, 'doi'] = base_metadata.loc[mask_has_merged_doi, 'merged_dois_list'] + + # Process regular doi column for rows without merged_dois + base_metadata.loc[~mask_has_merged_doi, 'doi'] = base_metadata.loc[~mask_has_merged_doi, 'doi'].apply(remove_doi_prefix) + + # Drop temporary column + base_metadata = base_metadata.drop(columns=['merged_dois_list']) + else: + # No merged_dois column, process regular doi column + base_metadata.loc[:, 'doi'] = base_metadata['doi'].apply(remove_doi_prefix) + + return base_metadata + def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFrame) -> pd.DataFrame: self.logger.debug(f"Enriching metadata with base for ORCID {params.get('orcid')}") @@ -378,10 +423,9 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra raw_dois = metadata["doi"].tolist() dois = [doi for doi in raw_dois if doi and pd.notna(doi)] + dois = [doi for doi in raw_dois if doi and pd.notna(doi)] dois_for_base_query, doi_mapping = self._prepare_dois_for_base_query(dois) - self.logger.debug(f"Prepared DOIs for BASE query: {dois_for_base_query}") - self.logger.debug(f"DOI mapping for normalization: {doi_mapping}") base_metadata = self.request_base_metadata(dois_for_base_query, params) if self.logger.isEnabledFor(logging.DEBUG): @@ -398,6 +442,7 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra base_metadata = base_metadata.reindex(columns=required_fields) + #base_metadata = self._explode_merged_dois(base_metadata) base_metadata['merged_dois'] = base_metadata['merged_dois'].apply(lambda x: x[0] if isinstance(x, list) and len(x) > 0 else x) base_metadata['merged_dois'] = base_metadata['merged_dois'].apply(lambda x: x.split(';') if isinstance(x, str) else []) base_metadata['merged_dois'] = base_metadata['merged_dois'].apply(lambda x: [x.strip() for x in x] if isinstance(x, list) else x) @@ -413,10 +458,8 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra base_metadata = self._match_dois_by_version(base_metadata, dois) base_metadata = base_metadata[base_metadata['doi'].isin(dois)] - # Sort ascending so oa_state=1 (open access) rows come before oa_state=2, - # ensuring the most open record is kept when deduplicating by DOI. - base_metadata = base_metadata.sort_values(by='oa_state', ascending=True).drop_duplicates(subset='doi', keep='first') + base_metadata = base_metadata.drop_duplicates(subset='doi', keep='first') if self.logger.isEnabledFor(logging.DEBUG): self._log_dataframe(base_metadata, params, 'base_metadata') @@ -433,12 +476,16 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra # Rename base metadata columns to avoid conflicts with original metadata base_metadata = base_metadata.rename(columns=fields_to_merge) + # Merge base metadata into the original metadata # Merge base metadata into the original metadata enriched_metadata = pd.merge( metadata, base_metadata[['doi'] + list(fields_to_merge.values())], # Use renamed columns from base_metadata on='doi', how='left' + base_metadata[['doi'] + list(fields_to_merge.values())], # Use renamed columns from base_metadata + on='doi', + how='left' ) # Custom merging functions @@ -468,6 +515,10 @@ def custom_merge(existing_value, new_value): lambda row: custom_merge(row['relation'], row['relation_base']), axis=1 ) + # Apply custom logic for link and oa_state and assign results + link_oa_state_values = enriched_metadata.apply(custom_merge_link_oa_state, axis=1) + enriched_metadata['link'], enriched_metadata['oa_state'] = zip(*link_oa_state_values) + enriched_metadata.drop(columns=['paper_abstract_base', 'subject_orig_base', 'subject_base', 'oa_state_base', 'link_base', 'relation_base'], inplace=True) if self.logger.isEnabledFor(logging.DEBUG): From b321c2fe54fe44f8fd3cc1e4371015ef81c3fc70 Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 20 Feb 2026 13:38:20 +0100 Subject: [PATCH 045/205] reconcile code divergence --- server/workers/orcid/src/orcid_service.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index ffa1b33a5..5c50d65e9 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -515,10 +515,6 @@ def custom_merge(existing_value, new_value): lambda row: custom_merge(row['relation'], row['relation_base']), axis=1 ) - # Apply custom logic for link and oa_state and assign results - link_oa_state_values = enriched_metadata.apply(custom_merge_link_oa_state, axis=1) - enriched_metadata['link'], enriched_metadata['oa_state'] = zip(*link_oa_state_values) - enriched_metadata.drop(columns=['paper_abstract_base', 'subject_orig_base', 'subject_base', 'oa_state_base', 'link_base', 'relation_base'], inplace=True) if self.logger.isEnabledFor(logging.DEBUG): From bc83ca36d9fef596f290bae7f4a782ed94f5b71a Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 20 Feb 2026 13:47:35 +0100 Subject: [PATCH 046/205] reconcile code divergence --- server/workers/orcid/src/orcid_service.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 5c50d65e9..428aaa3d4 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -223,6 +223,7 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D os.makedirs(folder) timing_df.to_csv(f'{folder}/stat_base_requests.csv', index=False) + base_metadata["oa_state"] = base_metadata["oa_state"].fillna("2").astype(int) return base_metadata def _prepare_dois_for_base_query(self, dois: List[str]) -> Tuple[List[str], Dict[str, List[str]]]: @@ -458,8 +459,9 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra base_metadata = self._match_dois_by_version(base_metadata, dois) base_metadata = base_metadata[base_metadata['doi'].isin(dois)] - - base_metadata = base_metadata.drop_duplicates(subset='doi', keep='first') + # Sort ascending so oa_state=1 (open access) rows come before oa_state=2, + # ensuring the most open record is kept when deduplicating by DOI. + base_metadata = base_metadata.sort_values(by='oa_state', ascending=True).drop_duplicates(subset='doi', keep='first') if self.logger.isEnabledFor(logging.DEBUG): self._log_dataframe(base_metadata, params, 'base_metadata') From 04bd131784d04f666cef6ae89987310d30b8b1d9 Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 20 Feb 2026 17:56:22 +0100 Subject: [PATCH 047/205] reconcile code divergence --- server/workers/orcid/src/orcid_service.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 428aaa3d4..bd408703c 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -485,9 +485,6 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra base_metadata[['doi'] + list(fields_to_merge.values())], # Use renamed columns from base_metadata on='doi', how='left' - base_metadata[['doi'] + list(fields_to_merge.values())], # Use renamed columns from base_metadata - on='doi', - how='left' ) # Custom merging functions From ed6641b687dbcdc2810a6fb7f49e6d7450e685c4 Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 20 Feb 2026 18:00:39 +0100 Subject: [PATCH 048/205] reconcile code divergence --- server/workers/orcid/src/orcid_service.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index bd408703c..d11b6480e 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -161,7 +161,6 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D for batch in batches: start_time = time.time() q_advanced = " OR ".join([f"dcdoi:{doi}" for doi in batch if doi]) - q_advanced = " OR ".join([f"dcdoi:{doi}" for doi in batch if doi]) request_id = str(uuid.uuid4()) @@ -205,6 +204,7 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D }) base_response: str = get_nested_value(result, ["input_data", "metadata"], '[]') # type: ignore + batch_df = pd.DataFrame(json.loads(base_response)) if len(batch_df) < len(batch): self.logger.warning( @@ -424,7 +424,6 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra raw_dois = metadata["doi"].tolist() dois = [doi for doi in raw_dois if doi and pd.notna(doi)] - dois = [doi for doi in raw_dois if doi and pd.notna(doi)] dois_for_base_query, doi_mapping = self._prepare_dois_for_base_query(dois) base_metadata = self.request_base_metadata(dois_for_base_query, params) @@ -478,7 +477,6 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra # Rename base metadata columns to avoid conflicts with original metadata base_metadata = base_metadata.rename(columns=fields_to_merge) - # Merge base metadata into the original metadata # Merge base metadata into the original metadata enriched_metadata = pd.merge( metadata, From b7fbc8ee9043eaa055647e85c2723c1ea412412d Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 20 Feb 2026 18:06:02 +0100 Subject: [PATCH 049/205] reconcile code divergence --- server/workers/orcid/src/orcid_service.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index d11b6480e..159db7ce4 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -204,8 +204,8 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D }) base_response: str = get_nested_value(result, ["input_data", "metadata"], '[]') # type: ignore - batch_df = pd.DataFrame(json.loads(base_response)) + self._log_is_base_response_missing_dois(batch, batch_df) if len(batch_df) < len(batch): self.logger.warning( f"BASE response shortfall: requested {len(batch)} DOIs, received {len(batch_df)} rows (limit or missing records?)" From b0570451e86105817890c6cab07e7bbc78fe353b Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 20 Feb 2026 18:29:57 +0100 Subject: [PATCH 050/205] enable enrichment for BASE --- server/workers/base/src/base.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/server/workers/base/src/base.py b/server/workers/base/src/base.py index d9277d19b..d0ec08bc2 100644 --- a/server/workers/base/src/base.py +++ b/server/workers/base/src/base.py @@ -274,16 +274,15 @@ def filter_duplicates(df, service): non_datasets = df.loc[df.index.difference(pure_datasets.index)] non_datasets = prioritize_OA_and_latest(non_datasets, dupind) - if service == "orcid": - non_datasets = prioritize_doi_and_provider(non_datasets, dupind) + non_datasets = prioritize_doi_and_provider(non_datasets, dupind) pure_datasets = mark_latest_doi(pure_datasets, dupind) pure_datasets_condition_mask = (pure_datasets.is_anchor == True) | (pure_datasets.is_duplicate == False) pure_datasets.loc[pure_datasets_condition_mask, "is_anchor"] = True - if service == "orcid": - non_datasets = enrich_anchor_using_duplicates(non_datasets, dupind) - pure_datasets = enrich_anchor_using_duplicates(pure_datasets, dupind) + + non_datasets = enrich_anchor_using_duplicates(non_datasets, dupind) + pure_datasets = enrich_anchor_using_duplicates(pure_datasets, dupind) filtered_non_datasets = non_datasets[non_datasets.is_anchor == True] filtered_datasets = pure_datasets[pure_datasets.is_anchor == True] From 08304908f72aa688d0ea9bc21e2a920ef3e8e8f7 Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 20 Feb 2026 18:45:25 +0100 Subject: [PATCH 051/205] reconcile code divergence --- server/workers/orcid/src/orcid_service.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 159db7ce4..bdfc33346 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -186,7 +186,8 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D 'limit': 360, 'list_size': 360, 'deduplicate_base': 'false', - 'exclude_date_filters': 'true' + 'exclude_date_filters': 'true', + 'q_advanced_only': 'true' }, "endpoint": "search" } From b79a7a001c6d18f43536436ae7dc0856ec3e97c0 Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 20 Feb 2026 18:45:25 +0100 Subject: [PATCH 052/205] reconcile code divergence --- server/workers/orcid/src/orcid_service.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 159db7ce4..bdfc33346 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -186,7 +186,8 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D 'limit': 360, 'list_size': 360, 'deduplicate_base': 'false', - 'exclude_date_filters': 'true' + 'exclude_date_filters': 'true', + 'q_advanced_only': 'true' }, "endpoint": "search" } From e6b3749edbde503da3746cc3276b31df2712157d Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 20 Feb 2026 18:59:49 +0100 Subject: [PATCH 053/205] bugfix for oa state priorisation --- server/workers/orcid/src/orcid_service.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index bdfc33346..7cbda12c7 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -459,9 +459,12 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra base_metadata = self._match_dois_by_version(base_metadata, dois) base_metadata = base_metadata[base_metadata['doi'].isin(dois)] - # Sort ascending so oa_state=1 (open access) rows come before oa_state=2, - # ensuring the most open record is kept when deduplicating by DOI. - base_metadata = base_metadata.sort_values(by='oa_state', ascending=True).drop_duplicates(subset='doi', keep='first') + # Sort by oa_state priority (1=open > 0=restricted > 2=unknown) so the + # most open record is kept when deduplicating by DOI. + oa_state_order = {1: 0, 0: 1, 2: 2} + base_metadata = base_metadata.assign( + _oa_sort=base_metadata['oa_state'].map(oa_state_order) + ).sort_values(by='_oa_sort').drop_duplicates(subset='doi', keep='first').drop(columns='_oa_sort') if self.logger.isEnabledFor(logging.DEBUG): self._log_dataframe(base_metadata, params, 'base_metadata') From 3b62fed30e62dd7a268e60a9b17af8eae8fee4a2 Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 20 Feb 2026 19:12:21 +0100 Subject: [PATCH 054/205] activate enrichment pipeline for base --- server/workers/base/src/base.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/server/workers/base/src/base.py b/server/workers/base/src/base.py index d9277d19b..071e63d71 100644 --- a/server/workers/base/src/base.py +++ b/server/workers/base/src/base.py @@ -274,16 +274,15 @@ def filter_duplicates(df, service): non_datasets = df.loc[df.index.difference(pure_datasets.index)] non_datasets = prioritize_OA_and_latest(non_datasets, dupind) - if service == "orcid": - non_datasets = prioritize_doi_and_provider(non_datasets, dupind) + + non_datasets = prioritize_doi_and_provider(non_datasets, dupind) pure_datasets = mark_latest_doi(pure_datasets, dupind) pure_datasets_condition_mask = (pure_datasets.is_anchor == True) | (pure_datasets.is_duplicate == False) pure_datasets.loc[pure_datasets_condition_mask, "is_anchor"] = True - if service == "orcid": - non_datasets = enrich_anchor_using_duplicates(non_datasets, dupind) - pure_datasets = enrich_anchor_using_duplicates(pure_datasets, dupind) + non_datasets = enrich_anchor_using_duplicates(non_datasets, dupind) + pure_datasets = enrich_anchor_using_duplicates(pure_datasets, dupind) filtered_non_datasets = non_datasets[non_datasets.is_anchor == True] filtered_datasets = pure_datasets[pure_datasets.is_anchor == True] From 8d388361667ec0343b92f3f93c96533354126c61 Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 20 Feb 2026 19:17:18 +0100 Subject: [PATCH 055/205] revert to original approach --- server/workers/orcid/src/orcid_service.py | 16 +++++----------- 1 file changed, 5 insertions(+), 11 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 7cbda12c7..224280527 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -421,7 +421,7 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra # run only if loglevel is debug, otherwise it is too expensive and we don't want it on production if self.logger.isEnabledFor(logging.DEBUG): - self._log_dataframe(metadata, params, '_original') + self._log_dataframe(metadata.sort_values(by='title'), params, '_original') raw_dois = metadata["doi"].tolist() dois = [doi for doi in raw_dois if doi and pd.notna(doi)] @@ -430,8 +430,7 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra base_metadata = self.request_base_metadata(dois_for_base_query, params) if self.logger.isEnabledFor(logging.DEBUG): - self._log_dataframe(base_metadata, params, 'base_metadata_raw') - + self._log_dataframe(base_metadata.sort_values(by='title'), params, 'base_metadata_raw') # dataframe # paper, doi= "10.17169/refubium-48053; 10.1371/journal.pone.0311918" # 1. step: split on "; " -> ["10.17169/refubium-48053", "10.1371/journal.pone.0311918"] @@ -459,14 +458,9 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra base_metadata = self._match_dois_by_version(base_metadata, dois) base_metadata = base_metadata[base_metadata['doi'].isin(dois)] - # Sort by oa_state priority (1=open > 0=restricted > 2=unknown) so the - # most open record is kept when deduplicating by DOI. - oa_state_order = {1: 0, 0: 1, 2: 2} - base_metadata = base_metadata.assign( - _oa_sort=base_metadata['oa_state'].map(oa_state_order) - ).sort_values(by='_oa_sort').drop_duplicates(subset='doi', keep='first').drop(columns='_oa_sort') + base_metadata = base_metadata.drop_duplicates(subset='doi', keep='first') if self.logger.isEnabledFor(logging.DEBUG): - self._log_dataframe(base_metadata, params, 'base_metadata') + self._log_dataframe(base_metadata.sort_values(by='title'), params, 'base_metadata') # Select and rename relevant fields from base_metadata, including subject_orig fields_to_merge = { @@ -519,7 +513,7 @@ def custom_merge(existing_value, new_value): enriched_metadata.drop(columns=['paper_abstract_base', 'subject_orig_base', 'subject_base', 'oa_state_base', 'link_base', 'relation_base'], inplace=True) if self.logger.isEnabledFor(logging.DEBUG): - self._log_dataframe(enriched_metadata, params, '_enriched') + self._log_dataframe(enriched_metadata.sort_values(by='title'), params, '_enriched') # temporal solution, for some reason if we have some undefined data, dataprocessing is failing enriched_metadata = enriched_metadata.reindex(columns=list(set(original_columns + ['oa_state', 'subject', 'subject_orig', 'paper_abstract', 'link', 'relation']))) From 11b8d86a803ba97f96038b856fa47af69c33eec7 Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 20 Feb 2026 20:11:48 +0100 Subject: [PATCH 056/205] bugfix for handling duplicates --- server/workers/orcid/src/orcid_service.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 224280527..cf6bc2ce0 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -458,7 +458,12 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra base_metadata = self._match_dois_by_version(base_metadata, dois) base_metadata = base_metadata[base_metadata['doi'].isin(dois)] - base_metadata = base_metadata.drop_duplicates(subset='doi', keep='first') + # Sort by oa_state priority (1=open > 0=restricted > 2=unknown) so the + # most open record is kept when deduplicating by DOI. + oa_state_order = {1: 0, 0: 1, 2: 2} + base_metadata = base_metadata.assign( + _oa_sort=base_metadata['oa_state'].map(oa_state_order) + ).sort_values(by='_oa_sort').drop_duplicates(subset='doi', keep='first').drop(columns='_oa_sort') if self.logger.isEnabledFor(logging.DEBUG): self._log_dataframe(base_metadata.sort_values(by='title'), params, 'base_metadata') From 5c8fb3a5c1e89be2d75ac90a17792b8a2b5901b3 Mon Sep 17 00:00:00 2001 From: chreman Date: Sun, 22 Feb 2026 17:13:17 +0100 Subject: [PATCH 057/205] bugfix for batch retrieval --- server/preprocessing/other-scripts/base.R | 57 +++++++++++------------ server/workers/base/src/base.py | 31 +++++++++++- server/workers/orcid/src/orcid_service.py | 3 +- 3 files changed, 56 insertions(+), 35 deletions(-) diff --git a/server/preprocessing/other-scripts/base.R b/server/preprocessing/other-scripts/base.R index 5a7ab46ef..9e45e4f71 100644 --- a/server/preprocessing/other-scripts/base.R +++ b/server/preprocessing/other-scripts/base.R @@ -156,37 +156,32 @@ get_papers <- function(query, params, has_custom_clustering_annotation <- unlist(lapply(metadata$subject_orig, function(x) grepl(paste0(cc, ":"), x, fixed=TRUE))) metadata <- metadata[has_custom_clustering_annotation,] }} - # don't deduplicate if params$deduplicate_base is set to FALSE - if (!is.null(params$deduplicate_base) && params$deduplicate_base != FALSE) { - # log to skip deduplication - blog$info(paste("vis_id:", .GlobalEnv$VIS_ID, "Deduplication skipped")) - } else { - while (nrow(metadata) - sum(metadata$is_duplicate) < limit && attr(res_raw, "numFound") > offset+120 && r < req_limit) { - offset <- offset+120 - res_raw <- get_raw_data(limit, - base_query, - return_fields, - sortby_string, - filter, - repo, - coll, - retry_opts, - offset, - non_public) - res <- bind_rows(res, res_raw$docs) - metadata <- etl(res, repo, non_public) - metadata <- unique(metadata, by = "id") - metadata <- sanitize_abstract(metadata) - metadata <- mark_duplicates(metadata) - metadata$has_dataset <- unlist(lapply(metadata$resulttype, function(x) "Dataset" %in% x)) - # check if custom clustering annotation param is in metadata - if (!is.null(cc)) { - if (!(cc %in% names(fieldmapper))) { - has_custom_clustering_annotation <- unlist(lapply(metadata$subject_orig, function(x) grepl(paste0(cc, ":"), x, fixed=TRUE))) - metadata <- metadata[has_custom_clustering_annotation,] - }} - r <- r+1 - } + + while (nrow(metadata) - sum(metadata$is_duplicate) < limit && attr(res_raw, "numFound") > offset+120 && r < req_limit) { + offset <- offset+120 + res_raw <- get_raw_data(limit, + base_query, + return_fields, + sortby_string, + filter, + repo, + coll, + retry_opts, + offset, + non_public) + res <- bind_rows(res, res_raw$docs) + metadata <- etl(res, repo, non_public) + metadata <- unique(metadata, by = "id") + metadata <- sanitize_abstract(metadata) + metadata <- mark_duplicates(metadata) + metadata$has_dataset <- unlist(lapply(metadata$resulttype, function(x) "Dataset" %in% x)) + # check if custom clustering annotation param is in metadata + if (!is.null(cc)) { + if (!(cc %in% names(fieldmapper))) { + has_custom_clustering_annotation <- unlist(lapply(metadata$subject_orig, function(x) grepl(paste0(cc, ":"), x, fixed=TRUE))) + metadata <- metadata[has_custom_clustering_annotation,] + }} + r <- r+1 } # check if custom clustering annotation param is in metadata if (!is.null(cc)) { diff --git a/server/workers/base/src/base.py b/server/workers/base/src/base.py index 071e63d71..320af9689 100644 --- a/server/workers/base/src/base.py +++ b/server/workers/base/src/base.py @@ -28,6 +28,7 @@ from datetime import datetime import dateparser import sys +from typing import Dict from common.rate_limiter import RateLimiter logger = logging.getLogger(__name__) @@ -86,7 +87,7 @@ def execute_search(self, params): else: metadata = pd.DataFrame(raw_metadata) metadata = self.sanitize_metadata(metadata) - metadata = filter_duplicates(metadata, original_service) + metadata = filter_duplicates(metadata, original_service, params) metadata = pd.concat( [metadata, parse_annotations_for_all(metadata, "subject_orig")], axis=1, @@ -240,7 +241,11 @@ def handle_contentproviders(self, request_id, params): pattern_annotations = re.compile(r"([A-Za-z]+:[\w'\- ]+);?") -def filter_duplicates(df, service): +def filter_duplicates(df, service, params): + if logger.isEnabledFor(logging.DEBUG): + logger.debug(f"Filtering duplicates for service: {service}") + logger.debug(f"Initial number of records: {len(df)}") + _log_dataframe(df, params, "initial_records") df.drop_duplicates("id", inplace=True, keep="first") df["is_anchor"] = False df["doi_duplicate"] = False @@ -303,6 +308,9 @@ def filter_duplicates(df, service): if c in filtered.columns: filtered.drop(c, axis=1, inplace=True) + if logger.isEnabledFor(logging.DEBUG): + logger.debug(f"Number of records after filtering: {len(filtered)}") + _log_dataframe(filtered, params, "filtered_records") return filtered @@ -362,3 +370,22 @@ def sanitize_year(year_str): sanitized_year = year_str # here we keep the original string return sanitized_year + +def _log_dataframe(df: pd.DataFrame, params: Dict[str, str], name: str, ): + vis_id = params.get('vis_id') + + columns_to_print = ['id', 'title', 'doi', 'merged_dois', 'paper_abstract', 'link', 'subject', 'subject_orig', 'oa_state'] + + available_columns = df.columns.tolist() + columns_to_print = [col for col in columns_to_print if col in available_columns] + + transformed = df.copy().reindex(columns=columns_to_print) + + transformed = transformed.fillna(value='missing') + + # create folder + folder = f'./output/{vis_id}' + if not os.path.exists(folder): + os.makedirs(folder) + file_path = f"{folder}/{name}.csv" + transformed.to_csv(file_path, index=False) \ No newline at end of file diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index cf6bc2ce0..4b00b64ec 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -152,7 +152,7 @@ def _log_dataframe(self, df: pd.DataFrame, params: Dict[str, str], name: str, ): def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.DataFrame: orcid = params.get('orcid') - batch_size = 15 + batch_size = 20 batches = [dois[i:i + batch_size] for i in range(0, len(dois), batch_size)] base_metadata = pd.DataFrame(dtype=object) @@ -185,7 +185,6 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D 'vis_id': request_id, 'limit': 360, 'list_size': 360, - 'deduplicate_base': 'false', 'exclude_date_filters': 'true', 'q_advanced_only': 'true' }, From f54237169e2147b49bf710c9e2417c22d74cc3af Mon Sep 17 00:00:00 2001 From: andrei Date: Mon, 23 Feb 2026 10:55:07 +0100 Subject: [PATCH 058/205] refactor: remove code doubling in logging --- server/workers/orcid/src/orcid_service.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 4b00b64ec..0e32e2f60 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -206,10 +206,6 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D base_response: str = get_nested_value(result, ["input_data", "metadata"], '[]') # type: ignore batch_df = pd.DataFrame(json.loads(base_response)) self._log_is_base_response_missing_dois(batch, batch_df) - if len(batch_df) < len(batch): - self.logger.warning( - f"BASE response shortfall: requested {len(batch)} DOIs, received {len(batch_df)} rows (limit or missing records?)" - ) base_metadata = pd.concat([ base_metadata, From a252eb16e947131356162618737f56581548d046 Mon Sep 17 00:00:00 2001 From: andrei Date: Mon, 23 Feb 2026 17:11:15 +0100 Subject: [PATCH 059/205] feat: a new filed that contains merged links --- server/workers/base/src/base.py | 1 + server/workers/common/common/enrichment.py | 6 ++++-- server/workers/orcid/src/orcid_service.py | 2 +- 3 files changed, 6 insertions(+), 3 deletions(-) diff --git a/server/workers/base/src/base.py b/server/workers/base/src/base.py index 320af9689..12fbd6626 100644 --- a/server/workers/base/src/base.py +++ b/server/workers/base/src/base.py @@ -251,6 +251,7 @@ def filter_duplicates(df, service, params): df["doi_duplicate"] = False df["has_relations"] = False df["link_duplicate"] = False + df["pdf_link_candidates_from_duplicates"] = "" df["duplicates"] = df.apply( lambda x: ",".join([x["id"], x["duplicates"]]) if len(x["duplicates"].split(",")) >= 1 diff --git a/server/workers/common/common/enrichment.py b/server/workers/common/common/enrichment.py index df58da470..fd11ef34e 100644 --- a/server/workers/common/common/enrichment.py +++ b/server/workers/common/common/enrichment.py @@ -261,7 +261,9 @@ def apply_oa_state_improvements(df, anchor_idx, accumulator): def apply_link_improvements(df, anchor_idx, all_links): """ - Applies improvements for link to the anchor element. + Applies improvements for link to the anchor element: set in + pdf_link_candidates_from_duplicates column if there are any links + from duplicates that can be used for PDF lookup. Args: df: DataFrame with data @@ -272,4 +274,4 @@ def apply_link_improvements(df, anchor_idx, all_links): unique_links = deduplicate_links(all_links) if unique_links: merged_links = '; '.join(sorted(unique_links)) - df.loc[anchor_idx, 'link'] = merged_links + df.loc[anchor_idx, 'pdf_link_candidates_from_duplicates'] = merged_links diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 0e32e2f60..e138ee274 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -400,7 +400,7 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra 'relations', 'annotations', 'repo', 'source', 'volume', 'issue', 'page', 'issn', 'citation_count', 'cited_by_wikipedia_count', 'cited_by_msm_count', 'cited_by_policies_count', 'cited_by_patents_count', 'cited_by_accounts_count', 'cited_by_fbwalls_count', - 'merged_dois', + 'merged_dois', 'pdf_link_candidates_from_duplicates', 'cited_by_feeds_count', 'cited_by_gplus_count', 'cited_by_rdts_count', From 3af393c55f80f0ba525c1dbe656d316fd80c0822 Mon Sep 17 00:00:00 2001 From: andrei Date: Mon, 23 Feb 2026 17:29:56 +0100 Subject: [PATCH 060/205] feat: remove anchor link from the array with links from doubles --- server/workers/common/common/enrichment.py | 17 ++++++++++++++++- 1 file changed, 16 insertions(+), 1 deletion(-) diff --git a/server/workers/common/common/enrichment.py b/server/workers/common/common/enrichment.py index fd11ef34e..79fa1931e 100644 --- a/server/workers/common/common/enrichment.py +++ b/server/workers/common/common/enrichment.py @@ -273,5 +273,20 @@ def apply_link_improvements(df, anchor_idx, all_links): if all_links: unique_links = deduplicate_links(all_links) if unique_links: - merged_links = '; '.join(sorted(unique_links)) + anchor_link = get_anchor_field_value(df, anchor_idx, 'link') + unique_links_without_anchor_link = [x for x in unique_links if x != anchor_link] + + merged_links = '; '.join(sorted(unique_links_without_anchor_link)) df.loc[anchor_idx, 'pdf_link_candidates_from_duplicates'] = merged_links + +def get_anchor_field_value(df, anchor_idx, column_name): + """ + Returns the value of the given column for the anchor row, or None if + the column is missing or the value is empty/NaN. + """ + if column_name not in df.columns: + return None + value = df.loc[anchor_idx, column_name] + if pd.isna(value) or value == '': + return None + return value From 3cc946e00c1abc257c296102d117e1ada567c4fe Mon Sep 17 00:00:00 2001 From: andrei Date: Tue, 24 Feb 2026 09:59:52 +0100 Subject: [PATCH 061/205] feat: parsing of pdf link candidates --- vis/js/dataprocessing/managers/DataManager.ts | 4 ++++ vis/js/types/models/paper.ts | 2 ++ vis/js/utils/data.ts | 19 +++++++++++++++++++ vis/test/data/papers.ts | 2 ++ 4 files changed, 27 insertions(+) diff --git a/vis/js/dataprocessing/managers/DataManager.ts b/vis/js/dataprocessing/managers/DataManager.ts index f5d7eced6..436bd1e65 100644 --- a/vis/js/dataprocessing/managers/DataManager.ts +++ b/vis/js/dataprocessing/managers/DataManager.ts @@ -14,6 +14,7 @@ import { getListLink, getOpenAccessLink, getOutlink, + getPdfLinkCandidatesFromDuplicates, getValueOrZero, getVisibleMetric, isOpenAccess, @@ -257,6 +258,9 @@ class DataManager { paper.oa_link = getOpenAccessLink(paper, this.config); paper.outlink = getOutlink(paper, this.config); paper.list_link = getListLink(paper, this.config, this.context); + + paper.pdf_link_candidates_from_duplicates = + getPdfLinkCandidatesFromDuplicates(paper); } __parseComments(paper: any) { diff --git a/vis/js/types/models/paper.ts b/vis/js/types/models/paper.ts index a87a440c6..cf43198ba 100644 --- a/vis/js/types/models/paper.ts +++ b/vis/js/types/models/paper.ts @@ -68,6 +68,8 @@ export interface CommonPaperDataForAllIntegrations { zoomedY: number; zoomedWidth: number; zoomedHeight: number; + + pdf_link_candidates_from_duplicates: string[] | null; } export interface PubmedPaper extends CommonPaperDataForAllIntegrations { diff --git a/vis/js/utils/data.ts b/vis/js/utils/data.ts index 76585c686..3f16568ab 100644 --- a/vis/js/utils/data.ts +++ b/vis/js/utils/data.ts @@ -285,6 +285,25 @@ export const getListLink = (paper, config, context) => { return {}; }; +/** + * Parses the paper's pdf link candidates from duplicates into an array of strings. + * + * @param {object} paper paper object + * + * @returns array of strings or null if no candidates are found + */ +export const getPdfLinkCandidatesFromDuplicates = (paper): string[] | null => { + if ( + typeof paper.pdf_link_candidates_from_duplicates !== "string" || + !paper.pdf_link_candidates_from_duplicates + ) { + return null; + } + + const links = paper.pdf_link_candidates_from_duplicates.split(";"); + return links.length > 0 ? links : null; +}; + /** * Parses the paper's authors string into an object array. * diff --git a/vis/test/data/papers.ts b/vis/test/data/papers.ts index 32f6f6c71..496602362 100644 --- a/vis/test/data/papers.ts +++ b/vis/test/data/papers.ts @@ -55,6 +55,8 @@ const MOCK_COMMON_PAPER_DATA: CommonPaperDataForAllIntegrations = { zoomedY: 1, zoomedWidth: 1, zoomedHeight: 1, + + pdf_link_candidates_from_duplicates: null, }; export const MOCK_BASE_PAPER_DATA: BasePaper = { From 2b22d6954fef82c5a7a831733ee6975ac4743a2b Mon Sep 17 00:00:00 2001 From: andrei Date: Tue, 24 Feb 2026 10:20:09 +0100 Subject: [PATCH 062/205] feat: usage of the pdf link candidates for pdf loading --- vis/js/utils/usePdfLookup.ts | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/vis/js/utils/usePdfLookup.ts b/vis/js/utils/usePdfLookup.ts index 02537fe2d..897b83dd8 100644 --- a/vis/js/utils/usePdfLookup.ts +++ b/vis/js/utils/usePdfLookup.ts @@ -50,6 +50,12 @@ const usePdfLookup = (paper: Paper, serverUrl: string, service: string) => { let possiblePDFs = ""; let fallbackUrl = ""; if (service === "base") { + let pdfLinkCandidatesFromDuplicates = null; + + if ("pdf_link_candidates_from_duplicates" in paper) { + pdfLinkCandidatesFromDuplicates = paper.pdf_link_candidates_from_duplicates as string[] | null; + } + possiblePDFs = encodeURIComponent(paper.link) + ";" + @@ -59,6 +65,10 @@ const usePdfLookup = (paper: Paper, serverUrl: string, service: string) => { .split("; ") .map((x) => encodeURIComponent(x)) .join("; "); + + if (pdfLinkCandidatesFromDuplicates) { + possiblePDFs += ";" + pdfLinkCandidatesFromDuplicates.map((x: string) => encodeURIComponent(x)).join("; "); + } } if (service === "openaire") { From c721d9128905e917505da401d79fbfdc8e95ec3c Mon Sep 17 00:00:00 2001 From: andrei Date: Tue, 24 Feb 2026 17:10:06 +0100 Subject: [PATCH 063/205] refactor: set initial batch size --- server/workers/orcid/src/orcid_service.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index e138ee274..982b6c7f6 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -152,7 +152,7 @@ def _log_dataframe(self, df: pd.DataFrame, params: Dict[str, str], name: str, ): def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.DataFrame: orcid = params.get('orcid') - batch_size = 20 + batch_size = 25 batches = [dois[i:i + batch_size] for i in range(0, len(dois), batch_size)] base_metadata = pd.DataFrame(dtype=object) From ca9fb8c11218b1c72ff289f7e3740553d343acd4 Mon Sep 17 00:00:00 2001 From: andrei Date: Tue, 24 Feb 2026 17:11:26 +0100 Subject: [PATCH 064/205] refactor: remove commented code --- server/workers/orcid/src/orcid_service.py | 45 ----------------------- 1 file changed, 45 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index e138ee274..055badf92 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -345,50 +345,6 @@ def get_unversioned_doi(doi_str): return base_metadata - def _explode_merged_dois(self, base_metadata: pd.DataFrame) -> pd.DataFrame: - """ - Explode merged_dois field to create separate rows for each DOI variant. - - If base_metadata contains a 'merged_dois' field with multiple DOIs separated by '; ', - this function creates separate rows for each DOI, allowing matching with ORCID metadata - by any of those DOIs. - - Parameters: - - base_metadata: DataFrame with BASE metadata, potentially containing 'merged_dois' field - - Returns: - - DataFrame with exploded rows where each row has a single DOI in the 'doi' column - """ - # Process merged_dois: explode to create separate rows for each DOI variant - # This allows us to match BASE records with multiple DOIs to ORCID records by any of those DOIs - if 'merged_dois' in base_metadata.columns: - # Split merged_dois by "; " and create a list of DOIs for each row - # If merged_dois is empty/NaN, create empty list; otherwise split and process each DOI - base_metadata['merged_dois_list'] = base_metadata['merged_dois'].apply( - lambda x: [remove_doi_prefix(doi.strip()) for doi in str(x).split('; ') if doi.strip()] - if pd.notna(x) and str(x).strip() else [] - ) - - # Use explode to create separate rows for each DOI in merged_dois_list - # Rows with empty lists will remain as single rows - base_metadata = base_metadata.explode('merged_dois_list', ignore_index=True) - - # For rows where merged_dois_list is not empty, use it as the DOI - # For rows where merged_dois_list is empty/NaN, use the regular doi column - mask_has_merged_doi = pd.notna(base_metadata['merged_dois_list']) & (base_metadata['merged_dois_list'] != '') - base_metadata.loc[mask_has_merged_doi, 'doi'] = base_metadata.loc[mask_has_merged_doi, 'merged_dois_list'] - - # Process regular doi column for rows without merged_dois - base_metadata.loc[~mask_has_merged_doi, 'doi'] = base_metadata.loc[~mask_has_merged_doi, 'doi'].apply(remove_doi_prefix) - - # Drop temporary column - base_metadata = base_metadata.drop(columns=['merged_dois_list']) - else: - # No merged_dois column, process regular doi column - base_metadata.loc[:, 'doi'] = base_metadata['doi'].apply(remove_doi_prefix) - - return base_metadata - def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFrame) -> pd.DataFrame: self.logger.debug(f"Enriching metadata with base for ORCID {params.get('orcid')}") @@ -437,7 +393,6 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra base_metadata = base_metadata.reindex(columns=required_fields) - #base_metadata = self._explode_merged_dois(base_metadata) base_metadata['merged_dois'] = base_metadata['merged_dois'].apply(lambda x: x[0] if isinstance(x, list) and len(x) > 0 else x) base_metadata['merged_dois'] = base_metadata['merged_dois'].apply(lambda x: x.split(';') if isinstance(x, str) else []) base_metadata['merged_dois'] = base_metadata['merged_dois'].apply(lambda x: [x.strip() for x in x] if isinstance(x, list) else x) From a4510cefb01e2e1d76c4a79ba6f9b14695a0f622 Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 27 Feb 2026 18:13:37 +0100 Subject: [PATCH 065/205] analytic tool update --- local_dev/tools/analytics/compare_orcid_logs.py | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/local_dev/tools/analytics/compare_orcid_logs.py b/local_dev/tools/analytics/compare_orcid_logs.py index 315cc6c14..de9ffd1d1 100644 --- a/local_dev/tools/analytics/compare_orcid_logs.py +++ b/local_dev/tools/analytics/compare_orcid_logs.py @@ -50,6 +50,21 @@ def main(): before_all = [] after_all = [] + # only keep orcids that have data in both logs for the per-ORCID comparison, remove all else + all_orcids = [orcid for orcid in all_orcids if orcid in before and orcid in after] + + # print which ORCIDs will be excluded due to missing data in either log + exluded_from_before = set(after.keys()) - set(before.keys()) + exluded_from_after = set(before.keys()) - set(after.keys()) + if exluded_from_before: + print(f"Excluded ORCID IDs due to missing data in BEFORE log: {', '.join(sorted(exluded_from_before))}") + if exluded_from_after: + print(f"Excluded ORCID IDs due to missing data in AFTER log: {', '.join(sorted(exluded_from_after))}") + + # filter before and after to only include ORCIDs that have data in both logs + before = {orcid: before[orcid] for orcid in all_orcids if orcid in before} + after = {orcid: after[orcid] for orcid in all_orcids if orcid in after} + for orcid in all_orcids: before_all.extend(before.get(orcid, [])) after_all.extend(after.get(orcid, [])) From 995749a08f79db908bc4ddb4ddc56f16104e70af Mon Sep 17 00:00:00 2001 From: chreman Date: Sun, 1 Mar 2026 22:41:38 +0100 Subject: [PATCH 066/205] group orcids before analysis --- .../tools/analytics/compare_orcid_logs.py | 21 ++++++++----------- 1 file changed, 9 insertions(+), 12 deletions(-) diff --git a/local_dev/tools/analytics/compare_orcid_logs.py b/local_dev/tools/analytics/compare_orcid_logs.py index de9ffd1d1..4077884f7 100644 --- a/local_dev/tools/analytics/compare_orcid_logs.py +++ b/local_dev/tools/analytics/compare_orcid_logs.py @@ -48,8 +48,6 @@ def main(): all_orcids = sorted(set(list(before.keys()) + list(after.keys()))) - before_all = [] - after_all = [] # only keep orcids that have data in both logs for the per-ORCID comparison, remove all else all_orcids = [orcid for orcid in all_orcids if orcid in before and orcid in after] @@ -65,10 +63,6 @@ def main(): before = {orcid: before[orcid] for orcid in all_orcids if orcid in before} after = {orcid: after[orcid] for orcid in all_orcids if orcid in after} - for orcid in all_orcids: - before_all.extend(before.get(orcid, [])) - after_all.extend(after.get(orcid, [])) - print("=" * 60) print("BEFORE vs AFTER comparison (ORCID worker)") print("=" * 60) @@ -90,17 +84,20 @@ def main(): print(f" Change in median: {a_stats['median'] - b_stats['median']:+.3f}s ({(a_stats['median'] / b_stats['median'] - 1) * 100:+.1f}%)") print(f" Change in mean: {a_stats['mean'] - b_stats['mean']:+.3f}s ({(a_stats['mean'] / b_stats['mean'] - 1) * 100:+.1f}%)") - # Overall stats + # Overall stats: aggregate per-ORCID means (one value per ORCID) + before_means = [statistics.mean(before[orcid]) for orcid in all_orcids] + after_means = [statistics.mean(after[orcid]) for orcid in all_orcids] + print(f"\n{'=' * 60}") - print("ALL ORCID IDs COMBINED") + print("ALL ORCID IDs COMBINED (per-ORCID means)") print("=" * 60) b_total = a_total = None - if before_all: - b_total = print_stats("Before", before_all) + if before_means: + b_total = print_stats("Before", before_means) else: print(" Before: (no data)") - if after_all: - a_total = print_stats("After", after_all) + if after_means: + a_total = print_stats("After", after_means) else: print(" After: (no data)") if b_total and a_total: From d8c66022871ea99a0a50d0a08859d131b98bf34f Mon Sep 17 00:00:00 2001 From: chreman Date: Mon, 11 May 2026 14:40:29 +0200 Subject: [PATCH 067/205] maintenance: remove replace strategy in enrichment --- server/workers/common/common/enrichment.py | 65 ++++++---------------- 1 file changed, 17 insertions(+), 48 deletions(-) diff --git a/server/workers/common/common/enrichment.py b/server/workers/common/common/enrichment.py index 79fa1931e..a813f04e8 100644 --- a/server/workers/common/common/enrichment.py +++ b/server/workers/common/common/enrichment.py @@ -2,11 +2,6 @@ import pandas as pd from common.deduplication import deduplicate_keywords, deduplicate_links -ENRICHMENT_STRATEGY_FOR_SUBJECT = os.getenv("ENRICHMENT_STRATEGY_FOR_SUBJECT") - -STRATEGY_REPLACE = 'replace' -STRATEGY_MERGE = 'merge' - KEYWORD_SIMILARITY_THRESHOLD = 85 OA_STATE_PRIORITY = { @@ -15,7 +10,7 @@ "2": 2, # unknown } -def enrich_anchor_using_duplicates(df, dupind, subject_strategy=ENRICHMENT_STRATEGY_FOR_SUBJECT): +def enrich_anchor_using_duplicates(df, dupind): """ Enriches anchor elements using data from duplicates in their groups. @@ -24,8 +19,8 @@ def enrich_anchor_using_duplicates(df, dupind, subject_strategy=ENRICHMENT_STRAT All improvements are done in a single pass through the group for efficiency. List of improvements: - - subject_orig: processed according to subject_strategy - - subject: processed according to subject_strategy + - subject_orig: processed according to merge strategy (merge all keywords from duplicates, remove duplicates, sort alphabetically) + - subject: processed according to merge strategy (merge all keywords from duplicates, remove duplicates, sort alphabetically) - paper_abstract: replaced with the longest description - oa_state: replaced with the highest priority status (yes > no > unknown) - link: merged from all duplicates, duplicates links are removed (https > http) @@ -33,9 +28,6 @@ def enrich_anchor_using_duplicates(df, dupind, subject_strategy=ENRICHMENT_STRAT Args: df: DataFrame with metadata, containing the column is_anchor dupind: Series with indices of duplicates for each id - subject_strategy: Strategy for processing keywords ('replace' or 'merge') - - 'replace': select the value with the highest number of keywords - - 'merge': combine all keywords from the group, removing duplicates Returns: DataFrame with improved anchor properties @@ -50,8 +42,6 @@ def enrich_anchor_using_duplicates(df, dupind, subject_strategy=ENRICHMENT_STRAT if is_all_columns_are_missing: return df - is_use_merge_strategy = subject_strategy == STRATEGY_MERGE - for _, idx in dupind.items(): idx = df.index.intersection(idx) @@ -80,11 +70,11 @@ def enrich_anchor_using_duplicates(df, dupind, subject_strategy=ENRICHMENT_STRAT for element_idx in idx: if has_subject_orig: subject_orig_value = group_data.loc[element_idx, 'subject_orig'] - process_subject_orig_element(subject_orig_value, subject_orig_acc, is_use_merge_strategy) + process_subject_orig_element(subject_orig_value, subject_orig_acc) if has_subject: subject_value = group_data.loc[element_idx, 'subject'] - process_subject_element(subject_value, subject_acc, is_use_merge_strategy) + process_subject_element(subject_value, subject_acc) if has_paper_abstract: paper_abstract_value = group_data.loc[element_idx, 'paper_abstract'] @@ -99,10 +89,10 @@ def enrich_anchor_using_duplicates(df, dupind, subject_strategy=ENRICHMENT_STRAT process_link_element(link_value, all_links) if has_subject_orig: - apply_subject_improvements(df, anchor_idx, subject_orig_acc, is_use_merge_strategy, 'subject_orig') + apply_subject_improvements(df, anchor_idx, subject_orig_acc, 'subject_orig') if has_subject: - apply_subject_improvements(df, anchor_idx, subject_acc, is_use_merge_strategy, 'subject') + apply_subject_improvements(df, anchor_idx, subject_acc, 'subject') if has_paper_abstract: apply_paper_abstract_improvements(df, anchor_idx, paper_abstract_acc) @@ -115,49 +105,35 @@ def enrich_anchor_using_duplicates(df, dupind, subject_strategy=ENRICHMENT_STRAT return df -def process_subject_orig_element(value, accumulator, use_merge_strategy): +def process_subject_orig_element(value, accumulator): """ Processes the subject_orig value for one element of the group. Args: value: The subject_orig value from the element accumulator: Dictionary with accumulative data - use_merge_strategy: If True, uses the merge strategy, otherwise replace """ is_not_empty = not (pd.isna(value) or value == '') if not is_not_empty: return keywords = [kw.strip() for kw in str(value).split(';') if kw.strip()] - if use_merge_strategy: - accumulator['all_keywords'].update(keywords) - else: - keyword_count = len(keywords) - if keyword_count > accumulator['best_count']: - accumulator['best_count'] = keyword_count - accumulator['best_value'] = value - -def process_subject_element(value, accumulator, use_merge_strategy): + accumulator['all_keywords'].update(keywords) + +def process_subject_element(value, accumulator): """ Processes the subject value for one element of the group. Args: value: The subject value from the element accumulator: Dictionary with accumulative data - use_merge_strategy: If True, uses the merge strategy, otherwise replace """ is_not_empty = not (pd.isna(value) or value == '') if not is_not_empty: return keywords = [kw.strip() for kw in str(value).split(';') if kw.strip()] - if use_merge_strategy: - accumulator['all_keywords'].update(keywords) - else: - keyword_count = len(keywords) - if keyword_count > accumulator['best_count']: - accumulator['best_count'] = keyword_count - accumulator['best_value'] = value + accumulator['all_keywords'].update(keywords) def process_paper_abstract_element(value, accumulator): """ @@ -209,7 +185,7 @@ def process_link_element(value, accumulator): links = [link.strip() for link in str(value).split(';') if link.strip()] accumulator.update(links) -def apply_subject_improvements(df, anchor_idx, accumulator, use_merge_strategy, column_name): +def apply_subject_improvements(df, anchor_idx, accumulator, column_name): """ Applies improvements for subject or subject_orig to the anchor element. @@ -217,19 +193,12 @@ def apply_subject_improvements(df, anchor_idx, accumulator, use_merge_strategy, df: DataFrame with data anchor_idx: Index of the anchor element accumulator: Dictionary with accumulative data - use_merge_strategy: If True, uses the merge strategy, otherwise replace column_name: Column name ('subject' or 'subject_orig') """ - if use_merge_strategy: - if accumulator['all_keywords']: - unique_keywords = deduplicate_keywords(accumulator['all_keywords'], KEYWORD_SIMILARITY_THRESHOLD) - merged_value = '; '.join(sorted(unique_keywords)) - df.loc[anchor_idx, column_name] = merged_value - else: - if accumulator['best_value'] is not None: - current = df.loc[anchor_idx, column_name] - if pd.isna(current) or str(current) != str(accumulator['best_value']): - df.loc[anchor_idx, column_name] = accumulator['best_value'] + if accumulator['all_keywords']: + unique_keywords = deduplicate_keywords(accumulator['all_keywords'], KEYWORD_SIMILARITY_THRESHOLD) + merged_value = '; '.join(sorted(unique_keywords)) + df.loc[anchor_idx, column_name] = merged_value def apply_paper_abstract_improvements(df, anchor_idx, accumulator): """ From 2286f7f7f57bb5a218fde3b1de093b6ae689624e Mon Sep 17 00:00:00 2001 From: chreman Date: Mon, 11 May 2026 18:44:48 +0200 Subject: [PATCH 068/205] update of contentprovider.json cache --- .../common/common/contentproviders.json | 246 +++++++++++++++--- 1 file changed, 217 insertions(+), 29 deletions(-) diff --git a/server/workers/common/common/contentproviders.json b/server/workers/common/common/contentproviders.json index b47f12af5..ce7caf2b3 100644 --- a/server/workers/common/common/contentproviders.json +++ b/server/workers/common/common/contentproviders.json @@ -1,4 +1,212 @@ [ + { + "name": "Revista Econômica do Nordeste", + "internal_name": "ftid14670" + }, + { + "name": "Cagayan State University (CSU)", + "internal_name": "ftid14732" + }, + { + "name": "Revista Internacional de Ciencias Económicas y Sociales RICESO", + "internal_name": "ftid14733" + }, + { + "name": "iEditore Journals", + "internal_name": "ftid14734" + }, + { + "name": "Journal of Engineering Research (Libya)", + "internal_name": "ftid14735" + }, + { + "name": "Journal of Biomedical Research & Environmental Sciences (JBRES)", + "internal_name": "ftid14740" + }, + { + "name": "Archives of Criminology and Forensic Sciences", + "internal_name": "ftid14741" + }, + { + "name": "Ellinger Publishing Media", + "internal_name": "ftid14742" + }, + { + "name": "Jurnal Universitas Sali Al", + "internal_name": "ftid14748" + }, + { + "name": "Global Insights in Biomedical & Multidisciplinary Research (GIBMR)", + "internal_name": "ftid14751" + }, + { + "name": "International Journal of Applied Artificial Intelligence and Robotics (IJAIC)", + "internal_name": "ftid14752" + }, + { + "name": "Edinburgh Multicultural Research Institute (EMRI) Journal Collection", + "internal_name": "ftid14753" + }, + { + "name": "Global Talent Foundation Inc", + "internal_name": "crid14737" + }, + { + "name": "TSARKA Science", + "internal_name": "crid14743" + }, + { + "name": "North American Publication Corp. (NAPOpen)", + "internal_name": "crid14789" + }, + { + "name": "Paradigma Journal of Social Sciences", + "internal_name": "crid14807" + }, + { + "name": "Journal Of Wound Medicine", + "internal_name": "ftid14709" + }, + { + "name": "Editorial Saberes Científicos", + "internal_name": "ftid14712" + }, + { + "name": "Editorial FIECYT", + "internal_name": "ftid14716" + }, + { + "name": "Universidad Nacional del Altiplano, Puno, Perú - Escuela de Posgrado", + "internal_name": "ftid14720" + }, + { + "name": "Department of Philosophy, Niger Delta University", + "internal_name": "ftid14723" + }, + { + "name": "AISI Journal of Sensory Integration and Child Development", + "internal_name": "ftid14724" + }, + { + "name": "منصة المجلات العلمية لجامعة المسيلة", + "internal_name": "ftid14727" + }, + { + "name": "Arado Business Journal (ABJ)", + "internal_name": "ftid14725" + }, + { + "name": "Universitätsbibliothek der Eberhard Karls Universität Tübingen / IxTheo: OJS", + "internal_name": "ftid14726" + }, + { + "name": "Periódicos - UFAM", + "internal_name": "ftid14728" + }, + { + "name": "Portal de Periódicos Eletrônicos FASIPE", + "internal_name": "ftid14730" + }, + { + "name": "Repositorio Institucional del Instituto de Investigaciones de la Amazonía Peruana - IIAP", + "internal_name": "ftid14705" + }, + { + "name": "Revista Inquietações", + "internal_name": "ftid14708" + }, + { + "name": "Advanced Social Science in Research Journal (ASSRJ)", + "internal_name": "ftid14701" + }, + { + "name": "Akrual: Jurnal Tugas Akhir Mahasiswa (JTAM)", + "internal_name": "ftid14702" + }, + { + "name": "International Journal of Pharmaceutical Drug Design (IJPDD)", + "internal_name": "ftid14703" + }, + { + "name": "Shodhbodhalaya", + "internal_name": "ftid14704" + }, + { + "name": "Current Pharmaceutical Research (CPR)", + "internal_name": "ftid14706" + }, + { + "name": "Jurnal Teologi Oligos", + "internal_name": "ftid14707" + }, + { + "name": "Journal of Digital Innovation and Social Research (JDISR)", + "internal_name": "ftid14710" + }, + { + "name": "Applied Planetary Health (APH)", + "internal_name": "ftid14711" + }, + { + "name": "HRL Journal Service", + "internal_name": "ftid14714" + }, + { + "name": "International Journal of Advanced Research and Innovations (IJARI)", + "internal_name": "ftid14717" + }, + { + "name": "Current Pharmaceutical Letters and Reviews (CPLR)", + "internal_name": "ftid14718" + }, + { + "name": "Creative Smart Visionary", + "internal_name": "ftid14719" + }, + { + "name": "International Journal of Constitutional and Administrative Law", + "internal_name": "ftid14721" + }, + { + "name": "J-MILE: Journal of Management and Industrial Engineering", + "internal_name": "ftid14722" + }, + { + "name": "електронний репозитарій Українського католицького університету", + "internal_name": "ftlvivcatholicun" + }, + { + "name": "Portal de Periódicos do IFSP Itapetininga (Instituto Federal de Educação Ciência e Tecnologia de São Paulo)", + "internal_name": "ftifspitapetinin" + }, + { + "name": "Repositorio Digital de la Universidad del Magdalena", + "internal_name": "ftunivmagdalena" + }, + { + "name": "İstanbul Medipol University Institutional Repository (DSpace@Medipol)", + "internal_name": "ftistanbulmedipo" + }, + { + "name": "Revista Tópicos", + "internal_name": "ftid14744" + }, + { + "name": "United Journal of Chemistry", + "internal_name": "ftid14745" + }, + { + "name": "Istanbul Medeniyet University", + "internal_name": "ftid14698" + }, + { + "name": "RepoWest: Open-Access-Publikationsserver der Fachhochschule Westküste", + "internal_name": "ftid14683" + }, + { + "name": "Ege University", + "internal_name": "ftid14691" + }, { "name": "INKUBIS: Jurnal Ekonomi dan Bisnis", "internal_name": "ftid14576" @@ -7,6 +215,14 @@ "name": "African Journal for Development Informatics (AJDI)", "internal_name": "ftid14673" }, + { + "name": "Revista Entropía Educativa", + "internal_name": "ftid14679" + }, + { + "name": "Repositorio de Conocimiento Entropía Educativa", + "internal_name": "ftid14682" + }, { "name": "Journal Azzahro", "internal_name": "ftid14697" @@ -63,10 +279,6 @@ "name": "Books, Proceedings and Seminars of Centre Mersenne", "internal_name": "ftid14678" }, - { - "name": "United Journal of Chemistry", - "internal_name": "crid14680" - }, { "name": "University of Biskra Journals", "internal_name": "ftid14681" @@ -227,10 +439,6 @@ "name": "EditorJDTET", "internal_name": "ftid14639" }, - { - "name": "Revista Tópicos", - "internal_name": "crid14640" - }, { "name": "International Journal of Health Concord", "internal_name": "ftid14642" @@ -20307,10 +20515,6 @@ "name": "International Academy Publishing (IAP)", "internal_name": "crintacademypubl" }, - { - "name": "Maxwell Scientific Publication Corp.", - "internal_name": "crmaxwellscipubl" - }, { "name": "oekom", "internal_name": "croekompubl" @@ -23971,10 +24175,6 @@ "name": "Online Journal System STAIDA (Sekolah Tinggi Agama Islam Daruttaqwa Gresik Jurnal Online)", "internal_name": "ftstaidaruttaqwa" }, - { - "name": "İstanbul Medipol University Institutional Repository (DSpace@Medipol)", - "internal_name": "ftistanbulmedipo" - }, { "name": "E-Jurnal STKIP Pesisir Selatan - Lembaga Penelitian dan Pengabdian kepada Masysrakat (LP2M)", "internal_name": "ftstkippesisirse" @@ -26567,10 +26767,6 @@ "name": "Indonesian Journal of Health Research (IJHR)", "internal_name": "ftjijhr" }, - { - "name": "The Scientific Journals of the University of Kurdistan Hewlêr", - "internal_name": "ftunikurdistanhe" - }, { "name": "Sistema Eletrônico de Periódicos - IFCH/Unicamp (Instituto de Filosofia e Ciências Humanas da Universidade Estadual de Campinas)", "internal_name": "ftuncampinasifch" @@ -37119,10 +37315,6 @@ "name": "Digital Commons @ Kettering University", "internal_name": "ftketteringuniv" }, - { - "name": "Portal de Periódicos do IFSP Itapetininga (Instituto Federal de Educação Ciência e Tecnologia de São Paulo)", - "internal_name": "ftifspitapetinin" - }, { "name": "東京海洋大学学術機関リポジトリ", "internal_name": "fttokyounivmst" @@ -37960,7 +38152,7 @@ "internal_name": "ftecoleminesnant" }, { - "name": "École Polytechnique, Université Paris-Saclay: HAL", + "name": "Portail HAL Polytechnique (École Polytechnique / Institut Polytechnik de Paris)", "internal_name": "ftepunivpsaclay" }, { @@ -38427,10 +38619,6 @@ "name": "DSpace-CRIS @ ANLIS (Administración Nacional de Laboratorios e Institutos de Salud)", "internal_name": "ftanlis" }, - { - "name": "електронний репозитарій Українського католицького університету", - "internal_name": "ftlvivcatholicun" - }, { "name": "九州保健福祉大学リポジトリ", "internal_name": "ftkyushuunivhw" From 2394d9dc392d7fbc7b3e3943f8f318d1303fab24 Mon Sep 17 00:00:00 2001 From: chreman Date: Mon, 11 May 2026 19:04:03 +0200 Subject: [PATCH 069/205] clean up container build workflow --- local_dev/dev.env.example | 6 ---- server/workers/build_docker_images.sh | 7 ---- server/workers/push_to_dockerhub.sh | 50 --------------------------- 3 files changed, 63 deletions(-) delete mode 100755 server/workers/push_to_dockerhub.sh diff --git a/local_dev/dev.env.example b/local_dev/dev.env.example index 161e1a65e..a102151d2 100644 --- a/local_dev/dev.env.example +++ b/local_dev/dev.env.example @@ -25,9 +25,3 @@ ORCID_CLIENT_SECRET= R_ALTMETRIC_APIKEY= R_CROSSREF_APIMAIL= - -ENRICHMENT_STRATEGY_FOR_SUBJECT=replace - -DOCKERHUB_USERNAME= -DOCKERHUB_TOKEN= -DOCKERHUB_ORG= \ No newline at end of file diff --git a/server/workers/build_docker_images.sh b/server/workers/build_docker_images.sh index ac39d8741..ca2bca7a0 100755 --- a/server/workers/build_docker_images.sh +++ b/server/workers/build_docker_images.sh @@ -16,9 +16,6 @@ for arg in "$@"; do --skip-contentproviders-update) skip_contentproviders_update=true ;; - --push) - push_to_dockerhub=true - ;; esac done @@ -60,7 +57,3 @@ done echo "" echo "Finished building services with version $service_version" echo "" - -if [ "$push_to_dockerhub" = true ]; then - bash "$SCRIPT_DIR/push_to_dockerhub.sh" "$service_version" "${services[@]}" -fi diff --git a/server/workers/push_to_dockerhub.sh b/server/workers/push_to_dockerhub.sh deleted file mode 100755 index 2fc2a83d8..000000000 --- a/server/workers/push_to_dockerhub.sh +++ /dev/null @@ -1,50 +0,0 @@ -#!/bin/bash - -# Defines the script directory -SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )" - -service_version="$1" -shift -services=("$@") - -# Load credentials from dev.env -DEV_ENV="$SCRIPT_DIR/../../local_dev/dev.env" -if [ ! -f "$DEV_ENV" ]; then - echo "Error: $DEV_ENV not found. Cannot push without credentials." - exit 1 -fi -set -a; source "$DEV_ENV"; set +a - -if [ -z "$DOCKERHUB_USERNAME" ] || [ -z "$DOCKERHUB_TOKEN" ] || [ -z "$DOCKERHUB_ORG" ]; then - echo "Error: DOCKERHUB_USERNAME, DOCKERHUB_TOKEN, and DOCKERHUB_ORG must be set in local_dev/dev.env" - exit 1 -fi - -echo "$DOCKERHUB_TOKEN" | docker login --username "$DOCKERHUB_USERNAME" --password-stdin - -# Obtain a Docker Hub API token to set repositories private -dockerhub_jwt=$(curl -s -X POST "https://hub.docker.com/v2/users/login" \ - -H "Content-Type: application/json" \ - -d "{\"username\": \"$DOCKERHUB_USERNAME\", \"password\": \"$DOCKERHUB_TOKEN\"}" \ - | grep -o '"token":"[^"]*"' | cut -d'"' -f4) - -if [ -z "$dockerhub_jwt" ]; then - echo "Error: Failed to obtain Docker Hub API token." - docker logout - exit 1 -fi - -for service in "${services[@]}"; do - remote_tag="$DOCKERHUB_ORG/$service:$service_version" - echo "Tagging $service:$service_version as $remote_tag" - docker tag "$service:$service_version" "$remote_tag" - echo "Pushing $remote_tag" - docker push "$remote_tag" - echo "Setting $DOCKERHUB_ORG/$service to private" - curl -s -X PATCH "https://hub.docker.com/v2/repositories/$DOCKERHUB_ORG/$service/" \ - -H "Authorization: Bearer $dockerhub_jwt" \ - -H "Content-Type: application/json" \ - -d '{"is_private": true}' > /dev/null -done - -docker logout From c39eeaed37216bdce829d9a3ac5fb83c0b8c542d Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 12 May 2026 12:32:29 +0200 Subject: [PATCH 070/205] e2e tests for ORCID enrichment tests --- e2e/keywordsEnrichment/ORCID.spec.ts | 84 ++++++++++++++++++++++++++-- tsconfig.json | 6 +- webpack.config.js | 1 + 3 files changed, 83 insertions(+), 8 deletions(-) diff --git a/e2e/keywordsEnrichment/ORCID.spec.ts b/e2e/keywordsEnrichment/ORCID.spec.ts index e3ad1fa63..5fd7ab358 100644 --- a/e2e/keywordsEnrichment/ORCID.spec.ts +++ b/e2e/keywordsEnrichment/ORCID.spec.ts @@ -1,8 +1,55 @@ import { test, expect } from "@playwright/test"; import { prepareVisualisation } from "../../vis/js/utils/e2eVisualisationLoader"; -test.describe("Tests to check how keywords enrichment works", () => { - test.skip("Keywords enrichment for the 'Asymmetric Power Boosts Extortion in an Economic Experiment' document (replace strategy)", async ({ +test.describe("Verify abstract and keywords are merged correctly, bugfix 1", () => { + test("Keywords enrichment for the 'Ten simple rules for innovative dissemination of research' document", async ({ + page, + }) => { + const VISUALISATION_DYNAMIC_URL = + "/search?type=get&vis_type=overview&orcid=0000-0002-5238-4195&service=orcid&embed=true"; + + await prepareVisualisation(page, VISUALISATION_DYNAMIC_URL); + + await expect(page.locator("#search-term-unique")).toContainText( + "Peter Kraker (0000-0002-5238-4195)", + ); + const paper = page.getByTitle( + "Ten simple rules for innovative dissemination of research", + ); + await expect(paper).toBeVisible(); + await paper.click(); + await expect(page.locator("#list_holder")).toContainText( + "Keywords: Biology (General); Humans [MeSH]; Information Dissemination [MeSH]; Innovative dissemination; Online Social Networking [MeSH]; Open Science; Research Personnel [MeSH]; Scientific publishing; QH301-705.5", + ); + }); +}); + +test.describe("Verify abstract and keywords are merged correctly, bugfix 1", () => { + test("Abstract enrichment for the 'Ten simple rules for innovative dissemination of research' document", async ({ + page, + }) => { + const VISUALISATION_DYNAMIC_URL = + "/search?type=get&vis_type=overview&orcid=0000-0002-5238-4195&service=orcid&embed=true"; + + await prepareVisualisation(page, VISUALISATION_DYNAMIC_URL); + + await expect(page.locator("#search-term-unique")).toContainText( + "Peter Kraker (0000-0002-5238-4195)", + ); + const paper = page.getByTitle( + "Ten simple rules for innovative dissemination of research", + ); + await expect(paper).toBeVisible(); + await paper.click(); + await expect(page.locator("#list_holder")).toContainText( + "How we communicate research is changing because of new (especially digital) possibilities. This article sets out 10 easy steps researchers can take to disseminate their work in novel and engaging ways, and hence increase the impact of their research on science and society.", + ); + }); +}); + + +test.describe("Verify abstract and keywords are merged correctly, bugfix 1", () => { + test("Keywords enrichment for the 'Social immunity modulates competition between coinfecting pathogens' document", async ({ page, }) => { const VISUALISATION_DYNAMIC_URL = @@ -13,11 +60,36 @@ test.describe("Tests to check how keywords enrichment works", () => { await expect(page.locator("#search-term-unique")).toContainText( "Christian Hilbe (0000-0001-5116-955X)", ); - await page - .getByTitle("Asymmetric Power Boosts Extortion in an Economic Experiment") - .click(); + const paper = page.getByTitle( + "Social immunity modulates competition between coinfecting pathogens", + ); + await expect(paper).toBeVisible(); + await paper.click(); + await expect(page.locator("#list_holder")).toContainText( + "Keywords: Animals [MeSH]; Ants [MeSH]; Argentine ants; ddc:570; grooming; Grooming [MeSH]; immune-mediated competition; infectious disease; host-pathogen interactions; Host-Pathogen Interactions [MeSH]; metarhizium fungus; Metarhizium [MeSH]; multiple infections; pathogen competition; pathogen diversity; Social Behavior [MeSH]; social insects; Virulence [MeSH];", + ); + }); +}); + +test.describe("Verify abstract and keywords are merged correctly, bugfix 1", () => { + test("Abstract enrichment for the 'Social immunity modulates competition between coinfecting pathogens' document", async ({ + page, + }) => { + const VISUALISATION_DYNAMIC_URL = + "/search?type=get&vis_type=overview&orcid=0000-0001-5116-955X&service=orcid&embed=true"; + + await prepareVisualisation(page, VISUALISATION_DYNAMIC_URL); + + await expect(page.locator("#search-term-unique")).toContainText( + "Christian Hilbe (0000-0001-5116-955X)", + ); + const paper = page.getByTitle( + "Social immunity modulates competition between coinfecting pathogens", + ); + await expect(paper).toBeVisible(); + await paper.click(); await expect(page.locator("#list_holder")).toContainText( - "Keywords: Physical Sciences; Mathematics; Applied Mathematics; Game Theory; Social Sciences; Economics; Experimental Economics; Prisoner's Dilemma; Biology and Life Sciences; Behavior; Psychology; Social Psychology; Experimental Design; Mathematical and Statistical Techniques; Statistical Methods; Generalized Linear Model; Statistics (Mathematics); Recreation; Games", + "Abstract Coinfections with multiple pathogens can result in complex within‐host dynamics affecting virulence and transmission. While multiple infections are intensively studied in solitary hosts, it is so far unresolved how social host interactions interfere with pathogen competition, and if this depends on coinfection diversity. We studied how the collective disease defences of ants – their social immunity – influence pathogen competition in coinfections of same or different fungal pathogen species. Social immunity reduced virulence for all pathogen combinations, but interfered with spore production only in different‐species coinfections. Here, it decreased overall pathogen sporulation success while increasing co‐sporulation on individual cadavers and maintaining a higher pathogen diversity at the community level. Mathematical modelling revealed that host sanitary care alone can modulate competitive outcomes between pathogens, giving advantage to fast‐germinating, thus less grooming‐sensitive ones. Host social interactions can hence modulate infection dynamics in coinfected group members, thereby altering pathogen communities at the host level and population level.", ); }); }); diff --git a/tsconfig.json b/tsconfig.json index 1614079ba..0ef37d653 100644 --- a/tsconfig.json +++ b/tsconfig.json @@ -1,10 +1,12 @@ { "compilerOptions": { - "target": "es5", + "target": "ES2015", "allowJs": true, "lib": ["es2022", "DOM"], "jsx": "react-jsx", - "moduleResolution": "node", + "module": "ESNext", + "moduleResolution": "bundler", + "skipLibCheck": true, "outDir": "./dist", "strict": true, "esModuleInterop": true, diff --git a/webpack.config.js b/webpack.config.js index 6da5b45c4..d603c82b0 100644 --- a/webpack.config.js +++ b/webpack.config.js @@ -148,6 +148,7 @@ module.exports = (env) => { additionalData: `$skin: "${process.env.SKIN || ""}";`, sassOptions: { includePaths: ["node_modules"], + silenceDeprecations: ["import"], }, }, }, From 10f58e6bdc5a7d1da932173062fbf8215d56fb0e Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 12 May 2026 15:24:32 +0200 Subject: [PATCH 071/205] first e2e tests for orcid enrichment bugfixes and regression testing --- e2e/keywordsEnrichment/ORCID.spec.ts | 261 ++++++++++++++++++--------- 1 file changed, 175 insertions(+), 86 deletions(-) diff --git a/e2e/keywordsEnrichment/ORCID.spec.ts b/e2e/keywordsEnrichment/ORCID.spec.ts index 5fd7ab358..d1a882dde 100644 --- a/e2e/keywordsEnrichment/ORCID.spec.ts +++ b/e2e/keywordsEnrichment/ORCID.spec.ts @@ -1,95 +1,184 @@ -import { test, expect } from "@playwright/test"; +import { test, expect, Page } from "@playwright/test"; import { prepareVisualisation } from "../../vis/js/utils/e2eVisualisationLoader"; -test.describe("Verify abstract and keywords are merged correctly, bugfix 1", () => { - test("Keywords enrichment for the 'Ten simple rules for innovative dissemination of research' document", async ({ - page, - }) => { - const VISUALISATION_DYNAMIC_URL = - "/search?type=get&vis_type=overview&orcid=0000-0002-5238-4195&service=orcid&embed=true"; +interface EnrichmentTestCase { + suiteName: string; + orcid: string; + paperTitle: string; + keywords?: string; + abstract?: string; +} - await prepareVisualisation(page, VISUALISATION_DYNAMIC_URL); - - await expect(page.locator("#search-term-unique")).toContainText( - "Peter Kraker (0000-0002-5238-4195)", - ); - const paper = page.getByTitle( - "Ten simple rules for innovative dissemination of research", - ); - await expect(paper).toBeVisible(); - await paper.click(); - await expect(page.locator("#list_holder")).toContainText( - "Keywords: Biology (General); Humans [MeSH]; Information Dissemination [MeSH]; Innovative dissemination; Online Social Networking [MeSH]; Open Science; Research Personnel [MeSH]; Scientific publishing; QH301-705.5", - ); - }); -}); - -test.describe("Verify abstract and keywords are merged correctly, bugfix 1", () => { - test("Abstract enrichment for the 'Ten simple rules for innovative dissemination of research' document", async ({ - page, - }) => { - const VISUALISATION_DYNAMIC_URL = - "/search?type=get&vis_type=overview&orcid=0000-0002-5238-4195&service=orcid&embed=true"; - - await prepareVisualisation(page, VISUALISATION_DYNAMIC_URL); - - await expect(page.locator("#search-term-unique")).toContainText( - "Peter Kraker (0000-0002-5238-4195)", - ); - const paper = page.getByTitle( - "Ten simple rules for innovative dissemination of research", - ); - await expect(paper).toBeVisible(); - await paper.click(); - await expect(page.locator("#list_holder")).toContainText( +const testCases: EnrichmentTestCase[] = [ + { + suiteName: "regression test", + orcid: "0000-0001-5116-955X", + paperTitle: "Humans choose representatives who enforce cooperation in social dilemmas through extortion", + abstract: + "Social dilemmas force players to balance between personal and collective gain. In many dilemmas, such as elected governments negotiating climate-change mitigation measures, the decisions are made not by individual players but by their representatives. However, the behaviour of representatives in social dilemmas has not been investigated experimentally. Here inspired by the negotiations for greenhouse-gas emissions reductions, we experimentally study a collective-risk social dilemma that involves representatives deciding on behalf of their fellow group members. Representatives can be re-elected or voted out after each consecutive collective-risk game. Selfish players are preferentially elected and are hence found most frequently in the ‘representatives’ treatment. Across all treatments, we identify the selfish players as extortioners. As predicted by our mathematical model, their steadfast strategies enforce cooperation from fair players who finally compensate almost completely the deficit caused by the extortionate co-players. Everybody gains, but the extortionate representatives and their groups gain the most.", + keywords: + "Climate Change [MeSH]; Cooperative Behavior [MeSH]; Female [MeSH]; Game Theory [MeSH]; Humans [MeSH]; Interpersonal Relations [MeSH]; Models, Theoretical [MeSH]; Q; Science; Young Adult [MeSH]; ddc:530; ddc:599", + }, + { + suiteName: "regression test", + orcid: "0000-0001-5116-955X", + paperTitle: "Extortion subdues human players but is finally punished in the prisoner’s dilemma", + abstract: + "Extortion is the practice of obtaining advantages through explicit forces and threats. Recently, it was demonstrated that even the repeated prisoner’s dilemma, one of the key models to explain mutual cooperation, allows for implicit forms of extortion. According to the theory, extortioners demand and receive an excessive share of any surplus, which allows them to outperform any adapting co-player. To explore the performance of such strategies against humans, we have designed an economic experiment in which participants were matched either with an extortioner or with a generous co-player. Although extortioners succeeded against each of their human opponents, extortion resulted in lower payoffs than generosity. Human subjects showed a strong concern for fairness: they punished extortion by refusing to fully cooperate, thereby reducing their own, and even more so, the extortioner’s gains. Thus, the prospects of extorting others in social relationships seem limited; in the long run, generosity is more profitable.", + keywords: + "Coercion [MeSH]; Cooperative Behavior [MeSH]; Game Theory [MeSH]; Humans [MeSH]", + }, + { + suiteName: "regression test", + orcid: "0000-0001-5116-955X", + paperTitle: "Asymmetric Power Boosts Extortion in an Economic Experiment", + abstract: + "Direct reciprocity is a major mechanism for the evolution of cooperation. Several classical studies have suggested that humans should quickly learn to adopt reciprocal strategies to establish mutual cooperation in repeated interactions. On the other hand, the recently discovered theory of ZD strategies has found that subjects who use extortionate strategies are able to exploit and subdue cooperators. Although such extortioners have been predicted to succeed in any population of adaptive opponents, theoretical follow-up studies questioned whether extortion can evolve in reality. However, most of these studies presumed that individuals have similar strategic possibilities and comparable outside options, whereas asymmetries are ubiquitous in real world applications. Here we show with a model and an economic experiment that extortionate strategies readily emerge once subjects differ in their strategic power. Our experiment combines a repeated social dilemma with asymmetric partner choice. In our main treatment there is one randomly chosen group member who is unilaterally allowed to exchange one of the other group members after every ten rounds of the social dilemma. We find that this asymmetric replacement opportunity generally promotes cooperation, but often the resulting payoff distribution reflects the underlying power structure. Almost half of the subjects in a better strategic position turn into extortioners, who quickly proceed to exploit their peers. By adapting their cooperation probabilities consistent with ZD theory, extortioners force their co-players to cooperate without being similarly cooperative themselves. Comparison to non-extortionate players under the same conditions indicates a substantial net gain to extortion. Our results thus highlight how power asymmetries can endanger mutually beneficial interactions, and transform them into exploitative relationships. In particular, our results indicate that the extortionate strategies predicted from ZD theory could play a more prominent role in our daily interactions than previously thought.", + keywords: + "Applied Mathematics; Behavior; Biology and Life Sciences; Coercion [MeSH]; Economics; Experimental Design; Experimental Economics; Game Theory; Game Theory [MeSH]; Games; Generalized Linear Model; Humans [MeSH]; Interpersonal Relations [MeSH]; Mathematical and Statistical Techniques; Mathematics; Medicine; Models, Theoretical [MeSH]; Physical Sciences; Prisoner's Dilemma; Psychology; Q; R; Recreation; Science; Social Behavior [MeSH]; Social Psychology; Social Sciences; Statistical Methods; Statistics (Mathematics); ddc:004", + }, + { + suiteName: "regression test", + orcid: "0000-0001-5116-955X", + paperTitle: "Democratic decisions establish stable authorities that overcome the paradox of second-order punishment", + abstract: + "Humans usually punish free riders but refuse to sanction those who cooperate but do not punish. However, such second-order punishment is essential to maintain cooperation. The central authorities established in modern societies punish both free riders and tax evaders. This is a paradox: would individuals who do not engage in second-order punishment strive for an authority that does? We address this puzzle with a mathematical model and an economic experiment. When individuals can choose between authorities by migrating between different communities, we find a costly bias against second-order punishment. When subjects use a majority vote instead, they vote for an authority with second-order punishment. These findings also suggest that other pressing social dilemmas could be solved by democratic voting.", + keywords: + "Computer Simulation [MeSH]; Cooperative Behavior [MeSH]; Democracy [MeSH]; Germany [MeSH]; Humans [MeSH]; Models, Theoretical [MeSH]; Politics [MeSH]; Punishment [MeSH]; Social Control Policies [MeSH]; evolution of cooperation; institution formation; pool punishment", + }, + { + suiteName: "regression test", + orcid: "0000-0001-5116-955X", + paperTitle: "The effect of environmental information on evolution of cooperation in stochastic games", + abstract: + "Many human interactions feature the characteristics of social dilemmas where individual actions have consequences for the group and the environment. The feedback between behavior and environment can be studied with the framework of stochastic games. In stochastic games, the state of the environment can change, depending on the choices made by group members. Past work suggests that such feedback can reinforce cooperative behaviors. In particular, cooperation can evolve in stochastic games even if it is infeasible in each separate repeated game. In stochastic games, participants have an interest in conditioning their strategies on the state of the environment. Yet in many applications, precise information about the state could be scarce. Here, we study how the availability of information (or lack thereof) shapes evolution of cooperation. Already for simple examples of two state games we find surprising effects. In some cases, cooperation is only possible if there is precise information about the state of the environment. In other cases, cooperation is most abundant when there is no information about the state of the environment. We systematically analyze all stochastic games of a given complexity class, to determine when receiving information about the environment is better, neutral, or worse for evolution of cooperation.", + keywords: + "B- ECONOMIE ET FINANCE; Cooperative Behavior [MeSH]; Humans [MeSH]; Mass Gatherings [MeSH]; Q; Science; [SHS.ECO]Humanities and Social Sciences/Economics and Finance; ddc:000", + }, + { + suiteName: "regression test", + orcid: "0000-0001-5116-955X", + paperTitle: "Social immunity modulates competition between coinfecting pathogens", + abstract: + "Coinfections with multiple pathogens can result in complex within‐host dynamics affecting virulence and transmission. While multiple infections are intensively studied in solitary hosts, it is so far unresolved how social host interactions interfere with pathogen competition, and if this depends on coinfection diversity. We studied how the collective disease defences of ants – their social immunity – influence pathogen competition in coinfections of same or different fungal pathogen species. Social immunity reduced virulence for all pathogen combinations, but interfered with spore production only in different‐species coinfections. Here, it decreased overall pathogen sporulation success while increasing co‐sporulation on individual cadavers and maintaining a higher pathogen diversity at the community level. Mathematical modelling revealed that host sanitary care alone can modulate competitive outcomes between pathogens, giving advantage to fast‐germinating, thus less grooming‐sensitive ones. Host social interactions can hence modulate infection dynamics in coinfected group members, thereby altering pathogen communities at the host level and population level.", + }, + { + suiteName: "regression test", + orcid: "0000-0001-5116-955X", + paperTitle: "Exact conditions for evolutionary stability in indirect reciprocity under noise", + abstract: + "Indirect reciprocity is a key mechanism for large-scale cooperation. This mechanism captures the insight that in part, people help others to build and maintain a good reputation. To enable such cooperation, appropriate social norms are essential. They specify how individuals should act based on each others' reputations, and how reputations are updated in response to individual actions. Although previous work has identified several norms that sustain cooperation, a complete analytical characterization of all evolutionarily stable norms remains lacking, especially when assessments or actions are noisy. In this study, we provide such a characterization for the public assessment regime. This characterization reproduces known results, such as the leading eight norms, but it extends to more general cases, allowing for various types of errors and additional actions including costly punishment. We also identify norms that impose a fixed payoff on any mutant strategy, analogous to the zero-determinant strategies in direct reciprocity. These results offer a rigorous foundation for understanding the evolution of cooperation through indirect reciprocity and the critical role of social norms.", + keywords: + "Biological Evolution [MeSH]; Biology (General); Computational Biology [MeSH]; Computer Simulation [MeSH]; Cooperative Behavior [MeSH]; Game Theory [MeSH]; Humans [MeSH]; QH301-705.5; Social Norms [MeSH]", + }, + { + suiteName: "regression test", + orcid: "0000-0003-4221-6275", + paperTitle: "Proportion of foetal and placental implantation abnormalities in Madagascar: A cross-sectional study of 35,919 women at public-sector primary healthcare facilities in central and southern Madagascar, 2017–2020", + abstract: ` +Background +Like other countries in sub-Saharan Africa, Madagascar has a high burden of maternal and neonatal mortality. However, as the proportion of foetal and placental abnormalities among the Malagasy population is unknown, strategies aimed at reducing maternal and neonatal mortality are challenging to define and implement. + + +Methods +We conducted a multi-year, cross-sectional study using secondary NGO data on obstetric ultrasound, including patient records of all pregnant women who received an obstetric ultrasound screening between July 1st, 2017, and September 30th, 2020, at 62 public-sector primary care facilities in urban and rural regions of Madagascar. We analysed demographic characteristics and determined the prevalence of foetal and placental abnormalities. + + +Results +The dataset included 38,688 ultrasound screening reports from 35,919 women, where 2,587/35,919 (7.20%) women had more than one ultrasound exam. Most women (68.63%, 26,550/38,688) received their first ultrasound during the third trimester of pregnancy. Foetal malpresentation at 36 weeks of gestation or later was diagnosed in 5.48% (176/3,211) of women with the breech presentation being most common (breech 3.99%, 128/3,211; transverse 0.84%, 27/3,211; mobile 0.5%, 16/3,211; oblique 0.16%, 5/3,211). Placenta previa was found in 2.31% (875/38,755) and multiple gestations in 1.03% (370/35,919) cases. Around one in every 150 women (0.66%, 234/38,702) had amniotic fluid disorders. + + +Conclusion +The proportion of foetal and placental abnormalities detected by obstetric ultrasound is consistent with findings from other countries in sub-Saharan Africa. In contrast to current WHO recommendations, pregnant women, particularly those from rural, resource-constrained settings attend obstetric ultrasound screenings most commonly during their third trimester of pregnancy. +`, + keywords: + "Adolescent [MeSH]; Adult [MeSH]; Cross-Sectional Studies [MeSH]; Female [MeSH]; Fetus [MeSH]; Humans [MeSH]; Madagascar [MeSH]; Medicine; Medizin und Gesundheit; Placenta [MeSH]; Pregnancy [MeSH]; Prevalence [MeSH]; Primary Health Care [MeSH]; Public Sector [MeSH]; Q; R; Science; Ultrasonography, Prenatal [MeSH]; Young Adult [MeSH]; ddc:610; foetal and placental abnormalities; foetal implantation; obstretric ultrasound; placental implantation", + }, + { + suiteName: "regression test", + orcid: "0000-0003-4221-6275", + paperTitle: "Body temperature measurement in mice during acute illness: implantable temperature transponder versus surface infrared thermometry.", + abstract: + "Abstract Body temperature is a valuable parameter in determining the wellbeing of laboratory animals. However, using body temperature to refine humane endpoints during acute illness generally lacks comprehensiveness and exposes to inter-observer bias. Here we compared two methods to assess body temperature in mice, namely implanted radio frequency identification (RFID) temperature transponders (method 1) to non-contact infrared thermometry (method 2) in 435 mice for up to 7 days during normothermia and lipopolysaccharide (LPS) endotoxin-induced hypothermia. There was excellent agreement between core and surface temperature as determined by method 1 and 2, respectively, whereas the intra- and inter-subject variation was higher for method 2. Nevertheless, using machine learning algorithms to determine temperature-based endpoints both methods had excellent accuracy in predicting death as an outcome event. Therefore, less expensive and cumbersome non-contact infrared thermometry can serve as a reliable alternative for implantable transponder-based systems for hypothermic responses, although requiring standardization between experimenters.", + keywords: + "600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit; Acute Disease; Acute Disease [MeSH]; Animals; Animals [MeSH]; Body Temperature; Body Temperature [MeSH]; Creative Commons Namensnennung – 4.0 International (CC BY 4.0); Electrodes; Electrodes, Implanted [MeSH]; Female; Female [MeSH]; Hypothermia [MeSH]; Hypothermia: chemically induced; Hypothermia: diagnosis; Hypothermia: mortality; Hypothermia: physiopathology; Implanted; Inbred C57BL; Infrared Rays; Infrared Rays [MeSH]; Lipopolysaccharides; Lipopolysaccharides [Chemical]; Lipopolysaccharides [MeSH]; Lipopolysaccharides: administration & dosage; Machine Learning; Machine Learning [MeSH]; Medicine; Mice; Mice [MeSH]; Mice, Inbred C57BL [MeSH]; Q; R; Radio Frequency Identification Device [MeSH]; Radio Frequency Identification Device: methods; Science; Sepsis [MeSH]; Sepsis: chemically induced; Sepsis: diagnosis; Sepsis: mortality; Sepsis: physiopathology; Survival Analysis; Survival Analysis [MeSH]; Text; Thermometers [MeSH]; Thermometers: classification; Thermometry: instrumentation; Thermometry: methods; article; ddc:610; info:eu-repo/classification/ddc/600; radio frequency identification (RFID); radio frequency identification RFID", + }, + { + suiteName: "regression test", + orcid: "0000-0003-4221-6275", + paperTitle: "Seismic Facies Analysis: A Deep Domain Adaptation Approach", + abstract: + "Deep neural networks (DNNs) can learn accurately from large quantities of labeled input data, but often fail to do so when labelled data are scarce. DNNs sometimes fail to generalize ontest data sampled from different input distributions. Unsupervised Deep Domain Adaptation (DDA)techniques have been proven useful when no labels are available, and when distribution shifts are observed in the target domain (TD). In the present study, experiments are performed on seismic images of the F3 block 3D dataset from offshore Netherlands (source domain; SD) and Penobscot 3D survey data from Canada (target domain; TD). Three geological classes from SD and TD that have similar reflection patterns are considered. A deep neural network architecture named EarthAdaptNet (EAN) is proposed to semantically segment the seismic images when few classes have data scarcity, and we use a transposed residual unit to replace the traditional dilated convolution in the decoder block. The EAN achieved a pixel-level accuracy >84% and an ... : 22 pages, 13 figures, 5 tables, and supplementary material included in the end of the paper ...", + keywords: + "Artificial Intelligence cs.AI; Computer Vision and Pattern Recognition cs.CV; FOS: Computer and information sciences; FOS: Electrical engineering, electronic engineering, information engineering; FOS: Physical sciences; Geophysics physics.geo-ph; Image and Video Processing eess.IV; Machine Learning cs.LG", + }, + { + suiteName: "regression test", + orcid: "0000-0003-4221-6275", + paperTitle: "Informing deep neural networks by multiscale principles of neuromodulatory systems ", + abstract: + "Our brains have evolved the ability to configure and adapt their processing states to match the unique challenges of acting and learning in diverse environments and behavioral contexts. In biological nervous systems, such state specification and adaptation arise in part from neuromodulators, including acetylcholine, noradrenaline, serotonin, and dopamine, whose diffuse release fine-tunes neuronal and synaptic dynamics and plasticity to complement the behavioral context in real-time. Despite the demonstrated effectiveness of deep neural networks for specific tasks, they remain relatively inflexible at generalizing across tasks or adapting to ever-changing behavioral demands. In this article, we provide an overview of neuromodulatory systems and their relationship to emerging pertinent principles in deep neural networks. We further outline opportunities for the integration of neuromodulatory principles into deep neural networks, towards endowing artificial intelligence with a key ingredient underlying the flexibility and learning capability of biological systems.", + keywords: + "610 Medicine & health; Artificial Intelligence [MeSH]; Dopamine [Chemical]; Dopamine [MeSH]; Humans [MeSH]; Neural Networks, Computer [MeSH]; Neurotransmitter Agents [Chemical]; Neurotransmitter Agents [MeSH]; Serotonin [Chemical]; Serotonin [MeSH]; acetylcholine; adaptive learning; dopamine; multiscale organization; noradrenaline; serotonin", + }, + { + suiteName: "bugfix 1", + orcid: "0000-0002-5238-4195", + paperTitle: "Ten simple rules for innovative dissemination of research", + abstract: "How we communicate research is changing because of new (especially digital) possibilities. This article sets out 10 easy steps researchers can take to disseminate their work in novel and engaging ways, and hence increase the impact of their research on science and society.", - ); - }); -}); - - -test.describe("Verify abstract and keywords are merged correctly, bugfix 1", () => { - test("Keywords enrichment for the 'Social immunity modulates competition between coinfecting pathogens' document", async ({ - page, - }) => { - const VISUALISATION_DYNAMIC_URL = - "/search?type=get&vis_type=overview&orcid=0000-0001-5116-955X&service=orcid&embed=true"; + keywords: + "Biology (General); Humans [MeSH]; Information Dissemination [MeSH]; Innovative dissemination; Online Social Networking [MeSH]; Open Science; Research Personnel [MeSH]; Scientific publishing; QH301-705.5", + }, + { + suiteName: "bugfix 1", + orcid: "0000-0001-5116-955X", + paperTitle: + "Social immunity modulates competition between coinfecting pathogens", + abstract: + "Coinfections with multiple pathogens can result in complex within‐host dynamics affecting virulence and transmission. While multiple infections are intensively studied in solitary hosts, it is so far unresolved how social host interactions interfere with pathogen competition, and if this depends on coinfection diversity. We studied how the collective disease defences of ants – their social immunity – influence pathogen competition in coinfections of same or different fungal pathogen species. Social immunity reduced virulence for all pathogen combinations, but interfered with spore production only in different‐species coinfections. Here, it decreased overall pathogen sporulation success while increasing co‐sporulation on individual cadavers and maintaining a higher pathogen diversity at the community level. Mathematical modelling revealed that host sanitary care alone can modulate competitive outcomes between pathogens, giving advantage to fast‐germinating, thus less grooming‐sensitive ones. Host social interactions can hence modulate infection dynamics in coinfected group members, thereby altering pathogen communities at the host level and population level.", + keywords: + "Animals [MeSH]; Ants [MeSH]; Argentine ants; ddc:570; grooming; Grooming [MeSH]; immune-mediated competition; infectious disease; host-pathogen interactions; Host-Pathogen Interactions [MeSH]; metarhizium fungus; Metarhizium [MeSH]; multiple infections; pathogen competition; pathogen diversity; Social Behavior [MeSH]; social insects; Virulence [MeSH];", + }, +]; - await prepareVisualisation(page, VISUALISATION_DYNAMIC_URL); - await expect(page.locator("#search-term-unique")).toContainText( - "Christian Hilbe (0000-0001-5116-955X)", - ); - const paper = page.getByTitle( - "Social immunity modulates competition between coinfecting pathogens", - ); - await expect(paper).toBeVisible(); - await paper.click(); - await expect(page.locator("#list_holder")).toContainText( - "Keywords: Animals [MeSH]; Ants [MeSH]; Argentine ants; ddc:570; grooming; Grooming [MeSH]; immune-mediated competition; infectious disease; host-pathogen interactions; Host-Pathogen Interactions [MeSH]; metarhizium fungus; Metarhizium [MeSH]; multiple infections; pathogen competition; pathogen diversity; Social Behavior [MeSH]; social insects; Virulence [MeSH];", - ); - }); -}); +for (const tc of testCases) { + test.describe( + `Verify abstract and keywords are merged correctly, ${tc.suiteName}`, + () => { + const url = `/search?type=get&vis_type=overview&orcid=${tc.orcid}&service=orcid&embed=true`; -test.describe("Verify abstract and keywords are merged correctly, bugfix 1", () => { - test("Abstract enrichment for the 'Social immunity modulates competition between coinfecting pathogens' document", async ({ - page, - }) => { - const VISUALISATION_DYNAMIC_URL = - "/search?type=get&vis_type=overview&orcid=0000-0001-5116-955X&service=orcid&embed=true"; + async function openPaper(page: Page) { + await prepareVisualisation(page, url); + await expect(page.locator("#search-term-unique")).toContainText( + `(${tc.orcid})`, + ); + const paper = page.getByTitle(tc.paperTitle); + await expect(paper).toBeVisible(); + await paper.click(); + return paper; + } - await prepareVisualisation(page, VISUALISATION_DYNAMIC_URL); + if (tc.keywords) { + const keywords = tc.keywords; + test(`Keywords enrichment for the '${tc.paperTitle}' document`, async ({ + page, + }) => { + await openPaper(page); + await expect(page.locator("#list_holder")).toContainText(keywords); + }); + } - await expect(page.locator("#search-term-unique")).toContainText( - "Christian Hilbe (0000-0001-5116-955X)", - ); - const paper = page.getByTitle( - "Social immunity modulates competition between coinfecting pathogens", - ); - await expect(paper).toBeVisible(); - await paper.click(); - await expect(page.locator("#list_holder")).toContainText( - "Abstract Coinfections with multiple pathogens can result in complex within‐host dynamics affecting virulence and transmission. While multiple infections are intensively studied in solitary hosts, it is so far unresolved how social host interactions interfere with pathogen competition, and if this depends on coinfection diversity. We studied how the collective disease defences of ants – their social immunity – influence pathogen competition in coinfections of same or different fungal pathogen species. Social immunity reduced virulence for all pathogen combinations, but interfered with spore production only in different‐species coinfections. Here, it decreased overall pathogen sporulation success while increasing co‐sporulation on individual cadavers and maintaining a higher pathogen diversity at the community level. Mathematical modelling revealed that host sanitary care alone can modulate competitive outcomes between pathogens, giving advantage to fast‐germinating, thus less grooming‐sensitive ones. Host social interactions can hence modulate infection dynamics in coinfected group members, thereby altering pathogen communities at the host level and population level.", - ); - }); -}); + if (tc.abstract) { + const abstract = tc.abstract; + test(`Abstract enrichment for the '${tc.paperTitle}' document`, async ({ + page, + }) => { + await openPaper(page); + await expect(page.locator("#list_holder")).toContainText(abstract); + }); + } + }, + ); +} From 95d82be79cc8e9d03e92a7e591311db101c80b6c Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 12 May 2026 15:31:41 +0200 Subject: [PATCH 072/205] add db warm-up and cache refresh to ORCID tests --- e2e/keywordsEnrichment/ORCID.spec.ts | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/e2e/keywordsEnrichment/ORCID.spec.ts b/e2e/keywordsEnrichment/ORCID.spec.ts index d1a882dde..3726619ed 100644 --- a/e2e/keywordsEnrichment/ORCID.spec.ts +++ b/e2e/keywordsEnrichment/ORCID.spec.ts @@ -142,6 +142,21 @@ const testCases: EnrichmentTestCase[] = [ }, ]; +const uniqueOrcids = [...new Set(testCases.map((tc) => tc.orcid).filter(Boolean))]; + +test.describe("Warm-up: pre-load unique ORCID profiles", () => { + for (const orcid of uniqueOrcids) { + test(`${orcid}`, async ({ page }) => { + const url = `/search?type=get&vis_type=overview&orcid=${orcid}&service=orcid&embed=true`; + await prepareVisualisation(page, url); + await expect(page.locator("#search-term-unique")).toContainText(`(${orcid})`); + }); + } + + test("wait for DB cache refresh", async () => { + await new Promise((resolve) => setTimeout(resolve, 180_000)); + }); +}); for (const tc of testCases) { test.describe( From 337453f77f44aab491c760de242433b1251c5d3f Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 12 May 2026 17:32:41 +0200 Subject: [PATCH 073/205] deduplication test updates --- e2e/keywordsEnrichment/ORCID.spec.ts | 4 - playwright.config.ts | 4 +- server/workers/base/tests/unit/test_base.py | 202 ++++++++++++++++---- 3 files changed, 163 insertions(+), 47 deletions(-) diff --git a/e2e/keywordsEnrichment/ORCID.spec.ts b/e2e/keywordsEnrichment/ORCID.spec.ts index 3726619ed..ae87d6d19 100644 --- a/e2e/keywordsEnrichment/ORCID.spec.ts +++ b/e2e/keywordsEnrichment/ORCID.spec.ts @@ -152,10 +152,6 @@ test.describe("Warm-up: pre-load unique ORCID profiles", () => { await expect(page.locator("#search-term-unique")).toContainText(`(${orcid})`); }); } - - test("wait for DB cache refresh", async () => { - await new Promise((resolve) => setTimeout(resolve, 180_000)); - }); }); for (const tc of testCases) { diff --git a/playwright.config.ts b/playwright.config.ts index 4034cd39f..0432185e7 100644 --- a/playwright.config.ts +++ b/playwright.config.ts @@ -15,8 +15,8 @@ export default defineConfig({ forbidOnly: !!process.env.CI, // Number of retries for failed tests retries: process.env.CI ? 2 : 0, - // In CI, limit to one worker for stability - workers: process.env.CI ? 1 : undefined, + // Single worker to avoid hammering the backend pipeline with parallel requests + workers: 1, // Report format after running reporter: "html", // Explicitly specify the test files to run diff --git a/server/workers/base/tests/unit/test_base.py b/server/workers/base/tests/unit/test_base.py index f77ce257e..86f98cc27 100644 --- a/server/workers/base/tests/unit/test_base.py +++ b/server/workers/base/tests/unit/test_base.py @@ -16,7 +16,7 @@ class DummyRedis: def __init__(self): self.store = {} self.queue = [] - + def blpop(self, key, timeout=0): if self.queue: return (key, self.queue.pop(0)) @@ -26,10 +26,10 @@ def blpop(self, key, timeout=0): def rpush(self, key, value): self.queue.append(value) - + def llen(self, key): return len(self.queue) - + def set(self, key, value): self.store[key] = value @@ -81,6 +81,37 @@ def client_base(): } return client + +def _make_record(id, title="Title", doi="", duplicates="", typenorm="1", + is_duplicate=False, link="", identifier="", oa_state="0", + year="2020", collection="", paper_abstract="Abstract", + authors="Author A", published_in="Journal", subject="", + subject_orig="", content_provider="cp1"): + """Build a minimal record dict with all columns required by filter_duplicates.""" + return { + "id": id, + "title": title, + "doi": doi, + "duplicates": duplicates, + "typenorm": typenorm, + "is_duplicate": is_duplicate, + "link": link, + "identifier": identifier, + "oa_state": oa_state, + "year": year, + "collection": collection, + "paper_abstract": paper_abstract, + "authors": authors, + "published_in": published_in, + "subject": subject, + "subject_orig": subject_orig, + "content_provider": content_provider, + } + + +_DEFAULT_PARAMS = {"vis_id": "test", "list_size": 100} + + # --- Tests for BaseClient methods --- def test_next_item(client_base): @@ -88,7 +119,7 @@ def test_next_item(client_base): message = {"id": "123", "params": {"q": "test"}, "endpoint": "search"} encoded_message = json.dumps(message).encode("utf-8") client_base.redis_store.queue.append(encoded_message) - + request_id, params, endpoint = client_base.next_item() assert request_id == "123" assert params.get("q") == "test" @@ -104,7 +135,7 @@ def __init__(self, stdout, stderr): self._stderr = stderr def communicate(self, input=None): return (self._stdout, self._stderr) - + def dummy_popen(cmd, stdin, stdout, stderr, encoding): # Simulate output with several lines. # Return a list of dictionaries containing required columns. @@ -119,17 +150,18 @@ def dummy_popen(cmd, stdin, stdout, stderr, encoding): dummy_stdout = "irrelevant line\n" + json.dumps([dummy_row]) + "\nextra line\n" dummy_stderr = "" return DummyProcess(dummy_stdout, dummy_stderr) - + monkeypatch.setattr(subprocess, "Popen", dummy_popen) - + # Patch methods used inside execute_search. monkeypatch.setattr(client_base, "sanitize_metadata", lambda df: df) - monkeypatch.setattr("base.filter_duplicates", lambda df: df) - monkeypatch.setattr("base.parse_annotations_for_all", lambda metadata, field: + # filter_duplicates takes (df, service, params) — use correct arity + monkeypatch.setattr("base.filter_duplicates", lambda df, service, params: df) + monkeypatch.setattr("base.parse_annotations_for_all", lambda metadata, field: pd.DataFrame({"annotations": [{}] * len(metadata)})) monkeypatch.setattr(client_base, "enrich_metadata", lambda df: pd.concat( [df, pd.DataFrame({"enriched": ["yes"] * len(df)})], axis=1)) - + params = {"q": "dummy query", "service": "base", "list_size": 100} res = client_base.execute_search(params) assert isinstance(res, dict) @@ -138,7 +170,7 @@ def dummy_popen(cmd, stdin, stdout, stderr, encoding): def test_sanitize_metadata(client_base): # Create a dummy DataFrame with an "authors" column. - df = pd.DataFrame({"authors": ["John Doe; Jane Smith"]}) + df = pd.DataFrame({"authors": ["John Doe; Jane Smith"], "year": ["2020"]}) sanitized = client_base.sanitize_metadata(df) assert "sanitized_authors" in sanitized.columns # Expect the authors string to be unchanged by our dummy sanitizer. @@ -166,11 +198,11 @@ def __init__(self, stdout, stderr): self._stderr = stderr def communicate(self, input=None): return (self._stdout, self._stderr) - + def dummy_popen_cp(cmd, stdin, stdout, stderr, encoding): dummy_stdout = json.dumps([{"name": "cp1", "internal_name": "Provider1"}]) + "\n" return DummyProcessCP(dummy_stdout, "") - + monkeypatch.setattr(subprocess, "Popen", dummy_popen_cp) res = client_base.get_contentproviders() cp_list = json.loads(res["contentproviders"]) @@ -178,34 +210,122 @@ def dummy_popen_cp(cmd, stdin, stdout, stderr, encoding): assert cp_list[0]["name"] == "cp1" assert cp_list[0]["internal_name"] == "Provider1" -# --- Tests for parser functions --- +# --- Tests for filter_duplicates --- + +def test_filter_duplicates_drops_internal_columns(): + """filter_duplicates must remove all working columns from the output.""" + df = pd.DataFrame([ + _make_record("1", doi="doi1", duplicates=""), + _make_record("2", doi="doi2", duplicates=""), + ]) + filtered = filter_duplicates(df.copy(), "test_service", _DEFAULT_PARAMS) + for col in ["doi_duplicate", "link_duplicate", "is_anchor", + "doi_version", "unversioned_doi", "publisher_doi", "has_relations"]: + assert col not in filtered.columns, f"Column {col!r} should have been dropped" -def test_filter_duplicates(): - # Create a dummy DataFrame simulating duplicate entries. - df = pd.DataFrame({ - "id": ["1", "1", "2"], # id as strings - "duplicates": ["1,1", "1,1", ""], - "doi": ["doi1", "doi1", "doi2"], - "typenorm": ["7", "7", "non7"], - "is_duplicate": [False, False, False], - "link": ["", "", ""] # Provide a link column to avoid KeyError - }) - # Add extra columns that filter_duplicates is supposed to drop. - df["doi_duplicate"] = False - df["link_duplicate"] = False - df["is_anchor"] = False - df["doi_version"] = ["v1", "v1", "v2"] - df["unversioned_doi"] = ["doi1", "doi1", "doi2"] - df["publisher_doi"] = ["pub1", "pub1", "pub2"] - df["has_relations"] = False - - filtered = filter_duplicates(df.copy()) - # Verify that the dropped columns are not present. - for col in [ - "doi_duplicate", "link_duplicate", "is_anchor", - "doi_version", "unversioned_doi", "publisher_doi", "has_relations" - ]: - assert col not in filtered.columns + +def test_filter_duplicates_removes_exact_id_duplicates(): + """Records sharing the same id must be deduplicated to one.""" + df = pd.DataFrame([ + _make_record("1", title="Paper A"), + _make_record("1", title="Paper A copy"), + _make_record("2", title="Paper B"), + ]) + filtered = filter_duplicates(df.copy(), "test_service", _DEFAULT_PARAMS) + assert len(filtered) == 2 + assert set(filtered["id"]) == {"1", "2"} + + +def test_filter_duplicates_keeps_unique_records(): + """Records that are genuinely unique must all survive.""" + df = pd.DataFrame([ + _make_record("1", doi="10.1/a"), + _make_record("2", doi="10.1/b"), + _make_record("3", doi="10.1/c"), + ]) + filtered = filter_duplicates(df.copy(), "test_service", _DEFAULT_PARAMS) + assert len(filtered) == 3 + + +def test_filter_duplicates_textual_duplicates_from_duplicates_column(): + """Records listed in each other's duplicates column should collapse to one anchor.""" + # R preprocessing identified "1" and "2" as textual duplicates + df = pd.DataFrame([ + _make_record("1", duplicates="2", oa_state="0", year="2020"), + _make_record("2", duplicates="1", oa_state="0", year="2021"), + _make_record("3", doi="10.1/c"), + ]) + filtered = filter_duplicates(df.copy(), "test_service", _DEFAULT_PARAMS) + # Only one of {1, 2} should survive plus record 3 + assert len(filtered) == 2 + ids = set(filtered["id"]) + assert "3" in ids + assert len(ids & {"1", "2"}) == 1 + + +def test_filter_duplicates_doi_duplicates_resolved(): + """Records with the same DOI (but not in duplicates column) should yield one anchor. + + This tests the add_false_negatives → prioritize path for doi-only duplicates + that the R script did not mark as textual duplicates. + """ + df = pd.DataFrame([ + _make_record("1", doi="10.1234/test", duplicates="", oa_state="1", year="2022"), + _make_record("2", doi="10.1234/test", duplicates="", oa_state="0", year="2020"), + _make_record("3", doi="10.1234/other"), + ]) + filtered = filter_duplicates(df.copy(), "test_service", _DEFAULT_PARAMS) + # Exactly one of {1, 2} should survive + doi_test_survivors = filtered[filtered["doi"] == "10.1234/test"] + assert len(doi_test_survivors) == 1, ( + f"Expected 1 anchor for doi 10.1234/test, got {len(doi_test_survivors)}: " + f"{doi_test_survivors['id'].tolist()}" + ) + + +def test_filter_duplicates_mixed_type_duplicates_no_double_anchor(): + """A dataset (typenorm=7) and a non-dataset that are textual duplicates must not + both appear in the output — the split into pure_datasets/non_datasets must not + accidentally give each sub-group an independent anchor.""" + df = pd.DataFrame([ + _make_record("dataset-A", typenorm="7", duplicates="non-dataset-B", + doi="10.1/x", oa_state="0", year="2020"), + _make_record("non-dataset-B", typenorm="1", duplicates="dataset-A", + doi="10.1/x", oa_state="0", year="2020"), + _make_record("unrelated-C", doi="10.1/c"), + ]) + filtered = filter_duplicates(df.copy(), "test_service", _DEFAULT_PARAMS) + ids = set(filtered["id"]) + assert "unrelated-C" in ids + duplicate_pair_survivors = ids & {"dataset-A", "non-dataset-B"} + assert len(duplicate_pair_survivors) == 1, ( + f"Both members of a duplicate pair survived: {duplicate_pair_survivors}" + ) + + +def test_filter_duplicates_oa_preferred_over_non_oa(): + """When prioritizing within a duplicate group, the OA record should be the anchor.""" + df = pd.DataFrame([ + _make_record("oa-version", duplicates="closed-version", oa_state="1", year="2020"), + _make_record("closed-version", duplicates="oa-version", oa_state="0", year="2021"), + ]) + filtered = filter_duplicates(df.copy(), "test_service", _DEFAULT_PARAMS) + assert len(filtered) == 1 + assert filtered.iloc[0]["id"] == "oa-version" + + +def test_filter_duplicates_latest_year_preferred_when_no_oa(): + """When no OA record exists, the newest record should be the anchor.""" + df = pd.DataFrame([ + _make_record("old", duplicates="new", oa_state="0", year="2018"), + _make_record("new", duplicates="old", oa_state="0", year="2022"), + ]) + filtered = filter_duplicates(df.copy(), "test_service", _DEFAULT_PARAMS) + assert len(filtered) == 1 + assert filtered.iloc[0]["id"] == "new" + + +# --- Tests for parser functions --- def test_parse_annotations_for_all(): # Create a dummy DataFrame with annotation strings. @@ -223,4 +343,4 @@ def test_sanitize_authors(): sanitized = sanitize_authors(authors, n=3) parts = authors.split("; ") expected = "; ".join(parts[:2] + [parts[-1]]) - assert sanitized == expected \ No newline at end of file + assert sanitized == expected From 02833dc88a6d6d31a199ecb6cb6c09f5c93541fe Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 12 May 2026 17:33:36 +0200 Subject: [PATCH 074/205] debug log expansion in deduplication pipeline --- server/workers/base/config.py | 2 +- server/workers/base/run_base.py | 1 + server/workers/base/src/base.py | 29 ++++++++++++++++++++++++++++- server/workers/orcid/src/config.py | 2 +- 4 files changed, 31 insertions(+), 3 deletions(-) diff --git a/server/workers/base/config.py b/server/workers/base/config.py index c263145b4..4e2d2ec2c 100644 --- a/server/workers/base/config.py +++ b/server/workers/base/config.py @@ -16,7 +16,7 @@ class RedisConfig(TypedDict): # Logging configuration LOGGING_CONFIG: LoggingConfig = { - "level": os.getenv("LOG_LEVEL", "INFO"), + "level": os.getenv("LOGLEVEL", "INFO"), "format": "%(asctime)s %(levelname)-8s %(message)s", "datefmt": "%Y-%m-%d %H:%M:%S" } diff --git a/server/workers/base/run_base.py b/server/workers/base/run_base.py index 36625ad8a..5483f43b0 100644 --- a/server/workers/base/run_base.py +++ b/server/workers/base/run_base.py @@ -18,6 +18,7 @@ def setup_logging(): if __name__ == "__main__": + setup_logging() redis_store = redis.StrictRedis(**REDIS_CONFIG) wrapper = BaseClient( "./other-scripts", diff --git a/server/workers/base/src/base.py b/server/workers/base/src/base.py index 455526946..cd4681201 100644 --- a/server/workers/base/src/base.py +++ b/server/workers/base/src/base.py @@ -241,6 +241,24 @@ def handle_contentproviders(self, request_id, params): pattern_annotations = re.compile(r"([A-Za-z]+:[\w'\- ]+);?") +def _log_dedup_state(df, step, params): + if not logger.isEnabledFor(logging.DEBUG): + return + n_dup = int(df["is_duplicate"].sum()) if "is_duplicate" in df.columns else "?" + n_anchor = int(df["is_anchor"].sum()) if "is_anchor" in df.columns else "?" + n_doi_dup = int(df["doi_duplicate"].sum()) if "doi_duplicate" in df.columns else "?" + n_link_dup = int(df["link_duplicate"].sum()) if "link_duplicate" in df.columns else "?" + logger.debug( + f"[dedup:{step}] total={len(df)} is_duplicate={n_dup} is_anchor={n_anchor}" + f" doi_duplicate={n_doi_dup} link_duplicate={n_link_dup}" + ) + if "id" in df.columns and "is_duplicate" in df.columns: + dup_ids = df.loc[df["is_duplicate"], "id"].tolist() + anchor_ids = df.loc[df["is_anchor"], "id"].tolist() if "is_anchor" in df.columns else [] + logger.debug(f"[dedup:{step}] duplicate_ids={dup_ids}") + logger.debug(f"[dedup:{step}] anchor_ids={anchor_ids}") + + def filter_duplicates(df, service, params): if logger.isEnabledFor(logging.DEBUG): logger.debug(f"Filtering duplicates for service: {service}") @@ -266,27 +284,36 @@ def filter_duplicates(df, service, params): ) df["publisher_doi"] = df.doi.map(lambda x: get_publisher_doi(x)) dupind = find_duplicate_indexes(df) + logger.debug(f"[dedup:find_duplicate_indexes] dupind groups: {len(dupind)}, multi-member groups: {sum(1 for idx in dupind if len(idx) > 1)}") df = mark_duplicate_dois(df) df = mark_duplicate_links(df) + _log_dedup_state(df, "after_mark_doi_link_duplicates", params) df = identify_relations(df) df = remove_false_positives_doi(df) df = remove_false_positives_link(df) + _log_dedup_state(df, "after_remove_false_positives", params) df = remove_textual_duplicates_from_different_sources(df, dupind) + _log_dedup_state(df, "after_remove_textual_duplicates", params) df = add_false_negatives(df) + _log_dedup_state(df, "after_add_false_negatives", params) df = mark_latest_doi(df, dupind) + _log_dedup_state(df, "after_mark_latest_doi", params) df.loc[df[~df.is_duplicate].index, "is_anchor"] = True + _log_dedup_state(df, "after_non_duplicate_anchors", params) pure_datasets = df[df.typenorm == "7"] non_datasets = df.loc[df.index.difference(pure_datasets.index)] + logger.debug(f"[dedup:split] non_datasets={len(non_datasets)} pure_datasets={len(pure_datasets)}") non_datasets = prioritize_OA_and_latest(non_datasets, dupind) non_datasets = prioritize_doi_and_provider(non_datasets, dupind) + _log_dedup_state(non_datasets, "non_datasets_after_prioritize", params) pure_datasets = mark_latest_doi(pure_datasets, dupind) pure_datasets_condition_mask = (pure_datasets.is_anchor == True) | (pure_datasets.is_duplicate == False) pure_datasets.loc[pure_datasets_condition_mask, "is_anchor"] = True + _log_dedup_state(pure_datasets, "pure_datasets_after_mark_latest", params) - non_datasets = enrich_anchor_using_duplicates(non_datasets, dupind) pure_datasets = enrich_anchor_using_duplicates(pure_datasets, dupind) diff --git a/server/workers/orcid/src/config.py b/server/workers/orcid/src/config.py index 92eb37737..f30038df5 100644 --- a/server/workers/orcid/src/config.py +++ b/server/workers/orcid/src/config.py @@ -21,7 +21,7 @@ class OrcidConfig(TypedDict): # Logging configuration LOGGING_CONFIG: LoggingConfig = { - "level": os.getenv("LOG_LEVEL", "INFO"), + "level": os.getenv("LOGLEVEL", "INFO"), "format": "%(asctime)s %(levelname)-8s %(message)s", "datefmt": "%Y-%m-%d %H:%M:%S" } From 172fc5191baf3f2a6d89be1f553fe7f551bc7bf6 Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 12 May 2026 17:34:19 +0200 Subject: [PATCH 075/205] bugfix for wrong anchor record merge --- server/workers/orcid/src/orcid_service.py | 36 +++++++++++++++++++---- 1 file changed, 31 insertions(+), 5 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index fb0125c72..4f6a0b8d0 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -396,10 +396,17 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra base_metadata['merged_dois'] = base_metadata['merged_dois'].apply(lambda x: x[0] if isinstance(x, list) and len(x) > 0 else x) base_metadata['merged_dois'] = base_metadata['merged_dois'].apply(lambda x: x.split(';') if isinstance(x, str) else []) base_metadata['merged_dois'] = base_metadata['merged_dois'].apply(lambda x: [x.strip() for x in x] if isinstance(x, list) else x) + # Save the normalized original doi before explode so we can rank direct fetches + # above rows whose doi was reassigned from merged_dois after explosion. + base_metadata['_fetch_doi'] = base_metadata['doi'].apply(remove_doi_prefix) base_metadata = base_metadata.explode('merged_dois', ignore_index=True) # replace doi with merged_dois if merged_dois is not empty, otherwise keep doi base_metadata.loc[base_metadata['merged_dois'].notna() & (base_metadata['merged_dois'] != ''), 'doi'] = base_metadata.loc[base_metadata['merged_dois'].notna() & (base_metadata['merged_dois'] != ''), 'merged_dois'] base_metadata.loc[:, 'doi'] = base_metadata['doi'].apply(remove_doi_prefix) + # True for rows whose final doi still matches the original fetched doi (direct); + # False for rows where doi was replaced by a merged_dois value (exploded). + base_metadata['_is_direct_fetch'] = base_metadata['doi'] == base_metadata['_fetch_doi'] + base_metadata.drop(columns='_fetch_doi', inplace=True) # Remove rows where 'doi' is pd.NaN base_metadata = base_metadata[pd.notna(base_metadata['doi'])] @@ -408,14 +415,33 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra base_metadata = self._match_dois_by_version(base_metadata, dois) base_metadata = base_metadata[base_metadata['doi'].isin(dois)] - # Sort by oa_state priority (1=open > 0=restricted > 2=unknown) so the - # most open record is kept when deduplicating by DOI. + if self.logger.isEnabledFor(logging.DEBUG): + self._log_dataframe(base_metadata.sort_values(by='title'), params, 'base_metadata_before_doi_dedup') + doi_counts = base_metadata['doi'].value_counts() + duplicate_dois = doi_counts[doi_counts > 1].index.tolist() + if duplicate_dois: + self.logger.debug( + f"[doi_dedup] {len(base_metadata)} records before dedup, " + f"{len(duplicate_dois)} DOIs with multiple records: {duplicate_dois}" + ) + for doi in duplicate_dois: + group = base_metadata[base_metadata['doi'] == doi][['doi', 'title', 'paper_abstract', 'subject_orig', 'oa_state']] + self.logger.debug(f"[doi_dedup] duplicate group for doi={doi!r}:\n{group.to_string()}") + else: + self.logger.debug(f"[doi_dedup] {len(base_metadata)} records, no duplicate DOIs — dedup step is a no-op here") + # Sort by: (1) direct fetch before exploded-from-merged_dois rows, + # (2) oa_state priority (1=open > 0=restricted > 2=unknown) as tiebreaker. + # This ensures the record actually fetched for a DOI wins over a record + # that acquired that DOI via merged_dois expansion. oa_state_order = {1: 0, 0: 1, 2: 2} base_metadata = base_metadata.assign( - _oa_sort=base_metadata['oa_state'].map(oa_state_order) - ).sort_values(by='_oa_sort').drop_duplicates(subset='doi', keep='first').drop(columns='_oa_sort') + _oa_sort=base_metadata['oa_state'].map(oa_state_order), + _direct_sort=(~base_metadata['_is_direct_fetch']).astype(int), + ).sort_values(by=['_direct_sort', '_oa_sort']).drop_duplicates( + subset='doi', keep='first' + ).drop(columns=['_oa_sort', '_direct_sort', '_is_direct_fetch']) if self.logger.isEnabledFor(logging.DEBUG): - self._log_dataframe(base_metadata.sort_values(by='title'), params, 'base_metadata') + self._log_dataframe(base_metadata.sort_values(by='title'), params, 'base_metadata_after_doi_dedup') # Select and rename relevant fields from base_metadata, including subject_orig fields_to_merge = { From a7e86ae03bb8be9a3ccd00ccb0391b2216ad1da6 Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 12 May 2026 17:52:52 +0200 Subject: [PATCH 076/205] deduplication test updates --- e2e/keywordsEnrichment/ORCID.spec.ts | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/e2e/keywordsEnrichment/ORCID.spec.ts b/e2e/keywordsEnrichment/ORCID.spec.ts index ae87d6d19..abcd2d745 100644 --- a/e2e/keywordsEnrichment/ORCID.spec.ts +++ b/e2e/keywordsEnrichment/ORCID.spec.ts @@ -17,7 +17,7 @@ const testCases: EnrichmentTestCase[] = [ abstract: "Social dilemmas force players to balance between personal and collective gain. In many dilemmas, such as elected governments negotiating climate-change mitigation measures, the decisions are made not by individual players but by their representatives. However, the behaviour of representatives in social dilemmas has not been investigated experimentally. Here inspired by the negotiations for greenhouse-gas emissions reductions, we experimentally study a collective-risk social dilemma that involves representatives deciding on behalf of their fellow group members. Representatives can be re-elected or voted out after each consecutive collective-risk game. Selfish players are preferentially elected and are hence found most frequently in the ‘representatives’ treatment. Across all treatments, we identify the selfish players as extortioners. As predicted by our mathematical model, their steadfast strategies enforce cooperation from fair players who finally compensate almost completely the deficit caused by the extortionate co-players. Everybody gains, but the extortionate representatives and their groups gain the most.", keywords: - "Climate Change [MeSH]; Cooperative Behavior [MeSH]; Female [MeSH]; Game Theory [MeSH]; Humans [MeSH]; Interpersonal Relations [MeSH]; Models, Theoretical [MeSH]; Q; Science; Young Adult [MeSH]; ddc:530; ddc:599", + "Climate Change [MeSH]; Cooperative Behavior [MeSH]; Female [MeSH]; Game Theory [MeSH]; Humans [MeSH]; Interpersonal Relations [MeSH]; Models, Theoretical [MeSH]; Q; Science; Young Adult [MeSH]; ddc:519; ddc:530", }, { suiteName: "regression test", @@ -101,7 +101,7 @@ const testCases: EnrichmentTestCase[] = [ abstract: "Abstract Body temperature is a valuable parameter in determining the wellbeing of laboratory animals. However, using body temperature to refine humane endpoints during acute illness generally lacks comprehensiveness and exposes to inter-observer bias. Here we compared two methods to assess body temperature in mice, namely implanted radio frequency identification (RFID) temperature transponders (method 1) to non-contact infrared thermometry (method 2) in 435 mice for up to 7 days during normothermia and lipopolysaccharide (LPS) endotoxin-induced hypothermia. There was excellent agreement between core and surface temperature as determined by method 1 and 2, respectively, whereas the intra- and inter-subject variation was higher for method 2. Nevertheless, using machine learning algorithms to determine temperature-based endpoints both methods had excellent accuracy in predicting death as an outcome event. Therefore, less expensive and cumbersome non-contact infrared thermometry can serve as a reliable alternative for implantable transponder-based systems for hypothermic responses, although requiring standardization between experimenters.", keywords: - "600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit; Acute Disease; Acute Disease [MeSH]; Animals; Animals [MeSH]; Body Temperature; Body Temperature [MeSH]; Creative Commons Namensnennung – 4.0 International (CC BY 4.0); Electrodes; Electrodes, Implanted [MeSH]; Female; Female [MeSH]; Hypothermia [MeSH]; Hypothermia: chemically induced; Hypothermia: diagnosis; Hypothermia: mortality; Hypothermia: physiopathology; Implanted; Inbred C57BL; Infrared Rays; Infrared Rays [MeSH]; Lipopolysaccharides; Lipopolysaccharides [Chemical]; Lipopolysaccharides [MeSH]; Lipopolysaccharides: administration & dosage; Machine Learning; Machine Learning [MeSH]; Medicine; Mice; Mice [MeSH]; Mice, Inbred C57BL [MeSH]; Q; R; Radio Frequency Identification Device [MeSH]; Radio Frequency Identification Device: methods; Science; Sepsis [MeSH]; Sepsis: chemically induced; Sepsis: diagnosis; Sepsis: mortality; Sepsis: physiopathology; Survival Analysis; Survival Analysis [MeSH]; Text; Thermometers [MeSH]; Thermometers: classification; Thermometry: instrumentation; Thermometry: methods; article; ddc:610; info:eu-repo/classification/ddc/600; radio frequency identification (RFID); radio frequency identification RFID", + "600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit; Acute Disease; Acute Disease [MeSH]; Animals; Animals [MeSH]; Body Temperature [MeSH]; Creative Commons Namensnennung – 4.0 International (CC BY 4.0); Electrodes; Electrodes, Implanted [MeSH]; Female; Female [MeSH]; Hypothermia [MeSH]; Hypothermia: chemically induced; Hypothermia: diagnosis; Hypothermia: mortality; Hypothermia: physiopathology; Implanted; Inbred C57BL; Infrared Rays; Infrared Rays [MeSH]; Lipopolysaccharides; Lipopolysaccharides [Chemical]; Lipopolysaccharides [MeSH]; Lipopolysaccharides: administration & dosage; Machine Learning; Machine Learning [MeSH]; Medicine; Mice; Mice [MeSH]; Mice, Inbred C57BL [MeSH]; Q; R; Radio Frequency Identification Device [MeSH]; Radio Frequency Identification Device: methods; Science; Sepsis [MeSH]; Sepsis: chemically induced; Sepsis: diagnosis; Sepsis: mortality; Sepsis: physiopathology; Survival Analysis; Survival Analysis [MeSH]; Text; Thermometers [MeSH]; Thermometers: classification; Thermometry: instrumentation; Thermometry: methods; article; body temperature; ddc:610; info:eu-repo/classification/ddc/600; radio frequency identification (RFID); radio frequency identification RFID", }, { suiteName: "regression test", @@ -128,7 +128,7 @@ const testCases: EnrichmentTestCase[] = [ abstract: "How we communicate research is changing because of new (especially digital) possibilities. This article sets out 10 easy steps researchers can take to disseminate their work in novel and engaging ways, and hence increase the impact of their research on science and society.", keywords: - "Biology (General); Humans [MeSH]; Information Dissemination [MeSH]; Innovative dissemination; Online Social Networking [MeSH]; Open Science; Research Personnel [MeSH]; Scientific publishing; QH301-705.5", + "Biology (General); Humans [MeSH]; Information Dissemination [MeSH]; Innovative dissemination; Online Social Networking [MeSH]; Open Science; QH301-705.5; Research Personnel [MeSH]; Scientific publishing", }, { suiteName: "bugfix 1", @@ -138,7 +138,7 @@ const testCases: EnrichmentTestCase[] = [ abstract: "Coinfections with multiple pathogens can result in complex within‐host dynamics affecting virulence and transmission. While multiple infections are intensively studied in solitary hosts, it is so far unresolved how social host interactions interfere with pathogen competition, and if this depends on coinfection diversity. We studied how the collective disease defences of ants – their social immunity – influence pathogen competition in coinfections of same or different fungal pathogen species. Social immunity reduced virulence for all pathogen combinations, but interfered with spore production only in different‐species coinfections. Here, it decreased overall pathogen sporulation success while increasing co‐sporulation on individual cadavers and maintaining a higher pathogen diversity at the community level. Mathematical modelling revealed that host sanitary care alone can modulate competitive outcomes between pathogens, giving advantage to fast‐germinating, thus less grooming‐sensitive ones. Host social interactions can hence modulate infection dynamics in coinfected group members, thereby altering pathogen communities at the host level and population level.", keywords: - "Animals [MeSH]; Ants [MeSH]; Argentine ants; ddc:570; grooming; Grooming [MeSH]; immune-mediated competition; infectious disease; host-pathogen interactions; Host-Pathogen Interactions [MeSH]; metarhizium fungus; Metarhizium [MeSH]; multiple infections; pathogen competition; pathogen diversity; Social Behavior [MeSH]; social insects; Virulence [MeSH];", + "Animals [MeSH]; Ants [MeSH]; Argentine ants; Grooming [MeSH]; Host-Pathogen Interactions [MeSH]; Metarhizium [MeSH]; Metarhizium fungus; Social Behavior [MeSH]; Virulence [MeSH]; ddc:570; grooming; immune-mediated competition; infectious disease; multiple infections; pathogen competition; pathogen diversity; social insects", }, ]; From bc508fd2d9d0044966e3f12555a0188a2510fd11 Mon Sep 17 00:00:00 2001 From: chreman Date: Wed, 13 May 2026 11:14:25 +0200 Subject: [PATCH 077/205] added test cases for wrong DOIs in duplicate groups --- e2e/keywordsEnrichment/ORCID.spec.ts | 24 ++++++++++++++++++++++-- 1 file changed, 22 insertions(+), 2 deletions(-) diff --git a/e2e/keywordsEnrichment/ORCID.spec.ts b/e2e/keywordsEnrichment/ORCID.spec.ts index abcd2d745..4fa621809 100644 --- a/e2e/keywordsEnrichment/ORCID.spec.ts +++ b/e2e/keywordsEnrichment/ORCID.spec.ts @@ -122,7 +122,7 @@ const testCases: EnrichmentTestCase[] = [ "610 Medicine & health; Artificial Intelligence [MeSH]; Dopamine [Chemical]; Dopamine [MeSH]; Humans [MeSH]; Neural Networks, Computer [MeSH]; Neurotransmitter Agents [Chemical]; Neurotransmitter Agents [MeSH]; Serotonin [Chemical]; Serotonin [MeSH]; acetylcholine; adaptive learning; dopamine; multiscale organization; noradrenaline; serotonin", }, { - suiteName: "bugfix 1", + suiteName: "bugfix merge of keywords and abstracts", orcid: "0000-0002-5238-4195", paperTitle: "Ten simple rules for innovative dissemination of research", abstract: @@ -131,7 +131,7 @@ const testCases: EnrichmentTestCase[] = [ "Biology (General); Humans [MeSH]; Information Dissemination [MeSH]; Innovative dissemination; Online Social Networking [MeSH]; Open Science; QH301-705.5; Research Personnel [MeSH]; Scientific publishing", }, { - suiteName: "bugfix 1", + suiteName: "bugfix merge of keywords and abstracts", orcid: "0000-0001-5116-955X", paperTitle: "Social immunity modulates competition between coinfecting pathogens", @@ -140,6 +140,26 @@ const testCases: EnrichmentTestCase[] = [ keywords: "Animals [MeSH]; Ants [MeSH]; Argentine ants; Grooming [MeSH]; Host-Pathogen Interactions [MeSH]; Metarhizium [MeSH]; Metarhizium fungus; Social Behavior [MeSH]; Virulence [MeSH]; ddc:570; grooming; immune-mediated competition; infectious disease; multiple infections; pathogen competition; pathogen diversity; social insects", }, + { + suiteName: "bugfix wrong DOIs in duplicates", + orcid: "0000-0002-5238-4195", + paperTitle: + "Comparison of downloads, citations and readership data for two information systems journals", + abstract: + "No abstract available", + keywords: + "Keywords: not available", + }, + { + suiteName: "bugfix wrong DOIs in duplicates", + orcid: "0000-0002-5238-4195", + paperTitle: + "Research data explored: an extended analysis of citations and altmetrics", + abstract: + 'In this study, we explore the citedness of research data, its distribution over time and its relation to the availability of a digital object identifier (DOI) in the Thomson Reuters database Data Citation Index (DCI). We investigate if cited research data "impacts" the (social) web, reflected by altmetrics scores, and if there is any relationship between the number of citations and the sum of altmetrics scores from various social media platforms. Three tools are used to collect altmetrics scores, namely PlumX, ImpactStory, and Altmetric.com, and the corresponding results are compared. We found that out of the three altmetrics tools, PlumX has the best coverage. Our experiments revealed that research data remain mostly uncited (about 85 %), although there has been an increase in citing data sets published since 2008. The percentage of the number of cited research data with a DOI in DCI has decreased in the last years. Only nine repositories are responsible for research data with DOIs and two or more citations. The number of cited research data with altmetrics "foot-prints" is even lower (4-9 %) but shows a higher coverage of research data from the last decade. In our study, we also found no correlation between the number of citations and the total number of altmetrics scores. Yet, certain data types (i.e. survey, aggregate data, and sequence data) are more often cited and also receive higher altmetrics scores. Additionally, we performed citation and altmetric analyses of all research data published between 2011 and 2013 in four different disciplines covered by the DCI. In general, these results correspond very well with the ones obtained for research data cited at least twice and also show low numbers in citations and in altmetrics. Finally, we observed that there are disciplinary differences in the availability and extent of altmetrics scores.', + keywords: + "Altmetrics; Citedness; Co-citation analysis; Data Citation Index; Research data", + }, ]; const uniqueOrcids = [...new Set(testCases.map((tc) => tc.orcid).filter(Boolean))]; From 9e7031f9b7b89ba6ed7e277b2c12abc3672b8806 Mon Sep 17 00:00:00 2001 From: chreman Date: Wed, 13 May 2026 14:50:05 +0200 Subject: [PATCH 078/205] added unit test for the doi_title_filter function --- .../base/tests/unit/test_doi_title_filter.py | 309 ++++++++++++++++++ 1 file changed, 309 insertions(+) create mode 100644 server/workers/base/tests/unit/test_doi_title_filter.py diff --git a/server/workers/base/tests/unit/test_doi_title_filter.py b/server/workers/base/tests/unit/test_doi_title_filter.py new file mode 100644 index 000000000..42d889890 --- /dev/null +++ b/server/workers/base/tests/unit/test_doi_title_filter.py @@ -0,0 +1,309 @@ +""" +Tests for the doi_title_filter function. + +The function signature is: + doi_title_filter(anchor_title: str, candidate_title: str) -> bool + +Returns True → titles refer to the same paper (keep candidate) +Returns False → titles are genuinely different papers (filter candidate out) + +All test cases below are benign pairs extracted from real container logs +(doi_dup_groups across 12 ORCID runs). None should be filtered out. + +Patterns observed: +- Exact matches (ratio 100): identical titles from different repository records +- Near-matches (90–99): trailing period, trailing/leading whitespace, Unicode vs + ASCII punctuation (curly vs straight quote) +- Journal-prefix prepended (65–89): one version carries "Journal Name / Title", + the other carries only "Title" — caught by substring matching after lowercasing +- ALL-CAPS vs title-case (23–36): very short titles; case-insensitive comparison + resolves these +""" + +import pytest +from common.deduplication import doi_title_filter + + +# --------------------------------------------------------------------------- +# Parametrize helpers +# --------------------------------------------------------------------------- + +def _case(anchor, candidate, doi=""): + return pytest.param(anchor, candidate, id=doi or f"{anchor[:40]}…") + + +# --------------------------------------------------------------------------- +# Ratio ~100 — identical titles, different repository records +# --------------------------------------------------------------------------- + +EXACT_CASES = [ + _case( + "Crosstalk in concurrent repeated games impedes direct reciprocity and requires stronger levels of forgiveness", + "Crosstalk in concurrent repeated games impedes direct reciprocity and requires stronger levels of forgiveness", + "10.1038/s41467-017-02721-8", + ), + _case( + "Enhancing satellite-based emergency mapping: Identifying wildfires through geo-social media analysis", + "Enhancing satellite-based emergency mapping: Identifying wildfires through geo-social media analysis", + "10.1080/20964471.2025.2454526", + ), + _case( + "Les fondements scientifiques et métaphysiques du monisme haeckelien", + "Les fondements scientifiques et métaphysiques du monisme haeckelien", + "10.1163/19552343-1009596005", + ), + _case( + "Introspection dynamics: a simple model of counterfactual learning in asymmetric games", + "Introspection dynamics: a simple model of counterfactual learning in asymmetric games", + "10.1088/1367-2630/ac6f76", + ), + _case( + "Maximum-entropy large-scale structures of Boolean networks optimized for criticality", + "Maximum-entropy large-scale structures of Boolean networks optimized for criticality", + "10.1088/1367-2630/17/4/043021", + ), +] + + +@pytest.mark.parametrize("anchor,candidate", EXACT_CASES) +def test_exact_match_not_filtered(anchor, candidate): + assert doi_title_filter(anchor, candidate) is True + + +# --------------------------------------------------------------------------- +# Ratio 90–99 — near-identical: trailing period, punctuation, version tag +# --------------------------------------------------------------------------- + +NEAR_MATCH_CASES = [ + _case( + "Enhanced geocoding precision for location inference of tweet text using spaCy, Nominatim and Google Maps. A comparative analysis of the influence of data selection.", + "Enhanced geocoding precision for location inference of tweet text using spaCy, Nominatim and Google Maps. A comparative analysis of the influence of data selection", + "10.1371/journal.pone.0282942", + ), + _case( + "Exact conditions for evolutionary stability in indirect reciprocity under noise.", + "Exact conditions for evolutionary stability in indirect reciprocity under noise", + "10.1371/journal.pcbi.1013584", + ), + _case( + "Direct reciprocity between individuals that use different strategy spaces.", + "Direct reciprocity between individuals that use different strategy spaces", + "10.1371/journal.pcbi.1010149", + ), + _case( + "Defining discovery: Is Google Scholar a discovery platform? An essay on the need for a new approach to scholarly discovery [version 2; peer review: 2 approved]", + "Defining discovery: Is Google Scholar a discovery platform? An essay on the need for a new approach to scholarly discovery", + "10.12688/openreseurope.14318.2", + ), + _case( + "A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and Why?", + "A diachronic analysis of paradigm shifts in NLP research: when, how, and why?", + "10.18653/v1/2023.emnlp-main.142", + ), + _case( + "The evolution of strategic timing in collective-risk dilemmas.", + "The Evolution of Strategic Timing in Collective-Risk Dilemmas", + "10.1371/journal.pone.0066490", + ), + _case( + "Urban emotion sensing beyond 'affective capture': Advancing critical interdisciplinary methods", + "Urban Emotion Sensing Beyond 'Affective Capture': Advancing Critical Interdisciplinary Methods", + "10.3390/ijerph17239003", + ), +] + + +@pytest.mark.parametrize("anchor,candidate", NEAR_MATCH_CASES) +def test_near_match_not_filtered(anchor, candidate): + assert doi_title_filter(anchor, candidate) is True + + +# --------------------------------------------------------------------------- +# Ratio 65–89 — journal name prepended as title prefix +# One version: "Journal Name / Paper Title" +# Other version: "Paper Title" +# Substring matching (lowercased) resolves these. +# --------------------------------------------------------------------------- + +JOURNAL_PREFIX_CASES = [ + _case( + "Assessing the spatial accuracy of geocoding flood-related imagery using Vision Language Models", + "Spatial Information Research / Assessing the spatial accuracy of geocoding flood-related imagery using Vision Language Models", + "10.1007/s41324-025-00609-0", + ), + _case( + "Spatial Economic Analysis / Analysing the spatial manifestation of sustainability-engaged inter-firm networks in Germany, Austria and Switzerland", + "Analysing the spatial manifestation of sustainability-engaged inter-firm networks in Germany, Austria and Switzerland", + "10.1080/17421772.2025.2573061", + ), + _case( + "Adapting mobile map application designs to map use context: a review and call for action on potential future research themes", + "Cartography and Geographic Information Science / Adapting mobile map application designs to map use context : a review and call for action on potential future research themes", + "10.1080/15230406.2021.2015720", + ), + _case( + "Developing a Citizen Social Science approach to understand urban stress and promote wellbeing in urban communities", + "Palgrave Communications / Developing a Citizen Social Science approach to understand urban stress and promote wellbeing in urban communities", + "10.1057/s41599-020-0460-1", + ), + _case( + "Spatial crime distribution and prediction for sporting events using social media", + "International Journal of Geographical Information Science / Spatial crime distribution and prediction for sporting events using social media", + "10.1080/13658816.2020.1719495", + ), + _case( + "Composition of place: towards a compositional view of functional space", + "Cartography and Geographic Information Science / Composition of place : towards a compositional view of functional space", + "10.1080/15230406.2019.1598894", + ), + _case( + "International Journal of Environmental Research and Public Health / Applying Spatial Video Geonarratives and Physiological Measurements to Explore Perceived Safety in Baton Rouge, Louisiana", + "Applying Spatial Video Geonarratives and Physiological Measurements to Explore Perceived Safety in Baton Rouge, Louisiana", + "10.3390/ijerph18031284", + ), + _case( + "Commuter Mobility Patterns in Social Media: Correlating Twitter and LODES Data", + "ISPRS International Journal of Geo-Information / Commuter Mobility Patterns in Social Media : Correlating Twitter and LODES Data", + "10.3390/ijgi11010015", + ), + _case( + "Sensors / Wearables and the quantified self : systematic benchmarking of physiological sensors", + "Wearables and the Quantified Self: Systematic Benchmarking of Physiological Sensors", + "10.3390/s19204448", + ), + _case( + "PLoS ONE / Abundant topological outliers in social media data and their effect on spatial analysis", + "Abundant Topological Outliers in Social Media Data and Their Effect on Spatial Analysis.", + "10.1371/journal.pone.0162360", + ), + _case( + "Urban Planning / Citizen-centric urban planning through extracting emotion information from Twitter in an interdisciplinary space-time-linguistics algorithm", + "Citizen-Centric Urban Planning through Extracting Emotion Information from Twitter in an Interdisciplinary Space-Time-Linguistics Algorithm", + "10.17645/up.v1i2.617", + ), + _case( + "Urban Planning / #London2012: Towards citizen-contributed urban planning through sentiment analysis of twitter data", + "#London2012: Towards Citizen-Contributed Urban Planning Through Sentiment Analysis of Twitter Data", + "10.17645/up.v3i1.1287", + ), + _case( + "ISPRS International Journal of Geo-Information / Geospatial analysis of the building heat demand and distribution losses in a district heating network", + "Geospatial Analysis of the Building Heat Demand and Distribution Losses in a District Heating Network", + "10.3390/ijgi5120219", + ), + _case( + "Estimating the Spatial Distribution of Crime Events around a Football Stadium from Georeferenced Tweets", + "ISPRS International Journal of Geo-Information / Estimating the Spatial Distribution of crime events around a football stadium from georeferenced tweets", + "10.3390/ijgi7020043", + ), + _case( + "Contextual Sensing: Integrating Contextual Information with Human and Technical Geo-Sensor Information for Smart Cities", + "Sensors / Contextual sensing : integrating contextual information with human and technical geo-sensor information for smart cities", + "10.3390/s150717013", + ), + _case( + "Geo-spatial Information Science / Routing through open spaces : a performance comparison of algorithms", + "Routing through open spaces – A performance comparison of algorithms", + "10.1080/10095020.2017.1399675", + ), + _case( + "Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment", + "Cartography and Geographic Information Science / Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment", + "10.1080/15230406.2017.1356242", + ), + _case( + "Urban Planning / Investigating the emotional responses of individuals to urban green space using twitter data : a critical comparison of three different methods of sentiment analysis", + "Investigating the Emotional Responses of Individuals to Urban Green Space Using Twitter Data: A Critical Comparison of Three Different Methods of Sentiment Analysis", + "10.17645/up.v3i1.1231", + ), + _case( + "ISPRS International Journal of Geo-Information / Analyzing and predicting micro-location patterns of software firms", + "Analyzing and Predicting Micro-Location Patterns of Software Firms", + "10.3390/ijgi7010001", + ), + _case( + "ISPRS International Journal of Geo-Information / Beyond Spatial Proximity : Classifying Parks and Their Visitors in London Based on Spatiotemporal and Sentiment Analysis of Twitter Data", + "Beyond Spatial Proximity—Classifying Parks and Their Visitors in London Based on Spatiotemporal and Sentiment Analysis of Twitter Data", + "10.3390/ijgi7090378", + ), + _case( + "E2mC: Improving Emergency Management Service Practice through Social Media and Crowdsourcing Analysis in Near Real Time", + "Sensors / E2mC: improving emergency management service practice through social media and crowdsourcing analysis in near real time", + "10.3390/s17122766", + ), + _case( + "User Experience Design in Professional Map-Based Geo-Portals", + "ISPRS International Journal of Geo-Information / User Experience Design in Professional Map-Based Geo-Portals", + "10.3390/ijgi2041015", + ), + _case( + "ISPRS International Journal of Geo-Information / GIS-based planning and modeling for renewable energy : challenges and future research avenues", + "GIS-Based Planning and Modeling for Renewable Energy: Challenges and Future Research Avenues", + "10.3390/ijgi3020662", + ), + _case( + "Determination of Suitable Areas for the Generation of Wind Energy in Germany: Potential Areas of the Present and Future", + "ISPRS International Journal of Geo-Information / Determination of suitable areas for the generation of wind energy in Germany : potential areas of the present and future", + "10.3390/ijgi3030942", + ), + _case( + "Collective Sensing: Integrating Geospatial Technologies to Understand Urban Systems—An Overview", + "Remote Sensing / Collective sensing: integrating geospatial technologies to understand urban systems : an overview", + "10.3390/rs3081743", + ), + _case( + "Sensors / Ubiquitous geo-sensing for context-aware analysis : exploring relationships between environmental and human dynamics", + "Ubiquitous Geo-Sensing for Context-Aware Analysis: Exploring Relationships between Environmental and Human Dynamics", + "10.3390/s120709800", + ), + _case( + "The Vienna Principles: A Vision for Scholarly Communication in the 21st Century", + "Mitteilungen der Vereinigung Österreichischer Bibliothekarinnen & Bibliothekare / The Vienna Principles: A Vision for Scholarly Communication in the 21st Century", + "10.31263/voebm.v69i3.1733", + ), + _case( + "Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM)", + "Österreichische Wasser- und Abfallwirtschaft / Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM)", + "10.1007/s00506-021-00767-z", + ), + _case( + "Social Sciences / Privacy Threats and Protection Recommendations for the Use of Geosocial Network Data in Research", + "Privacy Threats and Protection Recommendations for the Use of Geosocial Network Data in Research", + "10.3390/socsci7100191", + ), + _case( + "Social Sciences / The Spatial Structures in the Austrian COVID-19 Protest Movement: A Virtual and Geospatial User Network Analysis", + "The Spatial Structures in the Austrian COVID-19 Protest Movement: A Virtual and Geospatial User Network Analysis", + "10.3390/socsci13060282", + ), +] + + +@pytest.mark.parametrize("anchor,candidate", JOURNAL_PREFIX_CASES) +def test_journal_prefix_not_filtered(anchor, candidate): + assert doi_title_filter(anchor, candidate) is True + + +# --------------------------------------------------------------------------- +# Ratio 23–36 — ALL-CAPS vs title-case on short titles +# fuzz.ratio is case-sensitive so these score very low on raw comparison; +# lowercasing before comparison resolves them. +# --------------------------------------------------------------------------- + +CAPS_VARIANT_CASES = [ + _case( + "Recent Deaths", + "RECENT DEATHS", + "10.1126/science.96.2492.311.a", + ), + _case( + "TECHNOCHEMICAL LECTURES, 1942-1943, OF THE MELLON INSTITUTE", + "Technochemical Lectures, 1942-1943, of the Mellon Institute", + "10.1126/science.96.2492.311-a", + ), +] + + +@pytest.mark.parametrize("anchor,candidate", CAPS_VARIANT_CASES) +def test_caps_variant_not_filtered(anchor, candidate): + assert doi_title_filter(anchor, candidate) is True From 7ca69d00cd7bd8066da7c012a53ad129b1dd3f55 Mon Sep 17 00:00:00 2001 From: chreman Date: Wed, 13 May 2026 15:34:15 +0200 Subject: [PATCH 079/205] sanity check by DOI title filtering of duplicates --- server/workers/base/src/base.py | 70 ++++++++++++++++--- .../base/tests/unit/test_doi_title_filter.py | 34 +++++++-- server/workers/common/common/deduplication.py | 45 ++++++++---- server/workers/common/common/enrichment.py | 6 +- 4 files changed, 124 insertions(+), 31 deletions(-) diff --git a/server/workers/base/src/base.py b/server/workers/base/src/base.py index cd4681201..75d7f125d 100644 --- a/server/workers/base/src/base.py +++ b/server/workers/base/src/base.py @@ -3,12 +3,14 @@ import subprocess import pandas as pd import logging +from itertools import combinations +from rapidfuzz import fuzz from common.r_wrapper import RWrapper from common.deduplication import ( find_version_in_doi, get_unversioned_doi, get_publisher_doi, - find_duplicate_indexes, + find_duplicate_groups, mark_duplicate_dois, mark_duplicate_links, identify_relations, @@ -19,6 +21,7 @@ mark_latest_doi, prioritize_OA_and_latest, prioritize_doi_and_provider, + doi_title_filter, ) from common.enrichment import enrich_anchor_using_duplicates import re @@ -259,6 +262,22 @@ def _log_dedup_state(df, step, params): logger.debug(f"[dedup:{step}] anchor_ids={anchor_ids}") +def _log_group_similarity(df, indexes, group_type, group_key): + """Log titles, DOIs, and pairwise Levenshtein ratios for one duplicate group.""" + if not logger.isEnabledFor(logging.DEBUG): + return + rows = df.loc[list(indexes)] + titles = rows["title"].fillna("").tolist() + dois = rows["doi"].fillna("").tolist() + ids = rows["id"].fillna("").tolist() + logger.debug(f"[dedup:{group_type}] group={group_key!r} size={len(rows)}") + for i, (rid, doi, title) in enumerate(zip(ids, dois, titles)): + logger.debug(f" [{i}] id={rid!r} doi={doi!r} title={title!r}") + for (i, t1), (j, t2) in combinations(enumerate(titles), 2): + ratio = fuzz.ratio(t1, t2) + logger.debug(f" levenshtein[{i},{j}]={ratio:.1f}") + + def filter_duplicates(df, service, params): if logger.isEnabledFor(logging.DEBUG): logger.debug(f"Filtering duplicates for service: {service}") @@ -283,8 +302,8 @@ def filter_duplicates(df, service, params): lambda x: get_unversioned_doi(x) if type(x) is str else None ) df["publisher_doi"] = df.doi.map(lambda x: get_publisher_doi(x)) - dupind = find_duplicate_indexes(df) - logger.debug(f"[dedup:find_duplicate_indexes] dupind groups: {len(dupind)}, multi-member groups: {sum(1 for idx in dupind if len(idx) > 1)}") + duplicate_groups = find_duplicate_groups(df) + logger.debug(f"[dedup:find_duplicate_groups] duplicate_groups groups: {len(duplicate_groups)}, multi-member groups: {sum(1 for idx in duplicate_groups if len(idx) > 1)}") df = mark_duplicate_dois(df) df = mark_duplicate_links(df) _log_dedup_state(df, "after_mark_doi_link_duplicates", params) @@ -292,30 +311,61 @@ def filter_duplicates(df, service, params): df = remove_false_positives_doi(df) df = remove_false_positives_link(df) _log_dedup_state(df, "after_remove_false_positives", params) - df = remove_textual_duplicates_from_different_sources(df, dupind) + df = remove_textual_duplicates_from_different_sources(df, duplicate_groups) _log_dedup_state(df, "after_remove_textual_duplicates", params) df = add_false_negatives(df) _log_dedup_state(df, "after_add_false_negatives", params) - df = mark_latest_doi(df, dupind) + df = mark_latest_doi(df, duplicate_groups) _log_dedup_state(df, "after_mark_latest_doi", params) df.loc[df[~df.is_duplicate].index, "is_anchor"] = True _log_dedup_state(df, "after_non_duplicate_anchors", params) + false_positive_indexes = [] + for doi_val, grp in df[df["doi_duplicate"]].groupby("doi"): + if len(grp) < 2: + continue + anchors = grp[grp["is_anchor"]] + anchor_idx = anchors.index[0] if len(anchors) else grp.index[0] + anchor_title = df.at[anchor_idx, "title"] + for idx in grp.index: + if idx == anchor_idx: + continue + if doi_title_filter(anchor_title, df.at[idx, "title"]): + false_positive_indexes.append(idx) + logger.info( + f"[dedup:doi_title_filter] dropping false-positive DOI match " + f"doi={doi_val!r} anchor={anchor_title!r} " + f"candidate={df.at[idx, 'title']!r}" + ) + if false_positive_indexes: + df.drop(index=false_positive_indexes, inplace=True) + logger.info(f"[dedup:doi_title_filter] dropped {len(false_positive_indexes)} false-positive records") + + if logger.isEnabledFor(logging.DEBUG): + for idx_group in duplicate_groups: + if len(idx_group) > 1: + _log_group_similarity(df, idx_group, "textual_dup_group", group_key="duplicate_groups") + if logger.isEnabledFor(logging.DEBUG): + doi_groups = df[df["doi_duplicate"]].groupby("doi") + for doi_val, grp in doi_groups: + if len(grp) > 1: + _log_group_similarity(df, grp.index, "doi_dup_group", group_key=doi_val) + pure_datasets = df[df.typenorm == "7"] non_datasets = df.loc[df.index.difference(pure_datasets.index)] logger.debug(f"[dedup:split] non_datasets={len(non_datasets)} pure_datasets={len(pure_datasets)}") - non_datasets = prioritize_OA_and_latest(non_datasets, dupind) - non_datasets = prioritize_doi_and_provider(non_datasets, dupind) + non_datasets = prioritize_OA_and_latest(non_datasets, duplicate_groups) + non_datasets = prioritize_doi_and_provider(non_datasets, duplicate_groups) _log_dedup_state(non_datasets, "non_datasets_after_prioritize", params) - pure_datasets = mark_latest_doi(pure_datasets, dupind) + pure_datasets = mark_latest_doi(pure_datasets, duplicate_groups) pure_datasets_condition_mask = (pure_datasets.is_anchor == True) | (pure_datasets.is_duplicate == False) pure_datasets.loc[pure_datasets_condition_mask, "is_anchor"] = True _log_dedup_state(pure_datasets, "pure_datasets_after_mark_latest", params) - non_datasets = enrich_anchor_using_duplicates(non_datasets, dupind) - pure_datasets = enrich_anchor_using_duplicates(pure_datasets, dupind) + non_datasets = enrich_anchor_using_duplicates(non_datasets, duplicate_groups) + pure_datasets = enrich_anchor_using_duplicates(pure_datasets, duplicate_groups) filtered_non_datasets = non_datasets[non_datasets.is_anchor == True] filtered_datasets = pure_datasets[pure_datasets.is_anchor == True] diff --git a/server/workers/base/tests/unit/test_doi_title_filter.py b/server/workers/base/tests/unit/test_doi_title_filter.py index 42d889890..6aea97eb0 100644 --- a/server/workers/base/tests/unit/test_doi_title_filter.py +++ b/server/workers/base/tests/unit/test_doi_title_filter.py @@ -7,8 +7,8 @@ Returns True → titles refer to the same paper (keep candidate) Returns False → titles are genuinely different papers (filter candidate out) -All test cases below are benign pairs extracted from real container logs -(doi_dup_groups across 12 ORCID runs). None should be filtered out. +Tests include known test cases of false negatives which should be filtered out. +Tests include test cases of benign pairs extracted from real container logs. None of themshould be filtered out. Patterns observed: - Exact matches (ratio 100): identical titles from different repository records @@ -67,7 +67,7 @@ def _case(anchor, candidate, doi=""): @pytest.mark.parametrize("anchor,candidate", EXACT_CASES) def test_exact_match_not_filtered(anchor, candidate): - assert doi_title_filter(anchor, candidate) is True + assert doi_title_filter(anchor, candidate) is False # --------------------------------------------------------------------------- @@ -115,7 +115,7 @@ def test_exact_match_not_filtered(anchor, candidate): @pytest.mark.parametrize("anchor,candidate", NEAR_MATCH_CASES) def test_near_match_not_filtered(anchor, candidate): - assert doi_title_filter(anchor, candidate) is True + assert doi_title_filter(anchor, candidate) is False # --------------------------------------------------------------------------- @@ -281,7 +281,7 @@ def test_near_match_not_filtered(anchor, candidate): @pytest.mark.parametrize("anchor,candidate", JOURNAL_PREFIX_CASES) def test_journal_prefix_not_filtered(anchor, candidate): - assert doi_title_filter(anchor, candidate) is True + assert doi_title_filter(anchor, candidate) is False # --------------------------------------------------------------------------- @@ -306,4 +306,26 @@ def test_journal_prefix_not_filtered(anchor, candidate): @pytest.mark.parametrize("anchor,candidate", CAPS_VARIANT_CASES) def test_caps_variant_not_filtered(anchor, candidate): - assert doi_title_filter(anchor, candidate) is True + assert doi_title_filter(anchor, candidate) is False + + +# --------------------------------------------------------------------------- +# Known test cases of false negatives which should be filtered out +# --------------------------------------------------------------------------- + +FALSE_NEGATIVE_CASES = [ + _case( + "Comparison of downloads, citations and readership data for two information systems journals", + "Scientific publications – the bad, the good, for a fistful of dollars ; Научные публикации – хорошие, плохие, за пригоршню долларов", + "10.1007/s11192-014-1365-9", + ), + _case( + "Research data explored: an extended analysis of citations and altmetrics", + "Methodological issues of open research data: analysis of the datasets from SciELO included in Figshare ; Aspectos metodológicos de los datos abiertos de investigación: análisis de los conjuntos de datos de la colección SciELO incluidos en Figshare", + "10.1007/s11192-016-1887-4", + ), +] + +@pytest.mark.parametrize("anchor,candidate", FALSE_NEGATIVE_CASES) +def test_false_negatives_filtered(anchor, candidate): + assert doi_title_filter(anchor, candidate) is True \ No newline at end of file diff --git a/server/workers/common/common/deduplication.py b/server/workers/common/common/deduplication.py index 00f03d349..87734a392 100644 --- a/server/workers/common/common/deduplication.py +++ b/server/workers/common/common/deduplication.py @@ -6,6 +6,27 @@ from urllib.parse import urlparse pattern_doi = re.compile(r"\.v(\d)+$") +_pattern_punctuation = re.compile(r"[^\w\s]") +_DOI_TITLE_CUTOFF = 1/15.83*100 # ≈ 6.32 on rapidfuzz's 0–100 scale + + +def _normalize_title(title: str) -> str: + return _pattern_punctuation.sub("", title.lower()) + + +def doi_title_filter(anchor_title: str, candidate_title: str) -> bool: + """Return False if anchor and candidate likely not refer to the same paper. + + Uses case-folded, punctuation-stripped ratio matching so that + journal-name prefixes ("Journal Name / Paper Title" vs "Paper Title") and + ALL-CAPS vs title-case variants both resolve correctly. + Returns True only when the titles share so little text that they are + almost certainly unrelated papers mis-indexed under the same DOI. + """ + a = _normalize_title(anchor_title) + c = _normalize_title(candidate_title) + return fuzz.partial_ratio(a, c) <= 100 - _DOI_TITLE_CUTOFF + def find_version_in_doi(doi): m = pattern_doi.findall(doi) @@ -25,10 +46,10 @@ def get_publisher_doi(doi): else: return "" -def find_duplicate_indexes(df): - dupind = df.id.map(lambda x: df[df.duplicates.str.contains(x)].index) - tmp = pd.DataFrame(dupind).astype(str).drop_duplicates().index - return dupind[tmp] +def find_duplicate_groups(df): + duplicate_groups = df.id.map(lambda x: df[df.duplicates.str.contains(x)].index) + tmp = pd.DataFrame(duplicate_groups).astype(str).drop_duplicates().index + return duplicate_groups[tmp] def mark_duplicate_dois(df): for doi, index in df.groupby("doi").groups.items(): @@ -69,8 +90,8 @@ def add_false_negatives(df): df.loc[df[(~df.is_duplicate) & (df.doi_duplicate)].index, "is_duplicate"] = True return df -def remove_textual_duplicates_from_different_sources(df, dupind): - for _, idx in dupind.items(): +def remove_textual_duplicates_from_different_sources(df, duplicate_groups): + for _, idx in duplicate_groups.items(): if len(idx) > 1: tmp = df.loc[idx] df.loc[tmp.index, "is_duplicate"] = True @@ -83,8 +104,8 @@ def remove_textual_duplicates_from_different_sources(df, dupind): df.loc[tmp.sort_values(["doi", "year"], ascending=[False, False]).head(1).index, "is_anchor"] = True return df -def mark_latest_doi(df, dupind): - for _, idx in dupind.items(): +def mark_latest_doi(df, duplicate_groups): + for _, idx in duplicate_groups.items(): idx = df.index.intersection(idx) tmp = df.loc[idx] for udoi in list(filter(None, tmp.unversioned_doi.unique().tolist())): @@ -98,8 +119,8 @@ def mark_latest_doi(df, dupind): df.loc[latest.index, "is_anchor"] = True return df -def prioritize_OA_and_latest(df, dupind): - for _, idx in dupind.items(): +def prioritize_OA_and_latest(df, duplicate_groups): + for _, idx in duplicate_groups.items(): idx = df.index.intersection(idx) if len(idx) > 1: tmp = df.loc[idx] @@ -173,8 +194,8 @@ def compute_lv_matrix(titles, n): distance_matrix[j, i] = dist # Symmetric matrix return distance_matrix -def prioritize_doi_and_provider(df, dupind): - for _, idx in dupind.items(): +def prioritize_doi_and_provider(df, duplicate_groups): + for _, idx in duplicate_groups.items(): idx = df.index.intersection(idx) if len(idx) <= 1: diff --git a/server/workers/common/common/enrichment.py b/server/workers/common/common/enrichment.py index a813f04e8..8a25c22d7 100644 --- a/server/workers/common/common/enrichment.py +++ b/server/workers/common/common/enrichment.py @@ -10,7 +10,7 @@ "2": 2, # unknown } -def enrich_anchor_using_duplicates(df, dupind): +def enrich_anchor_using_duplicates(df, duplicate_groups): """ Enriches anchor elements using data from duplicates in their groups. @@ -27,7 +27,7 @@ def enrich_anchor_using_duplicates(df, dupind): Args: df: DataFrame with metadata, containing the column is_anchor - dupind: Series with indices of duplicates for each id + duplicate_groups: Series with indices of duplicates for each id Returns: DataFrame with improved anchor properties @@ -42,7 +42,7 @@ def enrich_anchor_using_duplicates(df, dupind): if is_all_columns_are_missing: return df - for _, idx in dupind.items(): + for _, idx in duplicate_groups.items(): idx = df.index.intersection(idx) is_group_has_only_one_element = len(idx) <= 1 From 4e54e0f34e093700ea2bc10476f65de182d01006 Mon Sep 17 00:00:00 2001 From: chreman Date: Wed, 13 May 2026 17:47:49 +0200 Subject: [PATCH 080/205] test case updates after bugfix --- e2e/keywordsEnrichment/ORCID.spec.ts | 31 +++++++++++++++++-- .../base/tests/unit/test_doi_title_filter.py | 6 ++-- 2 files changed, 32 insertions(+), 5 deletions(-) diff --git a/e2e/keywordsEnrichment/ORCID.spec.ts b/e2e/keywordsEnrichment/ORCID.spec.ts index 4fa621809..e4ffe15f7 100644 --- a/e2e/keywordsEnrichment/ORCID.spec.ts +++ b/e2e/keywordsEnrichment/ORCID.spec.ts @@ -101,7 +101,7 @@ const testCases: EnrichmentTestCase[] = [ abstract: "Abstract Body temperature is a valuable parameter in determining the wellbeing of laboratory animals. However, using body temperature to refine humane endpoints during acute illness generally lacks comprehensiveness and exposes to inter-observer bias. Here we compared two methods to assess body temperature in mice, namely implanted radio frequency identification (RFID) temperature transponders (method 1) to non-contact infrared thermometry (method 2) in 435 mice for up to 7 days during normothermia and lipopolysaccharide (LPS) endotoxin-induced hypothermia. There was excellent agreement between core and surface temperature as determined by method 1 and 2, respectively, whereas the intra- and inter-subject variation was higher for method 2. Nevertheless, using machine learning algorithms to determine temperature-based endpoints both methods had excellent accuracy in predicting death as an outcome event. Therefore, less expensive and cumbersome non-contact infrared thermometry can serve as a reliable alternative for implantable transponder-based systems for hypothermic responses, although requiring standardization between experimenters.", keywords: - "600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit; Acute Disease; Acute Disease [MeSH]; Animals; Animals [MeSH]; Body Temperature [MeSH]; Creative Commons Namensnennung – 4.0 International (CC BY 4.0); Electrodes; Electrodes, Implanted [MeSH]; Female; Female [MeSH]; Hypothermia [MeSH]; Hypothermia: chemically induced; Hypothermia: diagnosis; Hypothermia: mortality; Hypothermia: physiopathology; Implanted; Inbred C57BL; Infrared Rays; Infrared Rays [MeSH]; Lipopolysaccharides; Lipopolysaccharides [Chemical]; Lipopolysaccharides [MeSH]; Lipopolysaccharides: administration & dosage; Machine Learning; Machine Learning [MeSH]; Medicine; Mice; Mice [MeSH]; Mice, Inbred C57BL [MeSH]; Q; R; Radio Frequency Identification Device [MeSH]; Radio Frequency Identification Device: methods; Science; Sepsis [MeSH]; Sepsis: chemically induced; Sepsis: diagnosis; Sepsis: mortality; Sepsis: physiopathology; Survival Analysis; Survival Analysis [MeSH]; Text; Thermometers [MeSH]; Thermometers: classification; Thermometry: instrumentation; Thermometry: methods; article; body temperature; ddc:610; info:eu-repo/classification/ddc/600; radio frequency identification (RFID); radio frequency identification RFID", + "600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit; Acute Disease; Acute Disease [MeSH]; Animals; Animals [MeSH]; Body Temperature [MeSH]; Creative Commons Namensnennung – 4.0 International (CC BY 4.0); Electrodes; Electrodes, Implanted [MeSH]; Female; Female [MeSH]; Hypothermia [MeSH]; Hypothermia: chemically induced; Hypothermia: diagnosis; Hypothermia: mortality; Hypothermia: physiopathology; Implanted; Inbred C57BL; Infrared Rays; Infrared Rays [MeSH]; Lipopolysaccharides; Lipopolysaccharides [Chemical]; Lipopolysaccharides [MeSH]; Lipopolysaccharides: administration & dosage; Machine Learning; Machine Learning [MeSH]; Medicine; Mice; Mice [MeSH]; Mice, Inbred C57BL [MeSH]; Q; R; Radio Frequency Identification Device [MeSH]; Radio Frequency Identification Device: methods; Science; Sepsis [MeSH]; Sepsis: chemically induced; Sepsis: diagnosis; Sepsis: mortality; Sepsis: physiopathology; Survival Analysis; Survival Analysis [MeSH]; Text; Thermometers [MeSH]; Thermometers: classification; Thermometry: instrumentation; Thermometry: methods; article; ddc:610; info:eu-repo/classification/ddc/600; radio frequency identification (RFID); radio frequency identification RFID", }, { suiteName: "regression test", @@ -115,7 +115,7 @@ const testCases: EnrichmentTestCase[] = [ { suiteName: "regression test", orcid: "0000-0003-4221-6275", - paperTitle: "Informing deep neural networks by multiscale principles of neuromodulatory systems ", + paperTitle: "Informing deep neural networks by multiscale principles of neuromodulatory systems", abstract: "Our brains have evolved the ability to configure and adapt their processing states to match the unique challenges of acting and learning in diverse environments and behavioral contexts. In biological nervous systems, such state specification and adaptation arise in part from neuromodulators, including acetylcholine, noradrenaline, serotonin, and dopamine, whose diffuse release fine-tunes neuronal and synaptic dynamics and plasticity to complement the behavioral context in real-time. Despite the demonstrated effectiveness of deep neural networks for specific tasks, they remain relatively inflexible at generalizing across tasks or adapting to ever-changing behavioral demands. In this article, we provide an overview of neuromodulatory systems and their relationship to emerging pertinent principles in deep neural networks. We further outline opportunities for the integration of neuromodulatory principles into deep neural networks, towards endowing artificial intelligence with a key ingredient underlying the flexibility and learning capability of biological systems.", keywords: @@ -160,6 +160,33 @@ const testCases: EnrichmentTestCase[] = [ keywords: "Altmetrics; Citedness; Co-citation analysis; Data Citation Index; Research data", }, + { + suiteName: "regression test", + orcid: "0000-0002-2233-6926", + paperTitle: "Collective Sensing: Integrating Geospatial Technologies to Understand Urban Systems - An Overview", + abstract: + "Cities are complex systems composed of numerous interacting components that evolve over multiple spatio-temporal scales. Consequently, no single data source is sufficient to satisfy the information needs required to map, monitor, model, and ultimately understand and manage our interaction within such urban systems. Remote sensing technology provides a key data source for mapping such environments, but is not sufficient for fully understanding them. In this article we provide a condensed urban perspective of critical geospatial technologies and techniques: (i) Remote Sensing; (ii) Geographic Information Systems; (iii) object-based image analysis; and (iv) sensor webs, and recommend a holistic integration of these technologies within the language of open geospatial consortium (OGC) standards in-order to more fully understand urban systems. We then discuss the potential of this integration and conclude that this extends the monitoring and mapping options beyond “hard infrastructure” by addressing “humans as sensors”, mobility and human-environment interactions, and future improvements to quality of life and of social infrastructures.", + keywords: + "urban remote sensing; collective sensing; in situ sensing; sensor web; human-environment interactions; future trends; smart city", + }, + { + suiteName: "regression test title substring matching", + orcid: "0000-0001-9237-8606", + paperTitle: "Multibeam bathymetry raw data (Atlas Hydrosweep DS 3 echo sounder entire dataset) of RV POLARSTERN during cruise PS147/2", + abstract: + "Multibeam data were collected with RV Polarstern along the route of cruise PS147/2 and data acquisition was continuously monitored during the survey. Multibeam sonar system was Teledyne/Atlas Hydrosweep DS3. SVPs were retrieved from CTD data and synthetic profiles from World Ocean Atlas 18. SVPs were processed with HydrOffice SoundSpeedManager (https://www.hydroffice.org/soundspeed/main) and extended with World Ocean Atlas 18 (https://www.ncei.noaa.gov/archive/accession/NCEI-WOA18). SVP data were applied during acquisition. Multibeam data are unprocessed and may contain outliers and blunders and should not be used for grid calculations and charting projects without further editing. The raw multibeam sonar data in Teledyne Reson multibeam processing format (.s7k) were recorded with Teledyne PDS software. Raw data files can be processed using software packages like CARIS HIPS/SIPS. For updated vessel configuration files check further details.", + keywords: + 'Bathymetry; Multibeam Echosounder; Event label; Binary Object; Binary Object Media Type; Binary Object File Size; File content; Data file recording duration; Data file recording distance; Ship speed; Number of pings; Start of data file recording, date/time; Start of data file recording, latitude; Start of data file recording, longitude; Start of data file, heading; Start of data file, depth; Stop of data file recording, date/time; Stop of data file recording, latitude; Stop of data file recording, longitude; Stop of data file, heading; Stop of data file, depth; DATE/TIME; LATITUDE; LONGITUDE; ELEVATION; Comment; Swath-mapping system Atlas Hydrosweep DS-3; CTD/Rosette; Extracted with MB-System; PS147/2; Polarstern; BATHYmetric Long-Term Observations during Expeditions with RV Polarstern BATHY-LTO; DataHub Earth and Environment of the Helmholtz Association DataHub; Pilot study: "targeted underway bathymetry" for mapping uncharted seamounts SEAMAP', + }, + { + suiteName: "regression test title substring matching", + orcid: "0000-0002-5238-4195", + paperTitle: "The Vienna Principles: A Vision for Scholarly Communication in the 21st Century", + abstract: + 'Zur Zeit gibt es starke Bemühungen, die offensichtlichen Defizite des wissenschaftlichen Kommunikationssystems zu beheben. Open Science hat das Potenzial, die Produktion und Verbreitung von wissenschaftlichem Wissen positiv zu verändern; es existiert aber keine gemeinsam geteilte Vision, die das System wissenschaftlicher Kommunikation beschreibt, welches wir erschaffen wollen. Zwischen April 2015 und Juni 2016 trafen sich in Wien die Mitglieder der Open Access Network Austria (OANA) Arbeitsgruppe "Open Access and Scholarly Communication", um diese Angelegenheit zu diskutieren. Das Hauptergebnis unserer Überlegungen sind zwölf Prinzipien, die die Eckpfeiler eines künftigen wissenschaftlichen Kommunikationssystems dedarstellen. Diese Prinzipien sollen einen kohärenten Bezugsrahmen für die Debatte zur Verbesserung des derzeitigen Systems liefern. Mit diesem Dokument hoffen wir, eine breite Diskussion über eine gemeinsame Vision für die wissenschaftliche Kommunikation im 21. Jahrhundert anzustoßen. ; Currently, there is a strong push to address the apparent deficits of the scholarly communication system. Open Science has the potential to change the produc-tion and dissemination of scholarly knowledge for the better, but there is no commonly shared vision that describes the system that we want to create. Between April 2015 and June 2016, members of the Open Access Network Austria (OANA) working group "Open Access and Scholarly Communication" met in Vienna to discuss this matter. The main outcome of our considerations is a set of twelve principles that repre-sent the cornerstones of the future scholarly communication system. They are designed to provide a coherent frame of reference for the debate on how to improve the current system. With this document, we are hoping to inspire a widespread discussion towards a shared vision for scholarly communication in the 21st century ; Peter Kraker, Daniel Dörler, Andreas Ferus, Robert Gutounig, Florian Heigl, Christian Kaier, Katharina Rieck, Elena Šimukovič, Michela Vignol ', + keywords: + "Wissenschaftskommunikation; Open Access; Open Science; Vision; Bibliography. Library science. Information resources; Z", + }, ]; const uniqueOrcids = [...new Set(testCases.map((tc) => tc.orcid).filter(Boolean))]; diff --git a/server/workers/base/tests/unit/test_doi_title_filter.py b/server/workers/base/tests/unit/test_doi_title_filter.py index 6aea97eb0..06ec90fa3 100644 --- a/server/workers/base/tests/unit/test_doi_title_filter.py +++ b/server/workers/base/tests/unit/test_doi_title_filter.py @@ -286,7 +286,7 @@ def test_journal_prefix_not_filtered(anchor, candidate): # --------------------------------------------------------------------------- # Ratio 23–36 — ALL-CAPS vs title-case on short titles -# fuzz.ratio is case-sensitive so these score very low on raw comparison; +# fuzz.partial_ratio is case-sensitive so these score very low on raw comparison; # lowercasing before comparison resolves them. # --------------------------------------------------------------------------- @@ -294,12 +294,12 @@ def test_journal_prefix_not_filtered(anchor, candidate): _case( "Recent Deaths", "RECENT DEATHS", - "10.1126/science.96.2492.311.a", + "10.11/test_doi_title_filter_caps_variant", ), _case( "TECHNOCHEMICAL LECTURES, 1942-1943, OF THE MELLON INSTITUTE", "Technochemical Lectures, 1942-1943, of the Mellon Institute", - "10.1126/science.96.2492.311-a", + "10.11/test_doi_title_filter_caps_variant", ), ] From c186ee0db56dfd30793a18192e05bec81bcbf14d Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 14 May 2026 15:50:51 +0200 Subject: [PATCH 081/205] debug logging for DOI case merging --- e2e/keywordsEnrichment/ORCID.spec.ts | 24 +++++++- server/workers/base/src/base.py | 3 + server/workers/orcid/src/orcid_service.py | 69 +++++++++++++++++++---- 3 files changed, 84 insertions(+), 12 deletions(-) diff --git a/e2e/keywordsEnrichment/ORCID.spec.ts b/e2e/keywordsEnrichment/ORCID.spec.ts index e4ffe15f7..3e8c61c9c 100644 --- a/e2e/keywordsEnrichment/ORCID.spec.ts +++ b/e2e/keywordsEnrichment/ORCID.spec.ts @@ -191,10 +191,30 @@ const testCases: EnrichmentTestCase[] = [ const uniqueOrcids = [...new Set(testCases.map((tc) => tc.orcid).filter(Boolean))]; +const additionalUniqueOrcids = [ + "0000-0001-5116-955X", + "0000-0003-4221-6275", + "0000-0002-9843-6798", + "0000-0002-5238-4195", + "0000-0002-4505-0517", + "0000-0003-2897-6075", + "0000-0002-8911-7832", + "0000-0002-2233-6926", + "0000-0001-9287-3770", + "0000-0002-1193-6256", + "0000-0003-0108-7980", + "0000-0001-9237-8606" +]; + +const allUniqueOrcids = [...new Set([...uniqueOrcids, ...additionalUniqueOrcids])]; + + +// This can also be used to pre-load the unique ORCID profiles, so that the subsequent tests run faster +// npx playwright test "e2e/keywordsEnrichment/ORCID.spec.ts" --grep "Warm-up: pre-load unique ORCID profiles" test.describe("Warm-up: pre-load unique ORCID profiles", () => { - for (const orcid of uniqueOrcids) { + for (const orcid of allUniqueOrcids) { test(`${orcid}`, async ({ page }) => { - const url = `/search?type=get&vis_type=overview&orcid=${orcid}&service=orcid&embed=true`; + const url = `/search?type=get&vis_type=overview&orcid=${orcid}&service=orcid&embed=true&academic_age_offset=3`; await prepareVisualisation(page, url); await expect(page.locator("#search-term-unique")).toContainText(`(${orcid})`); }); diff --git a/server/workers/base/src/base.py b/server/workers/base/src/base.py index 75d7f125d..7c3c83691 100644 --- a/server/workers/base/src/base.py +++ b/server/workers/base/src/base.py @@ -156,6 +156,9 @@ def sanitize_metadata(self, metadata): lambda x: sanitize_authors(x) ) metadata["year"] = metadata["year"].map(lambda x: sanitize_year(x)) + # in anticipation of BASE API returning DOIs in inconsistent cases, + # we lowercase them here for better deduplication and enrichment + # metadata["doi"] = metadata["doi"].map(lambda x: x.lower() if type(x) is str else x) return metadata diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 4f6a0b8d0..da7078d58 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -345,16 +345,57 @@ def get_unversioned_doi(doi_str): return base_metadata + def _log_doi_casing_comparison(self, orcid_dois: List[str], base_metadata: pd.DataFrame, params: Dict[str, str]) -> None: + """Log how often ORCID and BASE have differently-cased versions of the same DOI.""" + orcid_id = params.get('orcid', 'unknown') + + raw_base_dois = base_metadata['doi'].dropna().unique().tolist() if not base_metadata.empty else [] + # Strip URL prefixes so both sides are bare DOIs before comparing + base_dois = [remove_doi_prefix(d) for d in raw_base_dois] + base_dois = [d for d in base_dois if d and not pd.isna(d)] + + orcid_lower_map: Dict[str, str] = {} + for doi in orcid_dois: + lower = doi.lower() + if lower not in orcid_lower_map: + orcid_lower_map[lower] = doi + + base_lower_map: Dict[str, str] = {} + for doi in base_dois: + lower = doi.lower() + if lower not in base_lower_map: + base_lower_map[lower] = doi + + case_mismatches = [] + for lower_doi, orcid_doi in orcid_lower_map.items(): + if lower_doi in base_lower_map: + base_doi = base_lower_map[lower_doi] + if orcid_doi != base_doi: + case_mismatches.append({'orcid': orcid_doi, 'base': base_doi}) + + self.logger.debug( + f"[doi_casing] orcid={orcid_id} sent {len(orcid_dois)} DOIs to BASE, " + f"BASE returned {len(base_dois)} unique DOIs (raw), " + f"{len(case_mismatches)} case mismatch(es) detected" + ) + for mismatch in case_mismatches: + self.logger.debug( + f"[doi_casing] mismatch: orcid_doi={mismatch['orcid']!r} base_doi={mismatch['base']!r}" + ) + if self.logger.isEnabledFor(logging.DEBUG): + self.logger.debug(f"[doi_casing] orcid_dois={orcid_dois}") + self.logger.debug(f"[doi_casing] base_dois={base_dois}") + def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFrame) -> pd.DataFrame: self.logger.debug(f"Enriching metadata with base for ORCID {params.get('orcid')}") original_columns = metadata.columns.to_list() - required_fields = ['id', 'identifier', 'relevance', 'relation', 'title', 'subtitle', 'doi', - 'paper_abstract', 'link', 'subject', 'oa_state', 'subject_orig', 'published_in', - 'year', 'authors', 'url', 'resulttype', 'type', 'typenorm', 'lang', 'language', - 'content_provider', 'coverage', 'is_duplicate', 'has_dataset', 'sanitized_authors', - 'relations', 'annotations', 'repo', 'source', 'volume', 'issue', 'page', 'issn', - 'citation_count', 'cited_by_wikipedia_count', 'cited_by_msm_count', 'cited_by_policies_count', + required_fields = ['id', 'identifier', 'relevance', 'relation', 'title', 'subtitle', 'doi', + 'paper_abstract', 'link', 'subject', 'oa_state', 'subject_orig', 'published_in', + 'year', 'authors', 'url', 'resulttype', 'type', 'typenorm', 'lang', 'language', + 'content_provider', 'coverage', 'is_duplicate', 'has_dataset', 'sanitized_authors', + 'relations', 'annotations', 'repo', 'source', 'volume', 'issue', 'page', 'issn', + 'citation_count', 'cited_by_wikipedia_count', 'cited_by_msm_count', 'cited_by_policies_count', 'cited_by_patents_count', 'cited_by_accounts_count', 'cited_by_fbwalls_count', 'merged_dois', 'pdf_link_candidates_from_duplicates', 'cited_by_feeds_count', @@ -369,7 +410,7 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra metadata = metadata.reindex(columns=required_fields) self.logger.debug('metadata reindexed') - + # run only if loglevel is debug, otherwise it is too expensive and we don't want it on production if self.logger.isEnabledFor(logging.DEBUG): self._log_dataframe(metadata.sort_values(by='title'), params, '_original') @@ -377,8 +418,19 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra raw_dois = metadata["doi"].tolist() dois = [doi for doi in raw_dois if doi and pd.notna(doi)] + doi_counts = pd.Series(dois).value_counts() + orcid_dup_dois = doi_counts[doi_counts > 1].index.tolist() + if orcid_dup_dois: + self.logger.info( + f"[doi_orcid_dedup] orcid={params.get('orcid')} has {len(orcid_dup_dois)} duplicate DOI(s) " + f"in ORCID metadata: {orcid_dup_dois}" + ) + else: + self.logger.debug(f"[doi_orcid_dedup] orcid={params.get('orcid')} no duplicate DOIs in ORCID metadata") + dois_for_base_query, doi_mapping = self._prepare_dois_for_base_query(dois) base_metadata = self.request_base_metadata(dois_for_base_query, params) + self._log_doi_casing_comparison(dois, base_metadata, params) if self.logger.isEnabledFor(logging.DEBUG): self._log_dataframe(base_metadata.sort_values(by='title'), params, 'base_metadata_raw') @@ -495,9 +547,6 @@ def custom_merge(existing_value, new_value): if self.logger.isEnabledFor(logging.DEBUG): self._log_dataframe(enriched_metadata.sort_values(by='title'), params, '_enriched') - - # temporal solution, for some reason if we have some undefined data, dataprocessing is failing - enriched_metadata = enriched_metadata.reindex(columns=list(set(original_columns + ['oa_state', 'subject', 'subject_orig', 'paper_abstract', 'link', 'relation']))) return enriched_metadata From 5ba798043896ba94d4b80d4f6cd270e140e81df3 Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 14 May 2026 17:21:18 +0200 Subject: [PATCH 082/205] cleanup DOI case-based enrichment, replace with simplified merge --- server/workers/orcid/src/orcid_service.py | 143 ++-------------------- 1 file changed, 10 insertions(+), 133 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index da7078d58..d83c17b9e 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -222,129 +222,6 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D base_metadata["oa_state"] = base_metadata["oa_state"].fillna("2").astype(int) return base_metadata - def _prepare_dois_for_base_query(self, dois: List[str]) -> Tuple[List[str], Dict[str, List[str]]]: - """ - Prepare DOI list for BASE query by adding lowercase variants for DOIs containing uppercase letters. - - For each DOI that contains uppercase letters, this function adds a lowercase version - to ensure case-insensitive matching in BASE search. - - Parameters: - - dois: List of original DOIs from ORCID - - Returns: - - Tuple of (list of DOIs including originals and lowercase variants, mapping from lowercase DOI to original DOIs) - """ - dois_for_base_query = [] - doi_mapping = {} - - for doi in dois: - dois_for_base_query.append(doi) - - if doi != doi.lower(): - lowercase_doi = doi.lower() - dois_for_base_query.append(lowercase_doi) - - if lowercase_doi not in doi_mapping: - doi_mapping[lowercase_doi] = [] - - doi_mapping[lowercase_doi].append(doi) - - dois_for_base_query = list(dict.fromkeys(dois_for_base_query)) - - return dois_for_base_query, doi_mapping - - def _normalize_base_results_to_original_dois( - self, - base_metadata: pd.DataFrame, - doi_mapping: Dict[str, List[str]] - ) -> pd.DataFrame: - """ - Normalize DOI values in BASE results to match original DOIs from ORCID. - - If BASE returns results with lowercase DOI variants, this function maps them back - to the original DOI format from ORCID to ensure proper merging. - - Parameters: - - base_metadata: DataFrame with results from BASE - - doi_mapping: Mapping from lowercase DOI to list of original DOIs - - Returns: - - DataFrame with normalized DOI values - """ - if base_metadata.empty: - return base_metadata - - def normalize_doi(doi_value): - if pd.isna(doi_value) or doi_value == '': - return doi_value - - if doi_value in doi_mapping: - original_dois_for_variant = doi_mapping[doi_value] - return original_dois_for_variant[0] - - return doi_value - - base_metadata = base_metadata.copy() - base_metadata.loc[:, 'doi'] = base_metadata['doi'].apply(normalize_doi) - - return base_metadata - - def _match_dois_by_version( - self, - base_metadata: pd.DataFrame, - original_dois: List[str], - ) -> pd.DataFrame: - """ - Match BASE results that have versioned DOIs (e.g. .v1, .v2) to original DOIs without version. - - If BASE returned a DOI with a version suffix but the original ORCID DOI is without version, - this function updates the base_metadata 'doi' column so that those rows match the original - DOI for merging. - - Parameters: - - base_metadata: DataFrame with 'doi' column (after explode and normalize) - - original_dois: List of original DOIs from ORCID - - Returns: - - DataFrame with 'doi' updated where versioned variants were matched to original DOIs - """ - pattern_doi_version = re.compile(r"\.v(\d)+$") - - def get_unversioned_doi(doi_str): - if pd.isna(doi_str) or doi_str == '': - return None - return pattern_doi_version.sub("", str(doi_str)) - - dois_received = base_metadata['doi'].unique().tolist() - base_unversioned_to_versioned = {} - for doi_from_base in dois_received: - unversioned = get_unversioned_doi(doi_from_base) - if unversioned and unversioned != doi_from_base: - if unversioned not in base_unversioned_to_versioned: - base_unversioned_to_versioned[unversioned] = [] - base_unversioned_to_versioned[unversioned].append(doi_from_base) - - dois_lost = [doi for doi in original_dois if doi not in dois_received] - dois_lost_with_versions = [] - for lost_doi in dois_lost: - unversioned_lost = get_unversioned_doi(lost_doi) - if unversioned_lost in base_unversioned_to_versioned: - dois_lost_with_versions.append({ - 'original': lost_doi, - 'versioned_variants_found': base_unversioned_to_versioned[unversioned_lost], - }) - - base_metadata = base_metadata.copy() - for lost_doi_info in dois_lost_with_versions: - original_doi = lost_doi_info['original'] - versioned_variants = lost_doi_info['versioned_variants_found'] - versioned_mask = base_metadata['doi'].isin(versioned_variants) - if versioned_mask.any(): - base_metadata.loc[versioned_mask, 'doi'] = original_doi - - return base_metadata - def _log_doi_casing_comparison(self, orcid_dois: List[str], base_metadata: pd.DataFrame, params: Dict[str, str]) -> None: """Log how often ORCID and BASE have differently-cased versions of the same DOI.""" orcid_id = params.get('orcid', 'unknown') @@ -428,8 +305,7 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra else: self.logger.debug(f"[doi_orcid_dedup] orcid={params.get('orcid')} no duplicate DOIs in ORCID metadata") - dois_for_base_query, doi_mapping = self._prepare_dois_for_base_query(dois) - base_metadata = self.request_base_metadata(dois_for_base_query, params) + base_metadata = self.request_base_metadata(dois, params) self._log_doi_casing_comparison(dois, base_metadata, params) if self.logger.isEnabledFor(logging.DEBUG): @@ -463,9 +339,6 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra # Remove rows where 'doi' is pd.NaN base_metadata = base_metadata[pd.notna(base_metadata['doi'])] - base_metadata = self._normalize_base_results_to_original_dois(base_metadata, doi_mapping) - base_metadata = self._match_dois_by_version(base_metadata, dois) - base_metadata = base_metadata[base_metadata['doi'].isin(dois)] if self.logger.isEnabledFor(logging.DEBUG): self._log_dataframe(base_metadata.sort_values(by='title'), params, 'base_metadata_before_doi_dedup') @@ -508,13 +381,17 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra # Rename base metadata columns to avoid conflicts with original metadata base_metadata = base_metadata.rename(columns=fields_to_merge) - # Merge base metadata into the original metadata + # Merge base metadata into the original metadata using a temporary lowercase key + # so that DOIs differing only in case (e.g. ORCID mixed-case vs BASE lowercase) are matched, + # while the original ORCID DOI casing is preserved in the output. enriched_metadata = pd.merge( - metadata, - base_metadata[['doi'] + list(fields_to_merge.values())], # Use renamed columns from base_metadata - on='doi', + metadata.assign(_doi_key=metadata['doi'].str.lower()), + base_metadata[['doi'] + list(fields_to_merge.values())] + .assign(_doi_key=base_metadata['doi'].str.lower()) + .drop(columns='doi'), + on='_doi_key', how='left' - ) + ).drop(columns='_doi_key') # Custom merging functions def custom_merge(existing_value, new_value): From 727ed60df5080388eb78cc2d26744b857f522caf Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 14 May 2026 18:29:27 +0200 Subject: [PATCH 083/205] reinstate some code --- server/workers/orcid/src/orcid_service.py | 72 ++++++++++++++++++++--- 1 file changed, 64 insertions(+), 8 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index d83c17b9e..62dda18b9 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -338,8 +338,9 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra # Remove rows where 'doi' is pd.NaN base_metadata = base_metadata[pd.notna(base_metadata['doi'])] - + base_metadata = self._match_dois_by_version(base_metadata, dois) base_metadata = base_metadata[base_metadata['doi'].isin(dois)] + if self.logger.isEnabledFor(logging.DEBUG): self._log_dataframe(base_metadata.sort_values(by='title'), params, 'base_metadata_before_doi_dedup') doi_counts = base_metadata['doi'].value_counts() @@ -354,17 +355,14 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra self.logger.debug(f"[doi_dedup] duplicate group for doi={doi!r}:\n{group.to_string()}") else: self.logger.debug(f"[doi_dedup] {len(base_metadata)} records, no duplicate DOIs — dedup step is a no-op here") - # Sort by: (1) direct fetch before exploded-from-merged_dois rows, - # (2) oa_state priority (1=open > 0=restricted > 2=unknown) as tiebreaker. + # Sort by: direct fetch before exploded-from-merged_dois rows, # This ensures the record actually fetched for a DOI wins over a record # that acquired that DOI via merged_dois expansion. - oa_state_order = {1: 0, 0: 1, 2: 2} base_metadata = base_metadata.assign( - _oa_sort=base_metadata['oa_state'].map(oa_state_order), _direct_sort=(~base_metadata['_is_direct_fetch']).astype(int), - ).sort_values(by=['_direct_sort', '_oa_sort']).drop_duplicates( + ).sort_values(by=['_direct_sort']).drop_duplicates( subset='doi', keep='first' - ).drop(columns=['_oa_sort', '_direct_sort', '_is_direct_fetch']) + ).drop(columns=['_direct_sort', '_is_direct_fetch']) if self.logger.isEnabledFor(logging.DEBUG): self._log_dataframe(base_metadata.sort_values(by='title'), params, 'base_metadata_after_doi_dedup') @@ -424,7 +422,10 @@ def custom_merge(existing_value, new_value): if self.logger.isEnabledFor(logging.DEBUG): self._log_dataframe(enriched_metadata.sort_values(by='title'), params, '_enriched') - + + # temporal solution, for some reason if we have some undefined data, dataprocessing is failing + enriched_metadata = enriched_metadata.reindex(columns=list(set(original_columns + ['oa_state', 'subject', 'subject_orig', 'paper_abstract', 'link', 'relation']))) + return enriched_metadata def enrich_author_info(self, author_info: AuthorInfo, metadata: pd.DataFrame, params: Dict[str, str]) -> AuthorInfo: @@ -601,3 +602,58 @@ def _log_is_base_response_missing_dois(self, batch: List[str], batch_df: pd.Data self.logger.debug( f"BASE response statistics: requested {len(batch)} DOIs, received {len(batch_df)} rows" ) + + def _match_dois_by_version( + self, + base_metadata: pd.DataFrame, + original_dois: List[str], + ) -> pd.DataFrame: + """ + Match BASE results that have versioned DOIs (e.g. .v1, .v2) to original DOIs without version. + + If BASE returned a DOI with a version suffix but the original ORCID DOI is without version, + this function updates the base_metadata 'doi' column so that those rows match the original + DOI for merging. + + Parameters: + - base_metadata: DataFrame with 'doi' column (after explode and normalize) + - original_dois: List of original DOIs from ORCID + + Returns: + - DataFrame with 'doi' updated where versioned variants were matched to original DOIs + """ + pattern_doi_version = re.compile(r"\.v(\d)+$") + + def get_unversioned_doi(doi_str): + if pd.isna(doi_str) or doi_str == '': + return None + return pattern_doi_version.sub("", str(doi_str)) + + dois_received = base_metadata['doi'].unique().tolist() + base_unversioned_to_versioned = {} + for doi_from_base in dois_received: + unversioned = get_unversioned_doi(doi_from_base) + if unversioned and unversioned != doi_from_base: + if unversioned not in base_unversioned_to_versioned: + base_unversioned_to_versioned[unversioned] = [] + base_unversioned_to_versioned[unversioned].append(doi_from_base) + + dois_lost = [doi for doi in original_dois if doi not in dois_received] + dois_lost_with_versions = [] + for lost_doi in dois_lost: + unversioned_lost = get_unversioned_doi(lost_doi) + if unversioned_lost in base_unversioned_to_versioned: + dois_lost_with_versions.append({ + 'original': lost_doi, + 'versioned_variants_found': base_unversioned_to_versioned[unversioned_lost], + }) + + base_metadata = base_metadata.copy() + for lost_doi_info in dois_lost_with_versions: + original_doi = lost_doi_info['original'] + versioned_variants = lost_doi_info['versioned_variants_found'] + versioned_mask = base_metadata['doi'].isin(versioned_variants) + if versioned_mask.any(): + base_metadata.loc[versioned_mask, 'doi'] = original_doi + + return base_metadata \ No newline at end of file From eed7e93ceea0f5334f6dc8e39096e1309a7b017a Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 14 May 2026 19:06:04 +0200 Subject: [PATCH 084/205] revert e2e test param change --- e2e/keywordsEnrichment/ORCID.spec.ts | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/e2e/keywordsEnrichment/ORCID.spec.ts b/e2e/keywordsEnrichment/ORCID.spec.ts index 3e8c61c9c..42391b162 100644 --- a/e2e/keywordsEnrichment/ORCID.spec.ts +++ b/e2e/keywordsEnrichment/ORCID.spec.ts @@ -214,7 +214,7 @@ const allUniqueOrcids = [...new Set([...uniqueOrcids, ...additionalUniqueOrcids] test.describe("Warm-up: pre-load unique ORCID profiles", () => { for (const orcid of allUniqueOrcids) { test(`${orcid}`, async ({ page }) => { - const url = `/search?type=get&vis_type=overview&orcid=${orcid}&service=orcid&embed=true&academic_age_offset=3`; + const url = `/search?type=get&vis_type=overview&orcid=${orcid}&service=orcid&embed=true`; await prepareVisualisation(page, url); await expect(page.locator("#search-term-unique")).toContainText(`(${orcid})`); }); From 41a982b88a4d159b778615ea445c62cf00965e06 Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 14 May 2026 19:39:33 +0200 Subject: [PATCH 085/205] added tests for DOI case variants --- e2e/keywordsEnrichment/ORCID.spec.ts | 27 +++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) diff --git a/e2e/keywordsEnrichment/ORCID.spec.ts b/e2e/keywordsEnrichment/ORCID.spec.ts index 42391b162..fdc2626ee 100644 --- a/e2e/keywordsEnrichment/ORCID.spec.ts +++ b/e2e/keywordsEnrichment/ORCID.spec.ts @@ -187,6 +187,33 @@ const testCases: EnrichmentTestCase[] = [ keywords: "Wissenschaftskommunikation; Open Access; Open Science; Vision; Bibliography. Library science. Information resources; Z", }, + { + suiteName: "regression test DOI case-insensitive merge", + orcid: "0000-0002-4505-0517", + paperTitle: "Network structure, metadata, and the prediction of missing nodes and annotations", + keywords: + "Machine Learning; Physics; Physics and Society; QC1-999; Social and Information Networks", + abstract: + "The empirical validation of community detection methods is often based on available annotations on the nodes that serve as putative indicators of the large-scale network structure.", + }, + { + suiteName: "regression test DOI case-insensitive merge", + orcid: "0000-0002-4505-0517", + paperTitle: "Hierarchical block structures and high-resolution model selection in large networks", + keywords: + "Data Analysis; Disordered Systems and Neural Networks; Machine Learning; Physics; Physics and Society; QC1-999; Social and Information Networks; Statistical Mechanics; Statistics and Probability", + abstract: + "Discovering and characterizing the large-scale topological features in empirical networks are crucial steps in understanding how complex systems function.", + }, + { + suiteName: "regression test DOI case-insensitive merge", + orcid: "0000-0002-9843-6798", + paperTitle: "Generalizing Tree–Level Sap Flow Across the European Continent", + keywords: + "Earth sciences; Geophysics. Cosmic physics; LSTMs; QC801-809; ddc:550; deep learning; ecohydrology; info:eu-repo/classification/ddc/550; sap flow; transpiration; vegetation dynamics", + abstract: + "Sap flow offers key insights about transpiration dynamics and forest‐climate interactions.", + }, ]; const uniqueOrcids = [...new Set(testCases.map((tc) => tc.orcid).filter(Boolean))]; From a3d97c182cff1404f460eaf5d935d4f19ee8d195 Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 15 May 2026 11:09:44 +0200 Subject: [PATCH 086/205] added tests for DOI case invariant enrichment --- e2e/keywordsEnrichment/ORCID.spec.ts | 19 ++++++++++++++----- 1 file changed, 14 insertions(+), 5 deletions(-) diff --git a/e2e/keywordsEnrichment/ORCID.spec.ts b/e2e/keywordsEnrichment/ORCID.spec.ts index fdc2626ee..eece5176c 100644 --- a/e2e/keywordsEnrichment/ORCID.spec.ts +++ b/e2e/keywordsEnrichment/ORCID.spec.ts @@ -191,28 +191,37 @@ const testCases: EnrichmentTestCase[] = [ suiteName: "regression test DOI case-insensitive merge", orcid: "0000-0002-4505-0517", paperTitle: "Network structure, metadata, and the prediction of missing nodes and annotations", - keywords: - "Machine Learning; Physics; Physics and Society; QC1-999; Social and Information Networks", abstract: "The empirical validation of community detection methods is often based on available annotations on the nodes that serve as putative indicators of the large-scale network structure.", + keywords: + "Machine Learning; Physics; Physics and Society; QC1-999; Social and Information Networks", }, { suiteName: "regression test DOI case-insensitive merge", orcid: "0000-0002-4505-0517", paperTitle: "Hierarchical block structures and high-resolution model selection in large networks", - keywords: - "Data Analysis; Disordered Systems and Neural Networks; Machine Learning; Physics; Physics and Society; QC1-999; Social and Information Networks; Statistical Mechanics; Statistics and Probability", abstract: "Discovering and characterizing the large-scale topological features in empirical networks are crucial steps in understanding how complex systems function.", + keywords: + "Data Analysis; Disordered Systems and Neural Networks; Machine Learning; Physics; Physics and Society; QC1-999; Social and Information Networks; Statistical Mechanics; Statistics and Probability", }, { suiteName: "regression test DOI case-insensitive merge", orcid: "0000-0002-9843-6798", paperTitle: "Generalizing Tree–Level Sap Flow Across the European Continent", + abstract: + "Sap flow offers key insights about transpiration dynamics and forest‐climate interactions.", keywords: "Earth sciences; Geophysics. Cosmic physics; LSTMs; QC801-809; ddc:550; deep learning; ecohydrology; info:eu-repo/classification/ddc/550; sap flow; transpiration; vegetation dynamics", + }, + { + suiteName: "regression test DOI case-insensitive merge", + orcid: "0000-0001-9237-8606", + paperTitle: "Multibeam bathymetry raw data (Atlas Hydrosweep DS 3 echo sounder entire dataset) of RV POLARSTERN during cruise PS147/2", abstract: - "Sap flow offers key insights about transpiration dynamics and forest‐climate interactions.", + 'Multibeam data were collected with RV Polarstern along the route of cruise PS147/2 and data acquisition was continuously monitored during the survey. Multibeam sonar system was Teledyne/Atlas Hydrosweep DS3. SVPs were retrieved from CTD data and synthetic profiles from World Ocean Atlas 18. SVPs were processed with HydrOffice SoundSpeedManager (https://www.hydroffice.org/soundspeed/main) and extended with World Ocean Atlas 18 (https://www.ncei.noaa.gov/archive/accession/NCEI-WOA18). SVP data were applied during acquisition. Multibeam data are unprocessed and may contain outliers and blunders and should not be used for grid calculations and charting projects without further editing. The raw multibeam sonar data in Teledyne Reson multibeam processing format (.s7k) were recorded with Teledyne PDS software. Raw data files can be processed using software packages like CARIS HIPS/SIPS. For updated vessel configuration files check further details.', + keywords: + 'Bathymetry ; Multibeam Echosounder ; Event label ; Binary Object ; Binary Object Media Type ; Binary Object File Size ; File content ; Data file recording duration ; Data file recording distance ; Ship speed ; Number of pings ; Start of data file recording, date/time ; Start of data file recording, latitude ; Start of data file recording, longitude ; Start of data file, heading ; Start of data file, depth ; Stop of data file recording, date/time ; Stop of data file recording, latitude ; Stop of data file recording, longitude ; Stop of data file, heading ; Stop of data file, depth ; DATE/TIME ; LATITUDE ; LONGITUDE ; ELEVATION ; Comment ; Swath-mapping system Atlas Hydrosweep DS-3 ; CTD/Rosette ; Extracted with MB-System ; PS147/2 ; Polarstern ; BATHYmetric Long-Term Observations during Expeditions with RV Polarstern BATHY-LTO ; DataHub Earth and Environment of the Helmholtz Association DataHub ; Pilot study: "targeted underway bathymetry" for mapping uncharted seamounts SEAMAP', }, ]; From 5cccb42f899008861bf4ad972f5a2ed747b5ee72 Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 15 May 2026 14:05:01 +0200 Subject: [PATCH 087/205] added BASE test cases for anchor record retention bugfix --- e2e/keywordsEnrichment/BASE.spec.ts | 148 ++++++++++++++++++++++++++++ 1 file changed, 148 insertions(+) create mode 100644 e2e/keywordsEnrichment/BASE.spec.ts diff --git a/e2e/keywordsEnrichment/BASE.spec.ts b/e2e/keywordsEnrichment/BASE.spec.ts new file mode 100644 index 000000000..3501f087c --- /dev/null +++ b/e2e/keywordsEnrichment/BASE.spec.ts @@ -0,0 +1,148 @@ +import { test, expect, Page } from "@playwright/test"; +import { prepareVisualisation } from "../../vis/js/utils/e2eVisualisationLoader"; + +interface EnrichmentTestCase { + suiteName: string; + query: string; + search_params: string; + paperTitle: string; + keywords?: string; + abstract?: string; +} + +const testCases: EnrichmentTestCase[] = [ + { + suiteName: "bugfix: retain anchor record", + query: '“Solar eclipse”', + search_params: "&service=base&sorting=most-relevant&document_types%5B%5D=121&lang_id%5B%5D=all-lang&min_descsize=300", + paperTitle: "Why every solar eclipse viewing event needs a disco ball", + abstract: + "Solar eclipses offer unparalleled opportunities for public engagement in astronomy. Large groups of people often gather to view eclipses, and these events require affordable and easy to use tools to safely observe the Sun. One unique way to observe a solar eclipse is by using a disco ball. Here, we present an analysis of the experiences of educators who used a disco ball as a solar projector during various public outreach events. Through a survey conducted shortly after the April 2024 total solar eclipse and the March 2025 partial solar eclipse, we collected data on the use, engagement, and perceived educational value of a disco ball projector from 31 individual events. The results suggest that disco balls were not only affordable and safe, but also popular and educational. ... : 22 pages, 8 figures, to be published in CAPJournal Volume 19 Issue 2 ...", + keywords: + "Instrumentation and Methods for Astrophysics astro-ph.IM; Physics Education physics.ed-ph; FOS: Physical sciences", + }, + { + suiteName: "bugfix: retain anchor record", + query: 'Clinical trials', + search_params: "&service=base&sorting=most-relevant&document_types%5B%5D=121&lang_id%5B%5D=all-lang&min_descsize=0", + paperTitle: "Clinical Trials and Clinical Research: A Comprehensive Review", + abstract: + "Clinical research is an alternative terminology used to describe medical research. Clinical research involves people, and it is generally carried out to evaluate the efficacy of a therapeutic drug, a medical/surgical procedure, or a device as a part of treatment and patient management. Moreover, any research that evaluates the aspects of a disease like the symptoms, risk factors, and pathophysiology, among others may be termed clinical research. However, clinical trials are those studies that assess the potential of a therapeutic drug/device in the management, control, and prevention of disease. In view of the increasing incidences of both communicable and non-communicable diseases, and especially after the effects that Coronavirus Disease-19 (COVID-19) had on public health worldwide, the emphasis on clinical research assumes extremely essential. The knowledge of clinical research will facilitate the discovery of drugs, devices, and vaccines, thereby improving preparedness during public health emergencies. Therefore, in this review, we comprehensively describe the critical elements of clinical research that include clinical trial phases, types, and designs of clinical trials, operations of trial, audit, and management, and ethical concerns.", + keywords: + "audit; clinical research; clinical trials; efficacy; ethical concerns; medical research; therapeutic drug", + }, + { + suiteName: "bugfix: retain anchor record", + query: 'Antibiotics prescription', + search_params: "&service=base&sorting=most-relevant&document_types%5B%5D=121&lang_id%5B%5D=all-lang&min_descsize=0", + paperTitle: "Clinical Trials and Clinical Research: A Comprehensive Review", + abstract: + "Purpose The purpose of the study was to assess the bacterial resistance and annual antibiotic consumption at a tertiary care hospital in Riyadh, Saudi Arabia over a two-year period. Methods This retrospective cohort study was conducted at a tertiary care hospital in Riyadh, Saudi Arabia from January 1, 2016, to December 31, 2017. Results The results showed that there was no significant difference between 2016 and 2017 data regarding patient characteristics like bed occupancy rate, the average length of stay, and the number of admissions; the same was true for bacterial characteristics like the number of bacteria, percentage of isolates in the group, and multidrug resistance (MDR) percentage (p: >0.05). Between 2016 and 2017, there was a slight reduction in the sensitivity of Escherichia​​​ coli (E. coli) carbapenem-resistant Enterobacteriaceae (CRE) (97%, 86%) and Klebsiella pneumoniae (K. pneumoniae) CRE (80%, 76%) towards colistin. There was also a decrease in the sensitivity of Acinetobacter baumannii (A. baumannii) multidrug-resistant organism (MDRO) from 42% to 29% against tigecycline, but an increase in the sensitivity of K. pneumoniae CRE (33%, 50%) and E. coli CRE (76%, 82%). The percentage of MDR strains in gram-positive bacteria showed that more than half of Staphylococcus aureus (S. aureus) were methicillin-resistant (61%, 59%) in 2016 and 2017 respectively. There was a reduction in the percentage of MDR strains in some gram-negative bacteria like Pseudomonas aeruginosa (P. aeruginosa) MDRO (24%, 19%),E. coli extended-spectrum beta-lactamases (ESBL) (56%, 50%), E. coli CRE (4%, 1%), K. pneumoniae CRE (49%, 33%), A. baumannii CRE (90%, 76%), and Proteus mirabilis​​​​​​​ (P. mirabilis) ESBL (54%, 50%). Conclusion MDRO bacteria are very common in the hospital where the study was conducted. Immediate action is required to tackle this problem.", + keywords: + "antibiotic; infection; infections; multidrug resistance organism; prescription; tertiary hospital", + }, + { + suiteName: "bugfix: retain anchor record", + query: '“Game theory”', + search_params: "&service=base&sorting=most-relevant&document_types%5B%5D=121&lang_id%5B%5D=all-lang&min_descsize=300", + paperTitle: "Game theory approaches for autonomy", + abstract: + "Game theory offers techniques for applying autonomy in the field. In this mini-review, we define autonomy, and briefly overview game theory with a focus on Nash and Stackleberg equilibria and Social dilemma. We provide a discussion of successful projects using game theory approaches applied to several autonomous systems.", + keywords: + "game theory; robotics; autonomous vehicles; nash equilibrium; self-driving cars; Physics; QC1-999", + }, + { + suiteName: "bugfix: retain anchor record", + query: 'Machine translation', + search_params: "&service=base&sorting=most-relevant&document_types%5B%5D=121&lang_id%5B%5D=all-lang&min_descsize=300", + paperTitle: "A Survey of Orthographic Information in Machine Translation", + abstract: + "Abstract Machine translation is one of the applications of natural language processing which has been explored in different languages. Recently researchers started paying attention towards machine translation for resource-poor languages and closely related languages. A widespread and underlying problem for these machine translation systems is the linguistic difference and variation in orthographic conventions which causes many issues to traditional approaches. Two languages written in two different orthographies are not easily comparable but orthographic information can also be used to improve the machine translation system. This article offers a survey of research regarding orthography’s influence on machine translation of under-resourced languages. It introduces under-resourced languages in terms of machine translation and how orthographic information can be utilised to improve machine translation. We describe previous work in this area, discussing what underlying assumptions were made, and showing how orthographic knowledge improves the performance of machine translation of under-resourced languages. We discuss different types of machine translation and demonstrate a recent trend that seeks to link orthographic information with well-established machine translation methods. Considerable attention is given to current efforts using cognate information at different levels of machine translation and the lessons that can be drawn from this. Additionally, multilingual neural machine translation of closely related languages is given a particular focus in this survey. This article ends with a discussion of the way forward in machine translation with orthographic information, focusing on multilingual settings and bilingual lexicon induction.", + keywords: + "Computation and Language cs.CL; FOS: Computer and information sciences; Machine translation ; Neural machine translation; Orthography; Rule-based machine translation; Statistical machine translation; Under-resourced languages", + }, + { + suiteName: "bugfix: retain anchor record", + query: '"climate change"', + search_params: "&service=base&sorting=most-relevant&document_types%5B%5D=7&lang_id%5B%5D=all-lang&min_descsize=0", + paperTitle: "LAICPMS_Pb.csv", + abstract: + "No abstract available", + keywords: + "Earth and Environmental Sciences; Medicine, Health and Life Sciences", + }, + { + suiteName: "bugfix: retain anchor record", + query: 'Big Data', + search_params: "&service=base&sorting=most-relevant&document_types%5B%5D=7&lang_id%5B%5D=all-lang&min_descsize=0", + paperTitle: "Big Data or Big Fail? The Good, the Bad and the Ugly and the missing role of Statistics", + abstract: + "The so called “Big Data” are data which we think as being “big” because of their volume, their amount per unit of time and because they are un- structured. The usual sources of big data are administrative repositories, transaction data or social media and social network feeds. Someone defines big data as those data which cannot be analyzed on a desktop machine or stored on one’s hard disk. These ways of defining big data completely miss the point of view of Statistics: they seem to be tailored more to advertising campaign of SaS or storage solution rather than to Science. Moreover, recent big fails, like e.g. the famous/infamous Google Flu Trend experiment, raised a series of popular news paper articles against the validity of information contained in these data and Statistics itself, even though none of these bad practices has been conducted by statisticians. While Information Technol- ogy and Computer Science are good at efficiently retrive and manage them, these data should be soon brought back into the field of Statistics to where data belong and this Special Issues of EJASA is one important step in this direction.", + keywords: + "Big data; social media; unstructured data; statistics; Settore SECS-S/01 - Statistica; Settore MAT/06 - Probabilita' e Statistica Matematica", + }, + { + suiteName: "bugfix: retain anchor record", + query: 'automation tools', + search_params: "&service=base&sorting=most-relevant&document_types%5B%5D=7&lang_id%5B%5D=all-lang&min_descsize=0", + paperTitle: "AEQUITAS. WP7. USE CASE HR1. DESC. v1.0", + abstract: + "The dataset contains the matching of job positions and hiring candidates; this data has been collected by a big Italian company, working in the HR sector - ADECCO. The detailed description of the data can be found in a README file within the compressed archive. This activity is part of the HORIZON-CL4-2021-HUMAN-01-24-AEQUITAS project (g.a. 101070363). The aim of AEQUITAS to address and tackle the multiple manifestations of bias and unfairness in Artificial Intelligence (AI) from a variety of dimensions, such as the development of AI tools, the data used to train, test and validate them or the interpretation practices developed around them. AEQUITAS offers an overall approach for tackling the problem, savant of the criticalities that automation and AI techniques bring about. This case study allows for detecting hiring dataset biases which are the primary source for training a novel AI system. For example, there are historical trends in the labour market in favour of men (higher levels of education, once of employment, hence of hiring) that might be reflected in the data history of ADECCO. Balancing these inequalities in data or leaving data biased and targeting debiasing or bias reducing algorithms is a key step for a fair AI system and dataset. The ADECCO data offer data were to compare selection decisions with regards to important bias, such as gender, age, economic background, etc.", + keywords: + "INF/01 Informatica; ING-INF/05 Sistemi di elaborazione delle informazioni", + }, +]; + +const uniqueSearches = [...new Set(testCases.map((tc) => tc.search_params).filter(Boolean))]; + +// This can also be used to pre-load the BASE searches, so that the subsequent tests run faster +// npx playwright test "e2e/keywordsEnrichment/BASE.spec.ts" --grep "Warm-up: pre-load BASE searches" +test.describe("Warm-up: pre-load BASE searches", () => { + for (const tc of testCases) { + test(`${tc.query}`, async ({ page }) => { + const url = `/search?type=get&vis_type=overview${tc.search_params}`; + await prepareVisualisation(page, url); + await expect(page.locator("#search-term-unique")).toContainText(`(${tc.query})`); + }); + } +}); + +for (const tc of testCases) { + test.describe( + `Verify abstract and keywords are merged correctly, ${tc.suiteName}`, + () => { + const url = `/search?type=get&vis_type=overview${tc.search_params}&service=base&embed=true`; + + async function openPaper(page: Page) { + await prepareVisualisation(page, url); + await expect(page.locator("#search-term-unique")).toContainText( + `(${tc.query})`, + ); + const paper = page.getByTitle(tc.paperTitle); + await expect(paper).toBeVisible(); + await paper.click(); + return paper; + } + + if (tc.keywords) { + const keywords = tc.keywords; + test(`Keywords enrichment for the '${tc.paperTitle}' document`, async ({ + page, + }) => { + await openPaper(page); + await expect(page.locator("#list_holder")).toContainText(keywords); + }); + } + + if (tc.abstract) { + const abstract = tc.abstract; + test(`Abstract enrichment for the '${tc.paperTitle}' document`, async ({ + page, + }) => { + await openPaper(page); + await expect(page.locator("#list_holder")).toContainText(abstract); + }); + } + }, + ); +} From 26fc56aa10b3385ccc2eb46eae0a53999cff0938 Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 15 May 2026 14:05:59 +0200 Subject: [PATCH 088/205] bugfix and logging for anchor document receiving best position in duplicate groups --- server/workers/base/src/base.py | 46 ++++++++++++++++++++++++++++++++- 1 file changed, 45 insertions(+), 1 deletion(-) diff --git a/server/workers/base/src/base.py b/server/workers/base/src/base.py index 7c3c83691..9f450714d 100644 --- a/server/workers/base/src/base.py +++ b/server/workers/base/src/base.py @@ -307,6 +307,12 @@ def filter_duplicates(df, service, params): df["publisher_doi"] = df.doi.map(lambda x: get_publisher_doi(x)) duplicate_groups = find_duplicate_groups(df) logger.debug(f"[dedup:find_duplicate_groups] duplicate_groups groups: {len(duplicate_groups)}, multi-member groups: {sum(1 for idx in duplicate_groups if len(idx) > 1)}") + for grp_id, idx in duplicate_groups.items(): + if len(idx) > 1: + logger.debug( + f"[dedup:position_check] group id={grp_id!r} size={len(idx)} " + f"member_original_indexes={sorted(idx.tolist())}" + ) df = mark_duplicate_dois(df) df = mark_duplicate_links(df) _log_dedup_state(df, "after_mark_doi_link_duplicates", params) @@ -335,7 +341,7 @@ def filter_duplicates(df, service, params): continue if doi_title_filter(anchor_title, df.at[idx, "title"]): false_positive_indexes.append(idx) - logger.info( + logger.debug( f"[dedup:doi_title_filter] dropping false-positive DOI match " f"doi={doi_val!r} anchor={anchor_title!r} " f"candidate={df.at[idx, 'title']!r}" @@ -373,8 +379,46 @@ def filter_duplicates(df, service, params): filtered_non_datasets = non_datasets[non_datasets.is_anchor == True] filtered_datasets = pure_datasets[pure_datasets.is_anchor == True] filtered = pd.concat([filtered_non_datasets, filtered_datasets]) + + # For each duplicate group whose anchor ended up at a higher index than + # another group member (which was dropped as non-anchor), move the anchor + # to the best-ranked (lowest) index in the group so it survives head(list_size). + seen_groups = set() + claimed_targets = set() + index_renames = {} + for _grp_id, idx in duplicate_groups.items(): + if len(idx) <= 1: + continue + idx_key = frozenset(idx.tolist()) + if idx_key in seen_groups: + continue + seen_groups.add(idx_key) + anchor_idxs = filtered.index.intersection(idx) + if len(anchor_idxs) == 0: + continue + min_idx = min(idx.tolist()) + if min_idx in filtered.index or min_idx in claimed_targets: + continue + for anchor_idx in sorted(anchor_idxs): + if anchor_idx > min_idx: + index_renames[anchor_idx] = min_idx + claimed_targets.add(min_idx) + break + if index_renames: + filtered.rename(index=index_renames, inplace=True) + logger.info(f"[dedup:index_fix] moved {len(index_renames)} anchor(s) to best-ranked group position: {index_renames}") + filtered.sort_index(inplace=True) + list_size = params.get("list_size") + for rank, (orig_idx, row) in enumerate(filtered.iterrows()): + beyond = list_size is not None and rank >= list_size + logger.debug( + f"[dedup:position_check] anchor id={row['id']!r} " + f"original_index={orig_idx} filtered_rank={rank} " + f"beyond_list_size={beyond} list_size={list_size}" + ) + for c in [ "doi_duplicate", "link_duplicate", From 258ca6dcce847080e9cf8fbc863af22f766132c2 Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 15 May 2026 14:08:11 +0200 Subject: [PATCH 089/205] update of contentprovider.json cache --- server/workers/common/common/contentproviders.json | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/server/workers/common/common/contentproviders.json b/server/workers/common/common/contentproviders.json index ce7caf2b3..27494edf9 100644 --- a/server/workers/common/common/contentproviders.json +++ b/server/workers/common/common/contentproviders.json @@ -247,10 +247,6 @@ "name": "CityUHK Scholars", "internal_name": "ftid14687" }, - { - "name": "Evolutio Press", - "internal_name": "ftid14688" - }, { "name": "Perry Research", "internal_name": "ftid14689" @@ -42719,10 +42715,6 @@ "name": "Pittsburgh Papers on the European Union (University of Pittsburgh)", "internal_name": "ftjppeu" }, - { - "name": "027.7 Zeitschrift für Bibliothekskultur", - "internal_name": "ftj0277" - }, { "name": "Bundesanstalt für Straßenwesen (BASt): Elektronisches BASt-Archiv (ELBA)", "internal_name": "ftbastrassen" @@ -43535,10 +43527,6 @@ "name": "Rollins College: Rollins Scholarship Online (RSO)", "internal_name": "ftrollinscollege" }, - { - "name": "公立はこだて未来大学", - "internal_name": "ftfunivhakodate" - }, { "name": "Revista de la Asociación Española de Neuropsiquiatría", "internal_name": "ftjaen" From a93949410af81bfa1d10792f5ec71ac1009cc074 Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 19 May 2026 10:33:54 +0200 Subject: [PATCH 090/205] test case update --- e2e/keywordsEnrichment/BASE.spec.ts | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/e2e/keywordsEnrichment/BASE.spec.ts b/e2e/keywordsEnrichment/BASE.spec.ts index 3501f087c..9d101ca0a 100644 --- a/e2e/keywordsEnrichment/BASE.spec.ts +++ b/e2e/keywordsEnrichment/BASE.spec.ts @@ -19,7 +19,7 @@ const testCases: EnrichmentTestCase[] = [ abstract: "Solar eclipses offer unparalleled opportunities for public engagement in astronomy. Large groups of people often gather to view eclipses, and these events require affordable and easy to use tools to safely observe the Sun. One unique way to observe a solar eclipse is by using a disco ball. Here, we present an analysis of the experiences of educators who used a disco ball as a solar projector during various public outreach events. Through a survey conducted shortly after the April 2024 total solar eclipse and the March 2025 partial solar eclipse, we collected data on the use, engagement, and perceived educational value of a disco ball projector from 31 individual events. The results suggest that disco balls were not only affordable and safe, but also popular and educational. ... : 22 pages, 8 figures, to be published in CAPJournal Volume 19 Issue 2 ...", keywords: - "Instrumentation and Methods for Astrophysics astro-ph.IM; Physics Education physics.ed-ph; FOS: Physical sciences", + "FOS: Physical sciences; Instrumentation and Methods for Astrophysics astro-ph.IM; Physics Education physics.ed-ph", }, { suiteName: "bugfix: retain anchor record", From 2e6d31caad7971096ba172a5ac32b9965e1b4255 Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 19 May 2026 17:43:03 +0200 Subject: [PATCH 091/205] make test case titles unique --- e2e/keywordsEnrichment/BASE.spec.ts | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/e2e/keywordsEnrichment/BASE.spec.ts b/e2e/keywordsEnrichment/BASE.spec.ts index 9d101ca0a..f0ed2ac47 100644 --- a/e2e/keywordsEnrichment/BASE.spec.ts +++ b/e2e/keywordsEnrichment/BASE.spec.ts @@ -109,7 +109,7 @@ test.describe("Warm-up: pre-load BASE searches", () => { for (const tc of testCases) { test.describe( - `Verify abstract and keywords are merged correctly, ${tc.suiteName}`, + `Verify abstract and keywords are merged correctly, ${tc.suiteName} [${tc.query}]`, () => { const url = `/search?type=get&vis_type=overview${tc.search_params}&service=base&embed=true`; From 7371d5b49890d3364fe6235aa11cba829d3731b1 Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 19 May 2026 18:05:07 +0200 Subject: [PATCH 092/205] fix query handling in tests --- e2e/keywordsEnrichment/BASE.spec.ts | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/e2e/keywordsEnrichment/BASE.spec.ts b/e2e/keywordsEnrichment/BASE.spec.ts index f0ed2ac47..d4a2609fc 100644 --- a/e2e/keywordsEnrichment/BASE.spec.ts +++ b/e2e/keywordsEnrichment/BASE.spec.ts @@ -13,7 +13,7 @@ interface EnrichmentTestCase { const testCases: EnrichmentTestCase[] = [ { suiteName: "bugfix: retain anchor record", - query: '“Solar eclipse”', + query: '"solar eclipse"', search_params: "&service=base&sorting=most-relevant&document_types%5B%5D=121&lang_id%5B%5D=all-lang&min_descsize=300", paperTitle: "Why every solar eclipse viewing event needs a disco ball", abstract: @@ -23,7 +23,7 @@ const testCases: EnrichmentTestCase[] = [ }, { suiteName: "bugfix: retain anchor record", - query: 'Clinical trials', + query: 'clinical trials', search_params: "&service=base&sorting=most-relevant&document_types%5B%5D=121&lang_id%5B%5D=all-lang&min_descsize=0", paperTitle: "Clinical Trials and Clinical Research: A Comprehensive Review", abstract: @@ -33,7 +33,7 @@ const testCases: EnrichmentTestCase[] = [ }, { suiteName: "bugfix: retain anchor record", - query: 'Antibiotics prescription', + query: 'antibiotics prescription', search_params: "&service=base&sorting=most-relevant&document_types%5B%5D=121&lang_id%5B%5D=all-lang&min_descsize=0", paperTitle: "Clinical Trials and Clinical Research: A Comprehensive Review", abstract: @@ -43,7 +43,7 @@ const testCases: EnrichmentTestCase[] = [ }, { suiteName: "bugfix: retain anchor record", - query: '“Game theory”', + query: '"game theory"', search_params: "&service=base&sorting=most-relevant&document_types%5B%5D=121&lang_id%5B%5D=all-lang&min_descsize=300", paperTitle: "Game theory approaches for autonomy", abstract: @@ -53,7 +53,7 @@ const testCases: EnrichmentTestCase[] = [ }, { suiteName: "bugfix: retain anchor record", - query: 'Machine translation', + query: 'machine translation', search_params: "&service=base&sorting=most-relevant&document_types%5B%5D=121&lang_id%5B%5D=all-lang&min_descsize=300", paperTitle: "A Survey of Orthographic Information in Machine Translation", abstract: @@ -73,7 +73,7 @@ const testCases: EnrichmentTestCase[] = [ }, { suiteName: "bugfix: retain anchor record", - query: 'Big Data', + query: 'big Data', search_params: "&service=base&sorting=most-relevant&document_types%5B%5D=7&lang_id%5B%5D=all-lang&min_descsize=0", paperTitle: "Big Data or Big Fail? The Good, the Bad and the Ugly and the missing role of Statistics", abstract: @@ -100,9 +100,9 @@ const uniqueSearches = [...new Set(testCases.map((tc) => tc.search_params).filte test.describe("Warm-up: pre-load BASE searches", () => { for (const tc of testCases) { test(`${tc.query}`, async ({ page }) => { - const url = `/search?type=get&vis_type=overview${tc.search_params}`; + const url = `/search?type=get&vis_type=overview&q=${tc.query}${tc.search_params}`; await prepareVisualisation(page, url); - await expect(page.locator("#search-term-unique")).toContainText(`(${tc.query})`); + await expect(page.locator("#search-term-unique")).toContainText(tc.query); }); } }); @@ -111,12 +111,12 @@ for (const tc of testCases) { test.describe( `Verify abstract and keywords are merged correctly, ${tc.suiteName} [${tc.query}]`, () => { - const url = `/search?type=get&vis_type=overview${tc.search_params}&service=base&embed=true`; + const url = `/search?type=get&vis_type=overview&q=${tc.query}${tc.search_params}`; async function openPaper(page: Page) { await prepareVisualisation(page, url); await expect(page.locator("#search-term-unique")).toContainText( - `(${tc.query})`, + tc.query, ); const paper = page.getByTitle(tc.paperTitle); await expect(paper).toBeVisible(); From 9f0368c2171f2b0c4e856a9002f4ed7656d96d42 Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 19 May 2026 18:28:31 +0200 Subject: [PATCH 093/205] bugfix and tests for anchor document enrichment in DOI versioned datasets --- e2e/keywordsEnrichment/BASE.spec.ts | 2 +- server/workers/common/common/deduplication.py | 12 +++++- server/workers/common/common/enrichment.py | 39 +++++++++++++++++++ 3 files changed, 51 insertions(+), 2 deletions(-) diff --git a/e2e/keywordsEnrichment/BASE.spec.ts b/e2e/keywordsEnrichment/BASE.spec.ts index d4a2609fc..e74a7d083 100644 --- a/e2e/keywordsEnrichment/BASE.spec.ts +++ b/e2e/keywordsEnrichment/BASE.spec.ts @@ -73,7 +73,7 @@ const testCases: EnrichmentTestCase[] = [ }, { suiteName: "bugfix: retain anchor record", - query: 'big Data', + query: 'big data', search_params: "&service=base&sorting=most-relevant&document_types%5B%5D=7&lang_id%5B%5D=all-lang&min_descsize=0", paperTitle: "Big Data or Big Fail? The Good, the Bad and the Ugly and the missing role of Statistics", abstract: diff --git a/server/workers/common/common/deduplication.py b/server/workers/common/common/deduplication.py index 87734a392..ae8140c4b 100644 --- a/server/workers/common/common/deduplication.py +++ b/server/workers/common/common/deduplication.py @@ -5,7 +5,17 @@ from rapidfuzz import fuzz from urllib.parse import urlparse -pattern_doi = re.compile(r"\.v(\d)+$") +# Strips dataset version/file suffixes to obtain a base DOI for grouping: +# Please consider those content providers only as examples, +# as the same DOI versioning patterns may be used by other providers as well. +# .v3 → Figshare, UCT, Loughborough, SAGE, Monash (10.1184/R1/6551801.v1) +# v3 → arxive, ICPSR (10.3886/e115525v3) +# .3 → Mendeley Data (10.17632/675v9chxnt.2) +# v3-104960 → ICPSR file-level sub-record (10.3886/e115525v3-104960) +# NOTE: the bare .N alternative is intentionally limited to 1-3 digits to avoid +# false positives on DOIs like 10.1594/pangaea.982329 where the numeric suffix +# is a record identifier, not a version number. +pattern_doi = re.compile(r"(?:\.?v|\.)([0-9]{1,3})(?:-\d+)?$") _pattern_punctuation = re.compile(r"[^\w\s]") _DOI_TITLE_CUTOFF = 1/15.83*100 # ≈ 6.32 on rapidfuzz's 0–100 scale diff --git a/server/workers/common/common/enrichment.py b/server/workers/common/common/enrichment.py index 8a25c22d7..acee1f554 100644 --- a/server/workers/common/common/enrichment.py +++ b/server/workers/common/common/enrichment.py @@ -1,7 +1,10 @@ import os +import logging import pandas as pd from common.deduplication import deduplicate_keywords, deduplicate_links +logger = logging.getLogger(__name__) + KEYWORD_SIMILARITY_THRESHOLD = 85 OA_STATE_PRIORITY = { @@ -10,6 +13,38 @@ "2": 2, # unknown } +def _log_anchor_state(tag, df, anchor_idx, group_data=None): + """ + Logs DOI, title, and keywords for an anchor record and, optionally, all + members of its duplicate group. Use tag='BEFORE'/'AFTER' for the anchor + state and tag='GROUP' to dump every group member. + + All messages share the prefix [ANCHOR_ENRICHMENT] so they can be extracted + with: grep 'ANCHOR_ENRICHMENT' + """ + doi = df.loc[anchor_idx, 'doi'] if 'doi' in df.columns else 'N/A' + title = df.loc[anchor_idx, 'title'] if 'title' in df.columns else 'N/A' + kw = df.loc[anchor_idx, 'subject_orig'] if 'subject_orig' in df.columns else 'N/A' + resulttype = df.loc[anchor_idx, 'resulttype'] if 'resulttype' in df.columns else 'N/A' + + logger.debug( + "[ANCHOR_ENRICHMENT] anchor_%s doi=%s | title=%s | keywords=%s | resulttype=%s", + tag, doi, title, kw, resulttype + ) + + if group_data is not None: + for _, member in group_data.iterrows(): + m_doi = member.get('doi', 'N/A') + m_title = member.get('title', 'N/A') + m_kw = member.get('subject_orig', 'N/A') + m_resulttype = member.get('resulttype', 'N/A') + is_anch = getattr(member, 'is_anchor', False) + logger.debug( + "[ANCHOR_ENRICHMENT] group_member is_anchor=%s doi=%s | title=%s | keywords=%s | resulttype=%s", + is_anch, m_doi, m_title, m_kw, m_resulttype + ) + + def enrich_anchor_using_duplicates(df, duplicate_groups): """ Enriches anchor elements using data from duplicates in their groups. @@ -61,6 +96,8 @@ def enrich_anchor_using_duplicates(df, duplicate_groups): anchor = anchors.iloc[0] anchor_idx = anchor.name + _log_anchor_state('BEFORE', df, anchor_idx, group_data=group_data) + subject_orig_acc = {'all_keywords': set(), 'best_value': None, 'best_count': 0} subject_acc = {'all_keywords': set(), 'best_value': None, 'best_count': 0} paper_abstract_acc = {'best_value': None, 'best_length': 0} @@ -103,6 +140,8 @@ def enrich_anchor_using_duplicates(df, duplicate_groups): if has_link: apply_link_improvements(df, anchor_idx, all_links) + _log_anchor_state('AFTER', df, anchor_idx) + return df def process_subject_orig_element(value, accumulator): From 1615c3846453c7362d52196fafe29ee68f3ca59c Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 19 May 2026 20:31:29 +0200 Subject: [PATCH 094/205] test fixes --- e2e/keywordsEnrichment/BASE.spec.ts | 25 ++++++++++++++++++++++--- e2e/keywordsEnrichment/ORCID.spec.ts | 4 ++-- 2 files changed, 24 insertions(+), 5 deletions(-) diff --git a/e2e/keywordsEnrichment/BASE.spec.ts b/e2e/keywordsEnrichment/BASE.spec.ts index e74a7d083..f34ccb1be 100644 --- a/e2e/keywordsEnrichment/BASE.spec.ts +++ b/e2e/keywordsEnrichment/BASE.spec.ts @@ -59,7 +59,7 @@ const testCases: EnrichmentTestCase[] = [ abstract: "Abstract Machine translation is one of the applications of natural language processing which has been explored in different languages. Recently researchers started paying attention towards machine translation for resource-poor languages and closely related languages. A widespread and underlying problem for these machine translation systems is the linguistic difference and variation in orthographic conventions which causes many issues to traditional approaches. Two languages written in two different orthographies are not easily comparable but orthographic information can also be used to improve the machine translation system. This article offers a survey of research regarding orthography’s influence on machine translation of under-resourced languages. It introduces under-resourced languages in terms of machine translation and how orthographic information can be utilised to improve machine translation. We describe previous work in this area, discussing what underlying assumptions were made, and showing how orthographic knowledge improves the performance of machine translation of under-resourced languages. We discuss different types of machine translation and demonstrate a recent trend that seeks to link orthographic information with well-established machine translation methods. Considerable attention is given to current efforts using cognate information at different levels of machine translation and the lessons that can be drawn from this. Additionally, multilingual neural machine translation of closely related languages is given a particular focus in this survey. This article ends with a discussion of the way forward in machine translation with orthographic information, focusing on multilingual settings and bilingual lexicon induction.", keywords: - "Computation and Language cs.CL; FOS: Computer and information sciences; Machine translation ; Neural machine translation; Orthography; Rule-based machine translation; Statistical machine translation; Under-resourced languages", + "Computation and Language cs.CL; FOS: Computer and information sciences; Machine translation; Neural machine translation; Orthography; Rule-based machine translation; Statistical machine translation; Under-resourced languages", }, { suiteName: "bugfix: retain anchor record", @@ -91,14 +91,33 @@ const testCases: EnrichmentTestCase[] = [ keywords: "INF/01 Informatica; ING-INF/05 Sistemi di elaborazione delle informazioni", }, + { + suiteName: "bugfix: retain anchor record", + query: '"game theory"', + search_params: "&service=base&sorting=most-relevant&document_types%5B%5D=121&lang_id%5B%5D=all-lang&min_descsize=300", + paperTitle: "Composing games into complex institutions", + keywords: + "Computer Science and Game Theory cs.GT; FOS: Computer and information sciences; Medicine; Q; R; Science; Social and Information Networks cs.SI", + }, + { + suiteName: "bugfix: retain anchor record", + query: '"game theory"', + search_params: "&service=base&sorting=most-relevant&document_types%5B%5D=121&lang_id%5B%5D=all-lang&min_descsize=300", + paperTitle: "Medical ethics, logic traps, and game theory: an illustrative tale of brain death", + keywords: + "Adult [MeSH]; Analytical Approach; Attitude to Death [MeSH]; Brain Death [MeSH]; Decision Making [MeSH]; Female [MeSH]; Game Theory [MeSH]; Humans [MeSH]; Life Support Care [MeSH]; Logic [MeSH]; Withholding Treatment [MeSH]", + }, ]; -const uniqueSearches = [...new Set(testCases.map((tc) => tc.search_params).filter(Boolean))]; +const uniqueWarmups = testCases.filter( + (tc, i, arr) => + arr.findIndex((t) => t.query === tc.query && t.search_params === tc.search_params) === i +); // This can also be used to pre-load the BASE searches, so that the subsequent tests run faster // npx playwright test "e2e/keywordsEnrichment/BASE.spec.ts" --grep "Warm-up: pre-load BASE searches" test.describe("Warm-up: pre-load BASE searches", () => { - for (const tc of testCases) { + for (const tc of uniqueWarmups) { test(`${tc.query}`, async ({ page }) => { const url = `/search?type=get&vis_type=overview&q=${tc.query}${tc.search_params}`; await prepareVisualisation(page, url); diff --git a/e2e/keywordsEnrichment/ORCID.spec.ts b/e2e/keywordsEnrichment/ORCID.spec.ts index eece5176c..aaf029052 100644 --- a/e2e/keywordsEnrichment/ORCID.spec.ts +++ b/e2e/keywordsEnrichment/ORCID.spec.ts @@ -183,7 +183,7 @@ const testCases: EnrichmentTestCase[] = [ orcid: "0000-0002-5238-4195", paperTitle: "The Vienna Principles: A Vision for Scholarly Communication in the 21st Century", abstract: - 'Zur Zeit gibt es starke Bemühungen, die offensichtlichen Defizite des wissenschaftlichen Kommunikationssystems zu beheben. Open Science hat das Potenzial, die Produktion und Verbreitung von wissenschaftlichem Wissen positiv zu verändern; es existiert aber keine gemeinsam geteilte Vision, die das System wissenschaftlicher Kommunikation beschreibt, welches wir erschaffen wollen. Zwischen April 2015 und Juni 2016 trafen sich in Wien die Mitglieder der Open Access Network Austria (OANA) Arbeitsgruppe "Open Access and Scholarly Communication", um diese Angelegenheit zu diskutieren. Das Hauptergebnis unserer Überlegungen sind zwölf Prinzipien, die die Eckpfeiler eines künftigen wissenschaftlichen Kommunikationssystems dedarstellen. Diese Prinzipien sollen einen kohärenten Bezugsrahmen für die Debatte zur Verbesserung des derzeitigen Systems liefern. Mit diesem Dokument hoffen wir, eine breite Diskussion über eine gemeinsame Vision für die wissenschaftliche Kommunikation im 21. Jahrhundert anzustoßen. ; Currently, there is a strong push to address the apparent deficits of the scholarly communication system. Open Science has the potential to change the produc-tion and dissemination of scholarly knowledge for the better, but there is no commonly shared vision that describes the system that we want to create. Between April 2015 and June 2016, members of the Open Access Network Austria (OANA) working group "Open Access and Scholarly Communication" met in Vienna to discuss this matter. The main outcome of our considerations is a set of twelve principles that repre-sent the cornerstones of the future scholarly communication system. They are designed to provide a coherent frame of reference for the debate on how to improve the current system. With this document, we are hoping to inspire a widespread discussion towards a shared vision for scholarly communication in the 21st century ; Peter Kraker, Daniel Dörler, Andreas Ferus, Robert Gutounig, Florian Heigl, Christian Kaier, Katharina Rieck, Elena Šimukovič, Michela Vignol ', + 'Zur Zeit gibt es starke Bemühungen, die offensichtlichen Defizite des wissenschaftlichen Kommunikationssystems zu beheben. Open Science hat das Potenzial, die Produktion und Verbreitung von wissenschaftlichem Wissen positiv zu verändern; es existiert aber keine gemeinsam geteilte Vision, die das System wissenschaftlicher Kommunikation beschreibt, welches wir erschaffen wollen. Zwischen April 2015 und Juni 2016 trafen sich in Wien die Mitglieder der Open Access Network Austria (OANA) Arbeitsgruppe "Open Access and Scholarly Communication", um diese Angelegenheit zu diskutieren. Das Hauptergebnis unserer Überlegungen sind zwölf Prinzipien, die die Eckpfeiler eines künftigen wissenschaftlichen Kommunikationssystems dedarstellen. Diese Prinzipien sollen einen kohärenten Bezugsrahmen für die Debatte zur Verbesserung des derzeitigen Systems liefern. Mit diesem Dokument hoffen wir, eine breite Diskussion über eine gemeinsame Vision für die wissenschaftliche Kommunikation im 21. Jahrhundert anzustoßen.', keywords: "Wissenschaftskommunikation; Open Access; Open Science; Vision; Bibliography. Library science. Information resources; Z", }, @@ -221,7 +221,7 @@ const testCases: EnrichmentTestCase[] = [ abstract: 'Multibeam data were collected with RV Polarstern along the route of cruise PS147/2 and data acquisition was continuously monitored during the survey. Multibeam sonar system was Teledyne/Atlas Hydrosweep DS3. SVPs were retrieved from CTD data and synthetic profiles from World Ocean Atlas 18. SVPs were processed with HydrOffice SoundSpeedManager (https://www.hydroffice.org/soundspeed/main) and extended with World Ocean Atlas 18 (https://www.ncei.noaa.gov/archive/accession/NCEI-WOA18). SVP data were applied during acquisition. Multibeam data are unprocessed and may contain outliers and blunders and should not be used for grid calculations and charting projects without further editing. The raw multibeam sonar data in Teledyne Reson multibeam processing format (.s7k) were recorded with Teledyne PDS software. Raw data files can be processed using software packages like CARIS HIPS/SIPS. For updated vessel configuration files check further details.', keywords: - 'Bathymetry ; Multibeam Echosounder ; Event label ; Binary Object ; Binary Object Media Type ; Binary Object File Size ; File content ; Data file recording duration ; Data file recording distance ; Ship speed ; Number of pings ; Start of data file recording, date/time ; Start of data file recording, latitude ; Start of data file recording, longitude ; Start of data file, heading ; Start of data file, depth ; Stop of data file recording, date/time ; Stop of data file recording, latitude ; Stop of data file recording, longitude ; Stop of data file, heading ; Stop of data file, depth ; DATE/TIME ; LATITUDE ; LONGITUDE ; ELEVATION ; Comment ; Swath-mapping system Atlas Hydrosweep DS-3 ; CTD/Rosette ; Extracted with MB-System ; PS147/2 ; Polarstern ; BATHYmetric Long-Term Observations during Expeditions with RV Polarstern BATHY-LTO ; DataHub Earth and Environment of the Helmholtz Association DataHub ; Pilot study: "targeted underway bathymetry" for mapping uncharted seamounts SEAMAP', + 'Bathymetry; Multibeam Echosounder; Event label; Binary Object; Binary Object Media Type; Binary Object File Size; File content; Data file recording duration; Data file recording distance; Ship speed; Number of pings; Start of data file recording, date/time; Start of data file recording, latitude; Start of data file recording, longitude; Start of data file, heading; Start of data file, depth; Stop of data file recording, date/time; Stop of data file recording, latitude; Stop of data file recording, longitude; Stop of data file, heading; Stop of data file, depth; DATE/TIME; LATITUDE; LONGITUDE; ELEVATION; Comment; Swath-mapping system Atlas Hydrosweep DS-3; CTD/Rosette; Extracted with MB-System; PS147/2; Polarstern; BATHYmetric Long-Term Observations during Expeditions with RV Polarstern BATHY-LTO; DataHub Earth and Environment of the Helmholtz Association DataHub; Pilot study: "targeted underway bathymetry" for mapping uncharted seamounts SEAMAP', }, ]; From 22667b4942f141c477ed47ec28febfc210f59321 Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 19 May 2026 21:44:29 +0200 Subject: [PATCH 095/205] test wrapup for flaky tests --- e2e/keywordsEnrichment/BASE.spec.ts | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/e2e/keywordsEnrichment/BASE.spec.ts b/e2e/keywordsEnrichment/BASE.spec.ts index f34ccb1be..714b16888 100644 --- a/e2e/keywordsEnrichment/BASE.spec.ts +++ b/e2e/keywordsEnrichment/BASE.spec.ts @@ -8,6 +8,7 @@ interface EnrichmentTestCase { paperTitle: string; keywords?: string; abstract?: string; + flaky?: boolean; } const testCases: EnrichmentTestCase[] = [ @@ -26,6 +27,7 @@ const testCases: EnrichmentTestCase[] = [ query: 'clinical trials', search_params: "&service=base&sorting=most-relevant&document_types%5B%5D=121&lang_id%5B%5D=all-lang&min_descsize=0", paperTitle: "Clinical Trials and Clinical Research: A Comprehensive Review", + flaky: true, abstract: "Clinical research is an alternative terminology used to describe medical research. Clinical research involves people, and it is generally carried out to evaluate the efficacy of a therapeutic drug, a medical/surgical procedure, or a device as a part of treatment and patient management. Moreover, any research that evaluates the aspects of a disease like the symptoms, risk factors, and pathophysiology, among others may be termed clinical research. However, clinical trials are those studies that assess the potential of a therapeutic drug/device in the management, control, and prevention of disease. In view of the increasing incidences of both communicable and non-communicable diseases, and especially after the effects that Coronavirus Disease-19 (COVID-19) had on public health worldwide, the emphasis on clinical research assumes extremely essential. The knowledge of clinical research will facilitate the discovery of drugs, devices, and vaccines, thereby improving preparedness during public health emergencies. Therefore, in this review, we comprehensively describe the critical elements of clinical research that include clinical trial phases, types, and designs of clinical trials, operations of trial, audit, and management, and ethical concerns.", keywords: @@ -36,6 +38,7 @@ const testCases: EnrichmentTestCase[] = [ query: 'antibiotics prescription', search_params: "&service=base&sorting=most-relevant&document_types%5B%5D=121&lang_id%5B%5D=all-lang&min_descsize=0", paperTitle: "Clinical Trials and Clinical Research: A Comprehensive Review", + flaky: true, abstract: "Purpose The purpose of the study was to assess the bacterial resistance and annual antibiotic consumption at a tertiary care hospital in Riyadh, Saudi Arabia over a two-year period. Methods This retrospective cohort study was conducted at a tertiary care hospital in Riyadh, Saudi Arabia from January 1, 2016, to December 31, 2017. Results The results showed that there was no significant difference between 2016 and 2017 data regarding patient characteristics like bed occupancy rate, the average length of stay, and the number of admissions; the same was true for bacterial characteristics like the number of bacteria, percentage of isolates in the group, and multidrug resistance (MDR) percentage (p: >0.05). Between 2016 and 2017, there was a slight reduction in the sensitivity of Escherichia​​​ coli (E. coli) carbapenem-resistant Enterobacteriaceae (CRE) (97%, 86%) and Klebsiella pneumoniae (K. pneumoniae) CRE (80%, 76%) towards colistin. There was also a decrease in the sensitivity of Acinetobacter baumannii (A. baumannii) multidrug-resistant organism (MDRO) from 42% to 29% against tigecycline, but an increase in the sensitivity of K. pneumoniae CRE (33%, 50%) and E. coli CRE (76%, 82%). The percentage of MDR strains in gram-positive bacteria showed that more than half of Staphylococcus aureus (S. aureus) were methicillin-resistant (61%, 59%) in 2016 and 2017 respectively. There was a reduction in the percentage of MDR strains in some gram-negative bacteria like Pseudomonas aeruginosa (P. aeruginosa) MDRO (24%, 19%),E. coli extended-spectrum beta-lactamases (ESBL) (56%, 50%), E. coli CRE (4%, 1%), K. pneumoniae CRE (49%, 33%), A. baumannii CRE (90%, 76%), and Proteus mirabilis​​​​​​​ (P. mirabilis) ESBL (54%, 50%). Conclusion MDRO bacteria are very common in the hospital where the study was conducted. Immediate action is required to tackle this problem.", keywords: @@ -49,7 +52,7 @@ const testCases: EnrichmentTestCase[] = [ abstract: "Game theory offers techniques for applying autonomy in the field. In this mini-review, we define autonomy, and briefly overview game theory with a focus on Nash and Stackleberg equilibria and Social dilemma. We provide a discussion of successful projects using game theory approaches applied to several autonomous systems.", keywords: - "game theory; robotics; autonomous vehicles; nash equilibrium; self-driving cars; Physics; QC1-999", + "Physics; QC1-999; autonomous vehicles; game theory; nash equilibrium; robotics; self-driving cars", }, { suiteName: "bugfix: retain anchor record", @@ -66,6 +69,7 @@ const testCases: EnrichmentTestCase[] = [ query: '"climate change"', search_params: "&service=base&sorting=most-relevant&document_types%5B%5D=7&lang_id%5B%5D=all-lang&min_descsize=0", paperTitle: "LAICPMS_Pb.csv", + flaky: true, abstract: "No abstract available", keywords: @@ -76,6 +80,7 @@ const testCases: EnrichmentTestCase[] = [ query: 'big data', search_params: "&service=base&sorting=most-relevant&document_types%5B%5D=7&lang_id%5B%5D=all-lang&min_descsize=0", paperTitle: "Big Data or Big Fail? The Good, the Bad and the Ugly and the missing role of Statistics", + flaky: true, abstract: "The so called “Big Data” are data which we think as being “big” because of their volume, their amount per unit of time and because they are un- structured. The usual sources of big data are administrative repositories, transaction data or social media and social network feeds. Someone defines big data as those data which cannot be analyzed on a desktop machine or stored on one’s hard disk. These ways of defining big data completely miss the point of view of Statistics: they seem to be tailored more to advertising campaign of SaS or storage solution rather than to Science. Moreover, recent big fails, like e.g. the famous/infamous Google Flu Trend experiment, raised a series of popular news paper articles against the validity of information contained in these data and Statistics itself, even though none of these bad practices has been conducted by statisticians. While Information Technol- ogy and Computer Science are good at efficiently retrive and manage them, these data should be soon brought back into the field of Statistics to where data belong and this Special Issues of EJASA is one important step in this direction.", keywords: @@ -86,6 +91,7 @@ const testCases: EnrichmentTestCase[] = [ query: 'automation tools', search_params: "&service=base&sorting=most-relevant&document_types%5B%5D=7&lang_id%5B%5D=all-lang&min_descsize=0", paperTitle: "AEQUITAS. WP7. USE CASE HR1. DESC. v1.0", + flaky: true, abstract: "The dataset contains the matching of job positions and hiring candidates; this data has been collected by a big Italian company, working in the HR sector - ADECCO. The detailed description of the data can be found in a README file within the compressed archive. This activity is part of the HORIZON-CL4-2021-HUMAN-01-24-AEQUITAS project (g.a. 101070363). The aim of AEQUITAS to address and tackle the multiple manifestations of bias and unfairness in Artificial Intelligence (AI) from a variety of dimensions, such as the development of AI tools, the data used to train, test and validate them or the interpretation practices developed around them. AEQUITAS offers an overall approach for tackling the problem, savant of the criticalities that automation and AI techniques bring about. This case study allows for detecting hiring dataset biases which are the primary source for training a novel AI system. For example, there are historical trends in the labour market in favour of men (higher levels of education, once of employment, hence of hiring) that might be reflected in the data history of ADECCO. Balancing these inequalities in data or leaving data biased and targeting debiasing or bias reducing algorithms is a key step for a fair AI system and dataset. The ADECCO data offer data were to compare selection decisions with regards to important bias, such as gender, age, economic background, etc.", keywords: @@ -105,7 +111,7 @@ const testCases: EnrichmentTestCase[] = [ search_params: "&service=base&sorting=most-relevant&document_types%5B%5D=121&lang_id%5B%5D=all-lang&min_descsize=300", paperTitle: "Medical ethics, logic traps, and game theory: an illustrative tale of brain death", keywords: - "Adult [MeSH]; Analytical Approach; Attitude to Death [MeSH]; Brain Death [MeSH]; Decision Making [MeSH]; Female [MeSH]; Game Theory [MeSH]; Humans [MeSH]; Life Support Care [MeSH]; Logic [MeSH]; Withholding Treatment [MeSH]", + "Adult [MeSH]; Analytical Approach; Attitude to Death [MeSH]; Brain Death [MeSH]; Death and Euthanasia; Decision Making [MeSH]; Female [MeSH]; Game Theory [MeSH]; Humans [MeSH]; Life Support Care [MeSH]; Logic [MeSH]; Withholding Treatment [MeSH]", }, ]; @@ -145,9 +151,11 @@ for (const tc of testCases) { if (tc.keywords) { const keywords = tc.keywords; + const flaky = tc.flaky; test(`Keywords enrichment for the '${tc.paperTitle}' document`, async ({ page, }) => { + test.fixme(!!flaky, "Flaky: depends on BASE API returning duplicate records consistently"); await openPaper(page); await expect(page.locator("#list_holder")).toContainText(keywords); }); @@ -155,9 +163,11 @@ for (const tc of testCases) { if (tc.abstract) { const abstract = tc.abstract; + const flaky = tc.flaky; test(`Abstract enrichment for the '${tc.paperTitle}' document`, async ({ page, }) => { + test.fixme(!!flaky, "Flaky: depends on BASE API returning duplicate records consistently"); await openPaper(page); await expect(page.locator("#list_holder")).toContainText(abstract); }); From 2b625460eed9d9280775e6081f5fa9b205929c17 Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 19 May 2026 21:49:32 +0200 Subject: [PATCH 096/205] update of contentprovider.json cache --- server/workers/common/common/contentproviders.json | 4 ---- 1 file changed, 4 deletions(-) diff --git a/server/workers/common/common/contentproviders.json b/server/workers/common/common/contentproviders.json index 27494edf9..2978d9d3b 100644 --- a/server/workers/common/common/contentproviders.json +++ b/server/workers/common/common/contentproviders.json @@ -46763,10 +46763,6 @@ "name": "Universiti Utara Malaysia: UUM IRepository", "internal_name": "ftunivutaramal" }, - { - "name": "WikiBooks - Inhaltsoffene Lehrbücher", - "internal_name": "ftwikibooks" - }, { "name": "岡山大学学術成果リポジトリ", "internal_name": "ftokayamaunivoud" From 395baebde3c00a3d29eb1c2e4ac6022cefce6452 Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 19 May 2026 21:54:31 +0200 Subject: [PATCH 097/205] test wrapup for flaky tests --- e2e/keywordsEnrichment/ORCID.spec.ts | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/e2e/keywordsEnrichment/ORCID.spec.ts b/e2e/keywordsEnrichment/ORCID.spec.ts index aaf029052..e6221f711 100644 --- a/e2e/keywordsEnrichment/ORCID.spec.ts +++ b/e2e/keywordsEnrichment/ORCID.spec.ts @@ -7,6 +7,7 @@ interface EnrichmentTestCase { paperTitle: string; keywords?: string; abstract?: string; + flaky?: boolean; } const testCases: EnrichmentTestCase[] = [ @@ -35,7 +36,7 @@ const testCases: EnrichmentTestCase[] = [ abstract: "Direct reciprocity is a major mechanism for the evolution of cooperation. Several classical studies have suggested that humans should quickly learn to adopt reciprocal strategies to establish mutual cooperation in repeated interactions. On the other hand, the recently discovered theory of ZD strategies has found that subjects who use extortionate strategies are able to exploit and subdue cooperators. Although such extortioners have been predicted to succeed in any population of adaptive opponents, theoretical follow-up studies questioned whether extortion can evolve in reality. However, most of these studies presumed that individuals have similar strategic possibilities and comparable outside options, whereas asymmetries are ubiquitous in real world applications. Here we show with a model and an economic experiment that extortionate strategies readily emerge once subjects differ in their strategic power. Our experiment combines a repeated social dilemma with asymmetric partner choice. In our main treatment there is one randomly chosen group member who is unilaterally allowed to exchange one of the other group members after every ten rounds of the social dilemma. We find that this asymmetric replacement opportunity generally promotes cooperation, but often the resulting payoff distribution reflects the underlying power structure. Almost half of the subjects in a better strategic position turn into extortioners, who quickly proceed to exploit their peers. By adapting their cooperation probabilities consistent with ZD theory, extortioners force their co-players to cooperate without being similarly cooperative themselves. Comparison to non-extortionate players under the same conditions indicates a substantial net gain to extortion. Our results thus highlight how power asymmetries can endanger mutually beneficial interactions, and transform them into exploitative relationships. In particular, our results indicate that the extortionate strategies predicted from ZD theory could play a more prominent role in our daily interactions than previously thought.", keywords: - "Applied Mathematics; Behavior; Biology and Life Sciences; Coercion [MeSH]; Economics; Experimental Design; Experimental Economics; Game Theory; Game Theory [MeSH]; Games; Generalized Linear Model; Humans [MeSH]; Interpersonal Relations [MeSH]; Mathematical and Statistical Techniques; Mathematics; Medicine; Models, Theoretical [MeSH]; Physical Sciences; Prisoner's Dilemma; Psychology; Q; R; Recreation; Science; Social Behavior [MeSH]; Social Psychology; Social Sciences; Statistical Methods; Statistics (Mathematics); ddc:004", + "Applied Mathematics; Behavior; Biology and Life Sciences; Coercion [MeSH]; Economics; Experimental Design; Experimental Economics; Game Theory; Game Theory [MeSH]; Games; Generalized Linear Model; Humans [MeSH]; Interpersonal Relations [MeSH]; Mathematical and Statistical Techniques; Mathematics; Medicine; Models, Theoretical [MeSH]; Physical Sciences; Prisoner's Dilemma; Psychology; Q; R; Recreation; Science; Social Behavior [MeSH]; Social Psychology; Social Sciences; Statistical Methods; Statistics (Mathematics); ddc:006", }, { suiteName: "regression test", @@ -66,8 +67,6 @@ const testCases: EnrichmentTestCase[] = [ suiteName: "regression test", orcid: "0000-0001-5116-955X", paperTitle: "Exact conditions for evolutionary stability in indirect reciprocity under noise", - abstract: - "Indirect reciprocity is a key mechanism for large-scale cooperation. This mechanism captures the insight that in part, people help others to build and maintain a good reputation. To enable such cooperation, appropriate social norms are essential. They specify how individuals should act based on each others' reputations, and how reputations are updated in response to individual actions. Although previous work has identified several norms that sustain cooperation, a complete analytical characterization of all evolutionarily stable norms remains lacking, especially when assessments or actions are noisy. In this study, we provide such a characterization for the public assessment regime. This characterization reproduces known results, such as the leading eight norms, but it extends to more general cases, allowing for various types of errors and additional actions including costly punishment. We also identify norms that impose a fixed payoff on any mutant strategy, analogous to the zero-determinant strategies in direct reciprocity. These results offer a rigorous foundation for understanding the evolution of cooperation through indirect reciprocity and the critical role of social norms.", keywords: "Biological Evolution [MeSH]; Biology (General); Computational Biology [MeSH]; Computer Simulation [MeSH]; Cooperative Behavior [MeSH]; Game Theory [MeSH]; Humans [MeSH]; QH301-705.5; Social Norms [MeSH]", }, @@ -98,6 +97,7 @@ const testCases: EnrichmentTestCase[] = [ suiteName: "regression test", orcid: "0000-0003-4221-6275", paperTitle: "Body temperature measurement in mice during acute illness: implantable temperature transponder versus surface infrared thermometry.", + flaky: true, abstract: "Abstract Body temperature is a valuable parameter in determining the wellbeing of laboratory animals. However, using body temperature to refine humane endpoints during acute illness generally lacks comprehensiveness and exposes to inter-observer bias. Here we compared two methods to assess body temperature in mice, namely implanted radio frequency identification (RFID) temperature transponders (method 1) to non-contact infrared thermometry (method 2) in 435 mice for up to 7 days during normothermia and lipopolysaccharide (LPS) endotoxin-induced hypothermia. There was excellent agreement between core and surface temperature as determined by method 1 and 2, respectively, whereas the intra- and inter-subject variation was higher for method 2. Nevertheless, using machine learning algorithms to determine temperature-based endpoints both methods had excellent accuracy in predicting death as an outcome event. Therefore, less expensive and cumbersome non-contact infrared thermometry can serve as a reliable alternative for implantable transponder-based systems for hypothermic responses, although requiring standardization between experimenters.", keywords: @@ -276,9 +276,11 @@ for (const tc of testCases) { if (tc.keywords) { const keywords = tc.keywords; + const flaky = tc.flaky; test(`Keywords enrichment for the '${tc.paperTitle}' document`, async ({ page, }) => { + test.fixme(!!flaky, "Flaky: depends on BASE API returning duplicate records consistently"); await openPaper(page); await expect(page.locator("#list_holder")).toContainText(keywords); }); @@ -286,9 +288,11 @@ for (const tc of testCases) { if (tc.abstract) { const abstract = tc.abstract; + const flaky = tc.flaky; test(`Abstract enrichment for the '${tc.paperTitle}' document`, async ({ page, }) => { + test.fixme(!!flaky, "Flaky: depends on BASE API returning duplicate records consistently"); await openPaper(page); await expect(page.locator("#list_holder")).toContainText(abstract); }); From db284e62482cc4b50187b651a8330f196aaed089 Mon Sep 17 00:00:00 2001 From: chreman Date: Mon, 1 Jun 2026 11:49:00 +0200 Subject: [PATCH 098/205] pin flask-restx version for compatibility with python 3.9 - bump again when updated to 3.10 or higher --- server/workers/persistence/Dockerfile | 1 - server/workers/persistence/requirements.txt | 2 +- server/workers/persistence/tests/requirements.txt | 2 +- server/workers/tests/Dockerfile.tests | 2 +- 4 files changed, 3 insertions(+), 4 deletions(-) diff --git a/server/workers/persistence/Dockerfile b/server/workers/persistence/Dockerfile index d339b08d7..2462af67a 100644 --- a/server/workers/persistence/Dockerfile +++ b/server/workers/persistence/Dockerfile @@ -9,7 +9,6 @@ WORKDIR /persistence COPY workers/persistence/requirements.txt . RUN pip install --upgrade pip RUN pip install --no-cache-dir -r requirements.txt -RUN pip install git+https://github.com/python-restx/flask-restx COPY workers/common ../common COPY workers/persistence/requirements-e.txt ./ diff --git a/server/workers/persistence/requirements.txt b/server/workers/persistence/requirements.txt index 1d31856d0..a18553f5f 100644 --- a/server/workers/persistence/requirements.txt +++ b/server/workers/persistence/requirements.txt @@ -19,4 +19,4 @@ SQLAlchemy-Utils==0.41.1 typing_extensions==4.7.1 Werkzeug==3.0.1 zipp==3.15.0 -flask-restx==1.3.0 \ No newline at end of file +flask-restx==1.3.2 \ No newline at end of file diff --git a/server/workers/persistence/tests/requirements.txt b/server/workers/persistence/tests/requirements.txt index 24b68d63d..9e573b089 100644 --- a/server/workers/persistence/tests/requirements.txt +++ b/server/workers/persistence/tests/requirements.txt @@ -1,6 +1,6 @@ pytest pytest-mock flask -flask_restx +flask_restx==1.3.2 sqlalchemy psycopg2 \ No newline at end of file diff --git a/server/workers/tests/Dockerfile.tests b/server/workers/tests/Dockerfile.tests index 351531b14..6e61b2d43 100644 --- a/server/workers/tests/Dockerfile.tests +++ b/server/workers/tests/Dockerfile.tests @@ -8,5 +8,5 @@ RUN apt-get install -y libpq-dev WORKDIR /app -RUN pip install git+https://github.com/python-restx/flask-restx +RUN pip install git+https://github.com/python-restx/flask-restx@1.3.2 RUN pip install pytest requests sqlalchemy sqlalchemy-utils psycopg2-binary \ No newline at end of file From 7a96d569d892e4ac7750d7884998f676a6384f82 Mon Sep 17 00:00:00 2001 From: chreman Date: Mon, 1 Jun 2026 19:24:50 +0200 Subject: [PATCH 099/205] pin flask-restx dependency for compatibility with python 3.9 - bump again when updated to 3.10 or higher --- server/workers/api/requirements.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/server/workers/api/requirements.txt b/server/workers/api/requirements.txt index 125c4b9f2..fcb4406af 100644 --- a/server/workers/api/requirements.txt +++ b/server/workers/api/requirements.txt @@ -8,7 +8,8 @@ dataclasses>=0.6 flasgger==0.9.7.1 Flask==3.0.0 Flask-Cors==4.0.0 -flask-restx==1.3.0 +flask-restx==1.3.2 +referencing==0.34.0 gunicorn==21.2.0 hiredis==2.0.0 importlib-metadata==4.8.3 From e955ba8633208810db4471bf1f5d0808705ecbee Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 2 Jun 2026 16:04:11 +0200 Subject: [PATCH 100/205] add logging for OA status field --- server/workers/common/common/enrichment.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/server/workers/common/common/enrichment.py b/server/workers/common/common/enrichment.py index acee1f554..30145590a 100644 --- a/server/workers/common/common/enrichment.py +++ b/server/workers/common/common/enrichment.py @@ -26,10 +26,11 @@ def _log_anchor_state(tag, df, anchor_idx, group_data=None): title = df.loc[anchor_idx, 'title'] if 'title' in df.columns else 'N/A' kw = df.loc[anchor_idx, 'subject_orig'] if 'subject_orig' in df.columns else 'N/A' resulttype = df.loc[anchor_idx, 'resulttype'] if 'resulttype' in df.columns else 'N/A' + oa_state = df.loc[anchor_idx, 'oa_state'] if 'oa_state' in df.columns else 'N/A' logger.debug( - "[ANCHOR_ENRICHMENT] anchor_%s doi=%s | title=%s | keywords=%s | resulttype=%s", - tag, doi, title, kw, resulttype + "[ANCHOR_ENRICHMENT] anchor_%s doi=%s | title=%s | keywords=%s | resulttype=%s | oa_state=%r (type=%s)", + tag, doi, title, kw, resulttype, oa_state, type(oa_state).__name__ ) if group_data is not None: @@ -38,10 +39,11 @@ def _log_anchor_state(tag, df, anchor_idx, group_data=None): m_title = member.get('title', 'N/A') m_kw = member.get('subject_orig', 'N/A') m_resulttype = member.get('resulttype', 'N/A') + m_oa_state = member.get('oa_state', 'N/A') is_anch = getattr(member, 'is_anchor', False) logger.debug( - "[ANCHOR_ENRICHMENT] group_member is_anchor=%s doi=%s | title=%s | keywords=%s | resulttype=%s", - is_anch, m_doi, m_title, m_kw, m_resulttype + "[ANCHOR_ENRICHMENT] group_member is_anchor=%s doi=%s | title=%s | keywords=%s | resulttype=%s | oa_state=%r (type=%s)", + is_anch, m_doi, m_title, m_kw, m_resulttype, m_oa_state, type(m_oa_state).__name__ ) From 9aa3450cc2c8dc7a8850b1caa610384c4b5876f7 Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 2 Jun 2026 16:05:36 +0200 Subject: [PATCH 101/205] add logging for PDF link field --- server/workers/common/common/enrichment.py | 22 ++++++++++++++++++---- 1 file changed, 18 insertions(+), 4 deletions(-) diff --git a/server/workers/common/common/enrichment.py b/server/workers/common/common/enrichment.py index 30145590a..791236b8d 100644 --- a/server/workers/common/common/enrichment.py +++ b/server/workers/common/common/enrichment.py @@ -27,10 +27,15 @@ def _log_anchor_state(tag, df, anchor_idx, group_data=None): kw = df.loc[anchor_idx, 'subject_orig'] if 'subject_orig' in df.columns else 'N/A' resulttype = df.loc[anchor_idx, 'resulttype'] if 'resulttype' in df.columns else 'N/A' oa_state = df.loc[anchor_idx, 'oa_state'] if 'oa_state' in df.columns else 'N/A' + link = df.loc[anchor_idx, 'link'] if 'link' in df.columns else 'N/A' + pdf_candidates = ( + df.loc[anchor_idx, 'pdf_link_candidates_from_duplicates'] + if 'pdf_link_candidates_from_duplicates' in df.columns else 'N/A' + ) logger.debug( - "[ANCHOR_ENRICHMENT] anchor_%s doi=%s | title=%s | keywords=%s | resulttype=%s | oa_state=%r (type=%s)", - tag, doi, title, kw, resulttype, oa_state, type(oa_state).__name__ + "[ANCHOR_ENRICHMENT] anchor_%s doi=%s | title=%s | keywords=%s | resulttype=%s | oa_state=%r (type=%s) | link=%s | pdf_link_candidates_from_duplicates=%s", + tag, doi, title, kw, resulttype, oa_state, type(oa_state).__name__, link, pdf_candidates ) if group_data is not None: @@ -40,10 +45,11 @@ def _log_anchor_state(tag, df, anchor_idx, group_data=None): m_kw = member.get('subject_orig', 'N/A') m_resulttype = member.get('resulttype', 'N/A') m_oa_state = member.get('oa_state', 'N/A') + m_link = member.get('link', 'N/A') is_anch = getattr(member, 'is_anchor', False) logger.debug( - "[ANCHOR_ENRICHMENT] group_member is_anchor=%s doi=%s | title=%s | keywords=%s | resulttype=%s | oa_state=%r (type=%s)", - is_anch, m_doi, m_title, m_kw, m_resulttype, m_oa_state, type(m_oa_state).__name__ + "[ANCHOR_ENRICHMENT] group_member is_anchor=%s doi=%s | title=%s | keywords=%s | resulttype=%s | oa_state=%r (type=%s) | link=%s", + is_anch, m_doi, m_title, m_kw, m_resulttype, m_oa_state, type(m_oa_state).__name__, m_link ) @@ -289,6 +295,14 @@ def apply_link_improvements(df, anchor_idx, all_links): merged_links = '; '.join(sorted(unique_links_without_anchor_link)) df.loc[anchor_idx, 'pdf_link_candidates_from_duplicates'] = merged_links + if merged_links: + anchor_doi = df.loc[anchor_idx, 'doi'] if 'doi' in df.columns else 'N/A' + anchor_title = df.loc[anchor_idx, 'title'] if 'title' in df.columns else 'N/A' + logger.debug( + "[ENRICHMENT_APPLIED] link doi=%s | title=%s | anchor_link=%s | candidates_from_duplicates=%s", + anchor_doi, anchor_title, anchor_link, merged_links + ) + def get_anchor_field_value(df, anchor_idx, column_name): """ Returns the value of the given column for the anchor row, or None if From 4aa0f84b46325870c39a9c96fb4c80486cf1126d Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 2 Jun 2026 16:05:54 +0200 Subject: [PATCH 102/205] fix build order issue in renv --- server/workers/dataprocessing/Dockerfile | 1 + 1 file changed, 1 insertion(+) diff --git a/server/workers/dataprocessing/Dockerfile b/server/workers/dataprocessing/Dockerfile index ca78a079d..c7f49f90a 100644 --- a/server/workers/dataprocessing/Dockerfile +++ b/server/workers/dataprocessing/Dockerfile @@ -153,6 +153,7 @@ COPY workers/dataprocessing/renv.lock . COPY workers/dataprocessing/activate.R . RUN R -e 'renv::consent(provided = TRUE)' && \ + R -e 'install.packages(c("gplots", "fossil", "foreach", "doParallel"), repos = "https://cran.wu.ac.at")' && \ R -e 'setwd("./"); renv::activate(); renv::restore(lockfile = "./renv.lock")' COPY workers/common ../common From 55724bbbd78320b753b6c9ddcc3fb769aae99ee2 Mon Sep 17 00:00:00 2001 From: chreman Date: Wed, 3 Jun 2026 12:40:21 +0200 Subject: [PATCH 103/205] e2e tests for OA status and PDF link enrichment; with some helper functions --- e2e/linkEnrichment/BASE.spec.ts | 84 +++++++++ e2e/linkEnrichment/ORCID.spec.ts | 78 +++++++++ e2e/linkEnrichment/helpers.ts | 95 +++++++++++ e2e/oaStatusEnrichment/ORCID.spec.ts | 243 +++++++++++++++++++++++++++ 4 files changed, 500 insertions(+) create mode 100644 e2e/linkEnrichment/BASE.spec.ts create mode 100644 e2e/linkEnrichment/ORCID.spec.ts create mode 100644 e2e/linkEnrichment/helpers.ts create mode 100644 e2e/oaStatusEnrichment/ORCID.spec.ts diff --git a/e2e/linkEnrichment/BASE.spec.ts b/e2e/linkEnrichment/BASE.spec.ts new file mode 100644 index 000000000..5583d43e0 --- /dev/null +++ b/e2e/linkEnrichment/BASE.spec.ts @@ -0,0 +1,84 @@ +import { test, expect } from "@playwright/test"; +import { prepareVisualisation } from "../../vis/js/utils/e2eVisualisationLoader"; +import { + capturePapersFromLatestRevision, + papersWithLinkEnrichment, + candidatesAsString, +} from "./helpers"; + +/** + * BASE link-enrichment smoke test. + * + * Query/params chosen from dev-base-1 logs: a `"game theory"` BASE search + * with the same param set used in the existing keyword-enrichment BASE spec + * produced 27 `[ENRICHMENT_APPLIED] link` events in a recent test run. The + * URL shape mirrors keywordsEnrichment/BASE.spec.ts so any future param + * drift surfaces in both tests at once. + * + * Two assertions are made: + * 1. Volume — at least MIN_ENRICHED_PAPERS papers have non-empty + * pdf_link_candidates_from_duplicates. This catches a wholesale + * regression (enrichment silently turning off). + * 2. Containment on a specific paper — the survey on Game Theory & WSN + * security gains a PubMed URL via duplicate grouping. Containment + * (rather than equality) means the test stays green if the duplicate + * group picks up additional sibling URLs over time. + */ +const QUERY = '"game theory"'; +const SEARCH_PARAMS = + "&service=base&sorting=most-relevant&document_types%5B%5D=121&lang_id%5B%5D=all-lang&min_descsize=300"; +const URL = `/search?type=get&vis_type=overview&q=${encodeURIComponent(QUERY)}${SEARCH_PARAMS}&embed=true`; + +const MIN_ENRICHED_PAPERS = 1; + +const PINNED_PAPER_TITLE = + "Game Theory Meets Wireless Sensor Networks Security Requirements and Threats Mitigation: A Survey"; +// PubMed identifiers are stable for the life of the record, so this URL +// fragment is a sturdy assertion target. +const PINNED_EXPECTED_CANDIDATE_SUBSTRING = + "pubmed.ncbi.nlm.nih.gov/27367700"; + +test.describe("BASE link enrichment smoke", () => { + test(`at least ${MIN_ENRICHED_PAPERS} paper has pdf_link_candidates_from_duplicates for query '${QUERY}'`, async ({ + page, + }) => { + const papers = await capturePapersFromLatestRevision(page, async () => { + await prepareVisualisation(page, URL); + await expect(page.locator("#search-term-unique")).toContainText( + "game theory", + ); + }); + + const enriched = papersWithLinkEnrichment(papers); + + if (enriched.length > 0) { + const sample = enriched.slice(0, 3).map((p) => ({ + title: p.title?.slice(0, 80), + doi: p.doi, + candidates: p.pdf_link_candidates_from_duplicates, + })); + // eslint-disable-next-line no-console + console.log("[linkEnrichment/BASE] sample enriched papers:", sample); + } + + expect(enriched.length).toBeGreaterThanOrEqual(MIN_ENRICHED_PAPERS); + }); + + test(`pinned paper '${PINNED_PAPER_TITLE}' has candidate containing '${PINNED_EXPECTED_CANDIDATE_SUBSTRING}'`, async ({ + page, + }) => { + const papers = await capturePapersFromLatestRevision(page, async () => { + await prepareVisualisation(page, URL); + await expect(page.locator("#search-term-unique")).toContainText( + "game theory", + ); + }); + + const pinned = papers.find((p) => p?.title === PINNED_PAPER_TITLE); + expect(pinned, `paper '${PINNED_PAPER_TITLE}' not found in results`) + .toBeDefined(); + + const candidates = candidatesAsString(pinned); + expect(candidates).toContain(PINNED_EXPECTED_CANDIDATE_SUBSTRING); + }); +}); diff --git a/e2e/linkEnrichment/ORCID.spec.ts b/e2e/linkEnrichment/ORCID.spec.ts new file mode 100644 index 000000000..5354b353c --- /dev/null +++ b/e2e/linkEnrichment/ORCID.spec.ts @@ -0,0 +1,78 @@ +import { test, expect } from "@playwright/test"; +import { prepareVisualisation } from "../../vis/js/utils/e2eVisualisationLoader"; +import { + capturePapersFromLatestRevision, + papersWithLinkEnrichment, + candidatesAsString, +} from "./helpers"; + +/** + * ORCID link-enrichment smoke test. + * + * 0000-0001-5116-955X is a heavy contributor in the dev-base-1 enrichment + * logs — a recent run produced dozens of `[ENRICHMENT_APPLIED] link` events + * for this profile. Two assertions are made: + * + * 1. Volume — at least MIN_ENRICHED_PAPERS papers have non-empty + * pdf_link_candidates_from_duplicates. Catches a wholesale regression. + * 2. Containment on a specific paper — "Local Replicator Dynamics..." + * gains a PubMed URL via duplicate grouping. Containment (rather than + * equality) keeps the test green if the dedup graph picks up more + * sibling URLs later. The same paper is also tracked in + * oaStatusEnrichment/ORCID.spec.ts as a non-OA negative case, so the + * two specs share fate if BASE stops returning this record at all. + */ +const ORCID = "0000-0001-5116-955X"; +const URL = `/search?type=get&vis_type=overview&orcid=${ORCID}&service=orcid&embed=true`; + +const MIN_ENRICHED_PAPERS = 1; + +const PINNED_PAPER_TITLE = + "Local Replicator Dynamics: A Simple Link Between Deterministic and Stochastic Models of Evolutionary Game Theory"; +const PINNED_EXPECTED_CANDIDATE_SUBSTRING = + "pubmed.ncbi.nlm.nih.gov/21181502"; + +test.describe("ORCID link enrichment smoke", () => { + test(`at least ${MIN_ENRICHED_PAPERS} paper has pdf_link_candidates_from_duplicates for ORCID ${ORCID}`, async ({ + page, + }) => { + const papers = await capturePapersFromLatestRevision(page, async () => { + await prepareVisualisation(page, URL); + await expect(page.locator("#search-term-unique")).toContainText( + `(${ORCID})`, + ); + }); + + const enriched = papersWithLinkEnrichment(papers); + + if (enriched.length > 0) { + const sample = enriched.slice(0, 3).map((p) => ({ + title: p.title?.slice(0, 80), + doi: p.doi, + candidates: p.pdf_link_candidates_from_duplicates, + })); + // eslint-disable-next-line no-console + console.log("[linkEnrichment/ORCID] sample enriched papers:", sample); + } + + expect(enriched.length).toBeGreaterThanOrEqual(MIN_ENRICHED_PAPERS); + }); + + test(`pinned paper '${PINNED_PAPER_TITLE}' has candidate containing '${PINNED_EXPECTED_CANDIDATE_SUBSTRING}'`, async ({ + page, + }) => { + const papers = await capturePapersFromLatestRevision(page, async () => { + await prepareVisualisation(page, URL); + await expect(page.locator("#search-term-unique")).toContainText( + `(${ORCID})`, + ); + }); + + const pinned = papers.find((p) => p?.title === PINNED_PAPER_TITLE); + expect(pinned, `paper '${PINNED_PAPER_TITLE}' not found in results`) + .toBeDefined(); + + const candidates = candidatesAsString(pinned); + expect(candidates).toContain(PINNED_EXPECTED_CANDIDATE_SUBSTRING); + }); +}); diff --git a/e2e/linkEnrichment/helpers.ts b/e2e/linkEnrichment/helpers.ts new file mode 100644 index 000000000..9d42bcd59 --- /dev/null +++ b/e2e/linkEnrichment/helpers.ts @@ -0,0 +1,95 @@ +import { Page, Response } from "@playwright/test"; + +/** + * Capture the JSON payload returned by `services/getLatestRevision.php`. + * + * `prepareVisualisation` triggers a fetch of this endpoint when the + * visualisation loads. We register the waiter *before* prepareVisualisation + * runs so the response can't be missed. + * + * The response body wraps the paper array inside a `data` field that is + * itself a JSON-encoded string — this helper handles that unpacking so call + * sites just receive `papers[]`. + */ +export async function capturePapersFromLatestRevision( + page: Page, + trigger: () => Promise, +): Promise { + const responsePromise = page.waitForResponse( + (r: Response) => + r.url().includes("services/getLatestRevision.php") && r.status() === 200, + { timeout: 60_000 }, + ); + + await trigger(); + + const response = await responsePromise; + const body = await response.json(); + + // Walk through the wrapping layers until we land on the paper array. + // Observed shape from BASE/ORCID responses: + // body.data is a JSON-encoded string that parses to + // { author, documents, ... } + // where `documents` is itself a JSON-encoded string holding the paper + // array. So two parse steps + one key lookup. Other endpoints may + // deliver the array directly or under `data`/`papers`/`results`, so the + // loop is tolerant of those too. + let cursor: any = body?.data; + for (let i = 0; i < 6; i++) { + if (Array.isArray(cursor)) return cursor; + if (typeof cursor === "string") { + try { + cursor = JSON.parse(cursor); + continue; + } catch { + break; + } + } + if (cursor && typeof cursor === "object") { + const nextKey = ["documents", "data", "papers", "results"].find( + (k) => k in cursor, + ); + if (nextKey) { + cursor = cursor[nextKey]; + continue; + } + } + break; + } + + throw new Error( + "Unexpected getLatestRevision payload shape — could not find papers array. body keys=" + + JSON.stringify(Object.keys(body ?? {})) + + ", data type=" + + typeof body?.data + + ", sample=" + + JSON.stringify(body).slice(0, 500), + ); +} + +/** + * Returns papers whose `pdf_link_candidates_from_duplicates` field carries + * at least one URL — i.e. papers where link enrichment actually fired. + */ +export function papersWithLinkEnrichment(papers: any[]): any[] { + return papers.filter((p) => { + const v = p?.pdf_link_candidates_from_duplicates; + if (!v) return false; + if (typeof v === "string") return v.trim().length > 0; + if (Array.isArray(v)) return v.length > 0; + return false; + }); +} + +/** + * Return the candidates field for a paper as a single string, regardless + * of whether the raw payload delivers it as a string or array. Returns "" + * when the field is absent or empty. + */ +export function candidatesAsString(paper: any): string { + const v = paper?.pdf_link_candidates_from_duplicates; + if (!v) return ""; + if (typeof v === "string") return v; + if (Array.isArray(v)) return v.join("; "); + return ""; +} diff --git a/e2e/oaStatusEnrichment/ORCID.spec.ts b/e2e/oaStatusEnrichment/ORCID.spec.ts new file mode 100644 index 000000000..ca099158c --- /dev/null +++ b/e2e/oaStatusEnrichment/ORCID.spec.ts @@ -0,0 +1,243 @@ +import { test, expect, Page } from "@playwright/test"; +import { prepareVisualisation } from "../../vis/js/utils/e2eVisualisationLoader"; + +interface OaStatusTestCase { + suiteName: string; + orcid: string; + paperTitle: string; + // "1" = open access (yes), "0" = no, "2" = unknown. + // Only "1" papers should render the `paper-tag open-access-tag` element. + expectedOaState: "1" | "0" | "2"; + flaky?: boolean; + // Known regression: the assertion currently fails. Marked with test.fail() + // so the suite tracks it — when the underlying bug is fixed, the test will + // start passing, the framework will report it as an unexpected pass, and + // this annotation should be removed. + regression?: string; +} + +const testCases: OaStatusTestCase[] = [ + // --- OA = 1 (open access expected) --- + { + suiteName: "open access tag rendered", + orcid: "0000-0001-5116-955X", + paperTitle: "Humans choose representatives who enforce cooperation in social dilemmas through extortion", + expectedOaState: "1", + }, + { + suiteName: "open access tag rendered", + orcid: "0000-0001-5116-955X", + paperTitle: "Extortion subdues human players but is finally punished in the prisoner’s dilemma", + expectedOaState: "1", + }, + { + suiteName: "open access tag rendered", + orcid: "0000-0001-5116-955X", + paperTitle: "Asymmetric Power Boosts Extortion in an Economic Experiment", + expectedOaState: "1", + }, + { + suiteName: "open access tag rendered", + orcid: "0000-0001-5116-955X", + paperTitle: "Democratic decisions establish stable authorities that overcome the paradox of second-order punishment", + expectedOaState: "1", + }, + { + suiteName: "open access tag rendered", + orcid: "0000-0001-5116-955X", + paperTitle: "The effect of environmental information on evolution of cooperation in stochastic games", + expectedOaState: "1", + }, + { + suiteName: "open access tag rendered", + orcid: "0000-0001-5116-955X", + paperTitle: "Social immunity modulates competition between coinfecting pathogens", + expectedOaState: "1", + }, + { + suiteName: "open access tag rendered", + orcid: "0000-0001-5116-955X", + paperTitle: "Exact conditions for evolutionary stability in indirect reciprocity under noise", + expectedOaState: "1", + }, + { + suiteName: "open access tag rendered", + orcid: "0000-0003-4221-6275", + paperTitle: "Proportion of foetal and placental implantation abnormalities in Madagascar: A cross-sectional study of 35,919 women at public-sector primary healthcare facilities in central and southern Madagascar, 2017–2020", + expectedOaState: "1", + }, + { + suiteName: "open access tag rendered", + orcid: "0000-0003-4221-6275", + paperTitle: "Body temperature measurement in mice during acute illness: implantable temperature transponder versus surface infrared thermometry.", + expectedOaState: "1", + flaky: true, + }, + { + suiteName: "open access tag rendered", + orcid: "0000-0003-4221-6275", + paperTitle: "Seismic Facies Analysis: A Deep Domain Adaptation Approach", + expectedOaState: "1", + }, + { + suiteName: "open access tag rendered", + orcid: "0000-0003-4221-6275", + paperTitle: "Informing deep neural networks by multiscale principles of neuromodulatory systems", + expectedOaState: "1", + }, + { + suiteName: "open access tag rendered", + orcid: "0000-0002-5238-4195", + paperTitle: "Ten simple rules for innovative dissemination of research", + expectedOaState: "1", + }, + { + suiteName: "open access tag rendered", + orcid: "0000-0002-5238-4195", + paperTitle: "Research data explored: an extended analysis of citations and altmetrics", + expectedOaState: "1", + }, + + // --- OA != 1 (no open access tag expected) --- + // Negative cases keep the suite falsifiable: without them, an always-OA UI + // bug would pass the positive cases trivially. + { + suiteName: "no open access tag", + orcid: "0000-0001-5116-955X", + paperTitle: "Evolution of cooperation through cumulative reciprocity", + expectedOaState: "0", + flaky: true, + }, + { + suiteName: "no open access tag", + orcid: "0000-0001-5116-955X", + paperTitle: "Local Replicator Dynamics: A Simple Link Between Deterministic and Stochastic Models of Evolutionary Game Theory", + expectedOaState: "0", + flaky: true, + }, + + // --- Known regression: case-sensitive DOI matching --- + // These papers should be OA but currently render without the open-access + // tag because the duplicate group that carries oa_state="1" is no longer + // being matched. Tracked with test.fail() until the DOI-matching + // regression is fixed. + { + suiteName: "regression: case-sensitive DOI matching", + orcid: "0000-0003-4221-6275", + paperTitle: "Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling", + expectedOaState: "1", + regression: "case-sensitive DOI matching regression", + }, + { + suiteName: "regression: case-sensitive DOI matching", + orcid: "0000-0003-4221-6275", + paperTitle: "Improving the adaptive and continuous learning capabilities of artificial neural networks: Lessons from multi-neuromodulatory dynamics", + expectedOaState: "1", + regression: "case-sensitive DOI matching regression", + }, + { + suiteName: "regression: case-sensitive DOI matching", + orcid: "0000-0003-4221-6275", + paperTitle: "Neural mechanisms of predictive processing: a collaborative community experiment through the OpenScope program", + expectedOaState: "1", + regression: "case-sensitive DOI matching regression", + }, + { + suiteName: "regression: case-sensitive DOI matching", + orcid: "0000-0003-4221-6275", + paperTitle: "Refining Humane Endpoints in Mouse Models of Disease by Systematic Review and Machine Learning-Based Endpoint Definition", + expectedOaState: "1", + regression: "case-sensitive DOI matching regression", + }, + { + suiteName: "regression: case-sensitive DOI matching", + orcid: "0000-0003-4221-6275", + paperTitle: "Sepsis-associated cognitive dysfunction: an investigation using stress-free, automated behavioral tests", + expectedOaState: "1", + regression: "case-sensitive DOI matching regression", + }, + { + suiteName: "regression: case-sensitive DOI matching", + orcid: "0000-0003-4221-6275", + paperTitle: "The role of gain neuromodulation in layer-5 pyramidal neurons", + expectedOaState: "1", + regression: "case-sensitive DOI matching regression", + }, +]; + +const uniqueOrcids = [...new Set(testCases.map((tc) => tc.orcid).filter(Boolean))]; + +// Mirror the warm-up list from keywordsEnrichment/ORCID.spec.ts so cold-cache +// runs don't pay the profile-fetch cost in the assertion tests. +const additionalUniqueOrcids = [ + "0000-0001-5116-955X", + "0000-0003-4221-6275", + "0000-0002-9843-6798", + "0000-0002-5238-4195", + "0000-0002-4505-0517", + "0000-0003-2897-6075", + "0000-0002-8911-7832", + "0000-0002-2233-6926", + "0000-0001-9287-3770", + "0000-0002-1193-6256", + "0000-0003-0108-7980", + "0000-0001-9237-8606", +]; + +const allUniqueOrcids = [...new Set([...uniqueOrcids, ...additionalUniqueOrcids])]; + +// Pre-load unique ORCID profiles so later assertion tests run faster. +// npx playwright test "e2e/oaStatusEnrichment/ORCID.spec.ts" --grep "Warm-up: pre-load unique ORCID profiles" +test.describe("Warm-up: pre-load unique ORCID profiles", () => { + for (const orcid of allUniqueOrcids) { + test(`${orcid}`, async ({ page }) => { + const url = `/search?type=get&vis_type=overview&orcid=${orcid}&service=orcid&embed=true`; + await prepareVisualisation(page, url); + await expect(page.locator("#search-term-unique")).toContainText(`(${orcid})`); + }); + } +}); + +for (const tc of testCases) { + test.describe( + `Verify OA status, ${tc.suiteName}`, + () => { + const url = `/search?type=get&vis_type=overview&orcid=${tc.orcid}&service=orcid&embed=true`; + + async function openPaper(page: Page) { + await prepareVisualisation(page, url); + await expect(page.locator("#search-term-unique")).toContainText( + `(${tc.orcid})`, + ); + const paper = page.getByTitle(tc.paperTitle); + await expect(paper).toBeVisible(); + await paper.click(); + return paper; + } + + const expectedOaState = tc.expectedOaState; + const flaky = tc.flaky; + const regression = tc.regression; + test(`OA status (${expectedOaState}) for the '${tc.paperTitle}' document`, async ({ + page, + }) => { + test.fixme(!!flaky, "Flaky: depends on BASE API returning duplicate records consistently"); + test.fail(!!regression, regression ?? ""); + await openPaper(page); + + // Scope to the specific paper's list entry so neighbouring tags can't + // satisfy the assertion. + const paperEntry = page + .locator(".list_entry") + .filter({ has: page.getByTitle(tc.paperTitle) }); + const openAccessTag = paperEntry.locator(".paper-tag.open-access-tag"); + + if (expectedOaState === "1") { + await expect(openAccessTag).toBeVisible(); + } else { + await expect(openAccessTag).toHaveCount(0); + } + }); + }, + ); +} From 77cf6ad4a3359ff4946e2f3e51e18294d0db7992 Mon Sep 17 00:00:00 2001 From: chreman Date: Wed, 3 Jun 2026 14:39:08 +0200 Subject: [PATCH 104/205] remove link enrichment tests --- e2e/linkEnrichment/BASE.spec.ts | 84 ---------------------------- e2e/linkEnrichment/ORCID.spec.ts | 78 -------------------------- e2e/linkEnrichment/helpers.ts | 95 -------------------------------- 3 files changed, 257 deletions(-) delete mode 100644 e2e/linkEnrichment/BASE.spec.ts delete mode 100644 e2e/linkEnrichment/ORCID.spec.ts delete mode 100644 e2e/linkEnrichment/helpers.ts diff --git a/e2e/linkEnrichment/BASE.spec.ts b/e2e/linkEnrichment/BASE.spec.ts deleted file mode 100644 index 5583d43e0..000000000 --- a/e2e/linkEnrichment/BASE.spec.ts +++ /dev/null @@ -1,84 +0,0 @@ -import { test, expect } from "@playwright/test"; -import { prepareVisualisation } from "../../vis/js/utils/e2eVisualisationLoader"; -import { - capturePapersFromLatestRevision, - papersWithLinkEnrichment, - candidatesAsString, -} from "./helpers"; - -/** - * BASE link-enrichment smoke test. - * - * Query/params chosen from dev-base-1 logs: a `"game theory"` BASE search - * with the same param set used in the existing keyword-enrichment BASE spec - * produced 27 `[ENRICHMENT_APPLIED] link` events in a recent test run. The - * URL shape mirrors keywordsEnrichment/BASE.spec.ts so any future param - * drift surfaces in both tests at once. - * - * Two assertions are made: - * 1. Volume — at least MIN_ENRICHED_PAPERS papers have non-empty - * pdf_link_candidates_from_duplicates. This catches a wholesale - * regression (enrichment silently turning off). - * 2. Containment on a specific paper — the survey on Game Theory & WSN - * security gains a PubMed URL via duplicate grouping. Containment - * (rather than equality) means the test stays green if the duplicate - * group picks up additional sibling URLs over time. - */ -const QUERY = '"game theory"'; -const SEARCH_PARAMS = - "&service=base&sorting=most-relevant&document_types%5B%5D=121&lang_id%5B%5D=all-lang&min_descsize=300"; -const URL = `/search?type=get&vis_type=overview&q=${encodeURIComponent(QUERY)}${SEARCH_PARAMS}&embed=true`; - -const MIN_ENRICHED_PAPERS = 1; - -const PINNED_PAPER_TITLE = - "Game Theory Meets Wireless Sensor Networks Security Requirements and Threats Mitigation: A Survey"; -// PubMed identifiers are stable for the life of the record, so this URL -// fragment is a sturdy assertion target. -const PINNED_EXPECTED_CANDIDATE_SUBSTRING = - "pubmed.ncbi.nlm.nih.gov/27367700"; - -test.describe("BASE link enrichment smoke", () => { - test(`at least ${MIN_ENRICHED_PAPERS} paper has pdf_link_candidates_from_duplicates for query '${QUERY}'`, async ({ - page, - }) => { - const papers = await capturePapersFromLatestRevision(page, async () => { - await prepareVisualisation(page, URL); - await expect(page.locator("#search-term-unique")).toContainText( - "game theory", - ); - }); - - const enriched = papersWithLinkEnrichment(papers); - - if (enriched.length > 0) { - const sample = enriched.slice(0, 3).map((p) => ({ - title: p.title?.slice(0, 80), - doi: p.doi, - candidates: p.pdf_link_candidates_from_duplicates, - })); - // eslint-disable-next-line no-console - console.log("[linkEnrichment/BASE] sample enriched papers:", sample); - } - - expect(enriched.length).toBeGreaterThanOrEqual(MIN_ENRICHED_PAPERS); - }); - - test(`pinned paper '${PINNED_PAPER_TITLE}' has candidate containing '${PINNED_EXPECTED_CANDIDATE_SUBSTRING}'`, async ({ - page, - }) => { - const papers = await capturePapersFromLatestRevision(page, async () => { - await prepareVisualisation(page, URL); - await expect(page.locator("#search-term-unique")).toContainText( - "game theory", - ); - }); - - const pinned = papers.find((p) => p?.title === PINNED_PAPER_TITLE); - expect(pinned, `paper '${PINNED_PAPER_TITLE}' not found in results`) - .toBeDefined(); - - const candidates = candidatesAsString(pinned); - expect(candidates).toContain(PINNED_EXPECTED_CANDIDATE_SUBSTRING); - }); -}); diff --git a/e2e/linkEnrichment/ORCID.spec.ts b/e2e/linkEnrichment/ORCID.spec.ts deleted file mode 100644 index 5354b353c..000000000 --- a/e2e/linkEnrichment/ORCID.spec.ts +++ /dev/null @@ -1,78 +0,0 @@ -import { test, expect } from "@playwright/test"; -import { prepareVisualisation } from "../../vis/js/utils/e2eVisualisationLoader"; -import { - capturePapersFromLatestRevision, - papersWithLinkEnrichment, - candidatesAsString, -} from "./helpers"; - -/** - * ORCID link-enrichment smoke test. - * - * 0000-0001-5116-955X is a heavy contributor in the dev-base-1 enrichment - * logs — a recent run produced dozens of `[ENRICHMENT_APPLIED] link` events - * for this profile. Two assertions are made: - * - * 1. Volume — at least MIN_ENRICHED_PAPERS papers have non-empty - * pdf_link_candidates_from_duplicates. Catches a wholesale regression. - * 2. Containment on a specific paper — "Local Replicator Dynamics..." - * gains a PubMed URL via duplicate grouping. Containment (rather than - * equality) keeps the test green if the dedup graph picks up more - * sibling URLs later. The same paper is also tracked in - * oaStatusEnrichment/ORCID.spec.ts as a non-OA negative case, so the - * two specs share fate if BASE stops returning this record at all. - */ -const ORCID = "0000-0001-5116-955X"; -const URL = `/search?type=get&vis_type=overview&orcid=${ORCID}&service=orcid&embed=true`; - -const MIN_ENRICHED_PAPERS = 1; - -const PINNED_PAPER_TITLE = - "Local Replicator Dynamics: A Simple Link Between Deterministic and Stochastic Models of Evolutionary Game Theory"; -const PINNED_EXPECTED_CANDIDATE_SUBSTRING = - "pubmed.ncbi.nlm.nih.gov/21181502"; - -test.describe("ORCID link enrichment smoke", () => { - test(`at least ${MIN_ENRICHED_PAPERS} paper has pdf_link_candidates_from_duplicates for ORCID ${ORCID}`, async ({ - page, - }) => { - const papers = await capturePapersFromLatestRevision(page, async () => { - await prepareVisualisation(page, URL); - await expect(page.locator("#search-term-unique")).toContainText( - `(${ORCID})`, - ); - }); - - const enriched = papersWithLinkEnrichment(papers); - - if (enriched.length > 0) { - const sample = enriched.slice(0, 3).map((p) => ({ - title: p.title?.slice(0, 80), - doi: p.doi, - candidates: p.pdf_link_candidates_from_duplicates, - })); - // eslint-disable-next-line no-console - console.log("[linkEnrichment/ORCID] sample enriched papers:", sample); - } - - expect(enriched.length).toBeGreaterThanOrEqual(MIN_ENRICHED_PAPERS); - }); - - test(`pinned paper '${PINNED_PAPER_TITLE}' has candidate containing '${PINNED_EXPECTED_CANDIDATE_SUBSTRING}'`, async ({ - page, - }) => { - const papers = await capturePapersFromLatestRevision(page, async () => { - await prepareVisualisation(page, URL); - await expect(page.locator("#search-term-unique")).toContainText( - `(${ORCID})`, - ); - }); - - const pinned = papers.find((p) => p?.title === PINNED_PAPER_TITLE); - expect(pinned, `paper '${PINNED_PAPER_TITLE}' not found in results`) - .toBeDefined(); - - const candidates = candidatesAsString(pinned); - expect(candidates).toContain(PINNED_EXPECTED_CANDIDATE_SUBSTRING); - }); -}); diff --git a/e2e/linkEnrichment/helpers.ts b/e2e/linkEnrichment/helpers.ts deleted file mode 100644 index 9d42bcd59..000000000 --- a/e2e/linkEnrichment/helpers.ts +++ /dev/null @@ -1,95 +0,0 @@ -import { Page, Response } from "@playwright/test"; - -/** - * Capture the JSON payload returned by `services/getLatestRevision.php`. - * - * `prepareVisualisation` triggers a fetch of this endpoint when the - * visualisation loads. We register the waiter *before* prepareVisualisation - * runs so the response can't be missed. - * - * The response body wraps the paper array inside a `data` field that is - * itself a JSON-encoded string — this helper handles that unpacking so call - * sites just receive `papers[]`. - */ -export async function capturePapersFromLatestRevision( - page: Page, - trigger: () => Promise, -): Promise { - const responsePromise = page.waitForResponse( - (r: Response) => - r.url().includes("services/getLatestRevision.php") && r.status() === 200, - { timeout: 60_000 }, - ); - - await trigger(); - - const response = await responsePromise; - const body = await response.json(); - - // Walk through the wrapping layers until we land on the paper array. - // Observed shape from BASE/ORCID responses: - // body.data is a JSON-encoded string that parses to - // { author, documents, ... } - // where `documents` is itself a JSON-encoded string holding the paper - // array. So two parse steps + one key lookup. Other endpoints may - // deliver the array directly or under `data`/`papers`/`results`, so the - // loop is tolerant of those too. - let cursor: any = body?.data; - for (let i = 0; i < 6; i++) { - if (Array.isArray(cursor)) return cursor; - if (typeof cursor === "string") { - try { - cursor = JSON.parse(cursor); - continue; - } catch { - break; - } - } - if (cursor && typeof cursor === "object") { - const nextKey = ["documents", "data", "papers", "results"].find( - (k) => k in cursor, - ); - if (nextKey) { - cursor = cursor[nextKey]; - continue; - } - } - break; - } - - throw new Error( - "Unexpected getLatestRevision payload shape — could not find papers array. body keys=" + - JSON.stringify(Object.keys(body ?? {})) + - ", data type=" + - typeof body?.data + - ", sample=" + - JSON.stringify(body).slice(0, 500), - ); -} - -/** - * Returns papers whose `pdf_link_candidates_from_duplicates` field carries - * at least one URL — i.e. papers where link enrichment actually fired. - */ -export function papersWithLinkEnrichment(papers: any[]): any[] { - return papers.filter((p) => { - const v = p?.pdf_link_candidates_from_duplicates; - if (!v) return false; - if (typeof v === "string") return v.trim().length > 0; - if (Array.isArray(v)) return v.length > 0; - return false; - }); -} - -/** - * Return the candidates field for a paper as a single string, regardless - * of whether the raw payload delivers it as a string or array. Returns "" - * when the field is absent or empty. - */ -export function candidatesAsString(paper: any): string { - const v = paper?.pdf_link_candidates_from_duplicates; - if (!v) return ""; - if (typeof v === "string") return v; - if (Array.isArray(v)) return v.join("; "); - return ""; -} From f226599ab042b931669dd53eddcdf8d4c7b63219 Mon Sep 17 00:00:00 2001 From: chreman Date: Wed, 3 Jun 2026 14:49:15 +0200 Subject: [PATCH 105/205] cleanup of PDF link related debug logging --- server/workers/common/common/enrichment.py | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/server/workers/common/common/enrichment.py b/server/workers/common/common/enrichment.py index 791236b8d..13848b1c6 100644 --- a/server/workers/common/common/enrichment.py +++ b/server/workers/common/common/enrichment.py @@ -28,14 +28,10 @@ def _log_anchor_state(tag, df, anchor_idx, group_data=None): resulttype = df.loc[anchor_idx, 'resulttype'] if 'resulttype' in df.columns else 'N/A' oa_state = df.loc[anchor_idx, 'oa_state'] if 'oa_state' in df.columns else 'N/A' link = df.loc[anchor_idx, 'link'] if 'link' in df.columns else 'N/A' - pdf_candidates = ( - df.loc[anchor_idx, 'pdf_link_candidates_from_duplicates'] - if 'pdf_link_candidates_from_duplicates' in df.columns else 'N/A' - ) logger.debug( - "[ANCHOR_ENRICHMENT] anchor_%s doi=%s | title=%s | keywords=%s | resulttype=%s | oa_state=%r (type=%s) | link=%s | pdf_link_candidates_from_duplicates=%s", - tag, doi, title, kw, resulttype, oa_state, type(oa_state).__name__, link, pdf_candidates + "[ANCHOR_ENRICHMENT] anchor_%s doi=%s | title=%s | keywords=%s | resulttype=%s | oa_state=%r (type=%s) | link=%s", + tag, doi, title, kw, resulttype, oa_state, type(oa_state).__name__, link ) if group_data is not None: From 7f626354309dd8b30b985b4170cf9796097f3eb5 Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 5 Jun 2026 13:10:16 +0200 Subject: [PATCH 106/205] rename data fields for additional DOIs --- server/preprocessing/other-scripts/base.R | 18 +++++++++++++++-- server/workers/base/src/base.py | 9 +++++++-- server/workers/orcid/src/orcid_service.py | 24 +++++++++++------------ 3 files changed, 35 insertions(+), 16 deletions(-) diff --git a/server/preprocessing/other-scripts/base.R b/server/preprocessing/other-scripts/base.R index 1c7b0dd69..7ae8163f8 100644 --- a/server/preprocessing/other-scripts/base.R +++ b/server/preprocessing/other-scripts/base.R @@ -283,8 +283,22 @@ etl <- function(res, repo, non_public) { metadata$type = check_metadata(res$dctype) metadata$typenorm = check_metadata(res$dctypenorm) metadata$doi = unlist(lapply(metadata$link, find_dois)) - metadata$merged_dois = check_metadata(res$dcdoi) - metadata$merged_dois = check_metadata(lapply(metadata$merged_dois, normalize_dois)) + metadata$additional_dois = check_metadata(res$dcdoi) + metadata$additional_dois = check_metadata(lapply(metadata$additional_dois, normalize_dois)) + # Fill primary doi from additional_dois when find_dois(link) returned nothing + # but dcdoi contains exactly one entry. Guarded to the single-entry case + # because multi-entry dcdoi can include related-but-different works + # so picking one from many would risk attaching foreign metadata to the record. + # additional_dois is a list-column (built via lapply), so flatten to a + # character vector before comparisons / assignment to keep `doi` typed. + additional_dois_char <- vapply(metadata$additional_dois, function(x) { + if (length(x) == 0) "" else as.character(x)[1] + }, character(1)) + needs_doi_fill <- (is.na(metadata$doi) | metadata$doi == "") & + !is.na(additional_dois_char) & + additional_dois_char != "" & + !grepl(";", additional_dois_char, fixed = TRUE) + metadata$doi[needs_doi_fill] <- additional_dois_char[needs_doi_fill] metadata$lang = check_metadata(res$dclang) metadata$language = check_metadata(res$dclanguage) metadata$content_provider = check_metadata(res$dcprovider) diff --git a/server/workers/base/src/base.py b/server/workers/base/src/base.py index 9f450714d..384562418 100644 --- a/server/workers/base/src/base.py +++ b/server/workers/base/src/base.py @@ -269,7 +269,12 @@ def _log_group_similarity(df, indexes, group_type, group_key): """Log titles, DOIs, and pairwise Levenshtein ratios for one duplicate group.""" if not logger.isEnabledFor(logging.DEBUG): return - rows = df.loc[list(indexes)] + # Intersect with df.index — group members can be dropped by the + # false-positive DOI/title filter before this log fires. + present = df.index.intersection(list(indexes)) + if len(present) == 0: + return + rows = df.loc[present] titles = rows["title"].fillna("").tolist() dois = rows["doi"].fillna("").tolist() ids = rows["id"].fillna("").tolist() @@ -499,7 +504,7 @@ def sanitize_year(year_str): def _log_dataframe(df: pd.DataFrame, params: Dict[str, str], name: str, ): vis_id = params.get('vis_id') - columns_to_print = ['id', 'title', 'doi', 'merged_dois', 'paper_abstract', 'link', 'subject', 'subject_orig', 'oa_state'] + columns_to_print = ['id', 'title', 'doi', 'additional_dois', 'paper_abstract', 'link', 'subject', 'subject_orig', 'oa_state'] available_columns = df.columns.tolist() columns_to_print = [col for col in columns_to_print if col in available_columns] diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 62dda18b9..7d9970b1a 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -134,7 +134,7 @@ def enrich_metadata(self, params: Dict[str, str], metadata: pd.DataFrame) -> pd. def _log_dataframe(self, df: pd.DataFrame, params: Dict[str, str], name: str, ): orcid = params.get('orcid') - columns_to_print = ['id', 'title', 'doi', 'merged_dois', 'paper_abstract', 'link', 'subject', 'subject_orig', 'oa_state'] + columns_to_print = ['id', 'title', 'doi', 'additional_dois', 'paper_abstract', 'link', 'subject', 'subject_orig', 'oa_state'] available_columns = df.columns.tolist() columns_to_print = [col for col in columns_to_print if col in available_columns] @@ -274,7 +274,7 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra 'relations', 'annotations', 'repo', 'source', 'volume', 'issue', 'page', 'issn', 'citation_count', 'cited_by_wikipedia_count', 'cited_by_msm_count', 'cited_by_policies_count', 'cited_by_patents_count', 'cited_by_accounts_count', 'cited_by_fbwalls_count', - 'merged_dois', 'pdf_link_candidates_from_duplicates', + 'additional_dois', 'pdf_link_candidates_from_duplicates', 'cited_by_feeds_count', 'cited_by_gplus_count', 'cited_by_rdts_count', @@ -321,18 +321,18 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra base_metadata = base_metadata.reindex(columns=required_fields) - base_metadata['merged_dois'] = base_metadata['merged_dois'].apply(lambda x: x[0] if isinstance(x, list) and len(x) > 0 else x) - base_metadata['merged_dois'] = base_metadata['merged_dois'].apply(lambda x: x.split(';') if isinstance(x, str) else []) - base_metadata['merged_dois'] = base_metadata['merged_dois'].apply(lambda x: [x.strip() for x in x] if isinstance(x, list) else x) + base_metadata['additional_dois'] = base_metadata['additional_dois'].apply(lambda x: x[0] if isinstance(x, list) and len(x) > 0 else x) + base_metadata['additional_dois'] = base_metadata['additional_dois'].apply(lambda x: x.split(';') if isinstance(x, str) else []) + base_metadata['additional_dois'] = base_metadata['additional_dois'].apply(lambda x: [x.strip() for x in x] if isinstance(x, list) else x) # Save the normalized original doi before explode so we can rank direct fetches - # above rows whose doi was reassigned from merged_dois after explosion. + # above rows whose doi was reassigned from additional_dois after explosion. base_metadata['_fetch_doi'] = base_metadata['doi'].apply(remove_doi_prefix) - base_metadata = base_metadata.explode('merged_dois', ignore_index=True) - # replace doi with merged_dois if merged_dois is not empty, otherwise keep doi - base_metadata.loc[base_metadata['merged_dois'].notna() & (base_metadata['merged_dois'] != ''), 'doi'] = base_metadata.loc[base_metadata['merged_dois'].notna() & (base_metadata['merged_dois'] != ''), 'merged_dois'] + base_metadata = base_metadata.explode('additional_dois', ignore_index=True) + # replace doi with additional_dois if additional_dois is not empty, otherwise keep doi + base_metadata.loc[base_metadata['additional_dois'].notna() & (base_metadata['additional_dois'] != ''), 'doi'] = base_metadata.loc[base_metadata['additional_dois'].notna() & (base_metadata['additional_dois'] != ''), 'additional_dois'] base_metadata.loc[:, 'doi'] = base_metadata['doi'].apply(remove_doi_prefix) # True for rows whose final doi still matches the original fetched doi (direct); - # False for rows where doi was replaced by a merged_dois value (exploded). + # False for rows where doi was replaced by a additional_dois value (exploded). base_metadata['_is_direct_fetch'] = base_metadata['doi'] == base_metadata['_fetch_doi'] base_metadata.drop(columns='_fetch_doi', inplace=True) @@ -355,9 +355,9 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra self.logger.debug(f"[doi_dedup] duplicate group for doi={doi!r}:\n{group.to_string()}") else: self.logger.debug(f"[doi_dedup] {len(base_metadata)} records, no duplicate DOIs — dedup step is a no-op here") - # Sort by: direct fetch before exploded-from-merged_dois rows, + # Sort by: direct fetch before exploded-from-additional_dois rows, # This ensures the record actually fetched for a DOI wins over a record - # that acquired that DOI via merged_dois expansion. + # that acquired that DOI via additional_dois expansion. base_metadata = base_metadata.assign( _direct_sort=(~base_metadata['_is_direct_fetch']).astype(int), ).sort_values(by=['_direct_sort']).drop_duplicates( From 79133b851f1320b7637d3591fb75c7ad51c8560d Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 5 Jun 2026 14:01:51 +0200 Subject: [PATCH 107/205] implement new data contract (guarded by exception) for DOI enrichment; add fallback mechanism in case DOI only available from secondary sources --- server/preprocessing/other-scripts/base.R | 10 ++-- server/workers/orcid/src/orcid_service.py | 59 ++++++++++++----------- 2 files changed, 37 insertions(+), 32 deletions(-) diff --git a/server/preprocessing/other-scripts/base.R b/server/preprocessing/other-scripts/base.R index 7ae8163f8..06e347140 100644 --- a/server/preprocessing/other-scripts/base.R +++ b/server/preprocessing/other-scripts/base.R @@ -283,22 +283,22 @@ etl <- function(res, repo, non_public) { metadata$type = check_metadata(res$dctype) metadata$typenorm = check_metadata(res$dctypenorm) metadata$doi = unlist(lapply(metadata$link, find_dois)) + metadata$doi_merge = unlist(lapply(metadata$link, find_dois)) metadata$additional_dois = check_metadata(res$dcdoi) metadata$additional_dois = check_metadata(lapply(metadata$additional_dois, normalize_dois)) - # Fill primary doi from additional_dois when find_dois(link) returned nothing + # Fill primary doi for the ORCID enrichment + #from additional_dois when find_dois(link) returned nothing # but dcdoi contains exactly one entry. Guarded to the single-entry case # because multi-entry dcdoi can include related-but-different works # so picking one from many would risk attaching foreign metadata to the record. - # additional_dois is a list-column (built via lapply), so flatten to a - # character vector before comparisons / assignment to keep `doi` typed. additional_dois_char <- vapply(metadata$additional_dois, function(x) { if (length(x) == 0) "" else as.character(x)[1] }, character(1)) - needs_doi_fill <- (is.na(metadata$doi) | metadata$doi == "") & + needs_doi_fill <- (is.na(metadata$doi_merge) | metadata$doi_merge == "") & !is.na(additional_dois_char) & additional_dois_char != "" & !grepl(";", additional_dois_char, fixed = TRUE) - metadata$doi[needs_doi_fill] <- additional_dois_char[needs_doi_fill] + metadata$doi_merge[needs_doi_fill] <- additional_dois_char[needs_doi_fill] metadata$lang = check_metadata(res$dclang) metadata$language = check_metadata(res$dclanguage) metadata$content_provider = check_metadata(res$dcprovider) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 7d9970b1a..0b901686f 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -134,7 +134,7 @@ def enrich_metadata(self, params: Dict[str, str], metadata: pd.DataFrame) -> pd. def _log_dataframe(self, df: pd.DataFrame, params: Dict[str, str], name: str, ): orcid = params.get('orcid') - columns_to_print = ['id', 'title', 'doi', 'additional_dois', 'paper_abstract', 'link', 'subject', 'subject_orig', 'oa_state'] + columns_to_print = ['id', 'title', 'doi_merge', 'doi_merge', 'additional_dois', 'paper_abstract', 'link', 'subject', 'subject_orig', 'oa_state'] available_columns = df.columns.tolist() columns_to_print = [col for col in columns_to_print if col in available_columns] @@ -226,7 +226,7 @@ def _log_doi_casing_comparison(self, orcid_dois: List[str], base_metadata: pd.Da """Log how often ORCID and BASE have differently-cased versions of the same DOI.""" orcid_id = params.get('orcid', 'unknown') - raw_base_dois = base_metadata['doi'].dropna().unique().tolist() if not base_metadata.empty else [] + raw_base_dois = base_metadata['doi_merge'].dropna().unique().tolist() if not base_metadata.empty else [] # Strip URL prefixes so both sides are bare DOIs before comparing base_dois = [remove_doi_prefix(d) for d in raw_base_dois] base_dois = [d for d in base_dois if d and not pd.isna(d)] @@ -306,6 +306,11 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra self.logger.debug(f"[doi_orcid_dedup] orcid={params.get('orcid')} no duplicate DOIs in ORCID metadata") base_metadata = self.request_base_metadata(dois, params) + + if "doi_merge" not in base_metadata.columns: + self.logger.error(f"BASE metadata is missing 'doi_merge' column, cannot proceed with enrichment. Params: {params}") + raise ValueError("BASE metadata is missing 'doi_merge' column") + self._log_doi_casing_comparison(dois, base_metadata, params) if self.logger.isEnabledFor(logging.DEBUG): @@ -314,9 +319,9 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra # paper, doi= "10.17169/refubium-48053; 10.1371/journal.pone.0311918" # 1. step: split on "; " -> ["10.17169/refubium-48053", "10.1371/journal.pone.0311918"] # use pandas explode to create new rows for each DOI variant, - # then we can merge on the 'doi' column with the original metadata - # paper identical metadata except doi 1: "10.17169/refubium-48053" - # paper identical metadata except doi 2: "10.1371/journal.pone.0311918" + # then we can merge on the 'doi_merge' column with the original metadata + # paper identical metadata except doi_merge 1: "10.17169/refubium-48053" + # paper identical metadata except doi_merge 2: "10.1371/journal.pone.0311918" # after that we can apply the merge, but for the base_metadata is has to use the doi_merge field, not doi base_metadata = base_metadata.reindex(columns=required_fields) @@ -326,24 +331,24 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra base_metadata['additional_dois'] = base_metadata['additional_dois'].apply(lambda x: [x.strip() for x in x] if isinstance(x, list) else x) # Save the normalized original doi before explode so we can rank direct fetches # above rows whose doi was reassigned from additional_dois after explosion. - base_metadata['_fetch_doi'] = base_metadata['doi'].apply(remove_doi_prefix) + base_metadata['_fetch_doi'] = base_metadata['doi_merge'].apply(remove_doi_prefix) base_metadata = base_metadata.explode('additional_dois', ignore_index=True) - # replace doi with additional_dois if additional_dois is not empty, otherwise keep doi - base_metadata.loc[base_metadata['additional_dois'].notna() & (base_metadata['additional_dois'] != ''), 'doi'] = base_metadata.loc[base_metadata['additional_dois'].notna() & (base_metadata['additional_dois'] != ''), 'additional_dois'] - base_metadata.loc[:, 'doi'] = base_metadata['doi'].apply(remove_doi_prefix) - # True for rows whose final doi still matches the original fetched doi (direct); - # False for rows where doi was replaced by a additional_dois value (exploded). - base_metadata['_is_direct_fetch'] = base_metadata['doi'] == base_metadata['_fetch_doi'] + # replace doi_merge with additional_dois if additional_dois is not empty, otherwise keep doi_merge + base_metadata.loc[base_metadata['additional_dois'].notna() & (base_metadata['additional_dois'] != ''), 'doi_merge'] = base_metadata.loc[base_metadata['additional_dois'].notna() & (base_metadata['additional_dois'] != ''), 'additional_dois'] + base_metadata.loc[:, 'doi_merge'] = base_metadata['doi_merge'].apply(remove_doi_prefix) + # True for rows whose final doi_merge still matches the original fetched doi_merge (direct); + # False for rows where doi_merge was replaced by a additional_dois value (exploded). + base_metadata['_is_direct_fetch'] = base_metadata['doi_merge'] == base_metadata['_fetch_doi'] base_metadata.drop(columns='_fetch_doi', inplace=True) - # Remove rows where 'doi' is pd.NaN - base_metadata = base_metadata[pd.notna(base_metadata['doi'])] + # Remove rows where 'doi_merge' is pd.NaN + base_metadata = base_metadata[pd.notna(base_metadata['doi_merge'])] base_metadata = self._match_dois_by_version(base_metadata, dois) - base_metadata = base_metadata[base_metadata['doi'].isin(dois)] + base_metadata = base_metadata[base_metadata['doi_merge'].isin(dois)] if self.logger.isEnabledFor(logging.DEBUG): self._log_dataframe(base_metadata.sort_values(by='title'), params, 'base_metadata_before_doi_dedup') - doi_counts = base_metadata['doi'].value_counts() + doi_counts = base_metadata['doi_merge'].value_counts() duplicate_dois = doi_counts[doi_counts > 1].index.tolist() if duplicate_dois: self.logger.debug( @@ -351,7 +356,7 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra f"{len(duplicate_dois)} DOIs with multiple records: {duplicate_dois}" ) for doi in duplicate_dois: - group = base_metadata[base_metadata['doi'] == doi][['doi', 'title', 'paper_abstract', 'subject_orig', 'oa_state']] + group = base_metadata[base_metadata['doi_merge'] == doi][['doi_merge', 'title', 'paper_abstract', 'subject_orig', 'oa_state']] self.logger.debug(f"[doi_dedup] duplicate group for doi={doi!r}:\n{group.to_string()}") else: self.logger.debug(f"[doi_dedup] {len(base_metadata)} records, no duplicate DOIs — dedup step is a no-op here") @@ -361,7 +366,7 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra base_metadata = base_metadata.assign( _direct_sort=(~base_metadata['_is_direct_fetch']).astype(int), ).sort_values(by=['_direct_sort']).drop_duplicates( - subset='doi', keep='first' + subset='doi_merge', keep='first' ).drop(columns=['_direct_sort', '_is_direct_fetch']) if self.logger.isEnabledFor(logging.DEBUG): self._log_dataframe(base_metadata.sort_values(by='title'), params, 'base_metadata_after_doi_dedup') @@ -384,9 +389,9 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra # while the original ORCID DOI casing is preserved in the output. enriched_metadata = pd.merge( metadata.assign(_doi_key=metadata['doi'].str.lower()), - base_metadata[['doi'] + list(fields_to_merge.values())] - .assign(_doi_key=base_metadata['doi'].str.lower()) - .drop(columns='doi'), + base_metadata[['doi_merge'] + list(fields_to_merge.values())] + .assign(_doi_key=base_metadata['doi_merge'].str.lower()) + .drop(columns='doi_merge'), on='_doi_key', how='left' ).drop(columns='_doi_key') @@ -612,15 +617,15 @@ def _match_dois_by_version( Match BASE results that have versioned DOIs (e.g. .v1, .v2) to original DOIs without version. If BASE returned a DOI with a version suffix but the original ORCID DOI is without version, - this function updates the base_metadata 'doi' column so that those rows match the original + this function updates the base_metadata 'doi_merge' column so that those rows match the original DOI for merging. Parameters: - - base_metadata: DataFrame with 'doi' column (after explode and normalize) + - base_metadata: DataFrame with 'doi_merge' column (after explode and normalize) - original_dois: List of original DOIs from ORCID Returns: - - DataFrame with 'doi' updated where versioned variants were matched to original DOIs + - DataFrame with 'doi_merge' updated where versioned variants were matched to original DOIs """ pattern_doi_version = re.compile(r"\.v(\d)+$") @@ -629,7 +634,7 @@ def get_unversioned_doi(doi_str): return None return pattern_doi_version.sub("", str(doi_str)) - dois_received = base_metadata['doi'].unique().tolist() + dois_received = base_metadata['doi_merge'].unique().tolist() base_unversioned_to_versioned = {} for doi_from_base in dois_received: unversioned = get_unversioned_doi(doi_from_base) @@ -652,8 +657,8 @@ def get_unversioned_doi(doi_str): for lost_doi_info in dois_lost_with_versions: original_doi = lost_doi_info['original'] versioned_variants = lost_doi_info['versioned_variants_found'] - versioned_mask = base_metadata['doi'].isin(versioned_variants) + versioned_mask = base_metadata['doi_merge'].isin(versioned_variants) if versioned_mask.any(): - base_metadata.loc[versioned_mask, 'doi'] = original_doi + base_metadata.loc[versioned_mask, 'doi_merge'] = original_doi return base_metadata \ No newline at end of file From ac978b8e766918be3e005a18d216727d02108b08 Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 5 Jun 2026 14:02:05 +0200 Subject: [PATCH 108/205] implement new data contract (guarded by exception) for DOI enrichment; add fallback mechanism in case DOI only available from secondary sources --- server/workers/orcid/src/orcid_service.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 0b901686f..25002ee4d 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -134,7 +134,7 @@ def enrich_metadata(self, params: Dict[str, str], metadata: pd.DataFrame) -> pd. def _log_dataframe(self, df: pd.DataFrame, params: Dict[str, str], name: str, ): orcid = params.get('orcid') - columns_to_print = ['id', 'title', 'doi_merge', 'doi_merge', 'additional_dois', 'paper_abstract', 'link', 'subject', 'subject_orig', 'oa_state'] + columns_to_print = ['id', 'title', 'doi', 'doi_merge', 'additional_dois', 'paper_abstract', 'link', 'subject', 'subject_orig', 'oa_state'] available_columns = df.columns.tolist() columns_to_print = [col for col in columns_to_print if col in available_columns] @@ -274,7 +274,7 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra 'relations', 'annotations', 'repo', 'source', 'volume', 'issue', 'page', 'issn', 'citation_count', 'cited_by_wikipedia_count', 'cited_by_msm_count', 'cited_by_policies_count', 'cited_by_patents_count', 'cited_by_accounts_count', 'cited_by_fbwalls_count', - 'additional_dois', 'pdf_link_candidates_from_duplicates', + 'additional_dois', 'doi_merge','pdf_link_candidates_from_duplicates', 'cited_by_feeds_count', 'cited_by_gplus_count', 'cited_by_rdts_count', From 6fa59e779c472e0ac776254a53db899027ebf477 Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 5 Jun 2026 14:17:05 +0200 Subject: [PATCH 109/205] fix test drift --- e2e/keywordsEnrichment/ORCID.spec.ts | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/e2e/keywordsEnrichment/ORCID.spec.ts b/e2e/keywordsEnrichment/ORCID.spec.ts index e6221f711..955def53e 100644 --- a/e2e/keywordsEnrichment/ORCID.spec.ts +++ b/e2e/keywordsEnrichment/ORCID.spec.ts @@ -36,7 +36,7 @@ const testCases: EnrichmentTestCase[] = [ abstract: "Direct reciprocity is a major mechanism for the evolution of cooperation. Several classical studies have suggested that humans should quickly learn to adopt reciprocal strategies to establish mutual cooperation in repeated interactions. On the other hand, the recently discovered theory of ZD strategies has found that subjects who use extortionate strategies are able to exploit and subdue cooperators. Although such extortioners have been predicted to succeed in any population of adaptive opponents, theoretical follow-up studies questioned whether extortion can evolve in reality. However, most of these studies presumed that individuals have similar strategic possibilities and comparable outside options, whereas asymmetries are ubiquitous in real world applications. Here we show with a model and an economic experiment that extortionate strategies readily emerge once subjects differ in their strategic power. Our experiment combines a repeated social dilemma with asymmetric partner choice. In our main treatment there is one randomly chosen group member who is unilaterally allowed to exchange one of the other group members after every ten rounds of the social dilemma. We find that this asymmetric replacement opportunity generally promotes cooperation, but often the resulting payoff distribution reflects the underlying power structure. Almost half of the subjects in a better strategic position turn into extortioners, who quickly proceed to exploit their peers. By adapting their cooperation probabilities consistent with ZD theory, extortioners force their co-players to cooperate without being similarly cooperative themselves. Comparison to non-extortionate players under the same conditions indicates a substantial net gain to extortion. Our results thus highlight how power asymmetries can endanger mutually beneficial interactions, and transform them into exploitative relationships. In particular, our results indicate that the extortionate strategies predicted from ZD theory could play a more prominent role in our daily interactions than previously thought.", keywords: - "Applied Mathematics; Behavior; Biology and Life Sciences; Coercion [MeSH]; Economics; Experimental Design; Experimental Economics; Game Theory; Game Theory [MeSH]; Games; Generalized Linear Model; Humans [MeSH]; Interpersonal Relations [MeSH]; Mathematical and Statistical Techniques; Mathematics; Medicine; Models, Theoretical [MeSH]; Physical Sciences; Prisoner's Dilemma; Psychology; Q; R; Recreation; Science; Social Behavior [MeSH]; Social Psychology; Social Sciences; Statistical Methods; Statistics (Mathematics); ddc:006", + "Applied Mathematics; Behavior; Biology and Life Sciences; Coercion [MeSH]; Economics; Experimental Design; Experimental Economics; Game Theory; Game Theory [MeSH]; Games; Generalized Linear Model; Humans [MeSH]; Interpersonal Relations [MeSH]; Mathematical and Statistical Techniques; Mathematics; Medicine; Models, Theoretical [MeSH]; Physical Sciences; Prisoner's Dilemma; Psychology; Q; R; Recreation; Science; Social Behavior [MeSH]; Social Psychology; Social Sciences; Statistical Methods; Statistics (Mathematics); ddc:004", }, { suiteName: "regression test", @@ -167,7 +167,7 @@ const testCases: EnrichmentTestCase[] = [ abstract: "Cities are complex systems composed of numerous interacting components that evolve over multiple spatio-temporal scales. Consequently, no single data source is sufficient to satisfy the information needs required to map, monitor, model, and ultimately understand and manage our interaction within such urban systems. Remote sensing technology provides a key data source for mapping such environments, but is not sufficient for fully understanding them. In this article we provide a condensed urban perspective of critical geospatial technologies and techniques: (i) Remote Sensing; (ii) Geographic Information Systems; (iii) object-based image analysis; and (iv) sensor webs, and recommend a holistic integration of these technologies within the language of open geospatial consortium (OGC) standards in-order to more fully understand urban systems. We then discuss the potential of this integration and conclude that this extends the monitoring and mapping options beyond “hard infrastructure” by addressing “humans as sensors”, mobility and human-environment interactions, and future improvements to quality of life and of social infrastructures.", keywords: - "urban remote sensing; collective sensing; in situ sensing; sensor web; human-environment interactions; future trends; smart city", + "Q; Science; collective sensing; future trends; human-environment interactions; in situ sensing; sensor web; smart city; urban remote sensing", }, { suiteName: "regression test title substring matching", From ff210174ec31a6a55b1486c49820be87e2e6f32c Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 5 Jun 2026 17:50:08 +0200 Subject: [PATCH 110/205] bugfix for gap in metadata enrichment in cases where DOIs are only present in additional_doi field --- server/preprocessing/other-scripts/base.R | 7 +- server/workers/common/common/enrichment.py | 105 +++++++++++++++++++-- server/workers/orcid/src/orcid_service.py | 15 +++ 3 files changed, 117 insertions(+), 10 deletions(-) diff --git a/server/preprocessing/other-scripts/base.R b/server/preprocessing/other-scripts/base.R index 06e347140..c2f2fac21 100644 --- a/server/preprocessing/other-scripts/base.R +++ b/server/preprocessing/other-scripts/base.R @@ -287,10 +287,11 @@ etl <- function(res, repo, non_public) { metadata$additional_dois = check_metadata(res$dcdoi) metadata$additional_dois = check_metadata(lapply(metadata$additional_dois, normalize_dois)) # Fill primary doi for the ORCID enrichment - #from additional_dois when find_dois(link) returned nothing + # from additional_dois when find_dois(link) returned nothing # but dcdoi contains exactly one entry. Guarded to the single-entry case - # because multi-entry dcdoi can include related-but-different works - # so picking one from many would risk attaching foreign metadata to the record. + # because doi_merge field expects to contain a single DOI + # and we want to avoid filling it with multiple DOIs separated by ; + # Enrichment from multiple DOIs is happening in the enrichment step and not in the search step additional_dois_char <- vapply(metadata$additional_dois, function(x) { if (length(x) == 0) "" else as.character(x)[1] }, character(1)) diff --git a/server/workers/common/common/enrichment.py b/server/workers/common/common/enrichment.py index 13848b1c6..2b0d52e36 100644 --- a/server/workers/common/common/enrichment.py +++ b/server/workers/common/common/enrichment.py @@ -1,4 +1,5 @@ import os +import re import logging import pandas as pd from common.deduplication import deduplicate_keywords, deduplicate_links @@ -76,8 +77,11 @@ def enrich_anchor_using_duplicates(df, duplicate_groups): has_paper_abstract = 'paper_abstract' in df.columns has_oa_state = 'oa_state' in df.columns has_link = 'link' in df.columns + has_additional_dois = 'additional_dois' in df.columns + has_doi = 'doi' in df.columns - is_all_columns_are_missing = not has_subject_orig and not has_subject and not has_paper_abstract and not has_oa_state and not has_link + is_all_columns_are_missing = (not has_subject_orig and not has_subject and not has_paper_abstract + and not has_oa_state and not has_link and not has_additional_dois) if is_all_columns_are_missing: return df @@ -107,6 +111,7 @@ def enrich_anchor_using_duplicates(df, duplicate_groups): paper_abstract_acc = {'best_value': None, 'best_length': 0} oa_state_acc = {'best_value': None, 'best_priority': float('inf')} all_links = set() + additional_dois_acc = {} for element_idx in idx: if has_subject_orig: @@ -129,6 +134,11 @@ def enrich_anchor_using_duplicates(df, duplicate_groups): link_value = group_data.loc[element_idx, 'link'] process_link_element(link_value, all_links) + if has_additional_dois or has_doi: + doi_value = group_data.loc[element_idx, 'doi'] if has_doi else None + additional_dois_value = group_data.loc[element_idx, 'additional_dois'] if has_additional_dois else None + process_additional_dois_element(doi_value, additional_dois_value, additional_dois_acc) + if has_subject_orig: apply_subject_improvements(df, anchor_idx, subject_orig_acc, 'subject_orig') @@ -144,6 +154,9 @@ def enrich_anchor_using_duplicates(df, duplicate_groups): if has_link: apply_link_improvements(df, anchor_idx, all_links) + if has_additional_dois: + apply_additional_dois_improvements(df, anchor_idx, additional_dois_acc) + _log_anchor_state('AFTER', df, anchor_idx) return df @@ -195,6 +208,25 @@ def process_paper_abstract_element(value, accumulator): accumulator['best_length'] = abstract_length accumulator['best_value'] = value +def oa_state_priority(value): + """ + Maps an oa_state value to its merge priority (lower wins: 1 yes > 0 no > 2 unknown). + + Accepts the canonical "0"/"1"/"2" strings produced by BASE as well as the int/float + forms seen after other workers cast oa_state (orcid -> int) or pandas upcasts it to + float on a left-join that introduced NaN (1 -> 1.0). NaN and unknown values map to + +inf so they never displace a known state. + """ + if pd.isna(value): + return float('inf') + + if isinstance(value, float) and value.is_integer(): + key = str(int(value)) + else: + key = str(value) + + return OA_STATE_PRIORITY.get(key, float('inf')) + def process_oa_state_element(value, accumulator): """ Processes the oa_state value for one element of the group. @@ -203,12 +235,7 @@ def process_oa_state_element(value, accumulator): value: The oa_state value from the element accumulator: Dictionary with accumulative data """ - is_not_empty = not pd.isna(value) - if not is_not_empty: - return - - oa_state_str = str(value) - priority = OA_STATE_PRIORITY.get(oa_state_str, float('inf')) + priority = oa_state_priority(value) if priority < accumulator['best_priority']: accumulator['best_priority'] = priority accumulator['best_value'] = value @@ -228,6 +255,70 @@ def process_link_element(value, accumulator): links = [link.strip() for link in str(value).split(';') if link.strip()] accumulator.update(links) +_DOI_PREFIX_RE = re.compile(r'^https?://(dx\.)?doi\.org/', re.IGNORECASE) + +def process_additional_dois_element(doi_value, additional_dois_value, accumulator): + """ + Collects every DOI a group member represents — its primary ``doi`` and any + entries in ``additional_dois`` — into the accumulator, so the anchor can later + advertise all of them. Mirrors process_link_element, but for DOIs. + + Duplicate group members are dropped after enrichment, so unless their DOIs are + folded into the anchor's additional_dois here, those DOIs become unmatchable + downstream (e.g. the ORCID worker's explode-and-merge on doi_merge). + + ``additional_dois`` follows the base.R contract: a one-element list whose single + element is a "; "-joined string of DOIs (see normalize_dois). The accumulator is a + dict mapping a lower-cased bare DOI (dedup key) to its bare display form. + + Args: + doi_value: The member's primary ``doi`` value (may be NaN/empty). + additional_dois_value: The member's ``additional_dois`` value (list/str/NaN). + accumulator: Dict collecting {lowercased_bare_doi: bare_doi}. + """ + def add_raw(raw): + if isinstance(raw, list): + parts = [] + for element in raw: + parts.extend(str(element).split(';')) + elif raw is None or (pd.isna(raw) or raw == ''): + return + else: + parts = str(raw).split(';') + + for part in parts: + bare = _DOI_PREFIX_RE.sub('', part.strip()) + if bare: + accumulator.setdefault(bare.lower(), bare) + + add_raw(doi_value) + add_raw(additional_dois_value) + +def apply_additional_dois_improvements(df, anchor_idx, accumulator): + """ + Writes the union of all group-member DOIs into the anchor's ``additional_dois``, + preserving the base.R contract (a one-element list holding a "; "-joined string of + ``https://doi.org/``-prefixed DOIs) so downstream explode/merge can match every DOI + the duplicate group represented back to this anchor. + + Args: + df: DataFrame with data + anchor_idx: Index of the anchor element + accumulator: Dict of {lowercased_bare_doi: bare_doi} + """ + if not accumulator: + return + + merged = '; '.join('https://doi.org/' + bare for bare in sorted(accumulator.values())) + df.at[anchor_idx, 'additional_dois'] = [merged] + + anchor_doi = df.loc[anchor_idx, 'doi'] if 'doi' in df.columns else 'N/A' + anchor_title = df.loc[anchor_idx, 'title'] if 'title' in df.columns else 'N/A' + logger.debug( + "[ENRICHMENT_APPLIED] additional_dois doi=%s | title=%s | additional_dois=%s", + anchor_doi, anchor_title, merged + ) + def apply_subject_improvements(df, anchor_idx, accumulator, column_name): """ Applies improvements for subject or subject_orig to the anchor element. diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 25002ee4d..6554ef4b9 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -5,6 +5,7 @@ import os import uuid from common.decorators import error_logging_aspect +from common.enrichment import oa_state_priority import numpy as np from pyorcid import Orcid, errors as pyorcid_errors from pyorcid.orcid_authentication import OrcidAuthentication @@ -360,6 +361,20 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra self.logger.debug(f"[doi_dedup] duplicate group for doi={doi!r}:\n{group.to_string()}") else: self.logger.debug(f"[doi_dedup] {len(base_metadata)} records, no duplicate DOIs — dedup step is a no-op here") + # Reduce oa_state across doi_merge duplicates to the best-priority value + # (1 yes > 0 no > 2 unknown) BEFORE dedup, mirroring process_oa_state_element. + # Without this, drop_duplicates(keep='first') below can discard an open-access + # record (oa_state=1) in favour of an unknown one (oa_state=2) for the same DOI, + # silently losing the open-access status during enrichment. + if 'oa_state' in base_metadata.columns and not base_metadata.empty: + oa_priority = base_metadata['oa_state'].map(oa_state_priority) + best_oa_state = ( + base_metadata.assign(_oa_priority=oa_priority) + .sort_values(by='_oa_priority') + .drop_duplicates(subset='doi_merge', keep='first') + .set_index('doi_merge')['oa_state'] + ) + base_metadata['oa_state'] = base_metadata['doi_merge'].map(best_oa_state) # Sort by: direct fetch before exploded-from-additional_dois rows, # This ensures the record actually fetched for a DOI wins over a record # that acquired that DOI via additional_dois expansion. From d5388041d0c716509f88f5fe9478f7df8b2d396a Mon Sep 17 00:00:00 2001 From: chreman Date: Mon, 8 Jun 2026 08:00:53 +0200 Subject: [PATCH 111/205] update of contentprovider.json cache --- .../common/common/contentproviders.json | 390 +++++++++++++++++- 1 file changed, 383 insertions(+), 7 deletions(-) diff --git a/server/workers/common/common/contentproviders.json b/server/workers/common/common/contentproviders.json index 2978d9d3b..3a60e5cbd 100644 --- a/server/workers/common/common/contentproviders.json +++ b/server/workers/common/common/contentproviders.json @@ -1,4 +1,384 @@ [ + { + "name": "Institutional Repository DAU", + "internal_name": "ftid14799" + }, + { + "name": "DIRENO - Digitales Repositorium Niederösterreich", + "internal_name": "ftid14801" + }, + { + "name": "Current Legal Problems", + "internal_name": "ftid14800" + }, + { + "name": "South American Publishing", + "internal_name": "ftid14798" + }, + { + "name": "IQVerse", + "internal_name": "ftid14810" + }, + { + "name": "DergiPark Akademik", + "internal_name": "ftid14822" + }, + { + "name": "Journal of Global Perspectives in Communication and Media (JGPCM)", + "internal_name": "ftid14823" + }, + { + "name": "Repositório Institucional da Fasipe", + "internal_name": "ftid14833" + }, + { + "name": "OpenXiv", + "internal_name": "ftid14852" + }, + { + "name": "CamEd Open Access Repository", + "internal_name": "ftid14855" + }, + { + "name": "Amoud University Institutional Repository", + "internal_name": "ftid14856" + }, + { + "name": "Editorial Didaxis", + "internal_name": "ftid14797" + }, + { + "name": "Nauka i tehnologija (International University Travnik)", + "internal_name": "ftid14819" + }, + { + "name": "GSMS Publications (Green and Sustainable Materials Society)", + "internal_name": "ftid14808" + }, + { + "name": "Jurnal di STMIK EL RAHMA Yogyakarta", + "internal_name": "ftid14811" + }, + { + "name": "Publishing House \"Scientometric Center\" (Журнал биологических наук и сельского хозяйства)", + "internal_name": "crid14812" + }, + { + "name": "Nexus House Editorial", + "internal_name": "ftid14818" + }, + { + "name": "Jurnal Ilmiah", + "internal_name": "ftid14820" + }, + { + "name": "Universitas Insan Budi Utomo", + "internal_name": "ftid14821" + }, + { + "name": "Pondok Jurnal", + "internal_name": "ftid14825" + }, + { + "name": "Michelangelo Scholar Publishing Limited (MSPL)", + "internal_name": "ftid14826" + }, + { + "name": "Athenaeum Scientific Publishers", + "internal_name": "crid14827" + }, + { + "name": "Omega Journals", + "internal_name": "ftid14828" + }, + { + "name": "Hala Foundation Journals", + "internal_name": "ftid14830" + }, + { + "name": "Sociological Studios", + "internal_name": "ftid14832" + }, + { + "name": "World Organization of Applied Sciences", + "internal_name": "ftid14835" + }, + { + "name": "Compusoft", + "internal_name": "ftid14837" + }, + { + "name": "International Academic Research Center (IARC) - Florida Global University (FGU)", + "internal_name": "ftid14838" + }, + { + "name": "Bhutan Journal of Business and Management", + "internal_name": "ftid14839" + }, + { + "name": "Interdisciplinary Applied Qualifications", + "internal_name": "ftid14840" + }, + { + "name": "TSUE Scientific Journal", + "internal_name": "ftid14843" + }, + { + "name": "DORA WSL", + "internal_name": "ftid14844" + }, + { + "name": "DORA PSI", + "internal_name": "ftid14845" + }, + { + "name": "Mahkota Science Publishers", + "internal_name": "ftid14846" + }, + { + "name": "TRESEARCH Technology Journals Platform", + "internal_name": "ftid14847" + }, + { + "name": "Pubris", + "internal_name": "ftid14849" + }, + { + "name": "E-Journal Al-Bayan Institute", + "internal_name": "ftid14850" + }, + { + "name": "NIBM Journal of Knowledge Innovation", + "internal_name": "ftid14851" + }, + { + "name": "International Multidisciplinary Journal of Emerging Technologies and Applications (IMJETA)", + "internal_name": "ftid14853" + }, + { + "name": "Jurnal Contradixie", + "internal_name": "ftid14854" + }, + { + "name": "Journal of national culture", + "internal_name": "ftid14780" + }, + { + "name": "Portal de Periódicos do IFRS", + "internal_name": "ftid14802" + }, + { + "name": "data.THM - Datenrepositorium der Technischen Hochschule Mittelhessen", + "internal_name": "ftid14729" + }, + { + "name": "İstanbul Esenyurt Üniversitesi Kurumsal Akademik Arşivi", + "internal_name": "ftid14731" + }, + { + "name": "SOLAV Journal (A Scholarly Platform for Interdisciplinary Applied Futures Research)", + "internal_name": "ftid14736" + }, + { + "name": "Sulaimani Dental Journal", + "internal_name": "ftid14738" + }, + { + "name": "Revista Entreparágrafos", + "internal_name": "ftid14739" + }, + { + "name": "CACTUS – Journal of Tourism Business, Management and Economics", + "internal_name": "ftid14746" + }, + { + "name": "Dandakaranya Review Journal", + "internal_name": "ftid14749" + }, + { + "name": "Journal of Lie Theory", + "internal_name": "ftid14763" + }, + { + "name": "STAI Di Al-Hikmah Jakarta", + "internal_name": "ftid14773" + }, + { + "name": "Didaxis. Revista Educativa, Social y Humanista", + "internal_name": "ftid14775" + }, + { + "name": "Physical and Mathematical Education", + "internal_name": "ftid14777" + }, + { + "name": "Education. Innovation. Practice", + "internal_name": "ftid14778" + }, + { + "name": "Publications Lawusn", + "internal_name": "ftid14781" + }, + { + "name": "Hessisches Kommunalrechtslexikon", + "internal_name": "ftid14782" + }, + { + "name": "Casting processes", + "internal_name": "ftid14783" + }, + { + "name": "Journal of Big Data and Artificial Intelligence", + "internal_name": "ftid14784" + }, + { + "name": "Luminis Editorial", + "internal_name": "ftid14785" + }, + { + "name": "Repositorio Institucional de la Superintendencia del Mercado de Valores (SMV)", + "internal_name": "ftid14786" + }, + { + "name": "Sorsogon Multidisciplinary Research Journal", + "internal_name": "ftid14787" + }, + { + "name": "Medical Journal of Soeradji", + "internal_name": "ftid14788" + }, + { + "name": "Revista Portuguesa de Doenças Infecciosas", + "internal_name": "ftid14790" + }, + { + "name": "Focuscholar", + "internal_name": "ftid14791" + }, + { + "name": "Australian Journal of Artificial Intelligence Review", + "internal_name": "ftid14792" + }, + { + "name": "Trends in Intellectual Property Research", + "internal_name": "ftid14793" + }, + { + "name": "Journal of Material Characterization and Applications", + "internal_name": "ftid14795" + }, + { + "name": "Jurnal Online Kesehatan Indonesia", + "internal_name": "ftid14796" + }, + { + "name": "UW Journal of Management Sciences (UWJMS)", + "internal_name": "ftid14803" + }, + { + "name": "UW Journal of Social Sciences (UWJSS)", + "internal_name": "ftid14805" + }, + { + "name": "University of Wah Journal of Computer Science (UWJCS)", + "internal_name": "ftid14806" + }, + { + "name": "New Journal of Biology and Medicine", + "internal_name": "ftid14813" + }, + { + "name": "Gemini Littera", + "internal_name": "ftid14814" + }, + { + "name": "İnsan İzi Dergisi", + "internal_name": "ftid14815" + }, + { + "name": "Gio Edutech", + "internal_name": "ftid14816" + }, + { + "name": "Pensamiento Numinal: Revista de Ciencias Sociales y Humanidades", + "internal_name": "ftid14817" + }, + { + "name": "Journal Regalia Institute", + "internal_name": "ftid14768" + }, + { + "name": "Literati Global Network", + "internal_name": "ftid14713" + }, + { + "name": "Afghan International Journal of Science (AIJS)", + "internal_name": "ftid14754" + }, + { + "name": "Dasinya Journal for Engineering and Informatics", + "internal_name": "ftid14755" + }, + { + "name": "Ceniiac Journal", + "internal_name": "ftid14756" + }, + { + "name": "REGRASP (Revista para Graduandos/Instituto Federal de Educação, Ciência e Tecnologia de São Paulo)", + "internal_name": "ftid14757" + }, + { + "name": "Journal Nacreva", + "internal_name": "ftid14758" + }, + { + "name": "Gabungan Riset Edukasi dan Eksplorasi Teori (GREET)", + "internal_name": "ftid14759" + }, + { + "name": "Era Scientific Publisher (ESP)", + "internal_name": "ftid14760" + }, + { + "name": "Revista IESB", + "internal_name": "ftid14761" + }, + { + "name": "PT. Berkah Smart Academica", + "internal_name": "ftid14762" + }, + { + "name": "Tér – Gazdaság – Ember / Journal of Region, Economy and Society", + "internal_name": "ftid14764" + }, + { + "name": "EL-IQTISHAD: Journal Economics, Business and Sharia Studies", + "internal_name": "ftid14765" + }, + { + "name": "Bangladesh Journal of Dental Research & Education (BJDRE)", + "internal_name": "ftid14766" + }, + { + "name": "Sofist: An International Journal of Philosophy", + "internal_name": "ftid14767" + }, + { + "name": "Education@ETMA", + "internal_name": "ftid14769" + }, + { + "name": "PEARL Multidisciplinary Journal", + "internal_name": "ftid14770" + }, + { + "name": "Ecosistema, revista de educación y medioambiente", + "internal_name": "ftid14771" + }, + { + "name": "Portal de Revistas Universidad del Atlántico", + "internal_name": "ftid14772" + }, { "name": "Revista Econômica do Nordeste", "internal_name": "ftid14670" @@ -18299,10 +18679,6 @@ "name": "Ufuk Universitesi Akademik Acikerisim Sistemi", "internal_name": "ftufukuniv" }, - { - "name": "Abdelhamid Mehri University Constantine2 ScholarlyWorks Repository", - "internal_name": "ftunivconst2" - }, { "name": "Jurnal Online IAIN Madura", "internal_name": "ftiainmaduojs" @@ -46024,7 +46400,7 @@ "internal_name": "ftlapress" }, { - "name": "University of South Africa: UNISA Institutional Repository", + "name": "Unisa Institutional Repository (UnisaIR - University of South Africa)", "internal_name": "ftunivsafrica" }, { @@ -46340,7 +46716,7 @@ "internal_name": "ftcochinuniv" }, { - "name": "The University of Western Ontario: Scholarship@Western", + "name": "Western University Open Repository", "internal_name": "ftunivwestonta" }, { @@ -46724,7 +47100,7 @@ "internal_name": "ftihe" }, { - "name": "TU Darmstadt: tuprints", + "name": "TUprints - Publikationsplattform der TU Darmstadt", "internal_name": "ftulbdarmstadt" }, { From 366d01497842f77b77f1a993af25fce9acea549c Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 9 Jun 2026 10:10:25 +0200 Subject: [PATCH 112/205] add tests for bugfix for escaping SOLR instructions in DOIs --- e2e/keywordsEnrichment/ORCID.spec.ts | 16 ++++++++++++++++ e2e/oaStatusEnrichment/ORCID.spec.ts | 6 ++++++ 2 files changed, 22 insertions(+) diff --git a/e2e/keywordsEnrichment/ORCID.spec.ts b/e2e/keywordsEnrichment/ORCID.spec.ts index 955def53e..891639539 100644 --- a/e2e/keywordsEnrichment/ORCID.spec.ts +++ b/e2e/keywordsEnrichment/ORCID.spec.ts @@ -223,6 +223,22 @@ const testCases: EnrichmentTestCase[] = [ keywords: 'Bathymetry; Multibeam Echosounder; Event label; Binary Object; Binary Object Media Type; Binary Object File Size; File content; Data file recording duration; Data file recording distance; Ship speed; Number of pings; Start of data file recording, date/time; Start of data file recording, latitude; Start of data file recording, longitude; Start of data file, heading; Start of data file, depth; Stop of data file recording, date/time; Stop of data file recording, latitude; Stop of data file recording, longitude; Stop of data file, heading; Stop of data file, depth; DATE/TIME; LATITUDE; LONGITUDE; ELEVATION; Comment; Swath-mapping system Atlas Hydrosweep DS-3; CTD/Rosette; Extracted with MB-System; PS147/2; Polarstern; BATHYmetric Long-Term Observations during Expeditions with RV Polarstern BATHY-LTO; DataHub Earth and Environment of the Helmholtz Association DataHub; Pilot study: "targeted underway bathymetry" for mapping uncharted seamounts SEAMAP', }, + { + suiteName: "bugfix SOLR parsing of DOI", + orcid: "0000-0002-1193-6256", + paperTitle: "A controlled CO2 release experiment in a fault zone at the In-Situ Laboratory in Western Australia", + abstract: + 'A controlled-release test at the In-Situ Laboratory Project in Western Australia injected 38 tonnes of gaseous CO2 between 336-342 m depth in a fault zone, and the gas was monitored by a wide range of downhole and surface monitoring technologies. Injection of CO2 at this depth fills the gap between shallow release (<25 m) and storage (>600 m) field trials. The main objectives of the controlled-release test were to assess the monitorability of shallow CO2 accumulations, and to investigate the impacts of a fault zone on CO2 migration. CO2 arrival was detected by distributed temperature sensing at the monitoring well (7 m away) after approximately 1.5 days and an injection volume of 5 tonnes. The CO2 plume was detected also by borehole seismic and electric resistivity imaging. The early detection of significantly less than 38 tonnes of CO2 in the shallow subsurface demonstrates rapid and sensitive monitorability of potential leaks in the overburden of a commercial-scale storage project, prior to reaching shallow groundwater, soil zones or the atmosphere. Observations suggest that the fault zone did not alter the CO2 migration along bedding at the scale and depth of the test. Contrary to model predictions, no vertical CO2 migration was detected beyond the perforated injection interval. CO2 and formation water escaped to the surface through the monitoring well at the end of the experiment due to unexpected damage to the well’s fibreglass casing. The well was successfully remediated without impact to the environment and the site is ready for future experiments.', + keywords: + 'Environmental engineering', + }, + { + suiteName: "bugfix SOLR parsing of DOI", + orcid: "0000-0002-1193-6256", + paperTitle: "Constraining probabilistic chloride mass-balance recharge estimates using baseflow and remotely sensed evapotranspiration: the Cambrian Limestone Aquifer in northern Australia", + abstract: + 'Abstract Regional-scale estimates of groundwater recharge are inherently uncertain, but this uncertainty is rarely quantified. Quantifying this uncertainty provides an understanding of the limitations of the estimates, and being able to reduce the uncertainty makes the recharge estimates more useful for water resources management. This paper describes the development of a method to constrain the uncertainty in upscaled recharge estimates using a rejection sampling procedure for baseflow and remotely sensed evapotranspiration data to constrain the lower and upper end of the recharge distribution, respectively. The recharge estimates come from probabilistic chloride mass-balance estimates from 3,575 points upscaled using regression kriging with rainfall, soils and vegetation as covariates. The method is successfully demonstrated for the 570,000-km 2 Cambrian Limestone Aquifer in northern Australia. The method developed here is able to reduce the uncertainty in the upscaled chloride mass-balance estimates of recharge by nearly a third using data that are readily available. The difference between the 5 th and 95 th percentiles of unconstrained recharge across the aquifer was 31 mm/yr (range 5–36 mm/yr) which was reduced to 22 mm/yr for the constrained case (9–31 mm/yr). The spatial distribution of recharge was dominated by the spatial distribution of rainfall but was comparatively reduced in areas with denser vegetation or finer textured soils. Recharge was highest in the north-west in the Daly River catchment with a catchment average of 101 (61–192) mm/yr and lowest in the south-east Georgina River catchment with 6 (4–12) mm/yr.', + }, ]; const uniqueOrcids = [...new Set(testCases.map((tc) => tc.orcid).filter(Boolean))]; diff --git a/e2e/oaStatusEnrichment/ORCID.spec.ts b/e2e/oaStatusEnrichment/ORCID.spec.ts index ca099158c..8726ab70f 100644 --- a/e2e/oaStatusEnrichment/ORCID.spec.ts +++ b/e2e/oaStatusEnrichment/ORCID.spec.ts @@ -163,6 +163,12 @@ const testCases: OaStatusTestCase[] = [ expectedOaState: "1", regression: "case-sensitive DOI matching regression", }, + { + suiteName: "bugfix SOLR parsing of DOI", + orcid: "0000-0002-1193-6256", + paperTitle: "A controlled CO2 release experiment in a fault zone at the In-Situ Laboratory in Western Australia", + expectedOaState: "1", + }, ]; const uniqueOrcids = [...new Set(testCases.map((tc) => tc.orcid).filter(Boolean))]; From e01500d26ddac1976351f556fa750da12cabf77f Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 9 Jun 2026 10:20:01 +0200 Subject: [PATCH 113/205] update of contentprovider.json cache --- server/workers/common/common/contentproviders.json | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/server/workers/common/common/contentproviders.json b/server/workers/common/common/contentproviders.json index 3a60e5cbd..ee7f175a8 100644 --- a/server/workers/common/common/contentproviders.json +++ b/server/workers/common/common/contentproviders.json @@ -3547,6 +3547,10 @@ "name": "Journal of the Institute for Biblical Aramaic Studies", "internal_name": "ftid13960" }, + { + "name": "Edu Science Indonesia", + "internal_name": "ftid13946" + }, { "name": "Orthotomeo - Jurnal Penelitian Ilmiah", "internal_name": "ftid13933" @@ -49367,10 +49371,6 @@ "name": "Media SuUB Bremen (Staats- und Universitätsbibliothek Bremen)", "internal_name": "ftsubbremen" }, - { - "name": "Edu Science Indonesia", - "internal_name": "ftid13946" - }, { "name": "GoTriple", "internal_name": "fttriple" From 96eeb025aa171c318b95b04c70f02d6ebf5e093e Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 11 Jun 2026 13:34:29 +0200 Subject: [PATCH 114/205] bugfix for SOLR parsing of DOIs with special characters --- server/workers/orcid/src/orcid_service.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 6554ef4b9..8a2740f5c 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -161,7 +161,12 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D for batch in batches: start_time = time.time() - q_advanced = " OR ".join([f"dcdoi:{doi}" for doi in batch if doi]) + # Quote DOIs so Lucene/Solr special characters (e.g. the parentheses + # in DOIs like 10.1061/40972(311)96) are treated as literal phrase + # content rather than query metacharacters. Without quoting, such a + # DOI degrades the exact dcdoi lookup into a free-text token search, + # poisoning the entire batch and silently dropping enrichment. + q_advanced = " OR ".join([f'dcdoi:"{doi}"' for doi in batch if doi]) request_id = str(uuid.uuid4()) From b753e717cbcd26d4478165307d567027868cf78e Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 16 Jun 2026 10:20:50 +0200 Subject: [PATCH 115/205] comment out debug logging --- server/workers/base/src/base.py | 49 +++++++++++----------- server/workers/orcid/src/orcid_service.py | 50 ++++++++++++----------- 2 files changed, 51 insertions(+), 48 deletions(-) diff --git a/server/workers/base/src/base.py b/server/workers/base/src/base.py index 54e43de20..44f6a0e8c 100644 --- a/server/workers/base/src/base.py +++ b/server/workers/base/src/base.py @@ -287,10 +287,11 @@ def _log_group_similarity(df, indexes, group_type, group_key): def filter_duplicates(df, service, params): - if logger.isEnabledFor(logging.DEBUG): - logger.debug(f"Filtering duplicates for service: {service}") - logger.debug(f"Initial number of records: {len(df)}") - _log_dataframe(df, params, "initial_records") + # if logger.isEnabledFor(logging.DEBUG): + # logger.debug(f"Filtering duplicates for service: {service}") + # logger.debug(f"Initial number of records: {len(df)}") + # _log_dataframe(df, params, "initial_records") + df.drop_duplicates("id", inplace=True, keep="first") df["is_anchor"] = False df["doi_duplicate"] = False @@ -320,19 +321,19 @@ def filter_duplicates(df, service, params): ) df = mark_duplicate_dois(df) df = mark_duplicate_links(df) - _log_dedup_state(df, "after_mark_doi_link_duplicates", params) + # _log_dedup_state(df, "after_mark_doi_link_duplicates", params) df = identify_relations(df) df = remove_false_positives_doi(df) df = remove_false_positives_link(df) - _log_dedup_state(df, "after_remove_false_positives", params) + # _log_dedup_state(df, "after_remove_false_positives", params) df = remove_textual_duplicates_from_different_sources(df, duplicate_groups) - _log_dedup_state(df, "after_remove_textual_duplicates", params) + # _log_dedup_state(df, "after_remove_textual_duplicates", params) df = add_false_negatives(df) - _log_dedup_state(df, "after_add_false_negatives", params) + # _log_dedup_state(df, "after_add_false_negatives", params) df = mark_latest_doi(df, duplicate_groups) - _log_dedup_state(df, "after_mark_latest_doi", params) + # _log_dedup_state(df, "after_mark_latest_doi", params) df.loc[df[~df.is_duplicate].index, "is_anchor"] = True - _log_dedup_state(df, "after_non_duplicate_anchors", params) + # _log_dedup_state(df, "after_non_duplicate_anchors", params) false_positive_indexes = [] for doi_val, grp in df[df["doi_duplicate"]].groupby("doi"): @@ -355,15 +356,15 @@ def filter_duplicates(df, service, params): df.drop(index=false_positive_indexes, inplace=True) logger.info(f"[dedup:doi_title_filter] dropped {len(false_positive_indexes)} false-positive records") - if logger.isEnabledFor(logging.DEBUG): - for idx_group in duplicate_groups: - if len(idx_group) > 1: - _log_group_similarity(df, idx_group, "textual_dup_group", group_key="duplicate_groups") - if logger.isEnabledFor(logging.DEBUG): - doi_groups = df[df["doi_duplicate"]].groupby("doi") - for doi_val, grp in doi_groups: - if len(grp) > 1: - _log_group_similarity(df, grp.index, "doi_dup_group", group_key=doi_val) + # if logger.isEnabledFor(logging.DEBUG): + # for idx_group in duplicate_groups: + # if len(idx_group) > 1: + # _log_group_similarity(df, idx_group, "textual_dup_group", group_key="duplicate_groups") + # if logger.isEnabledFor(logging.DEBUG): + # doi_groups = df[df["doi_duplicate"]].groupby("doi") + # for doi_val, grp in doi_groups: + # if len(grp) > 1: + # _log_group_similarity(df, grp.index, "doi_dup_group", group_key=doi_val) pure_datasets = df[df.typenorm == "7"] non_datasets = df.loc[df.index.difference(pure_datasets.index)] @@ -371,12 +372,12 @@ def filter_duplicates(df, service, params): non_datasets = prioritize_OA_and_latest(non_datasets, duplicate_groups) non_datasets = prioritize_doi_and_provider(non_datasets, duplicate_groups) - _log_dedup_state(non_datasets, "non_datasets_after_prioritize", params) + # _log_dedup_state(non_datasets, "non_datasets_after_prioritize", params) pure_datasets = mark_latest_doi(pure_datasets, duplicate_groups) pure_datasets_condition_mask = (pure_datasets.is_anchor == True) | (pure_datasets.is_duplicate == False) pure_datasets.loc[pure_datasets_condition_mask, "is_anchor"] = True - _log_dedup_state(pure_datasets, "pure_datasets_after_mark_latest", params) + # _log_dedup_state(pure_datasets, "pure_datasets_after_mark_latest", params) non_datasets = enrich_anchor_using_duplicates(non_datasets, duplicate_groups) pure_datasets = enrich_anchor_using_duplicates(pure_datasets, duplicate_groups) @@ -438,9 +439,9 @@ def filter_duplicates(df, service, params): if c in filtered.columns: filtered.drop(c, axis=1, inplace=True) - if logger.isEnabledFor(logging.DEBUG): - logger.debug(f"Number of records after filtering: {len(filtered)}") - _log_dataframe(filtered, params, "filtered_records") + # if logger.isEnabledFor(logging.DEBUG): + # logger.debug(f"Number of records after filtering: {len(filtered)}") + # _log_dataframe(filtered, params, "filtered_records") return filtered diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 8a2740f5c..d95b216e1 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -211,7 +211,7 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D base_response: str = get_nested_value(result, ["input_data", "metadata"], '[]') # type: ignore batch_df = pd.DataFrame(json.loads(base_response)) - self._log_is_base_response_missing_dois(batch, batch_df) + #self._log_is_base_response_missing_dois(batch, batch_df) base_metadata = pd.concat([ base_metadata, @@ -295,8 +295,8 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra self.logger.debug('metadata reindexed') # run only if loglevel is debug, otherwise it is too expensive and we don't want it on production - if self.logger.isEnabledFor(logging.DEBUG): - self._log_dataframe(metadata.sort_values(by='title'), params, '_original') + # if self.logger.isEnabledFor(logging.DEBUG): + # self._log_dataframe(metadata.sort_values(by='title'), params, '_original') raw_dois = metadata["doi"].tolist() dois = [doi for doi in raw_dois if doi and pd.notna(doi)] @@ -317,10 +317,11 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra self.logger.error(f"BASE metadata is missing 'doi_merge' column, cannot proceed with enrichment. Params: {params}") raise ValueError("BASE metadata is missing 'doi_merge' column") - self._log_doi_casing_comparison(dois, base_metadata, params) + # self._log_doi_casing_comparison(dois, base_metadata, params) + + # if self.logger.isEnabledFor(logging.DEBUG): + # self._log_dataframe(base_metadata.sort_values(by='title'), params, 'base_metadata_raw') - if self.logger.isEnabledFor(logging.DEBUG): - self._log_dataframe(base_metadata.sort_values(by='title'), params, 'base_metadata_raw') # dataframe # paper, doi= "10.17169/refubium-48053; 10.1371/journal.pone.0311918" # 1. step: split on "; " -> ["10.17169/refubium-48053", "10.1371/journal.pone.0311918"] @@ -352,20 +353,21 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra base_metadata = self._match_dois_by_version(base_metadata, dois) base_metadata = base_metadata[base_metadata['doi_merge'].isin(dois)] - if self.logger.isEnabledFor(logging.DEBUG): - self._log_dataframe(base_metadata.sort_values(by='title'), params, 'base_metadata_before_doi_dedup') - doi_counts = base_metadata['doi_merge'].value_counts() - duplicate_dois = doi_counts[doi_counts > 1].index.tolist() - if duplicate_dois: - self.logger.debug( - f"[doi_dedup] {len(base_metadata)} records before dedup, " - f"{len(duplicate_dois)} DOIs with multiple records: {duplicate_dois}" - ) - for doi in duplicate_dois: - group = base_metadata[base_metadata['doi_merge'] == doi][['doi_merge', 'title', 'paper_abstract', 'subject_orig', 'oa_state']] - self.logger.debug(f"[doi_dedup] duplicate group for doi={doi!r}:\n{group.to_string()}") - else: - self.logger.debug(f"[doi_dedup] {len(base_metadata)} records, no duplicate DOIs — dedup step is a no-op here") + # if self.logger.isEnabledFor(logging.DEBUG): + # self._log_dataframe(base_metadata.sort_values(by='title'), params, 'base_metadata_before_doi_dedup') + # doi_counts = base_metadata['doi_merge'].value_counts() + # duplicate_dois = doi_counts[doi_counts > 1].index.tolist() + # if duplicate_dois: + # self.logger.debug( + # f"[doi_dedup] {len(base_metadata)} records before dedup, " + # f"{len(duplicate_dois)} DOIs with multiple records: {duplicate_dois}" + # ) + # for doi in duplicate_dois: + # group = base_metadata[base_metadata['doi_merge'] == doi][['doi_merge', 'title', 'paper_abstract', 'subject_orig', 'oa_state']] + # self.logger.debug(f"[doi_dedup] duplicate group for doi={doi!r}:\n{group.to_string()}") + # else: + # self.logger.debug(f"[doi_dedup] {len(base_metadata)} records, no duplicate DOIs — dedup step is a no-op here") + # Reduce oa_state across doi_merge duplicates to the best-priority value # (1 yes > 0 no > 2 unknown) BEFORE dedup, mirroring process_oa_state_element. # Without this, drop_duplicates(keep='first') below can discard an open-access @@ -388,8 +390,8 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra ).sort_values(by=['_direct_sort']).drop_duplicates( subset='doi_merge', keep='first' ).drop(columns=['_direct_sort', '_is_direct_fetch']) - if self.logger.isEnabledFor(logging.DEBUG): - self._log_dataframe(base_metadata.sort_values(by='title'), params, 'base_metadata_after_doi_dedup') + # if self.logger.isEnabledFor(logging.DEBUG): + # self._log_dataframe(base_metadata.sort_values(by='title'), params, 'base_metadata_after_doi_dedup') # Select and rename relevant fields from base_metadata, including subject_orig fields_to_merge = { @@ -445,8 +447,8 @@ def custom_merge(existing_value, new_value): enriched_metadata.drop(columns=['paper_abstract_base', 'subject_orig_base', 'subject_base', 'oa_state_base', 'link_base', 'relation_base'], inplace=True) - if self.logger.isEnabledFor(logging.DEBUG): - self._log_dataframe(enriched_metadata.sort_values(by='title'), params, '_enriched') + # if self.logger.isEnabledFor(logging.DEBUG): + # self._log_dataframe(enriched_metadata.sort_values(by='title'), params, '_enriched') # temporal solution, for some reason if we have some undefined data, dataprocessing is failing enriched_metadata = enriched_metadata.reindex(columns=list(set(original_columns + ['oa_state', 'subject', 'subject_orig', 'paper_abstract', 'link', 'relation']))) From 9dd38d245dadb029322307d4957032c27ad6cba6 Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 16 Jun 2026 10:53:37 +0200 Subject: [PATCH 116/205] clean up debug logging --- server/workers/orcid/src/orcid_service.py | 37 ++++++++++++----------- 1 file changed, 19 insertions(+), 18 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index d95b216e1..f808c802c 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -70,7 +70,7 @@ def execute_search(self, params: Dict[str, str]) -> Union[SuccessResult, ErrorRe metadata = self._process_metadata(metadata, author_info, params) - self.logger.debug('metadata processed inside of _process_metadata') + # self.logger.debug('metadata processed inside of _process_metadata') return self._format_response(data=metadata, author_info=author_info, params=params) except ( @@ -202,12 +202,13 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D end_time = time.time() duration = end_time - start_time - timing_data.append({ - "request_id": request_id, - "batch_size": len(batch), - "duration": duration, - "timestamp": time.strftime("%Y-%m-%d %H:%M:%S", time.gmtime(start_time)) - }) + # timing_data.append({ + # "request_id": request_id, + # "batch_size": len(batch), + # "duration": duration, + # "timestamp": time.strftime("%Y-%m-%d %H:%M:%S", time.gmtime(start_time)) + # }) + self.logger.debug(f"BASE request for ORCID {orcid} batch of {len(batch)} DOIs took {duration:.2f} seconds") base_response: str = get_nested_value(result, ["input_data", "metadata"], '[]') # type: ignore batch_df = pd.DataFrame(json.loads(base_response)) @@ -218,12 +219,12 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D batch_df ], ignore_index=True) - if self.logger.isEnabledFor(logging.DEBUG): - timing_df = pd.DataFrame(timing_data) - folder = f'./output/{orcid}' - if not os.path.exists(folder): - os.makedirs(folder) - timing_df.to_csv(f'{folder}/stat_base_requests.csv', index=False) + # if self.logger.isEnabledFor(logging.DEBUG): + # timing_df = pd.DataFrame(timing_data) + # folder = f'./output/{orcid}' + # if not os.path.exists(folder): + # os.makedirs(folder) + # timing_df.to_csv(f'{folder}/stat_base_requests.csv', index=False) base_metadata["oa_state"] = base_metadata["oa_state"].fillna("2").astype(int) return base_metadata @@ -289,10 +290,10 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra 'cited_by_videos_count'] required_fields = list(set(required_fields + metadata.columns.to_list())) - self.logger.debug(f'fields to reindex: {required_fields}') + # self.logger.debug(f'fields to reindex: {required_fields}') metadata = metadata.reindex(columns=required_fields) - self.logger.debug('metadata reindexed') + # self.logger.debug('metadata reindexed') # run only if loglevel is debug, otherwise it is too expensive and we don't want it on production # if self.logger.isEnabledFor(logging.DEBUG): @@ -569,13 +570,13 @@ def _retrieve_author_info_and_metadata(self, orcid: Orcid) -> Tuple[AuthorInfo, def _process_metadata(self, metadata: pd.DataFrame, author_info: AuthorInfo, params: Dict[str, str]) -> pd.DataFrame: metadata["authors"] = metadata["authors"].replace("", author_info.author_name) metadata = self.enrich_metadata(params, metadata) - self.logger.debug(f'metadata shape after base enrichment: {metadata.shape}') + # self.logger.debug(f'metadata shape after base enrichment: {metadata.shape}') author_info = self.enrich_author_info(author_info, metadata, params) - self.logger.debug(f'metadata shape after enrichment: {metadata.shape}') + # self.logger.debug(f'metadata shape after enrichment: {metadata.shape}') limit = params.get("limit", '200') metadata = metadata.head(int(limit)) metadata = self.enrich_metadata_with_base(params, metadata) - self.logger.debug(f'metadata shape after processing: {metadata.shape}') + # self.logger.debug(f'metadata shape after processing: {metadata.shape}') return metadata def _format_response(self, data: pd.DataFrame, author_info: AuthorInfo, params: Dict[str, str]) -> SuccessResult: From 16c5ba94b2ef6c2922427baf1ab68cbad365ad29 Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 16 Jun 2026 10:53:56 +0200 Subject: [PATCH 117/205] add debug logging --- server/workers/base/src/base.py | 1 + 1 file changed, 1 insertion(+) diff --git a/server/workers/base/src/base.py b/server/workers/base/src/base.py index 44f6a0e8c..4e12ee63f 100644 --- a/server/workers/base/src/base.py +++ b/server/workers/base/src/base.py @@ -139,6 +139,7 @@ def execute_search(self, params): ) # clean up content, start with stripping whitespace text.content = text.content.map(lambda x: x.strip()) + _log_dataframe(metadata, params, "metadata_before_return") input_data = {} input_data["metadata"] = metadata.to_json(orient="records") input_data["text"] = text.to_json(orient="records") From f6d2a0a898dd466328c13bf937590d13264f8632 Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 16 Jun 2026 11:02:54 +0200 Subject: [PATCH 118/205] clean up debug logging --- server/workers/common/common/enrichment.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/server/workers/common/common/enrichment.py b/server/workers/common/common/enrichment.py index 2b0d52e36..632068fbc 100644 --- a/server/workers/common/common/enrichment.py +++ b/server/workers/common/common/enrichment.py @@ -104,7 +104,7 @@ def enrich_anchor_using_duplicates(df, duplicate_groups): anchor = anchors.iloc[0] anchor_idx = anchor.name - _log_anchor_state('BEFORE', df, anchor_idx, group_data=group_data) + # _log_anchor_state('BEFORE', df, anchor_idx, group_data=group_data) subject_orig_acc = {'all_keywords': set(), 'best_value': None, 'best_count': 0} subject_acc = {'all_keywords': set(), 'best_value': None, 'best_count': 0} @@ -157,7 +157,7 @@ def enrich_anchor_using_duplicates(df, duplicate_groups): if has_additional_dois: apply_additional_dois_improvements(df, anchor_idx, additional_dois_acc) - _log_anchor_state('AFTER', df, anchor_idx) + # _log_anchor_state('AFTER', df, anchor_idx) return df @@ -314,10 +314,10 @@ def apply_additional_dois_improvements(df, anchor_idx, accumulator): anchor_doi = df.loc[anchor_idx, 'doi'] if 'doi' in df.columns else 'N/A' anchor_title = df.loc[anchor_idx, 'title'] if 'title' in df.columns else 'N/A' - logger.debug( - "[ENRICHMENT_APPLIED] additional_dois doi=%s | title=%s | additional_dois=%s", - anchor_doi, anchor_title, merged - ) + # logger.debug( + # "[ENRICHMENT_APPLIED] additional_dois doi=%s | title=%s | additional_dois=%s", + # anchor_doi, anchor_title, merged + # ) def apply_subject_improvements(df, anchor_idx, accumulator, column_name): """ @@ -385,10 +385,10 @@ def apply_link_improvements(df, anchor_idx, all_links): if merged_links: anchor_doi = df.loc[anchor_idx, 'doi'] if 'doi' in df.columns else 'N/A' anchor_title = df.loc[anchor_idx, 'title'] if 'title' in df.columns else 'N/A' - logger.debug( - "[ENRICHMENT_APPLIED] link doi=%s | title=%s | anchor_link=%s | candidates_from_duplicates=%s", - anchor_doi, anchor_title, anchor_link, merged_links - ) + # logger.debug( + # "[ENRICHMENT_APPLIED] link doi=%s | title=%s | anchor_link=%s | candidates_from_duplicates=%s", + # anchor_doi, anchor_title, anchor_link, merged_links + # ) def get_anchor_field_value(df, anchor_idx, column_name): """ From 0fb25b4f766d8a73e0d30796e919f1fcb2147327 Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 19 Jun 2026 09:42:59 +0200 Subject: [PATCH 119/205] debug updates --- docker-compose.yml | 1 - server/workers/base/src/base.py | 50 +++++++++++------------ server/workers/orcid/src/orcid_service.py | 14 +++++-- 3 files changed, 36 insertions(+), 29 deletions(-) diff --git a/docker-compose.yml b/docker-compose.yml index 2f587d19c..9059d81f1 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -164,7 +164,6 @@ services: RENV_PATHS_CACHE: /renv/cache PYTHONIOENCODING: "utf-8" R_BASE_APIKEY: "${R_BASE_APIKEY}" - ENRICHMENT_STRATEGY_FOR_SUBJECT: "${ENRICHMENT_STRATEGY_FOR_SUBJECT}" restart: unless-stopped volumes: - ./local_dev/renv/cache:/renv/cache diff --git a/server/workers/base/src/base.py b/server/workers/base/src/base.py index 4e12ee63f..7429ebfc9 100644 --- a/server/workers/base/src/base.py +++ b/server/workers/base/src/base.py @@ -202,7 +202,7 @@ def get_contentproviders(self): def run(self): while True: while self.rate_limiter.rate_limit_reached(): - self.logger.debug("🛑 Request is limited") + self.logger.warning("🛑 Request is limited") time.sleep(0.1) request_id, params, endpoint = self.next_item() self.logger.debug(request_id) @@ -255,15 +255,15 @@ def _log_dedup_state(df, step, params): n_anchor = int(df["is_anchor"].sum()) if "is_anchor" in df.columns else "?" n_doi_dup = int(df["doi_duplicate"].sum()) if "doi_duplicate" in df.columns else "?" n_link_dup = int(df["link_duplicate"].sum()) if "link_duplicate" in df.columns else "?" - logger.debug( - f"[dedup:{step}] total={len(df)} is_duplicate={n_dup} is_anchor={n_anchor}" - f" doi_duplicate={n_doi_dup} link_duplicate={n_link_dup}" - ) + # logger.debug( + # f"[dedup:{step}] total={len(df)} is_duplicate={n_dup} is_anchor={n_anchor}" + # f" doi_duplicate={n_doi_dup} link_duplicate={n_link_dup}" + # ) if "id" in df.columns and "is_duplicate" in df.columns: dup_ids = df.loc[df["is_duplicate"], "id"].tolist() anchor_ids = df.loc[df["is_anchor"], "id"].tolist() if "is_anchor" in df.columns else [] - logger.debug(f"[dedup:{step}] duplicate_ids={dup_ids}") - logger.debug(f"[dedup:{step}] anchor_ids={anchor_ids}") + # logger.debug(f"[dedup:{step}] duplicate_ids={dup_ids}") + # logger.debug(f"[dedup:{step}] anchor_ids={anchor_ids}") def _log_group_similarity(df, indexes, group_type, group_key): @@ -313,13 +313,13 @@ def filter_duplicates(df, service, params): ) df["publisher_doi"] = df.doi.map(lambda x: get_publisher_doi(x)) duplicate_groups = find_duplicate_groups(df) - logger.debug(f"[dedup:find_duplicate_groups] duplicate_groups groups: {len(duplicate_groups)}, multi-member groups: {sum(1 for idx in duplicate_groups if len(idx) > 1)}") - for grp_id, idx in duplicate_groups.items(): - if len(idx) > 1: - logger.debug( - f"[dedup:position_check] group id={grp_id!r} size={len(idx)} " - f"member_original_indexes={sorted(idx.tolist())}" - ) + # logger.debug(f"[dedup:find_duplicate_groups] duplicate_groups groups: {len(duplicate_groups)}, multi-member groups: {sum(1 for idx in duplicate_groups if len(idx) > 1)}") + # for grp_id, idx in duplicate_groups.items(): + # if len(idx) > 1: + # logger.debug( + # f"[dedup:position_check] group id={grp_id!r} size={len(idx)} " + # f"member_original_indexes={sorted(idx.tolist())}" + # ) df = mark_duplicate_dois(df) df = mark_duplicate_links(df) # _log_dedup_state(df, "after_mark_doi_link_duplicates", params) @@ -348,11 +348,11 @@ def filter_duplicates(df, service, params): continue if doi_title_filter(anchor_title, df.at[idx, "title"]): false_positive_indexes.append(idx) - logger.debug( - f"[dedup:doi_title_filter] dropping false-positive DOI match " - f"doi={doi_val!r} anchor={anchor_title!r} " - f"candidate={df.at[idx, 'title']!r}" - ) + # logger.debug( + # f"[dedup:doi_title_filter] dropping false-positive DOI match " + # f"doi={doi_val!r} anchor={anchor_title!r} " + # f"candidate={df.at[idx, 'title']!r}" + # ) if false_positive_indexes: df.drop(index=false_positive_indexes, inplace=True) logger.info(f"[dedup:doi_title_filter] dropped {len(false_positive_indexes)} false-positive records") @@ -369,7 +369,7 @@ def filter_duplicates(df, service, params): pure_datasets = df[df.typenorm == "7"] non_datasets = df.loc[df.index.difference(pure_datasets.index)] - logger.debug(f"[dedup:split] non_datasets={len(non_datasets)} pure_datasets={len(pure_datasets)}") + # logger.debug(f"[dedup:split] non_datasets={len(non_datasets)} pure_datasets={len(pure_datasets)}") non_datasets = prioritize_OA_and_latest(non_datasets, duplicate_groups) non_datasets = prioritize_doi_and_provider(non_datasets, duplicate_groups) @@ -420,11 +420,11 @@ def filter_duplicates(df, service, params): list_size = params.get("list_size") for rank, (orig_idx, row) in enumerate(filtered.iterrows()): beyond = list_size is not None and rank >= list_size - logger.debug( - f"[dedup:position_check] anchor id={row['id']!r} " - f"original_index={orig_idx} filtered_rank={rank} " - f"beyond_list_size={beyond} list_size={list_size}" - ) + # logger.debug( + # f"[dedup:position_check] anchor id={row['id']!r} " + # f"original_index={orig_idx} filtered_rank={rank} " + # f"beyond_list_size={beyond} list_size={list_size}" + # ) for c in [ "doi_duplicate", diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index f808c802c..3ef6c8bc0 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -159,7 +159,7 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D timing_data = [] - for batch in batches: + for batch_index, batch in enumerate(batches): start_time = time.time() # Quote DOIs so Lucene/Solr special characters (e.g. the parentheses # in DOIs like 10.1061/40972(311)96) are treated as literal phrase @@ -170,6 +170,14 @@ def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.D request_id = str(uuid.uuid4()) + # Link this BASE run back to the originating ORCID profile. The + # request_id becomes the BASE vis_id and names the dataframe dump + # folder, so this is the join key for tying a BASE debug dump to an ORCID. + self.logger.debug( + f"[base_link] orcid={orcid} base_request_id={request_id} " + f"batch_index={batch_index} batch_size={len(batch)}" + ) + task_data = { "id": request_id, "params": { @@ -448,8 +456,8 @@ def custom_merge(existing_value, new_value): enriched_metadata.drop(columns=['paper_abstract_base', 'subject_orig_base', 'subject_base', 'oa_state_base', 'link_base', 'relation_base'], inplace=True) - # if self.logger.isEnabledFor(logging.DEBUG): - # self._log_dataframe(enriched_metadata.sort_values(by='title'), params, '_enriched') + if self.logger.isEnabledFor(logging.DEBUG): + self._log_dataframe(enriched_metadata.sort_values(by='title'), params, '_enriched') # temporal solution, for some reason if we have some undefined data, dataprocessing is failing enriched_metadata = enriched_metadata.reindex(columns=list(set(original_columns + ['oa_state', 'subject', 'subject_orig', 'paper_abstract', 'link', 'relation']))) From 80cf23558684529488c355d9a595b9c9da7fd670 Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 11 Jun 2026 13:37:41 +0200 Subject: [PATCH 120/205] bugfix for deduplication in orcid merge --- server/workers/orcid/src/orcid_service.py | 19 +++++++++++++------ 1 file changed, 13 insertions(+), 6 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 8a2740f5c..9b29eff5e 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -371,23 +371,30 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra # Without this, drop_duplicates(keep='first') below can discard an open-access # record (oa_state=1) in favour of an unknown one (oa_state=2) for the same DOI, # silently losing the open-access status during enrichment. + # DOIs are case-insensitive, but BASE/ORCID emit the same DOI in mixed + # casing (e.g. 10.1016/b978-... vs 10.1016/B978-...). Dedup on a lowercased + # key so case-only variants collapse into one record here, matching the + # case-insensitive join used later for enrichment. The stored 'doi_merge' + # value (and the ORCID DOI casing in the output) is left untouched. if 'oa_state' in base_metadata.columns and not base_metadata.empty: oa_priority = base_metadata['oa_state'].map(oa_state_priority) + doi_key = base_metadata['doi_merge'].str.lower() best_oa_state = ( - base_metadata.assign(_oa_priority=oa_priority) + base_metadata.assign(_oa_priority=oa_priority, _doi_key=doi_key) .sort_values(by='_oa_priority') - .drop_duplicates(subset='doi_merge', keep='first') - .set_index('doi_merge')['oa_state'] + .drop_duplicates(subset='_doi_key', keep='first') + .set_index('_doi_key')['oa_state'] ) - base_metadata['oa_state'] = base_metadata['doi_merge'].map(best_oa_state) + base_metadata['oa_state'] = doi_key.map(best_oa_state) # Sort by: direct fetch before exploded-from-additional_dois rows, # This ensures the record actually fetched for a DOI wins over a record # that acquired that DOI via additional_dois expansion. base_metadata = base_metadata.assign( _direct_sort=(~base_metadata['_is_direct_fetch']).astype(int), + _doi_key=base_metadata['doi_merge'].str.lower(), ).sort_values(by=['_direct_sort']).drop_duplicates( - subset='doi_merge', keep='first' - ).drop(columns=['_direct_sort', '_is_direct_fetch']) + subset='_doi_key', keep='first' + ).drop(columns=['_direct_sort', '_is_direct_fetch', '_doi_key']) if self.logger.isEnabledFor(logging.DEBUG): self._log_dataframe(base_metadata.sort_values(by='title'), params, 'base_metadata_after_doi_dedup') From 08edfc2b97170e771d592d078f650b534de4ebcd Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 19 Jun 2026 14:12:36 +0200 Subject: [PATCH 121/205] update of contentprovider.json cache --- .../common/common/contentproviders.json | 24 ------------------- 1 file changed, 24 deletions(-) diff --git a/server/workers/common/common/contentproviders.json b/server/workers/common/common/contentproviders.json index ee7f175a8..ea45f6d8c 100644 --- a/server/workers/common/common/contentproviders.json +++ b/server/workers/common/common/contentproviders.json @@ -3131,10 +3131,6 @@ "name": "Rumah Jurnal", "internal_name": "ftid14064" }, - { - "name": "UNEJ", - "internal_name": "ftid14068" - }, { "name": "Journals of Delitekno Media Mandiri", "internal_name": "ftid14070" @@ -15067,10 +15063,6 @@ "name": "Sekigen Akademi Dergileri (SA)", "internal_name": "ftsekizgenacad" }, - { - "name": "Sekolah Tinggi Agama Islam (STAI) Binamadani Tangerang OJS", - "internal_name": "ftstaibinamadani" - }, { "name": "Markas Jurnal Sekolah Tinggi Agama Islam Publisistik Thawalib Jakarta", "internal_name": "ftstaipublisisti" @@ -15607,10 +15599,6 @@ "name": "IMDEA Networks Institute Digital Repository", "internal_name": "ftimdeanetinst" }, - { - "name": "AMAD - „Archivum Medii Aevi Digitale - Interdisziplinäres Open-Access-Fachrepositorium und Wissenschaftsblog für Mittelalterforschung‟", - "internal_name": "ftamad" - }, { "name": "EIR NUOS - Репозитарій НУК (електронний інституційний репозитарій)", "internal_name": "ftnuosuniv" @@ -33327,10 +33315,6 @@ "name": "Assam Don Bosco University Journals", "internal_name": "ftdonboscouniojs" }, - { - "name": "Universitas Jember (UNEJ): Digital Repository", - "internal_name": "ftunivjember" - }, { "name": "Frontiers (Publisher)", "internal_name": "crfrontiers" @@ -33487,10 +33471,6 @@ "name": "ScienceDirect (Elsevier)", "internal_name": "crelsevierbv" }, - { - "name": "Jurnal University of Jember", - "internal_name": "ftunivjemberojs" - }, { "name": "Deakin University: openjournals@Deakin", "internal_name": "ftdeakinunivojs" @@ -41631,10 +41611,6 @@ "name": "Универзитет у Нишу", "internal_name": "ftunivnisojs" }, - { - "name": "MENAdoc (Virtuelle Fachbibliothek Vorderer Orient/Universitäts- und Landesbibliothek Sachsen-Anhalt, Halle)", - "internal_name": "ftulbhallemenado" - }, { "name": "Hamline University: DigitalCommons@Hamline", "internal_name": "fthamlineuniv" From 30f9da17a6ffcae42b88fb2d7d9a268c1a0df1ae Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 19 Jun 2026 23:04:20 +0200 Subject: [PATCH 122/205] add additional mesh term cleanup and MeSH term de-inversion; added mesh term de-inversion exclusion list; included unit test for new de-inversion function --- server/preprocessing/other-scripts/base.R | 76 +++++++++++++++++++ .../test/test_subject_cleaning.R | 58 ++++++++++++++ 2 files changed, 134 insertions(+) create mode 100644 server/preprocessing/other-scripts/test/test_subject_cleaning.R diff --git a/server/preprocessing/other-scripts/base.R b/server/preprocessing/other-scripts/base.R index c2f2fac21..9cd830eda 100644 --- a/server/preprocessing/other-scripts/base.R +++ b/server/preprocessing/other-scripts/base.R @@ -252,6 +252,11 @@ etl <- function(res, repo, non_public) { if (!is.null(params$vis_type) && params$vis_type == "timeline") { subject_cleaned = remove_keywords_with_text_in_square_brackets(subject_cleaned) } else { + # de-invert comma-inverted MeSH descriptors (marker preserved). + # Runs before the marker-stripping steps so [MeSH]/(mesh) are still present. + subject_cleaned = deinvert_marked_mesh_keywords(subject_cleaned) + # strip the "(mesh)" marker ("[MeSH]" is handled just below). + subject_cleaned = remove_mesh_round_bracket_marker(subject_cleaned) subject_cleaned = remove_text_in_square_brackets_from_keywords(subject_cleaned) } @@ -409,6 +414,77 @@ remove_text_in_square_brackets_from_keywords <- function(x) { gsub("\\[[^]]*\\]", "", x) } +# --- MeSH keyword handling (label improvements) ------------------- +# Applied only in the non-"timeline" branch (see vis_layout subject cleaning). + +remove_mesh_round_bracket_marker <- function(x) { + # remove the "(mesh)" marker from keywords (case-insensitive), + # mirroring remove_text_in_square_brackets_from_keywords for the "[MeSH]" form. + # Only the literal "(mesh)" marker is removed -- NOT other parentheses, which + # carry real keyword content. Example: 'Climate (mesh)' -> 'Climate'. + gsub("\\s*\\(mesh\\)", "", x, ignore.case = TRUE) +} + +# Reversal-exclusion set for MeSH de-inversion. Bare descriptors +# listed here are kept in their original order instead of being reversed. To be +# populated from the manual review of multi-comma MeSH descriptors; +MESH_DEINVERSION_EXCLUSIONS <- c( + "Human Papillomavirus Recombinant Vaccine Quadrivalent, Types 6, 11, 16, 18", + "Dibenz(b,f)(1,4)oxazepine-10(11H)-carboxylic acid, 8-chloro-, 2-acetylhydrazide", + "Technology, Industry, and Agriculture", + "Multi-Ingredient Cold, Flu, and Allergy Medications", + "National Heart, Lung, and Blood Institute (U.S.)", + "Disruptive, Impulse Control, and Conduct Disorders", + "Health Care Quality, Access, and Evaluation", + "Gram-Negative Anaerobic Straight, Curved, and Helical Rods", + "Hormones, Hormone Substitutes, and Hormone Antagonists", + "Vaginal Creams, Foams, and Jellies", + "Neoplasms, Ductal, Lobular, and Medullary", + "Congenital, Hereditary, and Neonatal Diseases and Abnormalities", + "Nucleobase, Nucleoside, Nucleotide, and Nucleic Acid Transport Proteins", + "Nucleic Acids, Nucleotides, and Nucleosides", + "Diet, Food, and Nutrition", + "Amino Acids, Peptides, and Proteins", + "Neoplasms, Cystic, Mucinous, and Serous", + "Benzenaminium, 4,4'-(3-oxo-1,5-pentanediyl)bis(N,N-dimethyl-N-2-propenyl-), Dibromide", + "3-Pyridinecarboxylic acid, 1,4-dihydro-2,6-dimethyl-5-nitro-4-(2-(trifluoromethyl)phenyl)-, Methyl ester", + "Pneumonia, Atypical Interstitial, of Cattle", + "Pneumonia, Progressive Interstitial, of Sheep", + "Epidermitis, Exudative, of Swine", + "Gastroenteritis, Transmissible, of Swine", + "Enteritis, Transmissible, of Turkeys", + "Anemia, Refractory, with Excess of Blasts" +) + +deinvert_mesh_term <- function(term) { + # Naive de-inversion: reverse the comma-separated parts and join with a space. + # "Adaptation, Physiological" -> "Physiological Adaptation"; "A, B, C" -> "C B A". + # Terms in MESH_DEINVERSION_EXCLUSIONS are left untouched. + if (term %in% MESH_DEINVERSION_EXCLUSIONS) return(term) + if (!grepl(",", term, fixed = TRUE)) return(term) + parts <- trimws(strsplit(term, ",", fixed = TRUE)[[1]]) + paste(rev(parts), collapse = " ") +} + +deinvert_marked_mesh_keywords <- function(x) { + # de-invert comma-inverted MeSH descriptors for readability. Only + # keywords carrying a [MeSH] or (mesh) marker are affected; the marker is + # preserved here and removed by the marker-stripping steps that follow. + marker_re <- "\\s*(\\[MeSH\\]|\\(mesh\\))\\s*$" + one <- function(subject) { + if (is.na(subject) || subject == "") return(subject) + kws <- trimws(strsplit(subject, ";", fixed = TRUE)[[1]]) + out <- vapply(kws, function(kw) { + if (!grepl(marker_re, kw, ignore.case = TRUE, perl = TRUE)) return(kw) + marker <- regmatches(kw, regexpr(marker_re, kw, ignore.case = TRUE, perl = TRUE)) + bare <- sub(marker_re, "", kw, ignore.case = TRUE, perl = TRUE) + paste0(deinvert_mesh_term(trimws(bare)), marker) + }, character(1), USE.NAMES = FALSE) + paste(out, collapse = "; ") + } + vapply(x, one, character(1), USE.NAMES = FALSE) +} + dctypenorm_decoder <- list( "4"="Audio", "11"="Book", diff --git a/server/preprocessing/other-scripts/test/test_subject_cleaning.R b/server/preprocessing/other-scripts/test/test_subject_cleaning.R new file mode 100644 index 000000000..6a0b4c8f8 --- /dev/null +++ b/server/preprocessing/other-scripts/test/test_subject_cleaning.R @@ -0,0 +1,58 @@ +# Unit & integration tests for the MeSH keyword cleaning. +# +# Run from the other-scripts directory in a healthy renv, e.g.: +# Rscript -e 'library(testthat); test_file("test/test_subject_cleaning.R")' +# +# base.R has top-level logging setup, so (as in test_base.R) utils.R must be +# sourced and logging initialised before base.R can be sourced. + +library(testthat) + +if (!exists("deinvert_marked_mesh_keywords")) { + source("utils.R") + setup_logging("INFO") + source("base.R") +} + +# The non-"timeline" MeSH slice of vis_layout's subject cleaning, in order. +mesh_clean <- function(s) { + s <- deinvert_marked_mesh_keywords(s) # de-invert (marker preserved) + s <- remove_mesh_round_bracket_marker(s) # strip "(mesh)" + s <- remove_text_in_square_brackets_from_keywords(s) # existing: strip "[MeSH]" + trimws(s) +} + +# --- marker removal --------------------------------------------- +test_that("the (mesh) marker is removed", { + expect_equal(mesh_clean("Cooperative Behavior (mesh)"), "Cooperative Behavior") +}) + +test_that("the [MeSH] marker is removed (existing behaviour preserved)", { + expect_equal(mesh_clean("Humans [MeSH]"), "Humans") +}) + +test_that("non-MeSH parentheses are NOT removed", { + expect_equal(mesh_clean("Statistics (Mathematics)"), "Statistics (Mathematics)") +}) + +# --- de-inversion ----------------------------------------------- +test_that("a single-comma MeSH term is de-inverted", { + expect_equal(mesh_clean("Adaptation, Physiological [MeSH]"), "Physiological Adaptation") +}) + +test_that("a multi-comma MeSH term is reversed (A, B, C, D -> D C B A)", { + expect_equal(mesh_clean("Leukemia, Lymphocytic, Chronic, B-Cell [MeSH]"), + "B-Cell Chronic Lymphocytic Leukemia") +}) + +test_that("an untagged comma keyword is NOT de-inverted", { + expect_equal(mesh_clean("Journalismus, Verlagswesen"), "Journalismus, Verlagswesen") +}) + +# --- Integration: I1, exclusion set honoured --------------------------------- +test_that("the reversal-exclusion set is honoured (kept in original order)", { + old <- MESH_DEINVERSION_EXCLUSIONS + on.exit(MESH_DEINVERSION_EXCLUSIONS <<- old) + MESH_DEINVERSION_EXCLUSIONS <<- c("Aged, 80 and over") + expect_equal(mesh_clean("Aged, 80 and over [MeSH]"), "Aged, 80 and over") +}) From de7051539320d32a462803ec5a55b8d4ab63de0d Mon Sep 17 00:00:00 2001 From: chreman Date: Sat, 20 Jun 2026 02:59:17 +0200 Subject: [PATCH 123/205] added classification cleanup in more structured approach than the chained gsub --- server/preprocessing/other-scripts/base.R | 212 ++++++++++++++++++ .../test/test_subject_cleaning.R | 58 +++++ 2 files changed, 270 insertions(+) diff --git a/server/preprocessing/other-scripts/base.R b/server/preprocessing/other-scripts/base.R index 9cd830eda..326ef2ac3 100644 --- a/server/preprocessing/other-scripts/base.R +++ b/server/preprocessing/other-scripts/base.R @@ -255,6 +255,10 @@ etl <- function(res, repo, non_public) { # de-invert comma-inverted MeSH descriptors (marker preserved). # Runs before the marker-stripping steps so [MeSH]/(mesh) are still present. subject_cleaned = deinvert_marked_mesh_keywords(subject_cleaned) + # drop whole keywords that are additional classifications. Runs before the + # bracket strip so leading-bracket classifications (e.g. HAL [SHS.ECO]...) + # are still intact. + subject_cleaned = clean_classification_keywords(subject_cleaned) # strip the "(mesh)" marker ("[MeSH]" is handled just below). subject_cleaned = remove_mesh_round_bracket_marker(subject_cleaned) subject_cleaned = remove_text_in_square_brackets_from_keywords(subject_cleaned) @@ -485,6 +489,214 @@ deinvert_marked_mesh_keywords <- function(x) { vapply(x, one, character(1), USE.NAMES = FALSE) } +# --- Classification keyword cleanup ------------------------------------------ +# Split the subject into a keyword vector, run individual filter functions that +# drop whole classification keywords, then rejoin. Each classification is one +# small, self-contained filter, which keeps the rules readable and testable. + +split_keywords <- function(subject) { + kws <- trimws(strsplit(subject, ";", fixed = TRUE)[[1]]) + kws[nzchar(kws)] +} + +join_keywords <- function(keywords) { + paste(keywords, collapse = "; ") +} + +# Each drop_* filter takes a character vector of keywords and returns it with +# the keywords belonging to that classification removed. + +drop_keyvalue <- function(keywords) { + # key=value annotations, e.g. "name=Bristol Population Health Science Institute". + keywords[!grepl("^name=", keywords, ignore.case = TRUE)] +} + +drop_rcdc <- function(keywords) { + # NIH RCDC, e.g. "Autism (rcdc)". + keywords[!grepl("\\(rcdc\\)\\s*$", keywords, ignore.case = TRUE)] +} + +drop_not_elsewhere_classified <- function(keywords) { + # "... not elsewhere classified" residue, e.g. "Biological Sciences not elsewhere classified". + keywords[!grepl("not elsewhere classified", keywords, ignore.case = TRUE)] +} + +drop_for <- function(keywords) { + # ANZSRC Fields of Research, three serialisations: + # "01 Mathematical Sciences (for)", "38 Economics (for-2020)", "FoR 03 (Chemical Sciences)". + # The FoR-prefix form requires a letter after "(" so a stray "... (89.8%)" is not matched. + keywords[!grepl("\\(for(-2020)?\\)\\s*$|^FoR [0-9]+ \\([A-Za-z]", keywords, ignore.case = TRUE)] +} + +drop_hrcs <- function(keywords) { + # UK Health Research Classification System, e.g. "... (hrcs-rac)", "... (hrcs-hc)". + keywords[!grepl("\\(hrcs-[a-z]+\\)\\s*$", keywords, ignore.case = TRUE)] +} + +drop_science_metrix <- function(keywords) { + # Science-Metrix, e.g. "Bioinformatics (science-metrix)". + keywords[!grepl("\\(science-metrix\\)\\s*$", keywords, ignore.case = TRUE)] +} + +drop_sdg <- function(keywords) { + # UN Sustainable Development Goals, e.g. "3 Good Health and Well Being (sdg)". + keywords[!grepl("\\(sdg\\)\\s*$", keywords, ignore.case = TRUE)] +} + +drop_acm_ccs <- function(keywords) { + # ACM Computing Classification System concept path, e.g. + # "Computing methodologies -> Machine learning" (the arrow is U+2192). + keywords[!grepl("→", keywords, fixed = TRUE)] +} + +drop_hal_shs <- function(keywords) { + # HAL domains: a leading bracket with one of the 13 top-level codes, optionally + # followed by dotted uppercase sub-codes, e.g. "[SHS.ECO]Humanities and ...". + # Case-sensitive: the codes are upper-case (lower-case look-alikes are not HAL). + pat <- "^\\[(CHIM|INFO|MATH|NLIN|PHYS|SCCO|SDE|SDU|SDV|SHS|SPI|STAT|QFIN)(\\.[A-Z-]+)*\\]" + keywords[!grepl(pat, keywords)] +} + +drop_url <- function(keywords) { + # URLs leaking in as keywords, e.g. supplementary-file links. + keywords[!grepl("^https?://", keywords, ignore.case = TRUE)] +} + +drop_numeric_path <- function(keywords) { + # Numeric ontology path codes (digits and slashes only), e.g. "/692/308/174". + keywords[!grepl("^[0-9/]*/[0-9/]*$", keywords)] +} + +# Toulouse Capitole (TSE) subject headings (top level + sub-categories), +# hardcoded from https://publications.ut-capitole.fr/view/subjects/ . A keyword +# equal to one of these (e.g. "B- ECONOMIE et FINANCE") is a subject +# classification, not a topic. +TOULOUSE_SUBJECTS <- c( + "A- DROIT", + "A1- Généralités", + "A1-1- Introduction au droit", + "A1-2- Philosophie du droit", + "A1-3- Sociologie juridique", + "A1-4- Droit de l'informatique", + "A1-5- Droit et religions", + "A1-6- Organisation judiciaire", + "A1-7- Sources du droit", + "A1-8- Principes généraux du droit", + "A1-9- Anthropologie juridique", + "A2- Histoire du droit", + "A2-1- Droit romain et droits de l'Antiquité", + "A2-2- Histoire du droit et des institutions", + "A2-3- Histoire du droit privé", + "A3- Droit public", + "3-1- Droit constitutionnel", + "3-3- Droit administratif", + "3-3- Libertés publiques", + "3-4- Finances publiques", + "3-5- Droit de l'urbanisme", + "3-6- Droit de la santé publique", + "3-7- Droit de l'information, de la communication, droit de la presse", + "3-8- Droit public économique et des affaires", + "A4- Droit privé", + "4-1- Droit civil", + "4-10- Droit de l'environnement", + "4-11- Droit médical", + "4-12- Droit de l'information, de la communication, droit de la presse", + "4-13- Droit du sport", + "4-14- Droit du tourisme", + "4-2- Droit des affaires – droit commercial", + "4-3- Droit social – droit du travail", + "4-4- Droit fiscal", + "4-5- Droit des transports", + "4-6- Droit des assurances", + "4-7- Droit de la propriété intellectuelle ( littéraire, artistique, industrielle)", + "4-8- Droit de la construction – droit immobilier", + "4-9- Droit rural", + "A5- Droit pénal", + "5-1- Droit pénal – Procédure pénale", + "5-2- Droit pénal des affaires – droit pénal spécial", + "5-3- Pénologie – Science pénitentiaire", + "5-4- Criminologie", + "A6- Droit international", + "6-1- Droit international privé", + "6-2- Droit international public", + "6-3- Droit international économique", + "A7- Droit comparé", + "7-1- Grands systèmes de droit", + "7-2- Droit des pays étrangers", + "A8- Droit de l'Union Européenne", + "8-1- Sources", + "8-2- Institutions", + "8-3- Droit substantiel", + "B- ECONOMIE ET FINANCE", + "B1- Généralités", + "B2- Production. Travail", + "B3- Transport et Communication", + "B4- Commerce et Affaire", + "B5- Finances", + "C- GESTION", + "C1- Généralités", + "C2- Comptabilité – Contrôle", + "C3- Gestion ressources humaines", + "C4- Management", + "C5- Marketing", + "C6- Stratégie", + "D- SCIENCES POLITIQUES", + "D1- Généralités", + "D2- Politique", + "D3- Institution et Administration", + "D4- Relations internationales", + "E- SCIENCES DE L'INFORMATION ET DE LA COMMUNICATION", + "E1- Généralités", + "E2- Communication", + "E3- Culture et Media", + "F- SCIENCES HUMAINES", + "F1- Histoire. Géographie", + "F2- Sociologie", + "G- MATHEMATIQUES", + "H- INFORMATIQUE", + "I- LANGUE", + "J- SPORT" +) + +normalize_subject <- function(x) { + # Case-fold and normalise curly apostrophes so keyword variants (lower-case, + # curly quotes) match the canonical list. + toupper(gsub("[‘’']", "'", trimws(x))) +} + +drop_letter_domain <- function(keywords) { + # Exact (normalised) match against the hardcoded Toulouse subject list. + keywords[!(normalize_subject(keywords) %in% normalize_subject(TOULOUSE_SUBJECTS))] +} + +# drop_domain_general <- function(keywords) { +# # Library of Congress "(General)" subjects, e.g. "Biology (General)". +# # Currently removed by the legacy gsub chain; enable when that is retired. +# keywords[!grepl("\\([Gg]eneral\\)\\s*$", keywords)] +# } + +clean_classification_keywords <- function(x) { + one <- function(subject) { + if (is.na(subject) || subject == "") return(subject) + keywords <- split_keywords(subject) + keywords <- drop_keyvalue(keywords) + keywords <- drop_rcdc(keywords) + keywords <- drop_not_elsewhere_classified(keywords) + keywords <- drop_for(keywords) + keywords <- drop_hrcs(keywords) + keywords <- drop_science_metrix(keywords) + keywords <- drop_sdg(keywords) + keywords <- drop_acm_ccs(keywords) + keywords <- drop_hal_shs(keywords) + keywords <- drop_url(keywords) + keywords <- drop_numeric_path(keywords) + keywords <- drop_letter_domain(keywords) + # keywords <- drop_domain_general(keywords) # handled by legacy chain for now + join_keywords(keywords) + } + vapply(x, one, character(1), USE.NAMES = FALSE) +} + dctypenorm_decoder <- list( "4"="Audio", "11"="Book", diff --git a/server/preprocessing/other-scripts/test/test_subject_cleaning.R b/server/preprocessing/other-scripts/test/test_subject_cleaning.R index 6a0b4c8f8..db80b9866 100644 --- a/server/preprocessing/other-scripts/test/test_subject_cleaning.R +++ b/server/preprocessing/other-scripts/test/test_subject_cleaning.R @@ -56,3 +56,61 @@ test_that("the reversal-exclusion set is honoured (kept in original order)", { MESH_DEINVERSION_EXCLUSIONS <<- c("Aged, 80 and over") expect_equal(mesh_clean("Aged, 80 and over [MeSH]"), "Aged, 80 and over") }) + +# --- classification cleanup -------------------------------------------------- +# Each classification keyword is dropped whole; the neighbour "cooperation" is +# kept, verifying removal with no side-effect on adjacent keywords. +drops_to_cooperation <- function(keyword) { + expect_equal(clean_classification_keywords(paste0(keyword, "; cooperation")), "cooperation") +} + +test_that("name= key-value keywords are dropped", { + drops_to_cooperation("name=Connected World") +}) +test_that("rcdc keywords are dropped", { + drops_to_cooperation("Autism (rcdc)") +}) +test_that("'not elsewhere classified' keywords are dropped", { + drops_to_cooperation("Biological Sciences not elsewhere classified") +}) +test_that("FoR keywords are dropped (all three serialisations)", { + drops_to_cooperation("01 Mathematical Sciences (for)") + drops_to_cooperation("38 Economics (for-2020)") + drops_to_cooperation("FoR 03 (Chemical Sciences)") +}) +test_that("hrcs keywords are dropped", { + drops_to_cooperation("2.1 Biological and endogenous factors (hrcs-rac)") +}) +test_that("science-metrix keywords are dropped", { + drops_to_cooperation("Bioinformatics (science-metrix)") +}) +test_that("sdg keywords are dropped", { + drops_to_cooperation("3 Good Health and Well Being (sdg)") +}) +test_that("ACM CCS keywords are dropped", { + drops_to_cooperation("Computing methodologies → Machine learning") +}) +test_that("HAL domain keywords are dropped", { + drops_to_cooperation("[SHS.ECO]Humanities and Social Sciences/Economics and Finance") + drops_to_cooperation("[SDV]Life Sciences [q-bio]") +}) +test_that("URL keywords are dropped", { + drops_to_cooperation("https://cdn.jamanetwork.com/x.pdf") +}) +test_that("numeric path keywords are dropped", { + drops_to_cooperation("/692/308/174") +}) +test_that("Toulouse letter-domain subjects are dropped (top level + sub-categories)", { + drops_to_cooperation("B- ECONOMIE ET FINANCE") + drops_to_cooperation("A1-4- Droit de l'informatique") + drops_to_cooperation("4-2- Droit des affaires – droit commercial") +}) + +# Guards: real keywords that look classification-ish must be kept. +test_that("look-alike keywords are NOT dropped", { + for (kw in c("J-PET", "for 1347 (89.8%)", "COVID-19/diagnosis", + "Statistics (Mathematics)", "Mixed/Augmented Reality", "[SHSX]not-a-code", + "2138", "B-cell lymphoma", "Marketing")) { + expect_equal(clean_classification_keywords(kw), kw) + } +}) From 04eb9d4121edc46504686907b605c934b53bdcfa Mon Sep 17 00:00:00 2001 From: chreman Date: Sat, 20 Jun 2026 03:06:33 +0200 Subject: [PATCH 124/205] move subject cleaning to separate R file; update test imports --- server/preprocessing/other-scripts/base.R | 292 +---------------- .../other-scripts/subject_cleaning.R | 301 ++++++++++++++++++ .../test/test_subject_cleaning.R | 12 +- 3 files changed, 307 insertions(+), 298 deletions(-) create mode 100644 server/preprocessing/other-scripts/subject_cleaning.R diff --git a/server/preprocessing/other-scripts/base.R b/server/preprocessing/other-scripts/base.R index 326ef2ac3..96205ab41 100644 --- a/server/preprocessing/other-scripts/base.R +++ b/server/preprocessing/other-scripts/base.R @@ -2,6 +2,7 @@ library(rbace) library(stringr) library(dplyr) source('preprocess.R') +source('subject_cleaning.R') # get_papers # @@ -406,297 +407,6 @@ decode_dctypenorm <- function(dctypestring) { return(typecodes) } -remove_keywords_with_text_in_square_brackets <- function(x) { - # This function removes whole keywords that contain text in square brackets. - # Example: 'Climate [MeSH]' | 'Some keywords [Chemical]'. - gsub("[^;]*\\[[^]]+\\][^;]*;?", "", x) -} - -remove_text_in_square_brackets_from_keywords <- function(x) { - # This function removes text in square brackets. - # Example: 'Climate [MeSH]' -> 'Climate'| 'Some keywords [Chemical]' -> 'Some keywords'. - gsub("\\[[^]]*\\]", "", x) -} - -# --- MeSH keyword handling (label improvements) ------------------- -# Applied only in the non-"timeline" branch (see vis_layout subject cleaning). - -remove_mesh_round_bracket_marker <- function(x) { - # remove the "(mesh)" marker from keywords (case-insensitive), - # mirroring remove_text_in_square_brackets_from_keywords for the "[MeSH]" form. - # Only the literal "(mesh)" marker is removed -- NOT other parentheses, which - # carry real keyword content. Example: 'Climate (mesh)' -> 'Climate'. - gsub("\\s*\\(mesh\\)", "", x, ignore.case = TRUE) -} - -# Reversal-exclusion set for MeSH de-inversion. Bare descriptors -# listed here are kept in their original order instead of being reversed. To be -# populated from the manual review of multi-comma MeSH descriptors; -MESH_DEINVERSION_EXCLUSIONS <- c( - "Human Papillomavirus Recombinant Vaccine Quadrivalent, Types 6, 11, 16, 18", - "Dibenz(b,f)(1,4)oxazepine-10(11H)-carboxylic acid, 8-chloro-, 2-acetylhydrazide", - "Technology, Industry, and Agriculture", - "Multi-Ingredient Cold, Flu, and Allergy Medications", - "National Heart, Lung, and Blood Institute (U.S.)", - "Disruptive, Impulse Control, and Conduct Disorders", - "Health Care Quality, Access, and Evaluation", - "Gram-Negative Anaerobic Straight, Curved, and Helical Rods", - "Hormones, Hormone Substitutes, and Hormone Antagonists", - "Vaginal Creams, Foams, and Jellies", - "Neoplasms, Ductal, Lobular, and Medullary", - "Congenital, Hereditary, and Neonatal Diseases and Abnormalities", - "Nucleobase, Nucleoside, Nucleotide, and Nucleic Acid Transport Proteins", - "Nucleic Acids, Nucleotides, and Nucleosides", - "Diet, Food, and Nutrition", - "Amino Acids, Peptides, and Proteins", - "Neoplasms, Cystic, Mucinous, and Serous", - "Benzenaminium, 4,4'-(3-oxo-1,5-pentanediyl)bis(N,N-dimethyl-N-2-propenyl-), Dibromide", - "3-Pyridinecarboxylic acid, 1,4-dihydro-2,6-dimethyl-5-nitro-4-(2-(trifluoromethyl)phenyl)-, Methyl ester", - "Pneumonia, Atypical Interstitial, of Cattle", - "Pneumonia, Progressive Interstitial, of Sheep", - "Epidermitis, Exudative, of Swine", - "Gastroenteritis, Transmissible, of Swine", - "Enteritis, Transmissible, of Turkeys", - "Anemia, Refractory, with Excess of Blasts" -) - -deinvert_mesh_term <- function(term) { - # Naive de-inversion: reverse the comma-separated parts and join with a space. - # "Adaptation, Physiological" -> "Physiological Adaptation"; "A, B, C" -> "C B A". - # Terms in MESH_DEINVERSION_EXCLUSIONS are left untouched. - if (term %in% MESH_DEINVERSION_EXCLUSIONS) return(term) - if (!grepl(",", term, fixed = TRUE)) return(term) - parts <- trimws(strsplit(term, ",", fixed = TRUE)[[1]]) - paste(rev(parts), collapse = " ") -} - -deinvert_marked_mesh_keywords <- function(x) { - # de-invert comma-inverted MeSH descriptors for readability. Only - # keywords carrying a [MeSH] or (mesh) marker are affected; the marker is - # preserved here and removed by the marker-stripping steps that follow. - marker_re <- "\\s*(\\[MeSH\\]|\\(mesh\\))\\s*$" - one <- function(subject) { - if (is.na(subject) || subject == "") return(subject) - kws <- trimws(strsplit(subject, ";", fixed = TRUE)[[1]]) - out <- vapply(kws, function(kw) { - if (!grepl(marker_re, kw, ignore.case = TRUE, perl = TRUE)) return(kw) - marker <- regmatches(kw, regexpr(marker_re, kw, ignore.case = TRUE, perl = TRUE)) - bare <- sub(marker_re, "", kw, ignore.case = TRUE, perl = TRUE) - paste0(deinvert_mesh_term(trimws(bare)), marker) - }, character(1), USE.NAMES = FALSE) - paste(out, collapse = "; ") - } - vapply(x, one, character(1), USE.NAMES = FALSE) -} - -# --- Classification keyword cleanup ------------------------------------------ -# Split the subject into a keyword vector, run individual filter functions that -# drop whole classification keywords, then rejoin. Each classification is one -# small, self-contained filter, which keeps the rules readable and testable. - -split_keywords <- function(subject) { - kws <- trimws(strsplit(subject, ";", fixed = TRUE)[[1]]) - kws[nzchar(kws)] -} - -join_keywords <- function(keywords) { - paste(keywords, collapse = "; ") -} - -# Each drop_* filter takes a character vector of keywords and returns it with -# the keywords belonging to that classification removed. - -drop_keyvalue <- function(keywords) { - # key=value annotations, e.g. "name=Bristol Population Health Science Institute". - keywords[!grepl("^name=", keywords, ignore.case = TRUE)] -} - -drop_rcdc <- function(keywords) { - # NIH RCDC, e.g. "Autism (rcdc)". - keywords[!grepl("\\(rcdc\\)\\s*$", keywords, ignore.case = TRUE)] -} - -drop_not_elsewhere_classified <- function(keywords) { - # "... not elsewhere classified" residue, e.g. "Biological Sciences not elsewhere classified". - keywords[!grepl("not elsewhere classified", keywords, ignore.case = TRUE)] -} - -drop_for <- function(keywords) { - # ANZSRC Fields of Research, three serialisations: - # "01 Mathematical Sciences (for)", "38 Economics (for-2020)", "FoR 03 (Chemical Sciences)". - # The FoR-prefix form requires a letter after "(" so a stray "... (89.8%)" is not matched. - keywords[!grepl("\\(for(-2020)?\\)\\s*$|^FoR [0-9]+ \\([A-Za-z]", keywords, ignore.case = TRUE)] -} - -drop_hrcs <- function(keywords) { - # UK Health Research Classification System, e.g. "... (hrcs-rac)", "... (hrcs-hc)". - keywords[!grepl("\\(hrcs-[a-z]+\\)\\s*$", keywords, ignore.case = TRUE)] -} - -drop_science_metrix <- function(keywords) { - # Science-Metrix, e.g. "Bioinformatics (science-metrix)". - keywords[!grepl("\\(science-metrix\\)\\s*$", keywords, ignore.case = TRUE)] -} - -drop_sdg <- function(keywords) { - # UN Sustainable Development Goals, e.g. "3 Good Health and Well Being (sdg)". - keywords[!grepl("\\(sdg\\)\\s*$", keywords, ignore.case = TRUE)] -} - -drop_acm_ccs <- function(keywords) { - # ACM Computing Classification System concept path, e.g. - # "Computing methodologies -> Machine learning" (the arrow is U+2192). - keywords[!grepl("→", keywords, fixed = TRUE)] -} - -drop_hal_shs <- function(keywords) { - # HAL domains: a leading bracket with one of the 13 top-level codes, optionally - # followed by dotted uppercase sub-codes, e.g. "[SHS.ECO]Humanities and ...". - # Case-sensitive: the codes are upper-case (lower-case look-alikes are not HAL). - pat <- "^\\[(CHIM|INFO|MATH|NLIN|PHYS|SCCO|SDE|SDU|SDV|SHS|SPI|STAT|QFIN)(\\.[A-Z-]+)*\\]" - keywords[!grepl(pat, keywords)] -} - -drop_url <- function(keywords) { - # URLs leaking in as keywords, e.g. supplementary-file links. - keywords[!grepl("^https?://", keywords, ignore.case = TRUE)] -} - -drop_numeric_path <- function(keywords) { - # Numeric ontology path codes (digits and slashes only), e.g. "/692/308/174". - keywords[!grepl("^[0-9/]*/[0-9/]*$", keywords)] -} - -# Toulouse Capitole (TSE) subject headings (top level + sub-categories), -# hardcoded from https://publications.ut-capitole.fr/view/subjects/ . A keyword -# equal to one of these (e.g. "B- ECONOMIE et FINANCE") is a subject -# classification, not a topic. -TOULOUSE_SUBJECTS <- c( - "A- DROIT", - "A1- Généralités", - "A1-1- Introduction au droit", - "A1-2- Philosophie du droit", - "A1-3- Sociologie juridique", - "A1-4- Droit de l'informatique", - "A1-5- Droit et religions", - "A1-6- Organisation judiciaire", - "A1-7- Sources du droit", - "A1-8- Principes généraux du droit", - "A1-9- Anthropologie juridique", - "A2- Histoire du droit", - "A2-1- Droit romain et droits de l'Antiquité", - "A2-2- Histoire du droit et des institutions", - "A2-3- Histoire du droit privé", - "A3- Droit public", - "3-1- Droit constitutionnel", - "3-3- Droit administratif", - "3-3- Libertés publiques", - "3-4- Finances publiques", - "3-5- Droit de l'urbanisme", - "3-6- Droit de la santé publique", - "3-7- Droit de l'information, de la communication, droit de la presse", - "3-8- Droit public économique et des affaires", - "A4- Droit privé", - "4-1- Droit civil", - "4-10- Droit de l'environnement", - "4-11- Droit médical", - "4-12- Droit de l'information, de la communication, droit de la presse", - "4-13- Droit du sport", - "4-14- Droit du tourisme", - "4-2- Droit des affaires – droit commercial", - "4-3- Droit social – droit du travail", - "4-4- Droit fiscal", - "4-5- Droit des transports", - "4-6- Droit des assurances", - "4-7- Droit de la propriété intellectuelle ( littéraire, artistique, industrielle)", - "4-8- Droit de la construction – droit immobilier", - "4-9- Droit rural", - "A5- Droit pénal", - "5-1- Droit pénal – Procédure pénale", - "5-2- Droit pénal des affaires – droit pénal spécial", - "5-3- Pénologie – Science pénitentiaire", - "5-4- Criminologie", - "A6- Droit international", - "6-1- Droit international privé", - "6-2- Droit international public", - "6-3- Droit international économique", - "A7- Droit comparé", - "7-1- Grands systèmes de droit", - "7-2- Droit des pays étrangers", - "A8- Droit de l'Union Européenne", - "8-1- Sources", - "8-2- Institutions", - "8-3- Droit substantiel", - "B- ECONOMIE ET FINANCE", - "B1- Généralités", - "B2- Production. Travail", - "B3- Transport et Communication", - "B4- Commerce et Affaire", - "B5- Finances", - "C- GESTION", - "C1- Généralités", - "C2- Comptabilité – Contrôle", - "C3- Gestion ressources humaines", - "C4- Management", - "C5- Marketing", - "C6- Stratégie", - "D- SCIENCES POLITIQUES", - "D1- Généralités", - "D2- Politique", - "D3- Institution et Administration", - "D4- Relations internationales", - "E- SCIENCES DE L'INFORMATION ET DE LA COMMUNICATION", - "E1- Généralités", - "E2- Communication", - "E3- Culture et Media", - "F- SCIENCES HUMAINES", - "F1- Histoire. Géographie", - "F2- Sociologie", - "G- MATHEMATIQUES", - "H- INFORMATIQUE", - "I- LANGUE", - "J- SPORT" -) - -normalize_subject <- function(x) { - # Case-fold and normalise curly apostrophes so keyword variants (lower-case, - # curly quotes) match the canonical list. - toupper(gsub("[‘’']", "'", trimws(x))) -} - -drop_letter_domain <- function(keywords) { - # Exact (normalised) match against the hardcoded Toulouse subject list. - keywords[!(normalize_subject(keywords) %in% normalize_subject(TOULOUSE_SUBJECTS))] -} - -# drop_domain_general <- function(keywords) { -# # Library of Congress "(General)" subjects, e.g. "Biology (General)". -# # Currently removed by the legacy gsub chain; enable when that is retired. -# keywords[!grepl("\\([Gg]eneral\\)\\s*$", keywords)] -# } - -clean_classification_keywords <- function(x) { - one <- function(subject) { - if (is.na(subject) || subject == "") return(subject) - keywords <- split_keywords(subject) - keywords <- drop_keyvalue(keywords) - keywords <- drop_rcdc(keywords) - keywords <- drop_not_elsewhere_classified(keywords) - keywords <- drop_for(keywords) - keywords <- drop_hrcs(keywords) - keywords <- drop_science_metrix(keywords) - keywords <- drop_sdg(keywords) - keywords <- drop_acm_ccs(keywords) - keywords <- drop_hal_shs(keywords) - keywords <- drop_url(keywords) - keywords <- drop_numeric_path(keywords) - keywords <- drop_letter_domain(keywords) - # keywords <- drop_domain_general(keywords) # handled by legacy chain for now - join_keywords(keywords) - } - vapply(x, one, character(1), USE.NAMES = FALSE) -} - dctypenorm_decoder <- list( "4"="Audio", "11"="Book", diff --git a/server/preprocessing/other-scripts/subject_cleaning.R b/server/preprocessing/other-scripts/subject_cleaning.R new file mode 100644 index 000000000..c6720c722 --- /dev/null +++ b/server/preprocessing/other-scripts/subject_cleaning.R @@ -0,0 +1,301 @@ +# subject_cleaning.R +# +# Keyword/subject cleaning helpers for the BASE worker, sourced from base.R. +# Pure base R (no external packages) so it can be sourced and tested in +# isolation. Two concerns: +# * MeSH labels: strip [MeSH]/(mesh) markers and de-invert comma-inverted +# descriptors for readability. +# * Classification removal: drop whole keywords that are subject +# classifications (split the subject, filter the keyword vector, rejoin). + + +remove_keywords_with_text_in_square_brackets <- function(x) { + # This function removes whole keywords that contain text in square brackets. + # Example: 'Climate [MeSH]' | 'Some keywords [Chemical]'. + gsub("[^;]*\\[[^]]+\\][^;]*;?", "", x) +} + +remove_text_in_square_brackets_from_keywords <- function(x) { + # This function removes text in square brackets. + # Example: 'Climate [MeSH]' -> 'Climate'| 'Some keywords [Chemical]' -> 'Some keywords'. + gsub("\\[[^]]*\\]", "", x) +} + +# --- MeSH keyword handling (label improvements) ------------------- +# Applied only in the non-"timeline" branch (see vis_layout subject cleaning). + +remove_mesh_round_bracket_marker <- function(x) { + # remove the "(mesh)" marker from keywords (case-insensitive), + # mirroring remove_text_in_square_brackets_from_keywords for the "[MeSH]" form. + # Only the literal "(mesh)" marker is removed -- NOT other parentheses, which + # carry real keyword content. Example: 'Climate (mesh)' -> 'Climate'. + gsub("\\s*\\(mesh\\)", "", x, ignore.case = TRUE) +} + +# Reversal-exclusion set for MeSH de-inversion. Bare descriptors +# listed here are kept in their original order instead of being reversed. To be +# populated from the manual review of multi-comma MeSH descriptors; +MESH_DEINVERSION_EXCLUSIONS <- c( + "Human Papillomavirus Recombinant Vaccine Quadrivalent, Types 6, 11, 16, 18", + "Dibenz(b,f)(1,4)oxazepine-10(11H)-carboxylic acid, 8-chloro-, 2-acetylhydrazide", + "Technology, Industry, and Agriculture", + "Multi-Ingredient Cold, Flu, and Allergy Medications", + "National Heart, Lung, and Blood Institute (U.S.)", + "Disruptive, Impulse Control, and Conduct Disorders", + "Health Care Quality, Access, and Evaluation", + "Gram-Negative Anaerobic Straight, Curved, and Helical Rods", + "Hormones, Hormone Substitutes, and Hormone Antagonists", + "Vaginal Creams, Foams, and Jellies", + "Neoplasms, Ductal, Lobular, and Medullary", + "Congenital, Hereditary, and Neonatal Diseases and Abnormalities", + "Nucleobase, Nucleoside, Nucleotide, and Nucleic Acid Transport Proteins", + "Nucleic Acids, Nucleotides, and Nucleosides", + "Diet, Food, and Nutrition", + "Amino Acids, Peptides, and Proteins", + "Neoplasms, Cystic, Mucinous, and Serous", + "Benzenaminium, 4,4'-(3-oxo-1,5-pentanediyl)bis(N,N-dimethyl-N-2-propenyl-), Dibromide", + "3-Pyridinecarboxylic acid, 1,4-dihydro-2,6-dimethyl-5-nitro-4-(2-(trifluoromethyl)phenyl)-, Methyl ester", + "Pneumonia, Atypical Interstitial, of Cattle", + "Pneumonia, Progressive Interstitial, of Sheep", + "Epidermitis, Exudative, of Swine", + "Gastroenteritis, Transmissible, of Swine", + "Enteritis, Transmissible, of Turkeys", + "Anemia, Refractory, with Excess of Blasts" +) + +deinvert_mesh_term <- function(term) { + # Naive de-inversion: reverse the comma-separated parts and join with a space. + # "Adaptation, Physiological" -> "Physiological Adaptation"; "A, B, C" -> "C B A". + # Terms in MESH_DEINVERSION_EXCLUSIONS are left untouched. + if (term %in% MESH_DEINVERSION_EXCLUSIONS) return(term) + if (!grepl(",", term, fixed = TRUE)) return(term) + parts <- trimws(strsplit(term, ",", fixed = TRUE)[[1]]) + paste(rev(parts), collapse = " ") +} + +deinvert_marked_mesh_keywords <- function(x) { + # de-invert comma-inverted MeSH descriptors for readability. Only + # keywords carrying a [MeSH] or (mesh) marker are affected; the marker is + # preserved here and removed by the marker-stripping steps that follow. + marker_re <- "\\s*(\\[MeSH\\]|\\(mesh\\))\\s*$" + one <- function(subject) { + if (is.na(subject) || subject == "") return(subject) + kws <- trimws(strsplit(subject, ";", fixed = TRUE)[[1]]) + out <- vapply(kws, function(kw) { + if (!grepl(marker_re, kw, ignore.case = TRUE, perl = TRUE)) return(kw) + marker <- regmatches(kw, regexpr(marker_re, kw, ignore.case = TRUE, perl = TRUE)) + bare <- sub(marker_re, "", kw, ignore.case = TRUE, perl = TRUE) + paste0(deinvert_mesh_term(trimws(bare)), marker) + }, character(1), USE.NAMES = FALSE) + paste(out, collapse = "; ") + } + vapply(x, one, character(1), USE.NAMES = FALSE) +} + +# --- Classification keyword cleanup ------------------------------------------ +# Split the subject into a keyword vector, run individual filter functions that +# drop whole classification keywords, then rejoin. Each classification is one +# small, self-contained filter, which keeps the rules readable and testable. + +split_keywords <- function(subject) { + kws <- trimws(strsplit(subject, ";", fixed = TRUE)[[1]]) + kws[nzchar(kws)] +} + +join_keywords <- function(keywords) { + paste(keywords, collapse = "; ") +} + +# Each drop_* filter takes a character vector of keywords and returns it with +# the keywords belonging to that classification removed. + +drop_keyvalue <- function(keywords) { + # key=value annotations, e.g. "name=Bristol Population Health Science Institute". + keywords[!grepl("^name=", keywords, ignore.case = TRUE)] +} + +drop_rcdc <- function(keywords) { + # NIH RCDC, e.g. "Autism (rcdc)". + keywords[!grepl("\\(rcdc\\)\\s*$", keywords, ignore.case = TRUE)] +} + +drop_not_elsewhere_classified <- function(keywords) { + # "... not elsewhere classified" residue, e.g. "Biological Sciences not elsewhere classified". + keywords[!grepl("not elsewhere classified", keywords, ignore.case = TRUE)] +} + +drop_for <- function(keywords) { + # ANZSRC Fields of Research, three serialisations: + # "01 Mathematical Sciences (for)", "38 Economics (for-2020)", "FoR 03 (Chemical Sciences)". + # The FoR-prefix form requires a letter after "(" so a stray "... (89.8%)" is not matched. + keywords[!grepl("\\(for(-2020)?\\)\\s*$|^FoR [0-9]+ \\([A-Za-z]", keywords, ignore.case = TRUE)] +} + +drop_hrcs <- function(keywords) { + # UK Health Research Classification System, e.g. "... (hrcs-rac)", "... (hrcs-hc)". + keywords[!grepl("\\(hrcs-[a-z]+\\)\\s*$", keywords, ignore.case = TRUE)] +} + +drop_science_metrix <- function(keywords) { + # Science-Metrix, e.g. "Bioinformatics (science-metrix)". + keywords[!grepl("\\(science-metrix\\)\\s*$", keywords, ignore.case = TRUE)] +} + +drop_sdg <- function(keywords) { + # UN Sustainable Development Goals, e.g. "3 Good Health and Well Being (sdg)". + keywords[!grepl("\\(sdg\\)\\s*$", keywords, ignore.case = TRUE)] +} + +drop_acm_ccs <- function(keywords) { + # ACM Computing Classification System concept path, e.g. + # "Computing methodologies -> Machine learning" (the arrow is U+2192). + keywords[!grepl("→", keywords, fixed = TRUE)] +} + +drop_hal_shs <- function(keywords) { + # HAL domains: a leading bracket with one of the 13 top-level codes, optionally + # followed by dotted uppercase sub-codes, e.g. "[SHS.ECO]Humanities and ...". + # Case-sensitive: the codes are upper-case (lower-case look-alikes are not HAL). + pat <- "^\\[(CHIM|INFO|MATH|NLIN|PHYS|SCCO|SDE|SDU|SDV|SHS|SPI|STAT|QFIN)(\\.[A-Z-]+)*\\]" + keywords[!grepl(pat, keywords)] +} + +drop_url <- function(keywords) { + # URLs leaking in as keywords, e.g. supplementary-file links. + keywords[!grepl("^https?://", keywords, ignore.case = TRUE)] +} + +drop_numeric_path <- function(keywords) { + # Numeric ontology path codes (digits and slashes only), e.g. "/692/308/174". + keywords[!grepl("^[0-9/]*/[0-9/]*$", keywords)] +} + +# Toulouse Capitole (TSE) subject headings (top level + sub-categories), +# hardcoded from https://publications.ut-capitole.fr/view/subjects/ . A keyword +# equal to one of these (e.g. "B- ECONOMIE et FINANCE") is a subject +# classification, not a topic. +TOULOUSE_SUBJECTS <- c( + "A- DROIT", + "A1- Généralités", + "A1-1- Introduction au droit", + "A1-2- Philosophie du droit", + "A1-3- Sociologie juridique", + "A1-4- Droit de l'informatique", + "A1-5- Droit et religions", + "A1-6- Organisation judiciaire", + "A1-7- Sources du droit", + "A1-8- Principes généraux du droit", + "A1-9- Anthropologie juridique", + "A2- Histoire du droit", + "A2-1- Droit romain et droits de l'Antiquité", + "A2-2- Histoire du droit et des institutions", + "A2-3- Histoire du droit privé", + "A3- Droit public", + "3-1- Droit constitutionnel", + "3-3- Droit administratif", + "3-3- Libertés publiques", + "3-4- Finances publiques", + "3-5- Droit de l'urbanisme", + "3-6- Droit de la santé publique", + "3-7- Droit de l'information, de la communication, droit de la presse", + "3-8- Droit public économique et des affaires", + "A4- Droit privé", + "4-1- Droit civil", + "4-10- Droit de l'environnement", + "4-11- Droit médical", + "4-12- Droit de l'information, de la communication, droit de la presse", + "4-13- Droit du sport", + "4-14- Droit du tourisme", + "4-2- Droit des affaires – droit commercial", + "4-3- Droit social – droit du travail", + "4-4- Droit fiscal", + "4-5- Droit des transports", + "4-6- Droit des assurances", + "4-7- Droit de la propriété intellectuelle ( littéraire, artistique, industrielle)", + "4-8- Droit de la construction – droit immobilier", + "4-9- Droit rural", + "A5- Droit pénal", + "5-1- Droit pénal – Procédure pénale", + "5-2- Droit pénal des affaires – droit pénal spécial", + "5-3- Pénologie – Science pénitentiaire", + "5-4- Criminologie", + "A6- Droit international", + "6-1- Droit international privé", + "6-2- Droit international public", + "6-3- Droit international économique", + "A7- Droit comparé", + "7-1- Grands systèmes de droit", + "7-2- Droit des pays étrangers", + "A8- Droit de l'Union Européenne", + "8-1- Sources", + "8-2- Institutions", + "8-3- Droit substantiel", + "B- ECONOMIE ET FINANCE", + "B1- Généralités", + "B2- Production. Travail", + "B3- Transport et Communication", + "B4- Commerce et Affaire", + "B5- Finances", + "C- GESTION", + "C1- Généralités", + "C2- Comptabilité – Contrôle", + "C3- Gestion ressources humaines", + "C4- Management", + "C5- Marketing", + "C6- Stratégie", + "D- SCIENCES POLITIQUES", + "D1- Généralités", + "D2- Politique", + "D3- Institution et Administration", + "D4- Relations internationales", + "E- SCIENCES DE L'INFORMATION ET DE LA COMMUNICATION", + "E1- Généralités", + "E2- Communication", + "E3- Culture et Media", + "F- SCIENCES HUMAINES", + "F1- Histoire. Géographie", + "F2- Sociologie", + "G- MATHEMATIQUES", + "H- INFORMATIQUE", + "I- LANGUE", + "J- SPORT" +) + +normalize_subject <- function(x) { + # Case-fold and normalise curly apostrophes so keyword variants (lower-case, + # curly quotes) match the canonical list. + toupper(gsub("[‘’']", "'", trimws(x))) +} + +drop_letter_domain <- function(keywords) { + # Exact (normalised) match against the hardcoded Toulouse subject list. + keywords[!(normalize_subject(keywords) %in% normalize_subject(TOULOUSE_SUBJECTS))] +} + +# drop_domain_general <- function(keywords) { +# # Library of Congress "(General)" subjects, e.g. "Biology (General)". +# # Currently removed by the legacy gsub chain; enable when that is retired. +# keywords[!grepl("\\([Gg]eneral\\)\\s*$", keywords)] +# } + +clean_classification_keywords <- function(x) { + one <- function(subject) { + if (is.na(subject) || subject == "") return(subject) + keywords <- split_keywords(subject) + keywords <- drop_keyvalue(keywords) + keywords <- drop_rcdc(keywords) + keywords <- drop_not_elsewhere_classified(keywords) + keywords <- drop_for(keywords) + keywords <- drop_hrcs(keywords) + keywords <- drop_science_metrix(keywords) + keywords <- drop_sdg(keywords) + keywords <- drop_acm_ccs(keywords) + keywords <- drop_hal_shs(keywords) + keywords <- drop_url(keywords) + keywords <- drop_numeric_path(keywords) + keywords <- drop_letter_domain(keywords) + # keywords <- drop_domain_general(keywords) # handled by legacy chain for now + join_keywords(keywords) + } + vapply(x, one, character(1), USE.NAMES = FALSE) +} diff --git a/server/preprocessing/other-scripts/test/test_subject_cleaning.R b/server/preprocessing/other-scripts/test/test_subject_cleaning.R index db80b9866..3888971d3 100644 --- a/server/preprocessing/other-scripts/test/test_subject_cleaning.R +++ b/server/preprocessing/other-scripts/test/test_subject_cleaning.R @@ -1,17 +1,15 @@ -# Unit & integration tests for the MeSH keyword cleaning. +# Unit & integration tests for the subject/keyword cleaning. # -# Run from the other-scripts directory in a healthy renv, e.g.: +# Run from the other-scripts directory, e.g.: # Rscript -e 'library(testthat); test_file("test/test_subject_cleaning.R")' # -# base.R has top-level logging setup, so (as in test_base.R) utils.R must be -# sourced and logging initialised before base.R can be sourced. +# subject_cleaning.R is pure base R (no packages, no logging), so it can be +# sourced and tested in isolation. library(testthat) if (!exists("deinvert_marked_mesh_keywords")) { - source("utils.R") - setup_logging("INFO") - source("base.R") + source("subject_cleaning.R") } # The non-"timeline" MeSH slice of vis_layout's subject cleaning, in order. From 89fbfaf724bb6ced553ee648cb6e9a2ccc7adca1 Mon Sep 17 00:00:00 2001 From: chreman Date: Mon, 22 Jun 2026 12:03:46 +0200 Subject: [PATCH 125/205] add docstring --- server/preprocessing/other-scripts/base.R | 2 ++ 1 file changed, 2 insertions(+) diff --git a/server/preprocessing/other-scripts/base.R b/server/preprocessing/other-scripts/base.R index 96205ab41..10d972a5b 100644 --- a/server/preprocessing/other-scripts/base.R +++ b/server/preprocessing/other-scripts/base.R @@ -251,6 +251,8 @@ etl <- function(res, repo, non_public) { subject_cleaned = gsub("\\[No keyword\\]", "", subject_cleaned) if (!is.null(params$vis_type) && params$vis_type == "timeline") { + # These classifications have not been cleaned for the streamgraph as the + # impact of cleaning them has not been evaluated subject_cleaned = remove_keywords_with_text_in_square_brackets(subject_cleaned) } else { # de-invert comma-inverted MeSH descriptors (marker preserved). From d11126636de792483431694b1d9d1fcf1a92308a Mon Sep 17 00:00:00 2001 From: chreman Date: Mon, 22 Jun 2026 17:17:34 +0200 Subject: [PATCH 126/205] add bugfix/expression for SDG --- server/preprocessing/other-scripts/subject_cleaning.R | 8 ++++++-- .../other-scripts/test/test_subject_cleaning.R | 6 ++++-- 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/server/preprocessing/other-scripts/subject_cleaning.R b/server/preprocessing/other-scripts/subject_cleaning.R index c6720c722..c2e753c6e 100644 --- a/server/preprocessing/other-scripts/subject_cleaning.R +++ b/server/preprocessing/other-scripts/subject_cleaning.R @@ -142,8 +142,12 @@ drop_science_metrix <- function(keywords) { } drop_sdg <- function(keywords) { - # UN Sustainable Development Goals, e.g. "3 Good Health and Well Being (sdg)". - keywords[!grepl("\\(sdg\\)\\s*$", keywords, ignore.case = TRUE)] + # UN Sustainable Development Goals, in two serialisations: + # suffix marker, e.g. "3 Good Health and Well Being (sdg)" + # numbered prefix, e.g. "SDG 10: Reduced inequalities" + is_sdg <- grepl("\\(sdg\\)\\s*$", keywords, ignore.case = TRUE) | + grepl("^SDG ?[0-9]+ ?[:.-]", keywords, ignore.case = TRUE) + keywords[!is_sdg] } drop_acm_ccs <- function(keywords) { diff --git a/server/preprocessing/other-scripts/test/test_subject_cleaning.R b/server/preprocessing/other-scripts/test/test_subject_cleaning.R index 3888971d3..15832946c 100644 --- a/server/preprocessing/other-scripts/test/test_subject_cleaning.R +++ b/server/preprocessing/other-scripts/test/test_subject_cleaning.R @@ -82,8 +82,10 @@ test_that("hrcs keywords are dropped", { test_that("science-metrix keywords are dropped", { drops_to_cooperation("Bioinformatics (science-metrix)") }) -test_that("sdg keywords are dropped", { +test_that("sdg keywords are dropped (suffix marker + numbered prefix)", { drops_to_cooperation("3 Good Health and Well Being (sdg)") + drops_to_cooperation("SDG 10: Reduced inequalities") + drops_to_cooperation("SDG 3: Good health and well-being") }) test_that("ACM CCS keywords are dropped", { drops_to_cooperation("Computing methodologies → Machine learning") @@ -108,7 +110,7 @@ test_that("Toulouse letter-domain subjects are dropped (top level + sub-categori test_that("look-alike keywords are NOT dropped", { for (kw in c("J-PET", "for 1347 (89.8%)", "COVID-19/diagnosis", "Statistics (Mathematics)", "Mixed/Augmented Reality", "[SHSX]not-a-code", - "2138", "B-cell lymphoma", "Marketing")) { + "2138", "B-cell lymphoma", "Marketing", "SDGs in practice")) { expect_equal(clean_classification_keywords(kw), kw) } }) From 56ca04b0055415bbaa98e97a239af880042d66ee Mon Sep 17 00:00:00 2001 From: chreman Date: Mon, 22 Jun 2026 17:32:36 +0200 Subject: [PATCH 127/205] add additional expression of FoR-classification --- server/preprocessing/other-scripts/subject_cleaning.R | 8 +++++--- .../other-scripts/test/test_subject_cleaning.R | 5 ++++- 2 files changed, 9 insertions(+), 4 deletions(-) diff --git a/server/preprocessing/other-scripts/subject_cleaning.R b/server/preprocessing/other-scripts/subject_cleaning.R index c2e753c6e..fb0ce8ce5 100644 --- a/server/preprocessing/other-scripts/subject_cleaning.R +++ b/server/preprocessing/other-scripts/subject_cleaning.R @@ -125,10 +125,12 @@ drop_not_elsewhere_classified <- function(keywords) { } drop_for <- function(keywords) { - # ANZSRC Fields of Research, three serialisations: - # "01 Mathematical Sciences (for)", "38 Economics (for-2020)", "FoR 03 (Chemical Sciences)". + # ANZSRC Fields of Research, serialisations seen in the data: + # "01 Mathematical Sciences (for)", "38 Economics (for-2020)", "FoR 03 (Chemical Sciences)", + # "anzsrc-for: 3402 Inorganic Chemistry". # The FoR-prefix form requires a letter after "(" so a stray "... (89.8%)" is not matched. - keywords[!grepl("\\(for(-2020)?\\)\\s*$|^FoR [0-9]+ \\([A-Za-z]", keywords, ignore.case = TRUE)] + keywords[!grepl("\\(for(-2020)?\\)\\s*$|^FoR [0-9]+ \\([A-Za-z]|^anzsrc-for ?: ?[0-9]+", + keywords, ignore.case = TRUE)] } drop_hrcs <- function(keywords) { diff --git a/server/preprocessing/other-scripts/test/test_subject_cleaning.R b/server/preprocessing/other-scripts/test/test_subject_cleaning.R index 15832946c..9f8663c60 100644 --- a/server/preprocessing/other-scripts/test/test_subject_cleaning.R +++ b/server/preprocessing/other-scripts/test/test_subject_cleaning.R @@ -71,10 +71,13 @@ test_that("rcdc keywords are dropped", { test_that("'not elsewhere classified' keywords are dropped", { drops_to_cooperation("Biological Sciences not elsewhere classified") }) -test_that("FoR keywords are dropped (all three serialisations)", { +test_that("FoR keywords are dropped (all serialisations)", { drops_to_cooperation("01 Mathematical Sciences (for)") drops_to_cooperation("38 Economics (for-2020)") drops_to_cooperation("FoR 03 (Chemical Sciences)") + drops_to_cooperation("anzsrc-for: 3402 Inorganic Chemistry") + drops_to_cooperation("anzsrc-for: 34 Chemical Sciences") + drops_to_cooperation("anzsrc-for: 03 Chemical Sciences") }) test_that("hrcs keywords are dropped", { drops_to_cooperation("2.1 Biological and endogenous factors (hrcs-rac)") From fad5772f807f917e258bed9cccb512b27457c214 Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 23 Jun 2026 15:48:11 +0200 Subject: [PATCH 128/205] add e2e keyword cleaning invariant test --- e2e/keywordsCleaning/ORCID.spec.ts | 159 +++++++++++++++++++++++++++++ 1 file changed, 159 insertions(+) create mode 100644 e2e/keywordsCleaning/ORCID.spec.ts diff --git a/e2e/keywordsCleaning/ORCID.spec.ts b/e2e/keywordsCleaning/ORCID.spec.ts new file mode 100644 index 000000000..179ca01e1 --- /dev/null +++ b/e2e/keywordsCleaning/ORCID.spec.ts @@ -0,0 +1,159 @@ +import { test, expect, Page } from "@playwright/test"; +import { prepareVisualisation } from "../../vis/js/utils/e2eVisualisationLoader"; + +interface EnrichmentTestCase { + suiteName: string; + orcid: string; + paperTitle: string; + keywords?: string; + abstract?: string; + flaky?: boolean; +} + +// const uniqueOrcids = [...new Set(testCases.map((tc) => tc.orcid).filter(Boolean))]; + +const additionalUniqueOrcids = [ + "0000-0001-6011-4382", + "0000-0003-0204-881X", + "0000-0001-5116-955X", + "0000-0003-2897-6075", + "0000-0002-8911-7832", + "0000-0002-9843-6798", + "0000-0003-4221-6275", + "0000-0002-4505-0517", + "0000-0002-2233-6926", + "0000-0002-2441-4043", + "0000-0001-9287-3770", + "0000-0001-9612-7791", + "0000-0002-4971-2944", + "0000-0001-9062-6039", + "0000-0002-3924-6636", + "0000-0001-5849-8137", + "0000-0003-0297-9614", +]; + +const allUniqueOrcids = [...new Set([ ...additionalUniqueOrcids])]; + + +// This can also be used to pre-load the unique ORCID profiles, so that the subsequent tests run faster +// npx playwright test "e2e/keywordsCleaning/ORCID.spec.ts" --grep "Warm-up: pre-load unique ORCID profiles" +test.describe("Warm-up: pre-load unique ORCID profiles", () => { + for (const orcid of allUniqueOrcids) { + test(`${orcid}`, async ({ page }) => { + const url = `/search?type=get&vis_type=overview&orcid=${orcid}&service=orcid&embed=true&academic_age_offset=1`; + await prepareVisualisation(page, url); + await expect(page.locator("#search-term-unique")).toContainText(`(${orcid})`); + }); + } +}); + +// --- Classification-leak invariant ------------------------------------------ +// +// The cleaning step removes classification-scheme keywords from the display +// `subject` field, and that cleaned field also feeds the cluster/area titles +// (summarize.R derives labels from metadata$subject). This is an *invariant* +// test, not an exact-output one: the live BASE API returns different records +// run-to-run, so we cannot pin the cleaned string. Instead we assert that no +// recognised classification marker survives anywhere in `subject` or in an +// area title, whatever BASE happens to return. +// +// Exact input -> output is covered deterministically by the R unit tests +// (other-scripts/test/test_subject_cleaning.R). Vocabulary-list schemes that +// carry no inline marker (e.g. the Toulouse letter-domain list) cannot be +// detected by pattern without false positives, so they are out of scope here +// and remain covered by the unit tests only. + +// Mirrors the marker-bearing schemes in kwlib.SCHEMES (the analysis oracle). +// Keep in sync when a new marker-based scheme is added to the cleaner. +const CLASSIFICATION_MARKERS: { name: string; re: RegExp }[] = [ + { name: "mesh [MeSH]", re: /\[MeSH\]/i }, + { name: "mesh (mesh)", re: /\(mesh\)/i }, + { name: "mesh-chemical", re: /\[Chemical\]/i }, + { name: "rcdc", re: /\(rcdc\)/i }, + { name: "for / for-2020", re: /\(for(-2020)?\)/i }, + { name: "science-metrix", re: /\(science-metrix\)/i }, + { name: "sdg", re: /\(sdg\)/i }, + { name: "hrcs", re: /\(hrcs-[a-z]+\)/i }, + { name: "acm-ccs arrow", re: /→/ }, + { name: "keyvalue name=", re: /^name=/i }, + { + name: "hal-shs domain code", + re: /^\[(CHIM|INFO|MATH|NLIN|PHYS|SCCO|SDE|SDU|SDV|SHS|SPI|STAT|QFIN)(\.[A-Z-]+)*\]/, + }, + { name: "url", re: /^https?:\/\// }, + { name: "not elsewhere classified", re: /not elsewhere classified/i }, + { name: "numeric path", re: /^\/[0-9/]+$/ }, +]; + +interface BackendDocument { + subject?: string; + area?: string; +} + +// getLatestRevision.php returns { context, data }, where data (a string that +// needs parsing) carries documents[] (itself sometimes a nested JSON string). +// Each document has the cleaned `subject` and the `area` (cluster) title. +function extractDocuments(json: any): BackendDocument[] { + let data = json?.data; + if (typeof data === "string") { + data = JSON.parse(data); + } + let documents = data?.documents ?? []; + if (typeof documents === "string") { + documents = JSON.parse(documents); + } + return Array.isArray(documents) ? documents : []; +} + +function splitKeywords(subject: string | undefined): string[] { + return String(subject ?? "") + .split(";") + .map((s) => s.trim()) + .filter(Boolean); +} + +test.describe("No classification markers leak into subject or area titles", () => { + for (const orcid of allUniqueOrcids) { + test(`subject + area titles are clean — ${orcid}`, async ({ page }) => { + const url = `/search?type=get&vis_type=overview&orcid=${orcid}&service=orcid&embed=true&academic_age_offset=1`; + + // Register before navigation so we capture the data load that populates + // the visualisation. The backend pipeline can take minutes, so match the + // global test timeout. + const responsePromise = page.waitForResponse( + (r) => r.url().includes("getLatestRevision.php") && r.ok(), + { timeout: 5 * 60 * 1000 }, + ); + + await prepareVisualisation(page, url); + await expect(page.locator("#search-term-unique")).toContainText(`(${orcid})`); + + const json = await (await responsePromise).json(); + const documents = extractDocuments(json); + + // A loaded profile should carry documents; if it is genuinely empty there + // is nothing to clean, which trivially satisfies the invariant. + const keywords = documents.flatMap((d) => splitKeywords(d.subject)); + for (const kw of keywords) { + for (const { name, re } of CLASSIFICATION_MARKERS) { + expect( + re.test(kw), + `subject keyword "${kw}" (${orcid}) still carries the ${name} classification marker`, + ).toBe(false); + } + } + + const areaTitles = [ + ...new Set(documents.map((d) => d.area).filter(Boolean) as string[]), + ]; + for (const title of areaTitles) { + for (const { name, re } of CLASSIFICATION_MARKERS) { + expect( + re.test(title), + `area title "${title}" (${orcid}) still carries the ${name} classification marker`, + ).toBe(false); + } + } + }); + } +}); From d4eca50246864688ba23f66df1b0b20a040c552b Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 23 Jun 2026 16:49:42 +0200 Subject: [PATCH 129/205] add new markers to e2e test --- e2e/keywordsCleaning/ORCID.spec.ts | 27 ++++++++++++++++++++++++--- 1 file changed, 24 insertions(+), 3 deletions(-) diff --git a/e2e/keywordsCleaning/ORCID.spec.ts b/e2e/keywordsCleaning/ORCID.spec.ts index 179ca01e1..de8440848 100644 --- a/e2e/keywordsCleaning/ORCID.spec.ts +++ b/e2e/keywordsCleaning/ORCID.spec.ts @@ -63,8 +63,16 @@ test.describe("Warm-up: pre-load unique ORCID profiles", () => { // detected by pattern without false positives, so they are out of scope here // and remain covered by the unit tests only. -// Mirrors the marker-bearing schemes in kwlib.SCHEMES (the analysis oracle). -// Keep in sync when a new marker-based scheme is added to the cleaner. +// Keep in sync when a new marker-based scheme is added to the cleaner. Every +// pattern below has been checked against real cleaned `subject` data to confirm +// it does not appear after cleaning (so the invariant holds) while the scheme is +// present in the source `subject_orig`. +// +// Deliberately NOT asserted: the "anzsrc-for: 3402 ..." form. In the current +// pipeline an earlier generic gsub strips its "for: ..." tail and leaves an +// orphan "anzsrc-" fragment, so neither the full marker nor a clean drop holds; +// the fix requires some refactoring of the existing cleanup, +// so it is left out rather than encoding a failing or misleading assertion. const CLASSIFICATION_MARKERS: { name: string; re: RegExp }[] = [ { name: "mesh [MeSH]", re: /\[MeSH\]/i }, { name: "mesh (mesh)", re: /\(mesh\)/i }, @@ -72,7 +80,8 @@ const CLASSIFICATION_MARKERS: { name: string; re: RegExp }[] = [ { name: "rcdc", re: /\(rcdc\)/i }, { name: "for / for-2020", re: /\(for(-2020)?\)/i }, { name: "science-metrix", re: /\(science-metrix\)/i }, - { name: "sdg", re: /\(sdg\)/i }, + { name: "sdg (suffix marker)", re: /\(sdg\)/i }, + { name: "sdg (numbered prefix)", re: /^SDG ?\d+ ?[:.-]/i }, { name: "hrcs", re: /\(hrcs-[a-z]+\)/i }, { name: "acm-ccs arrow", re: /→/ }, { name: "keyvalue name=", re: /^name=/i }, @@ -80,6 +89,18 @@ const CLASSIFICATION_MARKERS: { name: string; re: RegExp }[] = [ name: "hal-shs domain code", re: /^\[(CHIM|INFO|MATH|NLIN|PHYS|SCCO|SDE|SDU|SDV|SHS|SPI|STAT|QFIN)(\.[A-Z-]+)*\]/, }, + { name: "ddc", re: /\bddc:\s*\d/i }, + { name: "info:eu-repo", re: /info:eu-repo/i }, + { name: "pure ontology path", re: /\/dk\/atira/i }, + { name: "fos", re: /^FOS:\s/i }, + { + name: "arxiv category (dotted)", + re: /\b(cs|econ|eess|math|astro-ph|nlin|q-bio|q-fin|stat)\.[A-Z]{2}\b/, + }, + { + name: "arxiv category (bare)", + re: /\b(quant-ph|gr-qc|math-ph|cond-mat|hep-(ex|lat|ph|th)|nucl-(ex|th))\b/, + }, { name: "url", re: /^https?:\/\// }, { name: "not elsewhere classified", re: /not elsewhere classified/i }, { name: "numeric path", re: /^\/[0-9/]+$/ }, From 48c9605129fd1e5f95523db1fb57ddf8dfe6acb9 Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 23 Jun 2026 16:50:10 +0200 Subject: [PATCH 130/205] update for classification cleanup docstring with known issue --- server/preprocessing/other-scripts/subject_cleaning.R | 3 +++ 1 file changed, 3 insertions(+) diff --git a/server/preprocessing/other-scripts/subject_cleaning.R b/server/preprocessing/other-scripts/subject_cleaning.R index fb0ce8ce5..ade0230a3 100644 --- a/server/preprocessing/other-scripts/subject_cleaning.R +++ b/server/preprocessing/other-scripts/subject_cleaning.R @@ -129,6 +129,9 @@ drop_for <- function(keywords) { # "01 Mathematical Sciences (for)", "38 Economics (for-2020)", "FoR 03 (Chemical Sciences)", # "anzsrc-for: 3402 Inorganic Chemistry". # The FoR-prefix form requires a letter after "(" so a stray "... (89.8%)" is not matched. + # NOTE: in the current base.R order the "anzsrc-for: ..." form is already reduced to an + # orphan "anzsrc-" by an earlier generic "prefix:annotation" gsub, so this branch only + # takes effect if the per-scheme filters run before that legacy step. keywords[!grepl("\\(for(-2020)?\\)\\s*$|^FoR [0-9]+ \\([A-Za-z]|^anzsrc-for ?: ?[0-9]+", keywords, ignore.case = TRUE)] } From fdd7340285c4d11d6a83cb4f5ae8c4ff1770aef0 Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 23 Jun 2026 18:30:19 +0200 Subject: [PATCH 131/205] strip mesh qualifier terms; keep root keyword; includes tests --- server/preprocessing/other-scripts/base.R | 3 + .../other-scripts/subject_cleaning.R | 65 +++++++++++++++++++ .../test/test_subject_cleaning.R | 30 +++++++++ 3 files changed, 98 insertions(+) diff --git a/server/preprocessing/other-scripts/base.R b/server/preprocessing/other-scripts/base.R index 10d972a5b..40fbb7b3e 100644 --- a/server/preprocessing/other-scripts/base.R +++ b/server/preprocessing/other-scripts/base.R @@ -258,6 +258,9 @@ etl <- function(res, repo, non_public) { # de-invert comma-inverted MeSH descriptors (marker preserved). # Runs before the marker-stripping steps so [MeSH]/(mesh) are still present. subject_cleaned = deinvert_marked_mesh_keywords(subject_cleaned) + # strip MeSH subheading qualifiers, keeping the descriptor + # ("Autistic Disorder/genetics" -> "Autistic Disorder"). + subject_cleaned = strip_mesh_qualifier(subject_cleaned) # drop whole keywords that are additional classifications. Runs before the # bracket strip so leading-bracket classifications (e.g. HAL [SHS.ECO]...) # are still intact. diff --git a/server/preprocessing/other-scripts/subject_cleaning.R b/server/preprocessing/other-scripts/subject_cleaning.R index ade0230a3..52a52d23b 100644 --- a/server/preprocessing/other-scripts/subject_cleaning.R +++ b/server/preprocessing/other-scripts/subject_cleaning.R @@ -92,6 +92,71 @@ deinvert_marked_mesh_keywords <- function(x) { vapply(x, one, character(1), USE.NAMES = FALSE) } +# MeSH subheadings/qualifiers, the authoritative NLM list +# (https://www.nlm.nih.gov/mesh/subhierarchy.html), de-duplicated. Matching is +# case-insensitive. The page's "adminstration & dosage" spelling is kept next to +# the canonical "administration & dosage" so either form in the data is matched. +MESH_QUALIFIERS <- c( + "analysis", "blood", "cerebrospinal fluid", "isolation & purification", "urine", + "anatomy & histology", "blood supply", "cytology", "ultrastructure", "embryology", + "abnormalities", "innervation", "pathology", "chemistry", "agonists", + "analogs & derivatives", "antagonists & inhibitors", "chemical synthesis", + "diagnosis", "diagnostic imaging", "etiology", "chemically induced", "complications", + "secondary", "congenital", "genetics", "immunology", "microbiology", "virology", + "parasitology", "transmission", "organization & administration", "economics", + "legislation & jurisprudence", "standards", "supply & distribution", "trends", + "pharmacology", "adminstration & dosage", "administration & dosage", "adverse effects", + "poisoning", "toxicity", "pharmacokinetics", "physiology", "growth & development", + "metabolism", "biosynthesis", "deficiency", "enzymology", "physiopathology", + "statistics & numerical data", "epidemiology", "ethnology", "mortality", + "therapeutic use", "therapy", "diet therapy", "drug therapy", "nursing", + "prevention & control", "radiotherapy", "rehabilitation", "surgery", + "transplantation", "classification", "drug effects", "education", "ethics", + "history", "injuries", "instrumentation", "methods", "pathogenicity", "psychology", + "radiation effects", "veterinary" +) + +# Alternation built longest-first so multi-word qualifiers win over a substring +# (e.g. "drug therapy" before "therapy"); the qualifiers contain no regex +# metacharacters, so no escaping is needed. +MESH_QUALIFIER_ALTERNATION <- paste( + unique(MESH_QUALIFIERS)[order(nchar(unique(MESH_QUALIFIERS)), decreasing = TRUE)], + collapse = "|" +) + +strip_mesh_qualifier <- function(x) { + # MeSH descriptor/qualifier pairs: drop the trailing subheading, keep the + # descriptor. "Autistic Disorder/genetics" -> "Autistic Disorder", + # "Hospitals/*supply & distribution" -> "Hospitals". The separator is "/" or + # ":" (with or without surrounding spaces), the qualifier may carry a leading + # "*" major-topic marker, and a "*" left on the descriptor is trimmed too. + # Only subheadings from the authoritative NLM list are removed, so genuine + # compounds ("Mixed/Augmented Reality") are untouched. + # + # KNOWN LIMITATION: in the live base.R order the colon form ("Hypothermia: + # chemically induced") is usually already removed whole by the generic + # "prefix:annotation" gsub before this runs, taking the descriptor with it. So + # in practice this mainly affects the slash form; the colon form is the same + # legacy-ordering interference that trips up the classification drops. + pat <- paste0("^(.+?)\\s*[/:]\\s*\\*?\\s*(", MESH_QUALIFIER_ALTERNATION, ")\\s*$") + one <- function(subject) { + if (is.na(subject) || subject == "") return(subject) + kws <- trimws(strsplit(subject, ";", fixed = TRUE)[[1]]) + out <- vapply(kws, function(kw) { + stripped <- kw + repeat { # handle stacked qualifiers + s <- sub(pat, "\\1", stripped, ignore.case = TRUE, perl = TRUE) + if (identical(s, stripped)) break + stripped <- s + } + if (!identical(stripped, kw)) stripped <- trimws(gsub("*", "", stripped, fixed = TRUE)) + stripped + }, character(1), USE.NAMES = FALSE) + paste(out, collapse = "; ") + } + vapply(x, one, character(1), USE.NAMES = FALSE) +} + # --- Classification keyword cleanup ------------------------------------------ # Split the subject into a keyword vector, run individual filter functions that # drop whole classification keywords, then rejoin. Each classification is one diff --git a/server/preprocessing/other-scripts/test/test_subject_cleaning.R b/server/preprocessing/other-scripts/test/test_subject_cleaning.R index 9f8663c60..e888cfac9 100644 --- a/server/preprocessing/other-scripts/test/test_subject_cleaning.R +++ b/server/preprocessing/other-scripts/test/test_subject_cleaning.R @@ -55,6 +55,36 @@ test_that("the reversal-exclusion set is honoured (kept in original order)", { expect_equal(mesh_clean("Aged, 80 and over [MeSH]"), "Aged, 80 and over") }) +# --- MeSH qualifier (subheading) stripping ----------------------------------- +test_that("MeSH subheading qualifiers are stripped, descriptor kept", { + expect_equal(strip_mesh_qualifier("Autistic Disorder/genetics"), "Autistic Disorder") + expect_equal(strip_mesh_qualifier("Pain / complications"), "Pain") + expect_equal(strip_mesh_qualifier("Bed Occupancy/statistics & numerical data"), "Bed Occupancy") + expect_equal(strip_mesh_qualifier("COVID-19/*epidemiology"), "COVID-19") + expect_equal(strip_mesh_qualifier("Hospitals/*supply & distribution"), "Hospitals") + expect_equal(strip_mesh_qualifier("COVID-19/diagnosis"), "COVID-19") +}) +test_that("the major-topic '*' marker is trimmed from the descriptor", { + expect_equal(strip_mesh_qualifier("Raynaud Disease* / genetics"), "Raynaud Disease") +}) +test_that("the colon form is stripped in isolation (live pipeline removes it earlier)", { + expect_equal(strip_mesh_qualifier("Hypothermia: chemically induced"), "Hypothermia") +}) +test_that("stacked qualifiers are all stripped", { + expect_equal(strip_mesh_qualifier("Hypothermia/diagnosis/therapy"), "Hypothermia") +}) +test_that("qualifier stripping acts per keyword within a subject", { + expect_equal( + strip_mesh_qualifier("Autistic Disorder/genetics; cooperation; Pain / complications"), + "Autistic Disorder; cooperation; Pain") +}) +test_that("non-qualifier tails are left untouched", { + for (kw in c("Mixed/Augmented Reality", "Speech/Language", "Input/Output", + "Cost/benefit analysis")) { + expect_equal(strip_mesh_qualifier(kw), kw) + } +}) + # --- classification cleanup -------------------------------------------------- # Each classification keyword is dropped whole; the neighbour "cooperation" is # kept, verifying removal with no side-effect on adjacent keywords. From 6b5e931f2412bd1bdde5887432f02027099cfb2e Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 25 Jun 2026 12:10:32 +0200 Subject: [PATCH 132/205] add MeSH qualifier partial removal from keywords; include R tests and e2e invariant tests --- e2e/keywordsCleaning/BASE.spec.ts | 74 +++++++++ e2e/keywordsCleaning/ORCID.spec.ts | 125 +-------------- e2e/keywordsCleaning/invariant.ts | 149 ++++++++++++++++++ server/preprocessing/other-scripts/base.R | 8 +- .../other-scripts/subject_cleaning.R | 60 ++++--- .../test/test_subject_cleaning.R | 57 +++++++ 6 files changed, 333 insertions(+), 140 deletions(-) create mode 100644 e2e/keywordsCleaning/BASE.spec.ts create mode 100644 e2e/keywordsCleaning/invariant.ts diff --git a/e2e/keywordsCleaning/BASE.spec.ts b/e2e/keywordsCleaning/BASE.spec.ts new file mode 100644 index 000000000..6aa3a74c4 --- /dev/null +++ b/e2e/keywordsCleaning/BASE.spec.ts @@ -0,0 +1,74 @@ +import { test, expect } from "@playwright/test"; +import { prepareVisualisation } from "../../vis/js/utils/e2eVisualisationLoader"; +import { assertNoClassificationLeaks, extractDocuments } from "./invariant"; + +// Classification-leak invariant for BASE searches (mirrors ORCID.spec.ts). The +// queries are chosen to surface the schemes/forms the cleaner targets: +// * MeSH descriptors, qualifiers and space-joined blobs (biomedical queries: +// "pharmacology", "therapeutic use", "chemically induced", ...); +// * FOS / arXiv / ddc / LCC classifications (discipline queries: "game theory", +// "machine translation", ...). +// document_types=121 (articles) + min_descsize=300 reproduces the records the +// cleaning was validated against. See ./invariant.ts for the marker set. + +interface BaseSearch { + query: string; + search_params: string; +} + +// most-relevant articles, min description size 300 (the records the cleaning was +// validated against). +// +// NOTE: `type=get` returns the cached revision for a given (query, params, date) +// hash. So after a cleaning change, re-running the same search returns the stale +// pre-change map. To force a fresh recomputation against the current backend, +// vary a date param, e.g. append "&from=1665-02-05" (any unused value). A clean +// CI database computes fresh on first run, so the default below is fine there. +const ARTICLES = + "&service=base&sorting=most-relevant&document_types%5B%5D=121&lang_id%5B%5D=all-lang&min_descsize=300"; + +const searches: BaseSearch[] = [ + { query: "pharmacology", search_params: ARTICLES }, + { query: "therapeutic use", search_params: ARTICLES }, + { query: "chemically induced", search_params: ARTICLES }, + { query: "antagonists & inhibitors", search_params: ARTICLES }, + { query: "enzyme inhibitors", search_params: ARTICLES }, + { query: '"game theory"', search_params: ARTICLES }, + { query: "machine translation", search_params: ARTICLES }, + { query: "physics", search_params: ARTICLES }, + { query: "medicine", search_params: ARTICLES }, +]; + +// Pre-load the BASE searches so subsequent runs are faster. +// npx playwright test "e2e/keywordsCleaning/BASE.spec.ts" --grep "Warm-up: pre-load BASE searches" +test.describe("Warm-up: pre-load BASE searches", () => { + for (const { query, search_params } of searches) { + test(`${query}`, async ({ page }) => { + const url = `/search?type=get&vis_type=overview&q=${query}${search_params}`; + await prepareVisualisation(page, url); + await expect(page.locator("#search-term-unique")).toContainText(query.replace(/"/g, "")); + }); + } +}); + +test.describe("No classification markers leak into subject or area titles", () => { + for (const { query, search_params } of searches) { + test(`subject + area titles are clean — ${query}`, async ({ page }) => { + const url = `/search?type=get&vis_type=overview&q=${query}${search_params}`; + + // Register before navigation so we capture the data load that populates + // the visualisation. The backend pipeline can take minutes, so match the + // global test timeout. + const responsePromise = page.waitForResponse( + (r) => r.url().includes("getLatestRevision.php") && r.ok(), + { timeout: 5 * 60 * 1000 }, + ); + + await prepareVisualisation(page, url); + await expect(page.locator("#search-term-unique")).toContainText(query.replace(/"/g, "")); + + const json = await (await responsePromise).json(); + assertNoClassificationLeaks(extractDocuments(json), query); + }); + } +}); diff --git a/e2e/keywordsCleaning/ORCID.spec.ts b/e2e/keywordsCleaning/ORCID.spec.ts index de8440848..8cbf0035c 100644 --- a/e2e/keywordsCleaning/ORCID.spec.ts +++ b/e2e/keywordsCleaning/ORCID.spec.ts @@ -1,14 +1,6 @@ -import { test, expect, Page } from "@playwright/test"; +import { test, expect } from "@playwright/test"; import { prepareVisualisation } from "../../vis/js/utils/e2eVisualisationLoader"; - -interface EnrichmentTestCase { - suiteName: string; - orcid: string; - paperTitle: string; - keywords?: string; - abstract?: string; - flaky?: boolean; -} +import { assertNoClassificationLeaks, extractDocuments } from "./invariant"; // const uniqueOrcids = [...new Set(testCases.map((tc) => tc.orcid).filter(Boolean))]; @@ -47,92 +39,9 @@ test.describe("Warm-up: pre-load unique ORCID profiles", () => { } }); -// --- Classification-leak invariant ------------------------------------------ -// -// The cleaning step removes classification-scheme keywords from the display -// `subject` field, and that cleaned field also feeds the cluster/area titles -// (summarize.R derives labels from metadata$subject). This is an *invariant* -// test, not an exact-output one: the live BASE API returns different records -// run-to-run, so we cannot pin the cleaned string. Instead we assert that no -// recognised classification marker survives anywhere in `subject` or in an -// area title, whatever BASE happens to return. -// -// Exact input -> output is covered deterministically by the R unit tests -// (other-scripts/test/test_subject_cleaning.R). Vocabulary-list schemes that -// carry no inline marker (e.g. the Toulouse letter-domain list) cannot be -// detected by pattern without false positives, so they are out of scope here -// and remain covered by the unit tests only. - -// Keep in sync when a new marker-based scheme is added to the cleaner. Every -// pattern below has been checked against real cleaned `subject` data to confirm -// it does not appear after cleaning (so the invariant holds) while the scheme is -// present in the source `subject_orig`. -// -// Deliberately NOT asserted: the "anzsrc-for: 3402 ..." form. In the current -// pipeline an earlier generic gsub strips its "for: ..." tail and leaves an -// orphan "anzsrc-" fragment, so neither the full marker nor a clean drop holds; -// the fix requires some refactoring of the existing cleanup, -// so it is left out rather than encoding a failing or misleading assertion. -const CLASSIFICATION_MARKERS: { name: string; re: RegExp }[] = [ - { name: "mesh [MeSH]", re: /\[MeSH\]/i }, - { name: "mesh (mesh)", re: /\(mesh\)/i }, - { name: "mesh-chemical", re: /\[Chemical\]/i }, - { name: "rcdc", re: /\(rcdc\)/i }, - { name: "for / for-2020", re: /\(for(-2020)?\)/i }, - { name: "science-metrix", re: /\(science-metrix\)/i }, - { name: "sdg (suffix marker)", re: /\(sdg\)/i }, - { name: "sdg (numbered prefix)", re: /^SDG ?\d+ ?[:.-]/i }, - { name: "hrcs", re: /\(hrcs-[a-z]+\)/i }, - { name: "acm-ccs arrow", re: /→/ }, - { name: "keyvalue name=", re: /^name=/i }, - { - name: "hal-shs domain code", - re: /^\[(CHIM|INFO|MATH|NLIN|PHYS|SCCO|SDE|SDU|SDV|SHS|SPI|STAT|QFIN)(\.[A-Z-]+)*\]/, - }, - { name: "ddc", re: /\bddc:\s*\d/i }, - { name: "info:eu-repo", re: /info:eu-repo/i }, - { name: "pure ontology path", re: /\/dk\/atira/i }, - { name: "fos", re: /^FOS:\s/i }, - { - name: "arxiv category (dotted)", - re: /\b(cs|econ|eess|math|astro-ph|nlin|q-bio|q-fin|stat)\.[A-Z]{2}\b/, - }, - { - name: "arxiv category (bare)", - re: /\b(quant-ph|gr-qc|math-ph|cond-mat|hep-(ex|lat|ph|th)|nucl-(ex|th))\b/, - }, - { name: "url", re: /^https?:\/\// }, - { name: "not elsewhere classified", re: /not elsewhere classified/i }, - { name: "numeric path", re: /^\/[0-9/]+$/ }, -]; - -interface BackendDocument { - subject?: string; - area?: string; -} - -// getLatestRevision.php returns { context, data }, where data (a string that -// needs parsing) carries documents[] (itself sometimes a nested JSON string). -// Each document has the cleaned `subject` and the `area` (cluster) title. -function extractDocuments(json: any): BackendDocument[] { - let data = json?.data; - if (typeof data === "string") { - data = JSON.parse(data); - } - let documents = data?.documents ?? []; - if (typeof documents === "string") { - documents = JSON.parse(documents); - } - return Array.isArray(documents) ? documents : []; -} - -function splitKeywords(subject: string | undefined): string[] { - return String(subject ?? "") - .split(";") - .map((s) => s.trim()) - .filter(Boolean); -} - +// Classification-leak invariant: no classification marker or MeSH qualifier may +// survive in the cleaned `subject` or in an area title. See ./invariant.ts for +// the rationale and the marker set. test.describe("No classification markers leak into subject or area titles", () => { for (const orcid of allUniqueOrcids) { test(`subject + area titles are clean — ${orcid}`, async ({ page }) => { @@ -150,31 +59,9 @@ test.describe("No classification markers leak into subject or area titles", () = await expect(page.locator("#search-term-unique")).toContainText(`(${orcid})`); const json = await (await responsePromise).json(); - const documents = extractDocuments(json); - // A loaded profile should carry documents; if it is genuinely empty there // is nothing to clean, which trivially satisfies the invariant. - const keywords = documents.flatMap((d) => splitKeywords(d.subject)); - for (const kw of keywords) { - for (const { name, re } of CLASSIFICATION_MARKERS) { - expect( - re.test(kw), - `subject keyword "${kw}" (${orcid}) still carries the ${name} classification marker`, - ).toBe(false); - } - } - - const areaTitles = [ - ...new Set(documents.map((d) => d.area).filter(Boolean) as string[]), - ]; - for (const title of areaTitles) { - for (const { name, re } of CLASSIFICATION_MARKERS) { - expect( - re.test(title), - `area title "${title}" (${orcid}) still carries the ${name} classification marker`, - ).toBe(false); - } - } + assertNoClassificationLeaks(extractDocuments(json), orcid); }); } }); diff --git a/e2e/keywordsCleaning/invariant.ts b/e2e/keywordsCleaning/invariant.ts new file mode 100644 index 000000000..56aeb7fa5 --- /dev/null +++ b/e2e/keywordsCleaning/invariant.ts @@ -0,0 +1,149 @@ +import { expect } from "@playwright/test"; + +// Shared classification-leak invariant for the keyword-cleaning e2e tests +// (ORCID + BASE). The cleaning step removes classification-scheme keywords and +// MeSH subheading qualifiers from the display `subject` field, and that cleaned +// field also feeds the cluster/area titles (summarize.R derives labels from +// metadata$subject). These are *invariant* checks, not exact-output ones: the +// live BASE API returns different records run-to-run, so we cannot pin the +// cleaned string. Instead we assert that no recognised classification marker or +// MeSH qualifier survives anywhere in `subject` or in an area title, whatever +// BASE happens to return. Exact input -> output is covered deterministically by +// the R unit tests (other-scripts/test/test_subject_cleaning.R). +// +// Vocabulary-list schemes that carry no inline marker (e.g. the Toulouse +// letter-domain list) cannot be detected by pattern without false positives, so +// they are out of scope here and remain covered by the unit tests only. +// +// Deliberately NOT asserted: the "anzsrc-for: 3402 ..." form. In the current +// pipeline an earlier generic gsub strips its "for: ..." tail and leaves an +// orphan "anzsrc-" fragment, so neither the full marker nor a clean drop holds; +// the fix requires refactoring the existing cleanup, so it is left out rather +// than encoding a failing or misleading assertion. + +// Classification-scheme markers. Keep in sync with kwlib.SCHEMES / the cleaner. +// Each pattern has been checked against real cleaned `subject` data: it is absent +// after cleaning while the scheme is present in the source `subject_orig`. +export const CLASSIFICATION_MARKERS: { name: string; re: RegExp }[] = [ + { name: "mesh [MeSH]", re: /\[MeSH\]/i }, + { name: "mesh (mesh)", re: /\(mesh\)/i }, + { name: "mesh-chemical", re: /\[Chemical\]/i }, + { name: "rcdc", re: /\(rcdc\)/i }, + { name: "for / for-2020", re: /\(for(-2020)?\)/i }, + { name: "science-metrix", re: /\(science-metrix\)/i }, + { name: "sdg (suffix marker)", re: /\(sdg\)/i }, + { name: "sdg (numbered prefix)", re: /^SDG ?\d+ ?[:.-]/i }, + { name: "hrcs", re: /\(hrcs-[a-z]+\)/i }, + { name: "acm-ccs arrow", re: /→/ }, + { name: "keyvalue name=", re: /^name=/i }, + { + name: "hal-shs domain code", + re: /^\[(CHIM|INFO|MATH|NLIN|PHYS|SCCO|SDE|SDU|SDV|SHS|SPI|STAT|QFIN)(\.[A-Z-]+)*\]/, + }, + { name: "ddc", re: /\bddc:\s*\d/i }, + { name: "info:eu-repo", re: /info:eu-repo/i }, + { name: "pure ontology path", re: /\/dk\/atira/i }, + { name: "fos", re: /^FOS:\s/i }, + { + name: "arxiv category (dotted)", + re: /\b(cs|econ|eess|math|astro-ph|nlin|q-bio|q-fin|stat)\.[A-Z]{2}\b/, + }, + { + name: "arxiv category (bare)", + re: /\b(quant-ph|gr-qc|math-ph|cond-mat|hep-(ex|lat|ph|th)|nucl-(ex|th))\b/, + }, + { name: "url", re: /^https?:\/\// }, + { name: "not elsewhere classified", re: /not elsewhere classified/i }, + { name: "numeric path", re: /^\/[0-9/]+$/ }, +]; + +// MeSH subheadings/qualifiers (mirrors MESH_QUALIFIERS in subject_cleaning.R). +// A leak is a "Descriptor/qualifier" or "Descriptor - qualifier" pair, or a +// space-joined blob of headings, surviving into the cleaned subject. +const MESH_QUALIFIERS = [ + "analysis", "blood", "cerebrospinal fluid", "isolation & purification", "urine", + "anatomy & histology", "blood supply", "cytology", "ultrastructure", "embryology", + "abnormalities", "innervation", "pathology", "chemistry", "agonists", + "analogs & derivatives", "antagonists & inhibitors", "chemical synthesis", + "diagnosis", "diagnostic imaging", "etiology", "chemically induced", "complications", + "secondary", "congenital", "genetics", "immunology", "microbiology", "virology", + "parasitology", "transmission", "organization & administration", "economics", + "legislation & jurisprudence", "standards", "supply & distribution", "trends", + "pharmacology", "adverse effects", "poisoning", "toxicity", "pharmacokinetics", + "physiology", "growth & development", "metabolism", "biosynthesis", "deficiency", + "enzymology", "physiopathology", "statistics & numerical data", "epidemiology", + "ethnology", "mortality", "therapeutic use", "therapy", "diet therapy", + "drug therapy", "nursing", "prevention & control", "radiotherapy", "rehabilitation", + "surgery", "transplantation", "classification", "drug effects", "education", + "ethics", "history", "injuries", "instrumentation", "methods", "pathogenicity", + "psychology", "radiation effects", "veterinary", +]; +const QUAL = MESH_QUALIFIERS.slice() + .sort((a, b) => b.length - a.length) + .map((q) => q.replace(/[&]/g, "\\$&")) + .join("|"); + +export const QUALIFIER_MARKERS: { name: string; re: RegExp }[] = [ + { name: "mesh qualifier (slash)", re: new RegExp(`/\\s*\\*?\\s*(?:${QUAL})\\*?\\s*$`, "i") }, + { name: "mesh qualifier (dash)", re: new RegExp(`\\s-\\s\\*?(?:${QUAL})\\*?\\s*$`, "i") }, + { name: "mesh blob (space-joined headings)", re: /\S+\/\S+\s+\S+\/\S+/ }, +]; + +const ALL_MARKERS = [...CLASSIFICATION_MARKERS, ...QUALIFIER_MARKERS]; + +export interface BackendDocument { + subject?: string; + area?: string; +} + +// getLatestRevision.php returns { context, data }, where data (a string that +// needs parsing) carries documents[] (itself sometimes a nested JSON string). +// Each document has the cleaned `subject` and the `area` (cluster) title. +export function extractDocuments(json: any): BackendDocument[] { + let data = json?.data; + if (typeof data === "string") { + data = JSON.parse(data); + } + let documents = data?.documents ?? []; + if (typeof documents === "string") { + documents = JSON.parse(documents); + } + return Array.isArray(documents) ? documents : []; +} + +export function splitKeywords(subject: string | undefined): string[] { + return String(subject ?? "") + .split(";") + .map((s) => s.trim()) + .filter(Boolean); +} + +// Assert that no classification marker or MeSH qualifier survives in any +// `subject` keyword or in any area title. `label` identifies the search (ORCID / +// query) in the failure message. An empty document set trivially passes. +export function assertNoClassificationLeaks( + documents: BackendDocument[], + label: string, +): void { + const keywords = documents.flatMap((d) => splitKeywords(d.subject)); + for (const kw of keywords) { + for (const { name, re } of ALL_MARKERS) { + expect( + re.test(kw), + `subject keyword "${kw}" (${label}) still carries the ${name} marker`, + ).toBe(false); + } + } + + const areaTitles = [ + ...new Set(documents.map((d) => d.area).filter(Boolean) as string[]), + ]; + for (const title of areaTitles) { + for (const { name, re } of ALL_MARKERS) { + expect( + re.test(title), + `area title "${title}" (${label}) still carries the ${name} marker`, + ).toBe(false); + } + } +} diff --git a/server/preprocessing/other-scripts/base.R b/server/preprocessing/other-scripts/base.R index 40fbb7b3e..48252ede7 100644 --- a/server/preprocessing/other-scripts/base.R +++ b/server/preprocessing/other-scripts/base.R @@ -258,9 +258,6 @@ etl <- function(res, repo, non_public) { # de-invert comma-inverted MeSH descriptors (marker preserved). # Runs before the marker-stripping steps so [MeSH]/(mesh) are still present. subject_cleaned = deinvert_marked_mesh_keywords(subject_cleaned) - # strip MeSH subheading qualifiers, keeping the descriptor - # ("Autistic Disorder/genetics" -> "Autistic Disorder"). - subject_cleaned = strip_mesh_qualifier(subject_cleaned) # drop whole keywords that are additional classifications. Runs before the # bracket strip so leading-bracket classifications (e.g. HAL [SHS.ECO]...) # are still intact. @@ -268,6 +265,11 @@ etl <- function(res, repo, non_public) { # strip the "(mesh)" marker ("[MeSH]" is handled just below). subject_cleaned = remove_mesh_round_bracket_marker(subject_cleaned) subject_cleaned = remove_text_in_square_brackets_from_keywords(subject_cleaned) + # strip MeSH subheading qualifiers, keeping the descriptor + # ("Autistic Disorder/genetics" -> "Autistic Disorder"). Runs AFTER marker + # removal so a trailing "[MeSH]"/"(mesh)" does not sit between the qualifier + # and the heading boundary and block the strip. + subject_cleaned = strip_mesh_qualifier(subject_cleaned) } subject_cleaned = gsub("\\[[^\\[]+\\][^\\;]+(;|$)?", "", subject_cleaned) # remove classification diff --git a/server/preprocessing/other-scripts/subject_cleaning.R b/server/preprocessing/other-scripts/subject_cleaning.R index 52a52d23b..4eb0df9a2 100644 --- a/server/preprocessing/other-scripts/subject_cleaning.R +++ b/server/preprocessing/other-scripts/subject_cleaning.R @@ -125,32 +125,56 @@ MESH_QUALIFIER_ALTERNATION <- paste( ) strip_mesh_qualifier <- function(x) { - # MeSH descriptor/qualifier pairs: drop the trailing subheading, keep the - # descriptor. "Autistic Disorder/genetics" -> "Autistic Disorder", - # "Hospitals/*supply & distribution" -> "Hospitals". The separator is "/" or - # ":" (with or without surrounding spaces), the qualifier may carry a leading - # "*" major-topic marker, and a "*" left on the descriptor is trimmed too. - # Only subheadings from the authoritative NLM list are removed, so genuine - # compounds ("Mixed/Augmented Reality") are untouched. + # MeSH descriptor/qualifier handling. A qualifier always ends a heading, so a + # qualifier run is the anchor for both jobs: + # * strip the qualifier from a "Descriptor/qualifier" pair, keeping the + # descriptor: "Autistic Disorder/genetics" -> "Autistic Disorder". + # * split a space-delimited blob of headings (some sources deliver MeSH + # space-joined rather than "; "-separated) by inserting "; " where a + # qualifier run is followed by the next heading: + # "Cell Cycle Proteins/*genetics Cell Line" -> "Cell Cycle Proteins; Cell Line". + # The separator is "/", ":" (optional surrounding spaces) or " - " (a dash that + # must be surrounded by spaces, so hyphenated descriptors like "Alpha-Agonists" + # are not split); the qualifier may carry a "*" major-topic marker on either + # side. A run is only treated as a boundary when followed by end-of-text or the + # start of another heading (space + capital/'*'/digit/'('), so genuine compounds + # like "Health/economics policy" or "Mixed/Augmented Reality" are left intact. + # Only subheadings from the authoritative NLM list are matched. + # + # Blob splitting is best-effort and intentionally under-splits: it never breaks + # a real descriptor, but runs of qualifier-less headings (MeSH check tags such + # as "Animals", "Humans") stay merged because there is no qualifier to anchor on. # # KNOWN LIMITATION: in the live base.R order the colon form ("Hypothermia: # chemically induced") is usually already removed whole by the generic # "prefix:annotation" gsub before this runs, taking the descriptor with it. So - # in practice this mainly affects the slash form; the colon form is the same - # legacy-ordering interference that trips up the classification drops. - pat <- paste0("^(.+?)\\s*[/:]\\s*\\*?\\s*(", MESH_QUALIFIER_ALTERNATION, ")\\s*$") + # in practice this mainly affects the slash and dash forms; the colon form is + # the same legacy-ordering interference that trips up the classification drops. + qual <- MESH_QUALIFIER_ALTERNATION + # a single "<*?>qualifier<*?>" unit (separator is /, : or a spaced dash). + unit <- paste0("(?:\\s*[/:]\\s*|\\s+-\\s+)\\*?\\s*(?i:", qual, ")\\s*\\*?") + # A *stack* of 2+ qualifiers is unambiguously a MeSH descriptor/qualifier + # construction, so always split it (handles blobs whose next heading starts + # lower-case, e.g. a gene name "rab3A ..."). + stack <- paste0("(?:", unit, "){2,}") + # A *single* qualifier is only a boundary before end-of-text or an upper-case + # next heading. The next heading may follow with no space (some sources mash + # headings together, "therapeutic useAngiotensin..."), so the space is optional; + # the case-sensitive capital still leaves a lower-case compound continuation + # ("Health/economics policy") intact. + single <- paste0("(?:", unit, ")(?=\\s*$|\\s*[*A-Z0-9(])") one <- function(subject) { if (is.na(subject) || subject == "") return(subject) kws <- trimws(strsplit(subject, ";", fixed = TRUE)[[1]]) out <- vapply(kws, function(kw) { - stripped <- kw - repeat { # handle stacked qualifiers - s <- sub(pat, "\\1", stripped, ignore.case = TRUE, perl = TRUE) - if (identical(s, stripped)) break - stripped <- s - } - if (!identical(stripped, kw)) stripped <- trimws(gsub("*", "", stripped, fixed = TRUE)) - stripped + s <- gsub(stack, "; ", kw, perl = TRUE) + s <- gsub(single, "; ", s, perl = TRUE) + if (identical(s, kw)) return(kw) # no qualifier: not a MeSH pair/blob + # a "*" major-topic marker also starts a new heading; trim leftover "*". + s <- gsub("\\s+\\*(?=[A-Za-z(])", "; ", s, perl = TRUE) + s <- sub("^\\*", "", s) + parts <- trimws(gsub("*", "", trimws(strsplit(s, ";", fixed = TRUE)[[1]]), fixed = TRUE)) + paste(parts[nzchar(parts)], collapse = "; ") }, character(1), USE.NAMES = FALSE) paste(out, collapse = "; ") } diff --git a/server/preprocessing/other-scripts/test/test_subject_cleaning.R b/server/preprocessing/other-scripts/test/test_subject_cleaning.R index e888cfac9..f2ec36e45 100644 --- a/server/preprocessing/other-scripts/test/test_subject_cleaning.R +++ b/server/preprocessing/other-scripts/test/test_subject_cleaning.R @@ -67,6 +67,63 @@ test_that("MeSH subheading qualifiers are stripped, descriptor kept", { test_that("the major-topic '*' marker is trimmed from the descriptor", { expect_equal(strip_mesh_qualifier("Raynaud Disease* / genetics"), "Raynaud Disease") }) +test_that("a '*' marker after the qualifier is handled", { + expect_equal(strip_mesh_qualifier("Lung Neoplasms/genetics*"), "Lung Neoplasms") + expect_equal(strip_mesh_qualifier("Anti-Inflammatory Agents/pharmacology*"), "Anti-Inflammatory Agents") + expect_equal(strip_mesh_qualifier("Antimutagenic Agents / pharmacology*"), "Antimutagenic Agents") +}) +test_that("the ' - ' (spaced dash) separator is handled", { + expect_equal(strip_mesh_qualifier("Acyltransferases - genetics"), "Acyltransferases") + expect_equal(strip_mesh_qualifier("ATP-Binding Cassette Transporters - antagonists & inhibitors"), + "ATP-Binding Cassette Transporters") + expect_equal( + strip_mesh_qualifier("Adrenergic Alpha-Agonists - Antagonists & Inhibitors - Pharmacology"), + "Adrenergic Alpha-Agonists") +}) +test_that("hyphenated descriptors are not split by the dash separator", { + for (kw in c("B-cell lymphoma", "Self-Esteem", "Brain - Computer Interface")) { + expect_equal(strip_mesh_qualifier(kw), kw) + } +}) +test_that("space-delimited MeSH blobs are split at qualifier boundaries", { + expect_equal( + strip_mesh_qualifier("CXC/*antagonists & inhibitors/metabolism Chemotaxis/drug effects Docosahexaenoic Acids/pharmacology"), + "CXC; Chemotaxis; Docosahexaenoic Acids") + expect_equal( + strip_mesh_qualifier("Cell Cycle Proteins/*genetics Cell Line"), + "Cell Cycle Proteins; Cell Line") +}) +test_that("a '*' major-topic marker also starts a new heading", { + expect_equal(strip_mesh_qualifier("Cytokines/immunology *Immunity"), "Cytokines; Immunity") +}) +test_that("a stacked qualifier run splits even before a lower-case heading", { + # next heading is a gene name "rab3A" (lower-case); the 2+ qualifier stack is + # still unambiguous, so it splits and strips. + expect_equal( + strip_mesh_qualifier("Spermatozoa/cytology/drug effects/metabolism rab3A GTP-Binding Protein"), + "Spermatozoa; rab3A GTP-Binding Protein") +}) +test_that("headings concatenated with no delimiter are split at the qualifier", { + expect_equal( + strip_mesh_qualifier("Adrenergic beta-Antagonists/therapeutic useCalcium Channel Blockers/therapeutic use"), + "Adrenergic beta-Antagonists; Calcium Channel Blockers") +}) +test_that("qualifier-less headings stay merged (under-split, never wrongly broken)", { + # "Animals" has no qualifier to anchor on, so it stays glued to its neighbour. + expect_equal(strip_mesh_qualifier("Animals Cell Cycle Proteins/*genetics Cell Line"), + "Animals Cell Cycle Proteins; Cell Line") +}) +test_that("a qualifier word inside a compound is not a blob boundary", { + # "/economics" is followed by lowercase "policy", so it is a compound, not a pair. + expect_equal(strip_mesh_qualifier("Health/economics policy"), "Health/economics policy") +}) +test_that("a qualifier behind a MeSH marker is stripped (marker removed first, as in base.R)", { + # base.R strips [MeSH]/(mesh) before strip_mesh_qualifier, so the qualifier is no + # longer hidden behind the marker at the heading boundary. + s <- "Acetophenones/therapeutic use [MeSH]" + s <- remove_text_in_square_brackets_from_keywords(s) + expect_equal(strip_mesh_qualifier(s), "Acetophenones") +}) test_that("the colon form is stripped in isolation (live pipeline removes it earlier)", { expect_equal(strip_mesh_qualifier("Hypothermia: chemically induced"), "Hypothermia") }) From ec2da0b765b34228a209fc5a8b6269384848d36e Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 25 Jun 2026 17:52:38 +0200 Subject: [PATCH 133/205] add LoC subclass cleanup; include unit and e2e tests --- e2e/keywordsCleaning/invariant.ts | 14 +- server/preprocessing/other-scripts/base.R | 2 +- .../other-scripts/subject_cleaning.R | 173 ++++++++++++++++++ .../test/test_subject_cleaning.R | 45 ++++- 4 files changed, 231 insertions(+), 3 deletions(-) diff --git a/e2e/keywordsCleaning/invariant.ts b/e2e/keywordsCleaning/invariant.ts index 56aeb7fa5..25e7e57a7 100644 --- a/e2e/keywordsCleaning/invariant.ts +++ b/e2e/keywordsCleaning/invariant.ts @@ -89,7 +89,19 @@ export const QUALIFIER_MARKERS: { name: string; re: RegExp }[] = [ { name: "mesh blob (space-joined headings)", re: /\S+\/\S+\s+\S+\/\S+/ }, ]; -const ALL_MARKERS = [...CLASSIFICATION_MARKERS, ...QUALIFIER_MARKERS]; +// Library of Congress Classification — clear-cut forms only, as regression +// markers. Deliberately conservative: each pattern matches a form that is +// unambiguously a classification, so an ambiguous true-negative (a non-LCC +// keyword that the cleaner legitimately keeps) does NOT trip it. We do not assert +// the bare-code / code+caption forms here, as those are intentionally ambiguous. +export const LCC_MARKERS: { name: string; re: RegExp }[] = [ + // Numeric class-number range, e.g. "QC1-999", "QH301-705.5", "HF5001-6182". + // Both sides are pure digits, so MRI sequences ("T1-T2") and cell markers + // ("CD4-CD8") — which have a letter after the dash — are not matched. + { name: "lcc range", re: /^[A-Z]{1,3}\d{1,4}(\.\d+)?-\d{1,4}(\.\d+)?$/ }, +]; + +const ALL_MARKERS = [...CLASSIFICATION_MARKERS, ...QUALIFIER_MARKERS, ...LCC_MARKERS]; export interface BackendDocument { subject?: string; diff --git a/server/preprocessing/other-scripts/base.R b/server/preprocessing/other-scripts/base.R index 48252ede7..4437298cf 100644 --- a/server/preprocessing/other-scripts/base.R +++ b/server/preprocessing/other-scripts/base.R @@ -229,7 +229,7 @@ etl <- function(res, repo, non_public) { subject_cleaned = gsub("/dk/atira[^;]*(;|$)?", "", subject_cleaned) # remove atira classification subject_cleaned = gsub("ddc:[0-9]+(;|$)?", "", subject_cleaned) # remove Dewey Decimal Classification subject_cleaned = gsub("([\\w\\/\\:-])*?\\/ddc\\/([\\/0-9\\.])*", "", subject_cleaned) # remove Dewey Decimal Classification in URI form - subject_cleaned = gsub("[A-Z,0-9]{2,}-[A-Z,0-9\\.]{2,}(;|$)?", "", subject_cleaned) #remove LOC classification + subject_cleaned = gsub("[A-Z,0-9\\.]{2,}-[A-Z,0-9\\.]{2,}(;|$)?", "", subject_cleaned) #remove LOC classification (range form, incl. decimal-before-dash e.g. HT165.5-169.9) subject_cleaned = gsub("[^\\(;]+\\(General\\)(;|$)?", "", subject_cleaned) # remove general subjects subject_cleaned = gsub("[^\\(;]+\\(all\\)(;|$)?", "", subject_cleaned) # remove general subjects subject_cleaned = gsub("[^:;]+ ?:: ?[^;]+(;|$)?", "", subject_cleaned) #remove classification with separator :: diff --git a/server/preprocessing/other-scripts/subject_cleaning.R b/server/preprocessing/other-scripts/subject_cleaning.R index 4eb0df9a2..d956a9a50 100644 --- a/server/preprocessing/other-scripts/subject_cleaning.R +++ b/server/preprocessing/other-scripts/subject_cleaning.R @@ -370,6 +370,176 @@ drop_letter_domain <- function(keywords) { keywords[!(normalize_subject(keywords) %in% normalize_subject(TOULOUSE_SUBJECTS))] } +# Library of Congress Classification, top-level classes (single letter). The +# letters are the authoritative LCC class set (I, O, W, X, Y are not classes); +# the value is the class's subject heading, used to recognise the "code + caption" +# form. Subclasses (2-3 letters) are handled separately, with their own list. +LCC_TOPLEVEL_CAPTIONS <- c( + A = "General Works", + B = "Philosophy|Psychology|Religion", + C = "Auxiliary Sciences", + D = "World History|History", + E = "History", + F = "History", + G = "Geography|Anthropology|Recreation", + H = "Social Sciences", + J = "Political Science", + K = "Law", + L = "Education", + M = "Music", + N = "Fine Arts", + P = "Language|Literature|Philology|Linguistics", + Q = "Science", + R = "Medicine", + S = "Agriculture", + T = "Technology", + U = "Military Science", + V = "Naval Science", + Z = "Bibliography|Library Science|Information Resources" +) + +drop_lcc_toplevel <- function(keywords) { + # Drop LCC top-level classes in two forms: + # * a lone class letter, e.g. "Q"; + # * the "code + caption" form, e.g. "Q Science", "R Medicine (General)". + # The caption must match the class's own subject heading, so genuine science + # terms that start with a class letter ("B cell", "T test", "G protein") are + # kept. The bare caption on its own ("Science") is left alone (no class code). + letters <- names(LCC_TOPLEVEL_CAPTIONS) + drop <- keywords %in% letters + for (l in letters) { + drop <- drop | grepl(paste0("^", l, " (", LCC_TOPLEVEL_CAPTIONS[[l]], ")\\b"), + keywords, perl = TRUE) + } + keywords[!drop] +} + +# LCC subclasses (2-3 letter codes), crawled from the LC Classification Outline +# (itsmarc.com / US Library of Congress). The value is the significant words of +# the subclass heading, used to recognise the "code + caption" form. Bare codes +# are intentionally NOT matched here: ~27% of these subclasses collide with common +# abbreviations (AI, ML, QA, QC, CT, PR, ...), so a bare code is too ambiguous. +LCC_SUBCLASS_WORDS <- c( + AC = "collections series collected works", AE = "encyclopedias", AG = "dictionaries reference works", AI = "indexes", + AM = "museums collectors collecting", AN = "newspapers", AP = "periodicals", AS = "academies learned societies", + AY = "yearbooks almanacs directories", AZ = "history scholarship learning humanities", BC = "logic", BD = "speculative philosophy", + BF = "psychology", BH = "aesthetics", BJ = "ethics", BL = "religions mythology rationalism", + BM = "judaism", BP = "islam bahaism theosophy", BQ = "buddhism", BR = "christianity", + BS = "bible", BT = "doctrinal theology", BV = "practical theology", BX = "christian denominations", + CB = "history civilization", CC = "archaeology", CD = "diplomatics archives seals", CE = "technical chronology calendar", + CJ = "numismatics", CN = "inscriptions epigraphy", CR = "heraldry", CS = "genealogy", + CT = "biography", DA = "great britain", DAW = "central europe", DB = "austria liechtenstein hungary czechoslovakia", + DC = "france andorra monaco", DD = "germany", DE = "greco roman world", DF = "greece", + DG = "italy malta", DH = "low countries benelux", DJ = "netherlands holland", DJK = "eastern europe", + DK = "russia soviet union former republics poland", DL = "northern europe scandinavia", DP = "spain portugal", DQ = "switzerland", + DR = "balkan peninsula", DS = "asia", DT = "africa", DU = "oceania south seas", + DX = "romanies", GA = "mathematical geography cartography", GB = "physical geography", GC = "oceanography", + GE = "environmental sciences", GF = "human ecology anthropogeography", GN = "anthropology", GR = "folklore", + GT = "manners customs", GV = "recreation leisure", HA = "statistics", HB = "economic theory demography", + HC = "economic history conditions", HD = "industries land use labor", HE = "transportation communications", HF = "commerce", + HG = "finance", HJ = "public finance", HM = "sociology", HN = "social history conditions problems reform", + HQ = "family marriage women", HS = "societies secret benevolent", HT = "communities classes races", HV = "social pathology public welfare criminology", + HX = "socialism communism anarchism", JA = "political science", JC = "political theory", JF = "political institutions public administration", + JJ = "political institutions public administration north america", JK = "political institutions public administration united states", JL = "political institutions public administration canada latin america", JN = "political institutions public administration europe", + JQ = "political institutions public administration asia africa australia pacific area", JS = "local government municipal", JV = "colonies colonization emigration immigration international migration", JX = "obsolete", + JZ = "international relations", KB = "religious law comparative jurisprudence", KBM = "jewish law", KBP = "islamic law", + KBR = "history canon law", KBU = "law roman catholic church holy see", KDZ = "america north", KE = "canada", + KF = "united states", KG = "latin america mexico central west indies caribbean area", KH = "south america", KZ = "law nations", + LA = "history education", LB = "theory practice education", LC = "special aspects education", LD = "individual institutions united states", + LE = "individual institutions america except united states", LF = "individual institutions europe", LG = "individual institutions asia africa indian ocean islands australia new zealand pacific", LH = "college school magazines papers", + LJ = "student fraternities societies united states", LT = "textbooks", ML = "literature on music", MT = "instruction study", + "NA" = "architecture", NB = "sculpture", NC = "drawing design illustration", ND = "painting", + NE = "print media", NK = "decorative arts", NX = "arts", PA = "greek language literature latin", + PB = "modern languages celtic", PC = "romanic languages", PD = "germanic languages scandinavian", PE = "english language", + PF = "west germanic languages", PG = "slavic languages baltic albanian language", PH = "uralic languages basque language", PJ = "oriental languages literatures", + PK = "indo iranian languages literatures", PL = "languages literatures eastern asia africa oceania", PM = "hyperborean indian artificial languages", PN = "literature", + PQ = "french literature italian spanish portuguese", PR = "english literature", PS = "american literature", PT = "german literature dutch flemish since afrikaans scandinavian old norse icelandic norwegian modern faroese danish swedish", + PZ = "fiction juvenile belles lettres", QA = "mathematics", QB = "astronomy", QC = "physics", + QD = "chemistry", QE = "geology", QH = "natural history biology", QK = "botany", + QL = "zoology", QM = "human anatomy", QP = "physiology", QR = "microbiology", + RA = "public aspects medicine", RB = "pathology", RC = "internal medicine", RD = "surgery", + RE = "ophthalmology", RF = "otorhinolaryngology", RG = "gynecology obstetrics", RJ = "pediatrics", + RK = "dentistry", RL = "dermatology", RM = "therapeutics pharmacology", RS = "pharmacy materia medica", + RT = "nursing", RV = "botanic thomsonian eclectic medicine", RX = "homeopathy", RZ = "systems medicine", + SB = "plant culture", SD = "forestry", SF = "animal culture", SH = "aquaculture fisheries angling", + SK = "hunting sports", TA = "engineering civil", TC = "hydraulic engineering ocean", TD = "environmental technology sanitary engineering", + TE = "highway engineering roads pavements", TF = "railroad engineering operation", TG = "bridge engineering", TH = "building construction", + TJ = "mechanical engineering machinery", TK = "electrical engineering electronics nuclear", TL = "motor vehicles aeronautics astronautics", TN = "mining engineering metallurgy", + TP = "chemical technology", TR = "photography", TS = "manufactures", TT = "handicrafts arts crafts", + TX = "home economics", UA = "armies organization distribution military situation", UB = "military administration", UC = "maintenance transportation", + UD = "infantry", UE = "cavalry armor", UF = "artillery", UG = "military engineering air forces", + UH = "services", VA = "navies organization distribution naval situation", VB = "naval administration", VC = "naval maintenance", + VD = "naval seamen", VE = "marines", VF = "naval ordnance", VG = "minor services navies", + VK = "navigation merchant marine", VM = "naval architecture shipbuilding marine engineering", ZA = "information resources" +) + +# Subclass codes that also read as a common abbreviation in scientific/everyday +# use (curated, not exhaustive). A *bare* code from this set is too ambiguous to +# treat as LCC (e.g. ML machine learning, QA quality assurance, CT scan, PR public +# relations, NA not applicable), so only the code+caption form is removed for these. +LCC_SUBCLASS_ABBREV <- c( + "AC", "AE", "AG", "AI", "AM", "AN", "AP", "AS", "AZ", + "BC", "BD", "BF", "BL", "BM", "BP", "BR", "BS", "BT", "BV", + "CB", "CC", "CD", "CE", "CN", "CR", "CS", "CT", + "DA", "DAW", "DB", "DC", "DE", "DF", "DJ", "DK", "DL", "DP", "DR", "DS", "DT", "DU", "DX", + "GA", "GB", "GC", "GE", "GF", "GN", "GR", "GT", + "HA", "HB", "HC", "HD", "HE", "HF", "HG", "HM", "HN", "HQ", "HS", "HT", "HV", "HX", + "JS", "JV", "KB", "KE", "KG", + "LA", "LB", "LC", "LD", "LF", "LG", "LH", "LT", "ML", "MT", + "NA", "NB", "NC", "ND", "NE", "NK", + "PA", "PB", "PC", "PD", "PE", "PG", "PH", "PK", "PL", "PM", "PN", "PR", "PS", "PT", + "QA", "QB", "QC", "QD", "QE", "QM", "QR", + "RA", "RB", "RC", "RD", "RE", "RF", "RL", "RM", "RS", "RT", "RV", "RX", + "SB", "SD", "SF", "SH", "SK", + "TA", "TC", "TD", "TE", "TF", "TG", "TH", "TL", "TN", "TP", "TR", "TS", "TX", + "UA", "UB", "UC", "UE", "UF", "UG", + "VA", "VB", "VC", "VD", "VF", "VM" +) + +# Collision-free subclasses: a bare code from this set is safe to remove as LCC, +# because it is not a common abbreviation. Derived so it always equals the full +# subclass set minus the abbreviation collisions above. +LCC_SUBCLASS_COLLISION_FREE <- setdiff(names(LCC_SUBCLASS_WORDS), LCC_SUBCLASS_ABBREV) + +drop_lcc_subclass <- function(keywords) { + # Drop LCC subclasses in the forms where the code is unambiguous: + # * code + digits, bare or with caption ("QA76", "QA76 Computer software"); + # * code + caption whose first word is part of the subclass heading + # ("QA Mathematics", "ML Literature of music"). + # The caption match keeps abbreviation expansions that share a subclass code + # ("AI Artificial Intelligence", "CT Computed Tomography", "QA testing"). + # The digit form drops bare codes too (e.g. "GF125"): the digits make it + # unambiguous LCC, and the rare biomedical-marker collisions (CD4, TP53) are an + # acceptable trade-off for cluster summarisation, where an LCC code surfacing in + # an area title is worse than dropping the occasional marker keyword. + # (A bare code WITHOUT digits is handled by drop_lcc_subclass_bare, which only + # touches the collision-free set.) + codes <- names(LCC_SUBCLASS_WORDS) + alt <- paste(codes[order(nchar(codes), decreasing = TRUE)], collapse = "|") # longest first + # code + digits (bare, or followed by a caption) + drop <- grepl(paste0("^(", alt, ")[0-9]{1,4}(\\.[0-9]+)?( |$)"), keywords, perl = TRUE) + # code + caption whose leading word belongs to the subclass heading + m <- regmatches(keywords, regexec(paste0("^(", alt, ") (.+)$"), keywords, perl = TRUE)) + for (i in which(!drop)) { + mm <- m[[i]] + if (length(mm) == 3) { + first <- tolower(sub("[^A-Za-z].*$", "", mm[[3]])) + if (nzchar(first) && + first %in% strsplit(LCC_SUBCLASS_WORDS[[mm[[2]]]], " ", fixed = TRUE)[[1]]) { + drop[i] <- TRUE + } + } + } + keywords[!drop] +} + +drop_lcc_subclass_bare <- function(keywords) { + # Drop a bare subclass code (no caption), but only from the collision-free set, + # so abbreviation collisions (ML, QA, CT, ...) are kept. Exact, case-sensitive + # match: a keyword that *is* exactly "QH"/"QK"/... and nothing else. + keywords[!(keywords %in% LCC_SUBCLASS_COLLISION_FREE)] +} + # drop_domain_general <- function(keywords) { # # Library of Congress "(General)" subjects, e.g. "Biology (General)". # # Currently removed by the legacy gsub chain; enable when that is retired. @@ -392,6 +562,9 @@ clean_classification_keywords <- function(x) { keywords <- drop_url(keywords) keywords <- drop_numeric_path(keywords) keywords <- drop_letter_domain(keywords) + keywords <- drop_lcc_toplevel(keywords) + keywords <- drop_lcc_subclass(keywords) + keywords <- drop_lcc_subclass_bare(keywords) # keywords <- drop_domain_general(keywords) # handled by legacy chain for now join_keywords(keywords) } diff --git a/server/preprocessing/other-scripts/test/test_subject_cleaning.R b/server/preprocessing/other-scripts/test/test_subject_cleaning.R index f2ec36e45..867881bfb 100644 --- a/server/preprocessing/other-scripts/test/test_subject_cleaning.R +++ b/server/preprocessing/other-scripts/test/test_subject_cleaning.R @@ -195,12 +195,55 @@ test_that("Toulouse letter-domain subjects are dropped (top level + sub-categori drops_to_cooperation("A1-4- Droit de l'informatique") drops_to_cooperation("4-2- Droit des affaires – droit commercial") }) +test_that("LCC top-level classes are dropped (lone letter + code + caption)", { + drops_to_cooperation("Q") # lone class letter + drops_to_cooperation("Q Science") # code + caption + drops_to_cooperation("R Medicine (General)") + drops_to_cooperation("B Philosophy (General)") + drops_to_cooperation("T Technology (General)") + drops_to_cooperation("H Social Sciences") +}) +test_that("LCC subclasses are dropped in the code+caption form", { + drops_to_cooperation("QA Mathematics") + drops_to_cooperation("QB Astronomy") + drops_to_cooperation("BF Psychology") + drops_to_cooperation("ML Literature of music") + drops_to_cooperation("QA75 Electronic computers. Computer science") # code + digits + caption + drops_to_cooperation("QA76 Computer software") + drops_to_cooperation("RC0321 Neuroscience. Biological psychiatry") +}) +test_that("bare subclass + digits codes are dropped", { + drops_to_cooperation("QA76") # bare code + digits, no caption + drops_to_cooperation("GF125") + drops_to_cooperation("RC321") + drops_to_cooperation("QA75.5") # decimal class number +}) +test_that("subclass codes shared with abbreviations survive the caption check", { + for (kw in c("ML Machine Learning", "AI Artificial Intelligence", "CT Computed Tomography", + "QA testing", "QC quality control", "PR public relations")) { + expect_equal(clean_classification_keywords(kw), kw) + } +}) +test_that("bare subclass codes are dropped only when collision-free", { + drops_to_cooperation("QH") # natural history/biology, not an abbreviation + drops_to_cooperation("QK") # botany + drops_to_cooperation("TJ") # mechanical engineering + drops_to_cooperation("RJ") # pediatrics +}) +test_that("bare subclass codes that are common abbreviations are kept", { + for (kw in c("ML", "AI", "QA", "QC", "CT", "PR", "NA", "RT", "RF", "PH")) { + expect_equal(clean_classification_keywords(kw), kw) + } +}) # Guards: real keywords that look classification-ish must be kept. test_that("look-alike keywords are NOT dropped", { for (kw in c("J-PET", "for 1347 (89.8%)", "COVID-19/diagnosis", "Statistics (Mathematics)", "Mixed/Augmented Reality", "[SHSX]not-a-code", - "2138", "B-cell lymphoma", "Marketing", "SDGs in practice")) { + "2138", "B-cell lymphoma", "Marketing", "SDGs in practice", + # LCC look-alikes: class letter + a non-caption word, bare caption, non-class letter + "B cell", "T cells", "T test", "G protein", "S phase", "R group", + "Q methodology", "Science", "I")) { expect_equal(clean_classification_keywords(kw), kw) } }) From fe0aa47b7f9c12e39eacd99a5a757679230aee5b Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 26 Jun 2026 12:07:07 +0200 Subject: [PATCH 134/205] add grant scheme cleanup; include tests --- e2e/keywordsCleaning/invariant.ts | 8 ++++++++ .../preprocessing/other-scripts/subject_cleaning.R | 13 +++++++++++++ .../other-scripts/test/test_subject_cleaning.R | 12 ++++++++++++ 3 files changed, 33 insertions(+) diff --git a/e2e/keywordsCleaning/invariant.ts b/e2e/keywordsCleaning/invariant.ts index 25e7e57a7..c71b61127 100644 --- a/e2e/keywordsCleaning/invariant.ts +++ b/e2e/keywordsCleaning/invariant.ts @@ -55,6 +55,14 @@ export const CLASSIFICATION_MARKERS: { name: string; re: RegExp }[] = [ { name: "url", re: /^https?:\/\// }, { name: "not elsewhere classified", re: /not elsewhere classified/i }, { name: "numeric path", re: /^\/[0-9/]+$/ }, + // Funder grant / scheme reference, e.g. "SP/19/3/34678", "MR/S003991/1". + // Conservative regression marker: requires 3+ slash-separated alphanumeric + // segments with both a letter and a digit, so 1-slash MeSH/gene forms + // ("COVID-19/epidemiology", "HER-2/neu") and all-digit dates are not matched. + { + name: "grant id (slash code)", + re: /^(?=.*[A-Za-z])(?=.*\d)[A-Za-z0-9][A-Za-z0-9-]*(\/[A-Za-z0-9][A-Za-z0-9-]*){2,}$/, + }, ]; // MeSH subheadings/qualifiers (mirrors MESH_QUALIFIERS in subject_cleaning.R). diff --git a/server/preprocessing/other-scripts/subject_cleaning.R b/server/preprocessing/other-scripts/subject_cleaning.R index d956a9a50..17cc7dac3 100644 --- a/server/preprocessing/other-scripts/subject_cleaning.R +++ b/server/preprocessing/other-scripts/subject_cleaning.R @@ -268,6 +268,18 @@ drop_numeric_path <- function(keywords) { keywords[!grepl("^[0-9/]*/[0-9/]*$", keywords)] } +drop_grant_id <- function(keywords) { + # Funder grant / scheme reference numbers: 3+ slash-separated alphanumeric + # segments (no spaces) containing a digit, e.g. "SP/19/3/34678", "HDRUK/CFC/01", + # "MR/S003991/1". Requiring 2+ slashes excludes the 1-slash forms that are MeSH + # descriptor/qualifier ("COVID-19/epidemiology") or gene names ("HER-2/neu"); + # all-digit numeric paths are handled by drop_numeric_path. False positives are + # negligible: no such keyword occurs in a broad BASE corpus outside grant IDs. + is_grant <- grepl("^[A-Za-z0-9][A-Za-z0-9-]*(/[A-Za-z0-9][A-Za-z0-9-]*){2,}$", keywords, perl = TRUE) & + grepl("[0-9]", keywords) & !grepl("^[0-9/]+$", keywords) + keywords[!is_grant] +} + # Toulouse Capitole (TSE) subject headings (top level + sub-categories), # hardcoded from https://publications.ut-capitole.fr/view/subjects/ . A keyword # equal to one of these (e.g. "B- ECONOMIE et FINANCE") is a subject @@ -561,6 +573,7 @@ clean_classification_keywords <- function(x) { keywords <- drop_hal_shs(keywords) keywords <- drop_url(keywords) keywords <- drop_numeric_path(keywords) + keywords <- drop_grant_id(keywords) keywords <- drop_letter_domain(keywords) keywords <- drop_lcc_toplevel(keywords) keywords <- drop_lcc_subclass(keywords) diff --git a/server/preprocessing/other-scripts/test/test_subject_cleaning.R b/server/preprocessing/other-scripts/test/test_subject_cleaning.R index 867881bfb..a66e9defe 100644 --- a/server/preprocessing/other-scripts/test/test_subject_cleaning.R +++ b/server/preprocessing/other-scripts/test/test_subject_cleaning.R @@ -190,6 +190,18 @@ test_that("URL keywords are dropped", { test_that("numeric path keywords are dropped", { drops_to_cooperation("/692/308/174") }) +test_that("funder grant / scheme IDs are dropped", { + drops_to_cooperation("SP/19/3/34678") + drops_to_cooperation("HDRUK/CFC/01") + drops_to_cooperation("MR/S003991/1") + drops_to_cooperation("FS/11/2/28579") +}) +test_that("grant-id look-alikes are NOT dropped", { + # 1-slash forms (MeSH qualifier / gene names), no-digit, and dates are kept. + for (kw in c("COVID-19/epidemiology", "HER-2/neu", "CD4/CD8", "A/B/C", "2019/12/31")) { + expect_equal(drop_grant_id(kw), kw) + } +}) test_that("Toulouse letter-domain subjects are dropped (top level + sub-categories)", { drops_to_cooperation("B- ECONOMIE ET FINANCE") drops_to_cooperation("A1-4- Droit de l'informatique") From de28cb32b5c9cdfaec7ca16090e9c4819d1711cb Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 26 Jun 2026 12:07:17 +0200 Subject: [PATCH 135/205] update of contentprovider.json cache --- .../workers/common/common/contentproviders.json | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/server/workers/common/common/contentproviders.json b/server/workers/common/common/contentproviders.json index ea45f6d8c..1506fa174 100644 --- a/server/workers/common/common/contentproviders.json +++ b/server/workers/common/common/contentproviders.json @@ -1,4 +1,16 @@ [ + { + "name": "Repositorio de la Universidad Industrial de Santander", + "internal_name": "ftid14824" + }, + { + "name": "AgEcon Frontiers", + "internal_name": "ftid14829" + }, + { + "name": "Institut Binamadani Indonesia (INBI): Scientific Ejournal", + "internal_name": "ftid14831" + }, { "name": "Institutional Repository DAU", "internal_name": "ftid14799" @@ -1868,7 +1880,7 @@ "internal_name": "ftid14416" }, { - "name": "Helmut-Schmidt-Universität Hamburg: Volltextserver der HSU", + "name": "openHSU - Helmut-Schmidt-Universität Hamburg / Universität der Bundeswehr", "internal_name": "fthsunivhamburg" }, { @@ -47832,7 +47844,7 @@ "internal_name": "ftunivtorun" }, { - "name": "Universität Koblenz-Landau: Hochschulschriftenserver", + "name": "OPUS der Universität Koblenz", "internal_name": "ftunivkoblenzlan" }, { From 5809a6daac32f028fffb5df6fd584ea22f4de8e8 Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 30 Jun 2026 16:14:07 +0200 Subject: [PATCH 136/205] add debug and logging functionality for summarize.R area title generation functions --- .../other-scripts/run_vis_layout.R | 10 +-- .../preprocessing/other-scripts/summarize.R | 89 +++++++++++++++++++ server/preprocessing/other-scripts/utils.R | 48 ++++++++-- 3 files changed, 133 insertions(+), 14 deletions(-) diff --git a/server/preprocessing/other-scripts/run_vis_layout.R b/server/preprocessing/other-scripts/run_vis_layout.R index 63272a8dd..22645a0e0 100644 --- a/server/preprocessing/other-scripts/run_vis_layout.R +++ b/server/preprocessing/other-scripts/run_vis_layout.R @@ -15,13 +15,11 @@ library(tibble) library(tidyr) source('utils.R') source("vis_layout.R") -if (Sys.getenv("LOGLEVEL") == "DEBUG") { - DEBUG <- FALSE -} else { - DEBUG <- TRUE -} +# DEBUG mirrors LOGLEVEL=DEBUG (the previous logic was inverted, so DEBUG had no +# effect). It gates both verbose logging and the debug data dumps (see dump_data). +DEBUG <- debug_enabled() -if (DEBUG==TRUE){ +if (DEBUG) { setup_logging('DEBUG') } else { setup_logging('INFO') diff --git a/server/preprocessing/other-scripts/summarize.R b/server/preprocessing/other-scripts/summarize.R index e28cf6b19..0e39b4d67 100644 --- a/server/preprocessing/other-scripts/summarize.R +++ b/server/preprocessing/other-scripts/summarize.R @@ -1,19 +1,38 @@ library(stringr) vslog <- getLogger('vis.summarize') +# summarize.R +# Cluster labelling for the overview visualisation: turns each cluster of papers +# into a short, human-readable area title. create_cluster_labels() is the entry +# point (called from vis_layout.R); the helpers build a per-cluster text corpus, +# rank candidate terms by tf-idf, prune and de-nest n-grams, and fix casing. +# Tokens are kept ";"-separated throughout because SplitTokenizer (the +# TermDocumentMatrix tokenizer) splits terms on ";". + +# Tokenizer used by the TermDocumentMatrix: splits a document into terms on ";". +# The corpus joins tokens (words and "_"-joined n-grams) with ";", so this +# recovers them as individual terms without re-splitting the n-grams. SplitTokenizer <- function(x) { tokens = unlist(lapply(strsplit(words(x), split=";"), paste), use.names = FALSE) return(tokens) } +# Strip leading and trailing whitespace from a string. trim <- function (x) gsub("^\\s+|\\s+$", "", x) +# Generate all contiguous n-grams of length n from each input string. The words +# within an n-gram are joined with "_" so a whitespace tokenizer keeps the n-gram +# intact. Returns, per input string, a single space-separated string of its n-grams. expand_ngrams <- function(text, n) { text <- trimws(text) lapply(lapply(text, function(x)unlist(lapply(ngrams(unlist(strsplit(x, split = " ")), n), paste, collapse = "_"))), paste, collapse = " ") } +# Drop low-value n-grams from a set of "_"-joined n-grams. Removes n-grams that +# start or end with a stopword, whose first and last token are identical, or that +# are shorter than 2 tokens; stopwords are checked in batches for speed. Returns, +# per input, the surviving n-grams joined with ";". prune_ngrams <- function(ngrams, stops){ ngrams = mapply(strsplit, ngrams, split=" |;") tokenized_ngrams = mapply(function(x) { @@ -48,17 +67,39 @@ prune_ngrams <- function(ngrams, stops){ return (pruned_ngrams) } +# Entry point: assign a short label ("area title") to every cluster. +# Builds one pseudo-document per cluster from its papers' subjects + title +# n-grams (or a custom field), ranks terms by tf-idf (SMART "ntn"), and keeps the +# top top_n as the label. Clusters with no surviving tf-idf terms fall back to the +# most frequent bi-/tri-grams of their papers' titles and abstracts. Casing is +# then normalised against the corpus. +# clusters : list with $groups (cluster id per paper) and $num_clusters. +# metadata : data frame with title, subject, paper_abstract (+ optional +# custom_clustering / annotations fields named in params). +# type_counts : term -> count map, used to restore original casing. +# top_n : number of terms kept per label. +# stops : stopword vector. +# taxonomy_separator : if set, taxonomy subjects keep only their last path segment. +# Returns clusters with $cluster_labels filled: one label per paper, identical +# for all papers in the same cluster. create_cluster_labels <- function(clusters, metadata, type_counts, weightingspec, top_n, stops, taxonomy_separator="/", params=NULL) { + vslog$debug(paste("create_cluster_labels:", clusters$num_clusters, "clusters,", + nrow(metadata), "papers")) + dump_data(clusters, "summarize_01_clusters") + dump_data(metadata[, intersect(c("id", "title", "subject", "subject_orig", "paper_abstract"), + names(metadata)), drop = FALSE], "summarize_02_metadata") + dump_data(type_counts, "summarize_03_type_counts") cc <- params$custom_clustering if (!(is.null(cc)) && (cc %in% names(metadata))) { nn_corpus <- get_custom_cluster_corpus(clusters, metadata, stops, taxonomy_separator, custom_clustering=cc) } else { nn_corpus <- get_cluster_corpus(clusters, metadata, stops, taxonomy_separator) } + dump_data(nn_corpus, "summarize_04_corpus") nn_tfidf <- TermDocumentMatrix(nn_corpus, control = list( tokenize = SplitTokenizer, weighting = function(x) weightSMART(x, spec="ntn"), @@ -67,8 +108,11 @@ create_cluster_labels <- function(clusters, metadata, )) tfidf_top <- apply(nn_tfidf, 2, function(x) {x2 <- sort(x, TRUE);x2[x2>0]}) empty_tfidf <- which(apply(nn_tfidf, 2, sum)==0) + vslog$debug(paste("create_cluster_labels: tf-idf matrix", nTerms(nn_tfidf), "terms x", + nDocs(nn_tfidf), "clusters;", length(empty_tfidf), "clusters need a fallback")) tfidf_top[c(empty_tfidf)] <- fill_empty_clusters(nn_tfidf, nn_corpus)[c(empty_tfidf)] tfidf_top_names <- get_top_names(tfidf_top, top_n, stops) + dump_data(tfidf_top_names, "summarize_05_tfidf_top_names") clusters$cluster_labels = "" batch_size <- 1000 total_length <- length(stops) @@ -76,6 +120,12 @@ create_cluster_labels <- function(clusters, metadata, matches = which(unname(clusters$groups == k) == TRUE) summary = tfidf_top_names[[k]] if (summary == "") { + # Cluster label generation fallback: + # This is applied to clusters that have no top names, + # and will be used to generate a label from the titles and + # abstracts of the papers in the cluster + vslog$debug(paste("create_cluster_labels: title/abstract fallback for cluster", k, + "with", length(matches), "papers")) candidates = mapply(paste, metadata$title[matches], metadata$paper_abstract[matches]) candidates = lapply(candidates, tolower) for (i in seq(1, total_length, batch_size)) { @@ -100,10 +150,16 @@ create_cluster_labels <- function(clusters, metadata, clusters$cluster_labels = metadata$annotations[[cc]] } clusters$cluster_labels <- fix_cluster_labels(clusters$cluster_labels, type_counts) + dump_data(data.frame(cluster = clusters$groups, label = clusters$cluster_labels), + "summarize_06_cluster_labels") + vslog$debug(paste("create_cluster_labels: done,", + length(unique(clusters$cluster_labels)), "distinct labels")) return(clusters) } +# Normalise a vector of cluster labels: restore term casing (fix_keyword_casing) +# and collapse repeated commas. Returns the cleaned labels. fix_cluster_labels <- function(clusterlabels, type_counts){ unlist(mclapply(clusterlabels, function(x) { x <- fix_keyword_casing(x, type_counts) @@ -112,6 +168,9 @@ fix_cluster_labels <- function(clusterlabels, type_counts){ })) } +# Restore the casing of each word in a single label and capitalise the first +# letter of every comma-separated phrase. Words are matched back to their +# original corpus casing via match_keyword_case(type_counts). fix_keyword_casing <- function(keyword, type_counts) { kw = strsplit(keyword, ", ") kw = lapply(kw, strsplit, " ")[[1]] @@ -122,11 +181,18 @@ fix_keyword_casing <- function(keyword, type_counts) { return(paste(kw, collapse = ", ")) } +# Return the canonical (original-corpus) casing of a token: looks it up in +# type_counts case-insensitively, ignoring hyphens. Falls back to the input +# token if there is no match. match_keyword_case <- function(x, type_counts) { y <- names(type_counts[which(tolower(names(type_counts)) == gsub("-", "", tolower(x)))][1]) if (!is.na(y)) return(y) else return(x) } +# Build the per-cluster corpus from a custom metadata field (custom_clustering) +# instead of subjects. Per cluster: removes stopwords, then joins and normalises +# the field values into a single ";"-separated token string. Returns a VCorpus +# with one document per cluster. get_custom_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, add_title_ngrams = T, custom_clustering=NULL) { subjectlist = list() @@ -152,6 +218,12 @@ get_custom_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separa return(nn_corpus) } +# Build the per-cluster corpus used for tf-idf labelling. Per cluster: combines +# the papers' `subject` keywords with bi-/tri-grams from their titles. Taxonomy +# subjects (containing taxonomy_separator) are reduced to their last path segment. +# Everything is normalised into one ";"-separated token string per cluster. +# Returns a VCorpus with one document per cluster. add_title_ngrams toggles the +# title n-gram contribution. get_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, add_title_ngrams = T, custom_clustering=NULL) { subjectlist = list() @@ -196,6 +268,9 @@ get_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, } +# Turn the ranked tf-idf terms of each cluster into a display label: prunes +# stopword-edged n-grams, removes n-grams nested inside others (keeping the more +# specific one), capitalises, and returns the top_n terms joined with ", ". get_top_names <- function(tfidf_top, top_n, stops) { tfidf_top_names <- lapply(tfidf_top, names) tfidf_top_names <- lapply(tfidf_top_names, function(x) {another_prune_ngrams(x, stops)}) @@ -206,6 +281,9 @@ get_top_names <- function(tfidf_top, top_n, stops) { return(tfidf_top_names) } +# Variant of prune_ngrams used on tf-idf term names: drops n-grams that start or +# end with a stopword or whose first and last token are identical. Tolerant of +# empty/NA tokens. Returns the surviving "_"-joined n-grams. another_prune_ngrams <- function(ngrams, stops){ # filter out stopwords from start or stop of ngrams tokens <- unname(unlist(ngrams)) @@ -253,6 +331,10 @@ another_prune_ngrams <- function(ngrams, stops){ return(tokens) } +# Provide fallback top terms for clusters that produced none under the strict +# tf-idf bound, by recomputing the TermDocumentMatrix with a looser local bound +# (terms appearing at least once, instead of at least twice). Returns the +# per-cluster sorted term lists. fill_empty_clusters <- function(nn_tfidf, nn_corpus){ replacement_nn_tfidf <- TermDocumentMatrix(nn_corpus, control = list(tokenize = SplitTokenizer, weighting = function(x) weightSMART(x, spec="ntn"), @@ -263,6 +345,9 @@ fill_empty_clusters <- function(nn_tfidf, nn_corpus){ } +# Extract pruned bi- and tri-grams from a set of titles (see prune_ngrams) and +# return them concatenated. Note: ngram_lengths is currently unused — lengths 2 +# and 3 are hardcoded. get_title_ngrams <- function(titles, stops, ngram_lengths) { # for ngrams: we have to collapse with "_" or else tokenizers will split ngrams again at that point and we'll be left with unigrams titles_bigrams = prune_ngrams(expand_ngrams(titles, 2), stops) @@ -271,6 +356,10 @@ get_title_ngrams <- function(titles, stops, ngram_lengths) { } +# De-duplicate overlapping n-grams, preferring the more specific phrase: if a +# candidate is a substring of an already-kept name it is skipped; if a kept name +# is a substring of the candidate it is replaced by the candidate; otherwise the +# candidate is added. Returns up to top_n unique names. filter_out_nested_ngrams <- function(top_ngrams, top_n) { top_names <- list() for (ngram in top_ngrams) { diff --git a/server/preprocessing/other-scripts/utils.R b/server/preprocessing/other-scripts/utils.R index c5c20bc0e..d880c1aaa 100644 --- a/server/preprocessing/other-scripts/utils.R +++ b/server/preprocessing/other-scripts/utils.R @@ -53,24 +53,56 @@ get_stopwords <- function(languages) { } +# Configure logging at the given level. Sets the *root logger level* so that +# records at `loglevel` and above are actually emitted -- the previous version +# called getLogger(loglevel), which only created a logger named after the level +# and left the root at INFO, so every $debug() message was silently dropped. +# Records are written to LOGFILE when it is set, otherwise to the console. setup_logging <- function(loglevel) { - # checks if LOGFILE is defined, - # if not logs to console only + setLevel(loglevel) + removeHandler('basic.stdout') if (Sys.getenv("LOGFILE") == ""){ - getLogger(loglevel) - removeHandler('basic.stdout') - addHandler(writeToConsole) + addHandler(writeToConsole, level = loglevel) } else { if (!file.exists(Sys.getenv("LOGFILE"))) { file.create(Sys.getenv("LOGFILE")) } - getLogger(loglevel) - removeHandler('basic.stdout') - addHandler(writeToFile, file=Sys.getenv("LOGFILE")) + addHandler(writeToFile, file = Sys.getenv("LOGFILE"), level = loglevel) } } +# TRUE when debug logging and data dumping are enabled (LOGLEVEL=DEBUG). Single +# source of truth for the debug flag, reused for both verbose logs and dumps. +debug_enabled <- function() { + Sys.getenv("LOGLEVEL") == "DEBUG" +} + + +# Dump an intermediate object to a per-vis debug folder, for manual inspection +# and as test fixtures -- mirrors the _log_dataframe pattern of the BASE/ORCID +# workers. Only writes when debug_enabled(). Data frames are written as CSV, other +# R objects (lists, corpora, ...) as RDS, under //. +# (DUMP_DIR defaults to /headstart/output). Failures are logged, never fatal. +dump_data <- function(obj, stage) { + if (!debug_enabled()) return(invisible(NULL)) + vis_id <- .GlobalEnv$VIS_ID + if (is.null(vis_id) || identical(vis_id, "")) vis_id <- "unknown" + out_dir <- file.path(Sys.getenv("DUMP_DIR", unset = "/headstart/output"), vis_id) + tryCatch({ + dir.create(out_dir, recursive = TRUE, showWarnings = FALSE) + if (is.data.frame(obj)) { + write.csv(obj, file.path(out_dir, paste0(stage, ".csv")), row.names = FALSE) + } else { + saveRDS(obj, file.path(out_dir, paste0(stage, ".rds"))) + } + }, error = function(e) { + logwarn(paste("dump_data failed for stage", stage, ":", conditionMessage(e))) + }) + invisible(NULL) +} + + detect_error <- function(failed, service, params) { output <- list() reason <- list() From bed26f6a78eda4a5f7a791924e7ce31a242d5716 Mon Sep 17 00:00:00 2001 From: chreman Date: Mon, 6 Jul 2026 23:44:17 +0200 Subject: [PATCH 137/205] add wedge-in for ranking modes; config variables via env; basic tests --- docker-compose.yml | 5 + local_dev/dev.env.example | 11 +++ server/preprocessing/other-scripts/ranking.R | 69 +++++++++++++ .../preprocessing/other-scripts/summarize.R | 29 ++++-- .../other-scripts/test/run_tests.R | 44 +++++++++ .../other-scripts/test/test_ranking_config.R | 98 +++++++++++++++++++ .../other-scripts/test/test_ranking_wedge.R | 72 ++++++++++++++ .../test/test_subject_cleaning.R | 13 ++- .../other-scripts/test/testthat_shim.R | 78 +++++++++++++++ .../preprocessing/other-scripts/vis_layout.R | 3 +- 10 files changed, 412 insertions(+), 10 deletions(-) create mode 100644 server/preprocessing/other-scripts/ranking.R create mode 100644 server/preprocessing/other-scripts/test/run_tests.R create mode 100644 server/preprocessing/other-scripts/test/test_ranking_config.R create mode 100644 server/preprocessing/other-scripts/test/test_ranking_wedge.R create mode 100644 server/preprocessing/other-scripts/test/testthat_shim.R diff --git a/docker-compose.yml b/docker-compose.yml index 9059d81f1..ce179ce92 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -129,6 +129,11 @@ services: REDIS_PASSWORD: "${REDIS_PASSWORD}" LOGLEVEL: "${LOGLEVEL}" LOGFILE: "${LOGFILE}" + RANKING_MODE: "${RANKING_MODE}" + RANKING_MODE_BASE: "${RANKING_MODE_BASE}" + RANKING_MODE_PUBMED: "${RANKING_MODE_PUBMED}" + RANKING_MODE_ORCID: "${RANKING_MODE_ORCID}" + RANKING_MODE_OPENAIRE: "${RANKING_MODE_OPENAIRE}" RENV_VERSION: 0.14.0-5 CRAN_REPOS: https://cran.wu.ac.at LC_ALL: "en_US.UTF-8" diff --git a/local_dev/dev.env.example b/local_dev/dev.env.example index 1e028d723..485e39e27 100644 --- a/local_dev/dev.env.example +++ b/local_dev/dev.env.example @@ -14,6 +14,17 @@ REDIS_PASSWORD=testredispassword LOGLEVEL=DEBUG LOGFILE="/var/log/headstart/headstart.log" +# Area-title ranking mode. Only the +# dataprocessing worker reads these. Values: 0|1|2|3 (0 = legacy, no change). +# RANKING_MODE is the global default; the per-integration overrides +# RANKING_MODE_ take precedence when set. Leave a per-integration var +# empty to fall back to the global default. +RANKING_MODE=0 +RANKING_MODE_BASE= +RANKING_MODE_PUBMED= +RANKING_MODE_ORCID= +RANKING_MODE_OPENAIRE= + BEHIND_PROXY=True DEFAULT_DATABASE=dev FLASK_ENV=development diff --git a/server/preprocessing/other-scripts/ranking.R b/server/preprocessing/other-scripts/ranking.R new file mode 100644 index 000000000..e6ae0cd4a --- /dev/null +++ b/server/preprocessing/other-scripts/ranking.R @@ -0,0 +1,69 @@ +# ranking.R +# Area-title ranking modes (see docs/area_title_ranking_plan.md). +# +# Stage 0 wiring only: the per-integration config resolver `ranking_mode()` and +# the selection wedge `select_cluster_label_names()`. The wedge is a NO-OP for +# Mode 0 (the unchanged legacy selection) and, until the rank-aware selection +# lands (Stage 1+), falls back to the legacy selection for Modes 1-3 so an early +# config flag can never break labelling. +# +# This file is deliberately pure base R (no packages, no logging) so it can be +# sourced and unit-tested in isolation, mirroring subject_cleaning.R. + +RANKING_MODES <- c("0", "1", "2", "3") + +# Resolve the ranking mode for a data integration. Precedence: +# 1. per-integration override RANKING_MODE_ (e.g. RANKING_MODE_BASE) +# 2. global RANKING_MODE +# 3. default "0" (legacy) +# A value is only honoured if it is one of RANKING_MODES; an invalid value at one +# level falls through to the next, and if nothing valid is set the function +# returns "0". It warns only when something *was* configured but nothing valid +# resolved, so a misconfiguration degrades safely to legacy instead of breaking. +# service : integration name (e.g. "base", "pubmed", "orcid", "openaire"); the +# env var is RANKING_MODE_. NULL/"" skips step 1. +ranking_mode <- function(service = NULL) { + pick <- function(v) if (nzchar(v) && v %in% RANKING_MODES) v else NA_character_ + + raw_svc <- if (!is.null(service) && nzchar(service)) { + Sys.getenv(paste0("RANKING_MODE_", toupper(service))) + } else { + "" + } + raw_glob <- Sys.getenv("RANKING_MODE") + + mode <- pick(raw_svc) + if (is.na(mode)) mode <- pick(raw_glob) + if (!is.na(mode)) return(mode) + + # nothing valid resolved; warn if the user set *something* + if (nzchar(raw_svc) || nzchar(raw_glob)) { + msg <- sprintf( + "ranking_mode: no valid ranking mode for service '%s' (per-integration='%s', global='%s'); using legacy mode 0", + if (is.null(service)) "" else service, raw_svc, raw_glob) + if (exists("logwarn")) logwarn(msg) else warning(msg) + } + "0" +} + +# Selection wedge: turn the per-cluster ranked tf-idf term lists into display +# labels according to `mode`. Mode 0 is the unchanged legacy selection +# (`legacy_fn`, default get_top_names). Modes 1-3 will apply rank-aware selection; +# until that lands they fall back to `legacy_fn` with a warning (Stage 0), so the +# pipeline is never broken by an early config flag. +# tfidf_top : per-cluster named, descending tf-idf term lists. +# top_n : number of terms kept per label. +# stops : stopword vector. +# mode : ranking mode string (see RANKING_MODES). +# legacy_fn : the legacy selector; injectable for testing. +select_cluster_label_names <- function(tfidf_top, top_n, stops, mode = "0", + legacy_fn = get_top_names) { + if (identical(mode, "0")) { + return(legacy_fn(tfidf_top, top_n, stops)) + } + msg <- sprintf( + "select_cluster_label_names: ranking mode '%s' not yet implemented (Stage 0); using legacy selection", + mode) + if (exists("vslog")) vslog$warn(msg) else if (exists("logwarn")) logwarn(msg) else warning(msg) + legacy_fn(tfidf_top, top_n, stops) +} diff --git a/server/preprocessing/other-scripts/summarize.R b/server/preprocessing/other-scripts/summarize.R index 0e39b4d67..8c9f2438b 100644 --- a/server/preprocessing/other-scripts/summarize.R +++ b/server/preprocessing/other-scripts/summarize.R @@ -80,13 +80,15 @@ prune_ngrams <- function(ngrams, stops){ # top_n : number of terms kept per label. # stops : stopword vector. # taxonomy_separator : if set, taxonomy subjects keep only their last path segment. +# service : data integration name (base|pubmed|orcid|openaire|…), used +# to resolve the per-integration ranking mode (see ranking.R). # Returns clusters with $cluster_labels filled: one label per paper, identical # for all papers in the same cluster. create_cluster_labels <- function(clusters, metadata, type_counts, weightingspec, top_n, stops, taxonomy_separator="/", - params=NULL) { + params=NULL, service=NULL) { vslog$debug(paste("create_cluster_labels:", clusters$num_clusters, "clusters,", nrow(metadata), "papers")) dump_data(clusters, "summarize_01_clusters") @@ -111,7 +113,13 @@ create_cluster_labels <- function(clusters, metadata, vslog$debug(paste("create_cluster_labels: tf-idf matrix", nTerms(nn_tfidf), "terms x", nDocs(nn_tfidf), "clusters;", length(empty_tfidf), "clusters need a fallback")) tfidf_top[c(empty_tfidf)] <- fill_empty_clusters(nn_tfidf, nn_corpus)[c(empty_tfidf)] - tfidf_top_names <- get_top_names(tfidf_top, top_n, stops) + # Ranking-mode wedge (ranking.R): Mode 0 is the unchanged legacy selection; + # Modes 1-3 apply rank-aware selection (Stage 1+). The existing fallbacks below + # (title/abstract frequency) remain the final safety net in every mode. + mode <- ranking_mode(service) + vslog$debug(paste("create_cluster_labels: ranking mode", mode, "for service", + if (is.null(service)) "(none)" else service)) + tfidf_top_names <- select_cluster_label_names(tfidf_top, top_n, stops, mode = mode) dump_data(tfidf_top_names, "summarize_05_tfidf_top_names") clusters$cluster_labels = "" batch_size <- 1000 @@ -120,10 +128,10 @@ create_cluster_labels <- function(clusters, metadata, matches = which(unname(clusters$groups == k) == TRUE) summary = tfidf_top_names[[k]] if (summary == "") { - # Cluster label generation fallback: - # This is applied to clusters that have no top names, - # and will be used to generate a label from the titles and - # abstracts of the papers in the cluster + # Cluster label generation fallback: + # This is applied to clusters that have no top names, + # and will be used to generate a label from the titles and + # abstracts of the papers in the cluster vslog$debug(paste("create_cluster_labels: title/abstract fallback for cluster", k, "with", length(matches), "papers")) candidates = mapply(paste, metadata$title[matches], metadata$paper_abstract[matches]) @@ -227,6 +235,7 @@ get_custom_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separa get_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, add_title_ngrams = T, custom_clustering=NULL) { subjectlist = list() + subject_dbg = list(); title_ngram_dbg = list() # debug: keep the two sources apart for (k in seq(1, clusters$num_clusters)) { matches = which(unname(clusters$groups == k) == TRUE) titles = metadata$title[matches] @@ -252,6 +261,8 @@ get_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, subjects = lapply(subjects, function(x){paste(unlist(x), collapse=";")}) subjects = mapply(paste, subjects, taxons, collapse=";") } + subject_dbg[[k]] = paste(unlist(subjects), collapse=";") + title_ngram_dbg[[k]] = paste(unlist(title_ngrams), collapse=";") if (add_title_ngrams == T) { all_subjects = paste(subjects, title_ngrams, collapse=" ") } else { @@ -263,6 +274,12 @@ get_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, all_subjects <- str_replace_all(all_subjects, " +", ";") subjectlist = c(subjectlist, all_subjects) } + # Debug: record, per cluster, the tokens contributed by subjects vs. by title + # n-grams, so a label term can be attributed to its source (see get_title_ngrams). + dump_data(data.frame(cluster = seq_along(subjectlist), + subject_tokens = unlist(subject_dbg), + title_ngrams = unlist(title_ngram_dbg)), + "summarize_04a_corpus_sources") nn_corpus <- VCorpus(VectorSource(subjectlist)) return(nn_corpus) } diff --git a/server/preprocessing/other-scripts/test/run_tests.R b/server/preprocessing/other-scripts/test/run_tests.R new file mode 100644 index 000000000..1a9ed93dc --- /dev/null +++ b/server/preprocessing/other-scripts/test/run_tests.R @@ -0,0 +1,44 @@ +#!/usr/bin/env Rscript +# Test runner for the other-scripts R unit tests. +# +# Uses `testthat` if installed; otherwise falls back to test/testthat_shim.R so +# the suite runs inside the pipeline image (which has no testthat). Exits with a +# non-zero status if any test fails, so it is CI-friendly. +# +# Usage (from the other-scripts/ directory): +# Rscript test/run_tests.R # run the default suite +# Rscript test/run_tests.R test/test_ranking_config.R # run specific file(s) + +args <- commandArgs(trailingOnly = TRUE) + +use_testthat <- requireNamespace("testthat", quietly = TRUE) +if (use_testthat) { + library(testthat) + cat("Using installed testthat.\n") +} else { + source("test/testthat_shim.R") + cat("testthat not installed — using dependency-free shim.\n") +} + +# Default suite: the pure-base-R test files (run anywhere, no tm/logging needed). +default_files <- c( + "test/test_subject_cleaning.R", + "test/test_ranking_config.R", + "test/test_ranking_wedge.R" +) +test_files <- if (length(args) > 0) args else default_files + +for (f in test_files) { + cat("\n== ", f, " ==\n", sep = "") + if (use_testthat) testthat::test_file(f) else source(f) +} + +if (!use_testthat) { + cat(sprintf("\n---\n%d passed, %d failed\n", + .shim_results$pass, .shim_results$fail)) + if (.shim_results$fail > 0) { + cat("Failures:\n") + cat(paste0(" - ", .shim_results$failures, collapse = "\n"), "\n", sep = "") + quit(status = 1, save = "no") + } +} diff --git a/server/preprocessing/other-scripts/test/test_ranking_config.R b/server/preprocessing/other-scripts/test/test_ranking_config.R new file mode 100644 index 000000000..534751702 --- /dev/null +++ b/server/preprocessing/other-scripts/test/test_ranking_config.R @@ -0,0 +1,98 @@ +# Unit tests for the per-integration ranking-mode config resolver (ranking.R). +# +# Run via test/run_tests.R (from the other-scripts directory). ranking.R is pure +# base R, so it is sourced and tested in isolation. +# +# Covers the config surface (§8) and edge case §15-13 of +# docs/area_title_ranking_plan.md: unset, per-integration override, global +# fallback, precedence, case-insensitivity, and invalid/misconfigured values. + +if (!exists("ranking_mode")) { + source("ranking.R") +} + +# Clear every RANKING_MODE* var so each test starts from a known-empty env. +clear_ranking_env <- function() { + vars <- names(Sys.getenv()) + vars <- vars[startsWith(vars, "RANKING_MODE")] + if (length(vars)) Sys.unsetenv(vars) +} + +# --- default / unset --------------------------------------------------------- +test_that("unset env with no service defaults to mode 0", { + clear_ranking_env() + expect_equal(ranking_mode(), "0") +}) + +test_that("unset env with a service still defaults to mode 0", { + clear_ranking_env() + expect_equal(ranking_mode("base"), "0") +}) + +test_that("NULL and empty service are treated the same as no service", { + clear_ranking_env() + Sys.setenv(RANKING_MODE = "1") + expect_equal(ranking_mode(NULL), "1") + expect_equal(ranking_mode(""), "1") +}) + +# --- global RANKING_MODE ----------------------------------------------------- +test_that("a valid global RANKING_MODE is honoured", { + clear_ranking_env() + Sys.setenv(RANKING_MODE = "2") + expect_equal(ranking_mode(), "2") + expect_equal(ranking_mode("orcid"), "2") # no per-integration override -> global +}) + +# --- per-integration override ------------------------------------------------ +test_that("a per-integration override is used for its service", { + clear_ranking_env() + Sys.setenv(RANKING_MODE_BASE = "1") + expect_equal(ranking_mode("base"), "1") +}) + +test_that("per-integration override beats the global default", { + clear_ranking_env() + Sys.setenv(RANKING_MODE = "0", RANKING_MODE_PUBMED = "2") + expect_equal(ranking_mode("pubmed"), "2") # override wins + expect_equal(ranking_mode("base"), "0") # other services fall back to global +}) + +test_that("service lookup is case-insensitive", { + clear_ranking_env() + Sys.setenv(RANKING_MODE_BASE = "3") + expect_equal(ranking_mode("base"), "3") + expect_equal(ranking_mode("BASE"), "3") + expect_equal(ranking_mode("Base"), "3") +}) + +test_that("different integrations can run different modes simultaneously", { + clear_ranking_env() + Sys.setenv(RANKING_MODE_PUBMED = "2", RANKING_MODE_BASE = "0") + expect_equal(ranking_mode("pubmed"), "2") + expect_equal(ranking_mode("base"), "0") + expect_equal(ranking_mode("orcid"), "0") # unset -> default +}) + +# --- invalid / misconfigured values fall back safely to 0 -------------------- +test_that("an invalid global value falls back to mode 0", { + clear_ranking_env() + Sys.setenv(RANKING_MODE = "5") + expect_equal(ranking_mode(), "0") + Sys.setenv(RANKING_MODE = "banana") + expect_equal(ranking_mode("base"), "0") +}) + +test_that("an invalid per-integration value falls through to a valid global", { + clear_ranking_env() + Sys.setenv(RANKING_MODE = "2", RANKING_MODE_BASE = "banana") + expect_equal(ranking_mode("base"), "2") # invalid override ignored, global used +}) + +test_that("invalid at both levels yields mode 0", { + clear_ranking_env() + Sys.setenv(RANKING_MODE = "9", RANKING_MODE_BASE = "x") + expect_equal(ranking_mode("base"), "0") +}) + +clear_ranking_env() diff --git a/server/preprocessing/other-scripts/test/test_ranking_wedge.R b/server/preprocessing/other-scripts/test/test_ranking_wedge.R new file mode 100644 index 000000000..bd11ecf94 --- /dev/null +++ b/server/preprocessing/other-scripts/test/test_ranking_wedge.R @@ -0,0 +1,72 @@ +# Unit tests for the ranking-mode selection wedge (ranking.R). +# +# Run via test/run_tests.R (from the other-scripts directory). We inject a stub +# `legacy_fn` so the wedge can be tested in isolation, without the tm stack or +# summarize.R. +# +# Stage 0 invariant: the wedge is a NO-OP — Mode 0 uses the legacy selection, and +# Modes 1-3 (not yet implemented) also fall back to it. When Stage 1 lands, the +# Mode 1-3 cases below become the first behaviour to change, so these tests pin +# the current contract and will flag that change. + +if (!exists("select_cluster_label_names")) { + source("ranking.R") +} + +# A recording stub standing in for get_top_names: counts calls and captures the +# arguments it was passed, and returns a sentinel value. +make_stub <- function(ret = "STUB_LABELS") { + state <- new.env(parent = emptyenv()) + state$n <- 0L + state$last <- NULL + fn <- function(tfidf_top, top_n, stops) { + state$n <- state$n + 1L + state$last <- list(tfidf_top = tfidf_top, top_n = top_n, stops = stops) + ret + } + list(fn = fn, state = state) +} + +TT <- list(c(a = 3, b = 2), c(x = 1)) # dummy per-cluster tf-idf term lists +STOPS <- c("the", "of") + +# --- Mode 0: pure legacy pass-through ---------------------------------------- +test_that("mode 0 delegates to the legacy selector unchanged", { + s <- make_stub() + out <- select_cluster_label_names(TT, top_n = 3, stops = STOPS, mode = "0", legacy_fn = s$fn) + expect_equal(out, "STUB_LABELS") + expect_equal(s$state$n, 1L) +}) + +test_that("mode 0 forwards its arguments to the legacy selector verbatim", { + s <- make_stub() + select_cluster_label_names(TT, top_n = 3, stops = STOPS, mode = "0", legacy_fn = s$fn) + expect_identical(s$state$last$tfidf_top, TT) + expect_equal(s$state$last$top_n, 3) + expect_identical(s$state$last$stops, STOPS) +}) + +# --- Stage 0: modes 1-3 fall back to legacy (behaviour changes in Stage 1) ---- +test_that("modes 1-3 fall back to the legacy selector in Stage 0", { + for (m in c("1", "2", "3")) { + s <- make_stub() + out <- suppressWarnings( + select_cluster_label_names(TT, top_n = 3, stops = STOPS, mode = m, legacy_fn = s$fn)) + expect_equal(out, "STUB_LABELS") + expect_equal(s$state$n, 1L) + } +}) + +# --- Integration with the config resolver ------------------------------------ +test_that("a per-integration mode resolves and (Stage 0) still uses legacy", { + old <- Sys.getenv("RANKING_MODE_BASE", unset = NA) + on.exit(if (is.na(old)) Sys.unsetenv("RANKING_MODE_BASE") else Sys.setenv(RANKING_MODE_BASE = old)) + Sys.setenv(RANKING_MODE_BASE = "1") + mode <- ranking_mode("base") + expect_equal(mode, "1") + s <- make_stub() + out <- suppressWarnings( + select_cluster_label_names(TT, top_n = 3, stops = STOPS, mode = mode, legacy_fn = s$fn)) + expect_equal(out, "STUB_LABELS") # Stage 0: still legacy + expect_equal(s$state$n, 1L) +}) diff --git a/server/preprocessing/other-scripts/test/test_subject_cleaning.R b/server/preprocessing/other-scripts/test/test_subject_cleaning.R index a66e9defe..6940cfa0f 100644 --- a/server/preprocessing/other-scripts/test/test_subject_cleaning.R +++ b/server/preprocessing/other-scripts/test/test_subject_cleaning.R @@ -1,12 +1,19 @@ # Unit & integration tests for the subject/keyword cleaning. # -# Run from the other-scripts directory, e.g.: +# Run via the test runner (from the other-scripts directory): +# Rscript test/run_tests.R +# or, with testthat installed: # Rscript -e 'library(testthat); test_file("test/test_subject_cleaning.R")' # # subject_cleaning.R is pure base R (no packages, no logging), so it can be -# sourced and tested in isolation. +# sourced and tested in isolation. When testthat is not installed, the runner +# provides a dependency-free shim with the same test_that/expect_* API. -library(testthat) +if (!requireNamespace("testthat", quietly = TRUE)) { + if (!exists("test_that")) source("test/testthat_shim.R") +} else { + library(testthat) +} if (!exists("deinvert_marked_mesh_keywords")) { source("subject_cleaning.R") diff --git a/server/preprocessing/other-scripts/test/testthat_shim.R b/server/preprocessing/other-scripts/test/testthat_shim.R new file mode 100644 index 000000000..5e7632546 --- /dev/null +++ b/server/preprocessing/other-scripts/test/testthat_shim.R @@ -0,0 +1,78 @@ +# Minimal testthat-compatible shim. +# +# The pipeline image does not ship `testthat`, so this provides just the subset +# of the API our unit tests use, letting the suite run inside the container. When +# the real `testthat` is installed the runner uses it instead and this file is +# not sourced. Results accumulate in `.shim_results`; run_tests.R reports them. + +.shim_results <- new.env(parent = emptyenv()) +.shim_results$pass <- 0L +.shim_results$fail <- 0L +.shim_results$failures <- character(0) + +.shim_fail <- function(msg) { + stop(structure(class = c("expectation_failure", "error", "condition"), + list(message = msg, call = NULL))) +} + +test_that <- function(desc, code) { + ok <- tryCatch({ force(code); TRUE }, + expectation_failure = function(e) { + .shim_results$fail <- .shim_results$fail + 1L + .shim_results$failures <- c(.shim_results$failures, + paste0(desc, ": ", conditionMessage(e))) + cat(" FAIL: ", desc, " — ", conditionMessage(e), "\n", sep = "") + FALSE + }, + error = function(e) { + .shim_results$fail <- .shim_results$fail + 1L + .shim_results$failures <- c(.shim_results$failures, + paste0(desc, " [error]: ", conditionMessage(e))) + cat(" ERROR: ", desc, " — ", conditionMessage(e), "\n", sep = "") + FALSE + }) + if (isTRUE(ok)) { + .shim_results$pass <- .shim_results$pass + 1L + cat(" ok: ", desc, "\n", sep = "") + } + invisible(ok) +} + +expect_equal <- function(object, expected, ...) { + if (!isTRUE(all.equal(object, expected))) + .shim_fail(paste0("expected ", deparse(expected), " but got ", deparse(object))) + invisible(TRUE) +} + +expect_identical <- function(object, expected, ...) { + if (!identical(object, expected)) + .shim_fail(paste0("not identical: got ", deparse(object), " vs ", deparse(expected))) + invisible(TRUE) +} + +expect_true <- function(object, ...) { + if (!isTRUE(object)) .shim_fail("expected TRUE") + invisible(TRUE) +} + +expect_false <- function(object, ...) { + if (!isFALSE(object)) .shim_fail("expected FALSE") + invisible(TRUE) +} + +expect_null <- function(object, ...) { + if (!is.null(object)) .shim_fail(paste0("expected NULL but got ", deparse(object))) + invisible(TRUE) +} + +expect_match <- function(object, regexp, ...) { + if (!any(grepl(regexp, object))) + .shim_fail(paste0("'", paste(object, collapse=","), "' does not match /", regexp, "/")) + invisible(TRUE) +} + +expect_error <- function(object, ...) { + err <- tryCatch({ force(object); NULL }, error = function(e) e) + if (is.null(err)) .shim_fail("expected an error but none was raised") + invisible(TRUE) +} diff --git a/server/preprocessing/other-scripts/vis_layout.R b/server/preprocessing/other-scripts/vis_layout.R index da6f5f4be..88cb041ef 100644 --- a/server/preprocessing/other-scripts/vis_layout.R +++ b/server/preprocessing/other-scripts/vis_layout.R @@ -15,6 +15,7 @@ source('preprocess.R') source('features.R') source('cluster.R') source('summarize.R') +source('ranking.R') source('postprocess.R') registerDoParallel(detectCores(all.tests = FALSE, logical = TRUE)-1) @@ -56,7 +57,7 @@ vis_layout <- function(text, metadata, service, type_counts, weightingspec="ntn", top_n=3, stops=stops, taxonomy_separator, - params) + params, service=service) output <- create_overview_output(named_clusters, layout, metadata, list_size) } else { output <- create_streamgraph_output(metadata, list_size) From 58a6d3ae4ed4ae23374970cda53af6ea34c18a4c Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 7 Jul 2026 11:36:46 +0200 Subject: [PATCH 138/205] add integration tests and test harness for ranking mode before/after testing --- .../other-scripts/test/replay/README.md | 50 ++++++++ .../replay/base_cancer_research.expected.rds | Bin 0 -> 601 bytes .../replay/base_cancer_research.inputs.rds | Bin 0 -> 128053 bytes .../base_digital_education.expected.rds | Bin 0 -> 632 bytes .../replay/base_digital_education.inputs.rds | Bin 0 -> 114673 bytes .../replay/openaire_fight_ncov.expected.rds | Bin 0 -> 617 bytes .../replay/openaire_fight_ncov.inputs.rds | Bin 0 -> 107476 bytes .../test/replay/orcid_5116955x.expected.rds | Bin 0 -> 622 bytes .../test/replay/orcid_5116955x.inputs.rds | Bin 0 -> 66984 bytes .../test/replay/orcid_96127791.expected.rds | Bin 0 -> 784 bytes .../test/replay/orcid_96127791.inputs.rds | Bin 0 -> 196581 bytes .../test/replay/pubmed_infection.expected.rds | Bin 0 -> 621 bytes .../test/replay/pubmed_infection.inputs.rds | Bin 0 -> 237178 bytes .../other-scripts/test/replay_harness.R | 116 ++++++++++++++++++ .../other-scripts/test/run_tests.R | 16 ++- .../other-scripts/test/run_tests.sh | 14 +++ .../other-scripts/test/test_ranking_config.R | 4 +- .../other-scripts/test/test_replay_harness.R | 47 +++++++ .../other-scripts/test/test_replay_modes.R | 36 ++++++ 19 files changed, 278 insertions(+), 5 deletions(-) create mode 100644 server/preprocessing/other-scripts/test/replay/README.md create mode 100644 server/preprocessing/other-scripts/test/replay/base_cancer_research.expected.rds create mode 100644 server/preprocessing/other-scripts/test/replay/base_cancer_research.inputs.rds create mode 100644 server/preprocessing/other-scripts/test/replay/base_digital_education.expected.rds create mode 100644 server/preprocessing/other-scripts/test/replay/base_digital_education.inputs.rds create mode 100644 server/preprocessing/other-scripts/test/replay/openaire_fight_ncov.expected.rds create mode 100644 server/preprocessing/other-scripts/test/replay/openaire_fight_ncov.inputs.rds create mode 100644 server/preprocessing/other-scripts/test/replay/orcid_5116955x.expected.rds create mode 100644 server/preprocessing/other-scripts/test/replay/orcid_5116955x.inputs.rds create mode 100644 server/preprocessing/other-scripts/test/replay/orcid_96127791.expected.rds create mode 100644 server/preprocessing/other-scripts/test/replay/orcid_96127791.inputs.rds create mode 100644 server/preprocessing/other-scripts/test/replay/pubmed_infection.expected.rds create mode 100644 server/preprocessing/other-scripts/test/replay/pubmed_infection.inputs.rds create mode 100644 server/preprocessing/other-scripts/test/replay_harness.R create mode 100644 server/preprocessing/other-scripts/test/run_tests.sh create mode 100644 server/preprocessing/other-scripts/test/test_replay_harness.R create mode 100644 server/preprocessing/other-scripts/test/test_replay_modes.R diff --git a/server/preprocessing/other-scripts/test/replay/README.md b/server/preprocessing/other-scripts/test/replay/README.md new file mode 100644 index 000000000..855ca3036 --- /dev/null +++ b/server/preprocessing/other-scripts/test/replay/README.md @@ -0,0 +1,50 @@ +# Replay fixtures for area-title labelling + +This directory holds the **replay fixtures** and their **expected outputs** used by +`test/replay_harness.R` and `test/test_replay_modes.R`. + +- `.inputs.rds` — a frozen input bundle for `create_cluster_labels` + (clusters, metadata, type_counts, stops, params, service, …), captured from a + real map. This is what gets replayed. +- `.expected.rds` — the expected per-cluster labels under **Mode 0**. Created + automatically the first time `test_replay_modes.R` runs against a new fixture. + +The harness replays a fixture offline under a chosen ranking mode, so label output +is deterministic and independent of BASE/PubMed/etc. — this is our fast iteration +loop, **not** a replacement for end-to-end tests. + +## Capturing a fixture from a real map + +1. Ensure the dataprocessing worker runs with `LOGLEVEL=DEBUG` (it dumps + `summarize_00_label_inputs.rds` per map under `/headstart/output//`). +2. Generate the map (BASE / PubMed / ORCID / OpenAIRE) and note its `vis_id`. +3. Copy the bundle out of the container into this directory, naming it by + integration + topic, e.g.: + + ```sh + docker cp dev-dataprocessing-1:/headstart/output//summarize_00_label_inputs.rds \ + server/preprocessing/other-scripts/test/replay/pubmed_cancer.inputs.rds + ``` + +4. Run the suite; the first run records the Mode-0 expected output: + + ```sh + docker exec -w /headstart/other-scripts dev-dataprocessing-1 \ + sh test/run_tests.sh test/test_replay_modes.R + ``` + +5. Commit both the `.inputs.rds` and the generated `.expected.rds`. + +## Current fixtures + +One **generic** and/or one **biomedical** (MeSH-bearing) map per integration. Biomedical maps +are what exercise the specific/generic MeSH split in Modes 2/3. + +| fixture | integration | query / id | kind | +|---|---|---|---| +| `base_cancer_research` | BASE | `cancer research` | biomedical | +| `base_digital_education` | BASE | `digital education` | generic | +| `pubmed_infection` | PubMed | `infection` | biomedical (MeSH-rich) | +| `orcid_96127791` | ORCID | `0000-0001-9612-7791` | biomedical (health records) | +| `orcid_5116955x` | ORCID | `0000-0001-5116-955X` | generic (game theory) | +| `openaire_fight_ncov` | OpenAIRE | `Fight-nCoV` project | biomedical (COVID) | diff --git a/server/preprocessing/other-scripts/test/replay/base_cancer_research.expected.rds b/server/preprocessing/other-scripts/test/replay/base_cancer_research.expected.rds new file mode 100644 index 0000000000000000000000000000000000000000..502ff901dda4a9739c6dab4dc783f8839976de55 GIT binary patch literal 601 zcmV-f0;c^RiwFP!0000016@?fZWJ*PHQCr1?nubu033R5EV+ebl7UDhNF+$ykjGuq 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zypP-*bggkGR-+@oVy%>8kGoMa()!$rnzl1`{`rHS^&5Wfg`7v*YFRx}{H=@KR@*o( zpWl3>cr5?sW945}!MN@IgVXYTs-Bxm$d8^cRRL((rmG8zZS;YkiO|EFK|+_x^uY-^pP0R^<3tB0rDKXMnE?jw?N zr4yrD==xFY&4uYIX{FnYlC3hgV$ezR-tl~88NasbcDWN_U${z_2?aTyr>jh!MMM5^ zs(o2k(!>inH0^r7K~nuip>o^|&H+9gmiR@o(Am8-m7;Vf2M>7s`Y8QEy(i_`C*C)I zoGPQ`TH{6p-0FuXCH5)lTNSgYOX`bieKA}=4~6IJv}v2=w$4nc{MR=sFe=@^Jn-$C zPGfmNX85tvW$BFjEAUsH6xKLk2<-H0&csP5i zosaYBS+1_>FP0ENol8#Et`#Sw=eHjydSEG^NbN2=n=odrFx%>)BZQPs6FmvqmtFP!J_^M z^dZ&nHnrN{s{clahz_^%lXpDwz)*cpR$Qu#l%ogIRo}WuWO-{8d!bWH9mwNA zw6apzv(_y)(1U++|M%rZcCr1|Mfz5{z33qOdi+scQr@G#{lKHrSKUQ(A&cHP*?)Hb zF?*!{#qL7NM&kF*r}@c?N1q(%%{Y*C>S*IYvF;P0SPmy39lzDM80;ExUOzeA)LDDH z@E-X!x1=tX+eGV4oj1s(O{t+Z`mV8sF1P($?)j0ClC+1+C%+#G zRnAuT$?``k)~+rRA>q}9yOJH(!y#P=@zU5U@5#1ZeEPlL`}XRy@2}`~LVF<=s2jLwwvOZ>zE@j-NNF<3#LsWiBhZ{k(yPIlCHQ z?G1>Erj3jeg(e<|^XEz^An>68)brk(+vg^WR{`}NqMj%%pxzYkvj$H_c?Off4`eHm zh2MPM$nK>vuDOfDepFT%mq#<*o0Ov&w>SNS&zDsPD*5c-w31W>WM|>o i1`K<@!6aFv0Jo dump_data() no-ops + + clusters <- create_cluster_labels( + clusters = bundle$clusters, + metadata = bundle$metadata, + type_counts = bundle$type_counts, + weightingspec = bundle$weightingspec, + top_n = bundle$top_n, + stops = bundle$stops, + taxonomy_separator = bundle$taxonomy_separator, + params = bundle$params, + service = bundle$service) + labels_per_cluster(clusters) +} + +# File helpers. Fixtures: test/replay/.inputs.rds +# expected Mode-0 out: test/replay/.expected.rds +fixture_files <- function() list.files(REPLAY_DIR, pattern = "\\.inputs\\.rds$", full.names = TRUE) +fixture_name <- function(path) sub("\\.inputs\\.rds$", "", basename(path)) +expected_file <- function(name) file.path(REPLAY_DIR, paste0(name, ".expected.rds")) +read_expected <- function(name) readRDS(expected_file(name)) +write_expected <- function(name, labels) { + dir.create(REPLAY_DIR, showWarnings = FALSE, recursive = TRUE) + saveRDS(labels, expected_file(name)) +} + +# A small, self-contained input bundle for validating the harness without any +# external data: two clearly-separated clusters (climate vs. machine learning). +build_synthetic_bundle <- function() { + metadata <- data.frame( + title = c( + "Climate change and sea level rise", + "Global warming and climate change trends", + "Sea level rise projections under climate change", + "Deep learning for image recognition", + "Neural network training for machine learning", + "Machine learning applications and neural networks"), + subject = c( + "climate change; sea level rise", + "climate change; global warming", + "climate change; sea level rise", + "machine learning; neural networks", + "machine learning; deep learning", + "machine learning; neural networks"), + paper_abstract = c( + "climate change drives sea level rise across coastal regions", + "global warming and climate change increase temperatures", + "projections of sea level rise under continued climate change", + "deep learning improves image recognition accuracy", + "training neural networks for machine learning tasks", + "machine learning applications using neural networks"), + stringsAsFactors = FALSE) + + clusters <- list(groups = c(1, 1, 1, 2, 2, 2), num_clusters = 2) + + type_counts <- c(Climate = 6, Change = 6, Sea = 4, Level = 4, Rise = 4, + Global = 2, Warming = 2, Machine = 6, Learning = 6, + Neural = 4, Networks = 4, Deep = 2) + + stops <- c("and", "for", "of", "the", "a", "to", "under", "across", "trends", + "projections", "applications", "using", "improves", "drives") + + list(clusters = clusters, metadata = metadata, type_counts = type_counts, + weightingspec = "ntn", top_n = 3, stops = stops, + taxonomy_separator = NULL, params = list(), service = "base") +} diff --git a/server/preprocessing/other-scripts/test/run_tests.R b/server/preprocessing/other-scripts/test/run_tests.R index 1a9ed93dc..dbf230e6e 100644 --- a/server/preprocessing/other-scripts/test/run_tests.R +++ b/server/preprocessing/other-scripts/test/run_tests.R @@ -20,12 +20,24 @@ if (use_testthat) { cat("testthat not installed — using dependency-free shim.\n") } -# Default suite: the pure-base-R test files (run anywhere, no tm/logging needed). -default_files <- c( +# Pure-base-R test files (run anywhere, no tm/logging needed). +base_files <- c( "test/test_subject_cleaning.R", "test/test_ranking_config.R", "test/test_ranking_wedge.R" ) +# Replay tests need the tm stack — included only when tm is available (i.e. inside +# the pipeline image, run via test/run_tests.sh), skipped on a bare host. +replay_files <- c( + "test/test_replay_harness.R", + "test/test_replay_modes.R" +) +default_files <- base_files +if (requireNamespace("tm", quietly = TRUE)) { + default_files <- c(default_files, replay_files) +} else { + cat("tm not available — skipping replay tests (run inside the container via test/run_tests.sh).\n") +} test_files <- if (length(args) > 0) args else default_files for (f in test_files) { diff --git a/server/preprocessing/other-scripts/test/run_tests.sh b/server/preprocessing/other-scripts/test/run_tests.sh new file mode 100644 index 000000000..197d03702 --- /dev/null +++ b/server/preprocessing/other-scripts/test/run_tests.sh @@ -0,0 +1,14 @@ +#!/bin/sh +# Run the R test suite inside the pipeline image. +# +# Bypasses the renv profile (R_PROFILE_USER=/dev/null) so the container's +# site-library packages (tm, stringr, logging, …) are importable — the replay +# tests need them, and renv sandboxes them off the library path. The pure-base-R +# unit tests are unaffected. +# +# Usage (from the other-scripts directory, e.g. via docker exec -w /headstart/other-scripts): +# sh test/run_tests.sh # full suite +# sh test/run_tests.sh test/test_replay_modes.R # specific file(s) + +cd "$(dirname "$0")/.." || exit 1 +R_PROFILE_USER=/dev/null Rscript test/run_tests.R "$@" diff --git a/server/preprocessing/other-scripts/test/test_ranking_config.R b/server/preprocessing/other-scripts/test/test_ranking_config.R index 534751702..fead8565e 100644 --- a/server/preprocessing/other-scripts/test/test_ranking_config.R +++ b/server/preprocessing/other-scripts/test/test_ranking_config.R @@ -3,9 +3,7 @@ # Run via test/run_tests.R (from the other-scripts directory). ranking.R is pure # base R, so it is sourced and tested in isolation. # -# Covers the config surface (§8) and edge case §15-13 of -# docs/area_title_ranking_plan.md: unset, per-integration override, global -# fallback, precedence, case-insensitivity, and invalid/misconfigured values. + if (!exists("ranking_mode")) { source("ranking.R") diff --git a/server/preprocessing/other-scripts/test/test_replay_harness.R b/server/preprocessing/other-scripts/test/test_replay_harness.R new file mode 100644 index 000000000..96d9243ef --- /dev/null +++ b/server/preprocessing/other-scripts/test/test_replay_harness.R @@ -0,0 +1,47 @@ +# Plumbing tests for the replay harness (replay_harness.R). +# +# Validates the harness machinery on a synthetic input bundle — no external data, +# no docker fixture needed — so it runs as soon as tm is available. The +# data-driven regression over real fixtures lives in test_replay_modes.R. + +if (!exists("replay_labels")) source("test/replay_harness.R") +if (!requireNamespace("testthat", quietly = TRUE)) { + if (!exists("test_that")) source("test/testthat_shim.R") +} else { + library(testthat) +} + +bundle <- build_synthetic_bundle() + +test_that("replay produces one label per cluster", { + labels <- replay_labels(bundle, mode = "0") + expect_equal(length(labels), bundle$clusters$num_clusters) + expect_equal(names(labels), c("1", "2")) +}) + +test_that("labels reflect the two distinct clusters (non-empty, different)", { + labels <- replay_labels(bundle, mode = "0") + expect_true(all(nzchar(labels))) + expect_false(labels[["1"]] == labels[["2"]]) + # cluster 1 is the climate cluster, cluster 2 the ML cluster + expect_match(tolower(labels[["1"]]), "climate|sea level") + expect_match(tolower(labels[["2"]]), "machine|neural|learning") +}) + +test_that("replay is deterministic across repeated runs", { + expect_equal(replay_labels(bundle, mode = "0"), + replay_labels(bundle, mode = "0")) +}) + +test_that("Stage 0: mode 1 output equals mode 0 (wedge is a no-op)", { + expect_equal(replay_labels(bundle, mode = "1"), + replay_labels(bundle, mode = "0")) +}) + +test_that("replay restores the environment it changed", { + before_rank <- Sys.getenv("RANKING_MODE") + before_log <- Sys.getenv("LOGLEVEL") + invisible(replay_labels(bundle, mode = "1")) + expect_equal(Sys.getenv("RANKING_MODE"), before_rank) + expect_equal(Sys.getenv("LOGLEVEL"), before_log) +}) diff --git a/server/preprocessing/other-scripts/test/test_replay_modes.R b/server/preprocessing/other-scripts/test/test_replay_modes.R new file mode 100644 index 000000000..de6ab2ca7 --- /dev/null +++ b/server/preprocessing/other-scripts/test/test_replay_modes.R @@ -0,0 +1,36 @@ +# Replay regression over real fixtures (replay_harness.R). +# +# For every fixture bundle in test/replay/*.inputs.rds: +# - replay under Mode 0 and assert the labels match the stored expected output; +# - if no expected output exists yet, record it (bootstrap) and pass. +# Fixtures are created from real maps — see test/replay/README.md. +# +# Runs inside the pipeline image (needs tm). Skips cleanly when no fixtures exist, +# so it is safe to keep in the default suite before any datasets are captured. + +if (!exists("replay_labels")) source("test/replay_harness.R") +if (!requireNamespace("testthat", quietly = TRUE)) { + if (!exists("test_that")) source("test/testthat_shim.R") +} else { + library(testthat) +} + +fixtures <- fixture_files() + +if (length(fixtures) == 0) { + cat(" (no fixtures in ", REPLAY_DIR, " yet — see README.md to capture some)\n", sep = "") +} else { + for (fx in fixtures) { + name <- fixture_name(fx) + test_that(paste0("Mode-0 labels are stable for fixture '", name, "'"), { + labels <- replay_labels(fx, mode = "0") + if (!file.exists(expected_file(name))) { + write_expected(name, labels) + cat(" (recorded expected output for '", name, "')\n", sep = "") + expect_true(TRUE) + } else { + expect_equal(labels, read_expected(name)) + } + }) + } +} From b15d6996090a1dbc2a2cbd2cc21669f3c3dbe622 Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 7 Jul 2026 11:37:04 +0200 Subject: [PATCH 139/205] debug dump functionality --- server/preprocessing/other-scripts/summarize.R | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/server/preprocessing/other-scripts/summarize.R b/server/preprocessing/other-scripts/summarize.R index 8c9f2438b..7095d9401 100644 --- a/server/preprocessing/other-scripts/summarize.R +++ b/server/preprocessing/other-scripts/summarize.R @@ -95,6 +95,13 @@ create_cluster_labels <- function(clusters, metadata, dump_data(metadata[, intersect(c("id", "title", "subject", "subject_orig", "paper_abstract"), names(metadata)), drop = FALSE], "summarize_02_metadata") dump_data(type_counts, "summarize_03_type_counts") + # Replay-harness fixture: capture the complete input bundle so this map can be + # replayed offline under any ranking mode (see test/replay_harness.R). RDS only, + # debug-gated like the other dumps. + dump_data(list(clusters = clusters, metadata = metadata, type_counts = type_counts, + weightingspec = weightingspec, top_n = top_n, stops = stops, + taxonomy_separator = taxonomy_separator, params = params, service = service), + "summarize_00_label_inputs") cc <- params$custom_clustering if (!(is.null(cc)) && (cc %in% names(metadata))) { nn_corpus <- get_custom_cluster_corpus(clusters, metadata, stops, taxonomy_separator, custom_clustering=cc) From 4b090abfca2ccf07d70e97d04e085f53a3896c4a Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 7 Jul 2026 14:08:43 +0200 Subject: [PATCH 140/205] tests for ranking mode 1 --- .../other-scripts/test/run_tests.R | 3 +- .../other-scripts/test/test_ranking_select.R | 75 +++++++++++++++++++ .../other-scripts/test/test_ranking_wedge.R | 18 ++--- .../other-scripts/test/test_replay_harness.R | 13 +++- 4 files changed, 96 insertions(+), 13 deletions(-) create mode 100644 server/preprocessing/other-scripts/test/test_ranking_select.R diff --git a/server/preprocessing/other-scripts/test/run_tests.R b/server/preprocessing/other-scripts/test/run_tests.R index dbf230e6e..943492cd7 100644 --- a/server/preprocessing/other-scripts/test/run_tests.R +++ b/server/preprocessing/other-scripts/test/run_tests.R @@ -24,7 +24,8 @@ if (use_testthat) { base_files <- c( "test/test_subject_cleaning.R", "test/test_ranking_config.R", - "test/test_ranking_wedge.R" + "test/test_ranking_wedge.R", + "test/test_ranking_select.R" ) # Replay tests need the tm stack — included only when tm is available (i.e. inside # the pipeline image, run via test/run_tests.sh), skipped on a bare host. diff --git a/server/preprocessing/other-scripts/test/test_ranking_select.R b/server/preprocessing/other-scripts/test/test_ranking_select.R new file mode 100644 index 000000000..5ecfa72e5 --- /dev/null +++ b/server/preprocessing/other-scripts/test/test_ranking_select.R @@ -0,0 +1,75 @@ +# Unit tests for the rank-aware selection helpers (ranking.R): rank_policies, +# rank_of_terms, select_by_rank, format_label. +# +# Pure base R — the only summarize.R dependency (filter_out_nested_ngrams) is +# injected as a stub, so these run anywhere. The full path with the real prune / +# de-nest is exercised by the replay tests. + +if (!exists("select_by_rank")) source("ranking.R") +if (!requireNamespace("testthat", quietly = TRUE)) { + if (!exists("test_that")) source("test/testthat_shim.R") +} else { + library(testthat) +} + +spec1 <- rank_policies("1") +id_denest <- function(x, n) head(x, n) # identity-ish de-nester for isolation + +# --- rank_policies ----------------------------------------------------------- +test_that("rank_policies: mode 1 is topup rank1 then exclusive rank2", { + expect_equal(length(spec1), 2) + expect_equal(spec1[[1]]$rank, 1L); expect_equal(spec1[[1]]$policy, "topup") + expect_equal(spec1[[2]]$rank, 2L); expect_equal(spec1[[2]]$policy, "exclusive") +}) + +test_that("rank_policies: unimplemented modes return NULL", { + expect_null(rank_policies("2")) + expect_null(rank_policies("3")) + expect_null(rank_policies("9")) +}) + +# --- rank_of_terms ----------------------------------------------------------- +test_that("rank_of_terms: cleaned->1, heuristic-only->2, unknown->lowest rank", { + r <- rank_of_terms(c("a", "b", "c"), cleaned = c("a"), heuristic = c("b"), spec1) + expect_equal(r$ranks, c(1L, 2L, 2L)) # c is unknown -> lowest rank (2) + expect_equal(r$unknown, 1L) +}) + +test_that("rank_of_terms: highest-rank-wins when a term is in both sources", { + r <- rank_of_terms("x", cleaned = "x", heuristic = "x", spec1) + expect_equal(unname(r$ranks), 1L) + expect_equal(r$unknown, 0L) +}) + +# --- select_by_rank ---------------------------------------------------------- +test_that("select_by_rank: rank 1 fills up to top_n", { + lab <- select_by_rank(c("k1", "k2", "k3", "k4"), c(1L, 1L, 1L, 1L), 3, spec1, id_denest) + expect_equal(lab, c("k1", "k2", "k3")) +}) + +test_that("select_by_rank: exclusive rank 2 is skipped while rank 1 is non-empty", { + lab <- select_by_rank(c("k1", "h1", "h2"), c(1L, 2L, 2L), 3, spec1, id_denest) + expect_equal(lab, "k1") # < top_n is acceptable; no backfill (§15-0) +}) + +test_that("select_by_rank: falls to rank 2 only when rank 1 is empty", { + lab <- select_by_rank(c("h1", "h2"), c(2L, 2L), 3, spec1, id_denest) + expect_equal(lab, c("h1", "h2")) +}) + +test_that("select_by_rank: underscores become spaces", { + expect_equal(select_by_rank("sea_level_rise", 1L, 3, spec1, id_denest), "sea level rise") +}) + +test_that("select_by_rank: empty input yields an empty label", { + expect_equal(length(select_by_rank(character(0), integer(0), 3, spec1, id_denest)), 0) +}) + +# --- format_label ------------------------------------------------------------ +test_that("format_label capitalises each term and joins with ', '", { + expect_equal(format_label(c("climate change", "sea level")), "Climate change, Sea level") +}) + +test_that("format_label of nothing is the empty string", { + expect_equal(format_label(character(0)), "") +}) diff --git a/server/preprocessing/other-scripts/test/test_ranking_wedge.R b/server/preprocessing/other-scripts/test/test_ranking_wedge.R index bd11ecf94..0aed7beb0 100644 --- a/server/preprocessing/other-scripts/test/test_ranking_wedge.R +++ b/server/preprocessing/other-scripts/test/test_ranking_wedge.R @@ -4,10 +4,10 @@ # `legacy_fn` so the wedge can be tested in isolation, without the tm stack or # summarize.R. # -# Stage 0 invariant: the wedge is a NO-OP — Mode 0 uses the legacy selection, and -# Modes 1-3 (not yet implemented) also fall back to it. When Stage 1 lands, the -# Mode 1-3 cases below become the first behaviour to change, so these tests pin -# the current contract and will flag that change. +# These pin the wedge dispatch: Mode 0 uses the legacy selection, and any ranked +# mode falls back to legacy when no rank_sources are available (custom-clustering +# path) or the mode has no rank policy yet (Modes 2-3). The Mode-1 ranked path +# itself (with rank_sources) is exercised by the replay tests. if (!exists("select_cluster_label_names")) { source("ranking.R") @@ -46,19 +46,19 @@ test_that("mode 0 forwards its arguments to the legacy selector verbatim", { expect_identical(s$state$last$stops, STOPS) }) -# --- Stage 0: modes 1-3 fall back to legacy (behaviour changes in Stage 1) ---- -test_that("modes 1-3 fall back to the legacy selector in Stage 0", { +# --- ranked modes fall back to legacy when no rank_sources are available ------ +test_that("ranked modes fall back to legacy without rank_sources", { for (m in c("1", "2", "3")) { s <- make_stub() out <- suppressWarnings( select_cluster_label_names(TT, top_n = 3, stops = STOPS, mode = m, legacy_fn = s$fn)) - expect_equal(out, "STUB_LABELS") + expect_equal(out, "STUB_LABELS") # rank_sources is NULL (default) -> legacy expect_equal(s$state$n, 1L) } }) # --- Integration with the config resolver ------------------------------------ -test_that("a per-integration mode resolves and (Stage 0) still uses legacy", { +test_that("a per-integration mode resolves; no rank_sources -> legacy", { old <- Sys.getenv("RANKING_MODE_BASE", unset = NA) on.exit(if (is.na(old)) Sys.unsetenv("RANKING_MODE_BASE") else Sys.setenv(RANKING_MODE_BASE = old)) Sys.setenv(RANKING_MODE_BASE = "1") @@ -67,6 +67,6 @@ test_that("a per-integration mode resolves and (Stage 0) still uses legacy", { s <- make_stub() out <- suppressWarnings( select_cluster_label_names(TT, top_n = 3, stops = STOPS, mode = mode, legacy_fn = s$fn)) - expect_equal(out, "STUB_LABELS") # Stage 0: still legacy + expect_equal(out, "STUB_LABELS") # rank_sources NULL -> legacy expect_equal(s$state$n, 1L) }) diff --git a/server/preprocessing/other-scripts/test/test_replay_harness.R b/server/preprocessing/other-scripts/test/test_replay_harness.R index 96d9243ef..0c287f1a8 100644 --- a/server/preprocessing/other-scripts/test/test_replay_harness.R +++ b/server/preprocessing/other-scripts/test/test_replay_harness.R @@ -33,9 +33,16 @@ test_that("replay is deterministic across repeated runs", { replay_labels(bundle, mode = "0")) }) -test_that("Stage 0: mode 1 output equals mode 0 (wedge is a no-op)", { - expect_equal(replay_labels(bundle, mode = "1"), - replay_labels(bundle, mode = "0")) +test_that("mode 1 runs and yields sensible rank-1 labels", { + labels <- replay_labels(bundle, mode = "1") + expect_equal(length(labels), bundle$clusters$num_clusters) + expect_true(all(nzchar(labels))) + expect_false(labels[["1"]] == labels[["2"]]) + # rank 1 = the subject keywords; labels stay on-theme + expect_match(tolower(labels[["1"]]), "climate|sea level") + expect_match(tolower(labels[["2"]]), "machine|neural|learning") + # title-only heuristics (rank 2) are excluded while rank 1 is non-empty + expect_false(grepl("recognition|image", tolower(labels[["2"]]))) }) test_that("replay restores the environment it changed", { From 767b32f377937ea38e9e556eeb793bf3c916717b Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 7 Jul 2026 14:13:30 +0200 Subject: [PATCH 141/205] first implementation of ranking mode 1 --- server/preprocessing/other-scripts/ranking.R | 125 +++++++++++++++--- .../preprocessing/other-scripts/summarize.R | 93 +++++++++---- 2 files changed, 180 insertions(+), 38 deletions(-) diff --git a/server/preprocessing/other-scripts/ranking.R b/server/preprocessing/other-scripts/ranking.R index e6ae0cd4a..42d499c9e 100644 --- a/server/preprocessing/other-scripts/ranking.R +++ b/server/preprocessing/other-scripts/ranking.R @@ -46,24 +46,119 @@ ranking_mode <- function(service = NULL) { "0" } +# Ordered rank/fill-policy spec for a mode. Each entry is list(rank, policy) where +# policy is "topup" (fill open label slots) or "exclusive" (contribute only if the +# label is still empty). Returns NULL for modes with no ranked selection yet, which +# fall back to the legacy selection. +rank_policies <- function(mode) { + switch(as.character(mode), + "1" = list(list(rank = 1L, policy = "topup"), # keywords + MeSH (pooled) + list(rank = 2L, policy = "exclusive")), # heuristic title n-grams + NULL) +} + +# Assign a provenance rank to each candidate term (in "_"-joined, lowercased TDM +# form) from the separated rank sources: rank 1 if it is a cleaned keyword, rank 2 +# if it is only a heuristic title n-gram. As a fallback, unknown terms (in neither — e.g. a +# normalization straggler) take the lowest rank and are +# counted for debug purposes. Returns list(ranks = , unknown = ). +rank_of_terms <- function(terms, cleaned, heuristic, policy_spec) { + max_rank <- max(vapply(policy_spec, function(s) s$rank, integer(1))) + r <- ifelse(terms %in% cleaned, 1L, + ifelse(terms %in% heuristic, 2L, NA_integer_)) + unknown <- sum(is.na(r)) + r[is.na(r)] <- max_rank + list(ranks = r, unknown = unknown) +} + +# Waterfall selection over one cluster's ranked candidates. Walks the policy spec +# top->down: a "topup" rank fills open label slots; an "exclusive" rank contributes +# only if the label is still empty. De-nesting runs WITHIN each rank (decision #6). +# terms_us : "_"-joined, weight-ordered survivors (post-prune). +# term_ranks : aligned provenance-rank vector. +# top_n : max label terms. +# denest_fn : de-nester (filter_out_nested_ngrams); injectable for testing. +# Returns the label terms (space form), at most top_n. +select_by_rank <- function(terms_us, term_ranks, top_n, policy_spec, + denest_fn = filter_out_nested_ngrams) { + label <- character(0) + for (step in policy_spec) { + if (identical(step$policy, "exclusive") && length(label) > 0) next + open <- top_n - length(label) + if (open <= 0) break + sel <- terms_us[term_ranks == step$rank] # weight-ordered + if (length(sel) == 0) next + sel <- trimws(gsub("_", " ", sel)) + sel <- unlist(denest_fn(sel, top_n)) # de-nest within rank + if (length(sel) == 0) next + label <- c(label, head(sel, open)) + } + label +} + +# Format label terms exactly as the legacy path: capitalise each term's first +# letter, join with ", ". Empty -> "". +format_label <- function(terms) { + terms <- unlist(terms) + if (length(terms) == 0) return("") + terms <- trimws(terms) + cap <- paste0(toupper(substr(terms, 1, 1)), substr(terms, 2, nchar(terms))) + paste(cap, collapse = ", ") +} + # Selection wedge: turn the per-cluster ranked tf-idf term lists into display -# labels according to `mode`. Mode 0 is the unchanged legacy selection -# (`legacy_fn`, default get_top_names). Modes 1-3 will apply rank-aware selection; -# until that lands they fall back to `legacy_fn` with a warning (Stage 0), so the -# pipeline is never broken by an early config flag. -# tfidf_top : per-cluster named, descending tf-idf term lists. -# top_n : number of terms kept per label. -# stops : stopword vector. -# mode : ranking mode string (see RANKING_MODES). -# legacy_fn : the legacy selector; injectable for testing. +# labels according to `mode`. +# - Mode 0 (or no rank_sources, or an unimplemented mode) -> the unchanged legacy +# selection (`legacy_fn`, default get_top_names). +# - Modes with a rank policy (Stage 1: Mode 1) -> shared prune + rank-aware +# selection over the single global ranking, partitioned by `rank_sources`. +# tfidf_top : per-cluster named, descending tf-idf term lists. +# top_n : number of terms kept per label. +# stops : stopword vector. +# mode : ranking mode string (see RANKING_MODES). +# rank_sources : list(cleaned, heuristic), each a per-cluster list of lowercased +# "_"-joined tokens; NULL disables ranked selection. +# legacy_fn : the legacy selector; injectable for testing. select_cluster_label_names <- function(tfidf_top, top_n, stops, mode = "0", + rank_sources = NULL, legacy_fn = get_top_names) { - if (identical(mode, "0")) { + policy_spec <- rank_policies(mode) + if (identical(mode, "0") || is.null(policy_spec) || is.null(rank_sources)) { + if (!identical(mode, "0") && (is.null(policy_spec) || is.null(rank_sources))) { + msg <- sprintf( + "select_cluster_label_names: ranking mode '%s' not applied (no rank sources or mode unimplemented); using legacy selection", + mode) + if (exists("vslog")) vslog$warn(msg) else if (exists("logwarn")) logwarn(msg) else warning(msg) + } return(legacy_fn(tfidf_top, top_n, stops)) } - msg <- sprintf( - "select_cluster_label_names: ranking mode '%s' not yet implemented (Stage 0); using legacy selection", - mode) - if (exists("vslog")) vslog$warn(msg) else if (exists("logwarn")) logwarn(msg) else warning(msg) - legacy_fn(tfidf_top, top_n, stops) + + n <- length(tfidf_top) + out <- vector("list", n) + dbg <- vector("list", n) + unknown_total <- 0L + for (k in seq_len(n)) { + weights <- tfidf_top[[k]] + nms <- names(weights) + if (is.null(nms) || length(nms) == 0) { out[[k]] <- ""; next } + # Shared prune head (§3 step 1): same well-formedness filter as legacy, on the + # global weight-ordered list. another_prune_ngrams preserves order. + pruned <- unlist(another_prune_ngrams(nms, stops)) + if (length(pruned) == 0) { out[[k]] <- ""; next } + rr <- rank_of_terms(pruned, rank_sources$cleaned[[k]], rank_sources$heuristic[[k]], policy_spec) + unknown_total <- unknown_total + rr$unknown + out[[k]] <- format_label(select_by_rank(pruned, rr$ranks, top_n, policy_spec)) + dbg[[k]] <- data.frame(cluster = k, term = gsub("_", " ", pruned), + rank = rr$ranks, weight = as.numeric(weights[pruned]), + stringsAsFactors = FALSE) + } + if (unknown_total > 0L) { + msg <- sprintf("select_cluster_label_names: %d candidate term(s) had no rank source; assigned lowest rank", unknown_total) + if (exists("vslog")) vslog$warn(msg) else if (exists("logwarn")) logwarn(msg) + } + if (exists("dump_data")) { + dbg <- dbg[!vapply(dbg, is.null, logical(1))] + if (length(dbg)) dump_data(do.call(rbind, dbg), "summarize_04c_rank_candidates") + } + out } diff --git a/server/preprocessing/other-scripts/summarize.R b/server/preprocessing/other-scripts/summarize.R index 7095d9401..ae76ea00f 100644 --- a/server/preprocessing/other-scripts/summarize.R +++ b/server/preprocessing/other-scripts/summarize.R @@ -21,6 +21,21 @@ SplitTokenizer <- function(x) { trim <- function (x) gsub("^\\s+|\\s+$", "", x) +# Normalise a combined token string to the ";"-separated form the SplitTokenizer +# consumes: drop "?" artifacts, collapse repeated ";", trim spaces around ";", and +# turn any remaining whitespace into ";". This is the SINGLE source of truth for +# corpus tokenization, used both to build the corpus document and to derive the +# per-cluster rank sources — so a rank token can never drift out of the tf-idf term +# set (the §15-3 invariant; see get_cluster_corpus / ranking.R). +normalize_corpus_tokens <- function(s) { + s <- str_replace_all(s, "\\?+_\\?+|\\?+|\\?+ ", "") + s <- str_replace_all(s, ";+", ";") + s <- str_replace_all(s, " ?; ?", ";") + s <- str_replace_all(s, " +", ";") + s +} + + # Generate all contiguous n-grams of length n from each input string. The words # within an n-gram are joined with "_" so a whitespace tokenizer keeps the n-gram # intact. Returns, per input string, a single space-separated string of its n-grams. @@ -102,17 +117,36 @@ create_cluster_labels <- function(clusters, metadata, weightingspec = weightingspec, top_n = top_n, stops = stops, taxonomy_separator = taxonomy_separator, params = params, service = service), "summarize_00_label_inputs") + # Additive rank column on the metadata data frame (§16 R6): the rank-1 source. + # In Stage 1 this is subject_cleaned verbatim (keywords + MeSH pooled); Stage 2 + # carves MeSH out into an upstream keywords_rank_mesh column. subject_cleaned + # (metadata$subject) is left untouched, so Mode 0 is unaffected. + metadata$keywords_rank_cleaned <- metadata$subject cc <- params$custom_clustering if (!(is.null(cc)) && (cc %in% names(metadata))) { - nn_corpus <- get_custom_cluster_corpus(clusters, metadata, stops, taxonomy_separator, custom_clustering=cc) + corpus_out <- get_custom_cluster_corpus(clusters, metadata, stops, taxonomy_separator, custom_clustering=cc) } else { - nn_corpus <- get_cluster_corpus(clusters, metadata, stops, taxonomy_separator) + corpus_out <- get_cluster_corpus(clusters, metadata, stops, taxonomy_separator) } + # get_*_cluster_corpus returns the corpus plus per-cluster rank sources (the + # separated keyword/heuristic tokens used only for rank lookup, §3). rank_sources + # is NULL on the custom-clustering path, so ranked modes fall back to legacy there. + nn_corpus <- corpus_out$corpus + rank_sources <- corpus_out$rank_sources dump_data(nn_corpus, "summarize_04_corpus") + # Resolve the ranking mode BEFORE the TDM: the local frequency bound is + # mode-dependent (§6) — c(2, Inf) for Mode 0 (byte-identical legacy), c(1, Inf) + # for Modes 1-3 so low-frequency real keywords survive into the ranking. The + # corpus text and weighting are otherwise identical across modes. + mode <- ranking_mode(service) + local_bound <- if (identical(mode, "0")) c(2, Inf) else c(1, Inf) + vslog$debug(paste("create_cluster_labels: ranking mode", mode, "for service", + if (is.null(service)) "(none)" else service, + "; local bound", local_bound[1])) nn_tfidf <- TermDocumentMatrix(nn_corpus, control = list( tokenize = SplitTokenizer, weighting = function(x) weightSMART(x, spec="ntn"), - bounds = list(local = c(2, Inf)), + bounds = list(local = local_bound), tolower = TRUE )) tfidf_top <- apply(nn_tfidf, 2, function(x) {x2 <- sort(x, TRUE);x2[x2>0]}) @@ -121,12 +155,11 @@ create_cluster_labels <- function(clusters, metadata, nDocs(nn_tfidf), "clusters;", length(empty_tfidf), "clusters need a fallback")) tfidf_top[c(empty_tfidf)] <- fill_empty_clusters(nn_tfidf, nn_corpus)[c(empty_tfidf)] # Ranking-mode wedge (ranking.R): Mode 0 is the unchanged legacy selection; - # Modes 1-3 apply rank-aware selection (Stage 1+). The existing fallbacks below - # (title/abstract frequency) remain the final safety net in every mode. - mode <- ranking_mode(service) - vslog$debug(paste("create_cluster_labels: ranking mode", mode, "for service", - if (is.null(service)) "(none)" else service)) - tfidf_top_names <- select_cluster_label_names(tfidf_top, top_n, stops, mode = mode) + # Modes 1-3 apply rank-aware selection over the same global ranking, partitioned + # by rank_sources. The existing fallbacks below (title/abstract frequency) remain + # the final safety net in every mode. + tfidf_top_names <- select_cluster_label_names(tfidf_top, top_n, stops, mode = mode, + rank_sources = rank_sources) dump_data(tfidf_top_names, "summarize_05_tfidf_top_names") clusters$cluster_labels = "" batch_size <- 1000 @@ -206,8 +239,9 @@ match_keyword_case <- function(x, type_counts) { # Build the per-cluster corpus from a custom metadata field (custom_clustering) # instead of subjects. Per cluster: removes stopwords, then joins and normalises -# the field values into a single ";"-separated token string. Returns a VCorpus -# with one document per cluster. +# the field values into a single ";"-separated token string. Returns +# list(corpus, rank_sources) with rank_sources = NULL (no provenance split on this +# path, so ranked modes fall back to legacy selection). get_custom_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, add_title_ngrams = T, custom_clustering=NULL) { subjectlist = list() @@ -223,22 +257,23 @@ get_custom_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separa custom_input = mapply(gsub, custom_input, pattern=" ", replacement="_") all_subjects = paste(custom_input, collapse=" ") - all_subjects <- str_replace_all(all_subjects, "\\?+_\\?+|\\?+|\\?+ ", "") - all_subjects <- str_replace_all(all_subjects, ";+", ";") - all_subjects <- str_replace_all(all_subjects, " ?; ?", ";") - all_subjects <- str_replace_all(all_subjects, " +", ";") + all_subjects <- normalize_corpus_tokens(all_subjects) subjectlist = c(subjectlist, all_subjects) } + # Custom clustering has no keyword/heuristic provenance split, so rank_sources is + # NULL: ranked modes fall back to the legacy selection on this path (ranking.R). nn_corpus <- VCorpus(VectorSource(subjectlist)) - return(nn_corpus) + return(list(corpus = nn_corpus, rank_sources = NULL)) } # Build the per-cluster corpus used for tf-idf labelling. Per cluster: combines # the papers' `subject` keywords with bi-/tri-grams from their titles. Taxonomy # subjects (containing taxonomy_separator) are reduced to their last path segment. # Everything is normalised into one ";"-separated token string per cluster. -# Returns a VCorpus with one document per cluster. add_title_ngrams toggles the -# title n-gram contribution. +# Returns list(corpus, rank_sources): a VCorpus with one document per cluster, and +# per-cluster rank sources (cleaned = subject tokens, heuristic = title n-grams, +# lowercased) used by the ranked selection. add_title_ngrams toggles the title +# n-gram contribution. get_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, add_title_ngrams = T, custom_clustering=NULL) { subjectlist = list() @@ -275,10 +310,7 @@ get_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, } else { all_subjects = paste(subjects, collapse=" ") } - all_subjects <- str_replace_all(all_subjects, "\\?+_\\?+|\\?+|\\?+ ", "") - all_subjects <- str_replace_all(all_subjects, ";+", ";") - all_subjects <- str_replace_all(all_subjects, " ?; ?", ";") - all_subjects <- str_replace_all(all_subjects, " +", ";") + all_subjects <- normalize_corpus_tokens(all_subjects) subjectlist = c(subjectlist, all_subjects) } # Debug: record, per cluster, the tokens contributed by subjects vs. by title @@ -287,8 +319,23 @@ get_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, subject_tokens = unlist(subject_dbg), title_ngrams = unlist(title_ngram_dbg)), "summarize_04a_corpus_sources") + # Rank sources for the ranked selection (ranking.R): the same per-cluster tokens + # that feed the corpus, split by provenance and run through the SAME + # normalize_corpus_tokens() + lowercasing as the TDM terms — so every rank token + # matches a tf-idf term (the §15-3 invariant; without this, tokens like + # "population_growth " drift out of the map). cleaned = keywords_rank_cleaned + # (subject_cleaned, keywords + MeSH pooled in Stage 1); heuristic = + # keywords_rank_heuristic (title n-grams). Used only for rank lookup, never fed + # into the TDM. + split_tokens <- function(s) { + t <- tolower(unlist(strsplit(normalize_corpus_tokens(s), ";"))) + t <- gsub("^_+|_+$", "", t) # mirror the TDM tokenizer's edge-punctuation strip + t[nzchar(t)] + } + rank_sources <- list(cleaned = lapply(subject_dbg, split_tokens), + heuristic = lapply(title_ngram_dbg, split_tokens)) nn_corpus <- VCorpus(VectorSource(subjectlist)) - return(nn_corpus) + return(list(corpus = nn_corpus, rank_sources = rank_sources)) } From 43989c377f58bee4dcf6c89cf3b34aa8f0d3625d Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 9 Jul 2026 10:41:03 +0200 Subject: [PATCH 142/205] area label debug tooling --- .../other-scripts/test/mine_cases.R | 83 +++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 server/preprocessing/other-scripts/test/mine_cases.R diff --git a/server/preprocessing/other-scripts/test/mine_cases.R b/server/preprocessing/other-scripts/test/mine_cases.R new file mode 100644 index 000000000..8da906132 --- /dev/null +++ b/server/preprocessing/other-scripts/test/mine_cases.R @@ -0,0 +1,83 @@ +#!/usr/bin/env Rscript +# Mine Mode-1 replay fixtures for cluster-level test cases (Stage 1 selection). +# +# For every fixture bundle in test/replay/*.inputs.rds, replay the labelling under +# Mode 1 and classify each cluster into the situations we want to pin as tests: +# [RANK1-ONLY] label is all rank 1 AND rank-2 candidates existed but were +# excluded -> proves exclusivity / no backfill. +# [RANK2-FALL] no rank-1 candidates survived; label came from the heuristic rank. +# [DE-NEST] a nested term pair within one rank; de-nesting kept the specific one. +# Each hit is printed in the format a test case needs: the tf-idf-sorted terms per +# rank + the expected area label. +# +# Run inside the pipeline image, renv bypassed: +# sh test/run_tests.sh # (no — that runs the suite) +# R_PROFILE_USER=/dev/null Rscript test/mine_cases.R [fixture.inputs.rds ...] + +suppressWarnings(suppressMessages(source("test/replay_harness.R"))) + +TOP_N <- 3 +SPEC <- rank_policies("1") + +# word-sequence containment: is `a` a contiguous run of words inside `b`? +is_nested <- function(a, b) { + a != b && grepl(paste0(" ", a, " "), paste0(" ", b, " "), fixed = TRUE) +} + +# Recompute the Mode-1 per-cluster candidates exactly as create_cluster_labels does. +cluster_candidates <- function(bundle) { + md <- add_heuristic_keyword_fields(bundle$metadata, bundle$stops) + md$keywords_rank_cleaned <- md$subject + co <- get_cluster_corpus(bundle$clusters, md, bundle$stops, bundle$taxonomy_separator, heuristic_col = HEUR_MIN2) + tdm <- TermDocumentMatrix(co$corpus, control = list( + tokenize = SplitTokenizer, weighting = function(x) weightSMART(x, spec = "ntn"), + bounds = list(local = c(1, Inf)), tolower = TRUE)) + tt <- apply(tdm, 2, function(x) { x2 <- sort(x, TRUE); x2[x2 > 0] }) + empty <- which(apply(tdm, 2, sum) == 0) + if (length(empty)) { + fb <- get_cluster_corpus(bundle$clusters, md, bundle$stops, bundle$taxonomy_separator, heuristic_col = HEUR_MIN1)$corpus + tt[empty] <- fill_empty_clusters(fb)[empty] + } + + out <- vector("list", length(tt)) + for (k in seq_along(tt)) { + nms <- names(tt[[k]]); if (is.null(nms) || !length(nms)) next + pruned <- unlist(another_prune_ngrams(nms, bundle$stops)); if (!length(pruned)) next + rr <- rank_of_terms(pruned, co$rank_sources$cleaned[[k]], co$rank_sources$heuristic[[k]], SPEC) + sp <- trimws(gsub("_", " ", pruned)) + out[[k]] <- list( + r1 = sp[rr$ranks == 1], # rank-1 terms, weight-ordered + r2 = sp[rr$ranks == 2], # rank-2 terms, weight-ordered + label = format_label(select_by_rank(pruned, rr$ranks, TOP_N, SPEC))) + } + out +} + +fmt <- function(x, n = 8) if (length(x)) paste(head(x, n), collapse = " | ") else "(none)" + +mine <- function(path) { + name <- fixture_name(path) + cand <- cluster_candidates(readRDS(path)) + for (k in seq_along(cand)) { + c <- cand[[k]]; if (is.null(c) || !nzchar(c$label)) next + hits <- character(0) + if (length(c$r1) > 0 && length(c$r2) > 0) hits <- c(hits, "RANK1-ONLY") + if (length(c$r1) == 0 && length(c$r2) > 0) hits <- c(hits, "RANK2-FALL") + # de-nesting: a nested pair within the selected rank's top candidates + sel_rank <- if (length(c$r1) > 0) c$r1 else c$r2 + top <- head(sel_rank, 6) + nested <- FALSE + for (i in seq_along(top)) for (j in seq_along(top)) + if (i != j && is_nested(top[i], top[j])) nested <- TRUE + if (nested) hits <- c(hits, "DE-NEST") + if (!length(hits)) next + cat(sprintf("\n[%s] %s cluster %d\n", paste(hits, collapse = ","), name, k)) + cat(" rank1:", fmt(c$r1), "\n") + cat(" rank2:", fmt(c$r2), "\n") + cat(" label:", c$label, "\n") + } +} + +args <- commandArgs(trailingOnly = TRUE) +files <- if (length(args)) args else fixture_files() +for (f in files) mine(f) From 03ed43071ce5fc9f6f2ef170872efeb8533e19ea Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 9 Jul 2026 10:44:08 +0200 Subject: [PATCH 143/205] add two separate metadata fields for heuristically generated keywords with different document frequency thresholds --- .../preprocessing/other-scripts/summarize.R | 140 ++++++++++++------ 1 file changed, 97 insertions(+), 43 deletions(-) diff --git a/server/preprocessing/other-scripts/summarize.R b/server/preprocessing/other-scripts/summarize.R index ae76ea00f..8feef0956 100644 --- a/server/preprocessing/other-scripts/summarize.R +++ b/server/preprocessing/other-scripts/summarize.R @@ -1,6 +1,13 @@ library(stringr) vslog <- getLogger('vis.summarize') +# Metadata columns holding the heuristically generated (title n-gram) keywords, +# pre-binned by map-wide document frequency (see add_heuristic_keyword_fields: +# _min1 = n-grams appearing in >= 1 resource (all) -> used for the FALLBACK corpus +# _min2 = n-grams appearing in >= 2 resources -> used for the INITIAL corpus +HEUR_MIN1 <- "keywords_rank_heuristically_generated_min1" +HEUR_MIN2 <- "keywords_rank_heuristically_generated_min2" + # summarize.R # Cluster labelling for the overview visualisation: turns each cluster of papers # into a short, human-readable area title. create_cluster_labels() is the entry @@ -26,7 +33,7 @@ trim <- function (x) gsub("^\\s+|\\s+$", "", x) # turn any remaining whitespace into ";". This is the SINGLE source of truth for # corpus tokenization, used both to build the corpus document and to derive the # per-cluster rank sources — so a rank token can never drift out of the tf-idf term -# set (the §15-3 invariant; see get_cluster_corpus / ranking.R). +# set (see get_cluster_corpus / ranking.R). normalize_corpus_tokens <- function(s) { s <- str_replace_all(s, "\\?+_\\?+|\\?+|\\?+ ", "") s <- str_replace_all(s, ";+", ";") @@ -82,6 +89,47 @@ prune_ngrams <- function(ngrams, stops){ return (pruned_ngrams) } +# Heuristically generated keywords for a single paper: the bi- and tri-grams of +# its title, pruned of degenerate forms (n-grams that start or end with a +# stopword, or whose first and last token are identical). Words within an n-gram +# are "_"-joined so the tokenizer keeps them intact. Returns a character vector of +# unique "_"-joined n-grams (possibly empty). +paper_title_ngrams <- function(title, stops) { + clean <- gsub("[^[:alnum:]-]", " ", if (is.na(title)) "" else title) + clean <- trimws(gsub("\\s+", " ", clean)) + if (!nzchar(clean)) return(character(0)) + grams <- unlist(c(expand_ngrams(clean, 2), expand_ngrams(clean, 3))) + grams <- unlist(strsplit(paste(grams, collapse = " "), " ")) + grams <- grams[nzchar(grams)] + if (!length(grams)) return(character(0)) + keep <- vapply(grams, function(g) { + toks <- strsplit(g, "_", fixed = TRUE)[[1]] + length(toks) >= 2 && + !(tolower(toks[1]) %in% stops) && + !(tolower(toks[length(toks)]) %in% stops) && + toks[1] != toks[length(toks)] + }, logical(1), USE.NAMES = FALSE) + unique(grams[keep]) +} + +# Add the two heuristic-keyword metadata columns (HEUR_MIN1 / HEUR_MIN2) to the +# metadata data frame. Generates each paper's title n-grams, computes each +# n-gram's MAP-WIDE document frequency (number of distinct resources it appears +# in), and stores per paper, as "; "-joined "_"-n-gram strings: +# HEUR_MIN1 = all of the paper's n-grams (DF >= 1) +# HEUR_MIN2 = the paper's n-grams with document frequency >= 2 +# Computed once, early in labelling. +add_heuristic_keyword_fields <- function(metadata, stops) { + per_paper <- lapply(metadata$title, paper_title_ngrams, stops = stops) + df <- table(unlist(lapply(per_paper, unique))) # map-wide document frequency + min2_set <- names(df)[df >= 2] + metadata[[HEUR_MIN1]] <- vapply(per_paper, + function(g) paste(g, collapse = "; "), character(1)) + metadata[[HEUR_MIN2]] <- vapply(per_paper, + function(g) paste(g[g %in% min2_set], collapse = "; "), character(1)) + metadata +} + # Entry point: assign a short label ("area title") to every cluster. # Builds one pseudo-document per cluster from its papers' subjects + title # n-grams (or a custom field), ranks terms by tf-idf (SMART "ntn"), and keeps the @@ -117,25 +165,31 @@ create_cluster_labels <- function(clusters, metadata, weightingspec = weightingspec, top_n = top_n, stops = stops, taxonomy_separator = taxonomy_separator, params = params, service = service), "summarize_00_label_inputs") - # Additive rank column on the metadata data frame (§16 R6): the rank-1 source. - # In Stage 1 this is subject_cleaned verbatim (keywords + MeSH pooled); Stage 2 - # carves MeSH out into an upstream keywords_rank_mesh column. subject_cleaned - # (metadata$subject) is left untouched, so Mode 0 is unaffected. + # Additive rank columns on the metadata data frame: + # - keywords_rank_cleaned: the rank-1 source. Stage 1 = subject_cleaned verbatim + # (keywords + MeSH pooled); Stage 2 carves MeSH out into an upstream mesh column. + # - the two heuristic columns (min1/min2), pre-binned by map-wide document + # frequency. subject_cleaned (metadata$subject) is left untouched. + metadata <- add_heuristic_keyword_fields(metadata, stops) metadata$keywords_rank_cleaned <- metadata$subject cc <- params$custom_clustering if (!(is.null(cc)) && (cc %in% names(metadata))) { corpus_out <- get_custom_cluster_corpus(clusters, metadata, stops, taxonomy_separator, custom_clustering=cc) + fallback_corpus <- corpus_out$corpus # custom path: no heuristic min1/min2 split } else { - corpus_out <- get_cluster_corpus(clusters, metadata, stops, taxonomy_separator) + # Initial corpus uses the min2 heuristic set (DF >= 2); the fallback corpus + # swaps in min1 (all n-grams). + corpus_out <- get_cluster_corpus(clusters, metadata, stops, taxonomy_separator, heuristic_col = HEUR_MIN2) + fallback_corpus <- get_cluster_corpus(clusters, metadata, stops, taxonomy_separator, heuristic_col = HEUR_MIN1)$corpus } # get_*_cluster_corpus returns the corpus plus per-cluster rank sources (the - # separated keyword/heuristic tokens used only for rank lookup, §3). rank_sources + # separated keyword/heuristic tokens used only for rank lookup). rank_sources # is NULL on the custom-clustering path, so ranked modes fall back to legacy there. nn_corpus <- corpus_out$corpus rank_sources <- corpus_out$rank_sources dump_data(nn_corpus, "summarize_04_corpus") # Resolve the ranking mode BEFORE the TDM: the local frequency bound is - # mode-dependent (§6) — c(2, Inf) for Mode 0 (byte-identical legacy), c(1, Inf) + # mode-dependent c(2, Inf) for Mode 0 (byte-identical legacy), c(1, Inf) # for Modes 1-3 so low-frequency real keywords survive into the ranking. The # corpus text and weighting are otherwise identical across modes. mode <- ranking_mode(service) @@ -153,7 +207,13 @@ create_cluster_labels <- function(clusters, metadata, empty_tfidf <- which(apply(nn_tfidf, 2, sum)==0) vslog$debug(paste("create_cluster_labels: tf-idf matrix", nTerms(nn_tfidf), "terms x", nDocs(nn_tfidf), "clusters;", length(empty_tfidf), "clusters need a fallback")) - tfidf_top[c(empty_tfidf)] <- fill_empty_clusters(nn_tfidf, nn_corpus)[c(empty_tfidf)] + + # fallback mechanism if first attempt to find tf-idf terms fails: + # use the per-cluster corpus to get the top bi-/tri-grams from the titles of the papers in the cluster. + # This is a last-resort fallback, so it is only applied to clusters that have no tf-idf terms at all (empty_tfidf). + # The fallback is applied to the tfidf_top list, which is then used for label selection. + tfidf_top[c(empty_tfidf)] <- fill_empty_clusters(fallback_corpus)[c(empty_tfidf)] + # Ranking-mode wedge (ranking.R): Mode 0 is the unchanged legacy selection; # Modes 1-3 apply rank-aware selection over the same global ranking, partitioned # by rank_sources. The existing fallbacks below (title/abstract frequency) remain @@ -275,24 +335,23 @@ get_custom_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separa # lowercased) used by the ranked selection. add_title_ngrams toggles the title # n-gram contribution. get_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, - add_title_ngrams = T, custom_clustering=NULL) { + heuristic_col = HEUR_MIN2) { subjectlist = list() - subject_dbg = list(); title_ngram_dbg = list() # debug: keep the two sources apart + subject_dbg = list(); heuristic_dbg = list(); heuristic_min1_dbg = list() for (k in seq(1, clusters$num_clusters)) { matches = which(unname(clusters$groups == k) == TRUE) - titles = metadata$title[matches] subjects = metadata$subject[matches] - titles = lapply(titles, function(x) {gsub("[^[:alnum:]-]", " ", x)}) - titles = lapply(titles, gsub, pattern="\\s+", replacement=" ") - title_ngrams <- get_title_ngrams(titles, stops, c(2, 3)) - batch_size <- 1000 - total_length <- length(stops) - for (i in seq(1, total_length, batch_size)) { - titles = lapply(titles, function(x) {removeWords(x, stops[i:min(i+batch_size -1, total_length)])}) - } + # Heuristic keywords are pre-generated "_"-joined n-grams (see + # add_heuristic_keyword_fields): the corpus uses heuristic_col (min2 initial / + # min1 fallback); the rank map always uses min1 (the superset), so any heuristic + # term resolves to rank 2 regardless of which pass produced it. + heuristics = as.character(metadata[[heuristic_col]][matches]) + heuristics_min1 = as.character(metadata[[HEUR_MIN1]][matches]) + subjects = mapply(gsub, subjects, pattern = "; ", replacement=";") subjects = mapply(gsub, subjects, pattern=" ", replacement="_") - titles = mapply(gsub, titles, pattern=" ", replacement=";") + heuristics = gsub("; ", ";", heuristics) + heuristics_min1 = gsub("; ", ";", heuristics_min1) if (!is.null(taxonomy_separator)) { subjects = mapply(function(x){strsplit(x, ";")}, subjects) @@ -304,36 +363,30 @@ get_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, subjects = mapply(paste, subjects, taxons, collapse=";") } subject_dbg[[k]] = paste(unlist(subjects), collapse=";") - title_ngram_dbg[[k]] = paste(unlist(title_ngrams), collapse=";") - if (add_title_ngrams == T) { - all_subjects = paste(subjects, title_ngrams, collapse=" ") - } else { - all_subjects = paste(subjects, collapse=" ") - } + heuristic_dbg[[k]] = paste(unlist(heuristics), collapse=";") + heuristic_min1_dbg[[k]] = paste(unlist(heuristics_min1), collapse=";") + all_subjects = paste(subjects, heuristics, collapse=" ") all_subjects <- normalize_corpus_tokens(all_subjects) subjectlist = c(subjectlist, all_subjects) } - # Debug: record, per cluster, the tokens contributed by subjects vs. by title - # n-grams, so a label term can be attributed to its source (see get_title_ngrams). + # Debug: record, per cluster, the tokens contributed by subjects vs. by the + # heuristic (title n-gram) column, so a label term can be attributed to its source. dump_data(data.frame(cluster = seq_along(subjectlist), subject_tokens = unlist(subject_dbg), - title_ngrams = unlist(title_ngram_dbg)), + title_ngrams = unlist(heuristic_dbg)), "summarize_04a_corpus_sources") - # Rank sources for the ranked selection (ranking.R): the same per-cluster tokens - # that feed the corpus, split by provenance and run through the SAME - # normalize_corpus_tokens() + lowercasing as the TDM terms — so every rank token - # matches a tf-idf term (the §15-3 invariant; without this, tokens like - # "population_growth " drift out of the map). cleaned = keywords_rank_cleaned - # (subject_cleaned, keywords + MeSH pooled in Stage 1); heuristic = - # keywords_rank_heuristic (title n-grams). Used only for rank lookup, never fed - # into the TDM. + # Rank sources for the ranked selection (ranking.R): the same per-cluster tokens, + # split by provenance and run through the SAME normalize_corpus_tokens() + + # lowercasing + edge-strip as the TDM terms — so every rank token matches a tf-idf + # term. cleaned = keywords_rank_cleaned (subject_cleaned); + # heuristic = the min1 n-gram set. Used only for rank lookup, never fed to the TDM. split_tokens <- function(s) { t <- tolower(unlist(strsplit(normalize_corpus_tokens(s), ";"))) t <- gsub("^_+|_+$", "", t) # mirror the TDM tokenizer's edge-punctuation strip t[nzchar(t)] } - rank_sources <- list(cleaned = lapply(subject_dbg, split_tokens), - heuristic = lapply(title_ngram_dbg, split_tokens)) + rank_sources <- list(cleaned = lapply(subject_dbg, split_tokens), + heuristic = lapply(heuristic_min1_dbg, split_tokens)) nn_corpus <- VCorpus(VectorSource(subjectlist)) return(list(corpus = nn_corpus, rank_sources = rank_sources)) } @@ -406,10 +459,11 @@ another_prune_ngrams <- function(ngrams, stops){ # tf-idf bound, by recomputing the TermDocumentMatrix with a looser local bound # (terms appearing at least once, instead of at least twice). Returns the # per-cluster sorted term lists. -fill_empty_clusters <- function(nn_tfidf, nn_corpus){ - replacement_nn_tfidf <- TermDocumentMatrix(nn_corpus, control = list(tokenize = SplitTokenizer, +fill_empty_clusters <- function(fallback_corpus){ + replacement_nn_tfidf <- TermDocumentMatrix(fallback_corpus, control = list(tokenize = SplitTokenizer, weighting = function(x) weightSMART(x, spec="ntn"), - bounds = list(local = c(1, Inf)) + bounds = list(local = c(1, Inf)), + tolower = TRUE )) replacement_tfidf_top <- apply(replacement_nn_tfidf, 2, function(x) {x2 <- sort(x, TRUE);x2[x2>0]}) return(replacement_tfidf_top) From 2f24728a881adba2d0f087363da3de9a3df764b7 Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 9 Jul 2026 12:36:11 +0200 Subject: [PATCH 144/205] extract fallback into separate function; bugfix for fallback trigger logic --- .../preprocessing/other-scripts/summarize.R | 83 +++++++++++-------- 1 file changed, 50 insertions(+), 33 deletions(-) diff --git a/server/preprocessing/other-scripts/summarize.R b/server/preprocessing/other-scripts/summarize.R index 8feef0956..3730d275f 100644 --- a/server/preprocessing/other-scripts/summarize.R +++ b/server/preprocessing/other-scripts/summarize.R @@ -130,6 +130,33 @@ add_heuristic_keyword_fields <- function(metadata, stops) { metadata } +# Last-resort label for a cluster that produced no tf-idf label (empty even after +# the min1 fallback): build one from the most frequent bi-/tri-grams of the +# cluster's papers' titles + abstracts. Returns a single ", "-joined label string. +# matches : row indices of the cluster's papers in `metadata`. +# top_n : number of terms kept. +title_abstract_fallback_label <- function(matches, metadata, stops, top_n = 3) { + batch_size <- 1000 + total_length <- length(stops) + candidates = mapply(paste, metadata$title[matches], metadata$paper_abstract[matches]) + candidates = lapply(candidates, tolower) + for (i in seq(1, total_length, batch_size)) { + candidates = lapply(candidates, function(x) {paste(removeWords(x, stops[i:min(i+batch_size -1, total_length)]), collapse="")}) + } + candidates = lapply(candidates, function(x) {gsub("[^[:alpha:]]", " ", x)}) + candidates = lapply(candidates, function(x) {gsub(" +", " ", x)}) + candidates_bigrams = lapply(lapply(candidates, expand_ngrams, n=2), paste, collapse=" ") + candidates_trigrams = lapply(lapply(candidates, expand_ngrams, n=3), paste, collapse=" ") + candidates = unname(mapply(paste, candidates_bigrams, candidates_trigrams)) + candidates = unlist(lapply(candidates, str_split, " "), recursive = F) + candidates = unlist(lapply(candidates, function(x) {another_prune_ngrams(x, stops)})) + top_ngrams = sort(table(strsplit(paste(candidates, collapse=" "), " ")), decreasing = T) + summary <- filter_out_nested_ngrams(names(top_ngrams), top_n) + summary = lapply(summary, FUN = function(x) {paste(unlist(x), collapse="; ")}) + summary = gsub("_", " ", summary) + paste(summary, collapse=", ") +} + # Entry point: assign a short label ("area title") to every cluster. # Builds one pseudo-document per cluster from its papers' subjects + title # n-grams (or a custom field), ranks terms by tf-idf (SMART "ntn"), and keeps the @@ -204,53 +231,43 @@ create_cluster_labels <- function(clusters, metadata, tolower = TRUE )) tfidf_top <- apply(nn_tfidf, 2, function(x) {x2 <- sort(x, TRUE);x2[x2>0]}) - empty_tfidf <- which(apply(nn_tfidf, 2, sum)==0) vslog$debug(paste("create_cluster_labels: tf-idf matrix", nTerms(nn_tfidf), "terms x", - nDocs(nn_tfidf), "clusters;", length(empty_tfidf), "clusters need a fallback")) + nDocs(nn_tfidf), "clusters")) - # fallback mechanism if first attempt to find tf-idf terms fails: - # use the per-cluster corpus to get the top bi-/tri-grams from the titles of the papers in the cluster. - # This is a last-resort fallback, so it is only applied to clusters that have no tf-idf terms at all (empty_tfidf). - # The fallback is applied to the tfidf_top list, which is then used for label selection. - tfidf_top[c(empty_tfidf)] <- fill_empty_clusters(fallback_corpus)[c(empty_tfidf)] - # Ranking-mode wedge (ranking.R): Mode 0 is the unchanged legacy selection; # Modes 1-3 apply rank-aware selection over the same global ranking, partitioned - # by rank_sources. The existing fallbacks below (title/abstract frequency) remain - # the final safety net in every mode. + # by rank_sources. Initial labels come from the min2 (DF >= 2) corpus. tfidf_top_names <- select_cluster_label_names(tfidf_top, top_n, stops, mode = mode, rank_sources = rank_sources) + + # min1 fallback: any cluster whose label came out EMPTY is re-labelled from the + # min1 corpus (all title n-grams, bound 1). The trigger is "empty label", NOT + # "zero tf-idf sum": with the min2 DF filter a cluster can have a tiny tf-idf that + # prunes away to nothing, which the old zero-sum check missed, dropping it + # straight to the abstract-frequency fallback instead of the intended min1 rescue. + # The title/abstract-frequency fallback below remains the true last resort. + empty_label <- which(!vapply(tfidf_top_names, + function(x) { s <- if (length(x)) x[[1]] else ""; nzchar(s) }, + logical(1))) + if (length(empty_label) > 0) { + vslog$debug(paste("create_cluster_labels: min1 fallback for", length(empty_label), + "clusters with an empty min2 label")) + fallback_top <- fill_empty_clusters(fallback_corpus) + fallback_names <- select_cluster_label_names(fallback_top, top_n, stops, mode = mode, + rank_sources = rank_sources) + tfidf_top_names[empty_label] <- fallback_names[empty_label] + } dump_data(tfidf_top_names, "summarize_05_tfidf_top_names") clusters$cluster_labels = "" - batch_size <- 1000 - total_length <- length(stops) for (k in seq(1, clusters$num_clusters)) { matches = which(unname(clusters$groups == k) == TRUE) summary = tfidf_top_names[[k]] if (summary == "") { - # Cluster label generation fallback: - # This is applied to clusters that have no top names, - # and will be used to generate a label from the titles and - # abstracts of the papers in the cluster + # No tf-idf label survived even the min1 fallback: last-resort label built + # from the papers' titles + abstracts (see title_abstract_fallback_label). vslog$debug(paste("create_cluster_labels: title/abstract fallback for cluster", k, "with", length(matches), "papers")) - candidates = mapply(paste, metadata$title[matches], metadata$paper_abstract[matches]) - candidates = lapply(candidates, tolower) - for (i in seq(1, total_length, batch_size)) { - candidates = lapply(candidates, function(x) {paste(removeWords(x, stops[i:min(i+batch_size -1, total_length)]), collapse="")}) - } - candidates = lapply(candidates, function(x) {gsub("[^[:alpha:]]", " ", x)}) - candidates = lapply(candidates, function(x) {gsub(" +", " ", x)}) - candidates_bigrams = lapply(lapply(candidates, expand_ngrams, n=2), paste, collapse=" ") - candidates_trigrams = lapply(lapply(candidates, expand_ngrams, n=3), paste, collapse=" ") - candidates = unname(mapply(paste, candidates_bigrams, candidates_trigrams)) - candidates = unlist(lapply(candidates, str_split, " "), recursive = F) - candidates = unlist(lapply(candidates, function(x) {another_prune_ngrams(x, stops)})) - top_ngrams = sort(table(strsplit(paste(candidates, collapse=" "), " ")), decreasing = T) - summary <- filter_out_nested_ngrams(names(top_ngrams), 3) - summary = lapply(summary, FUN = function(x) {paste(unlist(x), collapse="; ")}) - summary = gsub("_", " ", summary) - summary = paste(summary, collapse=", ") + summary <- title_abstract_fallback_label(matches, metadata, stops, top_n) } clusters$cluster_labels[c(matches)] = summary } From 5c677da391baf42e2ef37e955c89c82c79c0087e Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 9 Jul 2026 12:36:16 +0200 Subject: [PATCH 145/205] update of contentprovider.json cache --- .../common/common/contentproviders.json | 196 ++++++++++++++---- 1 file changed, 160 insertions(+), 36 deletions(-) diff --git a/server/workers/common/common/contentproviders.json b/server/workers/common/common/contentproviders.json index 1506fa174..700ec7e86 100644 --- a/server/workers/common/common/contentproviders.json +++ b/server/workers/common/common/contentproviders.json @@ -1,4 +1,160 @@ [ + { + "name": "Universidad Americana", + "internal_name": "ftid14848" + }, + { + "name": "International Journal of Research Welfare Society", + "internal_name": "ftid14870" + }, + { + "name": "Biblioteca Digital Editora Poisson", + "internal_name": "ftid14876" + }, + { + "name": "Tourismnomic: Journal of Tourism and Economic", + "internal_name": "ftid14882" + }, + { + "name": "Dar El-Mahara Center", + "internal_name": "ftid14883" + }, + { + "name": "Revista Científica Prospherus", + "internal_name": "ftid14884" + }, + { + "name": "Social Lens", + "internal_name": "ftid14885" + }, + { + "name": "Acta Peruana de Ciencias Sociales y Humanidades", + "internal_name": "ftid14886" + }, + { + "name": "Horizons Intermediary Journal of Business Research (HIJBR)", + "internal_name": "ftid14887" + }, + { + "name": "EduLearn Revista Multidisciplinaria", + "internal_name": "ftid14888" + }, + { + "name": "Scripta Scientia", + "internal_name": "ftid14889" + }, + { + "name": "Journal of the Epidemiology Foundation of India", + "internal_name": "ftid14890" + }, + { + "name": "International Journal of Ayurveda & Modern Sciences (IJAMS)", + "internal_name": "ftid14891" + }, + { + "name": "Revista de Epidemiologia e Saúde Pública (RESP)", + "internal_name": "ftid14892" + }, + { + "name": "Subset Journal", + "internal_name": "ftid14894" + }, + { + "name": "PT. Global Pustaka Ilmiah", + "internal_name": "ftid14895" + }, + { + "name": "Nutrition and Metabolism Journal: Clinical and Experimental", + "internal_name": "ftid14896" + }, + { + "name": "Jurnal Kajian Pembaruan Hukum (JKPH)", + "internal_name": "ftid14900" + }, + { + "name": "International Journal of Eco-Innovation in Science and Engineering (IJEISE)", + "internal_name": "ftid14836" + }, + { + "name": "Asosiasi Transformasi Digital dan Bisnis Indonesia", + "internal_name": "ftid14841" + }, + { + "name": "Jurnal Online Fakultas Tarbiyah dan Keguruan UIN SMH Banten", + "internal_name": "ftid14842" + }, + { + "name": "Espacio ECP", + "internal_name": "ftid14857" + }, + { + "name": "SAP Multidisciplinary Open", + "internal_name": "ftid14858" + }, + { + "name": "Global Journals", + "internal_name": "ftid14859" + }, + { + "name": "Pelabuhan Jurnal IAI TABAH", + "internal_name": "ftid14860" + }, + { + "name": "Global Health Synapse", + "internal_name": "ftid14861" + }, + { + "name": "Incaper em Revista", + "internal_name": "ftid14862" + }, + { + "name": "International Journal of Economics and Project Management", + "internal_name": "ftid14863" + }, + { + "name": "Legal Research & Analysis", + "internal_name": "ftid14869" + }, + { + "name": "Insitut Muslim Cendekia", + "internal_name": "ftid14871" + }, + { + "name": "Margulan Readings", + "internal_name": "ftid14872" + }, + { + "name": "Civilization Research: Journal of Islamic Studies", + "internal_name": "ftid14873" + }, + { + "name": "Stanzaleaf International Journal of Multidisciplinary Studies", + "internal_name": "ftid14874" + }, + { + "name": "Edelweispublishing", + "internal_name": "ftid14875" + }, + { + "name": "Journal of Education and Development Lab", + "internal_name": "ftid14877" + }, + { + "name": "Journal of South Asian Issues (JSAI)", + "internal_name": "ftid14878" + }, + { + "name": "International Journal of Politics and International Relations (IJPIR)", + "internal_name": "ftid14879" + }, + { + "name": "Bielefeld University Press (BiUP)", + "internal_name": "crid14897" + }, + { + "name": "ACCScience Publishing", + "internal_name": "crid14899" + }, { "name": "Repositorio de la Universidad Industrial de Santander", "internal_name": "ftid14824" @@ -11,6 +167,10 @@ "name": "Institut Binamadani Indonesia (INBI): Scientific Ejournal", "internal_name": "ftid14831" }, + { + "name": "Mandailing Journal of Education and Sciences", + "internal_name": "ftid14834" + }, { "name": "Institutional Repository DAU", "internal_name": "ftid14799" @@ -1559,10 +1719,6 @@ "name": "Bulletin of Scientific Research in English Education", "internal_name": "ftid14458" }, - { - "name": "Scientific and Innovative Therapy", - "internal_name": "ftid14433" - }, { "name": "Qainar Journal of Social Science", "internal_name": "ftid14445" @@ -2839,10 +2995,6 @@ "name": "Repositorio Institucional CONACYT", "internal_name": "ftid14072" }, - { - "name": "Jurnal Yayasan Pendidikan Intan Cendekia", - "internal_name": "ftid14077" - }, { "name": "Revista Disserata", "internal_name": "ftid14078" @@ -13747,10 +13899,6 @@ "name": "Innovation and Sustainability", "internal_name": "ftjins" }, - { - "name": "Інноватика у вихованні", - "internal_name": "ftjiiu" - }, { "name": "Вісник Економіки (ЗУНУ - Західноукраїнський національний університет)", "internal_name": "ftwestukrnatuniv" @@ -17067,10 +17215,6 @@ "name": "Universidad, Ciencia y Tecnología", "internal_name": "ftjruct" }, - { - "name": "Company of Scientists Publishing (COSCIP - Company of Scientists & Physicians)", - "internal_name": "ftcoscipojs" - }, { "name": "DSpace@IIT Bombay (Indian Institute of Technology)", "internal_name": "ftiitbombay" @@ -26183,10 +26327,6 @@ "name": "UMB Digital Archive (University of Maryland, Baltimore)", "internal_name": "ftumarylandhshsl" }, - { - "name": "Digitalni repozitorij je RIT Croatia", - "internal_name": "ftritcroatia" - }, { "name": "VTDK VB (Vilniaus technologijų ir dizaino kolegija virtualią biblioteką)", "internal_name": "ftvilniuscolltd" @@ -28891,10 +29031,6 @@ "name": "Journal of Social Sciences (JSS)", "internal_name": "ftjss" }, - { - "name": "Journal in Humanities (International Black Sea University)", - "internal_name": "ftjinhumanities" - }, { "name": "Journal of Education in Black Sea Region (International Black Sea University)", "internal_name": "ftjebs" @@ -30263,10 +30399,6 @@ "name": "AFTI Scholar (Air Force Institute of Technology)", "internal_name": "ftairforceinstec" }, - { - "name": "LETRAS (Revista de la Facultad de Letras y Ciencias Humanas - Ciudad Universitaria de la UNMSM)", - "internal_name": "ftjletras" - }, { "name": "Digital Commons at St. Mary's University, San Antonio", "internal_name": "ftstmarysuniv" @@ -31543,10 +31675,6 @@ "name": "Вопросы статистики", "internal_name": "ftjvoprstat" }, - { - "name": "Karib - Nordic Journal for Caribbean Studies", - "internal_name": "ftjkarib" - }, { "name": "Romanian Journal of History and International Studies (RJHIS)", "internal_name": "ftjrjhis" @@ -46719,10 +46847,6 @@ "name": "Search4Dev (Digital documents by Dutch development organizations)", "internal_name": "ftdprn" }, - { - "name": "Volunteer Voices - Tennessee Electronic Library, The University of Tennessee", - "internal_name": "ftvolunteervoi" - }, { "name": "Tropicos.org (Missouri Botanical Garden)", "internal_name": "ftmbgarden" From e54e380a303c1d0aead037c1e339aceffb0ddcde Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 9 Jul 2026 14:17:05 +0200 Subject: [PATCH 146/205] ranking mode 2 implementation; include tests --- .../other-scripts/mesh_classification.R | 65 + server/preprocessing/other-scripts/ranking.R | 72 +- .../preprocessing/other-scripts/summarize.R | 51 +- .../other-scripts/test/mine_cases.R | 39 +- .../other-scripts/test/replay_harness.R | 65 + .../other-scripts/test/run_tests.R | 8 +- .../other-scripts/test/test_fallback.R | 53 + .../test/test_mesh_classification.R | 61 + .../other-scripts/test/test_mode1_selection.R | 77 + .../other-scripts/test/test_mode2.R | 31 + .../other-scripts/test/test_ranking_select.R | 63 +- .../resources/mesh_check_tags.txt | 27 + .../resources/mesh_tree_depth.tsv | 31109 ++++++++++++++++ 13 files changed, 31633 insertions(+), 88 deletions(-) create mode 100644 server/preprocessing/other-scripts/mesh_classification.R create mode 100644 server/preprocessing/other-scripts/test/test_fallback.R create mode 100644 server/preprocessing/other-scripts/test/test_mesh_classification.R create mode 100644 server/preprocessing/other-scripts/test/test_mode1_selection.R create mode 100644 server/preprocessing/other-scripts/test/test_mode2.R create mode 100644 server/preprocessing/resources/mesh_check_tags.txt create mode 100644 server/preprocessing/resources/mesh_tree_depth.tsv diff --git a/server/preprocessing/other-scripts/mesh_classification.R b/server/preprocessing/other-scripts/mesh_classification.R new file mode 100644 index 000000000..0c68b04de --- /dev/null +++ b/server/preprocessing/other-scripts/mesh_classification.R @@ -0,0 +1,65 @@ +# mesh_classification.R +# +# Classify MeSH descriptors as "specific" or "generic" for area-title ranking. +# A descriptor is GENERIC if it is a +# check tag (Humans, Animals, Male, …) OR its minimum MeSH tree depth is <= a +# threshold (top levels of the classification); everything else — including +# descriptors we cannot find in the tree — is SPECIFIC, so we never demote a term +# we failed to classify. +# +# Pure base R (no packages), so it can be sourced and unit-tested in isolation +# (mirrors subject_cleaning.R). Lookups are lazy-loaded once from: +# resources/mesh_tree_depth.tsv (descriptor \t min_depth \t max_depth \t n_locations) +# resources/mesh_check_tags.txt (one check tag per line) +# +# Lookup is by the MeSH descriptor's ORIGINAL form (as it appears in the NLM +# descriptor list, e.g. "Adaptation, Physiological"), matched case-insensitively — +# so classify BEFORE de-inverting/normalising the term. + +# Descriptors at min tree depth <= this are "generic". Depth = number of +# dot-separated components of a tree number ("C04.588" = depth 2). +MESH_GENERIC_MAX_DEPTH <- 2L + +.mesh_env <- new.env(parent = emptyenv()) + +# Find the resources directory the same way get_stopwords() does. +mesh_resources_dir <- function() { + for (d in c("../resources", "./resources", "../../resources")) { + if (dir.exists(d)) return(d) + } + stop("mesh_classification: could not locate the resources/ directory") +} + +# Load the tree-depth and check-tag lookups once (idempotent). `resources_dir` +# overrides the auto-detected path (used by tests). +load_mesh_resources <- function(resources_dir = NULL, force = FALSE) { + if (!force && !is.null(.mesh_env$depth)) return(invisible(NULL)) + dir <- if (is.null(resources_dir)) mesh_resources_dir() else resources_dir + d <- read.delim(file.path(dir, "mesh_tree_depth.tsv"), stringsAsFactors = FALSE, + quote = "", colClasses = c("character", "integer", "integer", "integer")) + .mesh_env$depth <- stats::setNames(d$min_depth, tolower(d$descriptor)) + tags <- readLines(file.path(dir, "mesh_check_tags.txt"), warn = FALSE) + tags <- tolower(trimws(tags)) + .mesh_env$checktags <- tags[nzchar(tags)] + invisible(NULL) +} + +# Minimum MeSH tree depth of a descriptor (original form), or NA if absent. +mesh_min_depth <- function(descriptor) { + load_mesh_resources() + unname(.mesh_env$depth[tolower(trimws(descriptor))]) +} + +# TRUE if the descriptor is a "generic" MeSH term (check tag or shallow tree depth). +# Vectorised over `descriptor`. +is_generic_mesh <- function(descriptor) { + load_mesh_resources() + key <- tolower(trimws(descriptor)) + d <- .mesh_env$depth[key] + unname((key %in% .mesh_env$checktags) | (!is.na(d) & d <= MESH_GENERIC_MAX_DEPTH)) +} + +# Classify each descriptor as "generic" or "specific". Vectorised. +classify_mesh <- function(descriptors) { + ifelse(is_generic_mesh(descriptors), "generic", "specific") +} diff --git a/server/preprocessing/other-scripts/ranking.R b/server/preprocessing/other-scripts/ranking.R index 42d499c9e..2cac95e21 100644 --- a/server/preprocessing/other-scripts/ranking.R +++ b/server/preprocessing/other-scripts/ranking.R @@ -46,29 +46,47 @@ ranking_mode <- function(service = NULL) { "0" } -# Ordered rank/fill-policy spec for a mode. Each entry is list(rank, policy) where -# policy is "topup" (fill open label slots) or "exclusive" (contribute only if the -# label is still empty). Returns NULL for modes with no ranked selection yet, which -# fall back to the legacy selection. -rank_policies <- function(mode) { +# Ordered rank spec for a mode. Each entry is list(rank, sources, policy): +# rank : the rank number (1 = highest priority). +# sources : names of the rank_sources token-sets that feed this rank (pooled). +# policy : "topup" (fill open label slots) or "exclusive" (only if still empty). +# Returns NULL for modes with no ranked selection (they fall back to legacy). +# The corpus is unchanged across modes; only this source->rank mapping differs. +rank_spec <- function(mode) { switch(as.character(mode), - "1" = list(list(rank = 1L, policy = "topup"), # keywords + MeSH (pooled) - list(rank = 2L, policy = "exclusive")), # heuristic title n-grams + "1" = list( + list(rank = 1L, sources = c("cleaned"), policy = "topup"), # keywords + MeSH pooled + list(rank = 2L, sources = c("heuristic"), policy = "exclusive")), # title n-grams + "2" = list( + list(rank = 1L, sources = c("cleaned_ex_mesh", "mesh_specific"), policy = "topup"), # keywords + specific MeSH + list(rank = 2L, sources = c("mesh_generic"), policy = "exclusive"), # generic MeSH + list(rank = 3L, sources = c("heuristic"), policy = "exclusive")), + "3" = list( + list(rank = 1L, sources = c("cleaned_ex_mesh"), policy = "topup"), # keywords + list(rank = 2L, sources = c("mesh_specific"), policy = "topup"), # specific MeSH tops up (decision #1) + list(rank = 3L, sources = c("mesh_generic"), policy = "exclusive"), # generic MeSH + list(rank = 4L, sources = c("heuristic"), policy = "exclusive")), NULL) } # Assign a provenance rank to each candidate term (in "_"-joined, lowercased TDM -# form) from the separated rank sources: rank 1 if it is a cleaned keyword, rank 2 -# if it is only a heuristic title n-gram. As a fallback, unknown terms (in neither — e.g. a -# normalization straggler) take the lowest rank and are -# counted for debug purposes. Returns list(ranks = , unknown = ). -rank_of_terms <- function(terms, cleaned, heuristic, policy_spec) { - max_rank <- max(vapply(policy_spec, function(s) s$rank, integer(1))) - r <- ifelse(terms %in% cleaned, 1L, - ifelse(terms %in% heuristic, 2L, NA_integer_)) - unknown <- sum(is.na(r)) - r[is.na(r)] <- max_rank - list(ranks = r, unknown = unknown) +# form) from the per-cluster source token-sets. `sources` is a named list of token +# vectors (cleaned, cleaned_ex_mesh, mesh_specific, mesh_generic, heuristic); `spec` +# is the ordered rank_spec. A term takes the LOWEST rank among the sources that +# contain it (highest-rank-wins). Unknown terms (in no source — e.g. a +# normalization straggler) take the last rank and are counted for the drift alarm. +# Returns list(ranks = , unknown = ). +rank_of_terms <- function(terms, sources, spec) { + ranks <- rep(NA_integer_, length(terms)) + for (s in spec) { # spec is ordered by rank ascending + pool <- unique(unlist(sources[s$sources], use.names = FALSE)) + hit <- is.na(ranks) & (terms %in% pool) + ranks[hit] <- s$rank + } + max_rank <- max(vapply(spec, function(s) s$rank, integer(1))) + unknown <- sum(is.na(ranks)) + ranks[is.na(ranks)] <- max_rank + list(ranks = ranks, unknown = unknown) } # Waterfall selection over one cluster's ranked candidates. Walks the policy spec @@ -116,15 +134,16 @@ format_label <- function(terms) { # top_n : number of terms kept per label. # stops : stopword vector. # mode : ranking mode string (see RANKING_MODES). -# rank_sources : list(cleaned, heuristic), each a per-cluster list of lowercased -# "_"-joined tokens; NULL disables ranked selection. +# rank_sources : named list of per-cluster token-sets (cleaned, cleaned_ex_mesh, +# mesh_specific, mesh_generic, heuristic), each a per-cluster list +# of lowercased "_"-joined tokens; NULL disables ranked selection. # legacy_fn : the legacy selector; injectable for testing. select_cluster_label_names <- function(tfidf_top, top_n, stops, mode = "0", rank_sources = NULL, legacy_fn = get_top_names) { - policy_spec <- rank_policies(mode) - if (identical(mode, "0") || is.null(policy_spec) || is.null(rank_sources)) { - if (!identical(mode, "0") && (is.null(policy_spec) || is.null(rank_sources))) { + spec <- rank_spec(mode) + if (identical(mode, "0") || is.null(spec) || is.null(rank_sources)) { + if (!identical(mode, "0") && (is.null(spec) || is.null(rank_sources))) { msg <- sprintf( "select_cluster_label_names: ranking mode '%s' not applied (no rank sources or mode unimplemented); using legacy selection", mode) @@ -141,13 +160,14 @@ select_cluster_label_names <- function(tfidf_top, top_n, stops, mode = "0", weights <- tfidf_top[[k]] nms <- names(weights) if (is.null(nms) || length(nms) == 0) { out[[k]] <- ""; next } - # Shared prune head (§3 step 1): same well-formedness filter as legacy, on the + # Shared prune head: same well-formedness filter as legacy, on the # global weight-ordered list. another_prune_ngrams preserves order. pruned <- unlist(another_prune_ngrams(nms, stops)) if (length(pruned) == 0) { out[[k]] <- ""; next } - rr <- rank_of_terms(pruned, rank_sources$cleaned[[k]], rank_sources$heuristic[[k]], policy_spec) + sources_k <- lapply(rank_sources, function(src) src[[k]]) # this cluster's token-sets + rr <- rank_of_terms(pruned, sources_k, spec) unknown_total <- unknown_total + rr$unknown - out[[k]] <- format_label(select_by_rank(pruned, rr$ranks, top_n, policy_spec)) + out[[k]] <- format_label(select_by_rank(pruned, rr$ranks, top_n, spec)) dbg[[k]] <- data.frame(cluster = k, term = gsub("_", " ", pruned), rank = rr$ranks, weight = as.numeric(weights[pruned]), stringsAsFactors = FALSE) diff --git a/server/preprocessing/other-scripts/summarize.R b/server/preprocessing/other-scripts/summarize.R index 3730d275f..8076f29d1 100644 --- a/server/preprocessing/other-scripts/summarize.R +++ b/server/preprocessing/other-scripts/summarize.R @@ -8,6 +8,13 @@ vslog <- getLogger('vis.summarize') HEUR_MIN1 <- "keywords_rank_heuristically_generated_min1" HEUR_MIN2 <- "keywords_rank_heuristically_generated_min2" +# MeSH rank columns, produced upstream by the data client (base.R, BASE only for +# now). Used by Modes 2/3 to split subject tokens into specific/generic. When +# absent (integrations without MeSH), the split is empty and Mode 2/3 degrade to +# Mode-1-like behaviour (see get_cluster_corpus). +KW_MESH_SPECIFIC <- "keywords_rank_mesh_specific" +KW_MESH_GENERIC <- "keywords_rank_mesh_generic" + # summarize.R # Cluster labelling for the overview visualisation: turns each cluster of papers # into a short, human-readable area title. create_cluster_labels() is the entry @@ -355,6 +362,8 @@ get_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, heuristic_col = HEUR_MIN2) { subjectlist = list() subject_dbg = list(); heuristic_dbg = list(); heuristic_min1_dbg = list() + mesh_spec_dbg = list(); mesh_gen_dbg = list() # normalised MeSH tokens (empty if no mesh columns) + has_mesh <- !is.null(metadata[[KW_MESH_SPECIFIC]]) for (k in seq(1, clusters$num_clusters)) { matches = which(unname(clusters$groups == k) == TRUE) subjects = metadata$subject[matches] @@ -382,6 +391,17 @@ get_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, subject_dbg[[k]] = paste(unlist(subjects), collapse=";") heuristic_dbg[[k]] = paste(unlist(heuristics), collapse=";") heuristic_min1_dbg[[k]] = paste(unlist(heuristics_min1), collapse=";") + # MeSH columns (Modes 2/3): aggregate per cluster, normalised the same way as + # subjects so their tokens match the subject tokens they classify. Empty when + # the client did not populate them (-> Mode 2/3 degrade to Mode-1 behaviour). + if (has_mesh) { + msp = gsub(" ", "_", gsub("; ", ";", as.character(metadata[[KW_MESH_SPECIFIC]][matches]))) + mge = gsub(" ", "_", gsub("; ", ";", as.character(metadata[[KW_MESH_GENERIC]][matches]))) + mesh_spec_dbg[[k]] = paste(unlist(msp), collapse=";") + mesh_gen_dbg[[k]] = paste(unlist(mge), collapse=";") + } else { + mesh_spec_dbg[[k]] = ""; mesh_gen_dbg[[k]] = "" + } all_subjects = paste(subjects, heuristics, collapse=" ") all_subjects <- normalize_corpus_tokens(all_subjects) subjectlist = c(subjectlist, all_subjects) @@ -392,18 +412,35 @@ get_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, subject_tokens = unlist(subject_dbg), title_ngrams = unlist(heuristic_dbg)), "summarize_04a_corpus_sources") - # Rank sources for the ranked selection (ranking.R): the same per-cluster tokens, - # split by provenance and run through the SAME normalize_corpus_tokens() + - # lowercasing + edge-strip as the TDM terms — so every rank token matches a tf-idf - # term. cleaned = keywords_rank_cleaned (subject_cleaned); - # heuristic = the min1 n-gram set. Used only for rank lookup, never fed to the TDM. + # Rank sources for the ranked selection (ranking.R): per-cluster token-sets, all + # run through the SAME normalize_corpus_tokens() + lowercasing + edge-strip as the + # TDM terms — so every rank token matches a tf-idf term. The MeSH split is + # derived by MEMBERSHIP on the (already-normalised) subject tokens: a subject token + # is mesh_specific/generic if it is in the corresponding MeSH column, else it is a + # cleaned (ex-mesh) keyword. This keeps every rank token a genuine subject token + # (so it matches the TDM) and needs no separate normalization path. + # cleaned = all subject tokens (keywords + MeSH pooled) — Mode 1 rank 1. + # mesh_specific/generic = subject tokens classified as specific/generic MeSH. + # cleaned_ex_mesh = subject tokens minus MeSH — Modes 2/3 rank 1. + # heuristic = the min1 title-n-gram tokens. + # Used only for rank lookup, never fed to the TDM. split_tokens <- function(s) { t <- tolower(unlist(strsplit(normalize_corpus_tokens(s), ";"))) t <- gsub("^_+|_+$", "", t) # mirror the TDM tokenizer's edge-punctuation strip t[nzchar(t)] } - rank_sources <- list(cleaned = lapply(subject_dbg, split_tokens), - heuristic = lapply(heuristic_min1_dbg, split_tokens)) + subj_tok <- lapply(subject_dbg, split_tokens) + spec_tok <- lapply(mesh_spec_dbg, split_tokens) + gen_tok <- lapply(mesh_gen_dbg, split_tokens) + mesh_specific <- mapply(intersect, subj_tok, spec_tok, SIMPLIFY = FALSE) + mesh_generic <- mapply(intersect, subj_tok, gen_tok, SIMPLIFY = FALSE) + cleaned_ex_mesh <- mapply(function(all, sp, ge) setdiff(all, c(sp, ge)), + subj_tok, mesh_specific, mesh_generic, SIMPLIFY = FALSE) + rank_sources <- list(cleaned = subj_tok, + cleaned_ex_mesh = cleaned_ex_mesh, + mesh_specific = mesh_specific, + mesh_generic = mesh_generic, + heuristic = lapply(heuristic_min1_dbg, split_tokens)) nn_corpus <- VCorpus(VectorSource(subjectlist)) return(list(corpus = nn_corpus, rank_sources = rank_sources)) } diff --git a/server/preprocessing/other-scripts/test/mine_cases.R b/server/preprocessing/other-scripts/test/mine_cases.R index 8da906132..b98e7602f 100644 --- a/server/preprocessing/other-scripts/test/mine_cases.R +++ b/server/preprocessing/other-scripts/test/mine_cases.R @@ -16,48 +16,13 @@ suppressWarnings(suppressMessages(source("test/replay_harness.R"))) -TOP_N <- 3 -SPEC <- rank_policies("1") - -# word-sequence containment: is `a` a contiguous run of words inside `b`? -is_nested <- function(a, b) { - a != b && grepl(paste0(" ", a, " "), paste0(" ", b, " "), fixed = TRUE) -} - -# Recompute the Mode-1 per-cluster candidates exactly as create_cluster_labels does. -cluster_candidates <- function(bundle) { - md <- add_heuristic_keyword_fields(bundle$metadata, bundle$stops) - md$keywords_rank_cleaned <- md$subject - co <- get_cluster_corpus(bundle$clusters, md, bundle$stops, bundle$taxonomy_separator, heuristic_col = HEUR_MIN2) - tdm <- TermDocumentMatrix(co$corpus, control = list( - tokenize = SplitTokenizer, weighting = function(x) weightSMART(x, spec = "ntn"), - bounds = list(local = c(1, Inf)), tolower = TRUE)) - tt <- apply(tdm, 2, function(x) { x2 <- sort(x, TRUE); x2[x2 > 0] }) - empty <- which(apply(tdm, 2, sum) == 0) - if (length(empty)) { - fb <- get_cluster_corpus(bundle$clusters, md, bundle$stops, bundle$taxonomy_separator, heuristic_col = HEUR_MIN1)$corpus - tt[empty] <- fill_empty_clusters(fb)[empty] - } - - out <- vector("list", length(tt)) - for (k in seq_along(tt)) { - nms <- names(tt[[k]]); if (is.null(nms) || !length(nms)) next - pruned <- unlist(another_prune_ngrams(nms, bundle$stops)); if (!length(pruned)) next - rr <- rank_of_terms(pruned, co$rank_sources$cleaned[[k]], co$rank_sources$heuristic[[k]], SPEC) - sp <- trimws(gsub("_", " ", pruned)) - out[[k]] <- list( - r1 = sp[rr$ranks == 1], # rank-1 terms, weight-ordered - r2 = sp[rr$ranks == 2], # rank-2 terms, weight-ordered - label = format_label(select_by_rank(pruned, rr$ranks, TOP_N, SPEC))) - } - out -} +# is_nested() and mode1_cluster_breakdown() are provided by replay_harness.R. fmt <- function(x, n = 8) if (length(x)) paste(head(x, n), collapse = " | ") else "(none)" mine <- function(path) { name <- fixture_name(path) - cand <- cluster_candidates(readRDS(path)) + cand <- mode1_cluster_breakdown(readRDS(path))$clusters for (k in seq_along(cand)) { c <- cand[[k]]; if (is.null(c) || !nzchar(c$label)) next hits <- character(0) diff --git a/server/preprocessing/other-scripts/test/replay_harness.R b/server/preprocessing/other-scripts/test/replay_harness.R index ca96ce9fd..799e99b42 100644 --- a/server/preprocessing/other-scripts/test/replay_harness.R +++ b/server/preprocessing/other-scripts/test/replay_harness.R @@ -74,6 +74,71 @@ write_expected <- function(name, labels) { saveRDS(labels, expected_file(name)) } +# Is `a` a contiguous run of words inside `b` (word-sequence containment)? +is_nested <- function(a, b) { + a != b && grepl(paste0(" ", a, " "), paste0(" ", b, " "), fixed = TRUE) +} + +# Per-cluster Mode-1 breakdown for a fixture bundle, computed exactly as +# create_cluster_labels does (initial corpus = min2, fallback = min1, bound +# c(1,Inf)). Returns list(clusters, unknown_total) where each cluster entry is +# list(r1, r2, label): the rank-1 / rank-2 candidate terms (space form, +# tf-idf-weight-ordered) and the resulting Mode-1 area label. Shared by the miner +# (mine_cases.R) and the Mode-1 regression tests. +mode1_cluster_breakdown <- function(bundle) { + md <- add_heuristic_keyword_fields(bundle$metadata, bundle$stops) + md$keywords_rank_cleaned <- md$subject + co <- get_cluster_corpus(bundle$clusters, md, bundle$stops, bundle$taxonomy_separator, + heuristic_col = HEUR_MIN2) + tdm <- TermDocumentMatrix(co$corpus, control = list( + tokenize = SplitTokenizer, weighting = function(x) weightSMART(x, spec = "ntn"), + bounds = list(local = c(1, Inf)), tolower = TRUE)) + tt <- apply(tdm, 2, function(x) { x2 <- sort(x, TRUE); x2[x2 > 0] }) + empty <- which(apply(tdm, 2, sum) == 0) + if (length(empty)) { + fb <- get_cluster_corpus(bundle$clusters, md, bundle$stops, bundle$taxonomy_separator, + heuristic_col = HEUR_MIN1)$corpus + tt[empty] <- fill_empty_clusters(fb)[empty] + } + spec <- rank_spec("1") + out <- vector("list", length(tt)); unknown_total <- 0L + for (k in seq_along(tt)) { + nms <- names(tt[[k]]); if (is.null(nms) || !length(nms)) next + pruned <- unlist(another_prune_ngrams(nms, bundle$stops)); if (!length(pruned)) next + sources_k <- lapply(co$rank_sources, function(src) src[[k]]) + rr <- rank_of_terms(pruned, sources_k, spec) + unknown_total <- unknown_total + rr$unknown + sp <- trimws(gsub("_", " ", pruned)) + lt <- select_by_rank(pruned, rr$ranks, 3, spec) # selected label terms (space form) + out[[k]] <- list(r1 = sp[rr$ranks == 1], r2 = sp[rr$ranks == 2], + label_terms = lt, label = format_label(lt)) + } + list(clusters = out, unknown_total = unknown_total) +} + +# Per-cluster Mode-0 fallback breakdown: the label each cluster would get from the +# min2 (initial) corpus vs. the min1 (fallback) corpus, under the Mode-0 bound +# c(2, Inf). Used to test the fallback trigger — a cluster whose min2 label is +# empty but whose min1 label is not must end up with the min1 label, NOT the +# abstract-frequency fallback (see create_cluster_labels). Returns list(min2, min1), +# each a per-cluster character vector. +mode0_fallback_breakdown <- function(bundle) { + md <- add_heuristic_keyword_fields(bundle$metadata, bundle$stops) + md$keywords_rank_cleaned <- md$subject + co2 <- get_cluster_corpus(bundle$clusters, md, bundle$stops, bundle$taxonomy_separator, + heuristic_col = HEUR_MIN2) + tdm2 <- TermDocumentMatrix(co2$corpus, control = list( + tokenize = SplitTokenizer, weighting = function(x) weightSMART(x, spec = "ntn"), + bounds = list(local = c(2, Inf)), tolower = TRUE)) + tt2 <- apply(tdm2, 2, function(x) { x2 <- sort(x, TRUE); x2[x2 > 0] }) + min2 <- unlist(get_top_names(tt2, 3, bundle$stops)) + + co1 <- get_cluster_corpus(bundle$clusters, md, bundle$stops, bundle$taxonomy_separator, + heuristic_col = HEUR_MIN1) + min1 <- unlist(get_top_names(fill_empty_clusters(co1$corpus), 3, bundle$stops)) + list(min2 = min2, min1 = min1) +} + # A small, self-contained input bundle for validating the harness without any # external data: two clearly-separated clusters (climate vs. machine learning). build_synthetic_bundle <- function() { diff --git a/server/preprocessing/other-scripts/test/run_tests.R b/server/preprocessing/other-scripts/test/run_tests.R index 943492cd7..cede324ba 100644 --- a/server/preprocessing/other-scripts/test/run_tests.R +++ b/server/preprocessing/other-scripts/test/run_tests.R @@ -25,13 +25,17 @@ base_files <- c( "test/test_subject_cleaning.R", "test/test_ranking_config.R", "test/test_ranking_wedge.R", - "test/test_ranking_select.R" + "test/test_ranking_select.R", + "test/test_mesh_classification.R" ) # Replay tests need the tm stack — included only when tm is available (i.e. inside # the pipeline image, run via test/run_tests.sh), skipped on a bare host. replay_files <- c( "test/test_replay_harness.R", - "test/test_replay_modes.R" + "test/test_replay_modes.R", + "test/test_mode1_selection.R", + "test/test_fallback.R", + "test/test_mode2.R" ) default_files <- base_files if (requireNamespace("tm", quietly = TRUE)) { diff --git a/server/preprocessing/other-scripts/test/test_fallback.R b/server/preprocessing/other-scripts/test/test_fallback.R new file mode 100644 index 000000000..e5b888198 --- /dev/null +++ b/server/preprocessing/other-scripts/test/test_fallback.R @@ -0,0 +1,53 @@ +# Regression for the min1 fallback trigger (create_cluster_labels). +# +# With the map-wide DF filter (§6.1), a cluster can end up with a tiny min2 tf-idf +# that prunes to an empty label. The min1 fallback must catch this (trigger = +# "empty label", not "zero tf-idf sum"); otherwise the cluster drops straight to +# the abstract-frequency fallback and surfaces affiliation/email boilerplate. +# +# base_cancer_fallback is the captured real map that exhibited this (cluster 15: +# the reference label is "Breast cancer survivors, Cancer recurrence fear, +# Disease-free survival", but the bug produced "People Republic ChinaEmail, …"). +# +# Runs inside the pipeline image (needs tm). + +if (!exists("mode0_fallback_breakdown")) source("test/replay_harness.R") +if (!requireNamespace("testthat", quietly = TRUE)) { + if (!exists("test_that")) source("test/testthat_shim.R") +} else { + library(testthat) +} + +FX <- file.path(REPLAY_DIR, "base_cancer_fallback.inputs.rds") + +if (!file.exists(FX)) { + cat(" (base_cancer_fallback fixture missing — skipping fallback regression)\n") +} else { + bundle <- readRDS(FX) + + # Invariant: whenever the min2 label is empty but the min1 label is not, the + # FINAL Mode-0 label must be the min1 label (the fallback fired before the + # abstract-frequency last resort). + test_that("min1 fallback fires on empty labels, not just zero tf-idf sum", { + bd <- mode0_fallback_breakdown(bundle) + final <- replay_labels(FX, mode = "0") + n <- bundle$clusters$num_clusters + checked <- 0 + for (k in seq_len(n)) { + if (!nzchar(bd$min2[k]) && nzchar(bd$min1[k])) { + checked <- checked + 1 + expect_equal(unname(final[[as.character(k)]]), unname(bd$min1[k])) + } + } + expect_true(checked > 0) # the fixture must actually exercise the fallback + }) + + # Concrete anchor: the cluster that motivated the fix reproduces its reference + # label rather than the abstract-fallback boilerplate. + test_that("base_cancer_fallback cluster 15 gets its reference (min1) label", { + final <- replay_labels(FX, mode = "0") + expect_equal(final[["15"]], + "Breast cancer survivors, Cancer recurrence fear, Disease-free survival") + expect_false(grepl("Email", final[["15"]])) + }) +} diff --git a/server/preprocessing/other-scripts/test/test_mesh_classification.R b/server/preprocessing/other-scripts/test/test_mesh_classification.R new file mode 100644 index 000000000..3a3b5e1ed --- /dev/null +++ b/server/preprocessing/other-scripts/test/test_mesh_classification.R @@ -0,0 +1,61 @@ +# Unit tests for the shared MeSH specific/generic classifier (mesh_classification.R). +# +# Pure base R; loads the tree/check-tag artifacts from resources/. If those are not +# present (e.g. a container image built before they were added), the tests skip. + +if (!exists("classify_mesh")) source("mesh_classification.R") +if (!requireNamespace("testthat", quietly = TRUE)) { + if (!exists("test_that")) source("test/testthat_shim.R") +} else { + library(testthat) +} + +.have_mesh <- tryCatch({ load_mesh_resources(); TRUE }, error = function(e) FALSE) + +if (!.have_mesh) { + cat(" (mesh resources not found in resources/ — skipping MeSH classifier tests)\n") +} else { + + # --- check tags are generic regardless of tree depth ------------------------ + test_that("check tags are generic even when their tree depth is deep", { + expect_true(is_generic_mesh("Humans")) # tree depth 11, but a check tag + expect_true(is_generic_mesh("Animals")) + expect_true(is_generic_mesh("Male")) + expect_true(is_generic_mesh("Female")) + expect_true(mesh_min_depth("Humans") > MESH_GENERIC_MAX_DEPTH) # depth alone wouldn't + }) + + # --- shallow tree depth (<= 2) is generic ----------------------------------- + test_that("descriptors at min tree depth <= 2 are generic", { + expect_true(is_generic_mesh("Neoplasms")) # depth 1 + expect_true(is_generic_mesh("Game Theory")) # depth 2 + expect_true(is_generic_mesh("Biological Evolution")) # depth 2 (multi-location, min 2) + }) + + # --- deeper descriptors are specific ---------------------------------------- + test_that("descriptors at min tree depth > 2 are specific", { + expect_false(is_generic_mesh("Breast Neoplasms")) # depth 3 + expect_false(is_generic_mesh("A549 Cells")) # depth 3 + }) + + # --- unknown descriptors default to specific -------------------------------- + test_that("descriptors not in the tree default to specific (never demoted)", { + expect_false(is_generic_mesh("Depressive Disorder, Major")) # comma-truncation artifact + expect_false(is_generic_mesh("Zzzz Not A Real Descriptor")) + expect_true(is.na(mesh_min_depth("Zzzz Not A Real Descriptor"))) + }) + + # --- lookup is on the ORIGINAL (non-de-inverted) form, case-insensitive ------ + test_that("lookup uses the original MeSH form, case-insensitively", { + expect_true(is_generic_mesh("Adaptation, Physiological")) # original form, depth 2 + expect_true(is_generic_mesh("adaptation, physiological")) # case-insensitive + # the de-inverted form is NOT in the tree -> classify BEFORE de-inverting + expect_false(is_generic_mesh("Physiological Adaptation")) + }) + + # --- classify_mesh is vectorised -------------------------------------------- + test_that("classify_mesh vectorises to generic/specific", { + expect_equal(classify_mesh(c("Humans", "Breast Neoplasms", "Neoplasms")), + c("generic", "specific", "generic")) + }) +} diff --git a/server/preprocessing/other-scripts/test/test_mode1_selection.R b/server/preprocessing/other-scripts/test/test_mode1_selection.R new file mode 100644 index 000000000..68c7d2252 --- /dev/null +++ b/server/preprocessing/other-scripts/test/test_mode1_selection.R @@ -0,0 +1,77 @@ +# Mode-1 selection regression tests, replayed over the real fixtures. +# +# These pin the Stage-1 ranking behaviour end-to-end (corpus + rank map + waterfall) +# on frozen real data, complementing the pure-unit tests in test_ranking_select.R: +# G1 §15-3 guard: no candidate term drifts out of the rank map (unknown == 0). +# G2 de-nesting: no area label contains a term nested inside another. +# C1 exclusivity: with rank 1 present, the label is drawn ONLY from rank 1. +# C2 fallback: with rank 1 empty, the label falls back to rank 2 (non-empty). +# Runs inside the pipeline image (needs tm). See docs §6, §15. + +if (!exists("mode1_cluster_breakdown")) source("test/replay_harness.R") +if (!requireNamespace("testthat", quietly = TRUE)) { + if (!exists("test_that")) source("test/testthat_shim.R") +} else { + library(testthat) +} + +fixtures <- fixture_files() + +# Cache the (relatively expensive) per-fixture breakdown across tests. +.bd_cache <- new.env(parent = emptyenv()) +breakdown <- function(fx) { + if (is.null(.bd_cache[[fx]])) .bd_cache[[fx]] <- mode1_cluster_breakdown(readRDS(fx)) + .bd_cache[[fx]] +} +each_cluster <- function(f) { + for (fx in fixtures) for (c in breakdown(fx)$clusters) if (!is.null(c) && nzchar(c$label)) f(c, fx) +} +# First cluster across all fixtures whose breakdown satisfies `pred`. +first_match <- function(pred) { + for (fx in fixtures) { + cl <- breakdown(fx)$clusters + for (k in seq_along(cl)) if (!is.null(cl[[k]]) && nzchar(cl[[k]]$label) && pred(cl[[k]])) + return(list(fx = fixture_name(fx), k = k, c = cl[[k]])) + } + NULL +} + +if (length(fixtures) == 0) { + cat(" (no fixtures in test/replay — skipping Mode-1 selection tests)\n") +} else { + + # G1 — §15-3: every pruned tf-idf term resolves to a rank (no drift). + test_that("Mode 1: zero unknown-rank terms across all fixtures (§15-3 guard)", { + total <- 0 + for (fx in fixtures) total <- total + breakdown(fx)$unknown_total + expect_equal(total, 0) + }) + + # G2 — within-rank de-nesting: no label carries a nested term pair. + test_that("Mode 1: no area label contains a term nested in another", { + bad <- character(0) + each_cluster(function(c, fx) { + t <- c$label_terms + for (i in seq_along(t)) for (j in seq_along(t)) + if (i != j && is_nested(t[i], t[j])) + bad <<- c(bad, paste0(fixture_name(fx), ": ", c$label)) + }) + expect_equal(unique(bad), character(0)) + }) + + # C1 — exclusivity: rank 1 and rank 2 both present -> label is all rank 1. + test_that("Mode 1: with rank 1 present, the label is drawn only from rank 1", { + m <- first_match(function(c) length(c$r1) > 0 && length(c$r2) > 0) + expect_true(!is.null(m)) + expect_true(all(m$c$label_terms %in% m$c$r1)) # no rank-2 heuristic leaked in + expect_true(length(m$c$label_terms) > 0) + }) + + # C2 — fallback: rank 1 empty -> label comes from rank 2, non-empty. + test_that("Mode 1: with rank 1 empty, the label falls back to rank 2 (non-empty)", { + m <- first_match(function(c) length(c$r1) == 0 && length(c$r2) > 0) + expect_true(!is.null(m)) + expect_true(all(m$c$label_terms %in% m$c$r2)) # label is heuristic, as expected + expect_true(nzchar(m$c$label)) + }) +} diff --git a/server/preprocessing/other-scripts/test/test_mode2.R b/server/preprocessing/other-scripts/test/test_mode2.R new file mode 100644 index 000000000..71a867700 --- /dev/null +++ b/server/preprocessing/other-scripts/test/test_mode2.R @@ -0,0 +1,31 @@ +# Mode-2 robustness regression. +# +# Mode 2 needs the MeSH rank columns (keywords_rank_mesh_specific/generic) which +# are produced upstream by the BASE client. When they are absent (any current +# fixture, and any integration without MeSH), Mode 2 must **degrade to Mode-1 +# behaviour**: rank 1 = keywords (cleaned_ex_mesh == cleaned), the empty MeSH ranks +# are skipped, heuristic is the last rank. This test pins that on a real fixture. +# +# Once a MeSH-bearing fixture exists (BASE client re-run), a differential Mode-2 +# case (generic MeSH demoted) will be added here. +# +# Runs inside the pipeline image (needs tm). + +if (!exists("replay_labels")) source("test/replay_harness.R") +if (!requireNamespace("testthat", quietly = TRUE)) { + if (!exists("test_that")) source("test/testthat_shim.R") +} else { + library(testthat) +} + +FX <- file.path(REPLAY_DIR, "base_cancer_research.inputs.rds") + +if (!file.exists(FX)) { + cat(" (base_cancer_research fixture missing — skipping Mode-2 robustness test)\n") +} else { + test_that("Mode 2 degrades to Mode 1 when no MeSH columns are present", { + m1 <- replay_labels(FX, mode = "1") + m2 <- replay_labels(FX, mode = "2") + expect_equal(unname(unlist(m2)), unname(unlist(m1))) + }) +} diff --git a/server/preprocessing/other-scripts/test/test_ranking_select.R b/server/preprocessing/other-scripts/test/test_ranking_select.R index 5ecfa72e5..349eb543d 100644 --- a/server/preprocessing/other-scripts/test/test_ranking_select.R +++ b/server/preprocessing/other-scripts/test/test_ranking_select.R @@ -1,4 +1,4 @@ -# Unit tests for the rank-aware selection helpers (ranking.R): rank_policies, +# Unit tests for the rank-aware selection helpers (ranking.R): rank_spec, # rank_of_terms, select_by_rank, format_label. # # Pure base R — the only summarize.R dependency (filter_out_nested_ngrams) is @@ -12,35 +12,66 @@ if (!requireNamespace("testthat", quietly = TRUE)) { library(testthat) } -spec1 <- rank_policies("1") +spec1 <- rank_spec("1") id_denest <- function(x, n) head(x, n) # identity-ish de-nester for isolation -# --- rank_policies ----------------------------------------------------------- -test_that("rank_policies: mode 1 is topup rank1 then exclusive rank2", { +# --- rank_spec --------------------------------------------------------------- +test_that("rank_spec: mode 1 is topup rank1 (cleaned) then exclusive rank2 (heuristic)", { expect_equal(length(spec1), 2) - expect_equal(spec1[[1]]$rank, 1L); expect_equal(spec1[[1]]$policy, "topup") - expect_equal(spec1[[2]]$rank, 2L); expect_equal(spec1[[2]]$policy, "exclusive") + expect_equal(spec1[[1]]$rank, 1L); expect_equal(spec1[[1]]$sources, "cleaned"); expect_equal(spec1[[1]]$policy, "topup") + expect_equal(spec1[[2]]$rank, 2L); expect_equal(spec1[[2]]$sources, "heuristic"); expect_equal(spec1[[2]]$policy, "exclusive") }) -test_that("rank_policies: unimplemented modes return NULL", { - expect_null(rank_policies("2")) - expect_null(rank_policies("3")) - expect_null(rank_policies("9")) +test_that("rank_spec: mode 2 pools cleaned_ex_mesh + specific in rank 1, generic exclusive", { + s <- rank_spec("2") + expect_equal(length(s), 3) + expect_equal(s[[1]]$sources, c("cleaned_ex_mesh", "mesh_specific")); expect_equal(s[[1]]$policy, "topup") + expect_equal(s[[2]]$sources, "mesh_generic"); expect_equal(s[[2]]$policy, "exclusive") + expect_equal(s[[3]]$sources, "heuristic"); expect_equal(s[[3]]$policy, "exclusive") }) -# --- rank_of_terms ----------------------------------------------------------- -test_that("rank_of_terms: cleaned->1, heuristic-only->2, unknown->lowest rank", { - r <- rank_of_terms(c("a", "b", "c"), cleaned = c("a"), heuristic = c("b"), spec1) +test_that("rank_spec: mode 3 tops up specific MeSH, generic + heuristic exclusive", { + s <- rank_spec("3") + expect_equal(length(s), 4) + expect_equal(s[[1]]$sources, "cleaned_ex_mesh"); expect_equal(s[[1]]$policy, "topup") + expect_equal(s[[2]]$sources, "mesh_specific"); expect_equal(s[[2]]$policy, "topup") # top-up (decision #1) + expect_equal(s[[3]]$sources, "mesh_generic"); expect_equal(s[[3]]$policy, "exclusive") + expect_equal(s[[4]]$sources, "heuristic"); expect_equal(s[[4]]$policy, "exclusive") +}) + +test_that("rank_spec: unimplemented modes return NULL", { + expect_null(rank_spec("9")) +}) + +# --- rank_of_terms (named source-sets) --------------------------------------- +test_that("rank_of_terms: mode 1 — cleaned->1, heuristic-only->2, unknown->lowest", { + r <- rank_of_terms(c("a", "b", "c"), list(cleaned = "a", heuristic = "b"), spec1) expect_equal(r$ranks, c(1L, 2L, 2L)) # c is unknown -> lowest rank (2) expect_equal(r$unknown, 1L) }) -test_that("rank_of_terms: highest-rank-wins when a term is in both sources", { - r <- rank_of_terms("x", cleaned = "x", heuristic = "x", spec1) - expect_equal(unname(r$ranks), 1L) +test_that("rank_of_terms: highest-rank-wins when a term is in two sources", { + r <- rank_of_terms("x", list(cleaned = "x", heuristic = "x"), spec1) + expect_equal(r$ranks, 1L) expect_equal(r$unknown, 0L) }) +test_that("rank_of_terms: mode 2 — specific pools into rank 1, generic is rank 2", { + s <- rank_spec("2") + srcs <- list(cleaned_ex_mesh = "kw", mesh_specific = "sp", mesh_generic = "gen", heuristic = "ng") + r <- rank_of_terms(c("kw", "sp", "gen", "ng"), srcs, s) + expect_equal(r$ranks, c(1L, 1L, 2L, 3L)) # kw & sp -> rank 1; gen -> 2; ng -> 3 +}) + +test_that("rank_of_terms: mode 2 degrades when mesh sources are empty", { + s <- rank_spec("2") + # no mesh: cleaned_ex_mesh carries the keywords, mesh sources empty + srcs <- list(cleaned_ex_mesh = c("kw1", "kw2"), mesh_specific = character(0), + mesh_generic = character(0), heuristic = "ng") + r <- rank_of_terms(c("kw1", "kw2", "ng"), srcs, s) + expect_equal(r$ranks, c(1L, 1L, 3L)) # behaves like Mode 1 (keywords rank 1, heuristic last) +}) + # --- select_by_rank ---------------------------------------------------------- test_that("select_by_rank: rank 1 fills up to top_n", { lab <- select_by_rank(c("k1", "k2", "k3", "k4"), c(1L, 1L, 1L, 1L), 3, spec1, id_denest) diff --git a/server/preprocessing/resources/mesh_check_tags.txt b/server/preprocessing/resources/mesh_check_tags.txt new file mode 100644 index 000000000..96eecabe6 --- /dev/null +++ b/server/preprocessing/resources/mesh_check_tags.txt @@ -0,0 +1,27 @@ +Humans +Animals +Male +Female +Adolescent +Adult +Aged +Aged, 80 and over +Child +Child, Preschool +Infant +Infant, Newborn +Middle Aged +Young Adult +Pregnancy +Rats +Mice +Cattle +Dogs +Cats +Rabbits +Swine +Sheep +Horses +Guinea Pigs +Chick Embryo +Haplorhini diff --git a/server/preprocessing/resources/mesh_tree_depth.tsv b/server/preprocessing/resources/mesh_tree_depth.tsv new file mode 100644 index 000000000..e343b6e76 --- /dev/null +++ b/server/preprocessing/resources/mesh_tree_depth.tsv @@ -0,0 +1,31109 @@ +descriptor min_depth max_depth n_tree_locations +(4-(m-Chlorophenylcarbamoyloxy)-2-butynyl)trimethylammonium Chloride 4 5 3 +1,2-Dihydroxybenzene-3,5-Disulfonic Acid Disodium Salt 7 8 3 +1,2-Dimethylhydrazine 5 5 1 +1,2-Dipalmitoylphosphatidylcholine 8 8 1 +1,4-alpha-Glucan Branching Enzyme 7 7 1 +1-(5-Isoquinolinesulfonyl)-2-Methylpiperazine 4 5 3 +1-Acylglycerol-3-Phosphate O-Acyltransferase 5 5 1 +1-Acylglycerophosphocholine O-Acyltransferase 5 5 1 +1-Alkyl-2-acetylglycerophosphocholine Esterase 9 9 1 +1-Butanol 4 5 2 +1-Carboxyglutamic Acid 5 5 3 +1-Deoxynojirimycin 3 5 4 +1-Methyl-3-isobutylxanthine 8 8 1 +1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine 4 4 1 +1-Methyl-4-phenylpyridinium 5 5 1 +1-Naphthylamine 3 7 3 +1-Naphthylisothiocyanate 4 7 4 +1-Octanol 4 5 2 +1-Phosphatidylinositol 4-Kinase 6 6 1 +1-Propanol 4 4 1 +1-Pyrroline-5-Carboxylate Dehydrogenase 5 5 1 +1-Sarcosine-8-Isoleucine Angiotensin II 6 6 2 +11-beta-Hydroxysteroid Dehydrogenase Type 1 7 7 2 +11-beta-Hydroxysteroid Dehydrogenase Type 2 7 7 2 +11-beta-Hydroxysteroid Dehydrogenases 6 6 2 +11-Hydroxycorticosteroids 5 5 1 +12-Hydroxy-5,8,10,14-eicosatetraenoic Acid 7 7 2 +12E7 Antigen 5 6 4 +14-3-3 Proteins 5 5 3 +14-alpha Demethylase Inhibitors 5 7 4 +15-Hydroxy-11 alpha,9 alpha-(epoxymethano)prosta-5,13-dienoic Acid 5 10 7 +15-Oxoprostaglandin 13-Reductase 5 5 1 +16,16-Dimethylprostaglandin E2 5 8 3 +17 alpha-Hydroxyprogesterone Caproate 9 10 2 +17-alpha-Hydroxypregnenolone 6 7 3 +17-alpha-Hydroxyprogesterone 8 9 2 +17-Hydroxycorticosteroids 5 5 1 +17-Hydroxysteroid Dehydrogenases 6 6 1 +17-Ketosteroids 4 5 2 +18-Hydroxycorticosterone 6 8 2 +18-Hydroxydesoxycorticosterone 6 8 2 +19-Iodocholesterol 5 7 3 +2',3'-Cyclic Nucleotide 3'-Phosphodiesterase 5 7 2 +2',3'-Cyclic-Nucleotide Phosphodiesterases 6 6 1 +2',5'-Oligoadenylate Synthetase 6 6 1 +2,2'-Dipyridyl 4 4 1 +2,3,4,5-Tetrahydro-7,8-dihydroxy-1-phenyl-1H-3-benzazepine 5 5 1 +2,3-Diketogulonic Acid 3 5 3 +2,3-Diphosphoglycerate 5 7 4 +2,4,5-Trichlorophenoxyacetic Acid 6 7 2 +2,4-Dichlorophenoxyacetic Acid 6 7 2 +2,4-Dinitrophenol 5 9 2 +2,6-Dichloroindophenol 8 8 1 +2-Acetolactate Mutase 5 5 1 +2-Acetylaminofluorene 4 7 4 +2-Amino-5-phosphonovalerate 4 5 2 +2-Aminoadipate Transaminase 6 6 1 +2-Aminoadipic Acid 4 6 2 +2-Aminopurine 6 6 1 +2-Chloroadenosine 5 7 3 +2-Hydroxy-5-nitrobenzyl Bromide 4 8 3 +2-Hydroxyphenethylamine 5 5 4 +2-Hydroxypropyl-beta-cyclodextrin 5 8 3 +2-Isopropylmalate Synthase 5 5 1 +2-Methoxyestradiol 7 7 2 +2-Methyl-4-chlorophenoxyacetic Acid 6 7 2 +2-Naphthylamine 3 7 3 +2-Oxoisovalerate Dehydrogenase (Acylating) 6 6 1 +2-Propanol 4 4 1 +2-Pyridinylmethylsulfinylbenzimidazoles 4 5 3 +20-alpha-Dihydroprogesterone 7 8 2 +20-alpha-Hydroxysteroid Dehydrogenase 7 8 3 +20-Hydroxysteroid Dehydrogenases 6 6 1 +2019-nCoV Vaccine mRNA-1273 6 7 4 +22q11 Deletion Syndrome 4 5 11 +24,25-Dihydroxyvitamin D 3 7 9 4 +25-Hydroxyvitamin D 2 5 7 4 +25-Hydroxyvitamin D3 1-alpha-Hydroxylase 5 8 6 +2H-Benzo(a)quinolizin-2-ol, 2-Ethyl-1,3,4,6,7,11b-hexahydro-3-isobutyl-9,10-dimethoxy- 5 5 1 +2S Albumins, Plant 5 5 1 +3' Flanking Region 6 7 2 +3' Untranslated Regions 6 8 4 +3',5'-Cyclic-AMP Phosphodiesterases 4 6 2 +3',5'-Cyclic-GMP Phosphodiesterases 4 6 2 +3,3'-Diaminobenzidine 8 8 1 +3,3'-Dichlorobenzidine 8 8 1 +3,4-Dichloro-N-methyl-N-(2-(1-pyrrolidinyl)-cyclohexyl)-benzeneacetamide, (trans)-Isomer 4 4 1 +3,4-Dihydroxyphenylacetic Acid 5 5 1 +3,4-Methylenedioxyamphetamine 6 6 1 +3-alpha-Hydroxysteroid Dehydrogenase (B-Specific) 6 7 2 +3-Deazauridine 5 6 3 +3-Deoxy-7-Phosphoheptulonate Synthase 5 5 1 +3-Hydroxyacyl CoA Dehydrogenases 6 6 1 +3-Hydroxyacyl-CoA Dehydrogenase 7 7 1 +3-Hydroxyanthranilate 3,4-Dioxygenase 6 6 1 +3-Hydroxyanthranilic Acid 5 9 4 +3-Hydroxybutyric Acid 4 6 4 +3-Hydroxysteroid Dehydrogenases 6 6 1 +3-Iodobenzylguanidine 4 7 3 +3-Isopropylmalate Dehydrogenase 6 6 1 +3-Mercaptopropionic Acid 4 5 2 +3-Methoxy-4-hydroxyphenylethanol 8 8 1 +3-Methyl-2-Oxobutanoate Dehydrogenase (Lipoamide) 6 6 1 +3-O-Methylglucose 5 5 2 +3-Oxo-5-alpha-Steroid 4-Dehydrogenase 5 5 1 +3-Oxoacyl-(Acyl-Carrier-Protein) Reductase 6 6 1 +3-Oxoacyl-(Acyl-Carrier-Protein) Synthase 5 5 1 +3-Phosphoinositide-Dependent Protein Kinases 5 8 2 +3-Phosphoshikimate 1-Carboxyvinyltransferase 5 5 1 +3-Pyridinecarboxylic acid, 1,4-dihydro-2,6-dimethyl-5-nitro-4-(2-(trifluoromethyl)phenyl)-, Methyl ester 5 5 2 +3C Viral Proteases 6 7 3 +3T3 Cells 4 4 2 +3T3-L1 Cells 6 6 2 +4,4'-Diisothiocyanostilbene-2,2'-Disulfonic Acid 4 8 3 +4,5-Dihydro-1-(3-(trifluoromethyl)phenyl)-1H-pyrazol-3-amine 5 5 1 +4-(3-Butoxy-4-methoxybenzyl)-2-imidazolidinone 5 5 1 +4-1BB Ligand 4 6 6 +4-Acetamido-4'-isothiocyanatostilbene-2,2'-disulfonic Acid 4 8 3 +4-Aminobenzoic Acid 7 9 2 +4-Aminobutyrate Transaminase 6 6 1 +4-Aminopyridine 4 5 2 +4-Butyrolactone 3 4 2 +4-Chloro-7-nitrobenzofurazan 6 6 1 +4-Chloromercuribenzenesulfonate 5 8 3 +4-Hydroxyaminoquinoline-1-oxide 4 6 3 +4-Hydroxybenzoate-3-Monooxygenase 6 6 1 +4-Hydroxycoumarins 6 6 2 +4-Hydroxyphenylpyruvate Dioxygenase 6 6 1 +4-Nitrophenylphosphatase 6 6 1 +4-Nitroquinoline-1-oxide 4 6 3 +4-Quinolones 6 6 1 +46, XX Disorders of Sex Development 4 6 5 +46, XX Testicular Disorders of Sex Development 5 7 5 +5' Flanking Region 6 7 2 +5' Untranslated Regions 6 8 4 +5'-Nucleotidase 7 7 1 +5,10-Methylenetetrahydrofolate Reductase (FADH2) 4 4 1 +5,6-Dihydroxytryptamine 7 7 1 +5,7-Dihydroxytryptamine 7 7 1 +5,8,11,14-Eicosatetraynoic Acid 5 5 1 +5-alpha Reductase Inhibitors 6 7 2 +5-alpha-Dihydroprogesterone 6 7 2 +5-Amino-3-((5-nitro-2-furyl)vinyl)-1,2,4-oxadiazole 4 6 3 +5-Aminolevulinate Synthetase 5 5 1 +5-Hydroxytryptophan 6 6 1 +5-Lipoxygenase-Activating Protein Inhibitors 5 5 1 +5-Lipoxygenase-Activating Proteins 4 5 3 +5-Methoxypsoralen 5 7 3 +5-Methoxytryptamine 7 7 2 +5-Methylcytosine 6 6 1 +5-Methyltetrahydrofolate-Homocysteine S-Methyltransferase 6 6 1 +6-Aminonicotinamide 5 6 2 +6-Cyano-7-nitroquinoxaline-2,3-dione 5 5 1 +6-Ketoprostaglandin F1 alpha 5 7 3 +6-Phytase 6 6 1 +7,8-Dihydro-7,8-dihydroxybenzo(a)pyrene 9,10-oxide 6 9 2 +7-Alkoxycoumarin O-Dealkylase 8 8 1 +8,11,14-Eicosatrienoic Acid 5 5 1 +8-Bromo Cyclic Adenosine Monophosphate 5 8 4 +8-Hydroxy-2'-Deoxyguanosine 5 8 3 +8-Hydroxy-2-(di-n-propylamino)tetralin 5 8 2 +9,10-Dimethyl-1,2-benzanthracene 4 7 2 +A Kinase Anchor Proteins 5 5 3 +A549 Cells 3 5 3 +AAA Domain 8 8 1 +AAA Proteins 4 5 2 +Abacavir 5 8 4 +Abatacept 8 8 2 +Abattoirs 3 6 2 +Abbreviated Injury Scale 3 8 4 +Abbreviations 2 2 1 +Abbreviations as Topic 6 6 1 +Abciximab 6 9 7 +Abdomen 3 3 1 +Abdomen, Acute 5 6 2 +Abdominal Abscess 4 4 1 +Abdominal Cavity 4 4 1 +Abdominal Core 3 3 1 +Abdominal Fat 5 5 1 +Abdominal Injuries 2 2 1 +Abdominal Muscles 4 4 1 +Abdominal Neoplasms 3 3 1 +Abdominal Oblique Muscles 5 5 1 +Abdominal Pain 4 5 4 +Abdominal Wall 4 4 2 +Abdominal Wound Closure Techniques 3 3 1 +Abdominoplasty 3 3 2 +Abducens Nerve 5 5 1 +Abducens Nerve Diseases 3 3 1 +Abducens Nerve Injury 4 5 4 +Abducens Nucleus 9 9 1 +Abelmoschus 10 10 1 +Abelson murine leukemia virus 6 6 2 +Aberrant Crypt Foci 3 3 1 +Aberrant Motor Behavior in Dementia 4 4 1 +Aberrometry 4 4 1 +Abetalipoproteinemia 7 7 4 +Abies 8 8 1 +Abietanes 4 7 3 +Abiotrophia 5 5 2 +Abiraterone Acetate 6 6 1 +Ablation Techniques 2 2 1 +Abnormal Involuntary Movement Scale 5 5 1 +Abnormal Karyotype 4 5 3 +Abnormalities, Drug-Induced 3 3 1 +Abnormalities, Multiple 3 3 1 +Abnormalities, Radiation-Induced 3 7 5 +Abnormalities, Severe Teratoid 3 3 1 +ABO Blood-Group System 5 5 2 +Abomasum 3 3 1 +Aborted Fetus 3 3 1 +Abortifacient Agents 5 5 2 +Abortifacient Agents, Nonsteroidal 6 6 2 +Abortifacient Agents, Steroidal 6 6 2 +Abortion Applicants 2 2 1 +Abortion, Eugenic 4 4 1 +Abortion, Habitual 5 5 1 +Abortion, Illegal 4 4 1 +Abortion, Incomplete 5 5 1 +Abortion, Induced 3 3 1 +Abortion, Legal 4 4 1 +Abortion, Missed 5 5 1 +Abortion, Septic 3 5 3 +Abortion, Spontaneous 4 6 2 +Abortion, Therapeutic 4 4 1 +Abortion, Threatened 4 4 1 +Abortion, Veterinary 2 5 2 +Abreaction 4 4 1 +Abrin 5 8 3 +Abruptio Placentae 5 5 2 +Abrus 8 8 1 +Abscess 3 5 2 +Abscisic Acid 5 9 5 +Absenteeism 4 4 1 +Absidia 5 5 1 +Absinthe 4 5 2 +Absorbable Implants 3 3 1 +Absorbent Pads 3 3 1 +Absorptiometry, Photon 5 5 2 +Absorption 2 4 5 +Absorption, Physicochemical 3 3 2 +Absorption, Physiological 3 5 3 +Absorption, Radiation 3 4 3 +Abstracting and Indexing 5 5 1 +Abstracts 2 2 2 +Abuse-Deterrent Formulations 3 6 6 +AC133 Antigen 5 5 3 +Acacia 8 8 1 +Academia 2 2 1 +Academic Dissertation 2 2 1 +Academic Dissertations as Topic 5 5 1 +Academic Failure 4 4 1 +Academic Medical Centers 3 3 1 +Academic Performance 3 3 1 +Academic Success 4 4 1 +Academies and Institutes 3 3 1 +Acalculous Cholecystitis 5 5 1 +Acalypha 10 10 1 +Acamprosate 7 7 2 +Acanthaceae 8 8 1 +Acanthamoeba 6 6 1 +Acanthamoeba castellanii 7 7 1 +Acanthamoeba Keratitis 4 5 4 +Acanthocephala 5 5 1 +Acanthocheilonema 9 9 1 +Acanthocheilonemiasis 8 8 1 +Acanthocytes 5 6 3 +Acantholysis 3 4 2 +Acanthoma 4 5 2 +Acanthopodina 5 5 1 +Acanthosis Nigricans 6 6 1 +Acarbose 5 5 1 +Acari 6 6 1 +Acaricides 4 5 2 +Acaridae 8 8 1 +Acatalasia 5 5 2 +Accelerated Idioventricular Rhythm 6 6 3 +Acceleration 3 3 1 +Accelerometry 2 2 1 +Acceptance and Commitment Therapy 5 5 1 +Access to Healthy Foods 6 6 2 +Access to Information 3 7 3 +Access to Primary Care 4 5 2 +Accessory Atrioventricular Bundle 3 3 1 +Accessory Nerve 5 5 1 +Accessory Nerve Diseases 3 3 1 +Accessory Nerve Injuries 4 5 4 +Accident Prevention 4 4 1 +Accident Proneness 3 3 1 +Accidental Falls 4 4 1 +Accidental Injuries 2 2 1 +Accidents 3 3 1 +Accidents, Aviation 4 4 1 +Accidents, Home 4 4 1 +Accidents, Occupational 4 4 1 +Accidents, Traffic 4 4 1 +Acclimatization 3 4 2 +Accommodation, Ocular 2 2 1 +Account Book 2 2 1 +Accountable Care Organizations 4 4 1 +Accounting 4 4 1 +Accounts Payable and Receivable 5 5 1 +Accreditation 4 4 2 +Acculturation 5 5 2 +Acebutolol 6 6 3 +Acecainide 6 9 4 +Acedapsone 5 5 1 +Acellular Dermis 4 4 1 +Acenaphthenes 4 7 2 +Acenocoumarol 7 7 2 +Acepromazine 4 5 2 +Acer 8 8 1 +Aceraceae 9 9 1 +Acetabularia 4 4 1 +Acetabuloplasty 3 3 2 +Acetabulum 6 6 1 +Acetaldehyde 3 3 1 +Acetaldehyde Dehydrogenase Inhibitors 5 5 1 +Acetals 3 3 1 +Acetamides 3 5 2 +Acetaminophen 5 6 2 +Acetanilides 4 5 2 +Acetate Kinase 6 6 1 +Acetate-CoA Ligase 6 6 1 +Acetates 4 4 2 +Acetazolamide 5 6 2 +Acetic Acid 5 5 2 +Acetic Anhydrides 3 5 2 +Acetivibrio thermocellus 4 5 4 +Acetoacetates 4 4 2 +Acetoanaerobium sticklandii 3 5 4 +Acetobacter 6 6 2 +Acetobacteraceae 5 5 2 +Acetobacterium 6 6 1 +Acetogenins 3 5 6 +Acetohexamide 5 7 5 +Acetoin 4 4 1 +Acetoin Dehydrogenase 6 6 1 +Acetolactate Synthase 4 5 2 +Acetone 3 3 1 +Acetonitriles 3 3 1 +Acetophenones 3 3 1 +Acetoxyacetylaminofluorene 5 8 4 +Acetrizoic Acid 7 9 4 +Acetyl Coenzyme A 5 9 4 +Acetyl-CoA C-Acetyltransferase 6 6 1 +Acetyl-CoA C-Acyltransferase 5 5 1 +Acetyl-CoA Carboxylase 4 5 2 +Acetyl-CoA Hydrolase 6 6 1 +Acetylation 3 4 3 +Acetylcarnitine 6 6 1 +Acetylcholine 4 4 1 +Acetylcholine Release Inhibitors 5 6 3 +Acetylcholinesterase 7 7 1 +Acetylcysteine 5 5 2 +Acetyldigitoxins 6 9 3 +Acetyldigoxins 6 9 3 +Acetylene 5 5 1 +Acetylesterase 6 6 1 +Acetylgalactosamine 5 5 1 +Acetylglucosamine 5 5 1 +Acetylglucosaminidase 7 7 1 +Acetylmuramyl-Alanyl-Isoglutamine 4 4 3 +Acetylserotonin O-Methyltransferase 6 6 1 +Acetylthiocholine 5 7 3 +Acetyltransferases 5 5 1 +Achaete-Scute Complex Genome Region 6 6 1 +Achievement 3 4 2 +Achillea 8 8 1 +Achilles Tendon 3 3 1 +Achlorhydria 4 4 2 +Achlya 4 4 1 +Acholeplasma 5 5 1 +Acholeplasma laidlawii 6 6 1 +Acholeplasmataceae 4 4 1 +Achondroplasia 4 5 3 +Achromobacter 6 6 2 +Achromobacter cycloclastes 7 7 2 +Achromobacter denitrificans 7 7 2 +Achyranthes 10 10 1 +Achyrocline 8 8 1 +Acid Anhydride Hydrolases 4 4 1 +Acid Ceramidase 6 6 1 +Acid Etching, Dental 4 4 1 +Acid Phosphatase 6 6 1 +Acid Rain 2 7 7 +Acid Sensing Ion Channel Blockers 6 6 1 +Acid Sensing Ion Channels 4 7 4 +Acid-Base Equilibrium 2 3 5 +Acid-Base Imbalance 3 3 1 +Acidaminococcus 3 5 2 +Acidianus 5 5 1 +Acidic Glycosphingolipids 4 5 3 +Acidiphilium 6 6 2 +Acidithiobacillus 4 5 2 +Acidithiobacillus thiooxidans 5 6 2 +Acidobacteria 2 4 2 +Acidosis 4 4 1 +Acidosis, Lactic 5 5 1 +Acidosis, Renal Tubular 4 7 5 +Acidosis, Respiratory 4 5 2 +Acids 2 2 1 +Acids, Acyclic 3 3 1 +Acids, Aldehydic 3 3 1 +Acids, Carbocyclic 3 3 1 +Acids, Heterocyclic 2 2 1 +Acids, Noncarboxylic 3 3 1 +Acidulated Phosphate Fluoride 3 7 6 +Acinar Cells 2 2 1 +Acinetobacter 5 6 2 +Acinetobacter baumannii 6 7 2 +Acinetobacter calcoaceticus 6 7 2 +Acinetobacter Infections 6 6 1 +Acinonyx 10 10 1 +Acitretin 5 8 3 +Aclarubicin 5 8 3 +Acne Conglobata 5 5 2 +Acne Keloid 4 6 4 +Acne Vulgaris 4 4 2 +Acneiform Eruptions 3 3 1 +Aconitate Hydratase 6 6 1 +Aconitic Acid 5 5 1 +Aconitine 4 6 2 +Aconitum 9 9 1 +Acoraceae 7 7 1 +Acorus 8 8 1 +Acoustic Impedance Tests 5 5 1 +Acoustic Maculae 5 6 2 +Acoustic Stimulation 2 4 3 +Acoustics 3 3 1 +Acquired Hyperostosis Syndrome 5 5 1 +Acquired Immunodeficiency Syndrome 4 7 8 +Acremonium 4 4 1 +Acridine Orange 6 6 1 +Acridines 4 4 1 +Acridones 3 5 2 +Acriflavine 6 6 1 +Acro-Osteolysis 4 5 2 +Acrocallosal Syndrome 4 5 2 +Acrocephalosyndactylia 5 7 10 +Acrodermatitis 4 4 3 +Acrodynia 4 6 3 +Acrolein 3 3 1 +Acromegaly 4 7 3 +Acromioclavicular Joint 4 4 1 +Acromion 6 6 1 +Acronine 4 6 2 +Acrosin 7 7 2 +Acrosome 5 9 3 +Acrosome Reaction 6 6 1 +Acrospiroma 6 6 2 +Acrylamide 4 6 2 +Acrylamides 3 5 2 +Acrylates 4 4 1 +Acrylic Resins 5 7 3 +Acrylonitrile 3 3 1 +Actaea 9 9 1 +ACTH Syndrome, Ectopic 4 4 1 +ACTH-Secreting Pituitary Adenoma 4 7 5 +Actigraphy 3 4 2 +Actihaemyl 3 3 1 +Actin Capping Proteins 5 5 2 +Actin Cytoskeleton 7 7 1 +Actin Depolymerizing Factors 5 5 2 +Actin-Related Protein 2 6 6 2 +Actin-Related Protein 2-3 Complex 5 5 2 +Actin-Related Protein 3 6 6 2 +Acting Out 3 3 1 +Actinidia 9 9 1 +Actinidiaceae 8 8 1 +Actinin 5 5 3 +Actinium 4 6 5 +Actinobacillosis 2 7 2 +Actinobacillus 4 5 2 +Actinobacillus equuli 5 6 2 +Actinobacillus Infections 6 6 1 +Actinobacillus pleuropneumoniae 5 6 2 +Actinobacillus seminis 5 6 2 +Actinobacillus suis 5 6 2 +Actinobacteria 3 6 2 +Actinoid Series Elements 3 5 3 +Actinomadura 5 5 1 +Actinomyces 6 8 2 +Actinomyces viscosus 7 9 2 +Actinomycetaceae 5 7 2 +Actinomycetales 4 4 1 +Actinomycetales Infections 5 5 1 +Actinomycosis 6 6 1 +Actinomycosis, Cervicofacial 4 7 4 +Actinoplanes 5 6 4 +Actins 5 5 3 +Action Potentials 3 4 3 +Action Spectrum 5 5 1 +Activated Protein C Resistance 4 5 4 +Activated-Leukocyte Cell Adhesion Molecule 5 8 5 +Activating Transcription Factor 1 6 6 2 +Activating Transcription Factor 2 6 6 2 +Activating Transcription Factor 3 6 6 2 +Activating Transcription Factor 4 6 6 2 +Activating Transcription Factor 6 4 6 4 +Activating Transcription Factors 5 5 2 +Activation Analysis 3 3 1 +Activation, Metabolic 3 5 3 +Activator Appliances 5 5 2 +Active Transport, Cell Nucleus 4 4 2 +Activin Receptors 5 8 3 +Activin Receptors, Type I 6 9 3 +Activin Receptors, Type II 6 9 3 +Activins 4 4 5 +Activities of Daily Living 2 6 4 +Activity Cycles 5 5 1 +Actomyosin 5 5 1 +Actuarial Analysis 4 5 3 +Acupressure 5 6 3 +Acupuncture 2 2 1 +Acupuncture Analgesia 3 4 2 +Acupuncture Points 5 5 1 +Acupuncture Therapy 3 3 1 +Acupuncture, Ear 4 4 2 +Acute Aortic Syndrome 4 4 1 +Acute Care Surgery 4 4 1 +Acute Chest Syndrome 3 7 6 +Acute Coronary Syndrome 4 4 2 +Acute Disease 4 4 1 +Acute Febrile Encephalopathy 4 4 1 +Acute Generalized Exanthematous Pustulosis 4 5 3 +Acute Kidney Injury 5 7 3 +Acute Lung Injury 4 4 1 +Acute Pain 5 5 3 +Acute Radiation Syndrome 3 7 4 +Acute Retroviral Syndrome 4 7 7 +Acute-On-Chronic Liver Failure 6 6 1 +Acute-Phase Proteins 4 4 1 +Acute-Phase Reaction 4 4 1 +Acyclic Monoterpenes 5 5 1 +Acyclovir 8 8 1 +Acyl Carrier Protein 4 4 1 +Acyl Coenzyme A 4 8 4 +Acyl-Butyrolactones 3 3 1 +Acyl-Carrier Protein S-Acetyltransferase 6 6 1 +Acyl-Carrier Protein S-Malonyltransferase 5 5 1 +Acyl-CoA Dehydrogenase 4 6 2 +Acyl-CoA Dehydrogenase, Long-Chain 4 6 2 +Acyl-CoA Dehydrogenases 5 5 1 +Acyl-CoA Oxidase 4 6 2 +Acylation 2 3 3 +Acylphosphatase 5 5 1 +Acyltransferases 4 4 1 +Ad26COVS1 6 6 1 +Adalimumab 9 9 3 +ADAM Proteins 4 7 3 +ADAM10 Protein 5 8 3 +ADAM12 Protein 5 8 3 +ADAM17 Protein 5 8 3 +Adamantane 5 6 2 +Adamantinoma 4 4 2 +Adams-Stokes Syndrome 5 5 3 +ADAMTS Proteins 5 8 4 +ADAMTS1 Protein 6 9 4 +ADAMTS13 Protein 6 9 4 +ADAMTS4 Protein 6 9 4 +ADAMTS5 Protein 6 9 4 +ADAMTS7 Protein 6 9 4 +ADAMTS9 Protein 6 9 4 +Adansonia 10 10 1 +Adapalene 4 10 6 +Adapalene, Benzoyl Peroxide Drug Combination 3 8 5 +Adaptation, Biological 2 2 1 +Adaptation, Ocular 2 2 1 +Adaptation, Physiological 2 3 2 +Adaptation, Psychological 2 2 1 +Adaptive Algorithms 4 5 3 +Adaptive Clinical Trial 4 4 1 +Adaptive Clinical Trials as Topic 6 7 3 +Adaptive Immunity 3 3 1 +Adaptor Protein Complex 1 6 6 1 +Adaptor Protein Complex 2 6 6 1 +Adaptor Protein Complex 3 6 6 1 +Adaptor Protein Complex 4 6 6 1 +Adaptor Protein Complex alpha Subunits 7 7 1 +Adaptor Protein Complex beta Subunits 7 7 1 +Adaptor Protein Complex delta Subunits 7 7 1 +Adaptor Protein Complex gamma Subunits 7 7 1 +Adaptor Protein Complex mu Subunits 7 7 1 +Adaptor Protein Complex sigma Subunits 7 7 1 +Adaptor Protein Complex Subunits 6 6 1 +Adaptor Proteins, Signal Transducing 4 4 3 +Adaptor Proteins, Vesicular Transport 5 5 1 +Addiction Medicine 3 3 1 +Addison Disease 3 4 2 +Address 2 2 1 +Adducins 4 5 3 +Adenine 5 5 1 +Adenine Nucleotide Translocator 1 6 8 7 +Adenine Nucleotide Translocator 2 6 8 7 +Adenine Nucleotide Translocator 3 6 8 7 +Adenine Nucleotides 4 6 3 +Adenine Phosphoribosyltransferase 6 6 1 +Adenocarcinoma 5 5 1 +Adenocarcinoma in Situ 3 6 3 +Adenocarcinoma of Lung 6 6 2 +Adenocarcinoma, Bronchiolo-Alveolar 7 7 2 +Adenocarcinoma, Clear Cell 6 6 1 +Adenocarcinoma, Follicular 6 6 1 +Adenocarcinoma, Mucinous 5 6 2 +Adenocarcinoma, Papillary 6 6 1 +Adenocarcinoma, Scirrhous 6 6 1 +Adenocarcinoma, Sebaceous 5 6 2 +Adenofibroma 5 7 2 +Adenoidectomy 3 3 1 +Adenoids 3 5 4 +Adenolymphoma 4 4 1 +Adenoma 4 4 1 +Adenoma, Acidophil 5 6 3 +Adenoma, Basophil 5 6 3 +Adenoma, Bile Duct 5 5 1 +Adenoma, Chromophobe 5 6 3 +Adenoma, Islet Cell 4 5 6 +Adenoma, Liver Cell 4 5 4 +Adenoma, Oxyphilic 5 5 1 +Adenoma, Pleomorphic 4 5 2 +Adenoma, Sweat Gland 5 5 2 +Adenoma, Villous 5 5 1 +Adenomatoid Tumor 5 5 2 +Adenomatosis, Pulmonary 5 5 1 +Adenomatous Polyposis Coli 3 7 9 +Adenomatous Polyposis Coli Protein 4 5 4 +Adenomatous Polyps 5 5 1 +Adenomyoepithelioma 4 4 1 +Adenomyoma 4 4 1 +Adenomyosis 5 6 2 +Adenophorea 6 6 1 +Adenophorea Infections 5 5 1 +Adenosarcoma 4 5 2 +Adenosine 4 6 3 +Adenosine A1 Receptor Agonists 8 8 2 +Adenosine A1 Receptor Antagonists 8 8 2 +Adenosine A2 Receptor Agonists 8 8 2 +Adenosine A2 Receptor Antagonists 8 8 2 +Adenosine A3 Receptor Agonists 8 8 2 +Adenosine A3 Receptor Antagonists 8 8 2 +Adenosine Deaminase 6 6 1 +Adenosine Deaminase Inhibitors 5 5 1 +Adenosine Diphosphate 5 7 3 +Adenosine Diphosphate Glucose 6 9 5 +Adenosine Diphosphate Ribose 6 9 5 +Adenosine Diphosphate Sugars 5 8 5 +Adenosine Kinase 6 6 1 +Adenosine Monophosphate 5 7 3 +Adenosine Phosphosulfate 6 8 3 +Adenosine Triphosphatases 5 5 1 +Adenosine Triphosphate 5 7 3 +Adenosine-5'-(N-ethylcarboxamide) 5 7 3 +Adenosylhomocysteinase 4 4 1 +Adenosylmethionine Decarboxylase 6 6 1 +Adenoviridae 3 3 1 +Adenoviridae Infections 4 4 1 +Adenovirus E1 Proteins 4 7 5 +Adenovirus E1A Proteins 4 8 7 +Adenovirus E1B Proteins 4 8 7 +Adenovirus E2 Proteins 4 7 5 +Adenovirus E3 Proteins 5 7 4 +Adenovirus E4 Proteins 5 7 4 +Adenovirus Early Proteins 4 6 4 +Adenovirus Infections, Human 5 5 1 +Adenovirus Vaccines 5 5 1 +Adenoviruses, Canine 3 5 2 +Adenoviruses, Human 5 5 1 +Adenoviruses, Porcine 5 5 1 +Adenoviruses, Simian 5 5 1 +Adenylate Cyclase Toxin 5 6 3 +Adenylate Kinase 6 6 1 +Adenylosuccinate Lyase 6 6 1 +Adenylosuccinate Synthase 5 5 1 +Adenylyl Cyclase Inhibitors 5 5 1 +Adenylyl Cyclases 4 5 2 +Adenylyl Imidodiphosphate 6 8 3 +Adherence Interventions 4 4 1 +Adherens Junctions 6 6 1 +Adhesins, Bacterial 4 5 4 +Adhesins, Escherichia coli 5 6 5 +Adhesiveness 3 3 1 +Adhesives 3 3 2 +Adiantum 8 8 1 +Adie Syndrome 3 5 4 +Adipates 5 5 1 +Adipocytes 3 3 1 +Adipocytes, Beige 4 4 1 +Adipocytes, Brown 4 4 1 +Adipocytes, White 4 4 1 +Adipogenesis 3 3 1 +Adipokines 3 4 5 +Adiponectin 4 5 5 +Adipose Tissue 3 3 1 +Adipose Tissue, Beige 4 4 1 +Adipose Tissue, Brown 4 4 1 +Adipose Tissue, White 4 4 1 +Adiposis Dolorosa 4 5 2 +Adiposity 4 7 4 +Adjustment Disorders 3 3 1 +Adjuvants, Anesthesia 5 5 1 +Adjuvants, Immunologic 5 5 1 +Adjuvants, Pharmaceutic 3 4 2 +Adjuvants, Vaccine 4 6 3 +Administration, Buccal 5 5 2 +Administration, Cutaneous 5 5 1 +Administration, Inhalation 4 4 1 +Administration, Intranasal 6 6 1 +Administration, Intravaginal 5 5 1 +Administration, Intravenous 4 4 1 +Administration, Intravesical 5 5 1 +Administration, Metronomic 4 4 1 +Administration, Mucosal 5 5 1 +Administration, Ophthalmic 5 5 1 +Administration, Oral 4 4 1 +Administration, Rectal 6 6 1 +Administration, Sublingual 5 5 1 +Administration, Topical 4 4 1 +Administrative Claims, Healthcare 5 5 1 +Administrative Personnel 3 3 1 +Admitting Department, Hospital 6 6 2 +Adnexa Uteri 4 4 1 +Adnexal Diseases 4 5 2 +Ado-Trastuzumab Emtansine 5 10 5 +Adolescent 3 3 1 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+Anesthesia, General 3 3 1 +Anesthesia, Inhalation 4 4 1 +Anesthesia, Intravenous 3 3 1 +Anesthesia, Local 4 4 1 +Anesthesia, Obstetrical 3 3 1 +Anesthesia, Rectal 4 4 1 +Anesthesia, Spinal 4 4 1 +Anesthesiologists 4 5 4 +Anesthesiology 3 3 1 +Anesthetics 5 6 2 +Anesthetics, Combined 6 7 2 +Anesthetics, Dissociative 8 9 2 +Anesthetics, General 6 7 2 +Anesthetics, Inhalation 7 8 2 +Anesthetics, Intravenous 7 8 2 +Anesthetics, Local 6 7 3 +Anesthetists 3 4 2 +Anestrus 4 4 1 +Anethole Trithione 4 9 4 +Anethum graveolens 8 8 1 +Anetoderma 3 4 2 +Aneugens 5 5 1 +Aneuploidy 3 5 3 +Aneurysm 3 3 1 +Aneurysm, Aortic Arch 6 6 2 +Aneurysm, Ascending Aorta 6 6 2 +Aneurysm, False 3 3 1 +Aneurysm, Infected 2 4 2 +Aneurysm, Ruptured 4 4 1 +Angelica 8 8 1 +Angelica archangelica 9 9 1 +Angelica sinensis 9 9 1 +Angelman Syndrome 4 4 5 +Anger 3 3 1 +Anger Management Therapy 4 4 1 +Angina Pectoris 4 6 3 +Angina Pectoris, Variant 6 8 3 +Angina, Stable 5 7 3 +Angina, Unstable 5 7 3 +Angiocardiography 5 6 4 +Angiodysplasia 3 3 1 +Angioedema 3 5 3 +Angioedemas, Hereditary 4 6 5 +Angiofibroma 4 4 1 +Angiogenesis 3 3 1 +Angiogenesis Inducing Agents 6 6 1 +Angiogenesis Inhibitors 5 6 3 +Angiogenesis Modulating Agents 5 5 1 +Angiogenic Proteins 3 4 3 +Angiography 4 5 2 +Angiography, Digital Subtraction 5 7 5 +Angioid Streaks 3 3 1 +Angiokeratoma 4 4 1 +Angiolipoma 5 5 1 +Angiolymphoid Hyperplasia with Eosinophilia 3 5 3 +Angiomatosis 3 3 1 +Angiomatosis, Bacillary 4 7 6 +Angiomotins 5 5 2 +Angiomyolipoma 5 5 2 +Angiomyoma 6 6 1 +Angioplasty 3 5 4 +Angioplasty, Balloon 4 6 4 +Angioplasty, Balloon, Coronary 5 7 8 +Angioplasty, Balloon, Laser-Assisted 4 7 10 +Angioplasty, Laser 3 6 6 +Angiopoietin-1 5 6 3 +Angiopoietin-2 5 6 3 +Angiopoietin-Like Protein 1 5 6 3 +Angiopoietin-Like Protein 2 5 6 3 +Angiopoietin-Like Protein 3 5 6 3 +Angiopoietin-Like Protein 4 5 6 3 +Angiopoietin-Like Protein 6 5 6 3 +Angiopoietin-Like Protein 7 5 6 3 +Angiopoietin-Like Protein 8 5 6 3 +Angiopoietin-like Proteins 4 5 3 +Angiopoietins 4 5 3 +Angioscopes 4 4 2 +Angioscopy 4 5 5 +Angiostatic Proteins 4 5 3 +Angiostatins 4 7 7 +Angiostrongylus 9 9 1 +Angiostrongylus cantonensis 10 10 1 +Angiotensin Amide 6 6 2 +Angiotensin I 5 6 6 +Angiotensin II 5 6 6 +Angiotensin II Type 1 Receptor Blockers 5 5 1 +Angiotensin II Type 2 Receptor Blockers 5 5 1 +Angiotensin III 5 6 6 +Angiotensin Receptor Antagonists 4 4 1 +Angiotensin-Converting Enzyme 2 7 7 1 +Angiotensin-Converting Enzyme Inhibitors 6 6 1 +Angiotensinogen 4 5 4 +Angiotensins 4 5 6 +Angola 5 5 1 +Angucyclines and Angucyclinones 4 7 5 +Anguilla 7 7 1 +Anhedonia 3 5 3 +Anhydrides 2 2 1 +Anidulafungin 5 5 1 +Anilides 3 4 2 +Aniline Compounds 3 3 1 +Aniline Hydroxylase 5 8 3 +Aniline Mustard 6 6 1 +Anilino Naphthalenesulfonates 4 8 4 +Animal Assisted Therapy 3 6 6 +Animal Care Committees 5 5 1 +Animal Communication 4 4 1 +Animal Culling 4 5 2 +Animal Diseases 1 1 1 +Animal Distribution 2 4 2 +Animal Experimentation 2 4 2 +Animal Feed 4 5 2 +Animal Fins 2 2 1 +Animal Fur 2 3 2 +Animal Husbandry 3 3 1 +Animal Identification Systems 2 2 1 +Animal Migration 5 5 1 +Animal Nutrition Sciences 3 3 1 +Animal Nutritional Physiological Phenomena 4 4 1 +Animal Population Groups 3 3 1 +Animal Proteins, Dietary 4 5 3 +Animal Rights 5 5 1 +Animal Scales 2 2 2 +Animal Shells 2 2 1 +Animal Structures 1 1 1 +Animal Technicians 4 5 2 +Animal Testing Alternatives 4 4 1 +Animal Use Alternatives 3 3 1 +Animal Welfare 4 4 1 +Animals 2 2 1 +Animals, Congenic 6 6 1 +Animals, Domestic 4 4 1 +Animals, Exotic 4 4 1 +Animals, Genetically Modified 3 4 2 +Animals, Inbred Strains 5 5 1 +Animals, Laboratory 4 4 1 +Animals, Newborn 4 4 1 +Animals, Outbred Strains 4 4 1 +Animals, Poisonous 4 4 1 +Animals, Suckling 4 4 1 +Animals, Wild 4 4 1 +Animals, Zoo 4 4 1 +Animation 2 2 2 +Anion Exchange Protein 1, Erythrocyte 7 8 8 +Anion Exchange Resins 5 5 1 +Anion Transport Proteins 6 6 2 +Anions 4 4 1 +Aniridia 3 4 5 +Anisakiasis 4 7 3 +Anisakis 9 9 1 +Aniseikonia 3 3 1 +Anisocoria 3 5 3 +Anisoles 4 8 3 +Anisometropia 3 3 1 +Anisomycin 4 4 1 +Anisotropy 2 3 2 +Anistreplase 7 8 4 +Ankle 4 4 1 +Ankle Brachial Index 5 5 1 +Ankle Fractures 3 4 2 +Ankle Injuries 3 3 1 +Ankle Joint 5 5 1 +Ankyloglossia 2 2 1 +Ankylosis 3 3 1 +Ankyrin Repeat 6 9 4 +Ankyrins 4 4 1 +Annelida 4 4 1 +Annexin A1 6 6 1 +Annexin A2 6 6 1 +Annexin A3 6 6 2 +Annexin A4 6 6 1 +Annexin A5 6 6 1 +Annexin A6 6 6 1 +Annexin A7 6 6 1 +Annexins 5 5 1 +Anniversaries and Special Events 3 4 2 +Annona 8 8 1 +Annonaceae 7 7 1 +Annual Report 2 2 1 +Annual Reports as Topic 3 3 1 +Annulus Fibrosus 5 6 3 +Anoctamin-1 8 8 3 +Anoctamins 7 7 3 +Anodonta 7 7 1 +Anodontia 4 5 3 +Anoikis 5 5 1 +Anomalous Left Coronary Artery 5 6 3 +Anomia 6 7 2 +Anomie 4 5 2 +Anomura 7 7 1 +Anonymous Testing 4 8 6 +Anonyms and Pseudonyms 6 6 1 +Anopheles 13 13 1 +Anophthalmos 3 4 2 +Anoplura 7 7 1 +Anorectal Malformations 3 4 2 +Anorexia 4 4 1 +Anorexia Nervosa 3 3 1 +Anosmia 5 6 2 +Anostraca 6 6 1 +Anovulation 4 7 4 +Anoxybacillus 5 6 5 +Anseriformes 6 6 1 +Anserine 5 5 1 +Answering Services 4 6 3 +Ant Venoms 4 5 2 +Antacids 4 5 2 +Antagomirs 5 5 1 +Antarctic Regions 2 2 1 +Antazoline 5 5 1 +Antelopes 9 9 1 +Antennapedia Homeodomain Protein 4 5 3 +Anterior Capsular Rupture, Ocular 3 3 1 +Anterior Capsule of the Lens 3 3 1 +Anterior Cerebral Artery 5 5 1 +Anterior Chamber 4 4 1 +Anterior Commissure, Brain 7 7 1 +Anterior Compartment Syndrome 4 4 2 +Anterior Cruciate Ligament 4 5 3 +Anterior Cruciate Ligament Injuries 4 4 1 +Anterior Cruciate Ligament Reconstruction 4 4 2 +Anterior Eye Segment 3 3 1 +Anterior Horn Cells 5 5 3 +Anterior Hypothalamic Nucleus 7 8 2 +Anterior Spinal Artery Syndrome 5 6 2 +Anterior Temporal Lobectomy 2 3 2 +Anterior Thalamic Nuclei 8 8 1 +Anterior Wall Myocardial Infarction 5 6 4 +Anthelmintics 6 6 1 +Anthemis 8 8 1 +Anthocerotophyta 5 5 1 +Anthocidaris 6 6 1 +Anthocyanins 3 7 4 +Anthozoa 5 5 1 +Anthracenes 3 6 2 +Anthracosilicosis 4 6 5 +Anthracosis 5 5 2 +Anthracyclines 4 7 3 +Anthralin 5 8 2 +Anthramycin 4 7 2 +Anthranilate Phosphoribosyltransferase 4 6 2 +Anthranilate Synthase 4 6 2 +Anthraquinones 3 8 3 +Anthrax 6 6 1 +Anthrax Vaccines 5 5 1 +Anthrones 4 7 2 +Anthropogenic Effects 3 3 1 +Anthropology 2 4 2 +Anthropology, Cultural 3 3 1 +Anthropology, Medical 3 5 2 +Anthropology, Physical 3 3 1 +Anthropometry 2 5 3 +Anthroposophy 3 3 2 +Anti-Allergic Agents 4 4 1 +Anti-Anxiety Agents 6 7 3 +Anti-Arrhythmia Agents 5 5 1 +Anti-Asthmatic Agents 5 5 1 +Anti-Bacterial Agents 5 5 1 +Anti-Citrullinated Protein Antibodies 8 8 3 +Anti-Dyskinesia Agents 5 5 1 +Anti-Glomerular Basement Membrane Disease 3 8 5 +Anti-HIV Agents 7 7 1 +Anti-Infective Agents 4 4 1 +Anti-Infective Agents, Local 5 5 1 +Anti-Infective Agents, Urinary 5 5 1 +Anti-Inflammatory Agents 4 4 1 +Anti-Inflammatory Agents, Non-Steroidal 5 8 3 +Anti-Mullerian Hormone 4 5 3 +Anti-N-Methyl-D-Aspartate Receptor Encephalitis 4 5 6 +Anti-Neutrophil Cytoplasmic Antibody-Associated Vasculitis 3 5 3 +Anti-Obesity Agents 4 4 1 +Anti-Retroviral Agents 6 6 1 +Anti-Ulcer Agents 5 5 1 +Anti-Vaccination Movement 2 2 1 +Antiaris 10 10 1 +Antibiosis 2 3 2 +Antibiotic Prophylaxis 4 4 2 +Antibiotics, Antineoplastic 5 5 1 +Antibiotics, Antitubercular 7 7 1 +Antibodies 6 6 3 +Antibodies, Anti-Idiotypic 7 7 3 +Antibodies, Anticardiolipin 9 9 3 +Antibodies, Antineutrophil Cytoplasmic 3 8 4 +Antibodies, Antinuclear 8 8 3 +Antibodies, Antiphospholipid 8 8 3 +Antibodies, Archaeal 7 7 3 +Antibodies, Bacterial 7 7 3 +Antibodies, Bispecific 7 7 3 +Antibodies, Blocking 7 7 3 +Antibodies, Catalytic 7 7 3 +Antibodies, Fungal 7 7 3 +Antibodies, Helminth 7 7 3 +Antibodies, Heterophile 7 7 3 +Antibodies, Immobilized 4 7 4 +Antibodies, Monoclonal 7 7 3 +Antibodies, Monoclonal, Humanized 8 8 3 +Antibodies, Monoclonal, Murine-Derived 8 8 3 +Antibodies, Neoplasm 7 7 3 +Antibodies, Neutralizing 7 7 3 +Antibodies, Phospho-Specific 7 7 3 +Antibodies, Protozoan 7 7 3 +Antibodies, Viral 7 7 3 +Antibody Affinity 2 3 2 +Antibody Diversity 3 3 2 +Antibody Formation 5 5 1 +Antibody Specificity 2 2 1 +Antibody-Coated Bacteria Test, Urinary 4 8 9 +Antibody-Dependent Cell Cytotoxicity 3 3 1 +Antibody-Dependent Enhancement 2 3 2 +Antibody-Producing Cells 2 3 2 +Anticarcinogenic Agents 4 5 3 +Anticestodal Agents 8 8 1 +Anticholesteremic Agents 6 6 2 +Anticholinergic Syndrome 3 3 1 +Anticipation, Genetic 3 6 2 +Anticipation, Psychological 3 3 1 +Anticoagulant Reversal Agents 7 7 1 +Anticoagulants 5 5 1 +Anticoagulation Bridge 3 3 1 +Anticoagulation Reversal 3 3 1 +Anticodon 4 5 2 +Anticonvulsants 5 5 1 +Antidepressive Agents 6 6 1 +Antidepressive Agents, Second-Generation 7 7 1 +Antidepressive Agents, Tricyclic 7 7 1 +Antidiarrheals 5 5 1 +Antidiuretic Agents 5 5 1 +Antidiuretic Hormone Receptor Antagonists 4 5 2 +Antidotes 4 5 2 +Antiemetics 5 6 3 +Antifibrinolytic Agents 5 7 2 +Antifibrotic Agents 5 5 1 +Antifoaming Agents 4 4 2 +Antifreeze Proteins 3 3 1 +Antifreeze Proteins, Type I 4 4 1 +Antifreeze Proteins, Type II 4 4 1 +Antifreeze Proteins, Type III 4 4 1 +Antifreeze Proteins, Type IV 4 4 1 +Antifungal Agents 5 5 1 +Antigen Presentation 2 5 2 +Antigen-Antibody Complex 3 7 4 +Antigen-Antibody Reactions 2 2 1 +Antigen-Presenting Cells 2 3 2 +Antigenic Drift and Shift 4 4 5 +Antigenic Modulation 3 3 1 +Antigenic Variation 3 3 2 +Antigens 2 2 1 +Antigens, Archaeal 3 3 1 +Antigens, Bacterial 3 4 2 +Antigens, CD 4 5 2 +Antigens, CD1 5 6 4 +Antigens, CD19 5 6 6 +Antigens, CD1d 6 7 4 +Antigens, CD20 5 6 4 +Antigens, CD34 5 6 2 +Antigens, CD7 5 6 3 +Antigens, Dermatophagoides 3 3 1 +Antigens, Differentiation 3 4 2 +Antigens, Differentiation, B-Lymphocyte 4 5 2 +Antigens, Differentiation, Myelomonocytic 4 5 2 +Antigens, Differentiation, T-Lymphocyte 4 5 2 +Antigens, Fungal 3 4 2 +Antigens, Helminth 3 3 1 +Antigens, Heterophile 3 3 1 +Antigens, Human Platelet 4 4 1 +Antigens, Ly 4 5 2 +Antigens, Neoplasm 3 3 1 +Antigens, Nuclear 3 4 2 +Antigens, Plant 3 4 2 +Antigens, Polyomavirus Transforming 5 6 5 +Antigens, Protozoan 3 4 2 +Antigens, Surface 3 3 1 +Antigens, T-Independent 3 3 1 +Antigens, Tumor-Associated, Carbohydrate 4 4 3 +Antigens, Viral 3 4 2 +Antigens, Viral, Tumor 4 5 3 +Antiglaucoma Agents 4 4 1 +Antiglycation Agents 4 4 1 +Antigua and Barbuda 4 5 2 +Antihyperkalemic Agents 5 5 1 +Antihypertensive Agents 5 5 1 +Antilymphocyte Serum 4 8 5 +Antimalarials 7 7 1 +Antimanic Agents 6 7 3 +Antimetabolites 4 4 2 +Antimetabolites, Antineoplastic 5 5 3 +Antimicrobial Cationic Peptides 3 5 3 +Antimicrobial Peptides 3 3 1 +Antimicrobial Stewardship 5 6 3 +Antimitotic Agents 5 5 2 +Antimony 4 4 3 +Antimony Potassium Tartrate 2 2 1 +Antimony Sodium Gluconate 2 6 4 +Antimutagenic Agents 4 5 2 +Antimycin A 5 5 1 +Antinematodal Agents 7 7 1 +Antineoplastic Agents 4 4 1 +Antineoplastic Agents, Alkylating 5 5 3 +Antineoplastic Agents, Hormonal 5 5 1 +Antineoplastic Agents, Immunological 5 5 1 +Antineoplastic Agents, Phytogenic 5 5 1 +Antineoplastic Combined Chemotherapy Protocols 4 4 3 +Antineoplastic Protocols 3 6 3 +Antioxidant Response Elements 7 10 3 +Antioxidants 2 5 4 +Antipain 4 4 1 +Antiparasitic Agents 5 5 1 +Antiparkinson Agents 6 6 1 +Antiperspirants 4 4 1 +Antiphospholipid Syndrome 3 3 1 +Antiplatyhelmintic Agents 7 7 1 +Antiporters 5 6 3 +Antiprotozoal Agents 6 6 1 +Antipruritics 5 5 1 +Antipsychotic Agents 6 7 3 +Antipyretics 4 4 1 +Antipyrine 6 6 1 +Antiracism 5 7 4 +Antiretroviral Therapy, Highly Active 4 4 1 +Antirheumatic Agents 4 4 1 +Antirrhinum 9 9 1 +Antisense Elements (Genetics) 2 6 3 +Antisepsis 6 6 1 +Antisickling Agents 5 5 1 +Antisocial Personality Disorder 3 3 1 +Antispermatogenic Agents 4 7 4 +Antistatic Agents 3 3 1 +Antistreptolysin 8 8 3 +Antithrombin III 3 6 5 +Antithrombin III Deficiency 4 5 4 +Antithrombin Proteins 4 4 2 +Antithrombins 6 7 2 +Antithyroid Agents 3 6 2 +Antitoxins 4 8 4 +Antitreponemal Agents 6 6 1 +Antitrichomonal Agents 7 7 1 +Antitrust Laws 4 4 1 +Antitubercular Agents 6 6 1 +Antitussive Agents 5 5 2 +Antivenins 5 9 4 +Antiviral Agents 5 5 1 +Antiviral Restriction Factors 3 3 1 +Antlers 3 3 1 +Antley-Bixler Syndrome Phenotype 4 6 5 +Antrodia 6 6 1 +Ants 10 10 1 +Anura 6 6 1 +Anuria 4 6 6 +Anus Diseases 5 5 1 +Anus Neoplasms 6 8 6 +Anus, Imperforate 3 4 2 +Anxiety 3 3 1 +Anxiety Disorders 2 2 1 +Anxiety, Castration 4 4 1 +Anxiety, Separation 3 3 2 +Aorta 4 4 1 +Aorta, Abdominal 5 5 1 +Aorta, Thoracic 5 5 1 +Aortic Aneurysm 4 4 2 +Aortic Aneurysm, Abdominal 5 5 2 +Aortic Aneurysm, Thoracic 5 5 2 +Aortic Aneurysm, Thoracoabdominal 6 6 2 +Aortic Arch Syndromes 4 4 1 +Aortic Bodies 6 7 3 +Aortic Coarctation 4 5 3 +Aortic Diseases 3 3 1 +Aortic Dissection 5 5 2 +Aortic Intramural Hematoma 5 5 2 +Aortic Root Aneurysm 6 6 2 +Aortic Rupture 3 5 4 +Aortic Stenosis, Subvalvular 5 6 2 +Aortic Stenosis, Supravalvular 5 6 2 +Aortic Valve 4 4 1 +Aortic Valve Disease 4 4 1 +Aortic Valve Insufficiency 5 5 1 +Aortic Valve Prolapse 5 5 2 +Aortic Valve Stenosis 4 5 2 +Aortico-Ventricular Tunnel 4 5 3 +Aortitis 4 4 2 +Aortography 5 6 2 +Aortopulmonary Septal Defect 5 6 3 +Aotidae 10 10 1 +Aotus trivirgatus 11 11 1 +APACHE 7 10 4 +Apamin 5 6 2 +Apansporoblastina 6 6 1 +Apartheid 3 6 2 +Apathy 3 3 1 +Apatites 3 8 4 +Apazone 4 4 1 +Apc1 Subunit, Anaphase-Promoting Complex-Cyclosome 5 7 2 +Apc10 Subunit, Anaphase-Promoting Complex-Cyclosome 5 7 2 +Apc11 Subunit, Anaphase-Promoting Complex-Cyclosome 5 7 2 +Apc2 Subunit, Anaphase-Promoting Complex-Cyclosome 5 7 2 +Apc3 Subunit, Anaphase-Promoting Complex-Cyclosome 5 7 2 +Apc4 Subunit, Anaphase-Promoting Complex-Cyclosome 5 7 2 +Apc5 Subunit, Anaphase-Promoting Complex-Cyclosome 5 7 2 +Apc6 Subunit, Anaphase-Promoting Complex-Cyclosome 5 7 2 +Apc7 Subunit, Anaphase-Promoting Complex-Cyclosome 5 7 2 +Apc8 Subunit, Anaphase-Promoting Complex-Cyclosome 5 7 2 +Ape Diseases 3 3 1 +Apelin 3 4 2 +Apelin Receptors 6 6 1 +Apexification 4 4 1 +Apgar Score 4 4 1 +Aphakia 3 3 1 +Aphakia, Postcataract 4 4 1 +Aphanizomenon 3 5 2 +Aphanomyces 4 4 1 +Aphasia 7 8 2 +Aphasia, Broca 8 9 2 +Aphasia, Conduction 8 9 2 +Aphasia, Primary Progressive 4 9 4 +Aphasia, Wernicke 8 9 2 +Aphidicolin 5 5 1 +Aphids 7 7 1 +Aphonia 4 5 4 +Aphorisms and Proverbs 2 2 1 +Aphorisms and Proverbs as Topic 3 3 1 +Aphrodisiacs 5 6 2 +Aphthovirus 6 6 1 +Apiaceae 7 7 1 +Apical Hypertrophic Cardiomyopathy 5 5 2 +Apicoectomy 3 3 3 +Apicomplexa 3 3 1 +Apicoplasts 8 8 1 +Apigenin 8 8 2 +Apitherapy 2 2 1 +Apium 8 8 1 +Aplysia 6 6 1 +Apnea 3 4 2 +APOBEC Deaminases 7 7 1 +APOBEC-1 Deaminase 8 8 1 +APOBEC-3G Deaminase 4 8 2 +Apocrine Glands 4 4 2 +Apocynaceae 8 8 1 +Apocynum 9 9 1 +Apoenzymes 4 4 2 +Apoferritins 4 6 3 +Apolipoprotein A-I 5 6 3 +Apolipoprotein A-II 5 6 3 +Apolipoprotein A-V 5 6 3 +Apolipoprotein B-100 5 6 3 +Apolipoprotein B-48 5 6 3 +Apolipoprotein C-I 5 6 3 +Apolipoprotein C-II 5 6 3 +Apolipoprotein C-III 5 6 3 +Apolipoprotein E2 5 6 3 +Apolipoprotein E3 5 6 3 +Apolipoprotein E4 5 6 3 +Apolipoprotein L1 5 6 3 +Apolipoproteins 3 4 3 +Apolipoproteins A 4 5 3 +Apolipoproteins B 4 5 3 +Apolipoproteins C 4 5 3 +Apolipoproteins D 4 5 3 +Apolipoproteins E 4 5 3 +Apolipoproteins L 4 5 3 +Apolipoproteins M 4 5 4 +Apomixis 3 6 2 +Apomorphine 5 7 3 +Aponeurosis 2 2 1 +Apoprotein(a) 4 5 2 +Apoproteins 3 3 1 +Apoptosis 4 4 1 +Apoptosis Inducing Factor 4 5 6 +Apoptosis Regulatory Proteins 4 4 2 +Apoptosomes 3 3 1 +Apoptotic Protease-Activating Factor 1 6 6 5 +Aporphines 4 6 3 +Appalachian Region 5 5 1 +Appendectomy 3 3 1 +Appendiceal Neoplasms 6 7 5 +Appendicitis 3 5 3 +Appendix 6 6 2 +Appetite 3 5 3 +Appetite Depressants 5 5 1 +Appetite Regulation 4 6 3 +Appetite Stimulants 5 6 2 +Appetitive Behavior 4 4 1 +Applied Behavior Analysis 4 4 1 +Appointments and Schedules 3 3 1 +Apraxia, Ideomotor 5 7 3 +Apraxias 4 6 3 +Aprepitant 5 5 1 +Aprindine 5 8 2 +Aprotinin 3 3 1 +Aptamers, Nucleotide 5 5 1 +Aptamers, Peptide 3 3 1 +Aptitude 4 4 1 +Aptitude Tests 3 3 1 +APUD Cells 3 3 1 +Apudoma 5 6 3 +Apurinic Acid 4 5 4 +Apyrase 5 5 1 +Aquabirnavirus 5 5 1 +Aquablation 3 6 2 +Aquaculture 3 3 1 +Aquaglyceroporins 8 8 3 +Aquaporin 1 8 8 3 +Aquaporin 2 8 8 3 +Aquaporin 3 9 9 3 +Aquaporin 4 8 8 3 +Aquaporin 5 8 8 3 +Aquaporin 6 9 9 3 +Aquaporins 7 7 3 +Aquatic Organisms 2 7 2 +Aquatic Therapy 4 5 2 +Aqueous Humor 4 5 2 +Aquifex 3 3 1 +Aquifoliaceae 7 7 1 +Aquilegia 9 9 1 +Arab World 6 6 1 +Arabia 4 4 1 +Arabidopsis 8 8 1 +Arabidopsis Proteins 4 4 1 +Arabinofuranosylcytosine Triphosphate 4 6 3 +Arabinofuranosyluracil 4 6 3 +Arabinonucleosides 3 3 1 +Arabinonucleotides 3 3 1 +Arabinose 5 5 1 +Arabis 8 8 1 +Arabs 3 4 2 +AraC Transcription Factor 4 5 3 +Araceae 9 9 1 +Arachidonate 12-Lipoxygenase 8 8 2 +Arachidonate 15-Lipoxygenase 8 8 2 +Arachidonate 5-Lipoxygenase 8 8 2 +Arachidonate Lipoxygenases 7 7 2 +Arachidonic Acid 6 6 2 +Arachidonic Acids 5 5 2 +Arachis 8 8 1 +Arachnid Vectors 6 7 2 +Arachnida 5 5 1 +Arachnodactyly 4 5 2 +Arachnoid 4 4 1 +Arachnoid Cysts 3 6 5 +Arachnoiditis 4 4 1 +Aralia 8 8 1 +Araliaceae 7 7 1 +Araucaria 8 8 1 +Araucaria araucana 9 9 1 +Araucariaceae 7 7 1 +Arbacia 6 6 1 +Arbaprostil 5 8 3 +Arbovirus Infections 3 3 2 +Arboviruses 2 2 1 +Arbutin 4 4 1 +Arcanobacterium 6 8 2 +Archaea 1 1 1 +Archaeal Proteins 3 3 1 +Archaeal Viruses 2 2 1 +Archaeoglobales 3 3 1 +Archaeoglobus 4 4 1 +Archaeoglobus fulgidus 5 5 1 +Archaeology 4 4 1 +Archamoebae 3 3 1 +Architectural Accessibility 4 4 1 +Architectural Drawing 2 2 2 +Architecture 2 2 1 +Archives 3 4 3 +Arcidae 6 6 1 +Arcobacter 3 4 2 +Arctic Regions 2 2 1 +Arctium 8 8 1 +Arctostaphylos 9 9 1 +Arcuate Nucleus of Hypothalamus 7 8 2 +Arcus Senilis 4 4 1 +Ardisia 9 9 1 +Area Health Education Centers 4 4 1 +Area Postrema 4 8 3 +Area Under Curve 3 5 4 +Areca 8 8 1 +Arecaceae 7 7 1 +Arecoline 3 5 3 +Arenaria Plant 10 10 1 +Arenaviridae 4 4 1 +Arenaviridae Infections 4 4 1 +Arenavirus 5 5 1 +Arenaviruses, New World 6 6 1 +Arenaviruses, Old World 6 6 1 +Argas 9 9 1 +Argasidae 8 8 1 +Argemone 9 9 1 +Argentina 4 4 1 +Arginase 5 5 1 +Arginine 4 4 3 +Arginine Kinase 6 6 1 +Arginine Vasopressin 5 7 5 +Arginine-tRNA Ligase 6 6 1 +Argininosuccinate Lyase 6 6 1 +Argininosuccinate Synthase 5 5 1 +Argininosuccinic Acid 5 6 3 +Argininosuccinic Aciduria 6 7 6 +Argon 4 4 2 +Argon Plasma Coagulation 3 5 4 +Argonaute Proteins 7 8 4 +Arguloida 6 6 1 +Argyria 4 4 2 +Aripiprazole 4 6 2 +Arisaema 10 10 1 +Aristolochia 8 8 1 +Aristolochiaceae 7 7 1 +Aristolochic Acids 3 7 3 +Arizona 6 6 1 +Arkansas 6 6 1 +Arm 4 4 1 +Arm Bones 5 5 1 +Arm Injuries 2 2 1 +Armadillo Domain Proteins 3 3 1 +Armadillos 8 8 1 +Armed Conflicts 5 5 1 +Armenia 4 4 4 +Armillaria 5 5 1 +Armin 4 4 1 +Armoracia 8 8 1 +Arnica 8 8 1 +Arnold-Chiari Malformation 4 5 2 +ARNTL Transcription Factors 5 5 4 +Aroclors 4 6 3 +Aromatase 5 8 6 +Aromatase Inhibitors 6 7 3 +Aromatherapy 3 4 4 +Aromatic Amino Acid Decarboxylase Inhibitors 5 7 2 +Aromatic-L-Amino-Acid Decarboxylases 6 6 1 +Arousal 3 3 2 +Arrestin 4 6 6 +Arrestins 4 5 5 +Arrhythmia, Sinus 4 4 2 +Arrhythmias, Cardiac 3 3 2 +Arrhythmogenic Right Ventricular Dysplasia 4 5 4 +Arsanilic Acid 3 3 1 +Arsenamide 3 3 1 +Arsenate Reductases 4 4 1 +Arsenates 3 5 2 +Arsenazo III 3 3 2 +Arsenic 4 4 1 +Arsenic Poisoning 3 4 2 +Arsenic Trioxide 3 4 2 +Arsenicals 2 2 2 +Arsenite Transporting ATPases 6 7 3 +Arsenites 3 5 2 +Arsphenamine 3 3 1 +Art 2 2 1 +Art Therapy 3 6 4 +Artemether 5 6 3 +Artemether, Lumefantrine Drug Combination 3 8 6 +Artemia 7 7 1 +Artemisia 8 8 1 +Artemisia absinthium 9 9 1 +Artemisia annua 9 9 1 +Artemisinins 4 5 3 +Arterial Occlusive Diseases 3 3 1 +Arterial Pressure 5 5 1 +Arterial Switch Operation 4 4 2 +Arteries 3 3 1 +Arterio-Arterial Fistula 4 5 4 +Arterioles 4 4 2 +Arteriolosclerosis 5 5 1 +Arteriosclerosis 4 4 1 +Arteriosclerosis Obliterans 5 5 1 +Arteriovenous Anastomosis 4 4 1 +Arteriovenous Fistula 4 6 6 +Arteriovenous Malformations 3 5 3 +Arteriovenous Shunt, Surgical 3 5 2 +Arteritis 4 4 1 +Arteriviridae 5 5 1 +Arterivirus 6 6 1 +Arterivirus Infections 5 5 1 +Artesunate 5 6 3 +Arthralgia 3 5 4 +Arthritis 3 3 1 +Arthritis, Experimental 4 4 2 +Arthritis, Gouty 4 6 5 +Arthritis, Infectious 2 4 2 +Arthritis, Juvenile 3 4 4 +Arthritis, Psoriatic 4 7 4 +Arthritis, Reactive 3 7 5 +Arthritis, Rheumatoid 3 4 4 +Arthritis-Encephalitis Virus, Caprine 6 6 1 +Arthrobacter 5 7 2 +Arthrocentesis 4 6 4 +Arthrodermataceae 5 5 1 +Arthrodesis 3 3 1 +Arthrography 5 5 1 +Arthrogryposis 3 4 4 +Arthrometry, Articular 4 5 2 +Arthropathy, Neurogenic 3 3 1 +Arthroplasty 3 3 2 +Arthroplasty, Replacement 3 4 3 +Arthroplasty, Replacement, Ankle 4 5 3 +Arthroplasty, Replacement, Elbow 4 5 3 +Arthroplasty, Replacement, Finger 4 5 3 +Arthroplasty, Replacement, Hip 4 5 3 +Arthroplasty, Replacement, Knee 4 5 3 +Arthroplasty, Replacement, Shoulder 4 5 3 +Arthroplasty, Subchondral 4 4 2 +Arthropod Antennae 2 2 1 +Arthropod Proteins 3 3 1 +Arthropod Vectors 5 6 2 +Arthropod Venoms 3 4 2 +Arthropods 4 4 1 +Arthroscopes 4 4 2 +Arthroscopy 3 5 3 +Arthus Reaction 4 4 1 +Articulation Disorders 7 8 2 +Artifacts 2 2 1 +Artificial Cells 5 5 2 +Artificial Gene Fusion 4 4 1 +Artificial Intelligence 3 4 2 +Artificial Lens Implant Migration 3 4 2 +Artificial Life 4 5 2 +Artificial Limbs 3 4 3 +Artificial Organs 3 3 1 +Artificial Virus-Like Particles 2 2 1 +Artificially Sweetened Beverages 3 4 2 +Artiodactyla 7 7 1 +Artocarpus 10 10 1 +Aruba 4 4 2 +Arum 10 10 1 +Arvicolinae 9 9 1 +Aryl Hydrocarbon Hydroxylases 4 7 3 +Aryl Hydrocarbon Receptor Nuclear Translocator 6 6 4 +Arylalkylamine N-Acetyltransferase 6 6 1 +Arylamine N-Acetyltransferase 6 6 1 +Aryldialkylphosphatase 6 6 1 +Arylformamidase 5 5 1 +Arylsulfatases 6 6 1 +Arylsulfonates 6 6 1 +Arylsulfonic Acids 5 5 1 +Arylsulfotransferase 6 6 1 +Arytenoid Cartilage 4 5 3 +Asarum 8 8 1 +Asbestos 4 6 2 +Asbestos, Amosite 4 8 5 +Asbestos, Amphibole 5 7 2 +Asbestos, Crocidolite 6 8 2 +Asbestos, Serpentine 4 7 5 +Asbestosis 3 5 3 +Ascariasis 7 7 1 +Ascaridia 8 8 1 +Ascaridiasis 7 7 1 +Ascaridida 7 7 1 +Ascaridida Infections 6 6 1 +Ascaridoidea 8 8 1 +Ascaris 9 9 1 +Ascaris lumbricoides 10 10 1 +Ascaris suum 10 10 1 +Ascites 3 3 1 +Ascitic Fluid 3 3 1 +Asclepias 9 9 1 +Ascomycota 3 3 1 +Ascophyllum 4 4 1 +Ascorbate Oxidase 4 4 1 +Ascorbate Peroxidases 5 5 1 +Ascorbic Acid 3 5 3 +Ascorbic Acid Deficiency 6 6 1 +Ascoviridae 3 3 2 +Asepsis 7 7 1 +Asfarviridae 3 3 1 +Asia 2 2 1 +Asia, Central 3 3 1 +Asia, Eastern 3 3 1 +Asia, Northern 3 3 1 +Asia, Southeastern 3 3 1 +Asia, Southern 4 4 1 +Asia, Western 3 3 1 +Asialoglycoprotein Receptor 5 5 2 +Asialoglycoproteins 4 4 2 +Asian 4 6 3 +Asian American Native Hawaiian and Pacific Islander 3 3 1 +Asian People 3 3 1 +Asimina 8 8 1 +Aspalathus 8 8 1 +Asparagaceae 9 9 1 +Asparagales 8 8 1 +Asparaginase 5 5 1 +Asparagine 4 4 3 +Asparaginyl Endopeptidase 7 7 2 +Asparagus Plant 10 10 1 +Aspartame 5 5 1 +Aspartate Aminotransferase, Cytoplasmic 7 7 1 +Aspartate Aminotransferase, Mitochondrial 7 7 1 +Aspartate Aminotransferases 6 6 1 +Aspartate Ammonia-Lyase 6 6 1 +Aspartate Carbamoyltransferase 4 6 2 +Aspartate Kinase 6 6 1 +Aspartate-Ammonia Ligase 6 6 1 +Aspartate-Semialdehyde Dehydrogenase 6 6 1 +Aspartate-tRNA Ligase 6 6 1 +Aspartic Acid 4 4 3 +Aspartic Acid Endopeptidases 6 6 2 +Aspartic Acid Proteases 5 5 1 +Aspartokinase Homoserine Dehydrogenase 4 7 3 +Aspartylglucosaminuria 5 5 2 +Aspartylglucosylaminase 5 5 1 +Asperger Syndrome 5 5 1 +Aspergillosis 4 4 1 +Aspergillosis, Allergic Bronchopulmonary 3 6 7 +Aspergillus 4 4 1 +Aspergillus flavus 5 5 1 +Aspergillus fumigatus 5 5 1 +Aspergillus nidulans 5 5 1 +Aspergillus niger 5 5 1 +Aspergillus ochraceus 5 5 1 +Aspergillus oryzae 5 5 1 +Aspermia 5 5 3 +Asphodelaceae 9 9 1 +Asphyxia 2 4 2 +Asphyxia Neonatorum 3 3 1 +Aspidosperma 9 9 1 +Aspirations, Psychological 3 4 2 +Aspirin 9 9 1 +Aspirin, Dipyridamole Drug Combination 3 10 3 +Assertiveness 3 3 1 +Assisted Circulation 2 2 1 +Assisted Living Facilities 3 4 2 +Association 4 4 2 +Association Learning 5 5 1 +Astacoidea 7 7 1 +Astatine 4 5 3 +Astemizole 5 5 1 +Aster Plant 8 8 1 +Asteraceae 7 7 1 +Asterias 6 6 1 +Asterina 6 6 1 +Asthenia 3 3 1 +Asthenopia 2 2 1 +Asthenozoospermia 5 5 3 +Asthma 3 5 4 +Asthma, Aspirin-Induced 4 4 4 +Asthma, Exercise-Induced 4 6 4 +Asthma, Occupational 2 6 4 +Asthma-Chronic Obstructive Pulmonary Disease Overlap Syndrome 3 6 6 +Astigmatism 3 3 1 +Astragalus gummifer 9 9 1 +Astragalus Plant 8 8 1 +Astragalus propinquus 9 9 1 +Astringents 4 5 2 +Astrocytes 3 3 2 +Astrocytoma 6 7 3 +Astrology 3 3 1 +Astronauts 3 3 1 +Astronomical Objects 3 3 1 +Astronomical Phenomena 2 2 1 +Astronomy 3 3 1 +Astroviridae 4 4 1 +Astroviridae Infections 4 4 1 +Asymmetric Cell Division 3 7 5 +Asymptomatic Diseases 4 4 1 +Asymptomatic Infections 2 5 2 +AT Rich Sequence 4 5 2 +AT-Hook Motifs 8 8 1 +Atadenovirus 4 4 1 +Ataxia 4 5 2 +Ataxia Telangiectasia 3 7 8 +Ataxia Telangiectasia Mutated Proteins 5 8 4 +Ataxin-1 5 5 2 +Ataxin-10 5 5 1 +Ataxin-2 5 6 4 +Ataxin-3 4 5 3 +Ataxin-7 5 5 2 +Ataxins 4 4 2 +Atazanavir Sulfate 4 4 2 +Ateles geoffroyi 12 12 1 +Atelidae 10 10 1 +Atelinae 11 11 1 +Atenolol 6 6 3 +Atherectomy 4 6 4 +Atherectomy, Coronary 5 7 8 +Atherosclerosis 5 5 1 +Athetosis 4 5 2 +Athletes 2 2 1 +Athletic Injuries 2 2 1 +Athletic Performance 5 5 1 +Athletic Tape 3 5 2 +Athletic Trainers 4 5 2 +Atlantic Islands 3 3 1 +Atlantic Ocean 3 3 1 +Atlanto-Axial Joint 4 4 1 +Atlanto-Occipital Joint 4 4 1 +Atlas 2 2 1 +Atlases as Topic 7 7 1 +Atmosphere 4 4 2 +Atmosphere Exposure Chambers 2 2 1 +Atmospheric Pressure 4 5 2 +Atomic Bomb Survivors 3 3 1 +Atomoxetine Hydrochloride 4 4 1 +Atorvastatin 4 5 2 +Atovaquone 4 5 2 +ATP Binding Cassette Transporter 1 7 7 5 +ATP Binding Cassette Transporter, Subfamily A 6 6 5 +ATP Binding Cassette Transporter, Subfamily A, Member 4 7 7 5 +ATP Binding Cassette Transporter, Subfamily B 6 9 7 +ATP Binding Cassette Transporter, Subfamily B, Member 1 7 10 7 +ATP Binding Cassette Transporter, Subfamily B, Member 11 7 10 7 +ATP Binding Cassette Transporter, Subfamily B, Member 2 7 10 7 +ATP Binding Cassette Transporter, Subfamily B, Member 3 7 10 7 +ATP Binding Cassette Transporter, Subfamily D 6 6 5 +ATP Binding Cassette Transporter, Subfamily D, Member 1 7 7 5 +ATP Binding Cassette Transporter, Subfamily G 6 6 5 +ATP Binding Cassette Transporter, Subfamily G, Member 1 7 7 5 +ATP Binding Cassette Transporter, Subfamily G, Member 2 7 7 5 +ATP Binding Cassette Transporter, Subfamily G, Member 5 3 7 7 +ATP Binding Cassette Transporter, Subfamily G, Member 8 3 7 7 +ATP Citrate (pro-S)-Lyase 5 5 1 +ATP Phosphoribosyltransferase 6 6 1 +ATP Synthetase Complexes 6 6 1 +ATP-Binding Cassette Sub-Family B Member 4 7 10 7 +ATP-Binding Cassette Transporters 5 5 5 +ATP-Binding Cassette, Sub-Family C Proteins 6 9 4 +ATP-Dependent Endopeptidases 6 8 5 +ATP-Dependent Proteases 5 7 4 +ATPase Inhibitory Protein 4 4 1 +ATPases Associated with Diverse Cellular Activities 5 6 3 +Atractylis 8 8 1 +Atractylodes 8 8 1 +Atractyloside 3 5 2 +Atracurium 6 6 1 +Atrasentan 4 5 3 +Atrazine 4 4 1 +Atrial Appendage 4 4 1 +Atrial Fibrillation 4 4 2 +Atrial Flutter 4 4 2 +Atrial Function 3 3 1 +Atrial Function, Left 4 4 1 +Atrial Function, Right 4 4 1 +Atrial Myosins 8 10 4 +Atrial Natriuretic Factor 5 5 2 +Atrial Natriuretic Factor Receptor B 7 8 3 +Atrial Premature Complexes 5 5 3 +Atrial Pressure 4 4 2 +Atrial Remodeling 3 4 3 +Atrial Septum 4 4 1 +Atrioventricular Block 5 5 3 +Atrioventricular Node 4 4 1 +Atriplex 10 10 1 +Atropa 9 9 1 +Atropa belladonna 10 10 1 +Atrophic Maxilla 4 5 4 +Atrophic Vaginitis 6 7 2 +Atrophy 3 3 1 +Atropine 5 7 5 +Atropine Derivatives 4 6 5 +Attachment Sites, Microbiological 5 5 1 +Attention 4 4 1 +Attention Deficit and Disruptive Behavior Disorders 3 3 1 +Attention Deficit Disorder with Hyperactivity 4 4 1 +Attentional Bias 5 5 1 +Attentional Blink 5 5 1 +Attitude 2 2 1 +Attitude of Health Personnel 3 3 2 +Attitude to Computers 3 3 1 +Attitude to Death 3 3 2 +Attitude to Health 3 3 2 +Atypical Bacterial Forms 2 2 2 +Atypical Hemolytic Uremic Syndrome 6 8 6 +Atypical Squamous Cells of the Cervix 3 8 5 +AU Rich Elements 6 9 4 +Audioanalgesia 3 3 1 +Audiologists 3 4 2 +Audiology 3 3 1 +Audiometry 5 5 1 +Audiometry, Evoked Response 6 6 1 +Audiometry, Pure-Tone 6 6 1 +Audiometry, Speech 6 6 1 +Audiovisual Aids 4 5 2 +Auditory Acuity 4 5 2 +Auditory Brain Stem Implantation 3 4 2 +Auditory Brain Stem Implants 3 7 4 +Auditory Cortex 9 9 2 +Auditory Diseases, Central 4 4 2 +Auditory Fatigue 5 6 2 +Auditory Pathways 4 4 1 +Auditory Perception 3 4 2 +Auditory Perceptual Disorders 4 6 6 +Auditory Threshold 4 5 3 +Augmented Reality 3 4 2 +Auranofin 5 5 1 +Aureobasidium 4 4 1 +Auricularia 4 4 1 +Auriculotherapy 3 3 1 +Aurintricarboxylic Acid 5 5 1 +Aurodox 6 6 1 +Aurora Kinase A 5 9 3 +Aurora Kinase B 5 9 3 +Aurora Kinase C 5 9 3 +Aurora Kinases 4 8 3 +Aurothioglucose 4 4 1 +Aurovertins 4 4 1 +Auscultation 4 4 1 +Australasia 3 3 1 +Australasian People 4 4 1 +Australia 3 4 2 +Australian Aboriginal and Torres Strait Islander Peoples 4 5 2 +Australian Capital Territory 4 5 2 +Austria 3 3 1 +Austria-Hungary 3 3 1 +Austrobaileyales 7 7 1 +Autacoids 3 3 1 +Authoritarianism 3 3 1 +Authorship 5 5 1 +Autism Spectrum Disorder 4 4 1 +Autistic Disorder 5 5 1 +Autoanalysis 2 2 1 +Autoantibodies 7 7 3 +Autoantigens 3 3 1 +Autobiographies as Topic 4 4 1 +Autobiography 4 5 3 +Autocrine Communication 3 3 1 +Autoencoder 3 7 8 +Autoexperimentation 3 6 2 +Autogenic Training 4 5 2 +Autografts 3 3 1 +Autoimmune Diseases 2 2 1 +Autoimmune Diseases of the Nervous System 2 3 2 +Autoimmune Hypophysitis 3 7 3 +Autoimmune Inner Ear Disease 3 4 2 +Autoimmune Lymphoproliferative Syndrome 3 4 4 +Autoimmune Pancreatitis 3 6 3 +Autoimmune-Inflammatory Syndrome Induced by Adjuvants 3 3 1 +Autoimmunity 3 3 1 +Autolysis 6 6 1 +Automated Facial Recognition 5 6 2 +Automation 3 3 1 +Automation, Laboratory 2 4 2 +Automatism 5 5 1 +Automobile Driver Examination 3 3 1 +Automobile Driving 2 2 1 +Automobiles 4 4 1 +Autonomic Agents 5 5 1 +Autonomic Denervation 4 4 1 +Autonomic Dysreflexia 3 4 2 +Autonomic Fibers, Postganglionic 4 5 4 +Autonomic Fibers, Preganglionic 5 5 6 +Autonomic Nerve Block 5 5 2 +Autonomic Nervous System 3 3 1 +Autonomic Nervous System Diseases 2 2 1 +Autonomic Pathways 4 4 2 +Autonomous Robots 5 6 3 +Autonomous Vehicles 4 4 1 +Autophagic Cell Death 3 4 2 +Autophagosomes 9 9 1 +Autophagy 2 2 1 +Autophagy-Related Protein 12 4 4 2 +Autophagy-Related Protein 5 4 4 1 +Autophagy-Related Protein 7 4 4 1 +Autophagy-Related Protein 8 Family 4 6 3 +Autophagy-Related Protein-1 Homolog 4 8 3 +Autophagy-Related Proteins 3 3 1 +Autopsy 2 5 3 +Autoradiography 3 5 3 +Autoreceptors 5 7 2 +Autosomal Emery-Dreifuss Muscular Dystrophy 4 7 4 +Autosuggestion 5 6 2 +Autotrophic Processes 2 3 2 +Autovaccines 5 5 1 +Auxilins 5 5 1 +Avalanches 3 5 2 +Avapritinib 4 5 3 +Avastrovirus 5 5 1 +Avatar 4 5 3 +Avena 8 8 1 +Averrhoa 8 8 1 +Aversive Agents 3 7 6 +Aversive Therapy 4 4 1 +Aviadenovirus 4 4 1 +Avian Leukosis 3 5 6 +Avian Leukosis Virus 5 5 2 +Avian Myeloblastosis Virus 5 5 2 +Avian Proteins 3 3 1 +Avian Sarcoma Viruses 5 5 2 +Aviation 3 3 1 +Avibirnavirus 5 5 1 +Avicennia 9 9 1 +Avidin 4 7 5 +Avihepadnavirus 4 4 2 +Avipoxvirus 5 5 1 +Avitaminosis 5 5 1 +Avoidance Learning 5 6 2 +Avoidant Restrictive Food Intake Disorder 3 3 1 +Avulavirus 7 7 1 +Avulavirus Infections 6 6 1 +Awards and Prizes 2 2 1 +Awareness 4 4 1 +Axenic Culture 4 5 3 +Axial Length, Eye 3 7 2 +Axial Spondyloarthritis 4 7 3 +Axilla 4 4 1 +Axillary Artery 4 4 1 +Axillary Vein 4 4 1 +Axillofemoral Bypass Grafting 3 4 2 +Axin Protein 4 5 2 +Axin Signaling Complex 3 4 2 +Axinella 5 5 1 +Axis, Cervical Vertebra 6 6 1 +Axitinib 4 8 5 +Axl Receptor Tyrosine Kinase 6 9 4 +Axon Fasciculation 5 8 4 +Axon Guidance 5 8 4 +Axon Initial Segment 5 5 3 +Axonal Transport 3 4 3 +Axonemal Dyneins 6 8 5 +Axoneme 8 8 1 +Axons 4 4 3 +Axotomy 4 4 1 +Aza Compounds 2 2 1 +Azabicyclo Compounds 3 4 2 +Azacitidine 3 6 4 +Azacosterol 5 7 3 +Azadirachta 8 8 1 +Azaguanine 3 8 2 +Azaperone 4 4 1 +Azaserine 5 5 1 +Azasteroids 5 5 1 +Azathioprine 4 6 3 +Azauridine 3 6 4 +Azepines 3 3 1 +Azerbaijan 4 4 1 +Azetidinecarboxylic Acid 4 5 4 +Azetidines 4 4 1 +Azetines 3 3 1 +Azides 2 3 2 +Azinphosmethyl 5 5 3 +Aziridines 4 4 1 +Azirines 3 3 1 +Azithromycin 6 6 1 +Azlocillin 7 8 3 +Azo Compounds 2 2 1 +Azoarcus 3 5 3 +Azocines 3 3 1 +Azoles 3 3 1 +Azoospermia 5 5 3 +Azores 4 4 1 +Azorhizobium 3 5 3 +Azorhizobium caulinodans 4 6 3 +Azospirillum 6 6 2 +Azospirillum brasilense 3 7 3 +Azospirillum lipoferum 3 7 3 +Azotemia 3 7 4 +Azotobacter 5 6 2 +Azotobacter vinelandii 6 7 2 +Azoxymethane 3 3 1 +Aztreonam 5 6 4 +Azulenes 3 7 3 +Azure Stains 4 5 2 +Azurin 4 4 2 +Azygos Vein 4 4 1 +B-Cell Activating Factor 4 6 6 +B-Cell Activation Factor Receptor 8 8 1 +B-Cell CLL-Lymphoma 10 Protein 6 6 5 +B-Cell Lymphoma 3 Protein 4 6 2 +B-Cell Maturation Antigen 8 8 1 +B-Lymphocyte Subsets 4 8 8 +B-Lymphocytes 3 7 5 +B-Lymphocytes, Regulatory 5 9 7 +B30.2-SPRY Domain 9 9 1 +B7 Antigens 3 4 4 +B7-1 Antigen 4 5 4 +B7-2 Antigen 4 5 5 +B7-H1 Antigen 4 5 5 +Babesia 6 6 1 +Babesia bovis 7 7 1 +Babesia microti 7 7 1 +Babesiosis 4 5 5 +Babuvirus 4 4 1 +Baccharis 8 8 1 +Bacillaceae 4 5 5 +Bacillaceae Infections 5 5 1 +Bacillales 3 3 2 +Bacilloscopy 4 4 1 +Bacillota 2 2 1 +Bacillus 5 6 5 +Bacillus amyloliquefaciens 6 7 5 +Bacillus anthracis 6 7 5 +Bacillus cereus 6 7 5 +Bacillus clausii 6 7 5 +Bacillus coagulans 6 7 5 +Bacillus firmus 6 7 5 +Bacillus licheniformis 6 7 5 +Bacillus megaterium 6 7 5 +Bacillus Phages 3 3 1 +Bacillus pumilus 6 7 5 +Bacillus subtilis 6 7 5 +Bacillus thuringiensis 6 7 5 +Bacillus thuringiensis Toxins 4 4 1 +Bacitracin 4 4 2 +Back 3 3 1 +Back Injuries 2 2 1 +Back Muscles 4 4 1 +Back Pain 5 5 3 +Background Radiation 4 4 1 +Backtracking Algorithms 3 4 2 +Baclofen 7 7 1 +Bacopa 9 9 1 +Bacteremia 3 6 3 +Bacteria 1 1 1 +Bacteria, Aerobic 2 2 1 +Bacteria, Anaerobic 2 2 1 +Bacteria, Thermoduric 2 2 1 +Bacterial Adhesion 3 3 1 +Bacterial Capsules 2 2 1 +Bacterial Chromatophores 2 7 2 +Bacterial Infections 3 3 1 +Bacterial Infections and Mycoses 2 2 1 +Bacterial Load 3 6 5 +Bacterial Lysates 3 3 1 +Bacterial Outer Membrane 2 4 2 +Bacterial Outer Membrane Proteins 4 4 2 +Bacterial Physiological Phenomena 2 2 1 +Bacterial Proteins 3 3 1 +Bacterial Proton-Translocating ATPases 4 9 5 +Bacterial Secretion Systems 4 4 1 +Bacterial Shedding 2 2 1 +Bacterial Structures 1 1 1 +Bacterial Toxins 3 3 1 +Bacterial Transferrin Receptor Complex 5 6 3 +Bacterial Translocation 3 3 1 +Bacterial Typing Techniques 5 6 2 +Bacterial Vaccines 4 4 1 +Bacterial Zoonoses 3 4 3 +Bacteriochlorophyll A 5 5 1 +Bacteriochlorophylls 4 7 4 +Bacteriocin Plasmids 4 4 1 +Bacteriocins 4 5 2 +Bacteriological Techniques 4 5 2 +Bacteriology 5 5 1 +Bacteriolysis 3 3 1 +Bacteriophage HK022 4 5 3 +Bacteriophage IKe 5 5 2 +Bacteriophage lambda 4 5 3 +Bacteriophage M13 4 5 3 +Bacteriophage mu 4 5 3 +Bacteriophage N4 4 5 3 +Bacteriophage P1 4 5 3 +Bacteriophage P2 4 5 3 +Bacteriophage P22 4 5 3 +Bacteriophage Pf1 4 5 3 +Bacteriophage phi 6 4 5 3 +Bacteriophage phi X 174 4 5 3 +Bacteriophage PRD1 4 4 2 +Bacteriophage Receptors 5 6 3 +Bacteriophage T3 5 5 3 +Bacteriophage T4 5 5 3 +Bacteriophage T7 5 5 3 +Bacteriophage Typing 6 7 2 +Bacteriophages 2 2 1 +Bacteriorhodopsins 4 8 4 +Bacteriuria 3 6 4 +Bacteroidaceae 4 5 2 +Bacteroidaceae Infections 5 5 1 +Bacteroides 5 6 2 +Bacteroides fragilis 6 7 2 +Bacteroides Infections 6 6 1 +Bacteroides thetaiotaomicron 6 7 2 +Bacteroidetes 3 3 1 +Baculoviral IAP Repeat-Containing 3 Protein 6 7 3 +Baculoviridae 3 3 2 +Badnavirus 4 4 2 +Bahamas 4 5 2 +Bahrain 5 5 1 +Balaenoptera 9 9 1 +Balamuthia mandrillaris 3 3 1 +Balanced Anesthesia 4 4 1 +Balanites 8 8 1 +Balanitis 5 5 2 +Balanitis Xerotica Obliterans 6 6 2 +Balanophoraceae 7 7 1 +Balantidiasis 4 5 3 +Balantidium 7 7 1 +BALB 3T3 Cells 5 5 2 +Balkan Nephropathy 6 8 3 +Balkan Peninsula 3 3 1 +Ballistocardiography 5 5 1 +Balloon Embolectomy 3 5 3 +Balloon Enteroscopy 5 7 4 +Balloon Occlusion 3 4 4 +Balloon Valvuloplasty 3 3 2 +Ballota 9 9 1 +Balneology 2 2 1 +Balsaminaceae 8 8 1 +Balsams 5 5 2 +Baltic States 4 4 1 +Baltimore 3 7 2 +Bambermycins 4 4 1 +Bambusa 8 8 1 +Bandages 2 2 1 +Bandages, Hydrocolloid 3 3 1 +Bangladesh 5 5 1 +Banisteriopsis 10 10 1 +Bankart Lesions 4 4 2 +Banking, Personal 5 5 1 +Bankruptcy 4 4 1 +Barbados 4 5 2 +Barbarea 8 8 1 +Barber Surgeons 5 6 2 +Barbering 3 3 1 +Barbital 6 6 1 +Barbiturates 5 5 1 +Bardet-Biedl Syndrome 4 5 4 +Bariatric Medicine 3 3 1 +Bariatric Surgery 2 4 2 +Bariatrics 3 3 1 +Barium 4 4 4 +Barium Compounds 2 2 1 +Barium Enema 4 6 2 +Barium Radioisotopes 4 4 1 +Barium Sulfate 3 6 2 +Baroreflex 4 4 2 +Barotrauma 2 2 1 +Barrett Esophagus 3 4 2 +Barrington's Nucleus 8 8 1 +Barringtonia 9 9 1 +Barth Syndrome 4 5 8 +Bartholin's Glands 3 5 2 +Bartonella 4 5 2 +Bartonella bacilliformis 5 6 2 +Bartonella henselae 5 6 2 +Bartonella Infections 6 6 1 +Bartonella quintana 5 6 2 +Bartonellaceae 3 4 2 +Bartonellaceae Infections 5 5 1 +Bartter Syndrome 5 7 4 +Basal Bodies 8 8 1 +Basal Cell Carcinoma 5 6 4 +Basal Cell Nevus Syndrome 3 7 10 +Basal Forebrain 8 10 2 +Basal Ganglia 7 7 1 +Basal Ganglia Cerebrovascular Disease 4 5 3 +Basal Ganglia Diseases 4 4 1 +Basal Ganglia Hemorrhage 5 7 6 +Basal Metabolism 3 3 1 +Basal Nucleus of Meynert 9 9 1 +Base Composition 3 3 1 +Base Pair Mismatch 3 4 2 +Base Pairing 4 6 3 +Base Sequence 3 6 3 +Baseball 5 5 1 +Basement Membrane 3 5 2 +Bashkiria 5 5 1 +Basic Helix-Loop-Helix Leucine Zipper Transcription Factors 5 5 4 +Basic Helix-Loop-Helix Proteins 4 4 2 +Basic Reproduction Number 4 7 5 +Basic-Leucine Zipper Transcription Factors 4 4 2 +Basidiomycota 3 3 1 +Basigin 4 5 4 +Basilar Artery 4 4 1 +Basilar Membrane 4 5 2 +Basiliximab 9 9 3 +Basketball 5 5 1 +Basolateral Nuclear Complex 6 9 2 +Basophil Degranulation Test 4 5 3 +Basophils 4 6 4 +Bass 7 7 1 +Bassia scoparia 8 8 1 +Batch Cell Culture Techniques 5 5 1 +Bathing Beaches 3 3 1 +Bathroom Equipment 2 2 1 +Baths 3 3 1 +Batrachochytrium 4 4 1 +Batrachoidiformes 6 6 1 +Batrachotoxins 4 5 2 +Batroxobin 6 8 4 +Battered Child Syndrome 4 4 1 +Battered Men 3 3 1 +Battered Women 3 3 1 +Bauhinia 8 8 1 +Bay-Region, Polycyclic Aromatic Hydrocarbon 5 5 2 +Bayes Theorem 5 6 3 +Bays 4 4 1 +BCG Vaccine 6 6 1 +bcl-2 Homologous Antagonist-Killer Protein 6 6 2 +bcl-2-Associated X Protein 6 6 2 +Bcl-2-Like Protein 11 4 6 4 +bcl-Associated Death Protein 4 6 3 +bcl-X Protein 6 6 2 +Bdellovibrio 4 5 2 +Bdellovibrio bacteriovorus 5 6 2 +Beak 2 2 1 +Beauty 4 5 2 +Beauty Culture 3 3 1 +Beauveria 4 5 2 +Becaplermin 5 6 4 +Beckwith-Wiedemann Syndrome 4 4 4 +Beclin-1 4 5 3 +Beclomethasone 5 7 2 +Bed Conversion 5 5 1 +Bed Occupancy 3 3 1 +Bed Rest 2 2 1 +Bedbugs 9 9 1 +Bedding and Linens 3 3 2 +Bedridden Persons 2 2 1 +Beds 3 3 1 +Bee Venoms 4 5 2 +Beekeeping 3 3 1 +Beer 4 5 6 +Bees 10 10 1 +Beggiatoa 5 5 2 +Beginning of Human Life 4 4 1 +Begomovirus 4 4 2 +Begoniaceae 7 7 1 +Behavior 2 2 1 +Behavior and Behavior Mechanisms 1 1 1 +Behavior Control 2 4 3 +Behavior Observation Techniques 3 3 2 +Behavior Rating Scale 3 3 1 +Behavior Therapy 3 3 1 +Behavior, Addictive 5 5 1 +Behavior, Animal 3 3 1 +Behavioral Disciplines and Activities 1 1 1 +Behavioral Medicine 3 3 2 +Behavioral Research 3 4 2 +Behavioral Risk Factor Surveillance System 6 7 3 +Behavioral Sciences 2 2 1 +Behavioral Symptoms 3 3 1 +Behaviorism 3 3 1 +Behcet Syndrome 3 6 6 +Behind-the-Counter Drugs 3 3 1 +Beijerinckiaceae 3 4 2 +Beijing 3 5 2 +Belgium 3 3 1 +Belize 4 4 1 +Bell Palsy 3 5 4 +Belladonna Alkaloids 4 4 2 +Beloniformes 7 7 1 +Beluga Whale 9 9 1 +Bemegride 5 5 1 +Benactyzine 5 7 2 +Bence Jones Protein 7 7 3 +Benchmarking 3 5 5 +Bencyclane 7 7 1 +Bendamustine Hydrochloride 5 6 3 +Bender-Gestalt Test 4 4 2 +Bendroflumethiazide 5 6 3 +Beneficence 4 6 2 +Benign Paroxysmal Positional Vertigo 4 6 3 +Benin 5 5 1 +Benomyl 5 5 2 +Benperidol 4 4 1 +Benserazide 3 3 1 +Bentonite 5 7 4 +Benz(a)Anthracenes 3 6 2 +Benzaldehyde Dehydrogenase (NADP+) 6 6 1 +Benzaldehydes 3 3 1 +Benzalkonium Compounds 4 5 2 +Benzamides 3 7 3 +Benzamidines 3 3 1 +Benzazepines 4 4 1 +Benzbromarone 5 5 1 +Benzenaminium, 4,4'-(3-oxo-1,5-pentanediyl)bis(N,N-dimethyl-N-2-propenyl-), Dibromide 4 4 1 +Benzene 6 6 1 +Benzene Derivatives 5 5 1 +Benzeneacetamides 4 6 2 +Benzenesulfonamides 4 6 3 +Benzenesulfonates 6 7 2 +Benzethonium 4 5 2 +Benzhydryl Compounds 6 6 1 +Benzidines 7 7 1 +Benzilates 4 6 2 +Benzimidazoles 4 4 1 +Benzo(a)pyrene 5 8 2 +Benzoate 4-Monooxygenase 6 6 1 +Benzoates 4 6 2 +Benzocaine 7 9 2 +Benzocycloheptenes 3 6 2 +Benzodiazepines 5 5 1 +Benzodiazepinones 6 6 1 +Benzodioxoles 4 4 2 +Benzoflavones 7 7 2 +Benzofurans 4 4 1 +Benzoic Acid 5 7 2 +Benzoin 4 4 1 +Benzoisochromanequinones 4 6 5 +Benzolamide 4 6 4 +Benzomorphans 5 5 2 +Benzophenanthridines 3 5 3 +Benzophenoneidum 4 4 1 +Benzophenones 3 6 2 +Benzopyrans 4 4 2 +Benzopyrene Hydroxylase 5 8 3 +Benzopyrenes 4 7 2 +Benzoquinones 3 3 1 +Benzothiadiazines 4 5 3 +Benzothiazoles 4 5 2 +Benzothiepins 3 4 2 +Benzoxazines 4 4 2 +Benzoxazoles 4 4 1 +Benzoxepins 4 4 1 +Benzoyl Peroxide 5 7 2 +Benzoylarginine Nitroanilide 4 5 4 +Benzoylarginine-2-Naphthylamide 3 5 3 +Benzoylcholine 5 7 4 +Benzphetamine 7 7 1 +Benztropine 4 6 4 +Benzydamine 5 6 2 +Benzyl Alcohol 4 8 2 +Benzyl Alcohols 3 7 2 +Benzyl Compounds 6 6 1 +Benzyl Viologen 6 6 1 +Benzylamine Oxidase 6 6 1 +Benzylamines 3 7 2 +Benzylammonium Compounds 4 4 1 +Benzylidene Compounds 6 6 1 +Benzylisoquinolines 3 5 2 +Bephenium Compounds 4 5 2 +Bepridil 4 4 1 +Berberidaceae 7 7 1 +Berberine 5 5 2 +Berberine Alkaloids 4 4 2 +Berberis 8 8 1 +Bereavement 3 3 1 +Beriberi 8 8 1 +Berkelium 4 6 5 +Berlin 3 4 2 +Bermuda 4 4 1 +Bernard-Soulier Syndrome 4 5 4 +Bertholletia 9 9 1 +Berylliosis 3 5 3 +Beryllium 4 4 3 +Besnoitia 7 7 1 +Bestrophins 4 7 4 +beta 2-Glycoprotein I 4 4 3 +beta 2-Microglobulin 6 6 7 +beta Carotene 4 9 4 +beta Catenin 4 5 3 +beta Karyopherins 5 7 3 +beta Lactam Antibiotics 6 6 1 +Beta Particles 4 4 1 +Beta Rhythm 4 6 4 +Beta vulgaris 8 8 1 +beta-Adrenergic Receptor Kinases 6 9 2 +beta-Alanine 4 4 1 +beta-Alanine-Pyruvate Transaminase 6 6 1 +beta-Aminoethyl Isothiourea 4 5 2 +beta-Amylase 6 6 1 +beta-Arrestin 1 6 7 4 +beta-Arrestin 2 6 7 4 +beta-Arrestins 5 6 4 +beta-Carotene 15,15'-Monooxygenase 7 7 1 +Beta-Cryptoxanthin 6 11 4 +beta-Crystallin A Chain 6 6 1 +beta-Crystallin B Chain 6 6 1 +beta-Crystallins 5 5 1 +beta-Cyclodextrins 4 7 3 +beta-D-Galactoside alpha 2-6-Sialyltransferase 6 6 1 +beta-Defensins 5 7 3 +beta-Endorphin 6 7 8 +beta-Fructofuranosidase 5 5 1 +beta-Galactosidase 6 6 1 +beta-Galactoside alpha-2,3-Sialyltransferase 6 6 1 +beta-Globins 6 7 2 +Beta-Globulins 5 5 2 +beta-Glucans 4 4 1 +beta-Glucosidase 7 7 1 +beta-Hexosaminidase alpha Chain 8 8 1 +beta-Hexosaminidase beta Chain 8 8 2 +beta-Keratins 5 6 3 +beta-Lactam Resistance 4 7 3 +beta-Lactamase Inhibitors 5 6 2 +beta-Lactamases 5 5 1 +beta-Lactams 3 4 3 +beta-Lipotropin 6 7 6 +beta-Mannosidase 6 6 1 +beta-Mannosidosis 6 6 4 +beta-MSH 5 9 8 +beta-N-Acetyl-Galactosaminidase 6 6 1 +beta-N-Acetylglucosaminylglycopeptide beta-1,4-Galactosyltransferase 7 7 1 +beta-N-Acetylhexosaminidases 6 6 1 +beta-Naphthoflavone 8 8 2 +beta-Synuclein 5 5 1 +beta-Thalassemia 5 7 4 +beta-Thromboglobulin 3 6 9 +beta-Tocopherol 7 7 2 +beta-Transducin Repeat-Containing Proteins 5 5 1 +Betacellulin 4 5 3 +Betacoronavirus 7 7 1 +Betacoronavirus 1 8 8 1 +Betacyanins 4 7 5 +Betaherpesvirinae 4 4 1 +Betahistine 4 4 1 +betaIG-H3 Protein 5 5 1 +Betaine 4 5 2 +Betaine-Aldehyde Dehydrogenase 6 6 1 +Betaine-Homocysteine S-Methyltransferase 6 6 1 +Betainfluenzavirus 5 5 1 +Betalains 3 6 5 +Betamethasone 5 7 2 +Betamethasone Valerate 6 8 2 +Betapapillomavirus 5 5 2 +Betaproteobacteria 3 3 1 +Betaretrovirus 4 4 2 +Betaxanthins 4 5 4 +Betaxolol 6 6 3 +Betazole 5 5 1 +Bethanechol 5 6 3 +Bethanechol Compounds 4 5 3 +Bethanidine 4 4 1 +Betrayal 4 4 1 +Betula 10 10 1 +Betulaceae 9 9 1 +Betulinic Acid 7 7 1 +Bevacizumab 9 9 3 +Beverages 2 3 2 +Bexarotene 5 10 6 +Bezafibrate 4 9 8 +Bezoars 3 3 1 +BH3 Interacting Domain Death Agonist Protein 6 6 2 +Bhopal Accidental Release 5 5 2 +Bhutan 5 5 1 +Bias 4 4 2 +Bias, Implicit 4 5 2 +Bibenzyls 7 7 1 +Bible 3 3 1 +Bibliographies as Topic 4 5 2 +Bibliography 2 2 2 +Bibliography of Medicine 5 6 2 +Bibliography, Descriptive 5 5 1 +Bibliography, National 6 6 1 +Bibliometrics 5 6 2 +Bibliotherapy 3 6 3 +Bicarbonates 5 6 2 +Bicornuate Uterus 7 7 1 +Bicuculline 4 6 2 +Bicuspid 5 5 1 +Bicuspid Aortic Valve Disease 4 5 4 +Bicyclic Monoterpenes 5 6 2 +Bicycling 5 5 1 +Bidens 8 8 1 +Bifidobacteriales Infections 5 5 1 +Bifidobacterium 4 7 2 +Bifidobacterium adolescentis 5 8 2 +Bifidobacterium animalis 5 8 2 +Bifidobacterium bifidum 5 8 2 +Bifidobacterium breve 5 8 2 +Bifidobacterium longum 5 8 2 +Bifidobacterium longum subspecies infantis 6 9 2 +Bifidobacterium pseudocatenulatum 5 8 2 +Biflavonoids 7 7 2 +Big Data 6 6 1 +Biglycan 5 6 6 +Bignoniaceae 8 8 1 +Biguanides 4 4 1 +Bilateral Vestibulopathy 3 5 3 +Bile 3 3 1 +Bile Acids and Salts 4 4 1 +Bile Canaliculi 4 5 2 +Bile Duct Diseases 3 3 1 +Bile Duct Neoplasms 4 5 4 +Bile Ducts 3 3 1 +Bile Ducts, Extrahepatic 4 4 1 +Bile Ducts, Intrahepatic 3 4 2 +Bile Pigments 3 6 4 +Bile Reflux 3 5 2 +Biliary Atresia 3 4 3 +Biliary Dyskinesia 5 5 1 +Biliary Fistula 3 5 2 +Biliary Tract 2 2 1 +Biliary Tract Diseases 2 2 1 +Biliary Tract Neoplasms 3 4 3 +Biliary Tract Surgical Procedures 3 3 1 +Biliopancreatic Diversion 3 4 2 +Bilirubin 4 7 4 +Biliverdine 5 8 4 +Bilobalides 6 6 1 +Bilophila 5 6 2 +Bimatoprost 3 9 4 +Binding Sites 4 4 1 +Binding Sites, Antibody 2 6 4 +Binding, Competitive 3 5 3 +Binge Drinking 4 5 3 +Binge-Eating Disorder 3 3 1 +Binomial Distribution 3 6 4 +Bioaccumulation 4 6 3 +Bioactive Peptides, Dietary 3 5 4 +Bioartificial Organs 4 4 1 +Biobehavioral Sciences 5 5 1 +Biobibliography 3 3 2 +Biocatalysis 2 3 3 +Biochemical Phenomena 2 2 1 +Biochemistry 3 3 2 +Biocompatible Materials 2 4 3 +Biocuration 4 4 1 +Biodegradable Plastics 4 6 3 +Biodegradation, Environmental 5 5 2 +Biodiversity 4 5 2 +Bioelectric Energy Sources 4 4 1 +Bioengineering 3 3 1 +Bioenhancers 4 5 2 +Bioethical Issues 3 5 2 +Bioethics 3 5 2 +Biofeedback, Psychology 3 4 4 +Biofilms 2 2 2 +Biofortification 4 4 1 +Biofouling 3 4 3 +Biofuels 2 5 2 +Biogenic Amines 3 3 1 +Biogenic Monoamines 4 4 1 +Biogenic Polyamines 4 4 1 +Biographies as Topic 3 3 1 +Biography 2 3 3 +Biohazard Release 4 4 1 +Bioisosterism 5 5 1 +Biolistics 4 7 2 +Biological Assay 2 2 1 +Biological Availability 3 4 2 +Biological Clocks 4 4 1 +Biological Coevolution 3 3 2 +Biological Control Agents 3 3 1 +Biological Dressings 3 3 1 +Biological Evolution 2 2 2 +Biological Factors 1 1 1 +Biological Mimicry 3 3 1 +Biological Monitoring 5 6 2 +Biological Ontologies 6 7 3 +Biological Oxygen Demand Analysis 5 6 2 +Biological Phenomena 1 1 1 +Biological Products 2 2 1 +Biological Psychiatry 4 4 2 +Biological Science Disciplines 2 2 1 +Biological Specimen Banks 3 3 1 +Biological Therapy 2 2 1 +Biological Transport 2 2 1 +Biological Transport, Active 3 3 1 +Biological Variation, Individual 2 2 1 +Biological Variation, Population 2 2 1 +Biological Warfare 6 6 1 +Biological Warfare Agents 3 5 2 +Biology 3 3 1 +Bioluminescence Resonance Energy Transfer Techniques 5 5 1 +Biomarkers 2 2 1 +Biomarkers, Pharmacological 3 3 1 +Biomarkers, Tumor 3 3 1 +Biomass 4 5 2 +Biomechanical Phenomena 3 3 2 +Biomedical and Dental Materials 1 3 3 +Biomedical Engineering 2 3 2 +Biomedical Enhancement 2 5 2 +Biomedical Research 4 4 1 +Biomedical Technology 4 4 1 +Biometric Identification 4 5 2 +Biometry 4 4 2 +Biomimetic Materials 3 3 1 +Biomimetics 4 4 2 +Biomineralization 3 7 4 +Biomolecular Condensates 3 3 1 +Biomphalaria 7 7 1 +Bionics 4 4 3 +Biopharmaceutics 3 4 2 +Biophysical Phenomena 2 2 1 +Biophysics 3 3 2 +Biopolymers 3 5 3 +Bioprinting 2 4 2 +Bioprospecting 2 2 1 +Bioprosthesis 3 3 1 +Biopsy 2 6 7 +Biopsy, Fine-Needle 4 8 8 +Biopsy, Large-Core Needle 4 8 8 +Biopsy, Needle 3 7 8 +Biopterins 3 6 2 +Bioreactors 2 4 2 +Bioresonance Therapy 4 4 1 +Biosecurity 4 4 1 +Biosensing Techniques 3 3 1 +Biosimilar Pharmaceuticals 3 3 1 +Biosolids 4 4 1 +Biostatistics 4 4 1 +Biosurfactants 4 4 1 +Biosurveillance 5 5 1 +Biosynthetic Pathways 3 3 2 +Biota 5 6 2 +Biotechnology 3 3 2 +Bioterrorism 6 7 3 +Biotin 3 5 2 +Biotinidase 5 5 1 +Biotinidase Deficiency 6 6 4 +Biotinylation 2 3 3 +Biotransformation 2 4 3 +Biperiden 4 6 3 +Biphasic Insulins 3 6 3 +Biphenyl Compounds 6 6 1 +Bipolar and Related Disorders 3 3 1 +Bipolar Disorder 4 4 1 +Bipolaris 4 4 2 +Bird Diseases 2 2 1 +Bird Fancier's Lung 2 6 4 +Birds 5 5 1 +Birdshot Chorioretinopathy 3 8 5 +Birefringence 3 3 1 +Birnaviridae 4 4 1 +Birnaviridae Infections 4 4 1 +Birt-Hogg-Dube Syndrome 3 4 2 +Birth Certificates 4 6 5 +Birth Cohort 3 3 1 +Birth Injuries 2 3 2 +Birth Intervals 4 4 2 +Birth Order 4 6 4 +Birth Rate 5 7 4 +Birth Setting 6 6 1 +Birth Weight 4 8 7 +Birthing Centers 3 3 1 +Bis(4-Methyl-1-Homopiperazinylthiocarbonyl)disulfide 5 5 1 +Bis(5'-adenosyl)triphosphatase 5 5 1 +Bis(Chloromethyl) Ether 4 4 1 +Bis-Trimethylammonium Compounds 4 4 2 +Bisacodyl 8 8 1 +Bisbenzimidazole 5 5 1 +Bisexuality 4 5 2 +Bismuth 4 5 4 +Bison 9 9 1 +Bisoprolol 6 6 3 +Bisphenol A Compounds 8 8 1 +Bisphenol A-Glycidyl Methacrylate 5 9 7 +Bisphenol B Compounds 8 8 1 +Bisphenol F Compounds 7 8 2 +Bisphenol S Compounds 7 8 2 +Bisphenols 7 7 1 +Bisphosphoglycerate Mutase 6 6 1 +Bisphosphonate-Associated Osteonecrosis of the Jaw 3 5 4 +Bite Force 3 3 1 +Bites and Stings 2 3 2 +Bites, Human 3 3 1 +Bithionol 7 7 1 +Bitis 8 10 3 +Biureas 4 4 1 +Biuret 4 4 1 +Biuret Reaction 3 3 1 +Bivalvia 5 5 1 +Bixaceae 7 7 1 +BK Virus 6 6 2 +Black or African American 3 6 3 +Black People 3 3 1 +Black Sea 3 3 1 +Black Widow Spider 7 7 1 +Blackwater Fever 6 6 2 +Bladder Exstrophy 3 6 7 +Blade Implantation 5 6 4 +Blalock-Taussig Procedure 3 5 2 +Bland White Garland Syndrome 5 6 7 +Blast Crisis 5 6 6 +Blast Injuries 3 3 1 +Blastic Plasmacytoid Dendritic Cell Neoplasm 4 4 7 +Blastocladiella 4 4 1 +Blastocladiomycota 3 3 1 +Blastocyst 3 3 1 +Blastocyst Inner Cell Mass 4 4 1 +Blastocystina 5 5 1 +Blastocystis 6 6 1 +Blastocystis hominis 7 7 1 +Blastocystis Infections 4 5 3 +Blastoderm 3 3 1 +Blastodisc 3 3 1 +Blastomeres 2 5 2 +Blastomyces 4 4 1 +Blastomycosis 4 5 7 +Blastula 3 3 1 +Blattellidae 7 7 1 +Bleaching Agents 4 4 1 +Bleeding Time 3 6 3 +Bleomycin 4 4 2 +Blepharitis 3 3 1 +Blepharophimosis 3 4 3 +Blepharoplasty 3 4 2 +Blepharoptosis 3 3 1 +Blepharospasm 3 3 1 +Blighia 8 8 1 +Blind Loop Syndrome 4 5 2 +Blindness 3 6 3 +Blindness, Cortical 4 7 3 +Blinking 2 4 2 +Blister 3 4 2 +Blockchain 4 5 2 +Blog 3 4 3 +Blogging 3 5 3 +Blood 2 3 2 +Blood Alcohol Content 4 4 1 +Blood Bactericidal Activity 3 4 2 +Blood Banking 4 5 3 +Blood Banks 4 4 1 +Blood Buffy Coat 4 4 1 +Blood Cell Count 3 6 7 +Blood Cells 2 3 2 +Blood Chemical Analysis 4 5 2 +Blood Circulation 3 3 1 +Blood Circulation Time 4 4 1 +Blood Coagulation 4 4 1 +Blood Coagulation Disorders 3 3 1 +Blood Coagulation Disorders, Inherited 3 4 2 +Blood Coagulation Factor Inhibitors 2 2 1 +Blood Coagulation Factors 2 4 2 +Blood Coagulation Tests 4 5 2 +Blood Component Removal 2 2 1 +Blood Component Transfusion 4 4 1 +Blood Culture 4 5 2 +Blood Donation 4 4 1 +Blood Donors 3 3 1 +Blood Flow Restriction Therapy 4 7 3 +Blood Flow Velocity 4 5 2 +Blood Gas Analysis 5 6 3 +Blood Gas Monitoring, Transcutaneous 6 8 4 +Blood Glucose 6 6 1 +Blood Glucose Self-Monitoring 4 6 5 +Blood Group Antigens 4 4 2 +Blood Group Incompatibility 2 3 2 +Blood Grouping and Crossmatching 4 6 7 +Blood Loss, Surgical 4 4 2 +Blood Patch, Epidural 3 7 2 +Blood Physiological Phenomena 2 2 1 +Blood Platelet Disorders 3 3 1 +Blood Platelets 3 4 2 +Blood Preservation 4 4 2 +Blood Pressure 4 5 2 +Blood Pressure Determination 4 4 2 +Blood Pressure Monitoring, Ambulatory 5 5 2 +Blood Pressure Monitors 4 4 1 +Blood Protein Disorders 3 3 1 +Blood Protein Electrophoresis 4 6 4 +Blood Proteins 3 3 1 +Blood Safety 5 6 4 +Blood Sedimentation 4 5 2 +Blood Specimen Collection 3 5 3 +Blood Stains 5 5 1 +Blood Substitutes 4 5 2 +Blood Transfusion 3 3 1 +Blood Transfusion, Autologous 4 4 1 +Blood Transfusion, Intrauterine 3 4 2 +Blood Urea Nitrogen 5 6 3 +Blood Vessel Prosthesis 3 3 1 +Blood Vessel Prosthesis Implantation 3 5 2 +Blood Vessels 2 2 1 +Blood Viscosity 4 5 2 +Blood Volume 3 4 2 +Blood Volume Determination 4 4 1 +Blood-Air Barrier 2 4 2 +Blood-Aqueous Barrier 2 4 2 +Blood-Borne Infections 3 5 2 +Blood-Borne Pathogens 2 2 1 +Blood-Brain Barrier 2 4 2 +Blood-Nerve Barrier 2 4 2 +Blood-Retinal Barrier 2 4 2 +Blood-Spinal Cord Barrier 2 4 2 +Blood-Testis Barrier 2 6 2 +Bloodless Medical and Surgical Procedures 2 4 3 +Bloodletting 4 4 1 +Bloom Syndrome 4 4 4 +Blotting, Far-Western 4 6 6 +Blotting, Northern 3 4 3 +Blotting, Southern 3 4 3 +Blotting, Southwestern 3 4 3 +Blotting, Western 3 5 5 +Blue Cross Blue Shield Insurance Plans 7 7 1 +Blue Light 4 6 4 +Blue Toe Syndrome 4 7 2 +Blueberry Plants 10 10 1 +Bluetongue 3 5 4 +Bluetongue virus 6 6 1 +Blushing 3 5 2 +BNT162 Vaccine 6 7 4 +BNT162b5 5 7 5 +Bocavirus 5 5 1 +Bodily Secretions 2 2 1 +Body Burden 4 4 2 +Body Composition 2 3 3 +Body Constitution 2 4 2 +Body Contouring 3 3 2 +Body Dissatisfaction 6 6 1 +Body Dysmorphic Disorders 3 3 1 +Body Fat Distribution 3 6 4 +Body Fluid Compartments 2 3 2 +Body Fluids 2 2 1 +Body Height 4 7 6 +Body Image 4 5 2 +Body Integrity Identity Disorder 3 3 1 +Body Mass Index 4 6 4 +Body Modification, Non-Therapeutic 2 3 2 +Body Odor 3 3 1 +Body Packing 4 5 2 +Body Patterning 4 4 1 +Body Piercing 3 4 2 +Body Regions 1 1 1 +Body Remains 4 5 3 +Body Satisfaction 6 6 1 +Body Size 4 6 4 +Body Surface Area 4 6 3 +Body Surface Potential Mapping 6 7 2 +Body Temperature 2 5 2 +Body Temperature Changes 3 3 1 +Body Temperature Regulation 3 4 3 +Body Water 3 3 1 +Body Weight 3 7 7 +Body Weight Changes 4 6 2 +Body Weight Maintenance 6 6 1 +Body Weights and Measures 3 5 3 +Body-Weight Trajectory 5 7 2 +Boehmeria 10 10 1 +Boidae 8 8 1 +Bolivia 4 4 1 +Bombacaceae 7 7 1 +Bombax 10 10 1 +Bombesin 4 5 6 +Bombs 4 4 1 +Bombyx 11 11 1 +Bonamia Plant 8 8 1 +Bone and Bones 3 3 2 +Bone Anteversion 4 4 2 +Bone Banks 5 5 1 +Bone Cements 4 7 4 +Bone Conduction 4 5 3 +Bone Cysts 3 3 2 +Bone Cysts, Aneurysmal 4 4 2 +Bone Demineralization Technique 2 2 1 +Bone Demineralization, Pathologic 4 4 2 +Bone Density 3 3 1 +Bone Density Conservation Agents 4 4 1 +Bone Development 5 8 2 +Bone Diseases 2 2 1 +Bone Diseases, Developmental 3 3 1 +Bone Diseases, Endocrine 2 3 2 +Bone Diseases, Infectious 2 3 2 +Bone Diseases, Metabolic 3 3 2 +Bone Lengthening 3 3 1 +Bone Malalignment 3 3 1 +Bone Marrow 3 3 1 +Bone Marrow Cells 2 3 2 +Bone Marrow Diseases 3 3 1 +Bone Marrow Examination 4 5 2 +Bone Marrow Failure Disorders 4 4 1 +Bone Marrow Neoplasms 4 4 3 +Bone Marrow Purging 3 3 1 +Bone Marrow Stromal Antigen 2 4 6 5 +Bone Marrow Transplantation 4 5 2 +Bone Matrix 4 4 1 +Bone Morphogenetic Protein 1 4 10 8 +Bone Morphogenetic Protein 15 5 6 6 +Bone Morphogenetic Protein 2 5 6 3 +Bone Morphogenetic Protein 3 5 6 3 +Bone Morphogenetic Protein 4 5 6 3 +Bone Morphogenetic Protein 5 5 6 3 +Bone Morphogenetic Protein 6 5 6 3 +Bone Morphogenetic Protein 7 5 6 3 +Bone Morphogenetic Protein Receptors 5 8 3 +Bone Morphogenetic Protein Receptors, Type I 6 9 3 +Bone Morphogenetic Protein Receptors, Type II 6 9 3 +Bone Morphogenetic Proteins 4 5 3 +Bone Nails 4 6 3 +Bone Neoplasms 3 3 2 +Bone Plates 4 6 3 +Bone Regeneration 4 4 2 +Bone Remodeling 3 3 2 +Bone Resorption 3 4 2 +Bone Retroversion 4 4 2 +Bone Screws 4 6 3 +Bone Substitutes 3 5 2 +Bone Transplantation 3 5 3 +Bone Wires 4 6 3 +Bone-Anchored Prosthesis 3 3 1 +Bone-Implant Interface 3 4 3 +Bone-Patellar Tendon-Bone Grafting 4 5 4 +Bone-Patellar Tendon-Bone Grafts 3 3 1 +Bones of Lower Extremity 4 4 1 +Bones of Upper Extremity 4 4 1 +Bongkrekic Acid 5 5 1 +Bony Callus 4 4 1 +Book Classification 6 6 2 +Book Collecting 3 3 1 +Book Illustrations 2 2 1 +Book Imprints 4 6 2 +Book Industry 3 4 2 +Book Ornamentation 6 6 1 +Book Prices 5 5 1 +Book Review 2 2 1 +Book Reviews as Topic 5 5 1 +Book Selection 4 4 1 +Bookbinding 4 4 1 +Bookplate 2 2 1 +Bookplates as Topic 6 6 1 +Books 5 5 1 +Books, Illustrated 5 6 2 +Bookselling 4 4 1 +Boosting Machine Learning Algorithms 3 7 4 +Boraginaceae 7 7 1 +Borago 8 8 1 +Boranes 3 3 2 +Borates 4 5 3 +Border Disease 3 6 2 +Border disease virus 6 6 1 +Borderline Personality Disorder 3 3 1 +Bordetella 6 6 2 +Bordetella avium 7 7 2 +Bordetella bronchiseptica 7 7 2 +Bordetella Infections 5 5 1 +Bordetella parapertussis 7 7 2 +Bordetella pertussis 7 7 2 +Boredom 3 3 1 +Boric Acids 3 4 3 +Borinic Acids 3 4 3 +Borna Disease 2 5 2 +Borna disease virus 6 6 1 +Bornaviridae 5 5 1 +Borneo 3 4 2 +Borohydrides 4 4 2 +Boron 4 4 1 +Boron Compounds 2 2 2 +Boron Neutron Capture Therapy 5 5 1 +Boronic Acids 3 4 3 +Borrelia 4 6 2 +Borrelia burgdorferi 6 8 2 +Borrelia burgdorferi Group 5 7 2 +Borrelia Infections 6 6 1 +Bortezomib 4 5 4 +Bosentan 4 7 5 +Bosnia and Herzegovina 4 4 1 +Boston 3 7 2 +Boswellia 8 8 1 +Botany 4 4 1 +Bothrops 8 10 3 +Bothrops asper 9 11 3 +Bothrops atrox 9 11 3 +Bothrops jararaca 9 11 3 +Bothrops jararaca Venom 6 7 2 +Botrytis 4 4 1 +Botswana 5 5 1 +Bottle Feeding 3 6 4 +Bottle-Nosed Dolphin 9 9 1 +Botulinum Antitoxin 5 9 4 +Botulinum Toxins 4 7 4 +Botulinum Toxins, Type A 5 8 4 +Botulism 3 6 4 +Boutonneuse Fever 5 8 2 +Bovine papillomavirus 1 6 6 2 +Bovine papillomavirus 4 6 6 2 +Bovine Respiratory Disease Complex 3 3 3 +Bovine Virus Diarrhea-Mucosal Disease 3 6 2 +Bowen's Disease 6 7 3 +Bowhead Whale 9 9 1 +Bowman Capsule 6 6 1 +Bowman Membrane 5 5 1 +Boxing 5 5 1 +Braces 5 5 1 +Brachial Artery 4 4 1 +Brachial Plexus 5 5 1 +Brachial Plexus Block 5 5 1 +Brachial Plexus Neuritis 5 5 2 +Brachial Plexus Neuropathies 4 4 1 +Brachiaria 8 8 1 +Brachiocephalic Trunk 4 4 1 +Brachiocephalic Veins 4 4 1 +Brachydactyly 4 5 2 +Brachypodium 8 8 1 +Brachyspira 3 5 2 +Brachyspira hyodysenteriae 4 6 2 +Brachytherapy 3 3 1 +Brachyura 7 7 1 +Brachyury Protein 5 5 2 +Bradycardia 4 4 2 +Bradykinin 4 5 7 +Bradykinin B1 Receptor Antagonists 5 5 1 +Bradykinin B2 Receptor Antagonists 5 7 2 +Bradykinin Receptor Antagonists 4 4 1 +Bradyrhizobiaceae 4 5 2 +Bradyrhizobium 5 6 2 +Brain 3 3 1 +Brain Abscess 3 4 4 +Brain Chemistry 2 3 2 +Brain Concussion 4 6 6 +Brain Contusion 4 6 4 +Brain Cortical Thickness 3 5 3 +Brain Damage, Chronic 4 5 2 +Brain Death 4 7 3 +Brain Diseases 3 3 1 +Brain Diseases, Metabolic 3 4 2 +Brain Diseases, Metabolic, Inborn 4 5 4 +Brain Drain 6 8 3 +Brain Edema 4 4 1 +Brain Hemorrhage, Traumatic 5 7 7 +Brain Infarction 5 6 6 +Brain Injuries 4 4 3 +Brain Injuries, Diffuse 5 5 3 +Brain Injuries, Traumatic 5 5 3 +Brain Injury, Chronic 5 6 5 +Brain Ischemia 4 5 2 +Brain Mapping 4 6 3 +Brain Neoplasms 4 5 3 +Brain Regeneration 3 3 1 +Brain Stem 4 4 1 +Brain Stem Hemorrhage, Traumatic 6 8 7 +Brain Stem Infarctions 6 7 6 +Brain Stem Neoplasms 6 7 3 +Brain Tissue Transplantation 3 5 3 +Brain Waves 3 5 4 +Brain-Computer Interfaces 3 3 1 +Brain-Derived Neurotrophic Factor 4 5 4 +Brain-Gut Axis 3 3 1 +Brainwashing 4 4 1 +Branched DNA Signal Amplification Assay 3 4 2 +Branchial Region 2 2 1 +Branchio-Oto-Renal Syndrome 4 4 3 +Branchioma 3 3 1 +Brassica 8 8 1 +Brassica napus 9 9 1 +Brassica rapa 9 9 1 +Brassicaceae 7 7 1 +Brassinosteroids 4 7 3 +Brazil 4 4 1 +BRCA1 Protein 4 5 5 +BRCA2 Protein 4 5 3 +Bread 3 4 2 +Breakfast 4 5 2 +Breakthrough Infections 2 4 2 +Breakthrough Pain 5 5 3 +Breast 2 2 1 +Breast Cancer Lymphedema 4 4 2 +Breast Carcinoma In Situ 4 6 3 +Breast Cyst 3 4 2 +Breast Density 3 5 2 +Breast Diseases 3 3 1 +Breast Feeding 4 6 4 +Breast Implantation 3 4 3 +Breast Implants 3 3 1 +Breast Milk Expression 5 7 3 +Breast Neoplasms 3 4 2 +Breast Neoplasms, Male 4 5 2 +Breast Self-Examination 5 5 2 +Breath Holding 4 4 1 +Breath Tests 3 3 1 +Breathing Exercises 4 4 2 +Breech Presentation 4 7 3 +Breeding 2 3 2 +Brefeldin A 5 5 1 +Brenner Tumor 5 8 7 +Brentuximab Vedotin 4 9 4 +Brettanomyces 4 4 2 +Bretylium Compounds 5 5 1 +Bretylium Tosylate 6 6 1 +Brevibacillus 4 5 5 +Brevibacterium 4 7 2 +Brevibacterium flavum 5 5 1 +Brevican 6 7 3 +Bridge Therapy 2 2 1 +Bridged Bicyclo Compounds 3 5 2 +Bridged Bicyclo Compounds, Heterocyclic 3 3 1 +Bridged-Ring Compounds 2 4 2 +Brief Psychiatric Rating Scale 5 5 1 +Brief, Resolved, Unexplained Event 4 4 3 +Brimonidine Tartrate 5 5 1 +Brimonidine Tartrate, Timolol Maleate Drug Combination 3 7 7 +Brinolase 6 6 1 +British Columbia 5 5 1 +British Virgin Islands 5 5 1 +Broad Ligament 3 5 3 +Broadly Neutralizing Antibodies 8 8 3 +Broadside 2 2 1 +Broadsides as Topic 5 5 1 +Broca Area 10 10 1 +Brochothrix 4 6 3 +Brocresine 8 8 1 +Bromates 3 5 2 +Bromazepam 7 7 1 +Bromcresol Green 5 8 3 +Bromcresol Purple 5 8 3 +Bromelains 7 7 2 +Bromelia 8 8 1 +Bromeliaceae 7 7 1 +Bromhexine 4 4 2 +Bromides 4 5 2 +Bromine 4 4 1 +Bromine Compounds 2 2 1 +Bromine Radioisotopes 4 4 1 +Bromisovalum 4 4 1 +Bromobenzenes 5 6 2 +Bromobenzoates 5 7 2 +Bromochlorofluorocarbons 5 6 3 +Bromocriptine 5 5 3 +Bromodeoxycytidine 5 7 3 +Bromodeoxyuridine 5 7 3 +Bromodomain Containing Proteins 5 5 3 +Bromosuccinimide 4 6 2 +Bromotrichloromethane 5 6 2 +Bromouracil 6 6 1 +Bromoviridae 3 4 2 +Bromovirus 4 5 3 +Brompheniramine 5 5 1 +Bromphenol Blue 5 7 3 +Bromthymol Blue 5 7 3 +Bromus 8 8 1 +Bronchi 3 3 1 +Bronchial Arteries 4 4 1 +Bronchial Diseases 2 2 1 +Bronchial Fistula 3 5 3 +Bronchial Hyperreactivity 3 3 1 +Bronchial Neoplasms 3 6 3 +Bronchial Provocation Tests 5 5 1 +Bronchial Spasm 3 3 1 +Bronchial Thermoplasty 5 5 2 +Bronchiectasis 3 3 1 +Bronchioles 4 4 1 +Bronchiolitis 4 5 4 +Bronchiolitis Obliterans 5 6 2 +Bronchiolitis Obliterans Syndrome 3 8 3 +Bronchiolitis, Viral 3 6 5 +Bronchitis 3 4 4 +Bronchitis, Chronic 4 6 6 +Bronchoalveolar Lavage 3 3 1 +Bronchoalveolar Lavage Fluid 4 4 1 +Bronchoconstriction 5 5 1 +Bronchoconstrictor Agents 5 6 2 +Bronchodilator Agents 6 6 2 +Bronchogenic Cyst 3 4 4 +Bronchography 4 6 2 +Bronchomalacia 4 5 4 +Bronchopneumonia 3 4 4 +Bronchopulmonary Dysplasia 4 5 2 +Bronchopulmonary Sequestration 3 4 2 +Bronchoscopes 4 4 2 +Bronchoscopy 4 5 4 +Bronchospirometry 6 6 1 +Broussonetia 10 10 1 +Brown Recluse Spider 7 7 1 +Brown-Sequard Syndrome 5 6 2 +Brucea 8 8 1 +Brucea javanica 9 9 1 +Brucella 5 6 2 +Brucella abortus 6 7 2 +Brucella canis 6 7 2 +Brucella melitensis 6 7 2 +Brucella ovis 6 7 2 +Brucella suis 6 7 2 +Brucella Vaccine 5 5 1 +Brucellaceae 4 5 2 +Brucellosis 5 5 1 +Brucellosis, Bovine 3 6 3 +Bruch Membrane 4 5 2 +Brugada Syndrome 3 4 3 +Brugia 9 9 1 +Brugia malayi 10 10 1 +Brugia pahangi 10 10 1 +Brugmansia 9 9 1 +Brunei 4 4 1 +Brunner Glands 5 6 2 +Bruxism 3 4 3 +Bryonia 8 8 1 +Bryophyta 5 5 1 +Bryopsida 6 6 1 +Bryostatins 5 6 6 +Bryozoa 4 4 1 +BTB-POZ Domain 9 9 1 +Buchnera 3 5 2 +Bucladesine 5 8 4 +Bucrylate 4 7 7 +Budd-Chiari Syndrome 3 6 2 +Buddhism 3 3 1 +Buddleja 9 9 1 +Budesonide 7 7 1 +Budesonide, Formoterol Fumarate Drug Combination 3 8 4 +Budgets 4 4 1 +Bufanolides 6 6 1 +Bufexamac 5 7 4 +Buffaloes 9 9 1 +Buffers 4 4 1 +Bufo arenarum 8 8 1 +Bufo bufo 8 8 1 +Bufo marinus 8 8 1 +Bufonidae 7 7 1 +Buformin 5 5 1 +Bufotenin 4 7 4 +Building Codes 4 4 1 +Built Environment 4 4 1 +Bulbar Palsy, Progressive 4 4 2 +Bulbo-Spinal Atrophy, X-Linked 4 5 6 +Bulbourethral Glands 3 4 2 +Bulgaria 4 4 1 +Bulimia 5 5 1 +Bulimia Nervosa 3 3 1 +Bulinus 7 7 1 +Bulk Drugs 2 2 1 +Bullying 4 5 3 +Bumetanide 4 9 4 +Bunaftine 4 7 2 +Bundle of His 4 4 1 +Bundle-Branch Block 5 5 3 +Bungarotoxins 5 6 2 +Bungarus 7 9 3 +Bungarus multicinctus 8 10 3 +Bunion 4 4 1 +Bunion, Tailor's 5 5 1 +Bunolol 5 8 5 +Bunyamwera virus 6 6 1 +Bunyaviridae 4 4 1 +Bunyaviridae Infections 4 4 1 +Bupivacaine 4 5 2 +Bupleurum 8 8 1 +Bupranolol 6 6 3 +Buprenorphine 4 5 4 +Buprenorphine, Naloxone Drug Combination 3 6 9 +Bupropion 4 4 1 +Burial 6 6 1 +Burimamide 4 5 2 +Burkholderia 6 6 1 +Burkholderia cenocepacia 8 8 1 +Burkholderia cepacia 8 8 1 +Burkholderia cepacia complex 7 7 1 +Burkholderia gladioli 7 7 1 +Burkholderia Infections 5 5 1 +Burkholderia mallei 7 7 1 +Burkholderia pseudomallei 7 7 1 +Burkholderiaceae 5 5 2 +Burkholderiales 4 4 1 +Burkina Faso 5 5 1 +Burkitt Lymphoma 5 7 5 +Burn Units 5 5 1 +Burning Mouth Syndrome 3 3 1 +Burnout, Professional 3 6 3 +Burnout, Psychological 4 5 2 +Burns 2 2 1 +Burns, Chemical 3 3 1 +Burns, Electric 3 3 2 +Burns, Inhalation 3 3 1 +Bursa of Fabricius 2 5 3 +Bursa, Synovial 4 4 1 +Bursera 8 8 1 +Burseraceae 7 7 1 +Bursitis 3 3 1 +Buruli Ulcer 4 8 2 +Burundi 5 5 1 +Buschke-Lowenstein Tumor 5 9 11 +Buserelin 5 8 5 +Buspirone 3 5 4 +Busulfan 5 8 3 +Butaclamol 5 8 2 +Butadienes 7 7 1 +Butanes 5 5 1 +Butanols 3 4 2 +Butanones 3 3 1 +Butea 8 8 1 +Buthionine Sulfoximine 6 6 2 +Butirosin Sulfate 4 4 1 +Butorphanol 4 5 4 +Butoxamine 5 5 1 +Butter 4 5 5 +Butterflies 10 10 1 +Buttermilk 3 6 11 +Buttocks 4 4 1 +Butylamines 3 6 2 +Butylated Hydroxyanisole 5 9 3 +Butylated Hydroxytoluene 8 8 1 +Butylene Glycols 4 4 1 +Butylhydroxybutylnitrosamine 4 4 1 +Butylscopolammonium Bromide 4 7 5 +Butyrate Response Factor 1 4 5 4 +Butyrates 4 4 2 +Butyric Acid 5 5 2 +Butyrivibrio 3 3 1 +Butyrivibrio fibrisolvens 4 4 1 +Butyrophenones 3 3 1 +Butyrophilins 5 5 3 +Butyryl-CoA Dehydrogenase 4 6 2 +Butyrylcholinesterase 7 7 1 +Butyrylthiocholine 5 7 3 +Buxaceae 7 7 1 +Buxus 8 8 1 +Byssinosis 3 5 3 +Byssochlamys 5 5 1 +Bystander Effect 3 3 1 +Byzantium 4 4 1 +c-Mer Tyrosine Kinase 6 9 4 +C-Peptide 7 7 2 +C-Reactive Protein 4 6 3 +C2 Domains 8 8 1 +C9orf72 Protein 6 6 2 +Ca(2+) Mg(2+)-ATPase 6 6 1 +CA-125 Antigen 5 6 5 +CA-19-9 Antigen 5 6 5 +CA1 Region, Hippocampal 6 9 2 +CA2 Region, Hippocampal 6 9 2 +CA3 Region, Hippocampal 6 9 2 +Cabergoline 5 5 2 +Cabo Verde 4 5 2 +Cacao 10 10 1 +Cachexia 5 6 2 +Caco-2 Cells 3 5 3 +Cacodylic Acid 3 3 1 +Cactaceae 9 9 1 +CADASIL 3 8 13 +Cadaver 4 4 1 +Cadaverine 5 5 2 +Cadherin 5 7 7 1 +Cadherin Related Proteins 4 6 3 +Cadherins 5 6 4 +Cadmium 4 4 3 +Cadmium Chloride 3 5 2 +Cadmium Compounds 2 2 1 +Cadmium Poisoning 4 4 1 +Cadmium Radioisotopes 4 4 1 +Caenorhabditis 9 9 1 +Caenorhabditis elegans 10 10 1 +Caenorhabditis elegans Proteins 4 4 1 +Caesalpinia 8 8 1 +Cafe-au-Lait Spots 4 4 2 +Caffeic Acids 5 5 1 +Caffeine 4 7 2 +Cajanus 8 8 1 +Calamus 8 8 1 +Calbindin 1 4 6 2 +Calbindin 2 6 6 1 +Calbindins 5 5 1 +Calcaneus 7 7 1 +Calceolariaceae 8 8 1 +Calcifediol 6 8 4 +Calcification, Physiologic 4 9 3 +Calcifying Nanoparticles 3 5 2 +Calcimimetic Agents 3 6 2 +Calcimycin 5 5 5 +Calcineurin 4 7 3 +Calcineurin Inhibitors 5 5 1 +Calcinosis 4 4 1 +Calcinosis Cutis 3 5 2 +Calciphylaxis 5 5 1 +Calcitonin 4 5 5 +Calcitonin Gene-Related Peptide 4 5 2 +Calcitonin Gene-Related Peptide Receptor Antagonists 4 7 3 +Calcitonin Receptor-Like Protein 6 6 1 +Calcitriol 7 9 4 +Calcium 3 4 3 +Calcium Aluminosilicate 5 7 4 +Calcium Carbonate 3 5 3 +Calcium Channel Agonists 5 6 3 +Calcium Channel Blockers 5 5 3 +Calcium Channels 6 6 3 +Calcium Channels, L-Type 7 7 3 +Calcium Channels, N-Type 7 7 3 +Calcium Channels, P-Type 8 8 3 +Calcium Channels, Q-Type 8 8 3 +Calcium Channels, R-Type 8 8 3 +Calcium Channels, T-Type 7 7 3 +Calcium Chelating Agents 5 6 2 +Calcium Chloride 3 5 2 +Calcium Citrate 3 7 2 +Calcium Compounds 2 2 1 +Calcium Dobesilate 7 8 2 +Calcium Fluoride 3 5 2 +Calcium Gluconate 4 6 3 +Calcium Hydroxide 3 6 3 +Calcium Ionophores 4 6 2 +Calcium Isotopes 3 5 3 +Calcium Metabolism Disorders 3 3 1 +Calcium Oxalate 7 7 1 +Calcium Phosphates 3 7 3 +Calcium Pyrophosphate 4 9 5 +Calcium Radioisotopes 4 6 4 +Calcium Release Activated Calcium Channels 7 7 3 +Calcium Signaling 4 5 3 +Calcium Sulfate 3 6 3 +Calcium, Dietary 3 3 1 +Calcium-Binding Proteins 4 4 1 +Calcium-Calmodulin-Dependent Protein Kinase Kinase 6 9 2 +Calcium-Calmodulin-Dependent Protein Kinase Type 1 6 9 2 +Calcium-Calmodulin-Dependent Protein Kinase Type 2 6 9 2 +Calcium-Calmodulin-Dependent Protein Kinase Type 4 6 9 2 +Calcium-Calmodulin-Dependent Protein Kinases 5 8 2 +Calcium-Regulating Hormones and Agents 4 4 1 +Calcium-Transporting ATPases 6 7 5 +Calculi 3 3 1 +Caldicellulosiruptor 3 3 1 +Calendar 2 2 1 +Calendars as Topic 5 5 1 +Calendula 8 8 1 +Calgranulin A 5 7 3 +Calgranulin B 5 7 3 +Calibration 3 3 1 +Calicheamicins 4 8 3 +Caliciviridae 4 4 1 +Caliciviridae Infections 4 4 1 +Calicivirus, Feline 6 6 1 +California 6 6 2 +Californium 4 6 5 +Calixarenes 3 3 1 +Call Centers 2 4 2 +Calla Plant 10 10 1 +Callicarpa 9 9 1 +Callicebus 11 11 1 +Callilepis 8 8 1 +Callimico 12 12 1 +Calliphoridae 10 10 1 +Callithrix 12 12 1 +Callitrichinae 11 11 1 +Callosities 4 4 1 +Calluna 9 9 1 +Callyspongia 5 5 1 +Calmodulin 5 6 3 +Calmodulin-Binding Proteins 4 4 1 +Calnexin 4 6 5 +Calophyllum 8 8 1 +Caloric Restriction 4 6 2 +Caloric Tests 5 5 1 +Calorimetry 3 3 1 +Calorimetry, Differential Scanning 4 4 2 +Calorimetry, Indirect 4 4 1 +Calotropis 9 9 1 +Calpain 7 7 2 +Calponins 5 5 4 +Calreticulin 4 6 4 +Calsequestrin 5 5 2 +Calycanthaceae 8 8 1 +Calymmatobacterium 5 5 2 +Calystegia 8 8 1 +Camallanina 8 8 1 +Camassia 10 10 1 +Cambendazole 5 5 1 +Cambium 4 5 2 +Cambodia 4 4 1 +Camelidae 8 8 1 +Camelids, New World 9 9 1 +Camellia 9 9 1 +Camellia sinensis 10 10 1 +Camelus 9 9 1 +Cameroon 5 5 1 +Campanulaceae 7 7 1 +Camphanes 5 7 4 +Camphor 3 8 5 +Camphor 5-Monooxygenase 4 7 3 +Camping 4 4 1 +Campomelic Dysplasia 3 4 2 +Camptotheca 8 8 1 +Camptothecin 3 3 1 +Campylobacter 3 6 2 +Campylobacter coli 4 7 2 +Campylobacter fetus 4 7 2 +Campylobacter hyointestinalis 4 7 2 +Campylobacter Infections 5 5 1 +Campylobacter jejuni 4 7 2 +Campylobacter lari 4 7 2 +Campylobacter rectus 4 7 2 +Campylobacter sputorum 4 7 2 +Campylobacter upsaliensis 4 7 2 +Campylobacteraceae 5 5 1 +Campylobacterales 4 4 1 +Camurati-Engelmann Syndrome 3 5 2 +Canada 4 4 1 +Canadian Longitudinal Study on Aging 7 7 1 +Canagliflozin 4 4 3 +Canaliculitis 4 4 1 +Cananga 8 8 1 +Canaries 9 9 1 +Canarypox virus 6 6 1 +Canavalia 8 8 1 +Canavan Disease 4 7 8 +Canavanine 3 3 1 +Cancellous Bone 4 4 1 +Cancer Care Facilities 5 5 1 +Cancer Pain 5 5 3 +Cancer Survivors 3 3 1 +Cancer Vaccines 4 4 1 +Cancer-Associated Fibroblasts 4 4 1 +Candicidin 5 5 1 +Candida 4 5 3 +Candida albicans 5 6 3 +Candida auris 6 6 1 +Candida glabrata 5 6 3 +Candida parapsilosis 5 6 3 +Candida tropicalis 5 6 3 +Candidemia 4 7 4 +Candidiasis 4 4 1 +Candidiasis, Chronic Mucocutaneous 4 5 5 +Candidiasis, Cutaneous 4 5 4 +Candidiasis, Invasive 5 5 2 +Candidiasis, Oral 3 5 2 +Candidiasis, Vulvovaginal 5 8 5 +Candy 3 4 2 +Canes 4 4 1 +Canidae 9 9 1 +Caniformia 8 8 1 +Cannabaceae 9 9 1 +Cannabidiol 5 5 1 +Cannabinoid Hyperemesis Syndrome 4 4 2 +Cannabinoid Receptor Agonists 6 7 2 +Cannabinoid Receptor Antagonists 6 7 2 +Cannabinoid Receptor Modulators 5 6 2 +Cannabinoids 4 4 1 +Cannabinol 5 5 1 +Cannabis 10 10 1 +Cannibalism 5 5 1 +Cannula 3 3 1 +Canonical Correlation Analysis 6 7 3 +Canrenoic Acid 6 6 1 +Canrenone 6 6 1 +Cantharidin 5 5 1 +Canthaxanthin 5 10 4 +Capacity Building 2 6 4 +Capecitabine 5 7 4 +Capgras Syndrome 3 3 1 +Capillaria 9 9 1 +Capillaries 4 4 1 +Capillary Action 3 3 1 +Capillary Electrochromatography 5 5 3 +Capillary Fragility 3 4 2 +Capillary Isoelectric Focusing 5 5 2 +Capillary Leak Syndrome 3 3 1 +Capillary Permeability 3 3 2 +Capillary Resistance 5 5 1 +Capillary Tubing 2 2 1 +Capital Expenditures 5 5 1 +Capital Financing 4 4 1 +Capital Punishment 4 4 1 +Capitalism 3 3 2 +Capitate Bone 7 7 1 +Capitation Fee 4 4 1 +Caplan Syndrome 3 5 6 +Capnocytophaga 4 5 2 +Capnography 5 5 1 +Capparaceae 7 7 1 +Capparis 8 8 1 +Capreomycin 4 4 2 +Caprifoliaceae 8 8 1 +Capripoxvirus 5 5 1 +Caproates 4 4 2 +Caprolactam 4 4 3 +Caprylates 3 4 2 +Capsaicin 4 8 5 +Capsella 8 8 1 +Capsicum 9 9 1 +Capsid 4 4 1 +Capsid Proteins 6 6 1 +Capsule Endoscopes 5 5 1 +Capsule Endoscopy 7 7 1 +Capsule Opacification 4 4 1 +Capsules 3 3 1 +Capsulorhexis 5 5 1 +Captan 5 5 1 +Captopril 6 6 1 +CapZ Actin Capping Protein 5 6 3 +Caragana 8 8 1 +Carbachol 5 6 2 +Carbadox 5 5 2 +Carbamates 4 4 1 +Carbamazepine 5 5 1 +Carbamide Peroxide 4 7 5 +Carbamoyl Phosphate Synthetase I Activators 5 5 1 +Carbamoyl-Phosphate Synthase (Ammonia) 5 5 1 +Carbamoyl-Phosphate Synthase (Glutamine-Hydrolyzing) 6 6 1 +Carbamoyl-Phosphate Synthase I Deficiency Disease 4 7 7 +Carbamyl Phosphate 4 5 2 +Carbanilides 4 7 4 +Carbapenem-Resistant Enterobacteriaceae 5 5 2 +Carbapenems 5 5 2 +Carbaryl 4 7 3 +Carbasugars 2 2 1 +Carbazilquinone 4 5 2 +Carbazoles 4 5 2 +Carbenicillin 6 7 3 +Carbenoxolone 7 7 1 +Carbidopa 3 11 3 +Carbimazole 5 5 1 +Carbocyanines 5 5 1 +Carbocysteine 4 5 3 +Carbodiimides 3 3 1 +Carbofuran 6 6 1 +Carbohydrate Binding Modules 8 8 1 +Carbohydrate Biochemistry 4 4 1 +Carbohydrate Conformation 5 5 1 +Carbohydrate Dehydrogenases 5 5 1 +Carbohydrate Epimerases 5 5 1 +Carbohydrate Metabolism 2 3 2 +Carbohydrate Metabolism, Inborn Errors 4 4 2 +Carbohydrate Sequence 4 6 2 +Carbohydrate Sulfotransferases 6 6 1 +Carbohydrates 1 1 1 +Carbolines 4 6 3 +Carbon 3 3 1 +Carbon Compounds, Inorganic 2 2 1 +Carbon Cycle 3 3 2 +Carbon Dioxide 3 4 3 +Carbon Disulfide 3 5 2 +Carbon Fiber 3 5 4 +Carbon Footprint 3 4 2 +Carbon Isotopes 3 4 2 +Carbon Monoxide 3 4 3 +Carbon Monoxide Poisoning 4 4 1 +Carbon Nanomaterials 4 4 1 +Carbon Quantum Dots 5 6 2 +Carbon Radioisotopes 4 5 3 +Carbon Sequestration 4 4 2 +Carbon Tetrachloride 5 5 1 +Carbon Tetrachloride Poisoning 3 3 1 +Carbon-13 Magnetic Resonance Spectroscopy 5 5 1 +Carbon-Carbon Double Bond Isomerases 5 5 1 +Carbon-Carbon Ligases 4 4 1 +Carbon-Carbon Lyases 4 4 1 +Carbon-Nitrogen Ligases 4 4 1 +Carbon-Nitrogen Ligases with Glutamine as Amide-N-Donor 5 5 1 +Carbon-Nitrogen Lyases 4 4 1 +Carbon-Oxygen Ligases 4 4 1 +Carbon-Oxygen Lyases 4 4 1 +Carbon-Sulfur Ligases 4 4 1 +Carbon-Sulfur Lyases 4 4 1 +Carbonated Beverages 3 4 2 +Carbonated Water 4 8 7 +Carbonates 3 5 3 +Carbonic Acid 3 4 2 +Carbonic Anhydrase I 7 7 1 +Carbonic Anhydrase II 7 7 1 +Carbonic Anhydrase III 7 7 1 +Carbonic Anhydrase Inhibitors 5 5 1 +Carbonic Anhydrase IV 6 7 5 +Carbonic Anhydrase IX 4 7 3 +Carbonic Anhydrase V 7 7 1 +Carbonic Anhydrases 6 6 1 +Carbonyl Cyanide m-Chlorophenyl Hydrazone 3 4 2 +Carbonyl Cyanide p-Trifluoromethoxyphenylhydrazone 3 4 2 +Carbonyl Reductase (NADPH) 6 6 1 +Carboplatin 3 3 1 +Carboprost 5 8 3 +Carboranes 4 4 2 +Carboxin 4 5 3 +Carboxy-Lyases 5 5 1 +Carboxyhemoglobin 5 6 2 +Carboxyl and Carbamoyl Transferases 5 5 1 +Carboxylesterase 6 6 1 +Carboxylic Acids 2 2 1 +Carboxylic Ester Hydrolases 5 5 1 +Carboxymethylcellulose Sodium 6 6 1 +Carboxypeptidase B 6 7 4 +Carboxypeptidase B2 6 7 4 +Carboxypeptidase H 6 7 4 +Carboxypeptidases 6 6 1 +Carboxypeptidases A 7 7 3 +Carbuncle 6 8 4 +Carbutamide 5 7 5 +Carcinoembryonic Antigen 4 6 6 +Carcinogenesis 3 4 2 +Carcinogenicity Tests 3 3 1 +Carcinogens 4 4 1 +Carcinogens, Environmental 5 5 1 +Carcinoid Heart Disease 3 8 4 +Carcinoid Tumor 6 6 3 +Carcinoma 4 4 1 +Carcinoma 256, Walker 3 6 3 +Carcinoma in Situ 5 5 1 +Carcinoma, Acinar Cell 6 6 1 +Carcinoma, Adenoid Cystic 6 6 1 +Carcinoma, Adenosquamous 4 5 2 +Carcinoma, Basosquamous 5 5 2 +Carcinoma, Bronchogenic 4 7 3 +Carcinoma, Brown-Pearce 3 3 1 +Carcinoma, Ductal 5 6 2 +Carcinoma, Ductal, Breast 4 7 4 +Carcinoma, Ehrlich Tumor 3 5 2 +Carcinoma, Embryonal 4 4 1 +Carcinoma, Endometrioid 5 8 9 +Carcinoma, Giant Cell 5 5 1 +Carcinoma, Hepatocellular 4 6 4 +Carcinoma, Intraductal, Noninfiltrating 5 7 3 +Carcinoma, Islet Cell 4 6 6 +Carcinoma, Krebs 2 3 5 2 +Carcinoma, Large Cell 5 5 1 +Carcinoma, Lewis Lung 3 5 2 +Carcinoma, Lobular 4 6 4 +Carcinoma, Medullary 5 7 4 +Carcinoma, Merkel Cell 4 7 5 +Carcinoma, Mucoepidermoid 5 6 2 +Carcinoma, Neuroendocrine 6 6 3 +Carcinoma, Non-Small-Cell Lung 5 8 3 +Carcinoma, Ovarian Epithelial 4 8 8 +Carcinoma, Pancreatic Ductal 4 7 7 +Carcinoma, Papillary 5 5 2 +Carcinoma, Papillary, Follicular 7 7 2 +Carcinoma, Renal Cell 5 7 9 +Carcinoma, Signet Ring Cell 5 6 2 +Carcinoma, Skin Appendage 5 6 2 +Carcinoma, Small Cell 5 5 1 +Carcinoma, Squamous Cell 5 5 2 +Carcinoma, Transitional Cell 5 5 1 +Carcinoma, Verrucous 5 5 2 +Carcinosarcoma 4 5 2 +CARD Signaling Adaptor Proteins 5 5 5 +Cardamine 8 8 1 +Cardanolides 4 4 1 +Cardenolides 6 6 1 +Cardia 5 5 1 +Cardiac Care Facilities 5 5 1 +Cardiac Catheterization 3 5 3 +Cardiac Catheters 4 4 1 +Cardiac Complexes, Premature 4 4 3 +Cardiac Conduction System Disease 3 3 1 +Cardiac Electrophysiology 5 5 3 +Cardiac Glycosides 3 5 2 +Cardiac Imaging Techniques 4 4 1 +Cardiac Myosins 7 9 4 +Cardiac Output 4 5 2 +Cardiac Output, High 3 3 2 +Cardiac Output, Low 3 3 2 +Cardiac Pacing, Artificial 3 3 1 +Cardiac Papillary Fibroelastoma 4 5 2 +Cardiac Rehabilitation 3 6 4 +Cardiac Resynchronization Therapy 4 4 1 +Cardiac Resynchronization Therapy Devices 5 5 1 +Cardiac Surgical Procedures 3 3 2 +Cardiac Tamponade 3 3 1 +Cardiac Valve Annuloplasty 4 4 2 +Cardiac Volume 4 4 1 +Cardiac-Gated Imaging Techniques 5 5 1 +Cardiac-Gated Single-Photon Emission Computer-Assisted Tomography 6 7 5 +Cardiidae 6 6 1 +Cardio Ankle Vascular Index 5 5 1 +Cardio-Oncology 5 5 2 +Cardio-Renal Syndrome 4 7 4 +Cardiobacteriaceae 4 4 2 +Cardiobacterium 5 5 2 +Cardiography, Impedance 5 6 2 +Cardiolipins 5 8 2 +Cardiologists 4 5 2 +Cardiology 4 4 1 +Cardiology Service, Hospital 6 6 2 +Cardiomegaly 3 4 2 +Cardiomegaly, Exercise-Induced 3 3 1 +Cardiometabolic Risk Factors 7 9 5 +Cardiomyopathies 3 3 1 +Cardiomyopathy, Alcoholic 4 5 2 +Cardiomyopathy, Dilated 4 4 3 +Cardiomyopathy, Hypertrophic 4 7 2 +Cardiomyopathy, Hypertrophic, Familial 3 8 3 +Cardiomyopathy, Restrictive 4 4 1 +Cardiomyoplasty 4 4 2 +Cardioplegic Solutions 4 5 4 +Cardiopulmonary Bypass 3 3 1 +Cardiopulmonary Resuscitation 4 4 1 +Cardiorespiratory Fitness 3 7 5 +Cardiotocography 5 5 3 +Cardiotonic Agents 4 5 2 +Cardiotoxicity 3 5 5 +Cardiotoxins 4 4 1 +Cardiovascular Abnormalities 2 3 2 +Cardiovascular Agents 4 4 1 +Cardiovascular Deconditioning 3 3 1 +Cardiovascular Diseases 1 1 1 +Cardiovascular Infections 2 2 2 +Cardiovascular Nursing 4 4 2 +Cardiovascular Physiological Phenomena 2 2 1 +Cardiovascular Surgical Procedures 2 2 1 +Cardiovascular System 1 1 1 +Cardiovirus 6 6 1 +Cardiovirus Infections 5 5 1 +Carduus 8 8 1 +Career Choice 6 6 1 +Career Mobility 4 4 2 +Caregiver Burden 5 5 1 +Caregivers 2 4 3 +Carex Plant 8 8 1 +Carfecillin 7 8 3 +Caribbean Netherlands 4 4 2 +Caribbean People 3 3 1 +Caribbean Region 3 3 1 +Carica 8 8 1 +Caricaceae 7 7 1 +Caricature 3 3 2 +Caricatures as Topic 3 3 1 +Cariogenic Agents 2 4 3 +Cariostatic Agents 2 5 5 +Carisoprodol 5 5 1 +Carlavirus 4 5 2 +Carmine 4 9 3 +Carmovirus 4 5 2 +Carmustine 4 5 2 +Carney Complex 4 6 5 +Carnitine 5 5 1 +Carnitine Acyltransferases 5 5 1 +Carnitine O-Acetyltransferase 6 6 2 +Carnitine O-Palmitoyltransferase 6 6 1 +Carnivora 7 7 1 +Carnivorous Plant 3 3 1 +Carnivory 4 5 3 +Carnobacteriaceae 4 4 2 +Carnobacterium 5 5 2 +Carnosine 4 5 3 +Caroli Disease 4 5 5 +Carotenoids 3 8 4 +Carotid Arteries 4 4 1 +Carotid Artery Diseases 4 5 2 +Carotid Artery Injuries 4 6 6 +Carotid Artery Thrombosis 5 6 4 +Carotid Artery, Common 5 5 1 +Carotid Artery, External 6 6 1 +Carotid Artery, Internal 6 6 1 +Carotid Artery, Internal, Dissection 5 7 7 +Carotid Body 6 7 3 +Carotid Body Tumor 8 8 2 +Carotid Intima-Media Thickness 3 6 3 +Carotid Sinus 5 5 1 +Carotid Stenosis 4 6 3 +Carotid Webs 5 6 3 +Carotid-Cavernous Sinus Fistula 5 7 13 +Carotid-Femoral Pulse Wave Velocity 5 5 1 +Carpal Bones 6 6 1 +Carpal Joints 5 5 1 +Carpal Tunnel Syndrome 4 6 3 +Carpometacarpal Joints 5 5 1 +Carps 8 8 1 +Carpus, Animal 3 3 1 +Carrageenan 3 3 1 +Carrier Proteins 3 3 1 +Carrier State 4 4 1 +Carteolol 6 6 4 +Carthamus 8 8 1 +Carthamus tinctorius 9 9 1 +Carticaine 4 4 2 +Cartilage 2 3 2 +Cartilage Diseases 2 3 2 +Cartilage Oligomeric Matrix Protein 5 5 1 +Cartilage, Articular 4 4 2 +Cartoon 3 3 2 +Cartoons as Topic 4 4 1 +Carubicin 6 9 3 +Carum 8 8 1 +Carvedilol 5 6 5 +Carya 10 10 1 +Caryophyllaceae 9 9 1 +Caryophyllales 8 8 1 +Caryophyllanae 7 7 1 +Case Management 5 5 1 +Case Managers 3 4 3 +Case Reports 2 2 1 +Case Reports as Topic 6 6 1 +Case-Control Studies 5 6 3 +Casearia 10 10 1 +Casein Kinase 1 epsilon 5 10 3 +Casein Kinase I 4 9 3 +Casein Kinase Ialpha 5 10 3 +Casein Kinase Idelta 5 10 3 +Casein Kinase II 6 9 2 +Casein Kinases 5 8 2 +Caseins 4 6 2 +Casimiroa 8 8 1 +CASP8 and FADD-Like Apoptosis Regulating Protein 6 6 5 +Caspase 1 7 9 3 +Caspase 10 7 9 3 +Caspase 12 7 9 3 +Caspase 14 7 9 3 +Caspase 2 7 9 3 +Caspase 3 7 9 3 +Caspase 6 7 9 3 +Caspase 7 7 9 3 +Caspase 8 6 9 6 +Caspase 9 6 9 6 +Caspase Activation and Recruitment Domain 10 10 1 +Caspase Inhibitors 7 7 1 +Caspases 5 7 3 +Caspases, Effector 6 8 3 +Caspases, Initiator 6 8 3 +Caspian Sea 4 4 1 +Caspofungin 3 5 3 +Cassia 8 8 1 +Castanospermum 8 8 1 +Castleman Disease 4 4 2 +Castor Oil 4 5 3 +Castration 3 3 2 +Casts, Surgical 6 6 2 +Casuistry 4 6 2 +Cat Diseases 2 2 1 +Cat's Claw 10 10 1 +Cat-Scratch Disease 4 7 2 +Catabolite Repression 2 3 3 +Catalase 5 5 1 +Catalepsy 4 5 2 +Catalog 2 2 1 +Catalog, Bookseller 4 4 1 +Catalog, Commercial 3 3 1 +Catalog, Drug 4 4 1 +Catalog, Publisher 4 4 1 +Catalog, Union 3 3 1 +Cataloging 5 5 2 +Catalogs as Topic 5 5 1 +Catalogs, Commercial as Topic 6 6 1 +Catalogs, Drug as Topic 6 6 1 +Catalogs, Library 6 6 1 +Catalogs, Union as Topic 6 6 1 +Catalysis 2 2 1 +Catalytic Domain 5 8 2 +Cataplexy 7 7 2 +Cataract 3 3 1 +Cataract Extraction 4 4 1 +Catarrhini 9 9 1 +Catastrophic Illness 4 4 1 +Catastrophization 3 4 2 +Catatonia 3 5 4 +Catchment Area, Health 4 6 3 +Catechin 6 7 4 +Catechol 1,2-Dioxygenase 6 6 1 +Catechol 2,3-Dioxygenase 6 6 1 +Catechol O-Methyltransferase 6 6 1 +Catechol O-Methyltransferase Inhibitors 5 7 2 +Catechol Oxidase 6 6 1 +Catecholamine Plasma Membrane Transport Proteins 7 7 2 +Catecholamines 3 8 2 +Catechols 7 7 1 +Catenanes 2 2 1 +Catenins 4 4 1 +Catfishes 6 6 1 +Catgut 5 5 1 +Catha 10 10 1 +Catharanthus 9 9 1 +Catharsis 5 5 1 +Cathartics 5 5 1 +Cathelicidins 4 6 3 +Cathepsin A 6 7 3 +Cathepsin B 6 7 3 +Cathepsin C 6 7 3 +Cathepsin D 6 7 3 +Cathepsin E 6 7 3 +Cathepsin F 6 7 3 +Cathepsin G 6 7 3 +Cathepsin H 6 7 5 +Cathepsin K 6 7 3 +Cathepsin L 6 7 3 +Cathepsin W 6 7 3 +Cathepsin Z 6 7 3 +Cathepsins 5 5 1 +Catheter Ablation 4 4 2 +Catheter Obstruction 3 3 2 +Catheter-Related Infections 2 2 1 +Catheterization 2 2 2 +Catheterization, Central Venous 3 5 4 +Catheterization, Peripheral 3 5 4 +Catheterization, Swan-Ganz 4 6 7 +Catheters 2 2 1 +Catheters, Indwelling 3 3 1 +Cathexis 4 4 1 +Cathode Ray Tube 3 3 1 +Catholicism 4 4 1 +Cation Exchange Resins 5 5 1 +Cation Transport Proteins 6 6 2 +Cationic Amino Acid Transporter 1 7 8 4 +Cationic Amino Acid Transporter 2 7 8 4 +Cations 4 4 1 +Cations, Divalent 5 5 1 +Cations, Monovalent 5 5 1 +Cats 11 11 1 +Cattell Personality Factor Questionnaire 5 5 1 +Cattle 9 9 1 +Cattle Diseases 2 2 1 +Cauda Equina 6 6 1 +Cauda Equina Syndrome 5 6 2 +Caudata 6 6 1 +Caudate Nucleus 10 10 1 +Caudovirales 3 3 2 +Caulerpa 4 4 1 +Caulimoviridae 3 3 2 +Caulimovirus 4 4 3 +Caulobacter 4 6 3 +Caulobacter crescentus 5 7 3 +Caulobacteraceae 4 5 2 +Caulophyllum 8 8 1 +Causalgia 4 5 3 +Causality 4 4 2 +Cause of Death 5 7 4 +Caustics 3 4 2 +Cautery 2 3 2 +Caveolae 6 10 3 +Caveolin 1 4 6 5 +Caveolin 2 4 6 5 +Caveolin 3 5 6 2 +Caveolins 5 5 1 +Cavernous Sinus 5 5 1 +Cavernous Sinus Syndromes 4 4 1 +Cavernous Sinus Thrombosis 5 8 4 +Caves 3 4 3 +CCAAT-Binding Factor 5 6 3 +CCAAT-Enhancer-Binding Protein-alpha 5 6 3 +CCAAT-Enhancer-Binding Protein-beta 5 6 3 +CCAAT-Enhancer-Binding Protein-delta 5 6 3 +CCAAT-Enhancer-Binding Proteins 4 5 3 +CCCTC-Binding Factor 4 5 6 +CCN Intercellular Signaling Proteins 3 5 4 +CCR5 Receptor Antagonists 4 8 2 +CD-I 6 9 7 +CD-ROM 6 9 7 +CD11 Antigens 5 6 4 +CD11a Antigen 6 8 5 +CD11b Antigen 6 8 6 +CD11c Antigen 6 9 11 +CD13 Antigens 5 7 5 +CD146 Antigen 5 6 6 +CD163 Antigen 5 9 4 +CD18 Antigens 5 9 9 +CD2 Antigens 5 6 2 +CD24 Antigen 4 6 9 +CD27 Ligand 4 6 6 +CD28 Antigens 5 7 3 +CD3 Complex 5 6 2 +CD30 Ligand 5 6 6 +CD36 Antigens 5 9 9 +CD4 Antigens 5 9 4 +CD4 Immunoadhesins 5 8 12 +CD4 Lymphocyte Count 6 9 7 +CD4-CD8 Ratio 2 10 8 +CD4-Positive T-Lymphocytes 7 8 3 +CD40 Antigens 4 8 4 +CD40 Ligand 5 6 6 +CD47 Antigen 5 5 3 +CD48 Antigen 6 7 8 +CD5 Antigens 5 6 4 +CD52 Antigen 4 6 6 +CD55 Antigens 6 6 4 +CD56 Antigen 5 8 6 +CD57 Antigens 5 6 2 +CD58 Antigens 5 5 3 +CD59 Antigens 6 6 4 +CD68 Molecule 5 9 9 +CD69 Antigens 5 6 3 +CD79 Antigens 7 8 3 +CD8 Antigens 5 6 2 +CD8-Positive T-Lymphocytes 7 8 3 +CD83 Antigen 5 6 8 +CDC2 Protein Kinase 4 11 7 +CDC2-CDC28 Kinases 5 10 3 +Cdc20 Proteins 5 7 2 +cdc25 Phosphatases 4 8 6 +CDC28 Protein Kinase, S cerevisiae 5 11 4 +cdc42 GTP-Binding Protein 7 9 3 +cdc42 GTP-Binding Protein, Saccharomyces cerevisiae 5 9 4 +Cdh1 Proteins 5 7 2 +CDP-Diacylglycerol-Inositol 3-Phosphatidyltransferase 6 6 1 +CDPdiacylglycerol-Serine O-Phosphatidyltransferase 6 6 1 +CDX2 Transcription Factor 4 5 3 +CEACAM1 Protein 5 6 4 +Ceanothus 10 10 1 +Cebidae 10 10 1 +Cebinae 11 11 1 +Cebus 12 12 1 +Cebus capucinus 13 13 1 +Cecal Diseases 4 4 1 +Cecal Neoplasms 5 6 5 +Cecostomy 4 4 2 +Cecropia Plant 10 10 1 +Cecropins 4 6 3 +Cecum 5 5 2 +Cedrela 8 8 1 +Cedrus 8 8 1 +Cefaclor 6 7 3 +Cefadroxil 6 7 3 +Cefamandole 5 6 3 +Cefatrizine 7 8 3 +Cefazolin 5 6 3 +Cefdinir 5 6 3 +Cefepime 5 6 3 +Cefiderocol 5 6 3 +Cefixime 7 8 3 +Cefmenoxime 7 8 3 +Cefmetazole 6 7 3 +Cefonicid 5 6 3 +Cefoperazone 6 7 3 +Cefotaxime 6 7 3 +Cefotetan 5 7 4 +Cefotiam 7 8 3 +Cefoxitin 6 7 3 +Cefozopran 5 6 4 +Cefpirome 5 6 3 +Cefpodoxime 5 6 3 +Cefpodoxime Proxetil 6 7 3 +Cefprozil 5 6 3 +Cefsulodin 5 6 3 +Ceftaroline 5 6 3 +Ceftazidime 6 7 3 +Ceftibuten 5 6 3 +Ceftizoxime 7 8 3 +Ceftriaxone 7 8 3 +Cefuroxime 5 6 3 +Ceiba 10 10 1 +Celastraceae 9 9 1 +Celastrales 8 8 1 +Celastrus 10 10 1 +Celecoxib 5 7 5 +CELF Proteins 6 6 2 +CELF1 Protein 7 7 2 +Celiac Artery 4 4 1 +Celiac Disease 4 5 2 +Celiac Plexus 5 5 2 +Celiprolol 5 7 5 +Cell Adhesion 2 2 1 +Cell Adhesion Molecule-1 5 6 4 +Cell Adhesion Molecules 4 5 4 +Cell Adhesion Molecules, Neuron-Glia 6 7 4 +Cell Adhesion Molecules, Neuronal 5 6 4 +Cell Aggregation 3 3 1 +Cell Biology 4 4 2 +Cell Body 3 3 1 +Cell Communication 2 2 1 +Cell Compartmentation 2 2 1 +Cell Competition 2 2 1 +Cell Count 2 5 4 +Cell Culture Techniques 3 5 4 +Cell Culture Techniques, Three Dimensional 4 6 4 +Cell Cycle 2 2 1 +Cell Cycle Checkpoints 3 3 1 +Cell Cycle Proteins 3 3 1 +Cell Death 2 2 1 +Cell Death Reversal 3 3 1 +Cell Dedifferentiation 2 2 1 +Cell Degranulation 3 3 1 +Cell Differentiation 2 2 1 +Cell Division 2 6 4 +Cell Encapsulation 4 4 1 +Cell Engineering 4 4 2 +Cell Enlargement 3 5 2 +Cell Extracts 3 3 1 +Cell Fractionation 3 3 1 +Cell Fusion 2 3 2 +Cell Growth Processes 2 4 2 +Cell Hypoxia 3 3 2 +Cell Line 3 3 1 +Cell Line Authentication 4 5 2 +Cell Line, Transformed 4 4 1 +Cell Line, Tumor 4 4 2 +Cell Lineage 2 6 4 +Cell Membrane 3 3 1 +Cell Membrane Permeability 2 3 2 +Cell Membrane Structures 4 4 1 +Cell Migration Assays 3 5 6 +Cell Migration Assays, Leukocyte 4 6 6 +Cell Migration Assays, Macrophage 4 6 6 +Cell Migration Inhibition 3 3 1 +Cell Movement 2 4 2 +Cell Nucleolus 7 7 1 +Cell Nucleus 4 7 2 +Cell Nucleus Division 3 4 2 +Cell Nucleus Shape 3 3 1 +Cell Nucleus Size 3 3 1 +Cell Nucleus Structures 5 5 1 +Cell Phone 6 6 1 +Cell Phone Use 4 4 2 +Cell Physiological Phenomena 1 1 1 +Cell Plasticity 3 3 1 +Cell Polarity 2 2 1 +Cell Proliferation 3 5 2 +Cell Respiration 2 2 2 +Cell Self Renewal 3 7 4 +Cell Separation 3 5 3 +Cell Shape 2 2 1 +Cell Size 2 2 1 +Cell Surface Display Techniques 5 5 2 +Cell Surface Extensions 3 3 1 +Cell Survival 2 2 1 +Cell Tracking 3 5 4 +Cell Transdifferentiation 2 2 1 +Cell Transformation, Neoplastic 4 5 2 +Cell Transformation, Viral 3 6 3 +Cell Transplantation 3 4 2 +Cell Wall 3 3 1 +Cell Wall Skeleton 4 5 4 +Cell- and Tissue-Based Therapy 3 3 1 +Cell-Derived Microparticles 5 5 1 +Cell-Free Nucleic Acids 3 3 1 +Cell-Free System 4 4 1 +Cell-in-Cell Formation 2 2 1 +Cell-Matrix Junctions 5 5 1 +Cell-Penetrating Peptides 3 3 1 +Cellobiose 4 5 3 +Cellophane 3 7 3 +Cells 1 1 1 +Cells, Cultured 2 2 1 +Cells, Immobilized 2 2 1 +Cellular Apoptosis Susceptibility Protein 4 6 3 +Cellular Automata 3 4 3 +Cellular Microenvironment 2 2 1 +Cellular Neural Networks, Computer 3 6 2 +Cellular Reprogramming 2 3 2 +Cellular Reprogramming Techniques 3 4 3 +Cellular Senescence 2 4 2 +Cellular Structures 2 2 1 +Cellulase 7 7 1 +Cellulases 6 6 1 +Cellulite 4 4 1 +Cellulitis 3 5 4 +Cellulomonas 4 4 1 +Cellulose 4 6 4 +Cellulose 1,4-beta-Cellobiosidase 7 7 1 +Cellulose, Oxidized 5 6 2 +Cellulosomes 4 4 1 +Cellvibrio 5 6 2 +Celosia 10 10 1 +Cementation 2 3 2 +Cementogenesis 6 6 1 +Cementoma 4 4 1 +Cementoplasty 3 3 2 +Cemeteries 2 7 2 +Cenchrus 8 8 1 +Censorship, Research 4 6 3 +Census Tract 6 7 2 +Censuses 3 5 3 +Centaurea 8 8 1 +Centaurea benedicta 9 9 1 +Centaurium 9 9 1 +Centchroman 6 6 2 +Centella 8 8 1 +Centenarians 6 6 1 +Centers for Disease Control and Prevention, U.S. 7 8 2 +Centers for Medicare and Medicaid Services, U.S. 6 7 2 +Central African People 5 5 1 +Central African Republic 5 5 1 +Central America 3 3 1 +Central American People 3 3 1 +Central Amygdaloid Nucleus 6 9 2 +Central Asian People 4 4 1 +Central Cord Syndrome 3 5 3 +Central Nervous System 2 2 1 +Central Nervous System Agents 4 4 1 +Central Nervous System Bacterial Infections 3 4 3 +Central Nervous System Cysts 3 5 4 +Central Nervous System Depressants 4 5 2 +Central Nervous System Diseases 2 2 1 +Central Nervous System Fungal Infections 3 4 3 +Central Nervous System Helminthiasis 4 5 3 +Central Nervous System Infections 2 3 2 +Central Nervous System Neoplasms 3 4 2 +Central Nervous System Parasitic Infections 3 4 3 +Central Nervous System Protozoal Infections 4 5 4 +Central Nervous System Sensitization 3 3 1 +Central Nervous System Stimulants 4 5 2 +Central Nervous System Vascular Malformations 3 5 4 +Central Nervous System Venous Angioma 4 5 4 +Central Nervous System Viral Diseases 3 4 3 +Central Pattern Generators 3 3 1 +Central Serous Chorioretinopathy 3 3 1 +Central Supply, Hospital 6 6 2 +Central Tolerance 5 5 1 +Central Venous Catheters 4 4 1 +Central Venous Pressure 6 6 1 +Centralized Hospital Services 5 5 2 +Centric Relation 3 3 1 +Centrifugation 2 2 1 +Centrifugation, Density Gradient 4 4 2 +Centrifugation, Isopycnic 5 5 2 +Centrifugation, Zonal 5 5 2 +Centrioles 9 10 2 +Centromere 4 9 2 +Centromere Protein A 4 5 6 +Centromere Protein B 4 5 6 +Centrosomal Associated Proteins 4 4 1 +Centrosome 8 9 2 +Cephacetrile 5 6 3 +Cephaelis 9 9 1 +Cephalexin 5 6 3 +Cephalochordata 5 5 2 +Cephaloglycin 6 7 3 +Cephalometry 3 6 3 +Cephalopelvic Disproportion 5 5 1 +Cephalopoda 5 5 1 +Cephaloridine 5 6 3 +Cephalosporin Resistance 5 8 3 +Cephalosporinase 6 6 1 +Cephalosporins 4 5 3 +Cephalotaxus 8 8 1 +Cephalothin 6 7 3 +Cephamycins 5 6 3 +Cephapirin 6 7 3 +Cephradine 6 7 3 +Ceramics 3 3 1 +Ceramidases 5 5 1 +Ceramides 3 6 4 +Cerastes 8 10 3 +Ceratitis capitata 11 11 1 +Ceratocystis 4 4 1 +Ceratopogonidae 12 12 1 +Cercaria 4 7 2 +Cerclage, Cervical 3 3 1 +Cercocebus 12 12 1 +Cercocebus atys 13 13 1 +Cercopithecidae 10 10 1 +Cercopithecinae 11 11 1 +Cercopithecus 12 12 1 +Cercospora 4 4 1 +Cercozoa 3 3 1 +Cerebellar Ataxia 5 6 3 +Cerebellar Cognitive Affective Syndrome 5 5 1 +Cerebellar Cortex 8 8 1 +Cerebellar Diseases 4 4 1 +Cerebellar Golgi Cells 4 9 3 +Cerebellar Neoplasms 5 7 4 +Cerebellar Nuclei 8 8 1 +Cerebellar Vermis 9 9 1 +Cerebellopontine Angle 8 8 1 +Cerebellum 7 7 1 +Cerebral Amyloid Angiopathy 5 7 3 +Cerebral Amyloid Angiopathy, Familial 5 8 12 +Cerebral Angiography 4 6 6 +Cerebral Aqueduct 5 8 2 +Cerebral Arterial Diseases 5 6 2 +Cerebral Arteries 4 4 1 +Cerebral Blood Volume 4 5 2 +Cerebral Cortex 7 7 1 +Cerebral Cortical Thinning 3 4 3 +Cerebral Crus 7 7 1 +Cerebral Decortication 3 3 1 +Cerebral Hemorrhage 5 6 3 +Cerebral Hemorrhage, Traumatic 6 8 9 +Cerebral Infarction 6 7 6 +Cerebral Intraventricular Hemorrhage 6 7 3 +Cerebral Palsy 5 5 1 +Cerebral Peduncle 6 6 1 +Cerebral Phaeohyphomycosis 5 6 3 +Cerebral Revascularization 3 5 2 +Cerebral Small Vessel Diseases 4 5 2 +Cerebral Veins 4 4 1 +Cerebral Ventricle Neoplasms 5 6 3 +Cerebral Ventricles 4 4 1 +Cerebral Ventriculitis 4 5 4 +Cerebral Ventriculography 4 6 4 +Cerebroside-Sulfatase 7 7 1 +Cerebrosides 4 7 4 +Cerebrospinal Fluid 4 4 1 +Cerebrospinal Fluid Leak 3 4 4 +Cerebrospinal Fluid Otorrhea 4 5 4 +Cerebrospinal Fluid Pressure 3 3 1 +Cerebrospinal Fluid Proteins 3 3 1 +Cerebrospinal Fluid Rhinorrhea 4 5 5 +Cerebrospinal Fluid Shunts 3 3 2 +Cerebrovascular Circulation 4 4 1 +Cerebrovascular Disorders 3 4 2 +Cerebrovascular Trauma 3 5 4 +Cerebrum 6 6 1 +Ceremonial Behavior 4 5 2 +Ceriodaphnia dubia 7 7 1 +Cerium 5 5 2 +Cerium Isotopes 3 6 3 +Cerium Radioisotopes 4 7 4 +Cermet Cements 4 7 7 +Ceroid 2 3 2 +Certificate of Need 4 4 1 +Certification 4 4 2 +Certolizumab Pegol 4 9 8 +Cerulenin 3 3 1 +Ceruletide 4 4 1 +Ceruloplasmin 4 6 6 +Cerumen 3 3 1 +Cerumenolytic Agents 4 5 3 +Cervical Atlas 6 6 1 +Cervical Cord 4 4 1 +Cervical Dizziness 6 6 2 +Cervical Length Measurement 6 6 2 +Cervical Plexus 5 5 1 +Cervical Plexus Block 5 5 1 +Cervical Rib 6 6 1 +Cervical Rib Syndrome 4 6 3 +Cervical Ripening 6 6 1 +Cervical Vertebrae 5 5 1 +Cervicoplasty 3 3 1 +Cervix Mucus 4 4 1 +Cervix Uteri 5 5 1 +Cesarean Section 4 4 1 +Cesarean Section, Repeat 5 5 1 +Cesium 4 4 4 +Cesium Isotopes 3 5 5 +Cesium Radioisotopes 4 6 6 +Cestoda 6 6 1 +Cestode Infections 4 4 1 +Cestrum 9 9 1 +Cetacea 7 7 1 +Cetirizine 5 5 1 +Cetomacrogol 4 6 4 +Cetrimonium 5 6 2 +Cetrimonium Compounds 4 5 2 +Cetuximab 9 9 3 +Cetylpyridinium 5 5 1 +Cevanes 4 4 2 +cGAS-STING Signaling Pathway 3 4 2 +Chad 5 5 1 +ChAdOx1 nCoV-19 6 7 2 +Chaetomium 5 5 1 +Chagas Cardiomyopathy 4 7 3 +Chagas Disease 3 6 2 +Chain of Infection 4 4 1 +Chalazion 3 3 2 +Chalcogens 3 3 1 +Chalcone 5 8 2 +Chalcones 4 7 3 +Chalones 4 4 1 +Chamaecrista 8 8 1 +Chamaecyparis 8 8 1 +Chamaemelum 8 8 1 +Chamomile 8 8 1 +Chancre 7 8 2 +Chancroid 4 7 5 +Change Management 4 4 1 +Channa punctata 6 6 1 +Channel Islands 4 4 1 +Channelopathies 3 3 1 +Channelrhodopsins 5 6 4 +Chaperone-Mediated Autophagy 3 3 1 +Chaperonin 10 7 7 1 +Chaperonin 60 7 8 2 +Chaperonin Containing TCP-1 7 8 2 +Chaperonins 5 6 2 +Chaplaincy Service, Hospital 6 6 2 +Chara 6 6 1 +Characeae 5 5 1 +Characidae 7 7 1 +Characiformes 6 6 1 +Character 3 3 1 +Charadriiformes 6 6 1 +Charcoal 4 4 1 +Charcot-Marie-Tooth Disease 4 6 5 +CHARGE Syndrome 3 9 9 +Charities 3 4 2 +Charles Bonnet Syndrome 5 7 3 +Charophyceae 4 4 1 +Chart 2 2 2 +Charybdotoxin 3 6 3 +Checklist 5 5 1 +Checkpoint Kinase 1 5 8 2 +Checkpoint Kinase 2 5 8 2 +Chediak-Higashi Syndrome 4 5 5 +Cheek 2 4 2 +Cheese 4 6 5 +Cheilitis 4 4 1 +Cheirogaleidae 9 9 1 +Chelating Agents 4 5 2 +Chelation Therapy 3 3 1 +Chelidonium 9 9 1 +Chelidonium majus 10 10 1 +Chemexfoliation 3 4 2 +Chemical Actions and Uses 1 1 1 +Chemical and Drug Induced Liver Injury 3 3 3 +Chemical and Drug Induced Liver Injury, Chronic 4 5 4 +Chemical Engineering 3 3 1 +Chemical Fractionation 3 3 1 +Chemical Hazard Release 4 4 1 +Chemical Industry 4 4 1 +Chemical Phenomena 1 1 1 +Chemical Precipitation 2 3 2 +Chemical Safety 6 6 1 +Chemical Terrorism 6 7 3 +Chemical Warfare 6 6 1 +Chemical Warfare Agents 4 5 3 +Chemically-Induced Disorders 1 1 1 +Cheminformatics 3 3 2 +Chemistry 2 2 1 +Chemistry Techniques, Analytical 2 2 1 +Chemistry Techniques, Synthetic 2 4 2 +Chemistry, Agricultural 3 3 1 +Chemistry, Analytic 3 3 1 +Chemistry, Bioinorganic 4 4 3 +Chemistry, Clinical 3 3 1 +Chemistry, Inorganic 3 3 1 +Chemistry, Organic 3 3 1 +Chemistry, Pharmaceutical 3 4 2 +Chemistry, Physical 3 3 1 +Chemoautotrophic Growth 3 4 2 +Chemoembolization, Therapeutic 4 4 2 +Chemogenetics 3 3 1 +Chemokine CCL1 5 7 5 +Chemokine CCL11 5 7 5 +Chemokine CCL17 5 7 5 +Chemokine CCL18 5 7 10 +Chemokine CCL19 5 7 10 +Chemokine CCL2 6 8 5 +Chemokine CCL20 5 7 10 +Chemokine CCL21 5 7 5 +Chemokine CCL22 5 7 5 +Chemokine CCL24 5 7 5 +Chemokine CCL26 5 7 5 +Chemokine CCL27 5 7 5 +Chemokine CCL3 5 7 10 +Chemokine CCL4 5 7 9 +Chemokine CCL5 5 7 5 +Chemokine CCL7 6 8 5 +Chemokine CCL8 6 8 5 +Chemokine CX3CL1 4 7 6 +Chemokine CXCL1 5 7 6 +Chemokine CXCL10 5 7 5 +Chemokine CXCL11 5 7 5 +Chemokine CXCL12 5 7 5 +Chemokine CXCL13 5 7 5 +Chemokine CXCL16 5 9 9 +Chemokine CXCL2 5 7 10 +Chemokine CXCL5 5 7 5 +Chemokine CXCL6 5 7 5 +Chemokine CXCL9 5 7 5 +Chemokine Receptor D6 8 9 2 +Chemokines 3 5 5 +Chemokines, C 4 6 5 +Chemokines, CC 4 6 5 +Chemokines, CX3C 4 6 5 +Chemokines, CXC 4 6 5 +Chemometrics 3 4 3 +Chemoprevention 3 3 1 +Chemoradiotherapy 3 3 3 +Chemoradiotherapy, Adjuvant 4 4 3 +Chemoreceptor Cells 4 5 3 +Chemosterilants 4 5 2 +Chemotactic Factors 2 2 1 +Chemotactic Factors, Eosinophil 3 3 1 +Chemotaxis 3 6 5 +Chemotaxis, Leukocyte 4 4 1 +Chemotherapy, Adjuvant 3 3 2 +Chemotherapy, Cancer, Regional Perfusion 3 4 2 +Chemotherapy-Induced Febrile Neutropenia 8 8 2 +Chemotherapy-Related Cognitive Impairment 3 5 2 +Chemsex 4 4 1 +Chenodeoxycholic Acid 7 7 2 +Chenopodiaceae 7 7 1 +Chenopodium 10 10 1 +Chenopodium album 11 11 1 +Chenopodium ambrosioides 11 11 1 +Chenopodium quinoa 11 11 1 +Chernobyl Nuclear Accident 5 5 2 +Cherubism 3 6 5 +Chest Pain 5 5 3 +Chest Tubes 3 3 1 +Chest Wall Oscillation 3 3 1 +Chewing Gum 4 5 5 +Cheyne-Stokes Respiration 3 4 2 +Chi-Square Distribution 3 6 4 +Chiari-Frommel Syndrome 6 7 2 +Chicago 3 7 3 +Chick Embryo 3 3 2 +Chicken anemia virus 5 5 1 +Chickenpox 6 6 1 +Chickenpox Vaccine 6 6 1 +Chickens 7 7 2 +Chief Cells, Gastric 3 6 3 +Chief Executive Officers, Hospital 5 6 5 +Chikungunya Fever 4 6 4 +Chikungunya virus 6 6 1 +Chilaiditi Syndrome 5 5 1 +Chilblains 3 4 2 +Child 3 3 1 +Child Abuse 6 6 2 +Child Abuse, Sexual 5 7 3 +Child Advocacy 4 5 3 +Child Behavior 3 3 1 +Child Behavior Disorders 3 3 1 +Child Care 3 5 2 +Child Custody 5 5 1 +Child Day Care Centers 2 4 2 +Child Development 3 4 2 +Child Development Disorders, Pervasive 3 3 1 +Child Guidance 3 4 2 +Child Guidance Clinics 5 5 1 +Child Health 3 3 1 +Child Health Services 4 4 1 +Child Labor 4 4 2 +Child Language 5 5 1 +Child Mortality 5 7 4 +Child Nutrition Disorders 3 3 1 +Child Nutrition Sciences 3 3 1 +Child Nutritional Physiological Phenomena 4 4 1 +Child of Impaired Parents 2 2 1 +Child Poverty 5 5 1 +Child Protective Services 4 4 2 +Child Psychiatry 4 4 2 +Child Reactive Disorders 4 4 1 +Child Rearing 2 2 1 +Child Restraint Systems 3 4 3 +Child Welfare 4 4 1 +Child, Abandoned 2 2 1 +Child, Adopted 2 2 1 +Child, Exceptional 2 4 2 +Child, Foster 2 2 1 +Child, Gifted 3 5 2 +Child, Hospitalized 3 3 1 +Child, Institutionalized 3 3 1 +Child, Orphaned 2 2 1 +Child, Preschool 4 4 1 +Child, Unwanted 2 2 1 +Childhood-Onset Fluency Disorder 4 4 1 +Children with Disabilities 3 3 1 +Children's Health Insurance Program 5 6 2 +Chile 4 4 1 +Chills 3 3 1 +Chilopoda 5 5 1 +Chimera 2 2 1 +Chimerin 1 5 7 3 +Chimerin Proteins 4 6 3 +Chimerism 5 5 1 +Chin 4 8 3 +China 4 4 1 +Chinchilla 8 8 1 +Chironomidae 12 12 1 +Chiropractic 2 2 1 +Chiroptera 7 7 1 +Chitin 3 4 2 +Chitin Synthase 7 7 1 +Chitinase-3-Like Protein 1 4 6 2 +Chitinases 5 5 1 +Chitosan 4 5 2 +Chive 11 11 1 +Chlamydia 5 5 1 +Chlamydia Infections 4 6 5 +Chlamydia muridarum 6 6 1 +Chlamydia trachomatis 6 6 1 +Chlamydiaceae 4 4 1 +Chlamydiaceae Infections 5 5 1 +Chlamydial Pneumonia 5 7 6 +Chlamydiales 3 3 1 +Chlamydomonas 4 4 1 +Chlamydomonas reinhardtii 5 5 1 +Chlamydophila 5 5 1 +Chlamydophila Infections 6 6 1 +Chlamydophila pneumoniae 6 6 1 +Chlamydophila psittaci 6 6 1 +Chloracne 4 4 1 +Chloral Hydrate 5 5 1 +Chloralose 4 6 2 +Chlorambucil 6 6 1 +Chloramines 3 3 2 +Chloramphenicol 4 7 3 +Chloramphenicol O-Acetyltransferase 6 6 1 +Chloramphenicol Resistance 4 7 3 +Chloranil 4 4 1 +Chlorates 3 5 2 +Chlordan 5 5 1 +Chlordecone 5 5 1 +Chlordiazepoxide 6 6 1 +Chlorella 4 4 1 +Chlorella vulgaris 5 5 1 +Chlorfenvinphos 4 4 1 +Chlorhexidine 5 5 1 +Chloride Channel Agonists 5 5 1 +Chloride Channels 6 6 3 +Chloride Peroxidase 5 5 1 +Chloride-Bicarbonate Antiporters 6 7 8 +Chlorides 4 5 2 +Chlorine 3 4 2 +Chlorine Compounds 2 2 1 +Chlorisondamine 4 5 3 +Chlormadinone Acetate 5 6 2 +Chlormequat 4 5 2 +Chlormerodrin 5 5 1 +Chlormethiazole 4 5 2 +Chlormezanone 4 4 2 +Chloroacetates 5 5 2 +Chlorobenzenes 5 6 2 +Chlorobenzoates 5 7 2 +Chlorobi 2 2 1 +Chlorobium 3 5 2 +Chlorobutanol 4 5 3 +Chlorocebus aethiops 13 13 1 +Chlorodiphenyl (54% Chlorine) 5 7 3 +Chloroflexi 2 2 1 +Chloroflexus 3 3 2 +Chlorofluorocarbons 5 5 2 +Chlorofluorocarbons, Ethane 6 6 2 +Chlorofluorocarbons, Methane 6 6 2 +Chloroform 5 5 2 +Chlorogenic Acid 5 5 2 +Chlorohydrins 3 3 1 +Chloromercuribenzoates 6 8 5 +Chloromercurinitrophenols 5 5 1 +Chlorophenols 7 7 2 +Chlorophyceae 4 4 1 +Chlorophyll 4 6 3 +Chlorophyll A 5 7 3 +Chlorophyll Binding Proteins 6 8 4 +Chlorophyllides 5 7 3 +Chlorophyta 3 3 1 +Chloroplast Proteins 4 4 1 +Chloroplast Proton-Translocating ATPases 5 9 6 +Chloroplast Thioredoxins 4 5 2 +Chloroplasts 8 8 1 +Chloroprene 7 7 1 +Chloroquine 6 6 1 +Chloroquinolinols 7 7 1 +Chlorothiazide 5 6 3 +Chlorotrianisene 8 8 1 +Chlorphenamidine 3 3 1 +Chlorphenesin 5 5 1 +Chlorpheniramine 5 5 1 +Chlorphentermine 7 7 1 +Chlorpromazine 4 5 2 +Chlorpropamide 5 7 5 +Chlorpropham 6 6 1 +Chlorprothixene 4 6 2 +Chlorpyrifos 5 5 3 +Chlorquinaldol 8 8 1 +Chlortetracycline 5 8 2 +Chlorthalidone 4 7 8 +Chlorzoxazone 5 5 1 +CHO Cells 3 4 2 +Choanal Atresia 3 4 4 +Choanoflagellata 2 2 1 +Chocolate 3 4 2 +Choice Behavior 5 5 1 +Cholagogues and Choleretics 5 5 1 +Cholanes 4 4 1 +Cholangiocarcinoma 6 6 1 +Cholangiography 4 6 2 +Cholangiopancreatography, Endoscopic Retrograde 4 7 6 +Cholangiopancreatography, Magnetic Resonance 4 6 2 +Cholangitis 4 4 1 +Cholangitis, Sclerosing 5 5 1 +Cholates 7 7 2 +Cholecalciferol 4 6 4 +Cholecystectomy 4 4 1 +Cholecystectomy, Laparoscopic 5 5 2 +Cholecystitis 4 4 1 +Cholecystitis, Acute 5 5 1 +Cholecystography 4 6 2 +Cholecystokinin 3 4 2 +Cholecystolithiasis 4 4 2 +Cholecystostomy 3 4 2 +Choledochal Cyst 3 4 4 +Choledocholithiasis 4 5 2 +Choledochostomy 3 4 2 +Cholelithiasis 3 3 1 +Cholenes 5 5 1 +Cholera 6 6 1 +Cholera Morbus 4 4 1 +Cholera Toxin 4 7 3 +Cholera Vaccines 5 5 1 +Cholestadienes 6 6 1 +Cholestadienols 7 7 1 +Cholestanes 4 4 1 +Cholestanetriol 26-Monooxygenase 5 8 6 +Cholestanol 5 7 3 +Cholestanols 5 5 1 +Cholestanones 5 5 1 +Cholestasis 4 4 1 +Cholestasis, Extrahepatic 5 5 1 +Cholestasis, Intrahepatic 3 5 2 +Cholesteatoma 4 4 1 +Cholesteatoma, Middle Ear 3 5 2 +Cholestenes 5 5 1 +Cholestenone 5 alpha-Reductase 5 5 1 +Cholestenones 6 6 1 +Cholesterol 4 6 3 +Cholesterol 24-Hydroxylase 5 8 6 +Cholesterol 7-alpha-Hydroxylase 5 8 6 +Cholesterol Ester Storage Disease 5 6 5 +Cholesterol Ester Transfer Proteins 4 4 4 +Cholesterol Esters 5 7 3 +Cholesterol Oxidase 7 7 1 +Cholesterol Side-Chain Cleavage Enzyme 5 8 6 +Cholesterol, Dietary 4 7 3 +Cholesterol, HDL 4 7 4 +Cholesterol, LDL 4 7 4 +Cholesterol, VLDL 4 7 4 +Cholestyramine Resin 5 7 3 +Cholic Acid 6 6 2 +Cholic Acids 5 5 2 +Choline 4 5 4 +Choline Deficiency 7 7 1 +Choline Dehydrogenase 5 5 1 +Choline Kinase 6 6 1 +Choline O-Acetyltransferase 6 6 1 +Choline-Phosphate Cytidylyltransferase 6 6 1 +Cholinergic Agents 5 5 2 +Cholinergic Agonists 6 6 2 +Cholinergic Antagonists 6 6 2 +Cholinergic Fibers 4 4 4 +Cholinergic Neurons 3 3 2 +Cholinesterase Inhibitors 5 6 3 +Cholinesterase Reactivators 5 6 3 +Cholinesterases 6 6 1 +Chondro-4-Sulfatase 8 8 1 +Chondroblastoma 5 5 1 +Chondrocalcinosis 4 4 2 +Chondrocytes 3 3 1 +Chondrodysplasia Punctata 5 5 1 +Chondrodysplasia Punctata, Rhizomelic 5 6 3 +Chondrogenesis 4 7 2 +Chondroitin 4 4 1 +Chondroitin ABC Lyase 8 8 1 +Chondroitin Lyases 7 7 1 +Chondroitin Sulfate Proteoglycan 4 5 6 3 +Chondroitin Sulfate Proteoglycans 4 5 3 +Chondroitin Sulfates 5 5 1 +Chondroitinases and Chondroitin Lyases 6 6 2 +Chondroitinsulfatases 7 7 1 +Chondroma 5 5 1 +Chondromalacia Patellae 3 4 2 +Chondromatosis 6 6 1 +Chondromatosis, Synovial 3 3 1 +Chondrosarcoma 5 5 2 +Chondrosarcoma, Clear Cell 6 6 2 +Chondrosarcoma, Mesenchymal 6 6 2 +Chondrus 3 3 1 +Chorda Tympani Nerve 6 6 1 +Chordae Tendineae 4 4 1 +Chordata 3 3 1 +Chordata, Nonvertebrate 4 4 2 +Chordoma 4 4 1 +Chordopoxvirinae 4 4 1 +Chorea 4 5 3 +Chorea Gravidarum 4 6 3 +Chorioallantoic Membrane 3 4 3 +Chorioamnionitis 3 6 4 +Choriocarcinoma 4 6 4 +Choriocarcinoma, Non-gestational 5 7 4 +Chorion 4 4 2 +Chorionic Gonadotropin 4 5 4 +Chorionic Gonadotropin, beta Subunit, Human 3 6 6 +Chorionic Villi 3 5 3 +Chorionic Villi Sampling 3 7 8 +Chorioretinitis 4 7 3 +Chorismate Mutase 5 5 1 +Chorismic Acid 5 8 2 +Choristoma 3 3 1 +Choroid 4 4 1 +Choroid Diseases 3 3 1 +Choroid Hemorrhage 3 5 3 +Choroid Neoplasms 4 5 4 +Choroid Plexus 5 5 1 +Choroid Plexus Neoplasms 6 7 3 +Choroidal Effusions 3 4 2 +Choroidal Neovascularization 4 5 2 +Choroideremia 3 4 4 +Choroiditis 4 6 2 +Christian Science 4 4 1 +Christianity 3 3 1 +Chromadorea 6 6 1 +Chromaffin Cells 2 3 2 +Chromaffin Granules 4 9 3 +Chromaffin System 2 2 1 +Chromans 5 5 2 +Chromates 3 5 2 +Chromatiaceae 4 4 1 +Chromatids 4 9 2 +Chromatin 4 9 3 +Chromatin Assembly and Disassembly 3 3 3 +Chromatin Assembly Factor-1 5 5 1 +Chromatin Immunoprecipitation 3 4 2 +Chromatin Immunoprecipitation Sequencing 4 5 4 +Chromatium 5 5 2 +Chromatography 3 3 1 +Chromatography, Affinity 5 5 1 +Chromatography, Agarose 6 6 1 +Chromatography, DEAE-Cellulose 6 6 1 +Chromatography, Gas 4 4 1 +Chromatography, Gel 5 5 1 +Chromatography, High Pressure Liquid 5 5 1 +Chromatography, Ion Exchange 5 5 1 +Chromatography, Liquid 4 4 1 +Chromatography, Micellar Electrokinetic Capillary 4 4 1 +Chromatography, Paper 5 5 1 +Chromatography, Reverse-Phase 5 5 1 +Chromatography, Supercritical Fluid 4 4 1 +Chromatography, Thin Layer 5 5 1 +Chromatophores 3 3 1 +Chromium 4 4 3 +Chromium Alloys 3 6 6 +Chromium Compounds 2 2 1 +Chromium Isotopes 3 5 4 +Chromium Radioisotopes 4 6 5 +Chromobacterium 4 5 2 +Chromoblastomycosis 4 5 3 +Chromobox Protein Homolog 5 5 5 2 +Chromogenic Compounds 4 5 2 +Chromogranin A 5 5 1 +Chromogranin B 5 5 1 +Chromogranins 4 4 2 +Chromohalobacter 5 6 2 +Chromolaena 8 8 1 +Chromomycin A3 4 4 1 +Chromomycins 3 3 1 +Chromonar 6 6 2 +Chromones 5 5 2 +Chromophore-Assisted Light Inactivation 2 2 1 +Chromosomal Instability 3 5 4 +Chromosomal Position Effects 4 4 1 +Chromosomal Proteins, Non-Histone 4 4 2 +Chromosomal Puffs 9 11 2 +Chromosome Aberrations 3 4 2 +Chromosome Banding 4 7 8 +Chromosome Breakage 4 5 3 +Chromosome Breakpoints 4 4 1 +Chromosome Deletion 4 7 6 +Chromosome Disorders 3 3 2 +Chromosome Duplication 3 5 3 +Chromosome Fragile Sites 6 6 1 +Chromosome Fragility 4 6 4 +Chromosome Inversion 4 5 4 +Chromosome Mapping 3 3 1 +Chromosome Painting 5 9 6 +Chromosome Pairing 6 7 4 +Chromosome Positioning 3 3 1 +Chromosome Segregation 4 5 2 +Chromosome Structures 3 8 2 +Chromosome Walking 4 4 1 +Chromosomes 3 7 3 +Chromosomes, Archaeal 4 4 2 +Chromosomes, Artificial 4 4 3 +Chromosomes, Artificial, Bacterial 3 5 6 +Chromosomes, Artificial, Human 6 6 7 +Chromosomes, Artificial, Mammalian 5 5 5 +Chromosomes, Artificial, P1 Bacteriophage 5 5 3 +Chromosomes, Artificial, Yeast 3 5 6 +Chromosomes, Bacterial 2 4 3 +Chromosomes, Fungal 2 4 3 +Chromosomes, Human 5 5 2 +Chromosomes, Human, 1-3 6 6 2 +Chromosomes, Human, 13-15 6 6 2 +Chromosomes, Human, 16-18 6 6 2 +Chromosomes, Human, 19-20 6 6 2 +Chromosomes, Human, 21-22 and Y 6 6 2 +Chromosomes, Human, 4-5 6 6 2 +Chromosomes, Human, 6-12 and X 6 6 2 +Chromosomes, Human, Pair 1 7 7 2 +Chromosomes, Human, Pair 10 7 7 2 +Chromosomes, Human, Pair 11 7 7 2 +Chromosomes, Human, Pair 12 7 7 2 +Chromosomes, Human, Pair 13 7 7 2 +Chromosomes, Human, Pair 14 7 7 2 +Chromosomes, Human, Pair 15 7 7 2 +Chromosomes, Human, Pair 16 7 7 2 +Chromosomes, Human, Pair 17 7 7 2 +Chromosomes, Human, Pair 18 7 7 2 +Chromosomes, Human, Pair 19 7 7 2 +Chromosomes, Human, Pair 2 7 7 2 +Chromosomes, Human, Pair 20 7 7 2 +Chromosomes, Human, Pair 21 7 7 2 +Chromosomes, Human, Pair 22 7 7 2 +Chromosomes, Human, Pair 3 7 7 2 +Chromosomes, Human, Pair 4 7 7 2 +Chromosomes, Human, Pair 5 7 7 2 +Chromosomes, Human, Pair 6 7 7 2 +Chromosomes, Human, Pair 7 7 7 2 +Chromosomes, Human, Pair 8 7 7 2 +Chromosomes, Human, Pair 9 7 7 2 +Chromosomes, Human, X 6 7 4 +Chromosomes, Human, Y 6 7 4 +Chromosomes, Insect 4 4 2 +Chromosomes, Mammalian 4 4 2 +Chromosomes, Plant 2 4 3 +Chromothripsis 4 5 2 +Chronaxy 3 4 3 +Chronic Care Model 5 5 1 +Chronic Cough 4 5 2 +Chronic Disease 4 4 1 +Chronic Disease Indicators 7 8 3 +Chronic Exertional Compartment Syndrome 4 5 3 +Chronic Inducible Urticaria 6 6 3 +Chronic Kidney Disease-Mineral and Bone Disorder 4 8 9 +Chronic Kidney Diseases of Uncertain Etiology 3 8 5 +Chronic Limb-Threatening Ischemia 4 7 4 +Chronic Pain 5 5 3 +Chronic Periodontitis 5 5 2 +Chronic Traumatic Encephalopathy 3 7 8 +Chronic Urticaria 5 5 3 +Chronobiology Discipline 3 3 1 +Chronobiology Disorders 2 2 1 +Chronobiology Phenomena 2 2 1 +Chronology 2 2 1 +Chronology as Topic 3 3 1 +Chronopharmacokinetics 4 4 1 +Chronotherapy 2 2 1 +Chronotype 3 5 2 +Chrysanthemum 8 8 1 +Chrysanthemum cinerariifolium 9 9 1 +Chrysenes 4 7 2 +Chryseobacterium 5 6 2 +Chrysobalanaceae 9 9 1 +Chrysophyta 3 3 1 +Chrysopogon 8 8 1 +Chrysosporium 4 4 1 +Church of Jesus Christ of Latter-day Saints 4 4 1 +Churg-Strauss Syndrome 4 6 4 +Chyle 5 5 2 +Chylomicron Remnants 4 5 2 +Chylomicrons 3 4 2 +Chylothorax 3 3 1 +Chylous Ascites 3 3 1 +Chymases 7 7 2 +Chymopapain 7 7 2 +Chymosin 7 7 2 +Chymotrypsin 7 7 2 +Chymotrypsinogen 3 5 2 +Chytridiomycota 3 3 1 +Cialit 5 6 2 +Cicatrix 4 4 3 +Cicatrix, Hypertrophic 5 5 2 +Cicer 8 8 1 +Cichlids 7 7 1 +Cichorium intybus 8 8 1 +Ciclopirox 5 7 2 +Cicuta 8 8 1 +Cidofovir 4 6 2 +Cigar Smoking 5 5 2 +Cigarette Smoking 5 5 2 +Ciguatera Poisoning 4 4 1 +Ciguatoxins 4 6 9 +Cilastatin 5 7 2 +Cilastatin, Imipenem Drug Combination 3 8 5 +Cilazapril 4 4 1 +Cilia 4 4 1 +Ciliary Arteries 4 4 1 +Ciliary Body 4 4 2 +Ciliary Motility Disorders 2 5 4 +Ciliary Neurotrophic Factor 4 5 4 +Ciliary Neurotrophic Factor Receptor alpha Subunit 6 9 5 +Ciliopathies 3 4 2 +Ciliophora 3 3 1 +Ciliophora Infections 4 4 1 +Cilostazol 5 5 2 +Cimetidine 4 5 2 +Cimicidae 8 8 1 +Cimicifuga 9 9 1 +Cinacalcet 4 7 2 +Cinanserin 5 5 1 +Cinchona 9 9 1 +Cinchona Alkaloids 3 3 1 +Cineangiography 5 6 2 +Cineradiography 6 6 1 +Cinnamates 4 4 1 +Cinnamomum 9 9 1 +Cinnamomum aromaticum 10 10 1 +Cinnamomum camphora 10 10 1 +Cinnamomum zeylanicum 10 10 1 +Cinnarizine 4 4 1 +Cinoxacin 3 4 2 +Ciona 6 6 2 +Ciona intestinalis 7 7 2 +Ciprofloxacin 8 8 1 +Circadian Clocks 5 5 1 +Circadian Rhythm 4 4 1 +Circadian Rhythm Signaling Peptides and Proteins 4 4 2 +Circle of Willis 5 5 1 +Circoviridae 3 3 1 +Circoviridae Infections 4 4 1 +Circovirus 4 4 1 +Circuit-Based Exercise 4 7 2 +Circular Dichroism 4 4 1 +Circulating MicroRNA 4 7 4 +Circulating Tumor DNA 4 5 2 +Circulatory and Respiratory Physiological Phenomena 1 1 1 +Circulatory Arrest, Deep Hypothermia Induced 5 5 2 +Circumcision, Female 3 5 4 +Circumcision, Male 3 5 3 +Circumventricular Organs 3 3 2 +Cirsium 8 8 1 +cis-trans-Isomerases 4 4 1 +Cisapride 4 9 8 +Cisplatin 3 3 3 +Cissampelos 8 8 1 +Cissus 8 8 1 +Cistaceae 7 7 1 +Cistanche 9 9 1 +Cisterna Magna 6 6 1 +Cistus 8 8 1 +Citalopram 3 5 3 +Cities 2 4 3 +Citizen Science 5 5 2 +Citizenship 4 5 2 +Citraconic Anhydrides 3 4 2 +Citrate (si)-Synthase 5 5 1 +Citrates 5 5 1 +Citric Acid 6 6 1 +Citric Acid Cycle 3 3 3 +Citrinin 4 5 3 +Citrobacter 5 5 2 +Citrobacter freundii 6 6 2 +Citrobacter koseri 6 6 2 +Citrobacter rodentium 6 6 2 +Citrullination 5 7 4 +Citrulline 4 4 1 +Citrullinemia 6 7 6 +Citrullus 8 8 1 +Citrullus colocynthis 9 9 1 +Citrus 8 8 1 +Citrus aurantiifolia 9 9 1 +Citrus paradisi 9 9 1 +Citrus sinensis 9 9 1 +City Planning 4 4 1 +Civil Defense 3 3 1 +Civil Disorders 4 4 1 +Civil Rights 4 5 2 +Civilization 5 5 1 +Cladocera 6 6 1 +Cladosporium 4 4 1 +Cladribine 5 8 6 +Clarithromycin 6 6 1 +Clarkia 8 8 1 +Class I Phosphatidylinositol 3-Kinases 6 8 4 +Class Ia Phosphatidylinositol 3-Kinase 7 9 4 +Class Ib Phosphatidylinositol 3-Kinase 7 9 4 +Class II Phosphatidylinositol 3-Kinases 6 8 4 +Class III Phosphatidylinositol 3-Kinases 5 8 3 +Classical Lissencephalies and Subcortical Band Heterotopias 5 7 5 +Classical Swine Fever 3 6 2 +Classical Swine Fever Virus 6 6 1 +Classification 2 5 2 +Classification Algorithms 4 5 2 +Clathrin 5 5 1 +Clathrin Heavy Chains 6 6 1 +Clathrin Light Chains 6 6 1 +Clathrin-Coated Vesicles 10 10 1 +Claudin-1 6 6 1 +Claudin-2 6 6 1 +Claudin-3 6 6 1 +Claudin-4 6 6 1 +Claudin-5 6 6 1 +Claudins 5 5 1 +Clausena 8 8 1 +Claustrophobia 4 4 1 +Claustrum 8 8 1 +Clavibacter 4 7 2 +Claviceps 5 5 1 +Clavicle 5 5 1 +Clavulanic Acid 6 6 2 +Clavulanic Acids 5 5 2 +Clay 3 5 4 +CLC-2 Chloride Channels 7 7 3 +Cleavage And Polyadenylation Specificity Factor 6 6 2 +Cleavage Stage, Ovum 2 2 1 +Cleavage Stimulation Factor 6 6 2 +Cleft Lip 4 5 4 +Cleft Palate 4 7 9 +Cleidocranial Dysplasia 4 5 3 +Clemastine 4 4 1 +Clematis 9 9 1 +Clenbuterol 5 5 2 +Cleome 8 8 1 +Clergy 4 4 1 +Clerodendrum 9 9 1 +Clethraceae 8 8 1 +Click Chemistry 3 5 2 +Climacteric 3 4 2 +Climate 4 5 2 +Climate Anxiety 4 5 2 +Climate Change 4 4 1 +Climate Models 3 3 2 +Climatic Processes 3 3 1 +Climatotherapy 2 2 1 +Clindamycin 5 6 2 +Clinical Alarms 3 3 1 +Clinical Audit 3 4 2 +Clinical Chemistry Tests 3 4 2 +Clinical Clerkship 3 3 1 +Clinical Coding 5 7 4 +Clinical Competence 3 4 3 +Clinical Conference 2 2 1 +Clinical Decision Rules 3 6 2 +Clinical Decision-Making 2 2 1 +Clinical Deterioration 5 5 1 +Clinical Enzyme Tests 4 5 3 +Clinical Governance 3 3 1 +Clinical Laboratory Information Systems 4 4 1 +Clinical Laboratory Services 5 5 1 +Clinical Laboratory Techniques 2 3 2 +Clinical Medicine 3 3 1 +Clinical Nursing Research 4 6 3 +Clinical Observation Units 4 4 1 +Clinical Pharmacy Information Systems 4 5 2 +Clinical Protocols 2 5 2 +Clinical Reasoning 3 3 1 +Clinical Relevance 4 7 3 +Clinical Studies as Topic 4 5 3 +Clinical Study 2 2 1 +Clinical Trial 3 3 1 +Clinical Trial Protocol 3 3 1 +Clinical Trial Protocols as Topic 6 6 1 +Clinical Trial, Phase I 4 4 1 +Clinical Trial, Phase II 4 4 1 +Clinical Trial, Phase III 4 4 1 +Clinical Trial, Phase IV 4 4 1 +Clinical Trial, Veterinary 3 3 1 +Clinical Trials as Topic 5 6 3 +Clinical Trials Data Monitoring Committees 4 4 1 +Clinical Trials, Phase I as Topic 6 7 3 +Clinical Trials, Phase II as Topic 6 7 3 +Clinical Trials, Phase III as Topic 6 7 3 +Clinical Trials, Phase IV as Topic 4 7 4 +Clinical Trials, Veterinary as Topic 5 6 3 +Clione 6 6 1 +Clioquinol 7 7 1 +Clitoria 8 8 1 +Clitoris 5 5 1 +Cloaca 2 2 2 +Cloacal Exstrophy 4 7 9 +Cloacin 5 5 1 +Clobazam 6 6 1 +Clobetasol 6 6 1 +CLOCK Proteins 5 8 5 +Clodronic Acid 5 5 1 +Clofarabine 4 7 4 +Clofazimine 5 5 1 +Clofenapate 5 9 4 +Clofibrate 6 10 3 +Clofibric Acid 5 9 3 +Clomiphene 8 8 1 +Clomipramine 5 5 1 +Clonal Anergy 4 4 1 +Clonal Deletion 4 4 1 +Clonal Evolution 2 2 2 +Clonal Hematopoiesis 3 4 4 +Clonal Selection, Antigen-Mediated 2 4 2 +Clonazepam 7 7 1 +Clone Cells 3 3 1 +Clonidine 6 6 1 +Cloning, Molecular 3 3 1 +Cloning, Organism 3 3 2 +Clonixin 4 5 2 +Clonorchiasis 5 5 1 +Clonorchis sinensis 8 8 1 +Clopamide 4 5 3 +Clopenthixol 4 6 2 +Clopidogrel 6 6 4 +Clopidol 4 4 1 +Cloprostenol 5 8 3 +Clorazepate Dipotassium 6 6 1 +Clorgyline 4 4 1 +Closed Fracture Reduction 4 4 1 +Closing Volume 4 7 2 +Closterium 6 6 1 +Closteroviridae 3 4 2 +Closterovirus 4 5 2 +Clostridiaceae 4 4 1 +Clostridioides 3 3 1 +Clostridioides difficile 4 4 1 +Clostridium 5 5 4 +Clostridium acetobutylicum 6 6 4 +Clostridium beijerinckii 6 6 4 +Clostridium botulinum 6 6 4 +Clostridium botulinum type A 7 7 4 +Clostridium botulinum type B 7 7 4 +Clostridium botulinum type C 7 7 4 +Clostridium botulinum type D 7 7 4 +Clostridium botulinum type E 7 7 4 +Clostridium botulinum type F 7 7 4 +Clostridium botulinum type G 7 7 4 +Clostridium butyricum 6 6 4 +Clostridium cellulovorans 6 6 4 +Clostridium chauvoei 6 6 4 +Clostridium Infections 5 5 1 +Clostridium kluyveri 6 6 4 +Clostridium perfringens 6 6 4 +Clostridium septicum 6 6 4 +Clostridium symbiosum 6 6 4 +Clostridium tertium 6 6 4 +Clostridium tetani 6 6 4 +Clostridium tetanomorphum 6 6 4 +Clostridium tyrobutyricum 6 6 4 +Clot Retraction 5 6 3 +Clothing 3 3 1 +Clotrimazole 5 5 1 +Cloud Computing 3 3 1 +Clove Oil 5 5 1 +Cloxacillin 6 7 3 +Clozapine 5 5 1 +Clubfoot 5 8 4 +Clupeine 5 5 2 +Clusia 9 9 1 +Clusiaceae 8 8 1 +Cluster Analysis 4 5 3 +Cluster Headache 7 7 1 +Clustered Regularly Interspaced Short Palindromic Repeats 6 8 3 +Clusterin 4 4 3 +Clustering Algorithms 3 4 2 +Clutch Size 3 3 1 +CME-Carbodiimide 4 4 1 +Cnicus 8 8 1 +Cnidaria 4 4 1 +Cnidarian Venoms 3 4 3 +Cnidium 8 8 1 +Co-Repressor Proteins 5 5 1 +Coagulants 5 5 1 +Coagulase 4 6 2 +Coagulation Protein Disorders 4 4 1 +Coal 3 5 2 +Coal Ash 3 3 1 +Coal Industry 5 5 1 +Coal Mining 6 6 1 +Coal Tar 3 3 1 +Coat Protein Complex I 5 5 1 +Coated Materials, Biocompatible 3 5 2 +Coated Pits, Cell-Membrane 5 5 1 +Coated Vesicles 9 9 1 +Coatomer Protein 6 6 1 +Cobalt 4 4 3 +Cobalt Isotopes 3 5 4 +Cobalt Radioisotopes 4 6 5 +Cobamides 3 8 4 +Cobblestone Lissencephaly 6 7 3 +Cobicistat 4 5 3 +Cobra Cardiotoxin Proteins 4 6 3 +Cobra Neurotoxin Proteins 4 6 3 +Coca 8 8 1 +Cocaine 4 6 4 +Cocaine Smoking 5 5 1 +Cocaine- and Amphetamine-Regulated Transcript Protein 4 5 2 +Cocaine-Related Disorders 3 3 2 +Cocarcinogenesis 4 5 2 +Coccidia 4 4 1 +Coccidioidal Meningitis 5 6 5 +Coccidioides 4 4 1 +Coccidioidin 4 5 2 +Coccidioidomycosis 4 4 1 +Coccidiosis 4 4 1 +Coccidiostats 7 7 1 +Cocculus 8 8 1 +Coccyx 5 5 1 +Cochlea 4 4 1 +Cochlear Aqueduct 5 5 1 +Cochlear Diseases 4 4 1 +Cochlear Duct 5 5 1 +Cochlear Implantation 3 4 2 +Cochlear Implants 3 7 4 +Cochlear Microphonic Potentials 4 6 3 +Cochlear Nerve 6 6 1 +Cochlear Nucleus 8 8 1 +Cockatoos 7 7 1 +Cockayne Syndrome 4 5 6 +Cockroaches 6 6 1 +Coconut Oil 4 4 2 +Cocos 8 8 1 +Coculture Techniques 4 4 1 +Cod Liver Oil 4 5 3 +Codeine 5 6 4 +Codependency, Psychological 3 3 1 +Codes of Ethics 3 6 6 +Codon 4 7 3 +Codon Usage 5 5 1 +Codon, Initiator 5 8 3 +Codon, Nonsense 4 7 3 +Codon, Terminator 5 8 3 +Codonopsis 8 8 1 +Coelomomyces 4 4 1 +Coenzyme A 3 7 4 +Coenzyme A Ligases 5 5 1 +Coenzyme A-Transferases 5 5 1 +Coenzymes 2 2 1 +Coercion 4 4 2 +Coffea 9 9 1 +Coffee 3 4 3 +Coffin-Lowry Syndrome 5 6 3 +Cofilin 1 6 6 2 +Cofilin 2 5 6 3 +Coformycin 5 6 3 +Cogan Syndrome 2 4 4 +Cognition 3 3 1 +Cognition Disorders 3 3 1 +Cognitive Aging 4 4 1 +Cognitive Behavioral Therapy 4 4 1 +Cognitive Dissonance 4 4 1 +Cognitive Dysfunction 4 4 1 +Cognitive Enhancement 2 2 1 +Cognitive Flexibility 4 4 1 +Cognitive Neuroscience 4 5 2 +Cognitive Psychology 4 5 2 +Cognitive Reflection 4 4 1 +Cognitive Remediation 4 4 1 +Cognitive Reserve 4 4 1 +Cognitive Restructuring 5 5 1 +Cognitive Science 4 4 1 +Cognitive Training 4 7 4 +Cohesins 4 5 3 +Cohort Effect 5 5 2 +Cohort Studies 5 6 3 +Coiled Bodies 8 8 1 +Coinfection 2 2 1 +Coitus 4 4 2 +Coitus Interruptus 4 5 2 +Coix 8 8 1 +Coke 4 6 2 +Cola 10 10 1 +Colchicaceae 9 9 1 +Colchicine 3 3 1 +Colchicum 10 10 1 +Cold Climate 5 6 2 +Cold Injury 2 2 1 +Cold Ischemia 3 4 2 +Cold Shock Proteins and Peptides 4 4 2 +Cold Temperature 4 7 5 +Cold Urticaria 5 5 2 +Cold-Shock Response 3 3 1 +Colectomy 4 4 1 +Coleoptera 9 9 1 +Colesevelam Hydrochloride 4 7 2 +Colestipol 3 5 4 +Coleus 9 9 1 +Colforsin 5 5 1 +Colic 3 3 1 +Colicins 4 4 1 +Colinus 8 8 1 +Colipases 3 3 1 +Coliphages 3 3 1 +Colistin 4 7 6 +Colitis 4 5 2 +Colitis, Collagenous 6 7 2 +Colitis, Ischemic 3 6 3 +Colitis, Lymphocytic 6 7 2 +Colitis, Microscopic 5 6 2 +Colitis, Ulcerative 5 6 4 +Colitis-Associated Neoplasms 7 8 5 +Collaborative Cross Mice 12 12 1 +Collagen 4 5 2 +Collagen Diseases 3 3 1 +Collagen Type I 6 7 2 +Collagen Type I, alpha 1 Chain 7 7 1 +Collagen Type II 6 7 2 +Collagen Type III 6 7 2 +Collagen Type IV 7 7 1 +Collagen Type IX 8 8 1 +Collagen Type V 6 7 2 +Collagen Type VI 7 7 1 +Collagen Type VII 7 7 1 +Collagen Type VIII 7 7 1 +Collagen Type X 7 7 1 +Collagen Type XI 6 7 2 +Collagen Type XII 8 8 1 +Collagen Type XIII 7 7 1 +Collagen Type XVII 4 7 2 +Collagen Type XVIII 7 7 1 +Collagenases 7 7 2 +Collagenous Sprue 4 5 2 +Collapse Therapy 4 4 1 +Collapsin Response Mediator Protein 1 4 4 2 +Collateral Circulation 4 4 1 +Collateral Ligament, Ulnar 5 6 3 +Collateral Ligaments 4 5 3 +Collected Correspondence 3 3 1 +Collected Work 2 2 1 +Collectins 5 5 1 +Collection 2 2 1 +Collections as Topic 3 3 1 +Collective Bargaining 4 4 2 +Collective Efficacy 4 7 6 +College Admission Test 3 3 1 +College Fraternities and Sororities 3 3 1 +Colles' Fracture 4 5 3 +Colletotrichum 4 4 1 +Collodion 5 7 2 +Colloid Cysts 3 6 5 +Colloids 2 3 2 +Colobinae 11 11 1 +Coloboma 3 4 3 +Colobus 12 12 1 +Colocasia 10 10 1 +Colombia 4 4 1 +Colon 5 5 2 +Colon, Ascending 6 6 2 +Colon, Descending 6 6 2 +Colon, Sigmoid 6 6 2 +Colon, Transverse 6 6 2 +Colonialism 3 3 1 +Colonic Diseases 4 4 1 +Colonic Diseases, Functional 5 5 1 +Colonic Neoplasms 6 7 5 +Colonic Polyps 5 5 1 +Colonic Pouches 3 3 2 +Colonic Pseudo-Obstruction 6 7 2 +Colonography, Computed Tomographic 4 8 5 +Colonoscopes 5 5 2 +Colonoscopy 5 7 4 +Colony Collapse 2 2 1 +Colony Count, Microbial 4 5 2 +Colony-Forming Units Assay 3 5 3 +Colony-Stimulating Factors 4 6 5 +Color 4 4 1 +Color Perception 5 5 1 +Color Perception Tests 5 5 1 +Color Therapy 3 4 3 +Color Vision 3 5 3 +Color Vision Defects 3 6 4 +Colorado 6 6 1 +Colorado Tick Fever 4 5 3 +Colorado tick fever virus 6 6 1 +Colorectal Neoplasms 5 6 6 +Colorectal Neoplasms, Hereditary Nonpolyposis 3 7 8 +Colorectal Surgery 4 4 1 +Colorectal Surgical Procedures 3 3 1 +Colorimetry 4 4 1 +Coloring Agents 3 3 1 +Colostomy 4 4 2 +Colostrum 3 3 1 +Colposcopes 4 4 2 +Colposcopy 3 5 5 +Colpotomy 3 4 2 +Coltivirus 5 5 1 +Colubridae 8 8 1 +Colubrina 10 10 1 +Columbia SK virus 8 8 1 +Columbidae 7 7 1 +Columbiformes 6 6 1 +Coma 6 7 2 +Coma, Post-Head Injury 4 7 3 +Comamonadaceae 5 5 2 +Comamonas 6 6 2 +Comamonas testosteroni 7 7 2 +Comb and Wattles 2 2 1 +Combat Disorders 4 4 1 +Combat Medics 4 6 4 +Combinatorial Chemistry Techniques 3 5 2 +Combined Antibody Therapeutics 3 8 4 +Combined Modality Therapy 2 2 1 +Combretaceae 7 7 1 +Combretum 8 8 1 +Comet Assay 4 5 4 +Comfrey 8 8 1 +Comic Book 3 3 1 +Comic Books as Topic 6 7 2 +Commelina 8 8 1 +Commelinaceae 7 7 1 +Comment 2 2 2 +Commerce 2 2 1 +Commiphora 8 8 1 +Commission on Professional and Hospital Activities 5 6 2 +Commissural Interneurons 4 4 2 +Commitment of Persons with Psychiatric Disorders 5 5 2 +Committee Membership 3 3 1 +Commodification 3 5 2 +Common Bile Duct 5 5 1 +Common Bile Duct Diseases 4 4 1 +Common Bile Duct Neoplasms 5 6 5 +Common Cold 3 5 3 +Common Data Elements 4 4 1 +Common Dolphins 9 9 1 +Common Variable Immunodeficiency 3 3 1 +Commonwealth of Independent States 3 4 2 +Commotio Cordis 4 5 3 +Communicable Disease Control 4 4 1 +Communicable Diseases 2 4 2 +Communicable Diseases, Emerging 3 5 2 +Communicable Diseases, Imported 3 5 2 +Communication 2 3 2 +Communication Barriers 3 3 1 +Communication Devices for People with Disabilities 3 3 1 +Communication Disorders 3 5 3 +Communication Methods, Total 4 7 2 +Communications Media 3 3 1 +Communism 3 3 1 +Community Dentistry 5 5 1 +Community Health Centers 4 4 1 +Community Health Nursing 4 4 2 +Community Health Planning 4 4 1 +Community Health Services 3 3 1 +Community Health Workers 4 5 2 +Community Integration 4 4 1 +Community Medicine 3 3 1 +Community Mental Health Centers 4 4 1 +Community Mental Health Services 3 4 3 +Community Networks 4 6 3 +Community of Practice 4 6 2 +Community Participation 4 4 2 +Community Pharmacy Services 4 4 2 +Community Psychiatry 4 4 2 +Community Resources 2 4 2 +Community Support 5 6 2 +Community-Acquired Infections 2 2 1 +Community-Acquired Pneumonia 3 4 2 +Community-Based Health Insurance 6 6 1 +Community-Based Participatory Research 3 4 2 +Community-Institutional Relations 4 4 1 +Comorbidity 4 4 2 +Comoros 4 5 2 +Comovirus 4 6 3 +Compact Disks 5 8 7 +Comparative Effectiveness Research 3 6 2 +Comparative Genomic Hybridization 4 4 3 +Comparative Study 2 2 1 +Compartment Syndromes 3 3 2 +Compassion Fatigue 5 6 4 +Compassionate Use Trials 3 4 3 +Compensation and Redress 3 5 3 +Competency-Based Education 3 3 1 +Competitive Behavior 4 4 1 +Competitive Bidding 5 5 1 +Competitive Medical Plans 5 7 2 +Complement Activating Enzymes 4 6 2 +Complement Activation 2 2 1 +Complement C1 6 6 1 +Complement C1 Inactivator Proteins 4 7 3 +Complement C1 Inhibitor Protein 4 8 5 +Complement C1q 7 7 1 +Complement C1r 5 7 3 +Complement C1s 5 7 3 +Complement C2 6 6 1 +Complement C2a 7 7 1 +Complement C2b 7 7 1 +Complement C3 5 6 2 +Complement C3 Convertase, Alternative Pathway 9 9 1 +Complement C3 Convertase, Classical Pathway 9 9 1 +Complement C3 Nephritic Factor 7 8 4 +Complement C3-C5 Convertases 7 7 1 +Complement C3-C5 Convertases, Alternative Pathway 8 8 1 +Complement C3-C5 Convertases, Classical Pathway 8 8 1 +Complement C3a 7 7 2 +Complement C3b 7 7 1 +Complement C3b Inactivator Proteins 7 7 1 +Complement C3c 8 8 1 +Complement C3d 8 8 1 +Complement C4 6 6 1 +Complement C4a 7 7 2 +Complement C4b 7 7 1 +Complement C4b-Binding Protein 7 7 1 +Complement C5 6 6 1 +Complement C5 Convertase, Alternative Pathway 9 9 1 +Complement C5 Convertase, Classical Pathway 9 9 1 +Complement C5a 7 7 2 +Complement C5a, des-Arginine 8 8 2 +Complement C5b 7 7 1 +Complement C6 6 6 1 +Complement C7 6 6 1 +Complement C8 6 6 1 +Complement C9 6 6 1 +Complement Factor B 3 7 5 +Complement Factor D 5 7 4 +Complement Factor H 6 8 3 +Complement Factor I 7 8 3 +Complement Fixation Tests 5 6 3 +Complement Hemolytic Activity Assay 5 6 6 +Complement Inactivating Agents 6 6 1 +Complement Inactivator Proteins 6 6 1 +Complement Membrane Attack Complex 6 6 1 +Complement Pathway, Alternative 3 3 1 +Complement Pathway, Classical 3 3 1 +Complement Pathway, Mannose-Binding Lectin 3 3 1 +Complement System Proteins 5 5 1 +Complementarity Determining Regions 6 9 9 +Complementary Therapies 2 2 1 +Complex Mixtures 1 1 1 +Complex Regional Pain Syndromes 3 4 2 +Compliance 4 4 1 +Complicity 3 5 2 +Compomers 4 8 7 +Composite Lymphoma 4 5 4 +Composite Resins 4 7 5 +Composite Tissue Allografts 3 3 1 +Composting 7 8 2 +Compound Eye, Arthropod 2 2 1 +Comprehension 4 4 1 +Comprehensive Dental Care 4 4 1 +Comprehensive Health Care 3 3 1 +Comprehensive Metabolic Panel 5 6 2 +Compressed Air 2 6 3 +Compression Algorithms 3 4 2 +Compression Bandages 3 3 1 +Compressive Strength 3 3 1 +Compulsive Behavior 4 4 1 +Compulsive Exercise 3 6 2 +Compulsive Personality Disorder 3 3 1 +Compulsive Sexual Behavior Disorder 4 6 2 +Computational Biology 3 4 2 +Computational Chemistry 3 3 1 +Computed Tomography Angiography 5 8 6 +Computer Communication Networks 4 4 1 +Computer Graphics 3 3 2 +Computer Heuristics 5 5 1 +Computer Literacy 3 3 1 +Computer Peripherals 5 5 1 +Computer Security 3 4 2 +Computer Simulation 3 3 1 +Computer Storage Devices 6 6 1 +Computer Systems 3 3 1 +Computer Terminals 6 6 1 +Computer User Training 3 3 1 +Computer-Aided Design 4 4 2 +Computer-Assisted Instruction 5 5 1 +Computerized Adaptive Testing 3 3 1 +Computers 4 4 1 +Computers, Analog 5 5 1 +Computers, Handheld 6 6 1 +Computers, Hybrid 5 5 1 +Computers, Mainframe 5 5 1 +Computers, Molecular 3 5 2 +Computing Methodologies 2 2 1 +Conalbumin 4 6 5 +Concanavalin A 5 5 2 +Concentration Camps 2 6 2 +Concept Formation 4 4 1 +Concierge Medicine 6 6 1 +Concurrent Review 4 4 2 +Condiments 3 4 2 +Conditioning, Classical 5 5 1 +Conditioning, Eyelid 5 5 1 +Conditioning, Operant 5 5 1 +Conditioning, Psychological 4 4 1 +Condoms 4 4 1 +Conduct Disorder 4 4 1 +Conducted Energy Weapon Injuries 3 3 1 +Conductometry 3 4 2 +Condylomata Acuminata 4 8 10 +Cone Dystrophy 3 3 2 +Cone Opsins 5 5 2 +Cone-Beam Computed Tomography 7 7 2 +Cone-Rod Dystrophies 3 5 3 +Confederate States of America 3 3 1 +Conference Proceedings 2 2 1 +Confidence Intervals 4 5 3 +Confidentiality 4 6 5 +Confined Spaces 3 4 2 +Conflict of Interest 3 5 2 +Conflict, Psychological 3 3 1 +Confounding Factors, Epidemiologic 4 4 2 +Confucianism 4 4 1 +Confusion 3 5 3 +Congenital Abnormalities 2 2 1 +Congenital Bone Marrow Failure Syndromes 3 5 2 +Congenital Cranial Dysinnervation Disorders 3 5 4 +Congenital Disorders of Glycosylation 5 5 2 +Congenital Hyperinsulinism 3 5 4 +Congenital Hypothyroidism 3 5 5 +Congenital Microtia 3 3 2 +Congenital Portosystemic Shunt 4 5 2 +Congenital, Hereditary, and Neonatal Diseases and Abnormalities 1 1 1 +Congenitally Corrected Transposition of the Great Arteries 5 6 3 +Congo 5 5 1 +Congo Red 5 8 3 +Congresses as Topic 3 3 1 +Conidiobolus 5 5 1 +Conium 8 8 1 +Conization 3 7 7 +Conjugation, Genetic 3 3 1 +Conjunctiva 4 4 2 +Conjunctival Diseases 2 2 1 +Conjunctival Neoplasms 3 4 3 +Conjunctivitis 3 3 1 +Conjunctivitis, Acute Hemorrhagic 5 6 5 +Conjunctivitis, Allergic 4 4 2 +Conjunctivitis, Bacterial 4 5 4 +Conjunctivitis, Inclusion 5 7 5 +Conjunctivitis, Viral 4 4 4 +Connaraceae 9 9 1 +Connecticut 6 6 1 +Connectin 5 7 2 +Connective Tissue 2 2 1 +Connective Tissue Cells 2 2 1 +Connective Tissue Diseases 2 2 1 +Connective Tissue Growth Factor 4 6 4 +Connectome 5 7 3 +Connexin 26 6 6 1 +Connexin 30 6 6 1 +Connexin 43 6 6 1 +Connexin 50 4 6 2 +Connexins 5 5 1 +Conotoxins 4 5 3 +Consanguinity 2 4 2 +Conscience 4 4 2 +Conscientious Refusal to Treat 5 6 2 +Consciousness 3 4 2 +Consciousness Disorders 3 5 4 +Consciousness Monitors 3 3 1 +Consensus 4 5 2 +Consensus Development Conference, NIH 4 5 3 +Consensus Development Conferences, NIH as Topic 5 7 2 +Consensus Sequence 5 5 1 +Consensus Statement 3 4 4 +Consensus Statements as Topic 4 6 2 +Consent Forms 4 6 4 +Conservation of Energy Resources 4 4 1 +Conservation of Natural Resources 2 3 2 +Conservation of Water Resources 4 4 1 +Conservative Treatment 2 2 1 +Conserved Sequence 4 4 1 +Consolidation Chemotherapy 3 3 1 +Constipation 4 4 1 +Constitution and Bylaws 3 3 1 +Constitutive Androstane Receptor 4 4 1 +Constraint Induced Movement Therapy 4 5 2 +Constriction 2 2 1 +Constriction, Pathologic 3 3 1 +Construction Industry 4 4 1 +Construction Materials 3 3 1 +Consultants 2 2 1 +Consumer Advocacy 4 5 2 +Consumer Behavior 3 3 1 +Consumer Health Informatics 3 3 1 +Consumer Health Information 5 6 2 +Consumer Organizations 3 3 1 +Consumer Product Safety 3 3 1 +Consummatory Behavior 4 4 1 +Contact Inhibition 2 2 1 +Contact Lens Solutions 3 6 3 +Contact Lenses 4 4 1 +Contact Lenses, Extended-Wear 6 6 1 +Contact Lenses, Hydrophilic 5 5 1 +Contact Tracing 3 5 3 +Contactin 1 7 8 8 +Contactin 2 7 8 8 +Contactins 6 7 8 +Containment of Biohazards 2 6 2 +Contig Mapping 3 5 2 +Contingent Negative Variation 5 5 2 +Continuity of Patient Care 3 5 3 +Continuous Flow Chemistry 3 3 1 +Continuous Glucose Monitoring 4 6 4 +Continuous Positive Airway Pressure 5 5 2 +Continuous Renal Replacement Therapy 3 3 2 +Contraception 3 3 1 +Contraception Behavior 4 5 2 +Contraception, Barrier 4 4 1 +Contraception, Immunologic 4 4 1 +Contraception, Postcoital 4 4 1 +Contraceptive Agents 5 5 2 +Contraceptive Agents, Female 6 6 2 +Contraceptive Agents, Hormonal 6 6 2 +Contraceptive Agents, Male 6 6 2 +Contraceptive Devices 2 2 1 +Contraceptive Devices, Female 3 3 1 +Contraceptive Devices, Male 3 3 1 +Contraceptive Effectiveness 4 4 1 +Contraceptive Prevalence Surveys 5 6 3 +Contraceptives, Oral 7 7 2 +Contraceptives, Oral, Combined 3 8 3 +Contraceptives, Oral, Hormonal 7 8 4 +Contraceptives, Oral, Sequential 8 8 2 +Contraceptives, Oral, Synthetic 8 8 2 +Contraceptives, Postcoital 7 7 2 +Contraceptives, Postcoital, Hormonal 7 8 4 +Contraceptives, Postcoital, Synthetic 8 8 2 +Contract Services 4 4 1 +Contractile Proteins 3 3 1 +Contracts 4 5 2 +Contracture 3 3 2 +Contraindications 2 2 1 +Contraindications, Drug 3 3 1 +Contraindications, Procedure 3 3 1 +Contrast Media 3 4 2 +Contrast Sensitivity 3 6 5 +Contrecoup Injury 2 6 4 +Control Groups 4 4 2 +Controlled Before-After Studies 5 6 3 +Controlled Clinical Trial 4 4 1 +Controlled Clinical Trials as Topic 6 7 3 +Controlled Substances 2 2 1 +Contusions 3 3 1 +Conus Snail 7 7 1 +Convalescence 4 4 1 +Convallaria 10 10 1 +Convection 3 3 1 +Convergence, Ocular 3 3 1 +Conversion Disorder 3 3 1 +Conversion to Open Surgery 4 4 1 +Convolutional Neural Networks 3 6 2 +Convolvulaceae 7 7 1 +Convolvulus 8 8 1 +Convulsants 5 6 2 +Convulsive Therapy 3 3 1 +Conyza 8 8 1 +Cookbook 2 2 1 +Cookbooks as Topic 6 6 2 +Cooking 5 5 1 +Cooking and Eating Utensils 3 5 3 +Cool-Down Exercise 3 6 4 +Coombs Test 7 8 3 +Cooperative Behavior 4 4 1 +Coordination Complexes 2 2 2 +COP-Coated Vesicles 10 10 1 +COP9 Signalosome Complex 3 7 3 +Copepoda 6 6 1 +Coping Skills 3 4 2 +Copper 4 4 3 +Copper Radioisotopes 4 4 1 +Copper Sulfate 6 6 1 +Copper Transport Proteins 7 7 2 +Copper Transporter 1 7 9 4 +Copper-Transporting ATPases 6 8 5 +Coprinus 5 5 1 +Coprophagia 5 5 1 +Coproporphyria, Hereditary 4 5 4 +Coproporphyrinogen Oxidase 5 5 1 +Coproporphyrinogens 6 8 3 +Coproporphyrins 4 7 4 +Coptis 9 9 1 +Coptis chinensis 10 10 1 +Copulation 6 6 1 +Copying Processes 2 2 1 +Copyright 3 6 3 +Cor Triatriatum 4 5 3 +Coracoid Process 6 6 1 +Coral Bleaching 3 3 1 +Coral Reefs 4 5 2 +Coral Snakes 7 9 3 +Corbicula 6 6 1 +Corchorus 10 10 1 +Cord Blood Stem Cell Transplantation 5 6 2 +Cord Factors 3 4 2 +Cordia 8 8 1 +Cordocentesis 4 6 7 +Cordotomy 4 4 1 +Cordyceps 5 5 1 +Cordyline 10 10 1 +Core Binding Factor Alpha 1 Subunit 6 6 1 +Core Binding Factor Alpha 2 Subunit 6 6 1 +Core Binding Factor Alpha 3 Subunit 6 6 1 +Core Binding Factor alpha Subunits 5 5 1 +Core Binding Factor beta Subunit 5 5 1 +Core Binding Factors 4 4 1 +Core Stability 4 6 4 +Coreopsis 8 8 1 +Coriandrum 8 8 1 +Coriolaceae 5 5 1 +Coriolis Force 3 3 1 +Corn Oil 4 6 6 +Cornaceae 7 7 1 +Cornea 4 4 1 +Corneal Cross-Linking 4 4 3 +Corneal Diseases 2 2 1 +Corneal Dystrophies, Hereditary 3 4 3 +Corneal Dystrophy, Juvenile Epithelial of Meesmann 4 5 3 +Corneal Edema 3 3 1 +Corneal Endothelial Cell Loss 3 4 4 +Corneal Injuries 3 6 4 +Corneal Keratocytes 4 4 1 +Corneal Neovascularization 3 5 2 +Corneal Opacity 3 3 1 +Corneal Pachymetry 4 4 1 +Corneal Perforation 4 7 4 +Corneal Stroma 5 5 1 +Corneal Surgery, Laser 3 4 4 +Corneal Topography 4 4 1 +Corneal Transplantation 4 5 3 +Corneal Ulcer 3 4 3 +Corneal Wavefront Aberration 3 3 2 +Cornell Medical Index 4 4 1 +Cornified Envelope Proline-Rich Proteins 4 4 2 +Cornus 8 8 1 +Coronary Aneurysm 4 5 3 +Coronary Angiography 5 6 4 +Coronary Artery Bypass 5 5 3 +Coronary Artery Bypass, Off-Pump 6 6 3 +Coronary Artery Disease 5 5 3 +Coronary Care Units 5 5 1 +Coronary Circulation 4 4 1 +Coronary Disease 4 4 2 +Coronary Occlusion 5 5 2 +Coronary Restenosis 6 6 2 +Coronary Sinus 5 5 1 +Coronary Stenosis 5 5 2 +Coronary Thrombosis 5 5 3 +Coronary Vasospasm 5 5 2 +Coronary Vessel Anomalies 4 5 3 +Coronary Vessels 4 4 2 +Coronary-Subclavian Steal Syndrome 4 5 3 +Coronaviridae 5 5 1 +Coronaviridae Infections 5 5 1 +Coronavirus 6 6 1 +Coronavirus 229E, Human 8 8 1 +Coronavirus 3C Proteases 7 8 3 +Coronavirus Envelope Proteins 6 6 1 +Coronavirus Infections 6 6 1 +Coronavirus M Proteins 7 7 1 +Coronavirus NL63, Human 8 8 1 +Coronavirus Nucleocapsid Proteins 6 6 1 +Coronavirus OC43, Human 9 9 1 +Coronavirus Papain-Like Proteases 7 8 3 +Coronavirus Protease Inhibitors 7 7 2 +Coronavirus RNA-Dependent RNA Polymerase 6 8 2 +Coronavirus, Bovine 9 9 1 +Coronavirus, Canine 9 9 1 +Coronavirus, Feline 9 9 1 +Coronavirus, Rat 8 8 1 +Coronavirus, Turkey 8 8 1 +Coroners and Medical Examiners 3 4 2 +Corpora Allata 2 3 2 +Corpse Dismemberment 3 5 3 +Corpus Callosum 7 7 1 +Corpus Luteum 5 6 2 +Corpus Luteum Hormones 4 4 1 +Corpus Luteum Maintenance 6 6 2 +Corpus Striatum 8 8 1 +Corrected and Republished Article 2 2 1 +Correction of Hearing Impairment 3 6 4 +Correctional Facilities 2 2 1 +Correctional Facilities Personnel 3 3 1 +Correlation of Data 4 4 1 +Correspondence as Topic 5 5 1 +Corrinoids 4 6 3 +Corrosion 2 2 1 +Corrosion Casting 6 7 4 +Corsiaceae 9 9 1 +Cortactin 4 5 6 +Cortical Bone 4 4 1 +Cortical Excitability 3 3 2 +Cortical Spreading Depression 5 5 1 +Cortical Synchronization 4 6 3 +Corticobasal Degeneration 4 4 1 +Corticomedial Nuclear Complex 6 9 2 +Corticosterone 6 7 2 +Corticotrophs 3 11 7 +Corticotropin-Like Intermediate Lobe Peptide 6 7 6 +Corticotropin-Releasing Hormone 6 7 4 +Corticoviridae 3 3 1 +Cortinarius 5 5 1 +Cortisol Awakening Response 2 2 1 +Cortisone 6 6 2 +Cortisone Reductase 7 7 1 +Cortodoxone 6 7 2 +Corydalis 9 9 1 +Corylus 10 10 1 +Corynebacterium 4 6 2 +Corynebacterium diphtheriae 5 7 2 +Corynebacterium glutamicum 5 7 2 +Corynebacterium Infections 6 6 1 +Corynebacterium pseudotuberculosis 5 7 2 +Corynebacterium pyogenes 5 7 2 +COS Cells 4 5 2 +Cosmeceuticals 2 4 2 +Cosmetic Techniques 2 2 1 +Cosmetics 3 4 3 +Cosmic Dust 4 5 3 +Cosmic Radiation 3 5 4 +Cosmids 4 4 2 +Cost Allocation 4 4 1 +Cost Control 4 4 1 +Cost of Illness 4 8 3 +Cost Savings 5 5 1 +Cost Sharing 4 5 2 +Cost-Benefit Analysis 4 4 1 +Cost-Effectiveness Analysis 4 4 1 +Costa Rica 4 4 1 +Costal Cartilage 4 4 1 +Costameres 6 6 1 +Costello Syndrome 3 4 3 +Costimulatory and Inhibitory T-Cell Receptors 6 6 1 +Costs and Cost Analysis 3 3 1 +Costus 9 9 1 +Cosyntropin 8 9 6 +Cote d'Ivoire 5 5 1 +Cotinine 5 5 1 +Cotton Fiber 4 4 1 +Cottonseed Oil 4 6 6 +Cottontail rabbit papillomavirus 6 6 2 +Coturnix 8 8 1 +Cotyledon 4 5 2 +Cough 3 4 2 +Cough-Variant Asthma 4 6 4 +Coumaphos 5 7 5 +Coumaric Acids 5 5 1 +Coumarins 5 5 2 +Coumestrol 6 8 4 +Counseling 3 4 4 +Counselors 3 3 1 +Countercurrent Distribution 3 5 2 +Counterfeit Drugs 3 3 1 +Counterimmunoelectrophoresis 5 9 6 +Counterpulsation 3 3 1 +Countertransference 5 5 1 +COUP Transcription Factor I 6 6 2 +COUP Transcription Factor II 5 6 4 +COUP Transcription Factors 4 5 3 +Couples Therapy 5 5 1 +Courage 3 3 1 +Courtship 4 4 1 +COVID-19 4 7 5 +COVID-19 Drug Treatment 3 3 1 +COVID-19 Nucleic Acid Testing 4 5 2 +COVID-19 Serological Testing 4 6 5 +COVID-19 Serotherapy 6 8 2 +COVID-19 Testing 3 4 2 +COVID-19 Vaccines 5 5 1 +Cowpox 5 5 1 +Cowpox virus 6 6 1 +Coxa Magna 3 3 1 +Coxa Valga 3 5 3 +Coxa Vara 5 5 2 +Coxiella 5 6 2 +Coxiella burnetii 6 7 2 +Coxiellaceae 4 5 2 +Coxsackie and Adenovirus Receptor-Like Membrane Protein 6 7 6 +Coxsackievirus Infections 6 6 1 +Coyotes 10 10 1 +CpG Islands 5 6 3 +Crack Cocaine 3 7 5 +Cracked Tooth Syndrome 4 5 2 +CRADD Signaling Adaptor Protein 6 6 13 +Crambe Plant 8 8 1 +Crambe Sponge 5 5 1 +Crangonidae 7 7 1 +Cranial Fontanelles 5 5 1 +Cranial Fossa, Anterior 4 6 2 +Cranial Fossa, Middle 4 6 2 +Cranial Fossa, Posterior 4 6 2 +Cranial Irradiation 3 3 1 +Cranial Nerve Diseases 2 2 1 +Cranial Nerve Injuries 3 4 3 +Cranial Nerve Neoplasms 3 5 5 +Cranial Nerves 4 4 1 +Cranial Sinuses 4 4 1 +Cranial Sutures 5 5 1 +Craniocerebral Trauma 3 3 2 +Craniofacial Abnormalities 3 4 2 +Craniofacial Dysostosis 4 5 3 +Craniofacial Fibrous Dysplasia 6 6 1 +Craniology 4 4 1 +Craniomandibular Disorders 3 4 3 +Craniopharyngioma 5 5 2 +Craniospinal Irradiation 3 3 1 +Craniosynostoses 4 6 5 +Craniotomy 3 3 1 +Crassostrea 7 7 1 +Crassulaceae 9 9 1 +Crassulacean Acid Metabolism 3 5 7 +Crataegus 10 10 1 +Craterostigma 8 8 1 +Craving 4 4 1 +Creatine 3 4 2 +Creatine Kinase 6 6 1 +Creatine Kinase, BB Form 7 7 1 +Creatine Kinase, MB Form 7 7 1 +Creatine Kinase, Mitochondrial Form 4 7 2 +Creatine Kinase, MM Form 7 7 1 +Creatinine 5 5 1 +Creativity 3 4 2 +CREB-Binding Protein 5 9 2 +Credentialing 3 3 2 +Cremation 6 6 1 +Crenarchaeota 2 2 1 +Creosote 8 8 1 +Crepis 8 8 1 +Cresols 7 7 1 +CREST Syndrome 4 7 8 +Creutzfeldt-Jakob Syndrome 4 5 4 +Crew Resource Management, Healthcare 3 3 1 +CRF Receptor, Type 1 7 8 4 +Cri-du-Chat Syndrome 4 5 4 +Cricetinae 10 10 1 +Cricetulus 11 11 1 +Cricket Sport 5 5 1 +Cricoid Cartilage 4 5 3 +Crigler-Najjar Syndrome 5 5 2 +Crime 3 4 2 +Crime Victims 2 2 1 +Crimean War 5 6 2 +Criminal Behavior 3 3 1 +Criminal Law 3 5 2 +Criminal Psychology 3 3 1 +Criminals 2 2 1 +Criminology 2 2 1 +Crinivirus 4 5 2 +Crinum 10 10 1 +Crisis Intervention 3 3 1 +CRISPR-Associated Protein 9 4 7 3 +CRISPR-Associated Proteins 3 3 1 +CRISPR-Cas Systems 5 5 1 +Crisscross Heart 4 5 3 +Crithidia 5 5 1 +Crithidia fasciculata 6 6 1 +Critical Care 3 4 2 +Critical Care Nursing 4 4 2 +Critical Care Outcomes 6 7 2 +Critical Illness 4 4 1 +Critical Pathways 5 5 1 +Critical Period, Psychological 4 4 1 +Crizotinib 4 5 2 +Crk-Associated Substrate Protein 4 5 4 +Croatia 4 4 1 +Crocus 10 10 1 +Crohn Disease 5 5 2 +Cromakalim 5 5 3 +Cromolyn Sodium 6 6 2 +Cronkhite-Canada Syndrome 4 5 3 +Cronobacter 5 5 2 +Cronobacter sakazakii 6 6 2 +Crop Production 3 3 1 +Crop Protection 3 3 1 +Crop, Avian 2 2 1 +Crops, Agricultural 3 4 3 +Cross Circulation 3 3 1 +Cross Infection 2 5 2 +Cross Protection 3 3 1 +Cross Reactions 3 3 1 +Cross-Cultural Comparison 5 5 2 +Cross-Linking Reagents 5 5 1 +Cross-Over Studies 4 5 3 +Cross-Priming 3 6 2 +Cross-Sectional Studies 5 6 3 +Crosses, Genetic 3 3 1 +Crossing Over, Genetic 4 4 1 +Crotalaria 8 8 1 +Crotalid Venoms 5 6 2 +Crotalinae 7 9 3 +Crotalus 8 10 3 +Croton 10 10 1 +Croton Oil 4 5 3 +Crotonates 5 5 2 +Crotoxin 6 7 2 +Croup 4 4 2 +Crowding 4 4 1 +Crowdsourcing 4 4 1 +Crown Compounds 3 3 1 +Crown Ethers 4 4 3 +Crown Lengthening 4 4 1 +Crown-Rump Length 4 8 7 +Crowns 4 4 2 +Crows 8 8 1 +Crush Injuries 2 2 1 +Crush Syndrome 3 3 2 +Crustacea 5 5 1 +Crutches 4 4 1 +Crying 5 5 2 +Cryoanesthesia 3 3 1 +Cryobiology 4 4 1 +Cryoelectron Microscopy 4 6 2 +Cryogels 5 6 2 +Cryoglobulinemia 4 5 5 +Cryoglobulins 7 7 3 +Cryopreservation 3 7 6 +Cryoprotective Agents 4 5 2 +Cryopyrin-Associated Periodic Syndromes 4 7 7 +Cryosurgery 3 3 1 +Cryotherapy 2 2 1 +Cryoultramicrotomy 6 7 4 +Cryptocarya 9 9 1 +Cryptochromes 4 5 4 +Cryptococcosis 4 4 1 +Cryptococcus 4 4 2 +Cryptococcus gattii 5 5 2 +Cryptococcus neoformans 5 5 2 +Cryptogenic Organizing Pneumonia 7 8 3 +Cryptolepis 9 9 1 +Cryptomeria 8 8 1 +Cryptophyta 2 2 1 +Cryptorchidism 3 5 6 +Cryptosporidiidae 6 6 1 +Cryptosporidiosis 4 5 6 +Cryptosporidium 7 7 1 +Cryptosporidium parvum 8 8 1 +Cryptoxanthins 5 10 4 +Crystal Arthropathies 3 3 1 +Crystallins 4 4 1 +Crystallization 2 3 2 +Crystallography 3 4 2 +Crystallography, X-Ray 5 5 1 +Crystalloid Solutions 4 4 1 +Crystalluria 4 4 1 +CSK Tyrosine-Protein Kinase 6 9 2 +Ctenocephalides 10 10 1 +Ctenophora 4 4 1 +CTLA-4 Antigen 4 7 4 +Cuba 4 5 2 +Cubital Tunnel Syndrome 5 6 3 +Cubozoa 5 5 1 +Cucumaria 6 6 1 +Cucumber Mosaic Virus Satellite 5 5 1 +Cucumis 8 8 1 +Cucumis melo 9 9 1 +Cucumis sativus 9 9 1 +Cucumovirus 4 5 3 +Cucurbit(n)urils 4 6 2 +Cucurbita 8 8 1 +Cucurbitaceae 7 7 1 +Cucurbitacins 5 5 1 +Cues 4 4 1 +Culdoscopes 4 4 2 +Culdoscopy 3 5 5 +Culex 13 13 1 +Culicidae 12 12 1 +Culicomorpha 11 11 1 +Cullin Proteins 4 7 2 +Cultural Characteristics 5 5 2 +Cultural Competency 5 5 1 +Cultural Deprivation 5 6 2 +Cultural Diversity 5 5 2 +Cultural Evolution 5 5 1 +Culturally Appropriate Technology 3 3 1 +Culturally Competent Care 3 4 2 +Culture 4 4 2 +Culture Media 2 4 2 +Culture Media, Conditioned 3 5 2 +Culture Media, Serum-Free 3 5 2 +Culture Techniques 3 3 1 +Cultured Milk Products 3 5 5 +Cuminum 8 8 1 +Cumulative Trauma Disorders 3 3 1 +Cumulus Cells 4 8 4 +Cuniculidae 8 8 1 +Cunninghamella 5 5 1 +Cunninghamia 8 8 1 +Cuphea 10 10 1 +Cupping Therapy 3 3 1 +Cupressaceae 7 7 1 +Cupressus 8 8 1 +Cupriavidus 6 6 2 +Cupriavidus necator 7 7 2 +Cuprizone 7 7 1 +Cuproptosis 5 5 1 +Curacao 4 4 1 +Curare 5 5 1 +Curculigo 10 10 1 +Curcuma 10 10 1 +Curcumin 6 8 3 +Curettage 2 2 1 +Curing Lights, Dental 3 3 2 +Curium 4 6 5 +Current Procedural Terminology 6 6 1 +Curriculum 2 2 1 +Curvularia 4 4 2 +Cuscuta 8 8 1 +Cushing Syndrome 4 4 1 +Cuspid 5 5 1 +Custodial Care 3 4 2 +Cutaneous Elimination 3 5 3 +Cutaneous Fistula 3 4 2 +Cutaneous Malignant Melanoma 4 7 5 +Cutaneous Squamous Cell Carcinoma 5 6 3 +Cutis Laxa 3 4 3 +CX3C Chemokine Receptor 1 7 9 3 +Cyamopsis 8 8 1 +Cyanamide 4 5 2 +Cyanates 2 2 1 +Cyanides 4 5 2 +Cyanoacrylates 3 5 7 +Cyanobacteria 2 4 2 +Cyanobacteria Toxins 4 4 1 +Cyanogen Bromide 3 5 2 +Cyanoketone 7 7 1 +Cyanophora 3 3 1 +Cyanosis 3 3 1 +Cyanothece 3 5 2 +Cyathus 5 5 1 +Cyberbullying 5 6 3 +Cybernetics 3 3 1 +Cycadopsida 5 5 1 +Cycas 6 6 1 +Cycasin 4 4 1 +Cyclacillin 5 6 3 +Cyclamates 7 7 1 +Cyclamen 9 9 1 +Cyclams 3 4 2 +Cyclandelate 5 5 2 +Cyclazocine 3 4 2 +Cyclea 8 8 1 +Cyclic ADP-Ribose 7 10 5 +Cyclic AMP 4 7 4 +Cyclic AMP Receptor Protein 4 4 1 +Cyclic AMP Response Element Modulator 5 5 2 +Cyclic AMP Response Element-Binding Protein 5 5 2 +Cyclic AMP Response Element-Binding Protein A 5 5 2 +Cyclic AMP-Dependent Protein Kinase Catalytic Subunits 8 11 4 +Cyclic AMP-Dependent Protein Kinase RIalpha Subunit 8 11 2 +Cyclic AMP-Dependent Protein Kinase RIbeta Subunit 8 11 2 +Cyclic AMP-Dependent Protein Kinase RIIalpha Subunit 8 11 2 +Cyclic AMP-Dependent Protein Kinase RIIbeta Subunit 8 11 2 +Cyclic AMP-Dependent Protein Kinase Type I 7 10 2 +Cyclic AMP-Dependent Protein Kinase Type II 7 10 2 +Cyclic AMP-Dependent Protein Kinases 6 9 2 +Cyclic CMP 4 6 4 +Cyclic GMP 4 7 4 +Cyclic GMP-Dependent Protein Kinase Type I 7 10 2 +Cyclic GMP-Dependent Protein Kinase Type II 7 10 2 +Cyclic GMP-Dependent Protein Kinases 6 9 2 +Cyclic Guanosine Monophosphate-Adenosine Monophosphate Synthase 6 6 1 +Cyclic IMP 4 7 4 +Cyclic N-Oxides 3 3 1 +Cyclic Nucleotide Phosphodiesterases, Type 1 5 7 4 +Cyclic Nucleotide Phosphodiesterases, Type 2 5 7 4 +Cyclic Nucleotide Phosphodiesterases, Type 3 5 7 2 +Cyclic Nucleotide Phosphodiesterases, Type 4 5 7 2 +Cyclic Nucleotide Phosphodiesterases, Type 5 5 7 4 +Cyclic Nucleotide Phosphodiesterases, Type 6 5 7 4 +Cyclic Nucleotide Phosphodiesterases, Type 7 5 7 2 +Cyclic Nucleotide-Gated Cation Channels 6 6 3 +Cyclic Nucleotide-Regulated Protein Kinases 5 8 2 +Cyclic P-Oxides 3 3 1 +Cyclic S-Oxides 3 3 2 +Cyclin A 5 5 3 +Cyclin A1 6 6 3 +Cyclin A2 6 6 3 +Cyclin B 5 5 3 +Cyclin B1 6 6 3 +Cyclin B2 6 6 3 +Cyclin C 4 6 8 +Cyclin D 5 5 3 +Cyclin D1 6 6 4 +Cyclin D2 6 6 3 +Cyclin D3 6 6 3 +Cyclin E 5 5 3 +Cyclin G 5 5 3 +Cyclin G1 6 6 3 +Cyclin G2 6 6 3 +Cyclin H 5 5 3 +Cyclin I 5 5 3 +Cyclin T 5 6 4 +Cyclin-Dependent Kinase 2 5 10 3 +Cyclin-Dependent Kinase 3 5 10 3 +Cyclin-Dependent Kinase 4 5 10 3 +Cyclin-Dependent Kinase 5 6 11 3 +Cyclin-Dependent Kinase 6 5 10 3 +Cyclin-Dependent Kinase 8 4 10 7 +Cyclin-Dependent Kinase 9 4 11 5 +Cyclin-Dependent Kinase Inhibitor p15 5 6 4 +Cyclin-Dependent Kinase Inhibitor p16 5 6 4 +Cyclin-Dependent Kinase Inhibitor p18 5 6 4 +Cyclin-Dependent Kinase Inhibitor p19 5 6 4 +Cyclin-Dependent Kinase Inhibitor p21 5 6 6 +Cyclin-Dependent Kinase Inhibitor p27 5 6 4 +Cyclin-Dependent Kinase Inhibitor p57 5 6 4 +Cyclin-Dependent Kinase Inhibitor Proteins 4 5 4 +Cyclin-Dependent Kinase-Activating Kinase 5 10 3 +Cyclin-Dependent Kinases 4 9 3 +Cyclins 4 4 3 +Cyclitols 6 6 1 +Cyclization 2 3 3 +Cyclizine 4 4 1 +Cycloaddition Reaction 3 5 2 +Cyclobutanes 6 6 1 +Cyclodecanes 6 6 1 +Cyclodextrins 3 6 3 +Cyclodialysis Clefts 4 5 2 +Cyclofenil 8 8 1 +Cycloheptanes 6 6 1 +Cyclohexane Monoterpenes 5 7 2 +Cyclohexanecarboxylic Acids 4 7 2 +Cyclohexanes 6 6 1 +Cyclohexanols 4 7 3 +Cyclohexanones 3 7 2 +Cyclohexenes 7 7 1 +Cycloheximide 5 5 1 +Cyclohexylamines 3 7 2 +Cycloleucine 4 7 2 +Cyclonic Storms 4 5 2 +Cyclooctanes 6 6 1 +Cyclooxygenase 1 5 5 1 +Cyclooxygenase 2 5 5 1 +Cyclooxygenase 2 Inhibitors 6 10 4 +Cyclooxygenase Inhibitors 5 9 4 +Cycloparaffins 3 5 2 +Cyclopentane Monoterpenes 5 7 2 +Cyclopentanes 6 6 1 +Cyclopenthiazide 5 6 3 +Cyclopentolate 5 5 1 +Cyclophanes 5 5 1 +Cyclophilin A 6 8 3 +Cyclophilin C 6 8 3 +Cyclophilins 5 7 3 +Cyclophosphamide 5 7 2 +Cyclopia Plant 8 8 1 +Cyclopropanes 6 6 1 +Cycloserine 5 6 3 +Cyclospora 7 7 1 +Cyclosporiasis 5 5 1 +Cyclosporine 5 5 2 +Cyclosporins 4 4 2 +Cyclosteroids 4 4 1 +Cyclothymic Disorder 3 3 1 +Cyclotides 4 4 1 +Cyclotrons 4 4 1 +Cylindrospermopsis 3 5 2 +Cymarine 5 8 2 +Cymbopogon 8 8 1 +Cymenes 5 6 2 +Cynanchum 9 9 1 +Cynara 8 8 1 +Cynara scolymus 9 9 1 +Cynodon 8 8 1 +Cynomorium 8 8 1 +Cyperaceae 7 7 1 +Cyperus 8 8 1 +Cyprinidae 7 7 1 +Cypriniformes 6 6 1 +Cyprinodontiformes 7 7 1 +Cyproheptadine 4 8 3 +Cyproterone 5 6 2 +Cyproterone Acetate 6 7 2 +Cyprus 4 5 2 +Cyrtosperma 10 10 1 +CYS2-HIS2 Zinc Fingers 9 9 1 +Cyst Fluid 3 3 1 +Cystadenocarcinoma 5 6 2 +Cystadenocarcinoma, Mucinous 6 7 2 +Cystadenocarcinoma, Papillary 6 7 2 +Cystadenocarcinoma, Serous 6 7 2 +Cystadenofibroma 5 8 3 +Cystadenoma 5 5 2 +Cystadenoma, Mucinous 6 6 2 +Cystadenoma, Papillary 6 6 2 +Cystadenoma, Serous 6 6 2 +Cystamine 4 4 1 +Cystaphos 5 5 3 +Cystathionine 4 4 4 +Cystathionine beta-Synthase 6 6 1 +Cystathionine gamma-Lyase 5 5 1 +Cystatin A 4 4 1 +Cystatin B 4 4 1 +Cystatin C 4 4 1 +Cystatin M 4 5 2 +Cystatins 3 3 1 +Cysteamine 5 5 2 +Cystectomy 4 4 1 +Cysteic Acid 4 4 2 +Cysteine 4 4 4 +Cysteine Dioxygenase 6 6 1 +Cysteine Endopeptidases 6 6 2 +Cysteine Loop Ligand-Gated Ion Channel Receptors 5 7 4 +Cysteine Proteases 5 5 1 +Cysteine Proteinase Inhibitors 6 6 1 +Cysteine Synthase 5 5 1 +Cysteine-Rich Protein 61 4 6 4 +Cysteinyldopa 5 10 6 +Cystic Adenomatoid Malformation of Lung, Congenital 3 4 3 +Cystic Duct 5 5 1 +Cystic Fibrosis 3 3 4 +Cystic Fibrosis Transmembrane Conductance Regulator 7 10 7 +Cysticercosis 6 6 1 +Cysticercus 8 8 1 +Cystine 4 7 7 +Cystine Depleting Agents 4 4 1 +Cystine Knot Motifs 8 8 1 +Cystine-Knot Miniproteins 3 3 2 +Cystinosis 5 5 2 +Cystinuria 5 8 4 +Cystinyl Aminopeptidase 7 7 3 +Cystitis 4 6 3 +Cystitis, Hemorrhagic 5 7 3 +Cystitis, Interstitial 5 7 3 +Cystocele 4 6 4 +Cystography 5 6 2 +Cystoscopes 4 4 2 +Cystoscopy 4 5 4 +Cystostomy 3 5 2 +Cystotomy 4 4 1 +Cystoviridae 4 4 2 +Cysts 2 3 2 +Cytapheresis 3 5 4 +Cytarabine 4 6 3 +Cytidine 4 5 3 +Cytidine Deaminase 6 6 1 +Cytidine Diphosphate 5 6 3 +Cytidine Diphosphate Choline 5 7 5 +Cytidine Diphosphate Diglycerides 5 7 5 +Cytidine Monophosphate 5 6 3 +Cytidine Monophosphate N-Acetylneuraminic Acid 5 7 7 +Cytidine Triphosphate 5 6 3 +Cytisus 8 8 1 +Cytochalasin B 5 6 2 +Cytochalasin D 5 6 2 +Cytochalasins 4 5 2 +Cytochrome a Group 3 5 2 +Cytochrome b Group 3 5 2 +Cytochrome b6f Complex 5 8 5 +Cytochrome c Group 3 5 2 +Cytochrome d Group 3 5 2 +Cytochrome P-450 CYP11B2 5 8 6 +Cytochrome P-450 CYP1A1 5 8 6 +Cytochrome P-450 CYP1A2 5 8 6 +Cytochrome P-450 CYP1A2 Inducers 5 5 2 +Cytochrome P-450 CYP1A2 Inhibitors 5 6 2 +Cytochrome P-450 CYP1B1 5 8 6 +Cytochrome P-450 CYP2A6 5 8 3 +Cytochrome P-450 CYP2B1 5 8 6 +Cytochrome P-450 CYP2B6 5 8 6 +Cytochrome P-450 CYP2B6 Inducers 5 5 2 +Cytochrome P-450 CYP2B6 Inhibitors 5 6 2 +Cytochrome P-450 CYP2C19 6 9 3 +Cytochrome P-450 CYP2C19 Inducers 5 5 2 +Cytochrome P-450 CYP2C19 Inhibitors 5 6 2 +Cytochrome P-450 CYP2C8 5 8 6 +Cytochrome P-450 CYP2C8 Inducers 5 5 2 +Cytochrome P-450 CYP2C8 Inhibitors 5 6 2 +Cytochrome P-450 CYP2C9 6 9 3 +Cytochrome P-450 CYP2C9 Inducers 5 5 2 +Cytochrome P-450 CYP2C9 Inhibitors 5 6 2 +Cytochrome P-450 CYP2D6 5 8 6 +Cytochrome P-450 CYP2D6 Inducers 5 5 2 +Cytochrome P-450 CYP2D6 Inhibitors 5 6 2 +Cytochrome P-450 CYP2E1 5 8 4 +Cytochrome P-450 CYP2E1 Inducers 5 5 2 +Cytochrome P-450 CYP2E1 Inhibitors 5 6 2 +Cytochrome P-450 CYP2J2 4 7 4 +Cytochrome P-450 CYP3A 5 8 4 +Cytochrome P-450 CYP3A Inducers 5 5 2 +Cytochrome P-450 CYP3A Inhibitors 5 6 2 +Cytochrome P-450 CYP4A 5 8 3 +Cytochrome P-450 Enzyme Inducers 4 4 2 +Cytochrome P-450 Enzyme Inhibitors 4 5 2 +Cytochrome P-450 Enzyme System 3 6 3 +Cytochrome P450 Family 1 4 7 3 +Cytochrome P450 Family 11 4 7 3 +Cytochrome P450 Family 12 4 7 3 +Cytochrome P450 Family 17 4 7 3 +Cytochrome P450 Family 19 4 7 3 +Cytochrome P450 Family 2 4 7 3 +Cytochrome P450 Family 21 4 7 3 +Cytochrome P450 Family 24 4 7 3 +Cytochrome P450 Family 26 4 7 3 +Cytochrome P450 Family 27 4 7 3 +Cytochrome P450 Family 3 4 7 3 +Cytochrome P450 Family 4 4 7 3 +Cytochrome P450 Family 46 4 7 3 +Cytochrome P450 Family 51 4 7 3 +Cytochrome P450 Family 6 4 7 3 +Cytochrome P450 Family 7 4 7 3 +Cytochrome P450 Family 8 4 7 3 +Cytochrome Reductases 5 5 1 +Cytochrome-B(5) Reductase 4 6 2 +Cytochrome-c Oxidase Deficiency 4 4 2 +Cytochrome-c Peroxidase 5 5 1 +Cytochromes 2 4 2 +Cytochromes a 4 6 2 +Cytochromes a1 4 6 2 +Cytochromes a3 4 6 2 +Cytochromes b 4 6 2 +Cytochromes b5 4 6 2 +Cytochromes b6 4 9 7 +Cytochromes c 4 6 2 +Cytochromes c' 4 6 2 +Cytochromes c1 4 6 2 +Cytochromes c2 4 6 2 +Cytochromes c6 4 6 2 +Cytochromes f 3 9 7 +Cytodiagnosis 3 5 3 +Cytogenetic Analysis 3 5 4 +Cytogenetics 5 5 1 +Cytoglobin 5 5 1 +Cytokine Receptor Common beta Subunit 9 9 4 +Cytokine Receptor gp130 8 9 5 +Cytokine Release Syndrome 5 5 2 +Cytokine TWEAK 5 6 3 +Cytokine-Induced Killer Cells 3 6 4 +Cytokines 3 4 3 +Cytokinesis 4 4 1 +Cytokinins 6 6 1 +Cytological Techniques 2 4 3 +Cytology 4 5 2 +Cytomegalovirus 5 5 1 +Cytomegalovirus Infections 5 5 1 +Cytomegalovirus Retinitis 4 6 5 +Cytomegalovirus Vaccines 5 5 1 +Cytopathogenic Effect, Viral 3 6 4 +Cytopenia 3 3 1 +Cytophaga 5 6 2 +Cytophagaceae 4 5 2 +Cytophagaceae Infections 5 5 1 +Cytophagocytosis 3 5 5 +Cytophotometry 3 6 4 +Cytoplasm 4 4 1 +Cytoplasmic Dyneins 6 8 5 +Cytoplasmic Granules 6 8 2 +Cytoplasmic Ribonucleoprotein Granules 7 9 2 +Cytoplasmic Streaming 2 3 2 +Cytoplasmic Structures 5 5 1 +Cytoplasmic Vesicles 7 7 1 +Cytoprotection 4 4 1 +Cytoreduction Surgical Procedures 2 2 1 +Cytosine 5 5 1 +Cytosine Deaminase 6 6 1 +Cytosine Nucleotides 4 5 3 +Cytoskeletal Proteins 3 3 1 +Cytoskeleton 6 6 1 +Cytosol 5 5 3 +Cytostatic Agents 4 4 1 +Cytotoxicity Tests, Immunologic 3 5 4 +Cytotoxicity, Immunologic 2 2 1 +Cytotoxins 4 4 1 +Czech Republic 4 4 1 +Czechoslovakia 3 3 1 +D-Ala(2),MePhe(4),Met(0)-ol-enkephalin 7 8 2 +D-Alanine Transaminase 6 6 1 +D-Amino-Acid Oxidase 6 6 1 +D-Aspartate Oxidase 6 6 1 +D-Aspartic Acid 5 5 2 +D-Xylulose Reductase 6 7 2 +Dabigatran 4 5 2 +Daboia 8 10 3 +Dacarbazine 3 5 2 +Daclizumab 9 9 3 +Dacryocystitis 3 3 1 +Dacryocystography 4 4 1 +Dacryocystorhinostomy 3 3 2 +Dactinomycin 4 4 3 +Dactylis 8 8 1 +Dagestan 5 5 1 +Dahlia 8 8 1 +Dairy Products 3 4 2 +Dairying 3 3 1 +Dalbergia 8 8 1 +Dalteparin 6 6 1 +Dammaranes 5 5 1 +Danazol 6 6 1 +Dance Therapy 3 6 5 +Dancing 4 4 1 +Dander 3 3 1 +Dandruff 4 4 2 +Dandy-Walker Syndrome 3 5 4 +Dangerous Behavior 3 4 2 +Dansyl Compounds 4 7 3 +Dantrolene 7 7 1 +Daphne 8 8 1 +Daphnia 7 7 1 +Daphnia magna 8 8 1 +Daphnia pulex 8 8 1 +Daphniphyllaceae 8 8 1 +Daphniphyllum 9 9 1 +Dapsone 4 4 1 +Daptomycin 3 4 4 +Darbepoetin alfa 6 6 2 +Darier Disease 4 4 3 +Dark Adaptation 3 3 1 +Darkness 4 4 1 +Darunavir 4 5 4 +Dasatinib 4 5 3 +Dashboard Systems 5 5 2 +Dasyproctidae 8 8 1 +Data Accuracy 4 6 5 +Data Aggregation 4 4 1 +Data Analysis 3 3 1 +Data Analytics 3 3 1 +Data Anonymization 4 6 4 +Data Collection 3 4 4 +Data Compression 3 4 3 +Data Curation 5 5 2 +Data Display 2 4 2 +Data Interpretation, Statistical 3 6 5 +Data Management 3 4 3 +Data Mining 3 6 2 +Data Science 2 2 1 +Data Systems 4 4 1 +Data Visualization 3 3 1 +Data Warehousing 3 6 3 +Database 2 2 1 +Database Management Systems 3 4 3 +Databases as Topic 3 6 2 +Databases, Bibliographic 4 7 2 +Databases, Chemical 5 8 2 +Databases, Factual 4 7 2 +Databases, Genetic 5 8 2 +Databases, Nucleic Acid 6 9 4 +Databases, Pharmaceutical 5 8 2 +Databases, Protein 6 9 4 +Dataset 2 2 1 +Datasets as Topic 4 8 6 +Datura 9 9 1 +Datura metel 10 10 1 +Datura stramonium 10 10 1 +Daucus carota 8 8 1 +Daunorubicin 5 8 3 +DAX-1 Orphan Nuclear Receptor 5 6 2 +Day Care, Medical 3 4 2 +DC-Specific ICAM-3 Grabbing Nonintegrin 5 7 6 +DCC Receptor 5 6 4 +DCMP Deaminase 6 6 1 +DDT 5 5 1 +De Lange Syndrome 4 5 4 +De Quervain Disease 5 5 1 +DEAD Box Protein 20 5 9 4 +DEAD Box Protein 58 9 9 1 +DEAD-box RNA Helicases 8 8 1 +DEAE-Cellulose 5 5 1 +DEAE-Dextran 5 5 1 +Deaf Culture 6 6 2 +Deaf-Blind Disorders 4 8 7 +Deafness 5 7 3 +Dealkylation 2 3 3 +Deamination 2 3 3 +Deamino Arginine Vasopressin 6 8 5 +Deanol 5 5 2 +Death 3 3 1 +Death Certificates 4 6 5 +Death Domain 10 10 1 +Death Domain Receptor Signaling Adaptor Proteins 5 5 5 +Death Domain Superfamily 9 9 1 +Death Effector Domain 10 10 1 +Death, Sudden 4 4 1 +Death, Sudden, Cardiac 4 5 2 +Death-Associated Protein Kinases 6 9 2 +Debaryomyces 4 5 2 +Debridement 2 2 1 +Debrisoquin 5 5 1 +Decalcification Technique 4 5 2 +Decalcification, Pathologic 4 5 3 +Decamethonium Compounds 5 5 2 +Decanoates 4 4 1 +Decanoic Acids 3 3 1 +Decapitation 3 3 1 +Decapoda 6 6 1 +Decapodiformes 6 6 1 +Decarboxylation 2 3 3 +Deceleration 4 4 1 +Decellularized Extracellular Matrix 3 5 4 +Deception 4 4 1 +Decerebrate State 3 4 2 +Decidua 3 6 2 +Deciduoma 4 7 2 +Decision Making 4 4 1 +Decision Making, Computer-Assisted 5 5 1 +Decision Making, Organizational 3 3 1 +Decision Making, Shared 3 5 3 +Decision Support Systems, Clinical 6 6 1 +Decision Support Systems, Management 4 4 1 +Decision Support Techniques 2 5 2 +Decision Theory 2 2 1 +Decision Trees 3 3 1 +Decitabine 4 7 4 +Decompression 2 4 2 +Decompression Sickness 3 3 1 +Decompression, Explosive 5 5 1 +Decompression, Surgical 2 2 1 +Decompressive Craniectomy 3 4 2 +Decontamination 4 4 1 +Decoquinate 6 6 1 +Decorin 5 6 6 +Dectin-1 5 7 2 +Deductibles and Coinsurance 5 6 2 +Deep Brain Stimulation 2 3 2 +Deep Eutectic Solvents 4 4 1 +Deep Learning 3 6 4 +Deep Sedation 3 3 1 +Deer 9 9 1 +DEET 4 8 3 +Defamation 4 5 2 +Default Mode Network 3 3 1 +Defecation 3 3 1 +Defecography 6 6 1 +Defective Interfering Viruses 3 3 1 +Defective Viruses 2 2 1 +Defense Mechanisms 2 2 1 +Defensins 4 6 3 +Defensive Medicine 5 6 2 +Deferasirox 5 7 3 +Deferiprone 5 5 1 +Deferoxamine 5 5 2 +Defibrillators 4 4 1 +Defibrillators, Implantable 4 5 3 +Deficiency Diseases 4 4 1 +DEFICIENS Protein 4 6 3 +Defoliants, Chemical 5 6 2 +Degenerin Sodium Channels 7 7 3 +Degloving Injuries 3 3 1 +Deglutition 3 3 1 +Deglutition Disorders 3 4 2 +Degrons 7 7 1 +Dehalococcoides 3 3 1 +Dehumanization 4 4 1 +Dehydration 3 4 2 +Dehydroascorbatase 6 6 1 +Dehydroascorbic Acid 3 6 4 +Dehydrocholesterols 5 8 3 +Dehydrocholic Acid 6 6 2 +Dehydroepiandrosterone 5 7 4 +Dehydroepiandrosterone Sulfate 6 8 4 +Deinococcus 4 4 1 +Deinstitutionalization 4 5 2 +Deja Vu 5 5 1 +Dekkera 4 5 2 +Delavirdine 4 5 2 +Delaware 6 6 1 +Delay Discounting 6 6 1 +Delayed Diagnosis 2 4 3 +Delayed Emergence from Anesthesia 4 4 1 +Delayed Graft Function 3 3 1 +Delayed Rectifier Potassium Channels 8 8 3 +Delayed-Action Preparations 3 4 2 +Delegation, Professional 4 4 1 +Deleted in Azoospermia 1 Protein 6 6 2 +Delftia 6 6 2 +Delftia acidovorans 7 7 2 +Delirium 3 6 4 +Delivery of Health Care 2 3 2 +Delivery of Health Care, Integrated 3 4 2 +Delivery Rooms 4 4 1 +Delivery, Obstetric 3 3 1 +Delphi Technique 3 3 1 +Delphinium 9 9 1 +Delta Catenin 5 5 1 +Delta Rhythm 4 6 4 +Delta Sleep-Inducing Peptide 4 5 3 +delta-1-Pyrroline-5-Carboxylate Reductase 6 6 1 +Delta-5 Fatty Acid Desaturase 7 7 1 +delta-Crystallins 5 7 2 +delta-Globins 7 8 2 +delta-Thalassemia 5 7 4 +Deltacoronavirus 7 7 1 +Deltainfluenzavirus 5 5 1 +Deltapapillomavirus 5 5 2 +Deltaproteobacteria 3 3 1 +Deltaretrovirus 4 4 2 +Deltaretrovirus Antibodies 8 8 3 +Deltaretrovirus Antigens 4 5 2 +Deltaretrovirus Infections 3 5 2 +Deltoid Muscle 4 4 1 +Delusional Parasitosis 3 3 1 +Delusions 4 4 1 +Demeclocycline 5 8 2 +Demecolcine 4 4 1 +Dementia 3 4 2 +Dementia, Multi-Infarct 5 8 9 +Dementia, Vascular 4 7 7 +Demethylation 3 4 3 +Democracy 3 3 1 +Democratic People's Republic of Korea 5 5 1 +Democratic Republic of the Congo 5 5 1 +Demography 2 4 3 +Demoralization 4 4 1 +Demulcents 4 5 4 +Demyelinating Autoimmune Diseases, CNS 3 5 4 +Demyelinating Diseases 2 2 1 +Denaturing Gradient Gel Electrophoresis 5 5 2 +Dendrimers 3 5 3 +Dendrites 3 4 3 +Dendritic Cell Sarcoma, Follicular 4 5 2 +Dendritic Cell Sarcoma, Interdigitating 4 5 2 +Dendritic Cells 3 4 4 +Dendritic Cells, Follicular 3 7 4 +Dendritic Spines 4 5 3 +Dendroaspis 7 9 3 +Dendrobium 10 10 1 +Denervation 3 3 1 +Dengue 4 6 4 +Dengue Vaccines 5 5 1 +Dengue Virus 6 6 1 +Denial, Psychological 3 3 1 +Denitrification 4 4 3 +Denmark 4 4 1 +Dennstaedtiaceae 7 7 1 +Denosumab 9 9 3 +Dens in Dente 4 5 3 +Dense Core Vesicles 10 10 1 +Densitometry 4 4 1 +Density Functional Theory 5 5 1 +Densovirinae 3 4 2 +Densovirus 4 5 2 +Dent Disease 4 7 5 +Dental Abutments 4 4 2 +Dental Alloys 3 5 2 +Dental Amalgam 4 6 2 +Dental Anxiety 4 4 1 +Dental Arch 3 7 2 +Dental Articulators 3 3 2 +Dental Assistants 5 6 4 +Dental Atraumatic Restorative Treatment 2 2 1 +Dental Audit 4 5 2 +Dental Auxiliaries 4 5 4 +Dental Bonding 2 2 1 +Dental Calculus 4 4 2 +Dental Care 2 4 2 +Dental Care for Aged 3 5 2 +Dental Care for Children 3 5 2 +Dental Care for Chronically Ill 3 5 2 +Dental Care for Persons with Disabilities 3 5 2 +Dental Care Team 4 4 1 +Dental Caries 4 4 1 +Dental Caries Activity Tests 3 3 1 +Dental Caries Susceptibility 3 3 1 +Dental Casting Investment 3 5 2 +Dental Casting Technique 3 3 2 +Dental Cavity Lining 4 4 1 +Dental Cavity Preparation 3 3 1 +Dental Cements 3 5 2 +Dental Cementum 5 5 2 +Dental Clasps 4 4 2 +Dental Clinics 4 4 1 +Dental Debonding 2 2 1 +Dental Deposits 3 3 1 +Dental Devices, Home Care 3 4 3 +Dental Disinfectants 4 6 2 +Dental Enamel 5 5 1 +Dental Enamel Hypomineralization 5 6 3 +Dental Enamel Hypoplasia 5 6 3 +Dental Enamel Permeability 4 4 1 +Dental Enamel Proteins 3 3 1 +Dental Enamel Solubility 3 3 1 +Dental Equipment 2 2 2 +Dental Etching 3 3 1 +Dental Facilities 3 3 1 +Dental Fissures 4 5 2 +Dental Fistula 4 5 2 +Dental Health Services 3 3 1 +Dental Health Surveys 2 7 5 +Dental High-Speed Equipment 3 3 2 +Dental High-Speed Technique 2 2 1 +Dental Hygienists 5 6 4 +Dental Implant-Abutment Design 4 5 2 +Dental Implantation 3 4 4 +Dental Implantation, Endosseous 4 5 4 +Dental Implantation, Endosseous, Endodontic 3 6 5 +Dental Implantation, Subperiosteal 4 5 4 +Dental Implants 3 5 4 +Dental Implants, Single-Tooth 5 5 2 +Dental Impression Materials 3 5 2 +Dental Impression Technique 3 3 1 +Dental Informatics 3 3 1 +Dental Instruments 3 3 2 +Dental Leakage 3 3 1 +Dental Marginal Adaptation 3 3 2 +Dental Materials 2 4 3 +Dental Occlusion 2 3 2 +Dental Occlusion, Balanced 3 3 1 +Dental Occlusion, Centric 3 3 1 +Dental Occlusion, Traumatic 4 4 1 +Dental Offices 4 4 1 +Dental Papilla 6 6 1 +Dental Pellicle 6 6 1 +Dental Physiological Phenomena 2 2 1 +Dental Pins 2 2 1 +Dental Plaque 4 4 1 +Dental Plaque Index 3 8 5 +Dental Polishing 2 2 1 +Dental Porcelain 3 5 3 +Dental Prophylaxis 3 3 2 +Dental Prosthesis 3 3 2 +Dental Prosthesis Design 3 4 2 +Dental Prosthesis Repair 3 4 2 +Dental Prosthesis Retention 4 4 1 +Dental Prosthesis, Implant-Supported 4 4 2 +Dental Pulp 5 5 1 +Dental Pulp Calcification 4 4 1 +Dental Pulp Capping 3 3 1 +Dental Pulp Cavity 5 5 1 +Dental Pulp Devitalization 4 4 1 +Dental Pulp Diseases 3 3 1 +Dental Pulp Exposure 4 4 1 +Dental Pulp Necrosis 4 4 2 +Dental Pulp Test 3 3 1 +Dental Records 4 6 5 +Dental Research 3 5 2 +Dental Restoration Failure 4 4 1 +Dental Restoration Repair 5 5 2 +Dental Restoration Wear 5 5 1 +Dental Restoration, Permanent 4 4 2 +Dental Restoration, Temporary 4 4 2 +Dental Sac 6 6 1 +Dental Scaling 4 4 2 +Dental Service, Hospital 4 6 3 +Dental Soldering 3 3 1 +Dental Staff 3 4 2 +Dental Staff, Hospital 4 5 4 +Dental Stress Analysis 2 2 1 +Dental Technicians 5 6 4 +Dental Veneers 4 4 2 +Dental Waste 4 6 2 +Dentate Gyrus 6 9 2 +Dentifrices 2 4 3 +Dentigerous Cyst 5 6 3 +Dentin 5 5 1 +Dentin Desensitizing Agents 6 7 2 +Dentin Dysplasia 4 5 3 +Dentin Permeability 4 4 1 +Dentin Sensitivity 3 3 1 +Dentin Sialophosphoprotein 4 5 5 +Dentin Solubility 3 3 1 +Dentin, Secondary 4 6 2 +Dentin-Bonding Agents 4 6 2 +Dentinal Fluid 3 3 1 +Dentinogenesis 6 6 1 +Dentinogenesis Imperfecta 4 5 3 +Dentist's Role 6 6 1 +Dentist-Patient Relations 4 5 2 +Dentistry 1 2 2 +Dentistry, Operative 3 3 1 +Dentists 4 5 2 +Dentists, Women 3 6 3 +Dentition 3 4 2 +Dentition, Mixed 4 4 1 +Dentition, Permanent 4 4 1 +Dentofacial Deformities 4 6 4 +Denture Bases 5 5 2 +Denture Cleansers 3 5 2 +Denture Design 3 5 2 +Denture Identification Marking 3 3 1 +Denture Liners 5 5 2 +Denture Precision Attachment 6 6 2 +Denture Rebasing 3 5 2 +Denture Repair 3 5 2 +Denture Retention 5 5 1 +Denture, Complete 5 5 2 +Denture, Complete, Immediate 6 6 2 +Denture, Complete, Lower 6 6 2 +Denture, Complete, Upper 6 6 2 +Denture, Overlay 5 5 2 +Denture, Partial 5 5 2 +Denture, Partial, Fixed 6 6 2 +Denture, Partial, Fixed, Resin-Bonded 7 7 2 +Denture, Partial, Immediate 6 6 2 +Denture, Partial, Removable 6 6 2 +Denture, Partial, Temporary 6 6 2 +Dentures 4 4 2 +Denturists 5 6 4 +Denys-Drash Syndrome 4 8 16 +Deodorants 4 4 1 +Deoxy Sugars 2 2 1 +Deoxyadenine Nucleotides 4 7 3 +Deoxyadenosines 4 7 3 +Deoxycholic Acid 6 6 2 +Deoxycytidine 4 6 3 +Deoxycytidine Kinase 6 6 1 +Deoxycytidine Monophosphate 5 7 3 +Deoxycytosine Nucleotides 4 6 3 +Deoxyepinephrine 5 10 4 +Deoxyglucose 3 3 1 +Deoxyguanine Nucleotides 4 7 3 +Deoxyguanosine 4 7 3 +Deoxyribodipyrimidine Photo-Lyase 4 5 2 +Deoxyribonuclease (Pyrimidine Dimer) 7 7 1 +Deoxyribonuclease BamHI 6 9 3 +Deoxyribonuclease EcoRI 6 9 3 +Deoxyribonuclease HindIII 6 9 3 +Deoxyribonuclease HpaII 6 9 3 +Deoxyribonuclease I 7 7 1 +Deoxyribonuclease IV (Phage T4-Induced) 7 7 1 +Deoxyribonucleases 5 5 1 +Deoxyribonucleases, Type I Site-Specific 5 8 3 +Deoxyribonucleases, Type II Site-Specific 5 8 3 +Deoxyribonucleases, Type III Site-Specific 5 8 3 +Deoxyribonucleoproteins 4 4 1 +Deoxyribonucleosides 3 3 1 +Deoxyribonucleotides 3 3 1 +Deoxyribose 3 3 1 +Deoxyuracil Nucleotides 4 6 3 +Deoxyuridine 4 6 3 +Dependency, Psychological 3 3 1 +Dependent Ambulation 6 6 1 +Dependent Personality Disorder 3 3 1 +Dependovirus 5 5 1 +Depersonalization 4 4 1 +Deportation 5 5 1 +Depreciation 5 5 1 +Deprescriptions 3 3 1 +Depression 3 4 2 +Depression, Chemical 4 4 1 +Depression, Postpartum 4 5 2 +Depressive Disorder 3 3 1 +Depressive Disorder, Treatment-Resistant 4 4 1 +Depsides 5 8 4 +Depsipeptides 4 4 2 +Depth Perception 4 5 2 +Dequalinium 6 6 1 +Dermabrasion 3 4 2 +Dermacentor 9 9 1 +Dermal Fillers 4 4 1 +Dermatan Sulfate 5 5 1 +Dermatitis 3 3 1 +Dermatitis Herpetiformis 3 4 3 +Dermatitis, Allergic Contact 4 5 3 +Dermatitis, Atopic 4 4 5 +Dermatitis, Contact 4 4 2 +Dermatitis, Exfoliative 4 4 2 +Dermatitis, Irritant 5 5 2 +Dermatitis, Occupational 2 5 3 +Dermatitis, Perioral 4 4 2 +Dermatitis, Photoallergic 4 6 4 +Dermatitis, Phototoxic 4 6 3 +Dermatitis, Seborrheic 4 4 4 +Dermatitis, Toxicodendron 5 6 3 +Dermatofibrosarcoma 6 7 2 +Dermatoglyphics 5 6 3 +Dermatologic Agents 4 4 1 +Dermatologic Surgical Procedures 3 3 1 +Dermatologists 4 5 2 +Dermatology 3 3 1 +Dermatomycoses 3 4 3 +Dermatomyositis 3 6 4 +Dermatophagoides farinae 9 9 1 +Dermatophagoides pteronyssinus 9 9 1 +Dermatophilus 4 7 2 +Dermcidins 4 6 3 +Dermis 3 3 1 +Dermoid Cyst 3 5 2 +Dermoscopy 4 6 2 +Dermotoxins 4 4 1 +Derris 8 8 1 +Descemet Membrane 4 5 2 +Descemet Stripping Endothelial Keratoplasty 4 5 3 +Descending Thoracic Aortic Aneurysm 6 6 2 +Desegregation 6 6 1 +Desensitization, Immunologic 4 6 2 +Desensitization, Psychologic 4 4 1 +Desert Climate 5 6 2 +Desflurane 4 5 3 +Desiccation 2 3 2 +Designed Ankyrin Repeat Proteins 4 4 2 +Designer Drugs 3 3 1 +Desipramine 5 5 1 +Deslanoside 6 9 2 +Desmidiales 5 5 1 +Desmin 5 5 2 +Desmocollins 7 8 4 +Desmoglein 1 4 9 5 +Desmoglein 2 8 9 4 +Desmoglein 3 4 9 5 +Desmogleins 7 8 4 +Desmoid Tumors 7 7 1 +Desmoplakins 5 5 1 +Desmoplastic Small Round Cell Tumor 5 5 1 +Desmosine 4 5 2 +Desmosomal Cadherins 6 7 4 +Desmosomes 6 6 1 +Desmosterol 6 8 3 +Desogestrel 7 7 1 +Desonide 7 7 1 +Desoximetasone 6 7 2 +Desoxycorticosterone 5 7 2 +Desoxycorticosterone Acetate 6 8 2 +Destrin 6 6 2 +Desulfitobacterium 3 4 2 +Desulfotomaculum 3 5 5 +Desulfovibrio 3 6 3 +Desulfovibrio africanus 4 7 3 +Desulfovibrio desulfuricans 4 7 3 +Desulfovibrio gigas 4 7 3 +Desulfovibrio vulgaris 4 7 3 +Desulfovibrionaceae 3 5 3 +Desulfovibrionaceae Infections 5 5 1 +Desulfovibrionales 4 4 1 +Desulfurococcaceae 4 4 1 +Desulfurococcales 3 3 1 +Desulfuromonas 3 5 3 +Desvenlafaxine Succinate 5 8 4 +Detection Algorithms 3 4 2 +Detergents 3 4 2 +Deubiquitinating Enzyme CYLD 4 5 2 +Deubiquitinating Enzymes 3 3 1 +Deuterium 3 4 3 +Deuterium Exchange Measurement 3 3 1 +Deuterium Oxide 5 7 4 +Deuteroporphyrins 4 7 4 +Devazepide 7 7 1 +Developed Countries 4 4 1 +Developing Countries 4 4 1 +Developmental Biology 4 4 1 +Developmental Defects of Enamel 4 5 3 +Developmental Disabilities 3 3 1 +Developmental Disability Nursing 4 4 2 +Developmental Dysplasia of the Hip 3 5 3 +Developmental Origins of Health and Disease 6 6 1 +Device Approval 3 4 2 +Device Lead Extraction 3 3 1 +Device Removal 2 2 1 +Dexamethasone 5 7 2 +Dexamethasone Isonicotinate 6 8 2 +Dexetimide 5 5 1 +Dexfenfluramine 5 5 1 +Dexlansoprazole 6 7 3 +Dexmedetomidine 5 5 1 +Dexmethylphenidate Hydrochloride 5 6 2 +Dexpramipexole 5 6 2 +Dexrazoxane 6 6 1 +Dextran Sulfate 5 5 1 +Dextranase 5 5 1 +Dextrans 4 5 2 +Dextrins 4 6 3 +Dextroamphetamine 7 7 1 +Dextrocardia 4 5 4 +Dextromethorphan 4 5 4 +Dextromoramide 5 5 1 +Dextropropoxyphene 5 5 1 +Dextrorphan 4 5 4 +Dextrothyroxine 4 5 2 +Diabesity 4 6 2 +Diabetes Complications 3 3 1 +Diabetes Insipidus 3 6 4 +Diabetes Insipidus, Nephrogenic 5 7 3 +Diabetes Insipidus, Neurogenic 4 7 4 +Diabetes Mellitus 2 4 2 +Diabetes Mellitus, Experimental 3 5 3 +Diabetes Mellitus, Lipoatrophic 4 6 2 +Diabetes Mellitus, Type 1 3 5 3 +Diabetes Mellitus, Type 2 3 5 2 +Diabetes, Gestational 3 5 3 +Diabetic Angiopathies 3 4 2 +Diabetic Cardiomyopathies 4 4 2 +Diabetic Coma 4 4 1 +Diabetic Foot 4 6 4 +Diabetic Ketoacidosis 4 6 2 +Diabetic Nephropathies 4 6 4 +Diabetic Neuropathies 4 4 2 +Diabetic Retinopathy 3 5 3 +Diabulimia 3 3 1 +Diacetyl 4 4 1 +Diacylglycerol Cholinephosphotransferase 6 6 1 +Diacylglycerol Kinase 6 6 1 +Diacylglycerol O-Acyltransferase 5 5 1 +Diagnosis 1 1 1 +Diagnosis, Computer-Assisted 2 6 2 +Diagnosis, Differential 2 2 1 +Diagnosis, Dual (Psychiatry) 2 2 1 +Diagnosis, Oral 2 2 1 +Diagnosis-Related Groups 7 7 1 +Diagnostic and Statistical Manual of Mental Disorders 6 6 1 +Diagnostic Equipment 2 2 1 +Diagnostic Errors 2 4 2 +Diagnostic Imaging 3 3 1 +Diagnostic Reference Levels 4 5 3 +Diagnostic Screening Programs 5 5 1 +Diagnostic Self Evaluation 3 5 2 +Diagnostic Services 4 4 1 +Diagnostic Techniques and Procedures 2 2 1 +Diagnostic Techniques, Cardiovascular 3 3 1 +Diagnostic Techniques, Digestive System 3 3 1 +Diagnostic Techniques, Endocrine 3 3 1 +Diagnostic Techniques, Neurological 3 3 1 +Diagnostic Techniques, Obstetrical and Gynecological 3 3 1 +Diagnostic Techniques, Ophthalmological 3 3 1 +Diagnostic Techniques, Otological 3 3 1 +Diagnostic Techniques, Radioisotope 3 3 1 +Diagnostic Techniques, Respiratory System 3 3 1 +Diagnostic Techniques, Surgical 3 3 1 +Diagnostic Techniques, Urological 3 3 1 +Diagnostic Test Approval 4 5 2 +Diagnostic Tests, Routine 3 3 1 +Diagnostic Uses of Chemicals 3 3 1 +Diagonal Band of Broca 6 6 1 +Dialectical Behavior Therapy 4 4 1 +Dialysis 2 3 2 +Dialysis Solutions 4 5 3 +Diamfenetide 5 6 2 +Diamide 3 3 1 +Diamine N-Acetyltransferase 6 6 1 +Diamines 4 4 1 +Diaminopimelic Acid 4 6 2 +Diamond 4 4 1 +Dianhydrogalactitol 4 5 2 +Dianisidine 7 7 1 +Dianthus 10 10 1 +Diapause 6 6 1 +Diapause, Insect 7 7 1 +Diaper Rash 6 6 2 +Diapers, Adult 4 4 1 +Diapers, Infant 4 4 1 +Diaphragm 4 5 2 +Diaphragmatic Eventration 3 4 2 +Diaphyses 4 4 1 +Diaries as Topic 4 4 1 +Diarrhea 4 4 1 +Diarrhea Virus 1, Bovine Viral 7 7 1 +Diarrhea Virus 2, Bovine Viral 7 7 1 +Diarrhea Viruses, Bovine Viral 6 6 1 +Diarrhea, Infantile 5 5 1 +Diary 2 2 1 +Diarylheptanoids 5 6 2 +Diarylquinolines 5 5 1 +Diaschisis 4 4 1 +Diastasis, Bone 3 4 2 +Diastasis, Muscle 3 4 2 +Diastema 4 5 3 +Diastole 4 5 3 +Diathermy 3 3 1 +Diatomaceous Earth 4 5 3 +Diatoms 3 3 1 +Diatrizoate 7 9 2 +Diatrizoate Meglumine 5 10 5 +Diazepam 7 7 1 +Diazepam Binding Inhibitor 3 3 1 +Diazinon 5 5 3 +Diazomethane 4 4 1 +Diazonium Compounds 3 3 1 +Diazooxonorleucine 3 5 2 +Diazoxide 5 6 3 +Dibekacin 5 5 1 +Dibenz(b,f)(1,4)oxazepine-10(11H)-carboxylic acid, 8-chloro-, 2-acetylhydrazide 5 5 1 +Dibenzazepines 4 4 1 +Dibenzocycloheptenes 4 7 2 +Dibenzofurans 4 4 1 +Dibenzofurans, Polychlorinated 3 5 2 +Dibenzothiazepines 4 5 2 +Dibenzothiepins 4 4 2 +Dibenzoxazepines 4 4 1 +Dibenzoxepins 4 4 1 +Dibenzylchlorethamine 4 4 1 +Dibromothymoquinone 4 4 1 +Dibucaine 3 5 2 +Dibutyl Phthalate 5 5 1 +Dibutyryl Cyclic GMP 5 8 4 +Dicamba 6 9 6 +Dicarbethoxydihydrocollidine 5 5 1 +Dicarboxylic Acid Transporters 8 8 2 +Dicarboxylic Acids 4 4 1 +Dicentra 9 9 1 +Dichelobacter nodosus 5 5 3 +Dichloroacetic Acid 6 6 2 +Dichlorodiphenyl Dichloroethylene 6 6 2 +Dichlorodiphenyldichloroethane 5 5 1 +Dichloroethylenes 5 6 2 +Dichlorophen 8 8 1 +Dichlororibofuranosylbenzimidazole 4 4 1 +Dichlorphenamide 4 5 2 +Dichlorvos 4 4 1 +Dichotic Listening Tests 5 5 1 +Dicistroviridae 3 4 2 +Dickeya 4 4 1 +Dickeya chrysanthemi 5 5 1 +Diclofenac 5 5 1 +Dicloxacillin 7 8 3 +Dicofol 6 7 2 +Dicrocoeliasis 5 5 1 +Dicrocoeliidae 7 7 1 +Dicrocoelium 8 8 1 +Dictamnus 8 8 1 +Dictionaries as Topic 7 7 1 +Dictionaries, Chemical as Topic 8 8 1 +Dictionaries, Classical as Topic 8 8 1 +Dictionaries, Dental as Topic 8 8 1 +Dictionaries, Medical as Topic 8 8 2 +Dictionaries, Pharmaceutic as Topic 8 8 1 +Dictionary 2 2 1 +Dictionary, Chemical 3 3 1 +Dictionary, Classical 3 3 1 +Dictionary, Dental 3 3 1 +Dictionary, Medical 3 3 1 +Dictionary, Pharmaceutic 3 3 1 +Dictionary, Polyglot 3 3 1 +Dictyocaulus 9 9 1 +Dictyocaulus Infections 4 8 4 +Dictyosteliida 4 4 1 +Dictyostelium 5 5 1 +Dicumarol 7 7 2 +Dicyclohexylcarbodiimide 4 4 1 +Dicyclomine 5 7 2 +Didanosine 5 7 4 +Didelphis 8 8 1 +Dideoxyadenosine 5 8 4 +Dideoxynucleosides 4 4 1 +Dideoxynucleotides 3 3 1 +Dieldrin 5 5 1 +Dielectric Spectroscopy 4 4 1 +Diencephalon 5 5 1 +Dienestrol 7 9 4 +Dientamoeba 4 4 1 +Dientamoebiasis 4 5 3 +Diestrus 4 4 1 +Diet 4 4 1 +Diet Fads 2 5 2 +Diet Records 4 4 1 +Diet Surveys 5 7 4 +Diet Therapy 3 3 1 +Diet, Atherogenic 5 5 1 +Diet, Carbohydrate Loading 4 5 2 +Diet, Carbohydrate-Restricted 4 5 2 +Diet, Cariogenic 5 5 1 +Diet, Diabetic 4 5 2 +Diet, Fat-Restricted 4 5 2 +Diet, Food, and Nutrition 2 2 1 +Diet, Gluten-Free 4 5 2 +Diet, Healthy 5 5 2 +Diet, High-Fat 5 5 1 +Diet, High-Protein 4 5 2 +Diet, High-Protein Low-Carbohydrate 5 6 4 +Diet, Ketogenic 5 6 2 +Diet, Macrobiotic 6 7 2 +Diet, Mediterranean 5 6 2 +Diet, Paleolithic 4 5 2 +Diet, Plant-Based 4 5 2 +Diet, Protein-Restricted 4 5 2 +Diet, Reducing 4 5 2 +Diet, Sodium-Restricted 4 5 2 +Diet, Vegan 6 7 2 +Diet, Vegetarian 5 6 2 +Diet, Western 5 5 1 +Dietary Advanced Glycation End Products 3 4 4 +Dietary Approaches To Stop Hypertension 4 5 2 +Dietary Carbohydrates 2 4 3 +Dietary Exposure 5 5 1 +Dietary Fats 3 4 3 +Dietary Fats, Unsaturated 4 5 3 +Dietary Fiber 3 4 3 +Dietary Proteins 3 4 3 +Dietary Services 3 3 1 +Dietary Sucrose 4 9 9 +Dietary Sugars 3 5 4 +Dietary Supplements 3 4 2 +Dietetics 3 3 1 +Diethyl Pyrocarbonate 6 6 1 +Diethylamines 4 4 1 +Diethylcarbamazine 4 5 2 +Diethylhexyl Phthalate 5 5 1 +Diethylnitrosamine 4 4 1 +Diethylpropion 4 4 1 +Diethylstilbestrol 9 9 1 +Differential Thermal Analysis 3 3 1 +Differential Threshold 5 5 1 +Diffuse Axonal Injury 6 6 3 +Diffuse Cerebral Sclerosis of Schilder 4 6 5 +Diffuse Intrinsic Pontine Glioma 6 8 6 +Diffuse Neurofibrillary Tangles with Calcification 4 5 3 +Diffuse Noxious Inhibitory Control 3 3 2 +Diffusion 2 2 2 +Diffusion Chambers, Culture 2 2 1 +Diffusion Magnetic Resonance Imaging 6 6 1 +Diffusion of Innovation 3 3 1 +Diffusion Tensor Imaging 3 7 4 +Diflubenzuron 5 7 3 +Diflucortolone 6 8 2 +Diflunisal 9 9 1 +DiGeorge Syndrome 5 6 11 +Digestion 3 4 2 +Digestive System 1 1 1 +Digestive System Abnormalities 2 3 2 +Digestive System and Oral Physiological Phenomena 1 1 1 +Digestive System Diseases 1 1 1 +Digestive System Fistula 2 4 2 +Digestive System Neoplasms 2 3 2 +Digestive System Physiological Phenomena 2 2 1 +Digestive System Surgical Procedures 2 2 1 +Digit Ratios 4 6 3 +Digital Dermatitis 2 5 4 +Digital Divide 4 4 1 +Digital Health 3 5 4 +Digital Media 3 5 3 +Digital Rectal Examination 5 5 1 +Digital Technology 3 4 2 +Digitalis 9 9 1 +Digitalis Glycosides 4 7 2 +Digitaria 8 8 1 +Digitonin 5 8 2 +Digitoxigenin 8 8 1 +Digitoxin 5 8 3 +Diglycerides 3 3 1 +Dignity Therapy 3 3 1 +Digoxigenin 8 8 1 +Digoxin 5 8 3 +Dihematoporphyrin Ether 6 9 4 +Dihydralazine 6 6 1 +Dihydro-beta-Erythroidine 3 4 2 +Dihydroalprenolol 7 7 3 +Dihydrodipicolinate Reductase 5 5 1 +Dihydroergocornine 5 5 2 +Dihydroergocristine 5 5 2 +Dihydroergocryptine 5 5 2 +Dihydroergotamine 5 5 2 +Dihydroergotoxine 5 5 2 +Dihydrolipoamide Dehydrogenase 4 7 9 +Dihydrolipoyllysine-Residue Acetyltransferase 5 6 3 +Dihydromorphine 5 6 4 +Dihydroorotase 5 5 1 +Dihydroorotate Dehydrogenase 5 5 1 +Dihydroorotate Oxidase 5 5 1 +Dihydropteridine Reductase 6 6 1 +Dihydropteroate Synthase 5 5 1 +Dihydropyridines 4 4 1 +Dihydropyrimidine Dehydrogenase Deficiency 5 5 2 +Dihydrostilbenoids 8 8 2 +Dihydrostreptomycin Sulfate 5 5 1 +Dihydrotachysterol 5 7 4 +Dihydrotestosterone 6 6 2 +Dihydrouracil Dehydrogenase (NAD+) 5 5 1 +Dihydrouracil Dehydrogenase (NADP) 5 5 1 +Dihydroxyacetone 5 5 2 +Dihydroxyacetone Phosphate 3 3 1 +Dihydroxycholecalciferols 6 8 4 +Dihydroxydihydrobenzopyrenes 5 8 2 +Dihydroxyphenylalanine 4 9 4 +Dihydroxytryptamines 6 6 1 +Diiodothyronines 5 7 2 +Diiodotyrosine 4 6 2 +Diketopiperazines 4 4 1 +Dilatation 2 2 1 +Dilatation and Curettage 3 4 2 +Dilatation, Pathologic 3 3 1 +Dilazep 4 4 1 +Dilleniaceae 7 7 1 +Diltiazem 5 5 1 +Dimaprit 4 5 2 +Dimenhydrinate 3 8 4 +Dimensional Measurement Accuracy 4 6 4 +Dimensionality Reduction 3 6 5 +Dimercaprol 4 4 1 +Dimerization 2 2 2 +Dimethadione 5 5 1 +Dimethindene 4 7 3 +Dimethisterone 6 6 1 +Dimethoate 5 5 3 +Dimethoxyphenylethylamine 5 5 1 +Dimethyl Adipimidate 4 6 2 +Dimethyl Fumarate 6 6 1 +Dimethyl Suberimidate 4 4 1 +Dimethyl Sulfoxide 4 4 1 +Dimethylallyltranstransferase 5 5 1 +Dimethylamines 4 4 1 +Dimethyldithiocarbamate 4 6 2 +Dimethylformamide 4 6 2 +Dimethylglycine Dehydrogenase 6 6 1 +Dimethylhydrazines 4 4 1 +Dimethylnitrosamine 4 4 1 +Dimethylphenylpiperazinium Iodide 4 4 1 +Dimethylpolysiloxanes 5 7 4 +Dimetridazole 4 6 2 +Diminazene 3 4 2 +Dimyristoylphosphatidylcholine 8 8 1 +Dinitolmide 4 8 3 +Dinitrobenzenes 4 7 2 +Dinitrochlorobenzene 5 7 5 +Dinitrocresols 3 8 2 +Dinitrofluorobenzene 5 7 2 +Dinitrogenase Reductase 5 5 1 +Dinitrophenols 4 8 2 +Dinoflagellida 3 3 1 +Dinoprost 7 7 2 +Dinoprostone 7 7 2 +Dinosaurs 6 6 1 +Dinucleoside Phosphates 3 3 1 +Dinucleotide Repeats 7 8 3 +Dioclea 8 8 1 +Dioctophymatoidea 8 8 1 +Dioctyl Sulfosuccinic Acid 6 6 1 +Dioncophyllaceae 9 9 1 +Dioscorea 8 8 1 +Dioscoreaceae 7 7 1 +Diosgenin 6 6 1 +Diosmin 8 8 2 +Diospyros 9 9 1 +Dioxanes 3 3 1 +Dioxins 3 3 2 +Dioxins and Dioxin-like Compounds 2 2 1 +Dioxolanes 4 4 1 +Dioxoles 3 3 1 +Dioxygenases 5 5 1 +Dipeptidases 6 6 1 +Dipeptides 4 4 1 +Dipeptidyl Peptidase 4 5 7 4 +Dipeptidyl-Peptidase IV Inhibitors 5 6 2 +Dipeptidyl-Peptidases and Tripeptidyl-Peptidases 6 6 1 +Dipetalonema 9 9 1 +Dipetalonema Infections 8 8 1 +Diphenhydramine 4 7 2 +Diphenoxylate 4 5 2 +Diphenyl Sulfone Compounds 4 6 2 +Diphenylacetic Acids 5 5 1 +Diphenylamine 4 4 1 +Diphenylcarbazide 3 3 1 +Diphenylhexatriene 6 6 1 +Diphosphates 7 8 3 +Diphosphoglyceric Acids 4 6 4 +Diphosphonates 4 4 1 +Diphosphotransferases 5 5 1 +Diphtheria 7 7 1 +Diphtheria Antitoxin 5 9 4 +Diphtheria Toxin 4 7 2 +Diphtheria Toxoid 5 5 1 +Diphtheria-Tetanus Vaccine 5 6 4 +Diphtheria-Tetanus-acellular Pertussis Vaccines 5 6 5 +Diphtheria-Tetanus-Pertussis Vaccine 5 6 4 +Diphyllobothriasis 5 5 1 +Diphyllobothrium 7 7 1 +Diploidy 3 3 1 +Diplomacy 3 6 3 +Diplomonadida 2 2 1 +Diplopia 3 6 3 +Dipodascus 4 5 2 +Dipodomys 8 8 1 +Diprenorphine 4 5 4 +Dipsacaceae 8 8 1 +Dipsacales 7 7 1 +Diptera 9 9 1 +Dipterocarpaceae 8 8 1 +Dipteryx 8 8 1 +Dipyridamole 4 4 1 +Dipyrone 7 7 1 +Diquat 5 5 1 +Direct Service Costs 4 5 2 +Direct-to-Consumer Advertising 5 5 1 +Direct-To-Consumer Screening and Testing 3 5 2 +Directed Molecular Evolution 4 4 1 +Directed Tissue Donation 4 4 1 +Directive Counseling 4 5 3 +Directly Observed Therapy 8 8 3 +Directories as Topic 7 7 1 +Directory 2 2 1 +Dirofilaria 9 9 1 +Dirofilaria immitis 10 10 1 +Dirofilaria repens 10 10 1 +Dirofilariasis 4 8 5 +Disability Discrimination 5 5 2 +Disability Evaluation 3 3 1 +Disability Studies 4 4 1 +Disability-Adjusted Life Years 5 7 4 +Disaccharidases 5 5 1 +Disaccharides 3 4 2 +Disarticulation 4 4 1 +Disaster Medicine 3 3 1 +Disaster Nursing 4 4 2 +Disaster Planning 4 4 1 +Disaster Victims 2 2 1 +Disasters 3 3 1 +Discitis 4 5 3 +Disclosure 3 6 3 +Discoidin Domain 8 8 1 +Discoidin Domain Receptor 1 7 10 5 +Discoidin Domain Receptor 2 7 10 5 +Discoidin Domain Receptors 6 9 5 +Discoidins 4 4 2 +Discrete Subaortic Stenosis 6 7 2 +Discriminant Analysis 4 5 3 +Discrimination Learning 4 4 1 +Discrimination, Psychological 4 4 1 +Discs Large Homolog 1 Protein 4 7 5 +Disease 3 3 1 +Disease Attributes 3 3 1 +Disease Eradication 3 3 1 +Disease Hotspot 4 4 1 +Disease Management 3 3 1 +Disease Models, Animal 2 4 3 +Disease Notification 3 5 3 +Disease Outbreaks 3 3 1 +Disease Progression 4 4 1 +Disease Reservoirs 5 5 1 +Disease Resistance 3 4 4 +Disease Susceptibility 3 4 2 +Disease Transmission, Infectious 3 3 1 +Disease Vectors 4 5 2 +Disease-Free Survival 4 7 6 +Diseases in Twins 4 4 1 +Disenfranchised Grief 5 5 1 +Disgust 3 3 1 +Dishevelled Proteins 5 5 3 +Disinfectants 3 5 2 +Disinfection 7 7 1 +Disinformation 4 5 2 +Disintegrins 3 3 1 +Disk Diffusion Antimicrobial Tests 5 6 2 +Diskectomy 3 3 3 +Diskectomy, Percutaneous 4 4 3 +Disks Large Homolog 4 Protein 4 7 5 +Disopyramide 4 4 1 +Disorder of Sex Development, 46,XY 4 6 5 +Disorders of Environmental Origin 1 1 1 +Disorders of Excessive Somnolence 5 5 2 +Disorders of Sex Development 3 5 5 +Dispensatories as Topic 7 7 1 +Dispensatory 2 2 1 +Displacement, Psychological 3 3 1 +Disposable Equipment 2 2 1 +Disruptive Technology 3 3 1 +Disruptive, Impulse Control, and Conduct Disorders 2 2 1 +Dissection 2 5 3 +Dissection, Abdominal Aorta 6 6 2 +Dissection, Ascending Aorta 7 7 2 +Dissection, Blood Vessel 4 4 1 +Dissection, Thoracic Aorta 6 6 2 +Disseminated Intravascular Coagulation 4 4 3 +Dissent and Disputes 4 5 2 +Dissociative Disorders 2 2 1 +Dissociative Identity Disorder 3 3 1 +Dissolved Organic Matter 2 2 2 +Distal Myopathies 4 6 3 +Distamycins 3 3 1 +Distance Counseling 4 6 8 +Distance Perception 5 6 2 +Distemper 3 7 2 +Distemper Virus, Canine 8 8 1 +Distemper Virus, Phocine 8 8 1 +Distillation 4 4 1 +Distracted Driving 3 3 1 +District of Columbia 3 6 2 +Disulfides 4 6 3 +Disulfidptosis 4 4 1 +Disulfiram 5 7 3 +Disulfoton 5 5 3 +Diterpene Alkaloids 3 5 2 +Diterpenes 4 4 1 +Diterpenes, Clerodane 5 5 1 +Diterpenes, Kaurane 5 5 1 +Dithiazanine 4 6 3 +Dithioerythritol 3 4 3 +Dithionite 4 6 2 +Dithionitrobenzoic Acid 6 8 2 +Dithiothreitol 3 4 3 +Dithizone 3 4 2 +Ditiocarb 4 6 2 +Diuresis 3 3 1 +Diuretics 5 5 1 +Diuretics, Osmotic 6 6 1 +Diuretics, Potassium Sparing 6 6 1 +Diurnal Enuresis 4 7 5 +Diuron 5 7 2 +Diversity, Equity, Inclusion 6 6 2 +Diverticular Diseases 4 4 1 +Diverticulitis 5 5 1 +Diverticulitis, Colonic 6 6 2 +Diverticulosis, Colonic 5 5 1 +Diverticulosis, Esophageal 4 6 2 +Diverticulosis, Stomach 4 6 2 +Diverticulum 3 5 2 +Diverticulum, Colon 4 6 2 +Diverticulum, Esophageal 4 6 2 +Diverticulum, Stomach 4 6 2 +Diving 4 7 2 +Diving and Hyperbaric Medicine 4 4 1 +Diving Reflex 3 6 4 +Divorce 4 7 6 +Diynes 6 6 1 +Dizocilpine Maleate 5 8 2 +Dizziness 5 5 1 +Djibouti 5 5 1 +DMF Index 3 8 5 +DNA 3 3 1 +DNA (Cytosine-5-)-Methyltransferase 1 5 9 3 +DNA (Cytosine-5-)-Methyltransferases 8 8 1 +DNA Adducts 3 4 2 +DNA Barcoding, Taxonomic 4 5 2 +DNA Breaks 3 3 1 +DNA Breaks, Double-Stranded 4 4 1 +DNA Breaks, Single-Stranded 4 4 1 +DNA Cleavage 2 3 2 +DNA Contamination 4 6 5 +DNA Copy Number Variations 5 5 1 +DNA Damage 2 2 1 +DNA Damage Tolerance 3 4 2 +DNA Degradation, Necrotic 3 4 2 +DNA Demethylation 4 5 3 +DNA End-Joining Repair 3 4 2 +DNA Excision Repair Protein ERCC-5 4 7 4 +DNA Fingerprinting 3 6 4 +DNA Footprinting 3 3 1 +DNA Fragmentation 3 3 1 +DNA Glycosylases 4 6 2 +DNA Gyrase 4 6 2 +DNA Helicases 4 6 2 +DNA Ligase ATP 5 6 2 +DNA Ligases 4 5 2 +DNA Methylation 2 5 4 +DNA Methyltransferase 3A 9 9 1 +DNA Methyltransferase 3B 9 9 1 +DNA Mismatch Repair 3 4 2 +DNA Modification Methylases 4 6 2 +DNA Mutational Analysis 5 5 1 +DNA Nanostructures 4 4 1 +DNA Nucleotidylexotransferase 7 7 1 +DNA Nucleotidyltransferases 6 6 1 +DNA Packaging 2 2 2 +DNA Polymerase beta 8 8 1 +DNA Polymerase gamma 4 8 2 +DNA Polymerase I 8 8 1 +DNA Polymerase II 8 8 1 +DNA Polymerase III 8 8 1 +DNA Polymerase iota 9 9 1 +DNA Polymerase theta 8 8 1 +DNA Primase 8 8 1 +DNA Primers 6 7 2 +DNA Probes 4 6 3 +DNA Probes, HLA 5 7 3 +DNA Probes, HPV 5 7 3 +DNA Repair 2 3 2 +DNA Repair Enzymes 3 3 1 +DNA Repair-Deficiency Disorders 3 3 1 +DNA Repeat Expansion 3 7 6 +DNA Replication 2 3 2 +DNA Replication Timing 3 4 2 +DNA Restriction Enzymes 4 7 3 +DNA Restriction-Modification Enzymes 3 3 1 +DNA Sequence, Unstable 5 5 1 +DNA Shuffling 4 4 1 +DNA Topoisomerase IV 4 6 2 +DNA Topoisomerases 4 4 1 +DNA Topoisomerases, Type I 5 5 3 +DNA Topoisomerases, Type II 5 5 1 +DNA Transformation Competence 2 4 2 +DNA Transposable Elements 4 7 4 +DNA Tumor Viruses 3 3 2 +DNA Virus Infections 3 3 1 +DNA Viruses 2 2 1 +DNA, A-Form 4 6 3 +DNA, Algal 4 4 1 +DNA, Ancient 4 4 1 +DNA, Antisense 3 7 4 +DNA, Archaeal 4 4 1 +DNA, B-Form 4 6 3 +DNA, Bacterial 4 4 1 +DNA, C-Form 4 6 3 +DNA, Catalytic 3 7 3 +DNA, Catenated 3 7 4 +DNA, Chloroplast 5 5 2 +DNA, Circular 4 6 3 +DNA, Complementary 5 7 3 +DNA, Concatenated 4 6 3 +DNA, Cruciform 4 6 3 +DNA, Environmental 4 4 1 +DNA, Fungal 4 4 1 +DNA, Helminth 4 4 1 +DNA, Intergenic 4 5 2 +DNA, Kinetoplast 5 6 2 +DNA, Mitochondrial 5 5 1 +DNA, Neoplasm 4 4 1 +DNA, Plant 4 4 1 +DNA, Protozoan 4 4 1 +DNA, Recombinant 4 4 1 +DNA, Ribosomal 4 4 1 +DNA, Ribosomal Spacer 5 5 2 +DNA, Satellite 4 7 5 +DNA, Single-Stranded 4 6 3 +DNA, Superhelical 5 7 3 +DNA, Viral 4 4 1 +DNA, Z-Form 4 6 3 +DNA-(Apurinic or Apyrimidinic Site) Lyase 4 5 2 +DNA-Activated Protein Kinase 5 8 4 +DNA-Binding Proteins 3 3 1 +DNA-Cytosine Methylases 7 7 1 +DNA-Directed DNA Polymerase 7 7 1 +DNA-Directed RNA Polymerases 7 7 1 +DNA-Formamidopyrimidine Glycosylase 5 7 2 +DnaB Helicases 5 7 2 +Dobutamine 4 9 3 +Docetaxel 6 8 2 +Docosahexaenoic Acids 5 6 3 +Document Analysis 6 6 1 +Documentaries and Factual Films 2 2 1 +Documentation 4 4 1 +Dodecanol 3 4 2 +Dodecenoyl-CoA Isomerase 6 6 1 +Dog Diseases 2 2 1 +Dogfish 8 8 1 +Dogs 10 10 1 +Dolichol Monophosphate Mannose 5 7 3 +Dolichol Phosphates 4 5 6 +Dolichols 3 4 4 +Dolichos 8 8 1 +Dolichospermum flos-aquae 3 5 2 +Dolphins 8 8 1 +Dolutegravir 4 5 4 +DOM 2,5-Dimethoxy-4-Methylamphetamine 6 6 1 +Domestic Violence 5 5 2 +Domestication 3 3 1 +Dominance, Cerebral 3 3 2 +Dominance, Ocular 2 5 2 +Dominance-Subordination 5 5 1 +Dominica 4 5 2 +Dominican Republic 4 5 2 +Domperidone 4 5 2 +Donepezil 4 8 3 +Donohue Syndrome 3 5 5 +Donor Conception 4 4 2 +Donor Selection 4 4 2 +Dopa Decarboxylase 7 7 1 +Dopamine 4 9 3 +Dopamine Agents 5 5 2 +Dopamine Agonists 6 6 2 +Dopamine and cAMP-Regulated Phosphoprotein 32 4 4 2 +Dopamine Antagonists 6 6 2 +Dopamine beta-Hydroxylase 6 6 1 +Dopamine D2 Receptor Antagonists 7 7 2 +Dopamine Plasma Membrane Transport Proteins 6 8 6 +Dopamine Uptake Inhibitors 6 6 5 +Dopaminergic Imaging 4 6 6 +Dopaminergic Neurons 3 3 2 +Doping in Sports 4 4 1 +Doppler Effect 2 2 1 +Doripenem 6 6 2 +Dorsal Raphe Nucleus 9 9 1 +Dorsolateral Prefrontal Cortex 10 10 1 +Dorsomedial Hypothalamic Nucleus 7 8 2 +Dosage Compensation, Genetic 4 4 1 +Dosage Forms 2 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Varicella Zoster 5 7 8 +Encephalitis, Viral 4 6 7 +Encephalitozoon 7 7 1 +Encephalitozoon cuniculi 8 8 1 +Encephalitozoonosis 5 5 1 +Encephalocele 4 5 3 +Encephalomalacia 4 4 1 +Encephalomyelitis 3 4 3 +Encephalomyelitis Virus, Avian 6 6 1 +Encephalomyelitis, Acute Disseminated 4 6 5 +Encephalomyelitis, Autoimmune, Experimental 4 6 5 +Encephalomyelitis, Eastern Equine 5 8 15 +Encephalomyelitis, Enzootic Porcine 3 6 2 +Encephalomyelitis, Equine 4 7 16 +Encephalomyelitis, Venezuelan Equine 5 8 16 +Encephalomyelitis, Western Equine 5 8 16 +Encephalomyocarditis virus 7 7 1 +Encephalopathy, Bovine Spongiform 3 5 4 +Enchondromatosis 5 5 1 +Enclomiphene 9 9 1 +Encopresis 3 4 3 +Encyclopedia 2 2 1 +Encyclopedias as Topic 7 7 1 +End Stage Liver Disease 5 5 1 +Endangered Species 4 6 4 +Endarterectomy 4 4 1 +Endarterectomy, Carotid 5 5 1 +Endarteritis 5 5 1 +Endemic Diseases 3 3 1 +Endo-1,3(4)-beta-Glucanase 7 7 1 +Endo-1,4-beta Xylanases 6 6 1 +Endocannabinoids 3 3 1 +Endocardial Cushion Defects 5 6 3 +Endocardial Cushions 4 4 2 +Endocardial Fibroelastosis 4 4 1 +Endocarditis 3 3 1 +Endocarditis, Bacterial 3 4 4 +Endocarditis, Non-Infective 4 4 1 +Endocarditis, Subacute Bacterial 4 6 5 +Endocardium 3 3 1 +Endocrine Cells 2 2 1 +Endocrine Disruptors 3 4 2 +Endocrine Gland Neoplasms 2 3 2 +Endocrine Glands 2 2 1 +Endocrine Surgical Procedures 2 2 1 +Endocrine System 1 1 1 +Endocrine System Diseases 1 1 1 +Endocrinologists 4 5 2 +Endocrinology 4 4 2 +Endocytosis 2 2 1 +Endodeoxyribonucleases 6 6 2 +Endoderm 3 3 1 +Endodermal Sinus Tumor 5 5 1 +Endodontics 2 4 2 +Endodontists 5 6 2 +Endogenous Retroviruses 4 8 5 +Endoglin 5 5 1 +Endoleak 4 5 3 +Endolimax 6 6 1 +Endolymph 5 5 1 +Endolymphatic Duct 5 6 2 +Endolymphatic Hydrops 4 4 1 +Endolymphatic Sac 6 7 2 +Endolymphatic Shunt 3 4 2 +Endolyn 5 7 6 +Endometrial Ablation Techniques 3 4 2 +Endometrial Hyperplasia 5 6 2 +Endometrial Neoplasms 5 7 5 +Endometrial Stromal Tumors 5 8 6 +Endometriosis 4 5 2 +Endometritis 5 7 4 +Endometrium 5 5 1 +Endomyces 4 5 2 +Endomyocardial Fibrosis 4 4 1 +Endonucleases 5 5 1 +Endopeptidase Clp 4 9 7 +Endopeptidase K 7 7 2 +Endopeptidases 5 5 1 +Endophenotypes 3 3 1 +Endophthalmitis 3 3 2 +Endophytes 2 2 1 +Endoplasmic Reticulum 7 7 1 +Endoplasmic Reticulum Chaperone BiP 6 6 1 +Endoplasmic Reticulum Stress 2 2 1 +Endoplasmic Reticulum, Rough 8 9 2 +Endoplasmic Reticulum, Smooth 8 8 1 +Endoplasmic Reticulum-Associated Degradation 4 8 5 +Endoreduplication 3 3 1 +Endoribonucleases 6 6 2 +Endorphins 5 6 2 +Endoscopes 3 3 2 +Endoscopes, Gastrointestinal 4 4 2 +Endoscopic Mucosal Resection 5 7 4 +Endoscopic Ultrasound-Guided Fine Needle Aspiration 4 9 16 +Endoscopy 3 4 2 +Endoscopy, Digestive System 3 5 4 +Endoscopy, Gastrointestinal 4 6 4 +Endosomal Sorting Complexes Required for Transport 3 5 2 +Endosomes 9 9 1 +Endosonography 5 5 1 +Endosperm 5 5 1 +Endospore-Forming Bacteria 2 2 1 +Endostatins 5 8 4 +Endosulfan 4 5 2 +Endotamponade 3 3 1 +Endothelial Cells 3 3 1 +Endothelial Growth Factors 3 4 3 +Endothelial PAS Domain-Containing Protein 1 5 5 2 +Endothelial Progenitor Cells 3 4 2 +Endothelial Protein C Receptor 5 5 4 +Endothelial-Mesenchymal Transition 4 4 1 +Endothelin A Receptor Antagonists 5 5 1 +Endothelin B Receptor Antagonists 5 5 1 +Endothelin Receptor Antagonists 4 4 1 +Endothelin-1 4 5 3 +Endothelin-2 4 5 3 +Endothelin-3 4 5 3 +Endothelin-Converting Enzymes 7 7 4 +Endothelins 3 4 3 +Endothelium 3 3 1 +Endothelium, Corneal 4 5 3 +Endothelium, Lymphatic 4 5 2 +Endothelium, Vascular 4 4 2 +Endothelium-Dependent Relaxing Factors 6 6 1 +Endotoxemia 3 7 3 +Endotoxin Tolerance 4 4 1 +Endotoxins 4 4 1 +Endovascular Aneurysm Repair 4 6 4 +Endovascular Procedures 3 4 2 +Endpoint Determination 2 2 1 +Endrin 5 5 1 +Endurance Training 4 7 5 +Enediynes 5 7 2 +Enema 3 3 1 +Energy Drinks 3 4 2 +Energy Intake 5 5 1 +Energy Metabolism 2 2 1 +Energy Transfer 2 3 3 +Energy-Generating Resources 3 3 1 +Enflurane 4 4 1 +Enfuvirtide 4 9 8 +Engineering 2 2 1 +England 4 4 1 +English Abstract 2 2 1 +Engrailed 2 Protein 4 5 2 +Engraving and Engravings 3 3 1 +Enhanced Recovery After Surgery 3 3 1 +Enhancer Elements, Genetic 5 8 3 +Enhancer of Zeste Homolog 2 Protein 5 10 5 +Enhancer RNAs 5 5 1 +Enkephalin, Ala(2)-MePhe(4)-Gly(5)- 6 7 2 +Enkephalin, D-Penicillamine (2,5)- 6 7 2 +Enkephalin, Leucine 6 7 2 +Enkephalin, Leucine-2-Alanine 7 8 2 +Enkephalin, Methionine 6 7 2 +Enkephalins 5 6 2 +Enophthalmos 3 3 1 +Enoplida 7 7 1 +Enoplida Infections 6 6 1 +Enoxacin 8 8 1 +Enoxaparin 6 6 1 +Enoximone 5 5 1 +Enoyl-(Acyl-Carrier Protein) Reductase (NADPH, B-Specific) 5 5 1 +Enoyl-(Acyl-Carrier-Protein) Reductase (NADH) 5 5 1 +Enoyl-CoA Hydratase 6 6 1 +Enoyl-CoA Hydratase 2 6 6 1 +Enprostil 5 8 3 +Enrofloxacin 8 8 1 +Ensemble Learning 5 6 2 +Enslaved Persons 2 2 1 +Enslavement 5 5 1 +Entamoeba 6 6 1 +Entamoeba histolytica 7 7 1 +Entamoebiasis 5 5 1 +Enteral Nutrition 3 4 2 +Enteric Nervous System 4 4 1 +Enteritis 4 4 2 +Enteritis, Transmissible, of Turkeys 4 7 2 +Enterobacter 5 5 2 +Enterobacter aerogenes 6 6 2 +Enterobacter cloacae 6 6 2 +Enterobacteriaceae 4 4 2 +Enterobacteriaceae Infections 5 5 1 +Enterobactin 4 5 3 +Enterobiasis 8 8 1 +Enterobius 9 9 1 +Enterochromaffin Cells 3 6 7 +Enterochromaffin-like Cells 3 4 4 +Enterococcaceae 4 4 2 +Enterococcus 5 5 2 +Enterococcus faecalis 6 6 2 +Enterococcus faecium 6 6 2 +Enterococcus hirae 6 6 2 +Enterocolitis 4 4 2 +Enterocolitis, Necrotizing 5 5 2 +Enterocolitis, Neutropenic 5 5 2 +Enterocolitis, Pseudomembranous 5 6 3 +Enterocytes 3 5 3 +Enterocytozoon 7 7 1 +Enteroendocrine Cells 2 3 3 +Enterohemorrhagic Escherichia coli 8 8 2 +Enterohepatic Circulation 2 2 1 +Enteropathogenic Escherichia coli 7 7 2 +Enteropathy-Associated T-Cell Lymphoma 6 7 3 +Enteropeptidase 7 7 2 +Enterosorption 3 3 1 +Enterostomy 3 3 2 +Enterotoxemia 2 6 2 +Enterotoxigenic Escherichia coli 7 7 2 +Enterotoxins 3 3 1 +Enterovirus 6 6 1 +Enterovirus A, Human 7 7 1 +Enterovirus B, Human 7 7 1 +Enterovirus C, Human 7 7 1 +Enterovirus D, Human 7 7 1 +Enterovirus Infections 5 5 1 +Enterovirus, Bovine 7 7 1 +Enteroviruses, Porcine 7 7 1 +Enthesopathy 4 4 2 +Entomobirnavirus 5 5 1 +Entomology 5 5 1 +Entomophthora 5 5 1 +Entomophthorales 4 4 1 +Entomoplasmataceae 5 5 1 +Entomoplasmatales 4 4 1 +Entomopoxvirinae 3 4 2 +Entopeduncular Nucleus 7 7 1 +Entorhinal Cortex 7 10 4 +Entosis 3 3 1 +Entrepreneurship 3 4 2 +Entropion 3 3 1 +Entropy 3 3 1 +Enuresis 3 6 5 +env Gene Products, Human Immunodeficiency Virus 6 7 3 +Enviomycin 5 5 2 +Environment 2 3 2 +Environment and Public Health 1 1 1 +Environment Design 2 4 3 +Environment, Controlled 3 3 1 +Environmental Biomarkers 2 6 3 +Environmental Exposure 4 4 1 +Environmental Health 2 2 1 +Environmental Illness 2 3 2 +Environmental Indicators 6 6 1 +Environmental Justice 4 7 4 +Environmental Medicine 3 5 2 +Environmental Microbiology 3 5 2 +Environmental Monitoring 4 5 2 +Environmental Policy 3 6 4 +Environmental Pollutants 3 3 1 +Environmental Pollution 3 3 1 +Environmental Psychology 4 4 1 +Environmental Restoration and Remediation 4 4 2 +Environmental Science 2 2 1 +Environmentalism 3 3 2 +Enzootic Bovine Leukosis 3 6 5 +Enzyme Activation 2 3 2 +Enzyme Activators 4 4 1 +Enzyme Assays 3 3 1 +Enzyme Induction 4 4 1 +Enzyme Inhibitors 4 4 1 +Enzyme Multiplied Immunoassay Technique 5 5 3 +Enzyme Precursors 2 4 2 +Enzyme Reactivators 4 4 1 +Enzyme Replacement Therapy 4 4 1 +Enzyme Repression 4 4 1 +Enzyme Stability 3 4 2 +Enzyme Therapy 3 3 1 +Enzyme-Linked Immunosorbent Assay 5 5 5 +Enzyme-Linked Immunospot Assay 3 6 8 +Enzymes 2 2 1 +Enzymes and Coenzymes 1 1 1 +Enzymes, Immobilized 3 4 2 +Eosine I Bluish 4 6 3 +Eosine Yellowish-(YS) 4 6 3 +Eosinophil Cationic Protein 6 7 2 +Eosinophil Granule Proteins 5 5 1 +Eosinophil Major Basic Protein 6 6 1 +Eosinophil Peroxidase 5 6 2 +Eosinophil-Derived Neurotoxin 6 7 2 +Eosinophilia 4 4 1 +Eosinophilia-Myalgia Syndrome 3 5 3 +Eosinophilic Esophagitis 4 5 4 +Eosinophilic Granuloma 3 5 5 +Eosinophils 4 6 4 +Ependyma 3 5 2 +Ependymoglial Cells 3 3 2 +Ependymoma 6 7 3 +Ephedra 7 7 1 +Ephedra sinica 8 8 1 +Ephedrine 5 5 4 +Ephemera 2 2 1 +Ephemeral Fever 3 6 2 +Ephemeral Fever Virus, Bovine 7 7 1 +Ephemeroptera 8 8 1 +Ephemerovirus 6 6 1 +Ephrin-A1 4 6 8 +Ephrin-A2 4 6 8 +Ephrin-A3 4 6 8 +Ephrin-A4 4 6 8 +Ephrin-A5 4 6 8 +Ephrin-B1 4 5 4 +Ephrin-B2 4 5 4 +Ephrin-B3 4 5 4 +Ephrins 3 4 4 +Epicardial Adipose Tissue 4 4 1 +Epicardial Mapping 6 6 1 +Epichloe 5 5 1 +Epichlorohydrin 5 5 1 +Epidemics 4 4 1 +Epidemiologic Factors 3 3 2 +Epidemiologic Measurements 3 3 1 +Epidemiologic Methods 2 3 2 +Epidemiologic Research Design 3 4 3 +Epidemiologic Studies 4 5 3 +Epidemiologic Study Characteristics 3 4 3 +Epidemiological Models 4 4 1 +Epidemiological Monitoring 3 4 2 +Epidemiologists 3 4 2 +Epidemiology 4 4 1 +Epidermal Cells 2 2 1 +Epidermal Cyst 3 3 1 +Epidermal Growth Factor 4 5 4 +Epidermis 3 3 2 +Epidermitis, Exudative, of Swine 3 3 1 +Epidermodysplasia Verruciformis 5 6 4 +Epidermolysis Bullosa 4 4 5 +Epidermolysis Bullosa Acquisita 5 5 4 +Epidermolysis Bullosa Dystrophica 4 5 6 +Epidermolysis Bullosa Simplex 5 5 5 +Epidermolysis Bullosa, Junctional 5 5 5 +Epidermophyton 4 4 1 +Epididymal Secretory Proteins 3 3 1 +Epididymis 4 4 1 +Epididymitis 4 4 2 +Epidural Abscess 4 5 4 +Epidural Neoplasms 5 6 3 +Epidural Space 6 6 1 +Epigastric Arteries 4 4 1 +Epigen 4 5 3 +Epigenesis, Genetic 3 3 1 +Epigenetic Memory 4 4 1 +Epigenetic Repression 4 4 1 +Epigenome 4 4 1 +Epigenome Editing 4 4 1 +Epigenomics 6 6 2 +Epiglottis 4 5 3 +Epiglottitis 4 4 2 +Epikeratophakia 5 6 3 +Epilepsia Partialis Continua 5 6 2 +Epilepsies, Myoclonic 6 6 2 +Epilepsies, Partial 5 5 1 +Epilepsy 4 4 1 +Epilepsy, Absence 6 6 2 +Epilepsy, Benign Neonatal 3 5 2 +Epilepsy, Complex Partial 6 6 1 +Epilepsy, Frontal Lobe 6 6 2 +Epilepsy, Generalized 5 5 1 +Epilepsy, Partial, Motor 6 6 1 +Epilepsy, Partial, Sensory 6 6 1 +Epilepsy, Post-Traumatic 5 5 4 +Epilepsy, Reflex 5 5 1 +Epilepsy, Rolandic 6 6 2 +Epilepsy, Temporal Lobe 6 6 2 +Epilepsy, Tonic-Clonic 6 6 1 +Epileptic Syndromes 5 5 1 +Epilobium 8 8 1 +Epimedium 8 8 1 +Epimestrol 6 6 1 +Epinephrine 4 9 5 +Epiphyses 4 4 1 +Epiphyses, Slipped 3 3 1 +Epiregulin 4 5 3 +Epiretinal Membrane 3 3 1 +Epirizole 4 5 2 +Epirubicin 7 10 3 +Episiotomy 4 4 1 +Episode of Care 3 4 2 +Epispadias 3 6 7 +Epistasis, Genetic 3 3 1 +Epistaxis 3 4 4 +Epitestosterone 7 8 2 +Epithalamus 5 6 2 +Epithelial Attachment 5 5 1 +Epithelial Cell Adhesion Molecule 4 6 5 +Epithelial Cells 2 2 1 +Epithelial Sodium Channel Agonists 6 6 1 +Epithelial Sodium Channel Blockers 6 7 2 +Epithelial Sodium Channels 7 7 3 +Epithelial-Mesenchymal Transition 3 3 1 +Epithelioid Cells 4 5 5 +Epithelium 2 2 1 +Epithelium, Corneal 3 5 2 +Epitope Mapping 3 4 2 +Epitopes 3 3 1 +Epitopes, B-Lymphocyte 4 4 1 +Epitopes, T-Lymphocyte 4 4 1 +Epitranscriptome 4 5 3 +Epitranscriptomics 4 7 3 +Eplerenone 3 6 2 +Epoetin Alfa 6 8 5 +Eponyms 6 6 1 +Epoprostenol 7 7 2 +Epothilones 5 5 1 +Epoxide Hydrolases 4 4 1 +Epoxy Compounds 4 4 1 +Epoxy Resins 5 7 3 +epsilon-Crystallins 5 6 2 +epsilon-Globins 7 8 2 +Epsilonproteobacteria 3 3 1 +Epsilonretrovirus 4 4 2 +Epstein-Barr Virus Infections 4 5 2 +Epstein-Barr Virus Nuclear Antigens 4 5 3 +Eptifibatide 4 4 1 +Equartevirus 7 7 1 +Equatorial Guinea 5 5 1 +Equidae 8 8 1 +Equilenin 6 6 3 +Equilibrative Nucleoside Transport Proteins 6 7 4 +Equilibrative Nucleoside Transporter 1 7 8 4 +Equilibrative-Nucleoside Transporter 2 7 8 4 +Equilin 6 6 3 +Equine Infectious Anemia 3 6 3 +Equine-Assisted Therapy 4 7 3 +Equinus Deformity 5 8 4 +Equipment and Supplies 1 1 1 +Equipment and Supplies Utilization 4 5 2 +Equipment and Supplies, Hospital 2 2 1 +Equipment Contamination 3 3 1 +Equipment Design 2 2 1 +Equipment Failure 2 2 1 +Equipment Failure Analysis 3 3 1 +Equipment Reuse 2 3 2 +Equipment Safety 2 2 1 +Equisetum 5 5 1 +Equivalence Trial 6 6 1 +Equivalence Trials as Topic 8 9 3 +Equol 8 8 1 +Erabutoxins 3 3 1 +Eragrostis 8 8 1 +Erb-b2 Receptor Tyrosine Kinases 4 10 8 +ErbB Receptors 6 9 4 +Erbium 5 5 2 +Erbovirus 6 6 1 +Erdheim-Chester Disease 5 5 1 +Erectile Dysfunction 3 4 4 +Eremophila Plant 8 8 1 +Eremothecium 4 5 2 +ERG1 Potassium Channel 9 9 3 +Ergocalciferols 4 6 4 +Ergolines 4 4 2 +Ergoloid Mesylates 6 6 2 +Ergometry 2 2 1 +Ergonomics 3 3 2 +Ergonovine 5 5 2 +Ergosterol 6 6 1 +Ergot Alkaloids 3 3 1 +Ergotamine 5 5 2 +Ergotamines 4 4 2 +Ergothioneine 4 5 2 +Ergotism 4 4 1 +Ericaceae 8 8 1 +Ericales 7 7 1 +Erigeron 8 8 1 +Eriobotrya 10 10 1 +Eriocaulaceae 7 7 1 +Eriodictyon 8 8 1 +Eriogonum 8 8 1 +Eritrea 5 5 1 +Erlotinib Hydrochloride 5 5 1 +Erotica 3 5 2 +ERRalpha Estrogen-Related Receptor 6 6 2 +Ertapenem 6 6 2 +Erucic Acids 5 5 1 +Eructation 4 4 1 +Erwinia 5 5 2 +Erwinia amylovora 6 6 2 +Eryngium 8 8 1 +Eryptosis 5 5 1 +Erysimum 8 8 1 +Erysipelas 4 6 4 +Erysipeloid 6 6 1 +Erysipelothrix 3 6 2 +Erysipelothrix Infections 2 5 2 +Erysiphe 4 4 1 +Erythema 3 4 2 +Erythema Ab Igne 3 5 3 +Erythema Chronicum Migrans 4 8 7 +Erythema Induratum 4 6 6 +Erythema Infectiosum 4 5 4 +Erythema Multiforme 4 4 2 +Erythema Nodosum 4 5 3 +Erythrasma 4 7 4 +Erythrina 8 8 1 +Erythritol 3 4 2 +Erythrityl Tetranitrate 3 5 3 +Erythroblastosis, Fetal 2 5 5 +Erythroblasts 5 8 3 +Erythrocebus 12 12 1 +Erythrocebus patas 13 13 1 +Erythrocruorins 4 6 2 +Erythrocyte Aggregation 4 5 3 +Erythrocyte Aging 3 5 3 +Erythrocyte Count 4 7 7 +Erythrocyte Deformability 4 4 1 +Erythrocyte Inclusions 4 6 3 +Erythrocyte Indices 3 5 3 +Erythrocyte Membrane 4 5 3 +Erythrocyte Transfusion 5 5 1 +Erythrocyte Volume 4 5 2 +Erythrocytes 3 4 3 +Erythrocytes, Abnormal 4 5 3 +Erythroid Cells 2 2 1 +Erythroid Precursor Cells 4 7 4 +Erythroid-Specific DNA-Binding Factors 4 4 2 +Erythrokeratodermia Variabilis 4 4 4 +Erythromelalgia 4 4 1 +Erythromycin 5 5 1 +Erythromycin Estolate 6 6 1 +Erythromycin Ethylsuccinate 6 6 1 +Erythroplasia 3 3 1 +Erythropoiesis 4 4 2 +Erythropoietin 5 7 5 +Erythrosine 4 6 3 +Erythrovirus 5 5 1 +Erythroxylaceae 7 7 1 +Escape Reaction 3 6 5 +Escherichia 5 5 2 +Escherichia coli 6 6 2 +Escherichia coli Infections 6 6 1 +Escherichia coli K12 7 7 2 +Escherichia coli O104 9 9 2 +Escherichia coli O157 9 9 2 +Escherichia coli Proteins 4 4 1 +Escherichia coli Vaccines 5 5 1 +Eschscholzia 9 9 1 +Escin 4 6 2 +Escitalopram 3 5 3 +Esculin 4 6 3 +Esocidae 7 7 1 +Esociformes 6 6 1 +Esomeprazole 6 7 3 +Esophageal Achalasia 6 6 1 +Esophageal and Gastric Varices 4 4 2 +Esophageal Atresia 3 4 3 +Esophageal Cyst 3 4 2 +Esophageal Diseases 3 3 1 +Esophageal Fistula 3 5 3 +Esophageal Motility Disorders 5 5 1 +Esophageal Mucosa 4 5 2 +Esophageal Neoplasms 4 5 5 +Esophageal Perforation 2 4 2 +Esophageal pH Monitoring 4 4 2 +Esophageal Spasm, Diffuse 6 6 1 +Esophageal Sphincter, Lower 4 6 3 +Esophageal Sphincter, Upper 4 5 4 +Esophageal Squamous Cell Carcinoma 5 6 7 +Esophageal Stenosis 4 4 1 +Esophagectomy 3 3 1 +Esophagitis 4 4 2 +Esophagitis, Peptic 5 6 4 +Esophagogastric Junction 5 5 2 +Esophagoplasty 3 3 1 +Esophagoscopes 5 5 2 +Esophagoscopy 5 7 4 +Esophagostomy 3 3 2 +Esophagus 4 4 1 +Esotropia 4 5 2 +Essay 2 2 1 +Essential Hypertension 4 4 1 +Essential Tremor 4 4 1 +Estazolam 6 6 1 +Esterases 4 4 1 +Esterification 2 3 3 +Esters 3 3 1 +Estetrol 7 7 2 +Esthesioneuroblastoma, Olfactory 4 9 4 +Esthetics 3 4 2 +Esthetics, Dental 2 2 1 +Estivation 5 6 3 +Estonia 5 5 1 +Estradiol 6 6 2 +Estradiol Congeners 5 5 1 +Estradiol Dehydrogenases 7 7 1 +Estramustine 6 7 2 +Estranes 4 4 1 +Estrenes 5 5 1 +Estriol 6 6 2 +Estrogen Antagonists 3 6 2 +Estrogen Receptor alpha 6 6 1 +Estrogen Receptor Antagonists 4 7 2 +Estrogen Receptor beta 6 6 1 +Estrogen Receptor Modulators 3 6 2 +Estrogen Replacement Therapy 4 4 1 +Estrogenic Steroids, Alkylated 6 6 1 +Estrogens 6 6 1 +Estrogens, Catechol 6 8 2 +Estrogens, Conjugated (USP) 6 6 1 +Estrogens, Esterified (USP) 6 6 1 +Estrogens, Non-Steroidal 7 7 1 +Estrone 5 6 4 +Estrous Cycle 3 3 1 +Estrus 4 4 1 +Estrus Detection 4 4 1 +Estrus Synchronization 4 5 2 +Estuaries 4 4 1 +Eswatini 5 5 1 +Eszopiclone 4 4 3 +Etanercept 6 8 8 +Etanidazole 4 6 2 +Etazolate 4 5 2 +Ethacridine 6 6 1 +Ethacrynic Acid 6 7 2 +Ethambutol 6 6 1 +Ethamoxytriphetol 4 4 1 +Ethamsylate 7 8 2 +Ethane 5 5 1 +Ethanol 3 3 1 +Ethanolamine 5 5 3 +Ethanolamine Ammonia-Lyase 6 6 1 +Ethanolaminephosphotransferase 6 6 1 +Ethanolamines 4 4 3 +Ethchlorvynol 4 4 1 +Ethenoadenosine Triphosphate 6 8 3 +Ether 4 4 1 +Ether-A-Go-Go Potassium Channels 8 8 3 +Ethers 2 2 1 +Ethers, Cyclic 3 3 2 +Ethical Analysis 3 5 2 +Ethical Dilemmas 2 5 2 +Ethical Relativism 3 5 2 +Ethical Review 3 5 2 +Ethical Theory 3 5 2 +Ethicists 3 5 3 +Ethics 2 4 2 +Ethics Committees 3 5 4 +Ethics Committees, Clinical 4 6 4 +Ethics Committees, Research 4 6 4 +Ethics Consultation 4 6 3 +Ethics, Business 3 5 2 +Ethics, Clinical 4 6 4 +Ethics, Dental 4 7 3 +Ethics, Institutional 3 5 2 +Ethics, Medical 5 7 2 +Ethics, Nursing 4 7 3 +Ethics, Pharmacy 5 7 2 +Ethics, Professional 3 5 2 +Ethics, Research 3 5 2 +Ethidium 5 5 1 +Ethinyl Estradiol 7 7 2 +Ethinyl Estradiol-Norgestrel Combination 8 8 3 +Ethiodized Oil 5 6 2 +Ethionamide 4 5 2 +Ethionine 4 4 2 +Ethiopia 5 5 1 +Ethisterone 6 6 1 +Ethmoid Bone 5 5 1 +Ethmoid Sinus 4 4 1 +Ethmoid Sinusitis 4 5 4 +Ethnic and Racial Minorities 3 5 3 +Ethnic Cleansing 5 7 3 +Ethnic Violence 5 5 1 +Ethnicity 3 5 2 +Ethnobotany 5 5 1 +Ethnology 5 5 1 +Ethnopharmacology 3 6 3 +Ethnopsychology 4 4 2 +Ethoglucid 3 5 2 +Ethology 3 3 1 +Ethopabate 7 9 2 +Ethosuximide 4 6 2 +Ethoxyquin 5 5 1 +Ethoxzolamide 4 6 5 +Ethyl Biscoumacetate 7 7 2 +Ethyl Chloride 5 5 1 +Ethyl Ethers 3 3 1 +Ethyl Methanesulfonate 8 8 2 +Ethylamines 3 3 1 +Ethyldimethylaminopropyl Carbodiimide 4 4 1 +Ethylene Chlorohydrin 4 4 2 +Ethylene Dibromide 5 5 1 +Ethylene Dichlorides 5 6 2 +Ethylene Glycol 5 5 1 +Ethylene Glycols 4 4 1 +Ethylene Oxide 5 5 1 +Ethylenebis(dithiocarbamates) 4 6 2 +Ethylenediamines 5 5 1 +Ethylenes 5 5 1 +Ethylenethiourea 5 6 2 +Ethylestrenol 7 7 1 +Ethylketocyclazocine 4 5 2 +Ethylmaleimide 4 7 3 +Ethylmercuric Chloride 6 6 1 +Ethylmercury Compounds 5 5 1 +Ethylmorphine 5 6 4 +Ethylmorphine-N-Demethylase 6 6 1 +Ethylnitrosourea 4 5 2 +Ethynodiol Diacetate 7 7 1 +Etidocaine 5 6 2 +Etidronic Acid 5 5 1 +Etilefrine 6 6 2 +Etimizol 5 5 1 +Etiocholanolone 5 6 4 +Etiolation 3 4 2 +Etioporphyrins 4 7 4 +Etodolac 4 6 2 +Etomidate 5 5 1 +Etoposide 4 9 3 +Etoricoxib 4 4 2 +Etorphine 4 5 4 +Etretinate 5 8 3 +ETS Motif 10 10 1 +ETS Translocation Variant 6 Protein 5 7 5 +ets-Domain Protein Elk-1 6 8 3 +ets-Domain Protein Elk-4 6 8 3 +Eubacteriales 3 3 1 +Eubacterium 4 6 2 +Eucalyptol 5 8 4 +Eucalyptus 8 8 1 +Eucalyptus Oil 4 5 2 +Euchromatin 5 10 3 +Eucoccidiida 5 5 1 +Eucommiaceae 7 7 1 +Eugenia 8 8 1 +Eugenics 3 3 1 +Eugenol 6 7 3 +Euglena 4 4 1 +Euglena gracilis 5 5 1 +Euglena longa 5 5 1 +Euglenida 3 3 1 +Euglenozoa 2 2 1 +Euglenozoa Infections 4 4 1 +Eukaryota 1 1 1 +Eukaryotic Cells 2 2 1 +Eukaryotic Initiation Factor-1 6 6 1 +Eukaryotic Initiation Factor-2 6 6 1 +Eukaryotic Initiation Factor-2B 6 6 3 +Eukaryotic Initiation Factor-3 6 6 1 +Eukaryotic Initiation Factor-4A 7 8 2 +Eukaryotic Initiation Factor-4E 6 7 2 +Eukaryotic Initiation Factor-4F 6 6 2 +Eukaryotic Initiation Factor-4G 7 7 1 +Eukaryotic Initiation Factor-5 6 6 3 +Eukaryotic Initiation Factors 5 5 1 +Eukaryotic Translation Initiation Factor 5A 7 7 3 +Eulipotyphla 7 7 1 +Eulogy 2 2 1 +Eunuchism 4 4 1 +Euonymus 10 10 1 +Eupatorium 8 8 1 +Eupenicillium 5 5 1 +Euphausiacea 6 6 1 +Euphorbia 10 10 1 +Euphorbiaceae 9 9 1 +Euphoria 3 3 1 +Euphrasia 9 9 1 +Eupleridae 9 9 1 +Euplotes 7 7 1 +Europe 2 2 1 +Europe, Eastern 3 3 1 +European Alpine Region 3 3 1 +European People 3 3 1 +European Union 4 4 1 +Europium 5 5 2 +Eurotiales 4 4 1 +Eurotium 5 5 1 +Euryarchaeota 2 2 1 +Eurycoma 8 8 1 +Eustachian Tube 4 4 1 +Euterpe 8 8 1 +Euthanasia 4 5 3 +Euthanasia, Active 5 6 3 +Euthanasia, Active, Voluntary 6 7 3 +Euthanasia, Animal 2 7 3 +Euthanasia, Involuntary 4 4 1 +Euthanasia, Passive 4 6 4 +Eutheria 6 6 1 +Euthyroid Sick Syndromes 3 3 1 +Eutrophication 3 3 1 +Evaluation Studies as Topic 2 4 2 +Evaluation Study 2 2 1 +Evans Blue 3 8 4 +Event-Related Potentials, P300 5 5 2 +Everolimus 5 5 1 +Evidence Gaps 3 4 3 +Evidence Synthesis 2 3 2 +Evidence-Based Dentistry 3 3 2 +Evidence-Based Emergency Medicine 4 5 2 +Evidence-Based Facility Design 4 4 1 +Evidence-Based Medicine 3 4 4 +Evidence-Based Nursing 3 3 2 +Evidence-Based Pharmacy Practice 3 3 2 +Evidence-Based Practice 2 2 1 +Evodia 8 8 1 +Evoked Potentials 4 4 2 +Evoked Potentials, Auditory 3 5 3 +Evoked Potentials, Auditory, Brain Stem 4 6 3 +Evoked Potentials, Motor 5 5 2 +Evoked Potentials, Somatosensory 5 5 2 +Evoked Potentials, Visual 2 5 3 +Evolution, Chemical 2 4 2 +Evolution, Molecular 3 3 2 +Evolution, Planetary 3 3 2 +Ex utero Intrapartum Treatment Procedures 3 5 2 +Ex-Smokers 2 2 1 +Examination Questions 2 2 1 +Examination Tables 3 3 1 +Exanthema 3 3 1 +Exanthema Subitum 4 6 4 +Exchange Transfusion, Whole Blood 4 4 1 +Excipients 4 5 2 +Excision Repair 3 4 2 +Excitation Contraction Coupling 3 6 3 +Excitatory Amino Acid Agents 5 5 2 +Excitatory Amino Acid Agonists 6 6 2 +Excitatory Amino Acid Antagonists 6 6 2 +Excitatory Amino Acid Transporter 1 8 9 8 +Excitatory Amino Acid Transporter 2 8 9 8 +Excitatory Amino Acid Transporter 3 8 9 8 +Excitatory Amino Acid Transporter 4 8 9 8 +Excitatory Amino Acid Transporter 5 8 9 8 +Excitatory Amino Acids 3 3 1 +Excitatory Postsynaptic Potentials 4 5 5 +Excoriation Disorder 3 5 3 +Executive Function 3 3 1 +Exenatide 3 4 3 +Exercise 2 5 2 +Exercise Movement Techniques 3 3 1 +Exercise Test 3 5 3 +Exercise Therapy 3 6 3 +Exercise Tolerance 4 4 1 +Exercise-Induced Allergies 4 4 1 +Exergaming 3 6 4 +Exfoliatins 4 4 3 +Exfoliation Syndrome 4 4 1 +Exhalation 5 5 1 +Exhibition 2 2 1 +Exhibitionism 3 3 1 +Exhibitions as Topic 5 6 2 +Exhumation 5 5 1 +Exiguobacterium 5 5 1 +Existentialism 3 3 2 +Exobiology 4 4 1 +Exocrine Glands 2 2 1 +Exocrine Pancreatic Insufficiency 3 3 1 +Exocytosis 2 2 1 +Exodeoxyribonuclease V 7 7 1 +Exodeoxyribonucleases 6 6 2 +Exome 4 4 1 +Exome Sequencing 6 6 1 +Exons 7 7 1 +Exonucleases 5 5 1 +Exopeptidases 5 5 1 +Exophiala 4 4 2 +Exophthalmos 3 3 1 +Exoribonucleases 6 6 2 +Exoskeleton Device 2 2 1 +Exosome Multienzyme Ribonuclease Complex 4 6 3 +Exosomes 5 9 2 +Exostoses 4 4 1 +Exostoses, Multiple Hereditary 3 8 5 +Exostosin 1 6 7 2 +Exostosin 2 6 7 2 +Exostosin Glycosyltransferase Family 5 5 1 +Exotoxins 3 3 1 +Exotropia 4 5 2 +Expectorants 5 5 1 +Expeditions 3 3 1 +Expert Systems 5 5 1 +Expert Testimony 4 5 2 +Expiratory Reserve Volume 5 8 4 +Exploratory Behavior 3 3 2 +Explosions 3 3 1 +Explosive Agents 3 3 1 +Exportin 1 Protein 4 7 4 +Exposome 5 5 1 +Exposure to Violence 5 5 1 +Expressed Emotion 3 3 1 +Expressed Sequence Tags 7 7 1 +Expression of Concern 2 2 1 +Expropriation 3 3 1 +Exsanguination 4 4 1 +Exteins 5 5 1 +Extended Family 5 7 6 +Extensively Drug-Resistant Tuberculosis 9 9 1 +External Capsule 6 6 1 +External Debt 3 3 1 +External Fixators 5 5 2 +Extinction, Biological 2 2 1 +Extinction, Psychological 5 5 1 +Extracellular Fluid 3 4 2 +Extracellular Matrix 4 4 1 +Extracellular Matrix Proteins 4 4 1 +Extracellular Polymeric Substance Matrix 3 3 2 +Extracellular Signal-Regulated MAP Kinases 6 9 2 +Extracellular Space 3 3 2 +Extracellular Traps 4 4 1 +Extracellular Vesicles 4 4 1 +Extrachromosomal DNA 4 4 1 +Extrachromosomal Inheritance 3 3 1 +Extracorporeal Circulation 2 2 1 +Extracorporeal Membrane Oxygenation 3 3 2 +Extracorporeal Shockwave Therapy 3 5 3 +Extraction and Processing Industry 4 4 1 +Extraction, Obstetrical 4 4 1 +Extraembryonic Membranes 3 3 2 +Extraintestinal Pathogenic Escherichia coli 7 7 2 +Extramarital Relations 4 4 1 +Extranodal Extension 4 5 2 +Extraoral Traction Appliances 5 5 1 +Extrapyramidal Tracts 4 4 2 +Extraterrestrial Environment 4 4 2 +Extravasation of Diagnostic and Therapeutic Materials 2 3 2 +Extravascular Lung Water 4 4 1 +Extravehicular Activity 5 5 1 +Extraversion, Psychological 4 4 2 +Extravillous Trophoblasts 4 5 3 +Extreme Cold 6 8 4 +Extreme Environments 4 4 1 +Extreme Heat 6 8 4 +Extreme Learning Machines 4 7 2 +Extreme Weather 5 6 3 +Extremities 2 2 1 +Extremophiles 2 2 1 +Exudates and Transudates 2 2 1 +Eye 2 4 2 +Eye Abnormalities 2 3 2 +Eye Banks 5 5 1 +Eye Burns 3 6 3 +Eye Color 2 3 2 +Eye Diseases 1 1 1 +Eye Diseases, Hereditary 2 3 2 +Eye Enucleation 3 3 1 +Eye Evisceration 3 3 1 +Eye Foreign Bodies 3 6 3 +Eye Hemorrhage 2 4 3 +Eye Infections 2 2 2 +Eye Infections, Bacterial 3 4 3 +Eye Infections, Fungal 3 4 3 +Eye Infections, Parasitic 3 3 2 +Eye Infections, Viral 3 3 3 +Eye Injuries 2 5 3 +Eye Injuries, Penetrating 3 6 3 +Eye Manifestations 2 3 2 +Eye Movement Desensitization Reprocessing 5 5 1 +Eye Movement Measurements 4 4 2 +Eye Movements 2 4 2 +Eye Neoplasms 2 3 2 +Eye Pain 3 5 5 +Eye Protective Devices 4 5 2 +Eye Proteins 3 3 1 +Eye, Artificial 3 3 1 +Eye-Tracking Technology 4 5 3 +Eyebrows 3 5 2 +Eyeglasses 4 4 1 +Eyelashes 3 6 3 +Eyelid Diseases 2 2 1 +Eyelid Neoplasms 3 5 3 +Eyelids 3 5 2 +Ezetimibe 5 5 1 +Ezetimibe, Simvastatin Drug Combination 3 9 4 +Ezrin 4 4 1 +F Factor 4 4 1 +F-Box Motifs 8 8 1 +F-Box Proteins 4 4 1 +F-Box-WD Repeat-Containing Protein 7 4 7 3 +F2-Isoprostanes 5 7 3 +Fabaceae 7 7 1 +Fabavirus 4 6 2 +Fabry Disease 4 8 12 +Face 3 3 1 +Facial Asymmetry 3 3 1 +Facial Bones 5 5 1 +Facial Dermatoses 3 3 1 +Facial Expression 4 5 2 +Facial Hemiatrophy 3 4 3 +Facial Injuries 4 4 2 +Facial Muscles 2 4 2 +Facial Neoplasms 4 4 1 +Facial Nerve 5 5 4 +Facial Nerve Diseases 3 3 2 +Facial Nerve Injuries 4 5 5 +Facial Neuralgia 4 4 2 +Facial Nucleus 9 9 1 +Facial Pain 5 5 3 +Facial Paralysis 3 5 3 +Facial Recognition 6 6 3 +Facial Transplantation 5 5 2 +Facies 4 4 2 +Facilitated Diffusion 3 3 4 +Facilitated Tucking 5 5 1 +Facilities and Services Utilization 3 5 5 +Facility Design and Construction 3 3 2 +Facility Regulation and Control 3 3 2 +Factitious Disorders 3 3 1 +Factor Analysis, Statistical 4 5 3 +Factor For Inversion Stimulation Protein 4 4 2 +Factor IX 3 5 4 +Factor IXa 4 7 4 +Factor V 3 5 3 +Factor V Deficiency 4 5 4 +Factor Va 4 6 2 +Factor VII 3 5 4 +Factor VII Deficiency 4 5 4 +Factor VIIa 4 7 4 +Factor VIII 3 5 3 +Factor VIIIa 4 6 2 +Factor X 3 5 4 +Factor X Deficiency 4 5 4 +Factor Xa 4 7 4 +Factor Xa Inhibitors 7 8 2 +Factor XI 3 5 4 +Factor XI Deficiency 4 5 4 +Factor XIa 4 7 4 +Factor XII 3 5 4 +Factor XII Deficiency 4 5 4 +Factor XIIa 4 7 4 +Factor XIII 3 5 4 +Factor XIII Deficiency 4 5 4 +Factor XIIIa 4 7 3 +Faculty 4 4 1 +Faculty, Dental 3 5 3 +Faculty, Medical 3 5 3 +Faculty, Nursing 3 5 3 +Faculty, Pharmacy 5 5 1 +Fadrozole 3 5 2 +Faecalibacterium 4 5 2 +Faecalibacterium prausnitzii 5 6 2 +Fagaceae 9 9 1 +Fagales 8 8 1 +Fagopyrum 8 8 1 +Fagus 10 10 1 +Failed Back Surgery Syndrome 4 6 2 +Failure to Rescue, Health Care 5 8 4 +Failure to Thrive 3 3 1 +Faith Healing 4 4 1 +Faith-Based Organizations 3 3 1 +Falconiformes 7 7 1 +Falkland Islands 4 4 1 +Fallopia 8 8 1 +Fallopia japonica 9 9 1 +Fallopia multiflora 9 9 1 +Fallopian Tube Diseases 5 6 2 +Fallopian Tube Neoplasms 4 7 5 +Fallopian Tube Patency Tests 3 4 3 +Fallopian Tubes 3 5 2 +False Negative Reactions 3 3 1 +False Positive Reactions 3 3 1 +Famciclovir 6 6 1 +Familial Exudative Vitreoretinopathies 3 4 5 +Familial Hypophosphatemic Rickets 5 9 12 +Familial Mediterranean Fever 4 4 1 +Familial Multiple Lipomatosis 3 5 3 +Familial Primary Pulmonary Hypertension 4 4 1 +Family 3 4 2 +Family Characteristics 3 5 6 +Family Conflict 5 5 2 +Family Health 3 3 1 +Family Leave 5 5 2 +Family Nurse Practitioners 5 6 2 +Family Nursing 4 4 1 +Family Planning Policy 5 6 3 +Family Planning Services 4 4 2 +Family Practice 4 4 1 +Family Relations 4 5 2 +Family Separation 4 5 2 +Family Structure 4 6 7 +Family Support 5 6 3 +Family Therapy 5 5 1 +Famine 5 5 1 +Famotidine 4 5 2 +Famous Persons 2 4 2 +Fanconi Anemia 4 7 4 +Fanconi Anemia Complementation Group A Protein 4 4 3 +Fanconi Anemia Complementation Group C Protein 4 4 1 +Fanconi Anemia Complementation Group D2 Protein 4 4 3 +Fanconi Anemia Complementation Group E Protein 4 4 2 +Fanconi Anemia Complementation Group F Protein 4 4 2 +Fanconi Anemia Complementation Group G Protein 4 4 2 +Fanconi Anemia Complementation Group L Protein 4 6 2 +Fanconi Anemia Complementation Group N Protein 4 5 3 +Fanconi Anemia Complementation Group Proteins 3 3 1 +Fanconi Syndrome 4 7 4 +FANFT 4 5 4 +Fantasy 3 5 2 +Farber Lipogranulomatosis 7 8 9 +Farmer's Lung 3 6 4 +Farmers 3 3 1 +Farms 3 3 2 +Farnesol 3 5 3 +Farnesyl-Diphosphate Farnesyltransferase 5 5 1 +Farnesyltranstransferase 5 5 1 +Fas Ligand Protein 5 6 6 +fas Receptor 6 8 2 +Fas-Associated Death Domain Protein 6 6 8 +Fascia 2 3 2 +Fascia Lata 3 4 2 +Fasciculation 4 5 2 +Fasciitis 2 2 1 +Fasciitis, Necrotizing 3 3 1 +Fasciitis, Plantar 3 3 2 +Fasciola 8 8 1 +Fasciola hepatica 9 9 1 +Fascioliasis 4 5 3 +Fasciolidae 7 7 1 +Fascioloidiasis 4 5 4 +Fasciotomy 2 2 1 +Fascism 3 3 1 +Fast Foods 3 4 2 +Fast Neutrons 5 5 1 +Fasting 4 5 3 +Fat Body 2 2 1 +Fat Emulsions, Intravenous 5 6 4 +Fat Necrosis 4 4 1 +Fat Substitutes 5 6 3 +Fatal Outcome 5 7 4 +Father-Child Relations 6 6 1 +Fathers 3 6 3 +Fatigue 3 3 1 +Fatigue Syndrome, Chronic 3 5 4 +Fats 2 2 1 +Fats, Unsaturated 3 3 1 +Fatty Acid Amide Hydrolases 5 5 1 +Fatty Acid Binding Protein 3 5 5 1 +Fatty Acid Desaturases 6 6 1 +Fatty Acid Elongases 6 6 1 +Fatty Acid Synthase, Type I 4 8 6 +Fatty Acid Synthase, Type II 4 8 7 +Fatty Acid Synthases 6 7 5 +Fatty Acid Synthesis Inhibitors 6 6 1 +Fatty Acid Transport Proteins 5 5 2 +Fatty Acid-Binding Protein 7 4 5 2 +Fatty Acid-Binding Proteins 4 4 1 +Fatty Acids 2 2 1 +Fatty Acids, Essential 4 4 1 +Fatty Acids, Monounsaturated 4 4 1 +Fatty Acids, Nonesterified 3 3 1 +Fatty Acids, Omega-3 4 5 3 +Fatty Acids, Omega-6 4 4 1 +Fatty Acids, Unsaturated 3 3 1 +Fatty Acids, Volatile 3 3 1 +Fatty Alcohols 2 3 2 +Fatty Liver 3 3 1 +Fatty Liver, Alcoholic 4 6 3 +Favism 4 7 5 +Fear 3 3 1 +Feasibility Studies 3 5 4 +Feathers 2 2 1 +Febrile Neutropenia 7 7 2 +Febuxostat 4 5 2 +Fecal Impaction 5 5 1 +Fecal Incontinence 5 5 1 +Fecal Microbiota Transplantation 3 3 1 +Feces 2 2 1 +Federal Government 3 4 4 +Federated Learning 5 6 2 +Fee Schedules 3 3 1 +Fee-for-Service Plans 4 6 3 +Feedback 4 4 1 +Feedback, Physiological 3 3 1 +Feedback, Psychological 3 3 2 +Feedback, Sensory 3 5 4 +Feeder Cells 3 3 1 +Feedforward Neural Networks 3 6 2 +Feeding and Eating Disorders 2 4 2 +Feeding and Eating Disorders of Childhood 3 3 1 +Feeding Behavior 3 4 3 +Feeding Methods 2 2 1 +Fees and Charges 3 3 1 +Fees, Dental 4 4 2 +Fees, Medical 4 4 2 +Fees, Pharmaceutical 4 4 1 +Feijoa 8 8 1 +Felbamate 5 6 2 +Felidae 9 9 1 +Feliformia 8 8 1 +Feline Acquired Immunodeficiency Syndrome 3 6 3 +Feline Infectious Peritonitis 3 7 2 +Feline Panleukopenia 3 5 2 +Feline Panleukopenia Virus 6 6 1 +Felis 10 10 1 +Fellowships and Scholarships 5 5 1 +Felodipine 5 5 1 +Felty Syndrome 4 5 4 +Felypressin 6 8 5 +Female Athlete Triad Syndrome 4 4 1 +Female Urogenital Diseases 3 3 1 +Female Urogenital Diseases and Pregnancy Complications 2 2 1 +Femininity 4 6 3 +Feminism 5 6 2 +Feminization 3 3 1 +Femoracetabular Impingement 3 3 2 +Femoral Artery 4 4 1 +Femoral Fractures 3 3 2 +Femoral Fractures, Distal 4 4 2 +Femoral Neck Fractures 4 5 3 +Femoral Neoplasms 4 4 2 +Femoral Nerve 6 6 1 +Femoral Neuropathy 5 5 1 +Femoral Vein 4 4 1 +Femur 5 5 1 +Femur Head 6 6 1 +Femur Head Necrosis 4 5 2 +Femur Neck 6 6 1 +Fenamates 4 9 3 +Fenbendazole 5 5 2 +Fenclonine 6 6 1 +Fendiline 5 5 1 +Fenestration, Labyrinth 4 4 1 +Fenfluramine 5 5 1 +Fenitrothion 5 5 3 +Fenofibrate 4 9 5 +Fenoldopam 5 5 1 +Fenoprofen 5 5 1 +Fenoterol 5 10 4 +Fenretinide 5 10 4 +Fentanyl 4 4 1 +Fenthion 5 5 3 +Feprazone 7 7 1 +FERM Domains 9 9 1 +Fermentation 3 4 2 +Fermented Beverages 3 4 4 +Fermented Foods 2 3 2 +Fermium 4 6 5 +Ferns 6 6 1 +Ferredoxin-NADP Reductase 5 5 1 +Ferredoxin-Nitrite Reductase 6 7 3 +Ferredoxins 4 7 4 +Ferrets 10 10 1 +Ferric Compounds 3 3 1 +Ferric Oxide, Saccharated 4 6 4 +Ferrichrome 4 5 4 +Ferricyanides 4 6 3 +Ferritins 5 5 2 +Ferrochelatase 4 7 4 +Ferrocyanides 4 6 3 +Ferroportin 7 7 1 +Ferroptosis 4 4 1 +Ferrosoferric Oxide 3 4 3 +Ferrous Compounds 3 3 1 +Ferrozine 4 8 3 +Fertile Period 4 4 1 +Fertilins 5 8 5 +Fertility 3 3 1 +Fertility Agents 5 5 2 +Fertility Agents, Female 6 6 2 +Fertility Agents, Male 6 6 2 +Fertility Clinics 3 3 1 +Fertility Preservation 4 4 3 +Fertilization 4 4 1 +Fertilization in Vitro 4 4 2 +Fertilizers 4 4 1 +Ferula 8 8 1 +Festschrift 3 3 3 +Festuca 8 8 1 +Fetal Alcohol Spectrum Disorders 3 5 3 +Fetal Blood 3 4 3 +Fetal Death 4 4 2 +Fetal Development 5 5 2 +Fetal Diseases 2 4 2 +Fetal Distress 3 3 1 +Fetal Globulins 4 4 2 +Fetal Growth Retardation 3 5 3 +Fetal Heart 3 3 2 +Fetal Hemoglobin 5 6 2 +Fetal Hypoxia 3 5 3 +Fetal Macrosomia 3 5 6 +Fetal Monitoring 4 4 2 +Fetal Mortality 5 7 4 +Fetal Movement 6 6 2 +Fetal Nutrition Disorders 4 5 2 +Fetal Organ Maturity 6 6 3 +Fetal Proteins 3 3 1 +Fetal Research 2 5 2 +Fetal Resorption 5 5 2 +Fetal Stem Cells 3 3 1 +Fetal Therapies 2 2 1 +Fetal Tissue Transplantation 4 5 2 +Fetal Viability 2 6 3 +Fetal Weight 3 8 9 +Fetishism, Psychiatric 3 3 1 +Fetofetal Transfusion 4 5 2 +Fetomaternal Transfusion 4 5 2 +Fetoscopes 4 4 2 +Fetoscopy 3 5 5 +Fetuin-B 5 7 4 +Fetuins 4 6 4 +Fetus 2 2 1 +Fetus-in-Fetu 5 5 1 +Fever 4 4 1 +Fever of Unknown Origin 5 5 1 +Fiber Optic Technology 4 4 1 +Fibric Acids 4 8 3 +Fibril-Associated Collagens 7 7 1 +Fibrillar Collagens 5 6 2 +Fibrillin-1 5 6 4 +Fibrillin-2 5 6 3 +Fibrillins 4 5 3 +Fibrin 4 4 1 +Fibrin Clot Lysis Time 3 5 3 +Fibrin Fibrinogen Degradation Products 5 5 2 +Fibrin Foam 5 5 1 +Fibrin Modulating Agents 4 4 1 +Fibrin Tissue Adhesive 5 5 1 +Fibrinogen 3 5 4 +Fibrinogens, Abnormal 4 6 3 +Fibrinolysin 7 7 2 +Fibrinolysis 5 5 1 +Fibrinolytic Agents 5 5 3 +Fibrinopeptide A 3 5 6 +Fibrinopeptide B 3 5 5 +Fibroadenoma 5 7 2 +Fibrobacter 4 4 1 +Fibrobacteres 3 3 1 +Fibroblast Activation Protein Alpha 4 6 2 +Fibroblast Growth Factor 1 4 5 3 +Fibroblast Growth Factor 10 4 5 3 +Fibroblast Growth Factor 2 4 5 3 +Fibroblast Growth Factor 3 4 5 3 +Fibroblast Growth Factor 4 4 6 4 +Fibroblast Growth Factor 5 4 5 3 +Fibroblast Growth Factor 6 4 6 4 +Fibroblast Growth Factor 7 4 5 3 +Fibroblast Growth Factor 8 4 5 3 +Fibroblast Growth Factor 9 4 5 3 +Fibroblast Growth Factor-23 5 5 1 +Fibroblast Growth Factors 3 4 3 +Fibroblasts 3 3 1 +Fibrocartilage 3 4 2 +Fibrocystic Breast Disease 4 4 1 +Fibroins 5 5 2 +Fibroma 6 6 1 +Fibroma Virus, Rabbit 5 6 3 +Fibroma, Desmoplastic 7 7 1 +Fibroma, Ossifying 6 7 2 +Fibromatosis, Abdominal 7 7 1 +Fibromatosis, Gingival 4 6 4 +Fibromatosis, Plantar 3 7 5 +Fibromodulin 5 6 4 +Fibromuscular Dysplasia 4 4 1 +Fibromyalgia 3 4 3 +Fibronectin Type III Domain 9 9 1 +Fibronectins 5 5 5 +Fibrosarcoma 5 6 2 +Fibrosis 3 3 1 +Fibrous Dysplasia of Bone 5 5 1 +Fibrous Dysplasia, Monostotic 6 6 1 +Fibrous Dysplasia, Polyostotic 6 6 1 +Fibula 6 6 1 +Fibula Fractures 3 3 1 +Ficain 7 7 2 +Ficolins 4 4 1 +Ficoll 3 3 1 +Fictional Work 2 2 1 +Fictional Works as Topic 6 6 1 +Ficus 10 10 1 +Ficusin 5 7 3 +Fidaxomicin 4 5 3 +Fiducial Markers 3 4 3 +Field Dependence-Independence 4 4 1 +FIGLU Test 4 5 2 +Figural Aftereffect 2 5 2 +Fiji 5 5 2 +Filaggrin Proteins 5 6 2 +Filamins 4 8 4 +Filariasis 7 7 1 +Filaricides 8 8 1 +Filarioidea 8 8 1 +Filgrastim 6 8 5 +Filing 5 5 1 +Filipendula 10 10 1 +Filipin 5 6 2 +Film Dosimetry 4 5 2 +Filoviridae 5 5 1 +Filoviridae Infections 5 5 1 +Filtering Surgery 3 3 1 +Filtration 2 3 3 +Fimbriae Proteins 5 5 2 +Fimbriae, Bacterial 2 4 2 +Fin Whale 10 10 1 +Financial Audit 5 5 1 +Financial Management 3 3 1 +Financial Management, Hospital 4 5 3 +Financial Statements 5 5 1 +Financial Stress 5 5 1 +Financial Support 3 3 1 +Financing, Construction 4 4 1 +Financing, Government 4 4 1 +Financing, Organized 3 3 1 +Financing, Personal 3 3 1 +Finasteride 6 6 2 +Finches 8 8 1 +Finger Injuries 3 3 1 +Finger Joint 5 5 1 +Finger Phalanges 6 6 1 +Fingers 5 5 1 +Fingersucking 4 4 1 +Fingolimod Hydrochloride 5 5 3 +Finite Element Analysis 2 2 1 +Finland 4 4 1 +Fire Ants 5 11 2 +Fire Extinguishing Systems 4 4 1 +Firearms 4 4 1 +Firefighters 5 5 1 +Fireflies 10 10 1 +Firefly Luciferin 3 5 3 +Fires 3 3 1 +Firesetting Behavior 3 3 1 +First Aid 3 3 1 +First Generation Cephalosporins 7 7 1 +Fiscal Policy 3 3 1 +Fish Diseases 2 2 1 +Fish Flour 6 7 2 +Fish Oils 3 3 1 +Fish Products 5 6 2 +Fish Proteins 3 3 1 +Fish Proteins, Dietary 4 7 8 +Fish Venoms 3 4 3 +Fisheries 3 4 2 +Fishes 5 5 1 +Fishes, Poisonous 5 6 2 +Fissure in Ano 6 6 1 +Fistula 3 3 1 +Fitness Centers 3 3 2 +Fitness Trackers 3 4 2 +Fixation, Ocular 3 3 1 +Fixatives 3 3 1 +Flacourtia 10 10 1 +Flagella 4 4 1 +Flagellin 4 4 1 +Flail Chest 3 3 1 +Flame Ionization 5 5 1 +Flame Retardants 3 3 1 +Flammulina 5 5 1 +Flank Pain 5 5 3 +Flap Endonucleases 7 7 1 +Flatfishes 6 6 1 +Flatfoot 5 8 4 +Flatulence 4 4 1 +Flavanones 7 7 2 +Flaveria 8 8 1 +Flavin Mononucleotide 4 7 5 +Flavin-Adenine Dinucleotide 5 7 7 +Flavins 3 5 4 +Flaviviridae 4 4 1 +Flaviviridae Infections 4 4 1 +Flavivirus 5 5 1 +Flavivirus Infections 5 5 1 +Flavobacteriaceae 4 5 2 +Flavobacteriaceae Infections 5 5 1 +Flavobacterium 5 6 2 +Flavodoxin 4 4 2 +Flavones 7 7 2 +Flavonoids 6 6 2 +Flavonolignans 7 8 3 +Flavonols 7 7 2 +Flavoproteins 3 3 1 +Flavoring Agents 3 6 5 +Flavoxate 8 8 2 +Flax 10 10 1 +Flea Infestations 5 5 1 +Flecainide 4 4 1 +Fleroxacin 9 9 1 +Flexibacter 5 6 2 +Flexiviridae 3 4 2 +Flexural Strength 3 3 1 +Flicker Fusion 2 5 3 +Flight, Animal 5 5 2 +Flocculation 3 4 2 +Flocculation Tests 5 7 5 +Floods 4 5 2 +Floors and Floorcoverings 4 4 1 +Florida 6 6 1 +Florigen 4 4 1 +Flounder 7 7 1 +Flour 3 4 2 +Flow Cytometry 4 7 7 +Flow Injection Analysis 3 3 1 +Flower Essences 3 5 2 +Flowering Tops 3 3 1 +Flowers 4 4 1 +Flowmeters 3 3 1 +Floxacillin 7 8 3 +Floxuridine 5 7 3 +Fluconazole 5 5 1 +Flucytosine 6 6 1 +Fludrocortisone 8 8 1 +Flufenamic Acid 8 10 2 +Fluid Shifts 2 2 1 +Fluid Therapy 3 3 1 +Fluids and Secretions 1 1 1 +Flumazenil 7 7 1 +Flumethasone 5 7 2 +Flunarizine 4 4 1 +Flunitrazepam 7 7 1 +Fluocinolone Acetonide 5 6 2 +Fluocinonide 6 7 2 +Fluocortolone 5 7 2 +Fluorenes 3 6 2 +Fluorescamine 3 5 3 +Fluorescein 4 6 3 +Fluorescein Angiography 4 5 2 +Fluorescein-5-isothiocyanate 4 6 5 +Fluoresceins 3 5 3 +Fluorescence 5 7 2 +Fluorescence Polarization 6 6 1 +Fluorescence Polarization Immunoassay 5 9 7 +Fluorescence Recovery After Photobleaching 6 6 1 +Fluorescence Resonance Energy Transfer 4 7 3 +Fluorescent Antibody Technique 4 7 5 +Fluorescent Antibody Technique, Direct 5 8 5 +Fluorescent Antibody Technique, Indirect 5 8 5 +Fluorescent Chemosensor Compounds 5 7 3 +Fluorescent Dyes 4 6 2 +Fluorescent Treponemal Antibody-Absorption Test 6 7 3 +Fluoridation 3 3 2 +Fluoride Poisoning 3 3 1 +Fluoride Treatment 4 4 1 +Fluorides 4 5 2 +Fluorides, Topical 3 5 3 +Fluorine 4 4 1 +Fluorine Compounds 2 2 1 +Fluorine Radioisotopes 4 4 1 +Fluorine-19 Magnetic Resonance Imaging 6 6 1 +Fluoroacetates 5 5 2 +Fluorobenzenes 5 6 2 +Fluorocarbon Polymers 3 6 4 +Fluorocarbons 5 5 1 +Fluorodeoxyglucose F18 4 4 1 +Fluorodeoxyuridylate 5 7 3 +Fluoroimmunoassay 4 8 6 +Fluorometholone 5 7 2 +Fluorometry 5 5 1 +Fluorophotometry 4 6 2 +Fluoroquinolones 7 7 1 +Fluoroscopy 5 5 1 +Fluorosis, Dental 6 7 3 +Fluorouracil 6 6 1 +Fluoxetine 4 4 1 +Fluoxymesterone 5 8 2 +Flupenthixol 4 6 3 +Fluphenazine 4 5 2 +Fluprednisolone 5 8 2 +Flurandrenolone 5 7 2 +Flurazepam 7 7 1 +Flurbiprofen 5 7 2 +Flurogestone Acetate 5 7 2 +Flurothyl 4 5 2 +Flushing 4 4 1 +Fluspirilene 3 5 4 +Flutamide 4 5 2 +Fluticasone 7 7 1 +Fluticasone-Salmeterol Drug Combination 3 8 5 +Fluvastatin 4 5 2 +Fluvoxamine 5 5 1 +FMN Reductase 5 5 1 +FMRFamide 4 5 2 +fms-Like Tyrosine Kinase 3 6 9 4 +Foam Cells 4 5 5 +Focal Adhesion Kinase 1 4 9 3 +Focal Adhesion Kinase 2 6 9 4 +Focal Adhesion Protein-Tyrosine Kinases 5 8 2 +Focal Adhesions 6 6 1 +Focal Cortical Dysplasia 5 6 2 +Focal Dermal Hypoplasia 4 5 7 +Focal Epithelial Hyperplasia 3 3 1 +Focal Facial Dermal Dysplasias 5 5 5 +Focal Infection 2 2 1 +Focal Infection, Dental 3 3 2 +Focal Nodular Hyperplasia 3 3 1 +Focus Groups 4 5 3 +Focused Assessment with Sonography for Trauma 5 5 2 +FODMAP Diet 6 6 1 +Foeniculum 8 8 1 +Folate Receptor 1 4 10 8 +Folate Receptor 2 6 10 7 +Folate Receptors, GPI-Anchored 5 9 7 +Folic Acid 6 6 1 +Folic Acid Antagonists 5 5 1 +Folic Acid Deficiency 7 7 1 +Folic Acid Transporters 8 8 2 +Folklore 3 5 2 +Follicle Stimulating Hormone 6 7 3 +Follicle Stimulating Hormone, beta Subunit 7 8 3 +Follicle Stimulating Hormone, Human 8 8 1 +Follicular Atresia 3 3 1 +Follicular Cyst 3 3 1 +Follicular Fluid 4 7 3 +Follicular Phase 4 4 1 +Folliculitis 4 4 1 +Follistatin 4 4 1 +Follistatin-Related Proteins 4 4 1 +Follow-Up Studies 6 7 3 +Fomepizole 5 5 1 +Fomites 6 6 1 +Fondaparinux 4 4 1 +Fonofos 5 5 3 +Fonsecaea 4 4 1 +Fontan Procedure 4 6 6 +Food 2 3 2 +Food Addiction 3 6 2 +Food Additives 4 5 3 +Food Analysis 2 5 2 +Food and Beverages 1 1 1 +Food Assistance 5 6 2 +Food Chain 4 5 2 +Food Coloring Agents 4 5 2 +Food Contamination 4 7 3 +Food Contamination, Radioactive 4 8 4 +Food Deprivation 3 3 1 +Food Deserts 3 5 2 +Food Dispensers, Automatic 5 5 1 +Food Fussiness 4 4 1 +Food Handling 4 4 1 +Food Hypersensitivity 4 4 1 +Food Industry 3 3 1 +Food Ingredients 3 4 3 +Food Insecurity 5 5 1 +Food Inspection 5 7 3 +Food Intolerance 4 4 1 +Food Irradiation 7 7 1 +Food Labeling 5 6 2 +Food Loss and Waste 3 5 3 +Food Markets 5 5 1 +Food Microbiology 4 8 5 +Food Packaging 4 5 3 +Food Parasitology 5 8 4 +Food Preferences 4 5 2 +Food Preservation 5 5 1 +Food Preservatives 5 6 3 +Food Quality 3 5 2 +Food Safety 4 6 2 +Food Security 5 5 1 +Food Service, Hospital 4 6 4 +Food Services 4 4 1 +Food Storage 5 5 1 +Food Supply 4 4 1 +Food Technology 4 4 1 +Food, Formulated 4 5 2 +Food, Fortified 3 4 2 +Food, Genetically Modified 3 4 2 +Food, Organic 3 4 2 +Food, Preserved 3 4 2 +Food, Processed 3 4 2 +Food-Drug Interactions 5 5 1 +Food-Processing Industry 5 5 1 +Foodborne Diseases 3 3 1 +Foods, Specialized 3 4 2 +Foot 4 4 1 +Foot Bones 5 5 1 +Foot Deformities 2 2 1 +Foot Deformities, Acquired 3 3 1 +Foot Deformities, Congenital 3 6 3 +Foot Dermatoses 4 4 1 +Foot Diseases 2 3 2 +Foot Injuries 3 3 1 +Foot Joints 4 4 1 +Foot Orthoses 5 5 1 +Foot Rot 2 5 2 +Foot Ulcer 4 5 2 +Foot-and-Mouth Disease 2 5 2 +Foot-and-Mouth Disease Virus 7 7 1 +Football 5 5 1 +For-Profit Insurance Plans 6 6 1 +Foramen Magnum 6 6 1 +Foramen Ovale 4 4 1 +Foramen Ovale, Patent 6 7 3 +Foraminifera 3 3 1 +Foraminotomy 3 3 1 +Force Potentiation 4 4 1 +Forced Expiratory Flow Rates 4 6 2 +Forced Expiratory Volume 4 6 2 +Forced Labor, Employment 4 5 2 +Forearm 4 4 1 +Forearm Injuries 3 3 1 +Forecasting 2 2 1 +Forefoot, Human 5 5 1 +Forehead 4 4 1 +Foreign Bodies 2 2 1 +Foreign Medical Graduates 4 5 3 +Foreign Professional Personnel 3 3 1 +Foreign-Body Migration 3 3 1 +Foreign-Body Reaction 3 4 2 +Forelimb 2 2 1 +Forensic Anthropology 4 4 2 +Forensic Ballistics 4 4 1 +Forensic Dentistry 3 4 2 +Forensic Entomology 4 6 2 +Forensic Genetics 4 5 2 +Forensic Imaging 4 4 2 +Forensic Medicine 3 4 2 +Forensic Microbiology 4 5 2 +Forensic Nursing 4 4 2 +Forensic Pathology 4 5 3 +Forensic Psychiatry 4 5 5 +Forensic Psychology 4 4 2 +Forensic Sciences 3 3 1 +Forensic Toxicology 3 4 3 +Foreskin 5 5 1 +Forest Therapy 5 5 1 +Forestry 3 3 1 +Forests 4 5 2 +Forgiveness 3 4 2 +Forkhead Box Protein L2 6 6 2 +Forkhead Box Protein M1 6 6 2 +Forkhead Box Protein O1 6 6 2 +Forkhead Box Protein O3 6 6 2 +Forkhead Transcription Factors 5 5 2 +Form 2 2 1 +Form Perception 5 5 1 +Formaldehyde 3 3 1 +Formamides 3 5 2 +Formate Dehydrogenases 5 5 1 +Formate-Tetrahydrofolate Ligase 5 5 1 +Formates 4 4 1 +Formative Feedback 5 5 1 +Formazans 3 3 1 +Formic Acid Esters 5 5 1 +Formiminoglutamic Acid 6 6 1 +Formins 5 5 2 +Formocresols 4 8 2 +Formoterol Fumarate 5 5 2 +Forms and Records Control 6 6 1 +Forms as Topic 4 4 1 +Formularies as Topic 7 7 1 +Formularies, Dental as Topic 8 8 1 +Formularies, Homeopathic as Topic 8 8 1 +Formularies, Hospital as Topic 8 8 1 +Formulary 2 2 1 +Formulary, Dental 3 3 1 +Formulary, Homeopathic 3 3 1 +Formulary, Hospital 3 3 1 +Formycins 4 5 3 +Formyltetrahydrofolate Dehydrogenase 5 5 1 +Formyltetrahydrofolates 4 8 2 +Fornix, Brain 6 9 2 +Forssman Antigen 4 4 1 +Forsythia 9 9 1 +Fos-Related Antigen 1 5 7 6 +Fos-Related Antigen-2 5 5 2 +Foscarnet 5 6 2 +Fosfomycin 4 4 1 +Fosinopril 4 6 2 +Fossil Fuels 2 4 2 +Fossils 5 5 1 +Foster Home Care 3 4 4 +Foundations 4 4 2 +Founder Effect 2 2 1 +Four-Dimensional Computed Tomography 5 7 5 +Fourier Analysis 2 4 3 +Fournier Gangrene 4 4 3 +Fourth Ventricle 5 5 1 +Fovea Centralis 5 5 1 +Foveomacular Retinitis 3 7 6 +Fowl adenovirus A 5 5 1 +Fowlpox 3 5 2 +Fowlpox virus 6 6 1 +Fox-Fordyce Disease 5 5 1 +Foxes 10 10 1 +Fractals 2 3 2 +Fractional Exhaled Nitric Oxide Testing 4 4 1 +Fractional Flow Reserve, Myocardial 5 5 1 +Fractional Precipitation 4 4 1 +Fractionation, Field Flow 4 4 1 +Fracture Dislocation 3 4 3 +Fracture Fixation 3 3 2 +Fracture Fixation, Internal 4 4 2 +Fracture Fixation, Intramedullary 5 5 2 +Fracture Healing 4 4 1 +Fractures, Avulsion 3 3 1 +Fractures, Bone 2 2 1 +Fractures, Cartilage 2 2 1 +Fractures, Closed 3 3 1 +Fractures, Comminuted 3 3 1 +Fractures, Compression 3 3 1 +Fractures, Malunited 3 3 1 +Fractures, Multiple 3 3 2 +Fractures, Open 3 3 1 +Fractures, Spontaneous 3 3 1 +Fractures, Stress 3 3 1 +Fractures, Ununited 3 3 1 +Fragaria 10 10 1 +Fragile X Messenger Ribonucleoprotein 1 4 5 3 +Fragile X Syndrome 5 6 5 +Frail Elderly 5 5 1 +Frailty 3 3 1 +Frameshift Mutation 4 4 1 +Frameshifting, Ribosomal 3 5 3 +Framycetin 5 5 1 +France 3 3 1 +Francisella 4 5 2 +Francisella tularensis 5 6 2 +Francium 4 5 6 +Frankia 3 4 2 +Frankincense 5 5 2 +Fraser Syndrome 3 7 12 +Frasier Syndrome 3 9 10 +Frataxin 4 7 3 +Fraud 4 4 1 +Fraxinus 9 9 1 +Free Association 4 4 1 +Free Radical Scavengers 5 5 1 +Free Radicals 2 2 2 +Free Tissue Flaps 4 4 2 +Freedom 4 5 2 +Freedom of Movement 5 5 1 +Freedom of Religion 5 5 1 +Freemartinism 3 7 6 +Freeze Drying 4 8 6 +Freeze Etching 7 8 4 +Freeze Fracturing 6 7 4 +Freeze Substitution 5 9 6 +Freezing 3 5 3 +Freezing Reaction, Cataleptic 4 5 3 +French Guiana 4 4 1 +French Revolution 5 6 2 +Fresh Water 3 4 2 +Freshwater Biology 4 6 3 +Freudian Theory 4 4 1 +Freund's Adjuvant 2 2 1 +Friction 3 3 1 +Friedreich Ataxia 4 6 5 +Friend murine leukemia virus 6 6 2 +Friends 2 2 1 +Fritillaria 10 10 1 +Frizzled Receptors 6 6 2 +Frizzled-Related Proteins 4 4 1 +Frontal Bone 5 5 1 +Frontal Lobe 8 8 1 +Frontal Sinus 4 4 1 +Frontal Sinusitis 4 5 4 +Frontline Workers 3 3 1 +Frontotemporal Dementia 5 6 4 +Frontotemporal Lobar Degeneration 4 5 4 +Frostbite 2 3 2 +Frozen Foods 4 5 2 +Frozen Sections 7 8 4 +Fructans 3 3 1 +Fructokinases 6 6 1 +Fructosamine 4 4 1 +Fructose 5 5 2 +Fructose Intolerance 6 6 2 +Fructose Metabolism, Inborn Errors 5 5 2 +Fructose-1,6-Diphosphatase Deficiency 6 6 2 +Fructose-Bisphosphatase 6 6 1 +Fructose-Bisphosphate Aldolase 6 6 1 +Fructosediphosphates 5 5 2 +Fructosephosphates 4 4 1 +Fructuronate Reductase 6 6 1 +Fruit 3 4 3 +Fruit and Vegetable Juices 3 4 2 +Fruit Proteins 4 6 4 +Fruiting Bodies, Fungal 2 2 1 +Frullania 6 6 1 +Frustration 3 3 1 +Fuchs' Endothelial Dystrophy 4 5 3 +Fucose 3 3 1 +Fucosidosis 5 7 8 +Fucosyl Galactose alpha-N-Acetylgalactosaminyltransferase 7 7 1 +Fucosyltransferases 6 6 1 +Fucus 5 5 1 +Fuel Oils 4 6 2 +Fukushima Nuclear Accident 5 5 2 +Fullerenes 4 6 3 +Fulvestrant 7 7 2 +Fumarate Hydratase 6 6 1 +Fumarates 5 5 1 +Fumaria 9 9 1 +Fumarioideae 9 9 1 +Fumigation 5 5 1 +Fumonisins 4 5 2 +Functional Food 3 4 2 +Functional Laterality 4 4 2 +Functional Medicine 3 3 1 +Functional Neuroimaging 3 5 3 +Functional Residual Capacity 4 7 2 +Functional Status 5 6 2 +Fund Raising 4 4 1 +Fundoplication 3 3 1 +Fundulidae 8 8 1 +Fundulus heteroclitus 9 9 2 +Fundus Oculi 4 4 1 +Funeral Homes 2 2 1 +Funeral Rites 5 5 1 +Funeral Sermon 3 3 1 +Fungal Capsules 2 2 1 +Fungal Genus Humicola 5 5 1 +Fungal Genus Venturia 4 4 1 +Fungal Polysaccharides 3 5 3 +Fungal Proteins 3 3 1 +Fungal Structures 1 1 1 +Fungal Vaccines 4 4 1 +Fungal Viruses 2 2 1 +Fungemia 3 6 3 +Fungi 2 2 1 +Fungi, Unclassified 3 3 1 +Fungicides, Industrial 4 5 2 +Funnel Chest 3 4 3 +Fur Seals 9 9 1 +Fura-2 5 5 2 +Furagin 4 5 2 +Furaldehyde 3 4 2 +Furans 3 3 1 +Furazolidone 4 6 3 +Furcation Defects 4 4 1 +Furin 6 7 3 +Furocoumarins 4 6 3 +Furosemide 5 6 3 +Fursultiamin 5 6 3 +Furunculosis 3 7 5 +Furylfuramide 4 5 2 +Fusaric Acid 4 5 2 +Fusariosis 5 6 3 +Fusarium 4 4 1 +Fused Kidney 3 5 4 +Fused Teeth 4 5 3 +Fused-Ring Compounds 2 2 1 +Fuselloviridae 3 3 2 +Fushi Tarazu Transcription Factors 4 5 2 +Fusidic Acid 4 8 3 +Fusion Proteins, bcr-abl 6 9 5 +Fusion Proteins, gag-onc 6 7 6 +Fusion Proteins, gag-pol 5 8 7 +Fusion Regulatory Protein 1, Heavy Chain 8 9 6 +Fusion Regulatory Protein 1, Light Chains 8 9 6 +Fusion Regulatory Protein-1 7 8 6 +Fusobacteria 2 2 1 +Fusobacteriaceae Infections 5 5 1 +Fusobacterium 3 5 2 +Fusobacterium Infections 6 6 1 +Fusobacterium necrophorum 4 6 2 +Fusobacterium nucleatum 4 6 2 +Fuzzy Logic 3 5 3 +G Protein-Coupled Inwardly-Rectifying Potassium Channels 8 8 3 +G(M1) Ganglioside 6 7 3 +G(M2) Activator Protein 4 4 1 +G(M2) Ganglioside 6 7 3 +G(M3) Ganglioside 6 7 3 +G-Box Binding Factors 4 4 3 +G-Protein-Coupled Receptor Kinase 1 4 9 3 +G-Protein-Coupled Receptor Kinase 2 7 10 2 +G-Protein-Coupled Receptor Kinase 3 7 10 2 +G-Protein-Coupled Receptor Kinase 4 6 9 2 +G-Protein-Coupled Receptor Kinase 5 6 9 2 +G-Protein-Coupled Receptor Kinases 5 8 2 +G-Quadruplexes 4 6 2 +G1 Phase 4 4 1 +G1 Phase Cell Cycle Checkpoints 4 5 2 +G2 Phase 4 4 1 +G2 Phase Cell Cycle Checkpoints 4 5 2 +GA-Binding Protein Transcription Factor 5 5 2 +GABA Agents 5 5 2 +GABA Agonists 6 6 2 +GABA Antagonists 6 6 2 +GABA Modulators 6 6 2 +GABA Plasma Membrane Transport Proteins 7 7 4 +GABA Uptake Inhibitors 6 6 5 +GABA-A Receptor Agonists 7 7 2 +GABA-A Receptor Antagonists 7 7 2 +GABA-B Receptor Agonists 7 7 2 +GABA-B Receptor Antagonists 7 7 2 +GABAergic Neurons 3 3 2 +Gabapentin 3 8 5 +Gabexate 4 4 1 +Gabon 5 5 1 +GADD45 Proteins 4 4 2 +Gadiformes 6 6 1 +Gadolinium 5 5 2 +Gadolinium DTPA 3 6 3 +Gadus morhua 7 7 1 +Gaelic Football 5 5 1 +gag Gene Products, Human Immunodeficiency Virus 5 8 4 +Gagging 3 4 4 +Gain of Function Mutation 4 4 1 +Gait 4 6 2 +Gait Analysis 5 5 2 +Gait Apraxia 4 7 5 +Gait Ataxia 4 6 4 +Gait Disorders, Neurologic 3 4 2 +Galactans 3 3 1 +Galactitol 3 4 2 +Galactogogues 4 4 1 +Galactokinase 6 6 1 +Galactolipids 3 4 2 +Galactorrhea 5 6 2 +Galactosamine 4 4 1 +Galactose 5 5 1 +Galactose Dehydrogenases 6 6 1 +Galactose Oxidase 5 5 1 +Galactosemias 5 6 6 +Galactosephosphates 4 4 1 +Galactosidases 5 5 1 +Galactoside 2-alpha-L-fucosyltransferase 7 7 1 +Galactosides 3 3 1 +Galactosylceramidase 6 6 1 +Galactosylceramides 5 8 4 +Galactosylgalactosylglucosylceramidase 6 6 1 +Galactosyltransferases 6 6 1 +Galagidae 9 9 1 +Galago 10 10 1 +Galanin 4 5 2 +Galanin-Like Peptide 4 4 1 +Galantamine 4 5 2 +Galanthus 10 10 1 +Galaxies 4 4 1 +Galectin 1 5 5 1 +Galectin 2 5 5 1 +Galectin 3 5 5 1 +Galectin 4 5 5 1 +Galectins 4 4 1 +Galega 8 8 1 +Galium 9 9 1 +Gallamine Triethiodide 4 4 2 +Gallbladder 3 3 1 +Gallbladder Diseases 3 3 1 +Gallbladder Emptying 3 3 1 +Gallbladder Neoplasms 4 5 4 +Gallic Acid 5 8 4 +Galliformes 6 6 1 +Gallionellaceae 4 4 2 +Gallium 4 4 2 +Gallium Isotopes 3 5 3 +Gallium Radioisotopes 4 6 4 +Gallopamil 6 6 1 +Gallstones 4 5 3 +Galphimia 10 10 1 +Galvanic Skin Response 3 4 5 +Gambia 5 5 1 +Gambling 3 4 2 +Game Theory 2 2 1 +Games, Experimental 2 2 1 +Games, Recreational 5 5 1 +Gamete Intrafallopian Transfer 4 4 2 +Gametogenesis 3 4 2 +Gametogenesis, Plant 2 5 3 +Gamification 2 4 2 +Gamma Cameras 2 2 1 +gamma Catenin 4 5 2 +Gamma Rays 4 5 3 +Gamma Rhythm 4 6 4 +Gamma Secretase Inhibitors and Modulators 5 5 1 +gamma-Aminobutyric Acid 4 6 2 +gamma-Butyrobetaine Dioxygenase 6 6 1 +gamma-Crystallins 5 5 1 +gamma-Cyclodextrins 4 7 3 +gamma-Endorphin 6 7 8 +gamma-Globins 7 8 2 +gamma-Globulins 6 6 3 +gamma-Glutamyl Hydrolase 7 7 1 +gamma-Glutamylcyclotransferase 6 6 1 +gamma-Glutamyltransferase 6 6 1 +gamma-Linolenic Acid 5 6 2 +gamma-Lipotropin 6 7 6 +gamma-MSH 5 9 14 +gamma-Synuclein 5 5 1 +gamma-Tocopherol 7 7 2 +Gammacoronavirus 7 7 1 +Gammaherpesvirinae 4 4 3 +Gammainfluenzavirus 5 5 1 +Gammapapillomavirus 5 5 2 +Gammaproteobacteria 3 3 1 +Gammaretrovirus 4 4 2 +Ganciclovir 9 9 1 +Ganglia 2 2 1 +Ganglia, Autonomic 3 4 2 +Ganglia, Invertebrate 2 3 2 +Ganglia, Parasympathetic 4 5 3 +Ganglia, Sensory 3 3 2 +Ganglia, Spinal 4 6 3 +Ganglia, Sympathetic 4 5 3 +Ganglioglioma 6 7 3 +Ganglion Cysts 3 4 2 +Ganglionectomy 6 6 1 +Ganglioneuroblastoma 8 9 3 +Ganglioneuroma 5 6 3 +Ganglionic Blockers 6 6 1 +Ganglionic Eminence 3 9 2 +Ganglionic Stimulants 6 6 1 +Ganglioside Galactosyltransferase 7 7 1 +Gangliosides 5 6 3 +Gangliosidoses 7 8 9 +Gangliosidoses, GM2 8 9 9 +Gangliosidosis, GM1 8 9 9 +Gangrene 4 4 1 +Ganoderma 6 6 1 +Gap Junction alpha-4 Protein 6 6 1 +Gap Junction alpha-5 Protein 6 6 1 +Gap Junction beta-1 Protein 6 6 1 +Gap Junction delta-2 Protein 6 6 1 +Gap Junctions 6 6 1 +GAP-43 Protein 4 5 4 +Garbage 7 7 1 +Garcinia 9 9 1 +Garcinia cambogia 10 10 1 +Garcinia kola 10 10 1 +Garcinia mangostana 10 10 1 +Gardenia 9 9 1 +Gardening 4 5 2 +Gardens 2 3 2 +Gardner Syndrome 4 8 10 +Gardnerella 4 4 1 +Gardnerella vaginalis 5 5 1 +Garlic 11 11 1 +Gas Chromatography-Mass Spectrometry 4 5 2 +Gas Gangrene 6 6 1 +Gas Poisoning 3 3 1 +Gas Scavengers 2 2 1 +Gasdermins 6 6 3 +Gases 2 2 1 +Gaslighting 4 4 1 +Gasoline 4 6 2 +Gasotransmitters 3 5 2 +Gastrectomy 3 3 1 +Gastric Absorption 4 7 5 +Gastric Acid 4 4 1 +Gastric Acidity Determination 4 5 3 +Gastric Antral Vascular Ectasia 4 4 2 +Gastric Artery 4 4 1 +Gastric Balloon 2 2 1 +Gastric Bypass 3 5 4 +Gastric Dilatation 4 4 1 +Gastric Emptying 4 4 1 +Gastric Fistula 3 5 2 +Gastric Fundus 5 5 1 +Gastric Hypothermia 4 4 1 +Gastric Inhibitory Polypeptide 4 5 5 +Gastric Juice 3 3 1 +Gastric Lavage 3 3 1 +Gastric Mucins 6 6 2 +Gastric Mucosa 4 5 2 +Gastric Outlet Obstruction 4 4 1 +Gastric Stump 5 5 1 +Gastrin-Releasing Peptide 4 5 3 +Gastrin-Secreting Cells 3 6 5 +Gastrinoma 5 7 6 +Gastrins 4 5 5 +Gastritis 4 4 2 +Gastritis, Atrophic 5 5 2 +Gastritis, Hypertrophic 5 5 2 +Gastrodia 10 10 1 +Gastroenteritis 3 3 1 +Gastroenteritis, Transmissible, of Swine 3 7 2 +Gastroenterologists 4 5 2 +Gastroenterology 4 4 1 +Gastroenterostomy 3 3 2 +Gastroepiploic Artery 4 4 1 +Gastroesophageal Reflux 6 6 1 +Gastrointestinal Absorption 3 6 5 +Gastrointestinal Agents 4 4 1 +Gastrointestinal Contents 2 2 1 +Gastrointestinal Diseases 2 2 1 +Gastrointestinal Hemorrhage 3 4 2 +Gastrointestinal Hormones 3 3 1 +Gastrointestinal Microbiome 3 8 3 +Gastrointestinal Motility 3 3 1 +Gastrointestinal Neoplasms 3 4 3 +Gastrointestinal Stromal Tumors 4 5 3 +Gastrointestinal Tract 2 2 1 +Gastrointestinal Transit 4 4 2 +Gastroparesis 4 5 2 +Gastropexy 3 3 1 +Gastroplasty 3 5 3 +Gastropoda 5 5 1 +Gastroschisis 3 5 3 +Gastroscopes 5 5 2 +Gastroscopy 5 7 4 +Gastrostomy 3 3 2 +Gastrula 2 2 1 +Gastrulation 6 6 1 +GATA Transcription Factors 4 4 2 +GATA1 Transcription Factor 5 5 4 +GATA2 Deficiency 3 5 2 +GATA2 Transcription Factor 5 5 4 +GATA3 Transcription Factor 5 5 4 +GATA4 Transcription Factor 5 5 2 +GATA5 Transcription Factor 5 5 2 +GATA6 Transcription Factor 5 5 2 +Gated Blood-Pool Imaging 6 7 6 +Gatekeeping 5 5 1 +Gatifloxacin 8 8 1 +Gaucher Disease 7 8 9 +Gaultheria 9 9 1 +GB virus A 6 6 1 +GB virus B 3 6 2 +GB virus C 6 6 1 +GC Rich Sequence 4 5 2 +Geese 7 7 2 +Gefarnate 4 4 2 +Gefitinib 5 5 1 +Geigeria 8 8 1 +Gelatin 4 4 1 +Gelatin Sponge, Absorbable 4 4 1 +Gelatinases 7 7 2 +Gels 3 4 2 +Gelsemium 9 9 1 +Gelsolin 5 6 5 +Gemcitabine 2 7 2 +Gemella 4 4 2 +Gemfibrozil 5 9 5 +Gemifloxacin 5 8 2 +Gemini of Coiled Bodies 8 8 1 +Geminin 4 4 1 +Geminiviridae 3 3 2 +Gemtuzumab 5 9 4 +Gender Dysphoria 3 3 1 +Gender Equity 3 5 3 +Gender Identity 4 5 4 +Gender Role 4 4 1 +Gender-Affirming Care 3 4 2 +Gender-Affirming Procedures 2 5 3 +Gender-Affirming Surgery 3 6 5 +Gender-Based Violence 5 5 1 +Gender-Nonconforming Persons 4 4 1 +Gene Amplification 3 4 3 +Gene Components 6 6 1 +Gene Conversion 4 4 1 +Gene Deletion 4 5 2 +Gene Dosage 3 3 1 +Gene Drive Technology 5 5 1 +Gene Duplication 3 4 2 +Gene Editing 4 4 1 +Gene Expression 2 2 1 +Gene Expression Profiling 3 3 1 +Gene Expression Regulation 2 2 1 +Gene Expression Regulation, Archaeal 3 3 1 +Gene Expression Regulation, Bacterial 3 3 1 +Gene Expression Regulation, Developmental 3 3 1 +Gene Expression Regulation, Enzymologic 3 3 1 +Gene Expression Regulation, Fungal 3 3 1 +Gene Expression Regulation, Leukemic 4 4 1 +Gene Expression Regulation, Neoplastic 3 3 1 +Gene Expression Regulation, Plant 3 3 1 +Gene Expression Regulation, Viral 3 3 1 +Gene Flow 3 3 1 +Gene Frequency 2 2 1 +Gene Fusion 3 3 1 +Gene Knock-In Techniques 4 4 1 +Gene Knockdown Techniques 4 4 1 +Gene Knockout Techniques 4 4 1 +Gene Library 3 3 1 +Gene Ontology 6 8 5 +Gene Order 2 2 1 +Gene Pool 2 2 1 +Gene Products, env 4 6 3 +Gene Products, gag 4 7 3 +Gene Products, nef 5 5 2 +Gene Products, pol 4 7 4 +Gene Products, rev 5 6 3 +Gene Products, rex 5 7 4 +Gene Products, tat 5 6 4 +Gene Products, tax 6 7 4 +Gene Products, vif 5 5 2 +Gene Products, vpr 6 6 1 +Gene Rearrangement 2 2 1 +Gene Rearrangement, alpha-Chain T-Cell Antigen Receptor 4 4 2 +Gene Rearrangement, B-Lymphocyte 3 3 2 +Gene Rearrangement, B-Lymphocyte, Heavy Chain 4 4 2 +Gene Rearrangement, B-Lymphocyte, Light Chain 4 4 2 +Gene Rearrangement, beta-Chain T-Cell Antigen Receptor 4 4 2 +Gene Rearrangement, delta-Chain T-Cell Antigen Receptor 4 4 2 +Gene Rearrangement, gamma-Chain T-Cell Antigen Receptor 4 4 2 +Gene Rearrangement, T-Lymphocyte 3 3 2 +Gene Regulatory Networks 5 5 1 +Gene Silencing 4 4 1 +Gene Targeting 3 3 1 +Gene Therapy Agents 3 3 1 +Gene Transfer Techniques 3 3 1 +Gene Transfer, Horizontal 3 3 1 +Gene-Environment Interaction 3 3 1 +Genealogy and Heraldry 3 3 1 +General Adaptation Syndrome 5 5 1 +General Practice 3 3 1 +General Practice, Dental 3 3 1 +General Practitioners 4 5 2 +General Surgery 4 4 1 +Generalization, Psychological 4 4 1 +Generalization, Response 5 5 1 +Generalization, Stimulus 5 5 1 +Generalized Anxiety Disorder 3 3 1 +Generative Adversarial Networks 4 7 4 +Generative Artificial Intelligence 5 5 1 +Genes 5 5 1 +Genes, abl 9 9 1 +Genes, APC 8 8 2 +Genes, araC 7 7 1 +Genes, Archaeal 6 7 3 +Genes, Bacterial 6 7 3 +Genes, bcl-1 9 9 1 +Genes, bcl-2 9 9 1 +Genes, BRCA1 8 8 2 +Genes, BRCA2 8 8 2 +Genes, cdc 6 6 1 +Genes, Chloroplast 4 6 2 +Genes, DCC 8 8 2 +Genes, Developmental 6 6 1 +Genes, Dominant 3 6 2 +Genes, Duplicate 6 6 1 +Genes, env 7 8 3 +Genes, erbA 9 9 1 +Genes, erbB 9 9 1 +Genes, erbB-1 10 10 1 +Genes, erbB-2 10 10 1 +Genes, Essential 6 6 1 +Genes, fms 9 9 1 +Genes, fos 9 9 1 +Genes, Fungal 6 7 3 +Genes, gag 7 8 3 +Genes, Helminth 5 6 2 +Genes, Homeobox 7 7 1 +Genes, Immediate-Early 6 8 4 +Genes, Immunoglobulin 3 6 2 +Genes, Immunoglobulin Heavy Chain 7 7 1 +Genes, Immunoglobulin Light Chain 7 7 1 +Genes, Insect 5 6 2 +Genes, Intracisternal A-Particle 7 8 6 +Genes, jun 9 9 1 +Genes, Lethal 6 6 1 +Genes, Mating Type, Fungal 7 8 3 +Genes, MCC 8 8 2 +Genes, MDR 6 7 2 +Genes, MHC Class I 4 7 3 +Genes, MHC Class II 4 7 3 +Genes, Microbial 5 6 2 +Genes, Mitochondrial 4 6 2 +Genes, Modifier 6 6 1 +Genes, mos 9 9 1 +Genes, myb 9 9 1 +Genes, myc 9 9 1 +Genes, nef 7 8 4 +Genes, Neoplasm 6 6 1 +Genes, Neurofibromatosis 1 8 8 2 +Genes, Neurofibromatosis 2 8 8 2 +Genes, Overlapping 6 6 1 +Genes, p16 8 8 2 +Genes, p53 8 8 2 +Genes, Plant 5 6 2 +Genes, pol 7 8 3 +Genes, Protozoan 5 6 2 +Genes, pX 7 8 4 +Genes, RAG-1 6 6 1 +Genes, ras 9 9 1 +Genes, Recessive 3 6 2 +Genes, Regulator 6 6 1 +Genes, rel 9 9 1 +Genes, Reporter 6 6 1 +Genes, Retinoblastoma 8 8 2 +Genes, rev 7 8 4 +Genes, rRNA 7 7 1 +Genes, sis 9 9 1 +Genes, src 9 9 1 +Genes, sry 6 6 1 +Genes, Suppressor 6 6 1 +Genes, Switch 7 7 1 +Genes, Synthetic 6 6 1 +Genes, T-Cell Receptor 6 6 1 +Genes, T-Cell Receptor alpha 7 7 1 +Genes, T-Cell Receptor beta 7 7 1 +Genes, T-Cell Receptor delta 7 7 1 +Genes, T-Cell Receptor gamma 7 7 1 +Genes, tat 7 8 4 +Genes, Transgenic, Suicide 7 7 1 +Genes, Tumor Suppressor 7 7 2 +Genes, vif 7 8 4 +Genes, Viral 6 7 3 +Genes, vpr 7 8 4 +Genes, vpu 7 8 4 +Genes, Wilms Tumor 8 8 2 +Genes, X-Linked 3 6 2 +Genes, Y-Linked 3 6 2 +Genetic Algorithms 3 4 2 +Genetic Association Studies 3 3 1 +Genetic Background 2 2 1 +Genetic Carrier Screening 4 6 5 +Genetic Code 3 3 1 +Genetic Complementation Test 4 4 1 +Genetic Counseling 4 7 2 +Genetic Determinism 4 4 1 +Genetic Diseases, Inborn 2 2 1 +Genetic Diseases, X-Linked 3 3 1 +Genetic Diseases, Y-Linked 3 3 1 +Genetic Drift 3 3 3 +Genetic Engineering 3 3 1 +Genetic Enhancement 3 6 3 +Genetic Fitness 2 2 1 +Genetic Heterogeneity 3 3 1 +Genetic Introgression 4 5 2 +Genetic Linkage 2 2 1 +Genetic Load 2 2 1 +Genetic Loci 5 5 1 +Genetic Markers 3 3 2 +Genetic Phenomena 1 1 1 +Genetic Pleiotropy 3 3 2 +Genetic Predisposition to Disease 3 5 2 +Genetic Privacy 5 7 6 +Genetic Profile 3 3 1 +Genetic Research 5 5 2 +Genetic Risk Score 4 8 5 +Genetic Services 3 3 1 +Genetic Speciation 3 3 2 +Genetic Structures 2 2 1 +Genetic Techniques 2 2 1 +Genetic Testing 3 5 5 +Genetic Therapy 3 4 2 +Genetic Variation 2 2 1 +Genetic Vectors 3 3 1 +Genetics 4 4 1 +Genetics, Behavioral 3 5 2 +Genetics, Medical 3 6 2 +Genetics, Microbial 5 5 2 +Genetics, Population 5 5 1 +Geniculate Bodies 8 8 1 +Geniculate Ganglion 4 6 3 +Genioplasty 4 4 1 +Genista 8 8 1 +Genistein 8 8 2 +Genital Diseases 2 2 1 +Genital Diseases, Female 3 4 2 +Genital Diseases, Male 3 3 2 +Genital Neoplasms, Female 3 5 5 +Genital Neoplasms, Male 3 4 5 +Genitalia 2 2 1 +Genitalia, Female 3 3 1 +Genitalia, Male 3 3 1 +Genitourinary Agents 4 4 1 +Genocide 5 7 2 +Genome 3 3 1 +Genome Components 4 4 1 +Genome Size 4 4 1 +Genome, Archaeal 5 5 1 +Genome, Bacterial 5 5 1 +Genome, Chloroplast 5 5 1 +Genome, Fungal 5 5 1 +Genome, Helminth 4 4 1 +Genome, Human 4 4 1 +Genome, Insect 4 4 1 +Genome, Microbial 4 4 1 +Genome, Mitochondrial 4 4 1 +Genome, Plant 4 4 1 +Genome, Plastid 4 4 1 +Genome, Protozoan 4 4 1 +Genome, Viral 5 5 1 +Genome-Wide Association Study 4 5 7 +Genomic Imprinting 4 4 1 +Genomic Instability 2 4 3 +Genomic Islands 6 7 3 +Genomic Library 4 4 2 +Genomic Medicine 2 4 2 +Genomic Structural Variation 4 4 1 +Genomics 5 5 2 +Genotype 2 2 1 +Genotyping Techniques 3 3 1 +Gentamicins 4 4 1 +Gentian Violet 4 4 1 +Gentiana 9 9 1 +Gentianaceae 8 8 1 +Gentianales 7 7 1 +Gentianella 9 9 1 +Gentisates 6 9 4 +Genu Valgum 3 3 1 +Genu Varum 3 3 1 +Geobacillus 5 6 5 +Geobacillus stearothermophilus 6 7 5 +Geobacter 4 5 2 +Geodia 5 5 1 +Geographic Atrophy 5 5 1 +Geographic Information Systems 5 6 2 +Geographic Locations 1 1 1 +Geographic Mapping 3 6 4 +Geography 3 3 1 +Geography, Medical 3 4 2 +Geologic Sediments 3 3 2 +Geological Phenomena 2 2 1 +Geology 3 3 1 +Georgia 6 6 2 +Georgia (Republic) 4 4 3 +Geothermal Energy 5 5 1 +Geotrichosis 4 4 1 +Geotrichum 4 4 1 +Geraniaceae 7 7 1 +Geranium 8 8 1 +Geranylgeranyl-Diphosphate Geranylgeranyltransferase 5 5 1 +Geranyltranstransferase 5 5 1 +Gerbillinae 9 9 1 +Geriatric Anesthesia 3 3 1 +Geriatric Assessment 4 6 6 +Geriatric Dentistry 3 3 1 +Geriatric Nursing 4 4 2 +Geriatric Psychiatry 4 4 2 +Geriatricians 4 5 2 +Geriatrics 3 3 1 +Germ Cell Ribonucleoprotein Granules 8 10 2 +Germ Cells 2 3 2 +Germ Cells, Plant 3 5 2 +Germ Layers 2 2 1 +Germ Theory of Disease 4 4 1 +Germ-Free Life 2 2 1 +Germ-Line Mutation 4 4 1 +Germanium 4 4 3 +Germany 3 3 1 +Germany, East 3 3 1 +Germany, West 3 3 1 +Germinal Center 4 6 2 +Germinal Center Kinases 5 8 2 +Germination 3 4 2 +Germine Acetates 5 5 2 +Germinoma 4 4 1 +Geroscience 3 4 2 +Gerstmann Syndrome 5 7 3 +Gerstmann-Straussler-Scheinker Disease 4 5 5 +Gestalt Theory 3 3 1 +Gestalt Therapy 3 3 1 +Gestational Age 3 6 2 +Gestational Carriers 3 6 3 +Gestational Sac 2 2 1 +Gestational Trophoblastic Disease 4 6 3 +Gestational Weight Gain 3 8 3 +Gestonorone Caproate 7 7 2 +Gestrinone 8 8 1 +Gestures 6 6 1 +Geum 10 10 1 +Ghana 5 5 1 +Ghee 5 6 5 +Ghrelin 4 4 2 +Giant Axonal Neuropathy 4 6 6 +Giant Cell Arteritis 4 6 7 +Giant Cell Tumor of Bone 6 6 2 +Giant Cell Tumor of Tendon Sheath 4 6 3 +Giant Cell Tumors 5 5 1 +Giant Cells 2 2 1 +Giant Cells, Foreign-Body 3 5 6 +Giant Cells, Langhans 3 5 6 +Giant Viruses 3 3 1 +Giardia 3 3 1 +Giardia lamblia 4 4 1 +Giardiasis 4 5 3 +Giardiavirus 5 5 1 +Gibberella 5 5 1 +Gibberellins 6 6 1 +Gibraltar 3 3 1 +Gift Giving 4 4 1 +Gigantism 4 4 3 +Gilbert Disease 5 5 2 +Gills 2 2 1 +Gingipain Cysteine Endopeptidases 7 7 1 +Gingiva 5 5 1 +Gingival Crevicular Fluid 3 3 1 +Gingival Diseases 4 4 1 +Gingival Hemorrhage 4 5 3 +Gingival Hyperplasia 6 6 1 +Gingival Hypertrophy 6 6 1 +Gingival Neoplasms 4 5 3 +Gingival Overgrowth 5 5 1 +Gingival Pocket 3 6 2 +Gingival Recession 5 5 2 +Gingival Retraction Techniques 3 3 1 +Gingivectomy 3 3 3 +Gingivitis 2 5 2 +Gingivitis, Necrotizing Ulcerative 3 7 3 +Gingivoplasty 3 3 3 +Ginkgo biloba 6 6 1 +Ginkgo Extract 5 5 1 +Ginkgolides 5 5 1 +Ginsenosides 4 5 2 +Giraffes 9 9 1 +Gitelman Syndrome 4 7 4 +Gizzard, Avian 3 3 1 +Gizzard, Non-avian 2 2 1 +Glafenine 7 9 2 +Glanders 3 6 2 +Glare 2 4 2 +Glasgow Coma Scale 3 8 4 +Glasgow Outcome Scale 3 8 4 +Glass 3 3 1 +Glass Ionomer Cements 4 6 2 +Glatiramer Acetate 3 3 1 +Glaucarubin 4 6 2 +Glaucoma 3 3 1 +Glaucoma Drainage Implants 3 3 1 +Glaucoma, Angle-Closure 4 4 1 +Glaucoma, Neovascular 4 4 1 +Glaucoma, Open-Angle 4 4 1 +Glaucophyta 2 2 1 +Gleditsia 8 8 1 +Glenoid Cavity 6 6 1 +Glia Maturation Factor 4 5 4 +Gliadin 7 7 2 +Glial Cell Line-Derived Neurotrophic Factor 5 6 4 +Glial Cell Line-Derived Neurotrophic Factor Receptors 6 8 5 +Glial Cell Line-Derived Neurotrophic Factors 4 5 4 +Glial Fibrillary Acidic Protein 5 5 2 +Glicentin 5 5 1 +Gliclazide 5 7 5 +Glioblastoma 7 8 3 +Gliocladium 4 4 1 +Glioma 5 6 3 +Glioma, Subependymal 7 8 3 +Gliosarcoma 6 7 3 +Gliosis 3 3 1 +Gliotoxin 4 5 2 +Glipizide 5 5 1 +Global Burden of Disease 3 9 5 +Global Health 3 3 2 +Global Longitudinal Strain 4 4 1 +Global Warming 5 5 1 +Globins 4 4 1 +Globosides 4 7 4 +Globulins 3 3 1 +Globus Pallidus 9 9 1 +Globus Sensation 4 4 1 +Glomeromycota 3 3 1 +Glomerular Basement Membrane 4 7 4 +Glomerular Filtration Barrier 2 6 3 +Glomerular Filtration Rate 3 5 2 +Glomerular Mesangium 7 7 2 +Glomerulonephritis 5 7 3 +Glomerulonephritis, IGA 3 8 4 +Glomerulonephritis, Membranoproliferative 2 8 4 +Glomerulonephritis, Membranous 3 8 4 +Glomerulosclerosis, Focal Segmental 6 8 3 +Glomus Jugulare 6 7 3 +Glomus Jugulare Tumor 8 8 2 +Glomus Tumor 4 4 1 +Glomus Tympanicum 3 7 6 +Glomus Tympanicum Tumor 8 8 2 +Glossalgia 4 5 4 +Glossectomy 3 3 2 +Glossinidae 10 10 1 +Glossitis 4 4 1 +Glossitis, Benign Migratory 5 5 1 +Glossopharyngeal Nerve 5 5 4 +Glossopharyngeal Nerve Diseases 3 3 1 +Glossopharyngeal Nerve Injuries 4 5 4 +Glossoptosis 4 4 1 +Glottis 3 3 1 +Gloves, Protective 4 6 4 +Gloves, Surgical 4 7 5 +Glucagon 6 6 2 +Glucagon-Like Peptide 1 6 6 1 +Glucagon-Like Peptide 2 6 6 1 +Glucagon-Like Peptide Receptors 6 7 2 +Glucagon-Like Peptide-1 Receptor 7 8 2 +Glucagon-Like Peptide-1 Receptor Agonists 5 5 1 +Glucagon-Like Peptide-2 Receptor 7 8 2 +Glucagon-Like Peptides 5 5 1 +Glucagon-Secreting Cells 3 4 5 +Glucagonoma 5 7 6 +Glucan 1,3-beta-Glucosidase 7 7 1 +Glucan 1,4-alpha-Glucosidase 6 6 1 +Glucan 1,4-beta-Glucosidase 7 7 1 +Glucan Endo-1,3-beta-D-Glucosidase 7 7 1 +Glucans 3 4 2 +Glucaric Acid 3 5 3 +Glucocorticoid-Induced TNFR-Related Protein 8 8 1 +Glucocorticoids 4 6 2 +Glucokinase 6 6 1 +Gluconacetobacter 5 6 2 +Gluconacetobacter xylinus 6 7 2 +Gluconates 3 5 3 +Gluconeogenesis 3 4 2 +Gluconobacter 6 6 2 +Gluconobacter oxydans 7 7 2 +Glucosamine 4 4 1 +Glucosamine 6-Phosphate N-Acetyltransferase 6 6 1 +Glucose 5 5 1 +Glucose 1-Dehydrogenase 7 7 1 +Glucose Clamp Technique 3 6 3 +Glucose Dehydrogenases 6 6 1 +Glucose Intolerance 5 5 1 +Glucose Metabolism Disorders 3 3 1 +Glucose Oxidase 5 5 1 +Glucose Solution, Hypertonic 4 4 1 +Glucose Tolerance Test 4 6 3 +Glucose Transport Proteins, Facilitative 6 6 4 +Glucose Transporter Type 1 7 7 4 +Glucose Transporter Type 2 7 7 4 +Glucose Transporter Type 3 4 7 5 +Glucose Transporter Type 4 7 7 4 +Glucose Transporter Type 5 7 7 4 +Glucose-1-Phosphate Adenylyltransferase 6 6 1 +Glucose-6-Phosphatase 6 6 1 +Glucose-6-Phosphate 5 5 1 +Glucose-6-Phosphate Isomerase 6 6 1 +Glucosephosphate Dehydrogenase 6 6 1 +Glucosephosphate Dehydrogenase Deficiency 4 6 4 +Glucosephosphates 4 4 1 +Glucosidases 5 5 1 +Glucosides 3 3 1 +Glucosinolates 5 5 3 +Glucosylceramidase 6 6 1 +Glucosylceramides 5 8 4 +Glucosyltransferases 6 6 1 +Glucuronates 4 6 4 +Glucuronic Acid 5 7 4 +Glucuronidase 5 5 1 +Glucuronides 5 7 4 +Glucuronosyltransferase 6 6 1 +Glue Proteins, Drosophila 4 6 3 +Glugea 7 7 1 +GluK2 Kainate Receptor 9 10 4 +GluK3 Kainate Receptor 8 10 5 +Glutamate Carboxypeptidase II 7 7 3 +Glutamate Decarboxylase 6 6 1 +Glutamate Dehydrogenase 6 6 1 +Glutamate Dehydrogenase (NADP+) 6 6 1 +Glutamate Formimidoyltransferase 6 6 1 +Glutamate Plasma Membrane Transport Proteins 7 8 8 +Glutamate Synthase 6 6 2 +Glutamate Synthase (NADH) 4 6 2 +Glutamate-5-Semialdehyde Dehydrogenase 6 6 1 +Glutamate-Ammonia Ligase 6 6 1 +Glutamate-Cysteine Ligase 6 6 1 +Glutamate-tRNA Ligase 6 6 1 +Glutamates 4 4 2 +Glutamic Acid 4 5 3 +Glutaminase 5 5 1 +Glutamine 4 4 3 +Glutamine-Fructose-6-Phosphate Transaminase (Isomerizing) 6 6 1 +Glutamyl Aminopeptidase 7 7 3 +Glutaral 3 3 1 +Glutarates 5 5 1 +Glutaredoxins 5 5 1 +Glutaryl-CoA Dehydrogenase 5 5 1 +Glutathione 4 4 1 +Glutathione Disulfide 5 5 1 +Glutathione Peroxidase 4 5 2 +Glutathione Peroxidase GPX1 5 6 2 +Glutathione Reductase 5 5 1 +Glutathione S-Transferase pi 6 6 1 +Glutathione Synthase 6 6 1 +Glutathione Transferase 5 5 1 +Glutens 6 6 2 +Glutethimide 5 5 1 +Glyburide 5 5 2 +Glycated Hemoglobin 5 6 6 +Glycated Proteins 4 4 5 +Glycated Serum Albumin 5 6 7 +Glycated Serum Proteins 4 5 5 +Glycation End Products, Advanced 3 3 2 +Glycemic Control 2 2 1 +Glycemic Index 5 7 3 +Glycemic Load 5 7 3 +Glyceraldehyde 3 5 2 +Glyceraldehyde 3-Phosphate 4 4 1 +Glyceraldehyde 3-Phosphate Dehydrogenase (NADP+) 7 7 1 +Glyceraldehyde-3-Phosphate Dehydrogenase (NADP+)(Phosphorylating) 7 7 1 +Glyceraldehyde-3-Phosphate Dehydrogenase (Phosphorylating) 7 7 1 +Glyceraldehyde-3-Phosphate Dehydrogenases 6 6 1 +Glyceric Acids 3 5 3 +Glycerides 2 2 1 +Glycerol 4 5 2 +Glycerol Kinase 6 6 1 +Glycerol-3-Phosphate Dehydrogenase (NAD+) 6 8 2 +Glycerol-3-Phosphate O-Acyltransferase 5 5 1 +Glycerolphosphate Dehydrogenase 7 7 1 +Glycerophosphates 3 5 4 +Glycerophosphoinositol Inositolphosphodiesterase 6 6 1 +Glycerophospholipids 6 6 1 +Glyceryl Ethers 3 5 2 +Glycerylphosphorylcholine 5 9 4 +Glycine 3 3 1 +Glycine Agents 5 5 2 +Glycine Decarboxylase Complex 4 6 3 +Glycine Decarboxylase Complex H-Protein 5 5 2 +Glycine Dehydrogenase 6 6 1 +Glycine Dehydrogenase (Decarboxylating) 5 7 3 +Glycine Hydroxymethyltransferase 6 6 1 +Glycine max 8 8 1 +Glycine N-Methyltransferase 6 6 1 +Glycine Plasma Membrane Transport Proteins 7 7 5 +Glycine Transaminase 6 6 1 +Glycine-tRNA Ligase 6 6 1 +Glycobiology 4 4 1 +Glycocalyx 5 5 1 +Glycochenodeoxycholic Acid 7 8 7 +Glycocholic Acid 5 6 3 +Glycoconjugates 2 2 1 +Glycodelin 4 5 4 +Glycodeoxycholic Acid 6 7 5 +Glycogen 4 5 2 +Glycogen Debranching Enzyme System 6 7 2 +Glycogen Phosphorylase 8 8 1 +Glycogen Phosphorylase, Brain Form 9 9 1 +Glycogen Phosphorylase, Liver Form 9 9 1 +Glycogen Phosphorylase, Muscle Form 9 9 1 +Glycogen Storage Disease 5 5 2 +Glycogen Storage Disease Type I 6 6 2 +Glycogen Storage Disease Type II 6 7 8 +Glycogen Storage Disease Type IIb 4 6 5 +Glycogen Storage Disease Type III 6 6 2 +Glycogen Storage Disease Type IV 6 6 2 +Glycogen Storage Disease Type V 6 6 2 +Glycogen Storage Disease Type VI 6 6 2 +Glycogen Storage Disease Type VII 4 6 5 +Glycogen Storage Disease Type VIII 4 6 3 +Glycogen Synthase 7 7 1 +Glycogen Synthase Kinase 3 4 9 6 +Glycogen Synthase Kinase 3 beta 5 10 6 +Glycogen Synthase Kinases 5 8 2 +Glycogen-Synthase-D Phosphatase 5 7 2 +Glycogenolysis 3 4 2 +Glycolaldehyde Dehydrogenase 6 6 1 +Glycolates 4 5 2 +Glycolipids 2 3 2 +Glycols 3 3 1 +Glycolysis 3 4 4 +Glycomics 4 6 5 +Glycopeptides 3 3 2 +Glycophorins 5 5 3 +Glycoprotein Hormones, alpha Subunit 6 8 11 +Glycoproteins 3 3 2 +Glycopyrrolate 4 4 3 +Glycosaminoglycans 3 3 1 +Glycoside Hydrolase Inhibitors 5 5 2 +Glycoside Hydrolases 4 4 1 +Glycosides 2 2 1 +Glycosphingolipids 3 4 3 +Glycosuria 4 6 4 +Glycosuria, Renal 4 7 8 +Glycosylation 3 4 3 +Glycosylphosphatidylinositol Diacylglycerol-Lyase 8 8 1 +Glycosylphosphatidylinositols 3 8 3 +Glycosyltransferases 4 4 1 +Glycylglycine 5 5 1 +Glycyrrhetinic Acid 6 6 1 +Glycyrrhiza 8 8 1 +Glycyrrhiza uralensis 9 9 1 +Glycyrrhizic Acid 6 6 1 +Glymphatic System 2 4 3 +Glyoxal 3 3 1 +Glyoxylates 4 4 1 +Glyoxysomes 8 10 2 +Glyphosate 4 5 3 +Glypicans 6 6 6 +GMP Reductase 5 5 1 +Gnaphalium 8 8 1 +Gnathostoma 10 10 1 +Gnathostomiasis 7 7 1 +Gnetophyta 6 6 1 +Gnetum 7 7 1 +Goals 3 3 1 +Goat Diseases 2 2 1 +Goats 9 9 1 +Goblet Cells 3 6 10 +Goiter 3 3 1 +Goiter, Endemic 4 4 1 +Goiter, Nodular 4 4 1 +Goiter, Substernal 4 4 1 +Gold 4 4 3 +Gold Alloys 3 6 6 +Gold Colloid 3 3 1 +Gold Colloid, Radioactive 4 7 6 +Gold Compounds 2 2 1 +Gold Isotopes 3 5 4 +Gold Radioisotopes 4 6 5 +Gold Sodium Thiomalate 4 7 3 +Gold Sodium Thiosulfate 3 7 3 +Goldenhar Syndrome 6 7 3 +Goldfish 9 9 1 +Golf 5 5 1 +Golgi Apparatus 7 7 1 +Golgi Matrix Proteins 3 3 1 +Golgi-Mazzoni Corpuscles 5 6 3 +Gonadal Disorders 2 2 1 +Gonadal Dysgenesis 4 6 5 +Gonadal Dysgenesis, 46,XX 5 7 10 +Gonadal Dysgenesis, 46,XY 5 7 10 +Gonadal Dysgenesis, Mixed 5 7 12 +Gonadal Hormones 3 3 1 +Gonadal Steroid Hormones 4 4 1 +Gonadoblastoma 4 8 12 +Gonadotrophs 3 11 7 +Gonadotropin-Releasing Hormone 4 7 5 +Gonadotropins 4 4 1 +Gonadotropins, Equine 4 5 4 +Gonadotropins, Pituitary 5 6 3 +Gonads 3 3 2 +Gonanes 4 4 1 +Gonioscopy 4 4 1 +Goniothalamus 8 8 1 +Gonorrhea 4 6 5 +Goosecoid Protein 4 5 2 +Gophers 8 8 1 +Gordonia Bacterium 4 4 1 +Gorilla gorilla 11 11 1 +Goserelin 5 8 5 +Gossypium 10 10 1 +Gossypol 5 5 1 +Gout 3 5 5 +Gout Suppressants 5 5 1 +Governing Board 3 3 1 +Government 2 3 2 +Government Agencies 3 3 2 +Government Employees 3 3 1 +Government Programs 2 2 1 +Government Publication 2 2 1 +Government Publications as Topic 5 5 1 +Government Regulation 3 4 2 +gp100 Melanoma Antigen 4 5 3 +GPI-Linked Proteins 5 5 4 +Gracilaria 3 5 2 +Gracilis Muscle 4 4 1 +GRADE Approach 7 7 1 +Graft Enhancement, Immunologic 4 6 2 +Graft Occlusion, Vascular 4 4 1 +Graft Rejection 4 4 1 +Graft Survival 4 4 1 +Graft vs Host Disease 2 2 1 +Graft vs Host Reaction 3 3 1 +Graft vs Leukemia Effect 5 5 1 +Graft vs Tumor Effect 4 4 1 +Grain Proteins 4 6 4 +Gram-Negative Aerobic Bacteria 3 3 1 +Gram-Negative Aerobic Rods and Cocci 4 4 1 +Gram-Negative Anaerobic Bacteria 3 3 1 +Gram-Negative Anaerobic Cocci 4 4 1 +Gram-Negative Anaerobic Straight, Curved, and Helical Rods 4 4 1 +Gram-Negative Bacteria 2 2 1 +Gram-Negative Bacterial Infections 4 4 1 +Gram-Negative Chemolithotrophic Bacteria 4 4 1 +Gram-Negative Facultatively Anaerobic Rods 3 3 1 +Gram-Negative Oxygenic Photosynthetic Bacteria 3 3 1 +Gram-Positive Asporogenous Rods 4 4 1 +Gram-Positive Asporogenous Rods, Irregular 5 5 1 +Gram-Positive Asporogenous Rods, Regular 5 5 1 +Gram-Positive Bacteria 2 2 1 +Gram-Positive Bacterial Infections 4 4 1 +Gram-Positive Cocci 3 3 1 +Gram-Positive Endospore-Forming Bacteria 3 3 2 +Gram-Positive Endospore-Forming Rods 4 4 3 +Gram-Positive Rods 3 3 1 +Gramicidin 5 5 3 +Grandparents 2 5 3 +Granisetron 4 6 4 +Granular Cell Tumor 5 5 1 +Granulation Tissue 3 3 1 +Granulins 4 5 4 +Granulocyte Colony-Stimulating Factor 5 7 5 +Granulocyte Precursor Cells 4 7 7 +Granulocyte-Macrophage Colony-Stimulating Factor 5 7 5 +Granulocyte-Macrophage Progenitor Cells 4 6 4 +Granulocytes 3 5 6 +Granuloma 3 4 2 +Granuloma Annulare 4 5 3 +Granuloma Inguinale 4 6 8 +Granuloma, Foreign-Body 4 4 2 +Granuloma, Giant Cell 3 5 4 +Granuloma, Laryngeal 3 5 5 +Granuloma, Lethal Midline 3 3 2 +Granuloma, Plasma Cell 4 4 1 +Granuloma, Pyogenic 4 4 1 +Granuloma, Respiratory Tract 2 4 2 +Granulomatosis with Polyangiitis 4 6 4 +Granulomatosis, Orofacial 3 3 2 +Granulomatous Disease, Chronic 4 5 4 +Granulomatous Mastitis 5 6 2 +Granulosa Cell Tumor 4 8 8 +Granulosa Cells 3 7 4 +Granulovirus 4 4 2 +Granzymes 7 7 2 +Grape Seed Extract 3 5 2 +Graph Neural Networks 3 6 2 +Graphic Novel 4 4 1 +Graphic Novels as Topic 7 8 2 +Graphite 3 4 2 +Grasshoppers 7 7 1 +Grassland 4 5 2 +Grateful Med 4 6 2 +Grave Robbing 4 4 1 +Graves Disease 3 4 4 +Graves Ophthalmopathy 3 5 6 +Gravidity 3 5 3 +Gravitation 3 3 1 +Gravitropism 3 5 2 +Gravity Sensing 2 4 2 +Gravity Suits 2 2 1 +Gravity, Altered 4 4 1 +Gray Literature 5 5 1 +Gray Matter 4 4 2 +Gray Platelet Syndrome 4 4 2 +GRB10 Adaptor Protein 5 5 3 +GRB2 Adaptor Protein 5 5 3 +GRB7 Adaptor Protein 5 5 3 +Great Lakes Region 5 5 1 +Greece 3 3 1 +Greece, Ancient 4 4 1 +Greek World 7 7 1 +Green Chemistry Technology 3 3 1 +Green Fluorescent Proteins 4 4 1 +Green Light 4 7 4 +Greenhouse Effect 3 4 2 +Greenhouse Gases 3 5 2 +Greenland 3 5 3 +Grenada 4 5 2 +Grewia 10 10 1 +Grid Cells 3 3 2 +Grief 4 4 1 +Grief Therapy 3 3 1 +Griffonia 8 8 1 +Grifola 5 5 1 +Grindelia 8 8 1 +Griseofulvin 5 5 1 +Groin 4 4 1 +Grooming 5 5 1 +Gross Domestic Product 3 3 1 +Grossulariaceae 9 9 1 +Grounded Theory 5 5 1 +Groundwater 3 3 1 +Group Dynamics 4 4 1 +Group Homes 3 4 2 +Group I Chaperonins 6 7 2 +Group I Phospholipases A2 10 10 1 +Group IA Phospholipases A2 11 11 1 +Group IB Phospholipases A2 11 11 1 +Group II Chaperonins 6 7 2 +Group II Phospholipases A2 10 10 1 +Group III Histone Deacetylases 6 6 1 +Group III Phospholipases A2 10 10 1 +Group IV Phospholipases A2 10 10 1 +Group Practice 4 4 1 +Group Practice, Dental 5 5 1 +Group Practice, Prepaid 5 5 1 +Group Processes 3 3 1 +Group Purchasing 4 7 3 +Group Structure 4 4 1 +Group V Phospholipases A2 10 10 1 +Group VI Phospholipases A2 10 10 1 +Group X Phospholipases A2 10 10 1 +Growth 3 3 1 +Growth and Development 2 2 1 +Growth Arrest-Specific Protein 6 3 4 3 +Growth Charts 4 4 1 +Growth Cones 4 5 5 +Growth Differentiation Factor 1 5 6 6 +Growth Differentiation Factor 10 5 6 6 +Growth Differentiation Factor 15 4 6 6 +Growth Differentiation Factor 2 5 6 6 +Growth Differentiation Factor 3 5 6 6 +Growth Differentiation Factor 5 5 6 3 +Growth Differentiation Factor 6 5 6 3 +Growth Differentiation Factor 9 5 6 3 +Growth Differentiation Factors 4 5 3 +Growth Disorders 3 3 1 +Growth Hormone 6 6 2 +Growth Hormone-Releasing Hormone 6 7 4 +Growth Hormone-Secreting Pituitary Adenoma 4 7 5 +Growth Inhibitors 5 5 1 +Growth Plate 5 5 1 +Growth Substances 4 4 1 +Gryllidae 7 7 1 +GTP Cyclohydrolase 5 5 1 +GTP Phosphohydrolase Activators 5 5 1 +GTP Phosphohydrolase-Linked Elongation Factors 5 7 4 +GTP Phosphohydrolases 5 5 1 +GTP Pyrophosphokinase 6 6 1 +GTP-Binding Protein alpha Subunit, Gi2 7 10 4 +GTP-Binding Protein alpha Subunits 5 8 4 +GTP-Binding Protein alpha Subunits, G12-G13 6 9 4 +GTP-Binding Protein alpha Subunits, Gi-Go 6 9 4 +GTP-Binding Protein alpha Subunits, Gq-G11 6 9 4 +GTP-Binding Protein alpha Subunits, Gs 6 9 4 +GTP-Binding Protein beta Subunits 5 6 4 +GTP-Binding Protein gamma Subunits 5 6 4 +GTP-Binding Protein Regulators 4 4 2 +GTP-Binding Proteins 4 6 3 +GTPase-Activating Proteins 5 5 2 +Guadeloupe 4 5 2 +Guaiac 5 5 1 +Guaiacol 4 8 4 +Guaiacum 8 8 1 +Guaifenesin 5 9 4 +Guam 5 5 2 +Guanabenz 4 4 1 +Guanazole 5 5 1 +Guanethidine 4 4 1 +Guanfacine 4 5 2 +Guanidine 4 4 1 +Guanidines 3 3 1 +Guanidinoacetate N-Methyltransferase 6 6 1 +Guanine 7 7 1 +Guanine Deaminase 5 5 1 +Guanine Nucleotide Dissociation Inhibitors 5 5 2 +Guanine Nucleotide Exchange Factors 5 5 2 +Guanine Nucleotide-Releasing Factor 2 6 6 2 +Guanine Nucleotides 4 6 3 +Guanosine 4 6 3 +Guanosine 5'-O-(3-Thiotriphosphate) 4 6 4 +Guanosine Diphosphate 5 7 3 +Guanosine Diphosphate Fucose 6 9 5 +Guanosine Diphosphate Mannose 6 9 5 +Guanosine Diphosphate Sugars 5 8 5 +Guanosine Monophosphate 5 7 3 +Guanosine Pentaphosphate 5 7 3 +Guanosine Tetraphosphate 5 7 3 +Guanosine Triphosphate 5 7 3 +Guanylate Cyclase 4 5 2 +Guanylate Cyclase-Activating Proteins 4 7 5 +Guanylate Kinases 6 6 1 +Guanylthiourea 4 5 2 +Guanylyl Cyclase C Agonists 5 5 2 +Guanylyl Imidodiphosphate 6 8 3 +Guatemala 4 4 1 +Guatteria 8 8 1 +Gubernaculum 2 2 1 +Guernsey 5 5 1 +Guidebook 2 2 1 +Guided Tissue Regeneration 3 3 1 +Guided Tissue Regeneration, Periodontal 3 4 3 +Guideline 2 4 3 +Guideline Adherence 3 4 2 +Guidelines as Topic 3 4 2 +Guillain-Barre Syndrome 4 6 5 +Guilt 3 3 1 +Guinea 5 5 1 +Guinea Pigs 8 8 1 +Guinea-Bissau 5 5 1 +Gulf of America 4 4 1 +Gulf War 5 6 2 +Gum Arabic 4 5 3 +Gun Violence 5 5 2 +Gutta-Percha 4 6 4 +Guttate Psoriasis 5 5 1 +Guttaviridae 3 3 2 +Guyana 4 4 1 +Gymnastics 3 6 3 +Gymnema 9 9 1 +Gymnema sylvestre 10 10 1 +Gymnotiformes 7 7 1 +Gynatresia 4 5 2 +Gynecologic Surgical Procedures 3 3 1 +Gynecological Examination 4 4 1 +Gynecologists 4 5 2 +Gynecology 4 4 2 +Gynecomastia 4 4 1 +Gynostemma 8 8 1 +Gyrate Atrophy 3 4 3 +Gyrovirus 4 4 1 +Gyrus Cinguli 6 9 2 +H(+)-K(+)-Exchanging ATPase 6 9 5 +H-1 parvovirus 6 6 1 +H-2 Antigens 6 6 4 +H-Reflex 5 5 1 +H-Y Antigen 6 6 2 +Habenula 6 7 2 +Habits 3 3 1 +Habituation, Psychophysiologic 3 4 3 +HaCaT Cells 4 4 3 +Haemaphysalis longicornis 9 9 1 +Haemonchiasis 8 8 1 +Haemonchus 9 9 1 +Haemophilus 5 5 2 +Haemophilus ducreyi 6 6 2 +Haemophilus Infections 6 6 1 +Haemophilus influenzae 6 6 2 +Haemophilus influenzae type b 7 7 2 +Haemophilus paragallinarum 6 6 2 +Haemophilus parainfluenzae 6 6 2 +Haemophilus paraphrophilus 6 6 2 +Haemophilus parasuis 6 6 2 +Haemophilus somnus 6 6 2 +Haemophilus Vaccines 5 5 1 +Haemosporida 4 4 1 +Hafnia 5 5 2 +Hafnia alvei 6 6 2 +Hafnium 4 4 3 +Hagfishes 6 6 3 +Hair 2 2 1 +Hair Analysis 4 5 2 +Hair Bleaching Agents 5 5 2 +Hair Cells, Ampulla 4 6 2 +Hair Cells, Auditory 4 7 6 +Hair Cells, Auditory, Inner 5 8 6 +Hair Cells, Auditory, Outer 5 8 6 +Hair Cells, Vestibular 4 7 7 +Hair Color 2 3 2 +Hair Diseases 3 3 1 +Hair Dyes 4 5 2 +Hair Follicle 3 4 3 +Hair Preparations 4 4 1 +Hair Removal 3 3 1 +Haiti 4 5 2 +Hajdu-Cheney Syndrome 3 6 4 +Halcinonide 7 7 1 +Half-Life 3 3 1 +Halfway Houses 3 3 1 +Haliclona 5 5 1 +Halitosis 4 4 1 +Hallermann's Syndrome 5 6 3 +Hallucinations 4 6 3 +Hallucinogens 4 6 2 +Hallux 7 7 1 +Hallux Limitus 3 4 3 +Hallux Rigidus 3 4 2 +Hallux Valgus 3 3 1 +Hallux Varus 3 3 1 +Haloarcula 5 5 1 +Haloarcula marismortui 6 6 1 +Halobacillus 5 6 5 +Halobacteriaceae 4 4 1 +Halobacteriales 3 3 1 +Halobacterium 5 5 1 +Halobacterium salinarum 6 6 1 +Halococcus 5 5 1 +Halofenate 5 7 3 +Haloferax 5 5 1 +Haloferax mediterranei 6 6 1 +Haloferax volcanii 6 6 1 +Halogenated Diphenyl Ethers 4 8 2 +Halogenation 2 3 2 +Halogens 3 3 1 +Halomonadaceae 4 5 2 +Halomonas 5 6 2 +Haloperidol 4 4 1 +Halorhodopsins 4 7 4 +Halorhodospira halophila 5 6 2 +Halorubrum 5 5 1 +Halothane 4 4 1 +Halothiobacillus 5 5 2 +Hamamelidaceae 7 7 1 +Hamamelis 8 8 1 +Hamartoma 2 2 1 +Hamartoma Syndrome, Multiple 3 4 4 +Hamate Bone 7 7 1 +Hamelia 9 9 1 +Hamman-Rich Syndrome 6 6 1 +Hammer Toe Syndrome 3 3 1 +Hamstring Muscles 4 5 2 +Hamstring Tendons 3 3 1 +Hand 4 4 1 +Hand Bones 5 5 1 +Hand Deformities 2 2 1 +Hand Deformities, Acquired 3 3 1 +Hand Deformities, Congenital 3 6 3 +Hand Dermatoses 3 3 1 +Hand Disinfection 5 5 1 +Hand Hygiene 3 4 2 +Hand Injuries 2 2 1 +Hand Joints 4 4 1 +Hand Sanitizers 4 6 3 +Hand Strength 4 5 2 +Hand Transplantation 5 5 2 +Hand, Foot and Mouth Disease 7 7 1 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1 +Hospital Bed Capacity, 300 to 499 5 5 1 +Hospital Bed Capacity, 500 and over 5 5 1 +Hospital Bed Capacity, under 100 5 5 1 +Hospital Charges 4 4 2 +Hospital Communication Systems 5 5 2 +Hospital Costs 4 5 3 +Hospital Departments 5 5 2 +Hospital Design and Construction 4 4 2 +Hospital Distribution Systems 5 5 2 +Hospital Information Systems 4 5 2 +Hospital Medicine 3 3 1 +Hospital Mortality 5 7 4 +Hospital Planning 5 5 1 +Hospital Rapid Response Team 4 4 1 +Hospital Records 4 6 5 +Hospital Restructuring 5 5 2 +Hospital Shared Services 4 5 3 +Hospital Shops 5 5 2 +Hospital to Home Transition 4 5 2 +Hospital Units 3 3 1 +Hospital Volunteers 3 5 3 +Hospital-Patient Relations 4 5 2 +Hospital-Physician Joint Ventures 6 6 2 +Hospital-Physician Relations 4 5 4 +Hospitalists 4 6 6 +Hospitalization 3 4 2 +Hospitals 3 3 1 +Hospitals, Animal 4 6 2 +Hospitals, Chronic Disease 5 5 1 +Hospitals, Community 4 4 1 +Hospitals, Convalescent 5 5 1 +Hospitals, County 5 5 1 +Hospitals, District 5 5 1 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1 +Host-Parasite Interactions 4 4 1 +Host-Pathogen Interactions 2 3 2 +Host-Seeking Behavior 2 5 2 +Hosta 10 10 1 +Hostility 3 3 1 +Hot Flashes 3 3 1 +Hot Melt Extrusion Technology 3 4 3 +Hot Springs 5 5 2 +Hot Temperature 4 7 5 +Hotlines 4 4 1 +House Calls 4 4 1 +Houseflies 11 11 1 +Household Articles 2 2 1 +Household Products 2 2 1 +Household Work 2 2 1 +Housekeeping, Hospital 3 6 3 +Housing 2 6 4 +Housing for the Elderly 3 5 3 +Housing Instability 3 6 2 +Housing Quality 5 6 4 +Housing, Animal 3 5 2 +Houttuynia 8 8 1 +HSC70 Heat-Shock Proteins 6 6 1 +HSP110 Heat-Shock Proteins 6 6 1 +HSP20 Heat-Shock Proteins 6 6 1 +HSP27 Heat-Shock Proteins 6 6 1 +HSP30 Heat-Shock Proteins 6 6 1 +HSP40 Heat-Shock Proteins 5 5 1 +HSP47 Heat-Shock Proteins 4 5 5 +HSP70 Heat-Shock Proteins 5 5 1 +HSP72 Heat-Shock Proteins 6 6 1 +HSP90 Heat-Shock Proteins 5 5 1 +HT29 Cells 3 5 3 +HTLV-I Antibodies 9 9 3 +HTLV-I Antigens 5 6 2 +HTLV-I Infections 4 6 2 +HTLV-II Antibodies 9 9 3 +HTLV-II Antigens 5 6 2 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Cells 4 4 1 +Human-Animal Bond 4 4 1 +Human-Animal Interaction 3 3 1 +Humanism 3 5 2 +Humanities 1 1 1 +Humans 11 11 1 +Humeral Fractures 3 3 2 +Humeral Fractures, Distal 4 5 4 +Humeral Head 7 7 1 +Humerus 6 6 1 +Humic Substances 3 7 3 +Humidifiers 2 3 2 +Humidity 4 6 4 +Humoralism 4 4 1 +Humpback Whale 9 9 1 +Humulus 10 10 1 +Hungary 4 4 1 +Hunger 3 4 3 +Hunting 2 4 2 +Huntingtin Protein 3 3 1 +Huntington Disease 4 6 7 +Huperzia 6 6 1 +Hutchinson's Melanotic Freckle 5 7 3 +Hyacinthus 10 10 1 +Hyaenidae 9 9 1 +Hyalectins 5 6 3 +Hyalin 2 2 1 +Hyaline Cartilage 3 4 2 +Hyaline Fibromatosis Syndrome 4 4 2 +Hyaline Membrane Disease 5 5 3 +Hyalohyphomycosis 4 5 3 +Hyaluronan Receptors 5 7 8 +Hyaluronan Synthases 7 7 1 +Hyaluronic Acid 4 4 1 +Hyaluronoglucosaminidase 5 6 2 +Hybrid Cells 3 3 1 +Hybrid Renal Replacement Therapy 3 3 2 +Hybrid Vigor 2 2 1 +Hybridization, Genetic 3 4 2 +Hybridomas 4 4 2 +Hycanthone 4 6 2 +Hydantoins 6 6 1 +Hydatidiform Mole 5 7 3 +Hydatidiform Mole, Invasive 6 8 3 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Deuterium Exchange-Mass Spectrometry 4 4 2 +Hydrogen Peroxide 5 7 4 +Hydrogen Sulfide 3 4 3 +Hydrogen-Ion Concentration 2 2 1 +Hydrogenase 4 4 1 +Hydrogenation 2 3 3 +Hydrogenophilaceae 4 4 1 +Hydrogensulfite Reductase 5 5 1 +Hydrolases 3 3 1 +Hydrology 3 5 3 +Hydrolysis 2 2 1 +Hydrolyzable Tannins 5 9 5 +Hydromorphone 5 6 4 +Hydronephrosis 4 6 3 +Hydrophiidae 7 9 3 +Hydrophobic and Hydrophilic Interactions 2 2 1 +Hydrophthalmos 3 5 4 +Hydrophyllaceae 7 7 1 +Hydropneumothorax 3 3 1 +Hydroponics 3 3 1 +Hydrops Fetalis 3 6 7 +Hydroquinones 7 7 1 +Hydrostatic Pressure 4 4 1 +Hydrotherapy 3 4 2 +Hydrothermal Vents 5 6 2 +Hydrothorax 3 3 1 +Hydroxamic Acids 4 4 2 +Hydroxides 3 5 2 +Hydroxocobalamin 6 8 3 +Hydroxy Acids 3 3 1 +Hydroxyacetylaminofluorene 5 8 4 +Hydroxyacylglutathione Hydrolase 6 6 1 +Hydroxyapatites 4 9 4 +Hydroxybenzoate Ethers 4 8 5 +Hydroxybenzoates 4 7 4 +Hydroxybutyrate Dehydrogenase 6 6 1 +Hydroxybutyrates 4 5 3 +Hydroxychloroquine 7 7 1 +Hydroxycholecalciferols 5 7 4 +Hydroxycholesterols 6 8 3 +Hydroxycorticosteroids 4 4 1 +Hydroxydopamines 5 10 2 +Hydroxyeicosatetraenoic Acids 6 6 2 +Hydroxyestrones 7 9 4 +Hydroxyethyl Starch Derivatives 4 5 2 +Hydroxyethylrutoside 9 9 2 +Hydroxyindoleacetic Acid 4 6 2 +Hydroxyl Radical 4 6 3 +Hydroxylamine 4 4 1 +Hydroxylamines 3 3 1 +Hydroxylation 2 3 3 +Hydroxylysine 5 5 2 +Hydroxymercuribenzoates 5 8 7 +Hydroxymethyl and Formyl Transferases 5 5 1 +Hydroxymethylbilane Synthase 5 5 1 +Hydroxymethylglutaryl CoA Reductases 7 7 1 +Hydroxymethylglutaryl-CoA Reductase Inhibitors 5 7 3 +Hydroxymethylglutaryl-CoA Reductases, NAD-Dependent 8 8 1 +Hydroxymethylglutaryl-CoA Synthase 5 5 1 +Hydroxymethylglutaryl-CoA-Reductases, NADP-dependent 8 8 1 +Hydroxyphenylazouracil 6 6 1 +Hydroxyprogesterones 7 8 2 +Hydroxyproline 6 6 1 +Hydroxypropiophenone 4 4 1 +Hydroxyprostaglandin Dehydrogenases 6 6 1 +Hydroxypyruvate Reductase 6 6 1 +Hydroxyquinolines 5 5 1 +Hydroxysteroid Dehydrogenases 5 5 1 +Hydroxysteroids 4 4 1 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+Hyperekplexia 3 3 1 +Hyperemesis Gravidarum 5 6 2 +Hyperemia 3 3 1 +Hypereosinophilic Syndrome 5 5 1 +Hyperesthesia 5 6 2 +Hyperferritinemia 4 4 1 +Hypergammaglobulinemia 3 4 3 +Hyperglycemia 4 4 1 +Hyperglycemic Hyperosmolar Nonketotic Coma 5 5 1 +Hyperglycinemia, Nonketotic 5 6 6 +Hypergravity 5 5 1 +Hyperhidrosis 4 4 1 +Hyperhomocysteinemia 4 7 4 +Hypericum 9 9 1 +Hyperinsulinism 4 4 1 +Hyperkalemia 4 4 1 +Hyperkeratosis, Epidermolytic 5 6 6 +Hyperkinesis 4 5 2 +Hyperlactatemia 3 3 2 +Hyperlipidemia, Familial Combined 5 6 4 +Hyperlipidemias 5 5 1 +Hyperlipoproteinemia Type I 5 7 4 +Hyperlipoproteinemia Type II 5 7 4 +Hyperlipoproteinemia Type III 5 7 4 +Hyperlipoproteinemia Type IV 5 7 5 +Hyperlipoproteinemia Type V 5 7 5 +Hyperlipoproteinemias 6 6 1 +Hyperlysinemias 5 6 6 +Hypermastigia 3 3 1 +Hypermedia 4 4 1 +Hypernatremia 4 4 1 +Hyperopia 3 3 1 +Hyperostosis 3 3 1 +Hyperostosis Frontalis Interna 4 5 2 +Hyperostosis, Cortical, Congenital 3 5 3 +Hyperostosis, Diffuse Idiopathic Skeletal 4 5 2 +Hyperostosis, Sternocostoclavicular 3 4 3 +Hyperotreti 5 5 2 +Hyperoxaluria 4 6 3 +Hyperoxaluria, Primary 5 7 5 +Hyperoxia 4 4 1 +Hyperparathyroidism 3 3 1 +Hyperparathyroidism, Primary 4 4 1 +Hyperparathyroidism, Secondary 4 4 1 +Hyperphagia 4 4 1 +Hyperphosphatemia 4 4 1 +Hyperpigmentation 4 4 1 +Hyperpituitarism 3 6 2 +Hyperplasia 3 3 1 +Hyperpolarization-Activated Cyclic Nucleotide-Gated Channels 6 6 3 +Hyperprolactinemia 4 7 2 +Hypersensitivity 2 2 1 +Hypersensitivity, Delayed 3 3 1 +Hypersensitivity, Immediate 3 3 1 +Hyperspectral Imaging 4 4 1 +Hypersplenism 4 4 1 +Hypertelorism 5 6 3 +Hypertension 3 3 1 +Hypertension, Malignant 4 4 1 +Hypertension, Portal 3 3 1 +Hypertension, Pregnancy-Induced 4 4 2 +Hypertension, Pulmonary 3 4 2 +Hypertension, Renal 4 6 4 +Hypertension, Renovascular 5 7 4 +Hypertensive Crisis 4 4 1 +Hypertensive Encephalopathy 5 5 1 +Hypertensive Retinopathy 3 4 2 +Hyperthermia 3 4 2 +Hyperthermia, Induced 2 2 1 +Hyperthermic Intraperitoneal Chemotherapy 3 4 3 +Hyperthyroidism 3 3 1 +Hyperthyroxinemia 3 3 1 +Hyperthyroxinemia, Familial Dysalbuminemic 3 4 2 +Hypertonic Solutions 3 3 1 +Hypertrichosis 4 4 1 +Hypertriglyceridemia 6 6 1 +Hypertriglyceridemic Waist 3 7 2 +Hypertrophy 3 3 1 +Hypertrophy, Left Ventricular 4 5 2 +Hypertrophy, Right Ventricular 4 5 2 +Hyperuricemia 3 3 1 +Hyperventilation 3 4 2 +Hypervitaminosis A 3 3 1 +Hypesthesia 5 6 2 +Hyphae 3 3 1 +Hyphema 3 5 2 +Hyphomicrobiaceae 4 4 1 +Hyphomicrobium 5 5 1 +Hypnosis 3 4 2 +Hypnosis, Anesthetic 3 3 1 +Hypnosis, Dental 3 4 3 +Hypnotics and Sedatives 5 6 2 +Hypoadrenocorticism, Familial 5 5 1 +Hypoalbuminemia 5 5 1 +Hypoaldosteronism 4 4 1 +Hypoalphalipoproteinemias 6 6 4 +Hypobetalipoproteinemia, Familial, Apolipoprotein B 5 7 2 +Hypobetalipoproteinemias 6 6 4 +Hypocalcemia 4 4 2 +Hypocapnia 4 4 1 +Hypochlorous Acid 3 4 4 +Hypochondriasis 3 3 1 +Hypocotyl 3 4 3 +Hypocrea 5 5 1 +Hypocreales 4 4 1 +Hypodermoclysis 4 6 2 +Hypodermyiasis 6 6 1 +Hypogastric Plexus 5 5 2 +Hypoglossal Nerve 5 5 1 +Hypoglossal Nerve Diseases 3 3 1 +Hypoglossal Nerve Injuries 4 5 4 +Hypoglycemia 4 4 1 +Hypoglycemic Agents 4 4 1 +Hypoglycins 3 7 2 +Hypogonadism 3 3 1 +Hypogravity 5 5 1 +Hypohidrosis 4 4 1 +Hypokalemia 4 4 1 +Hypokalemic Periodic Paralysis 4 6 4 +Hypokinesia 4 5 2 +Hypolipidemic Agents 5 5 2 +Hypolipoproteinemias 5 5 4 +Hyponatremia 4 4 1 +Hypoparathyroidism 3 3 1 +Hypopharyngeal Neoplasms 4 6 4 +Hypopharynx 3 3 2 +Hypophosphatasia 5 5 2 +Hypophosphatemia 4 4 1 +Hypophosphatemia, Familial 4 7 7 +Hypophysectomy 3 3 2 +Hypophysectomy, Chemical 4 4 1 +Hypophysitis 3 6 2 +Hypopigmentation 4 4 1 +Hypopituitarism 3 6 2 +Hypoplastic Left Heart Syndrome 4 5 3 +Hypoproteinemia 4 4 1 +Hypoprothrombinemias 4 5 4 +Hypospadias 3 5 6 +Hypotension 3 3 1 +Hypotension, Controlled 2 2 1 +Hypotension, Orthostatic 4 5 2 +Hypothalamic Area, Lateral 6 7 2 +Hypothalamic Diseases 4 4 1 +Hypothalamic Hormones 4 5 4 +Hypothalamic Neoplasms 5 7 4 +Hypothalamic-Pituitary-Gonadal 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4 4 1 +Ibandronic Acid 5 5 1 +Ibogaine 5 8 4 +Ibotenic Acid 4 5 2 +Ibuprofen 5 5 1 +Ice 3 7 6 +Ice Cover 3 5 3 +Ice Cream 4 5 2 +Iceland 3 4 2 +Ichthyosiform Erythroderma, Congenital 4 5 6 +Ichthyosis 3 4 4 +Ichthyosis Bullosa of Siemens 4 5 6 +Ichthyosis Vulgaris 4 5 5 +Ichthyosis, Lamellar 5 6 6 +Ichthyosis, X-Linked 4 5 9 +Icodextrin 4 5 2 +Ictaluridae 7 7 1 +Ictalurivirus 4 4 1 +Id 4 4 2 +Idaho 6 6 1 +Idarubicin 6 9 3 +Idazoxan 4 5 3 +Ideal Body Weight 5 8 6 +Identification, Psychological 3 4 2 +Identity Crisis 4 4 1 +Identity Recognition 5 5 1 +Identity Theft 5 5 1 +Idiopathic Hypersomnia 6 6 2 +Idiopathic Interstitial Pneumonias 6 6 1 +Idiopathic Noncirrhotic Portal Hypertension 4 4 1 +Idiopathic Pulmonary Fibrosis 5 5 1 +Idoxuridine 5 7 3 +Iduronate Sulfatase 6 6 1 +Iduronic Acid 5 7 4 +Iduronidase 5 5 1 +Ifosfamide 4 8 3 +IgA Deficiency 4 5 2 +IgA Vasculitis 4 5 7 +IgG Deficiency 4 5 2 +Iguanas 7 7 1 +Ikaros Transcription Factor 5 5 2 +Ilarvirus 4 5 2 +Ileal Diseases 4 4 1 +Ileal Neoplasms 5 6 5 +Ileitis 5 5 3 +Ileocecal Valve 5 6 2 +Ileostomy 4 4 2 +Ileum 4 5 2 +Ileus 5 5 1 +Ilex 8 8 1 +Ilex guayusa 9 9 1 +Ilex paraguariensis 9 9 1 +Ilex vomitoria 9 9 1 +Iliac Aneurysm 4 4 1 +Iliac Artery 4 4 1 +Iliac Vein 4 4 1 +Iliotibial Band Syndrome 4 4 2 +Ilium 6 6 1 +Ilizarov Technique 4 4 2 +Ill-Housed Persons 2 2 1 +Illegitimacy 4 5 2 +Illicit Drugs 2 2 1 +Illicium 9 9 1 +Illinois 6 6 2 +Illness Behavior 3 3 1 +Illusions 4 6 4 +Iloprost 4 7 3 +Iltovirus 5 5 1 +Image Cytometry 4 7 4 +Image Enhancement 4 5 2 +Image Interpretation, Computer-Assisted 3 7 3 +Image Processing, Computer-Assisted 3 3 1 +Image-Guided Biopsy 3 7 7 +Imagery, Psychotherapy 3 4 2 +Imaginal Discs 2 2 1 +Imagination 4 4 1 +Imaging Genomics 4 6 3 +Imaging, Three-Dimensional 4 4 2 +Imatinib Mesylate 4 8 5 +Imidazoles 4 4 1 +Imidazolidines 5 5 1 +Imidazoline Receptors 4 4 1 +Imidazolines 5 5 1 +Imides 2 2 1 +Imidocarb 5 8 4 +Imidoesters 3 3 1 +Imines 2 2 1 +Imino Acids 3 4 3 +Imino Furanoses 3 4 3 +Imino Pyranoses 3 4 3 +Imino Sugars 2 3 2 +Imipenem 7 7 2 +Imipramine 5 5 1 +Imiquimod 6 6 1 +Imitative Behavior 3 3 1 +Immediate Dental Implant Loading 5 6 4 +Immediate-Early Proteins 3 5 2 +Immersion 2 2 1 +Immersion Foot 4 4 1 +Immobility Response, Tonic 4 5 3 +Immobilization 2 2 1 +Immobilized Nucleic Acids 3 3 1 +Immobilized Proteins 3 3 1 +Immune Adherence Reaction 4 5 3 +Immune Checkpoint Inhibitors 4 6 2 +Immune Checkpoint Proteins 2 3 2 +Immune Complex Diseases 3 3 1 +Immune Evasion 2 4 3 +Immune Privilege 4 4 1 +Immune Reconstitution 2 2 1 +Immune Reconstitution Inflammatory Syndrome 2 2 1 +Immune Sera 3 7 5 +Immune System 2 2 1 +Immune System Diseases 1 1 1 +Immune System Exhaustion 2 2 1 +Immune System Phenomena 1 1 1 +Immune Tolerance 3 3 1 +Immunity 2 2 1 +Immunity, Active 4 4 1 +Immunity, Cellular 4 4 1 +Immunity, Herd 3 3 1 +Immunity, Heterologous 3 3 1 +Immunity, Humoral 4 4 1 +Immunity, Innate 3 3 1 +Immunity, Maternally-Acquired 3 3 1 +Immunity, Mucosal 3 3 1 +Immunization 3 5 5 +Immunization Programs 4 4 1 +Immunization Schedule 4 6 2 +Immunization, Passive 4 6 2 +Immunization, Secondary 4 6 2 +Immunoassay 3 3 2 +Immunoblastic Lymphadenopathy 4 4 3 +Immunoblotting 4 4 2 +Immunochemistry 4 4 3 +Immunocompetence 2 2 1 +Immunocompromised Host 2 2 1 +Immunoconglutinins 8 8 3 +Immunoconjugates 5 7 3 +Immunodeficiency Virus, Bovine 6 6 1 +Immunodeficiency Virus, Feline 6 6 1 +Immunodiffusion 5 7 4 +Immunodominant Epitopes 4 4 1 +Immunoediting, Cancer 2 2 1 +Immunoelectrophoresis 4 8 6 +Immunoelectrophoresis, Two-Dimensional 5 9 6 +Immunoenzyme Techniques 4 4 3 +Immunogenetic Phenomena 2 2 1 +Immunogenetics 5 5 1 +Immunogenic Cell Death 4 4 1 +Immunogenicity, Vaccine 2 3 2 +Immunoglobulin A 8 8 3 +Immunoglobulin A, Secretory 9 9 3 +Immunoglobulin Allotypes 3 7 4 +Immunoglobulin alpha-Chains 8 9 6 +Immunoglobulin Class Switching 5 5 2 +Immunoglobulin Constant Regions 6 6 3 +Immunoglobulin D 8 8 3 +Immunoglobulin delta-Chains 8 9 6 +Immunoglobulin Domains 8 8 1 +Immunoglobulin E 8 8 3 +Immunoglobulin epsilon-Chains 8 9 6 +Immunoglobulin Fab Fragments 5 7 4 +Immunoglobulin Fc Fragments 5 7 7 +Immunoglobulin Fragments 4 6 4 +Immunoglobulin G 8 8 3 +Immunoglobulin G4-Related Disease 3 3 1 +Immunoglobulin gamma-Chains 8 9 6 +Immunoglobulin Gm Allotypes 4 10 10 +Immunoglobulin Heavy Chains 7 7 3 +Immunoglobulin Idiotypes 3 7 7 +Immunoglobulin Isotypes 7 7 3 +Immunoglobulin J Recombination Signal Sequence-Binding Protein 4 4 3 +Immunoglobulin J-Chains 7 7 3 +Immunoglobulin Joining Region 7 9 7 +Immunoglobulin kappa-Chains 8 8 3 +Immunoglobulin Km Allotypes 4 9 7 +Immunoglobulin lambda-Chains 8 8 3 +Immunoglobulin Light Chains 7 7 3 +Immunoglobulin Light Chains, Surrogate 8 9 6 +Immunoglobulin Light-chain Amyloidosis 4 5 4 +Immunoglobulin M 8 8 3 +Immunoglobulin mu-Chains 8 9 6 +Immunoglobulin Subunits 6 6 3 +Immunoglobulin Switch Region 7 8 2 +Immunoglobulin Variable Region 5 8 8 +Immunoglobulins 5 5 3 +Immunoglobulins, Intravenous 7 9 4 +Immunoglobulins, Thyroid-Stimulating 8 8 3 +Immunohistochemistry 3 6 10 +Immunoinformatics 4 5 3 +Immunologic Capping 3 3 2 +Immunologic Deficiency Syndromes 2 2 1 +Immunologic Factors 4 4 1 +Immunologic Memory 4 4 1 +Immunologic Surveillance 2 5 2 +Immunologic Techniques 2 2 1 +Immunologic Tests 3 4 3 +Immunological Memory Cells 6 6 1 +Immunological Synapses 3 6 2 +Immunomagnetic Separation 3 5 3 +Immunomodulating Agents 5 5 1 +Immunomodulation 2 3 2 +Immunonutrition Diet 5 5 1 +Immunophenotyping 4 5 3 +Immunophilins 4 6 3 +Immunoprecipitation 3 4 2 +Immunoproliferative Disorders 2 2 1 +Immunoproliferative Small Intestinal Disease 4 6 8 +Immunoproteins 4 4 1 +Immunoradiometric Assay 5 5 2 +Immunoreceptor Tyrosine-Based Activation Motif 8 8 1 +Immunoreceptor Tyrosine-Based Inhibition Motif 8 8 1 +Immunosenescence 3 4 2 +Immunosorbent Techniques 4 4 2 +Immunosorbents 4 4 1 +Immunosuppression Therapy 3 5 2 +Immunosuppressive Agents 5 5 1 +Immunotherapy 4 4 1 +Immunotherapy, Active 4 6 2 +Immunotherapy, Adoptive 6 8 2 +Immunotoxins 4 8 4 +Immunoturbidimetry 4 5 3 +IMP Dehydrogenase 6 6 1 +Impatiens 9 9 1 +Impetigo 5 7 5 +Implant Capsular Contracture 5 5 2 +Implantable Neurostimulators 4 5 2 +Implants, Experimental 3 3 1 +Implementation Science 4 4 1 +Implosive Therapy 5 5 1 +Impotence, Vasculogenic 5 5 3 +Imprinting Disorders 3 3 1 +Imprinting, Psychological 4 4 1 +Impromidine 4 5 2 +Impulsive Behavior 3 3 1 +In Situ Hybridization 4 7 6 +In Situ Hybridization, Fluorescence 4 8 7 +In Situ Nick-End Labeling 3 3 1 +In Vitro Meat 4 5 2 +In Vitro Oocyte Maturation Techniques 4 4 2 +In Vitro Techniques 2 2 1 +In Vivo Dosimetry 4 4 1 +Inactivation, Metabolic 3 5 3 +Inappropriate ADH Syndrome 3 6 3 +Inappropriate Prescribing 3 5 2 +Inbreeding 3 4 2 +Inbreeding Depression 2 2 1 +Incandescence 4 6 4 +Incarceration 4 4 2 +Incest 4 4 1 +Incidence 5 7 4 +Incidental Findings 2 2 1 +Incineration 7 7 1 +Incisional Hernia 4 4 2 +Incisor 5 5 1 +Incivility 4 4 2 +Inclusion Bodies 3 3 1 +Inclusion Bodies, Viral 3 4 2 +Income 3 3 1 +Income Tax 4 4 1 +Incontinence Pads 4 4 1 +Incontinentia Pigmenti 4 4 6 +Incretins 6 6 1 +Incubators 2 2 1 +Incubators, Infant 3 3 2 +Incunabula 2 2 1 +Incunabula as Topic 6 6 1 +Incus 5 5 1 +Indans 4 7 2 +Indapamide 4 5 3 +Indazoles 4 5 2 +INDEL Mutation 3 4 2 +Indenes 3 6 2 +Independent Living 3 6 3 +Independent Medical Evaluation 4 4 1 +Independent Practice Associations 5 7 4 +Independent State of Samoa 6 6 2 +Index 2 2 1 +Index of Orthodontic Treatment Need 3 8 5 +India 5 5 1 +Indian Ocean 3 3 1 +Indian Ocean Islands 3 3 1 +Indiana 6 6 2 +Indians, Central American 4 5 3 +Indians, North American 5 5 2 +Indians, South American 4 5 3 +Indican 5 5 1 +Indicator Dilution Techniques 2 2 1 +Indicators and Reagents 4 4 1 +Indigenous Canadians 6 6 2 +Indigenous Peoples 3 3 1 +Indigo Carmine 5 5 1 +Indigofera 8 8 1 +Indinavir 4 4 1 +Indium 4 4 2 +Indium Radioisotopes 4 4 1 +Individuality 3 3 1 +Individuation 4 4 1 +Indochina 4 4 1 +Indocyanine Green 5 5 1 +Indole Alkaloids 3 6 3 +Indole-3-Glycerol-Phosphate Synthase 6 6 1 +Indoleacetic Acids 3 5 2 +Indoleamine-Pyrrole 2,3,-Dioxygenase 6 6 1 +Indolequinones 3 5 2 +Indoles 4 4 1 +Indolizidines 5 5 1 +Indolizines 4 4 1 +Indolosesquiterpenes 3 6 3 +Indomethacin 5 5 1 +Indonesia 3 4 2 +Indophenol 7 7 1 +Indoprofen 5 5 2 +Indoramin 4 8 5 +Indriidae 9 9 1 +Induced Demand 3 3 1 +Induced Pluripotent Stem Cells 4 4 2 +Inducible T-Cell Co-Stimulator Ligand 4 5 4 +Inducible T-Cell Co-Stimulator Protein 5 7 3 +Induction Chemotherapy 3 3 2 +Industrial Development 3 4 2 +Industrial Microbiology 4 5 2 +Industrial Oils 3 3 1 +Industrial Waste 3 5 2 +Industry 2 2 1 +Inert Gas Narcosis 2 4 2 +Infant 3 3 1 +Infant Behavior 4 4 1 +Infant Care 4 4 1 +Infant Death 4 4 1 +Infant Equipment 2 2 1 +Infant Food 4 5 2 +Infant Formula 4 6 6 +Infant Health 3 3 1 +Infant Mortality 5 7 4 +Infant Nutrition Disorders 3 3 1 +Infant Nutritional Physiological Phenomena 5 5 1 +Infant Welfare 4 4 1 +Infant, Extremely Low Birth Weight 7 7 1 +Infant, Extremely Premature 6 6 1 +Infant, Large for Gestational Age 5 5 1 +Infant, Low Birth Weight 5 5 1 +Infant, Newborn 4 4 1 +Infant, Newborn, Diseases 2 2 1 +Infant, Postmature 5 5 1 +Infant, Premature 5 5 1 +Infant, Premature, Diseases 3 3 1 +Infant, Small for Gestational Age 6 6 1 +Infant, Very Low Birth Weight 6 6 1 +Infanticide 5 5 1 +Infarction 4 4 2 +Infarction, Anterior Cerebral Artery 6 8 8 +Infarction, Middle Cerebral Artery 6 8 8 +Infarction, Posterior Cerebral Artery 6 8 8 +Infection Control 5 5 1 +Infection Control Practitioners 3 4 2 +Infection Control, Dental 2 6 2 +Infections 1 1 1 +Infectious Anemia Virus, Equine 6 6 1 +Infectious Bovine Rhinotracheitis 3 5 2 +Infectious bronchitis virus 8 8 1 +Infectious bursal disease virus 6 6 1 +Infectious Disease Incubation Period 4 4 1 +Infectious Disease Medicine 4 4 1 +Infectious Disease Transmission, Patient-to-Professional 4 4 1 +Infectious Disease Transmission, Professional-to-Patient 4 4 1 +Infectious Disease Transmission, Vertical 4 4 1 +Infectious Encephalitis 3 5 4 +Infectious hematopoietic necrosis virus 7 7 1 +Infectious Mononucleosis 4 6 4 +Infectious pancreatic necrosis virus 6 6 1 +Inferior Colliculi 7 7 1 +Inferior Olivary Complex 8 8 2 +Inferior Wall Myocardial Infarction 5 6 4 +Infertility 3 3 1 +Infertility, Female 4 5 3 +Infertility, Male 4 4 3 +Inflammasomes 3 3 1 +Inflammation 3 3 1 +Inflammation Mediators 2 2 1 +Inflammatory Bowel Diseases 4 4 2 +Inflammatory Breast Neoplasms 4 5 2 +Inflation, Economic 3 3 1 +Infliximab 8 8 3 +Inflorescence 5 5 1 +Influenza A virus 6 6 1 +Influenza A Virus, H10N7 Subtype 7 7 1 +Influenza A Virus, H10N8 Subtype 7 7 1 +Influenza A Virus, H1N1 Subtype 7 7 1 +Influenza A Virus, H1N2 Subtype 7 7 1 +Influenza A Virus, H2N2 Subtype 7 7 1 +Influenza A Virus, H3N2 Subtype 7 7 1 +Influenza A Virus, H3N8 Subtype 7 7 1 +Influenza A Virus, H5N1 Subtype 7 7 1 +Influenza A Virus, H5N2 Subtype 7 7 1 +Influenza A Virus, H5N6 Subtype 7 7 1 +Influenza A Virus, H5N8 Subtype 7 7 1 +Influenza A Virus, H7N1 Subtype 7 7 1 +Influenza A Virus, H7N2 Subtype 7 7 1 +Influenza A Virus, H7N3 Subtype 7 7 1 +Influenza A Virus, H7N7 Subtype 7 7 1 +Influenza A Virus, H7N9 Subtype 7 7 1 +Influenza A Virus, H9N2 Subtype 7 7 1 +Influenza B virus 6 6 1 +Influenza in Birds 3 5 2 +Influenza Pandemic, 1918-1919 5 5 1 +Influenza Vaccines 5 5 1 +Influenza, Human 3 5 3 +Infodemic 4 4 1 +Infodemiology 4 4 1 +Infographic 2 2 1 +Infographics as Topic 4 6 3 +Informal Sector 3 3 1 +Informatics 2 2 1 +Information Avoidance 3 3 1 +Information Centers 2 3 2 +Information Dissemination 3 3 1 +Information Literacy 3 3 1 +Information Management 2 2 1 +Information Motivation Behavioral Skills Model 4 4 1 +Information Science 1 1 1 +Information Seeking Behavior 3 4 3 +Information Services 3 3 1 +Information Sources 2 2 1 +Information Storage and Retrieval 2 5 2 +Information Systems 5 5 1 +Information Technology 2 2 1 +Information Theory 2 2 1 +Informed Consent 4 6 4 +Informed Consent By Minors 5 7 4 +Infradian Rhythm 4 4 1 +Infrared Rays 4 7 7 +Infratemporal Fossa 4 6 2 +Infratentorial Neoplasms 5 6 3 +Infusion Pumps 2 4 2 +Infusion Pumps, Implantable 3 5 2 +Infusions, Intra-Arterial 5 5 1 +Infusions, Intralesional 5 5 1 +Infusions, Intraosseous 5 5 1 +Infusions, Intravenous 5 5 2 +Infusions, Intraventricular 5 5 1 +Infusions, Parenteral 4 4 1 +Infusions, Spinal 5 5 1 +Infusions, Subcutaneous 5 5 1 +Inguinal Canal 4 4 1 +Inhalant Abuse 3 3 2 +Inhalation 5 5 1 +Inhalation Exposure 5 5 1 +Inhalation Spacers 4 4 1 +Inheritance Patterns 2 2 1 +Inhibin-beta Subunits 5 5 10 +Inhibins 4 4 5 +Inhibition, Psychological 3 4 3 +Inhibitor of Apoptosis Proteins 5 6 3 +Inhibitor of Differentiation Protein 1 5 5 1 +Inhibitor of Differentiation Protein 2 5 5 1 +Inhibitor of Differentiation Proteins 4 4 1 +Inhibitor of Growth Protein 1 4 5 4 +Inhibitory Concentration 50 3 4 2 +Inhibitory Postsynaptic Potentials 4 5 7 +Injection Site Reaction 3 4 2 +Injection, Intratympanic 5 5 1 +Injections 4 4 1 +Injections, Epidural 6 6 1 +Injections, Intra-Arterial 5 5 1 +Injections, Intra-Articular 5 5 1 +Injections, Intradermal 6 6 1 +Injections, Intralesional 5 5 1 +Injections, Intralymphatic 5 5 1 +Injections, Intramuscular 5 5 1 +Injections, Intraocular 5 5 1 +Injections, Intraperitoneal 5 5 1 +Injections, Intravenous 5 5 2 +Injections, Intraventricular 5 5 1 +Injections, Jet 6 6 1 +Injections, Spinal 5 5 1 +Injections, Subcutaneous 5 5 1 +Injury Severity Score 3 8 4 +Ink 3 3 1 +Ink Blot Tests 5 5 1 +Inlay Casting Wax 4 6 2 +Inlays 5 5 2 +Innate Immunity Recognition 4 4 1 +Inonotus 4 4 1 +Inorganic Chemicals 1 1 1 +Inorganic Pyrophosphatase 6 8 3 +Inosine 4 6 3 +Inosine Diphosphate 5 7 3 +Inosine Monophosphate 5 7 3 +Inosine Nucleotides 4 6 3 +Inosine Pranobex 5 7 5 +Inosine Triphosphatase 6 6 1 +Inosine Triphosphate 5 7 3 +Inositol 3 4 2 +Inositol 1,4,5-Trisphosphate 4 6 3 +Inositol 1,4,5-Trisphosphate Receptors 4 7 4 +Inositol Oxygenase 5 6 2 +Inositol Phosphates 3 5 3 +Inositol Polyphosphate 5-Phosphatases 7 7 1 +Inotuzumab Ozogamicin 5 9 4 +Inoviridae 3 3 2 +Inovirus 4 4 2 +Inpatients 3 3 1 +Insanity Defense 5 6 4 +Insect Bites and Stings 3 4 2 +Insect Control 6 6 1 +Insect Hormones 4 4 1 +Insect Proteins 4 4 1 +Insect Repellents 4 5 3 +Insect Vectors 6 7 2 +Insect Viruses 2 2 1 +Insecta 5 5 1 +Insecticide Resistance 5 5 1 +Insecticide-Treated Bednets 4 4 1 +Insecticides 4 5 2 +Insemination 4 4 1 +Insemination, Artificial 4 5 3 +Insemination, Artificial, Heterologous 5 6 3 +Insemination, Artificial, Homologous 5 6 3 +Inservice Training 2 2 1 +Insomnia, Fatal Familial 4 6 4 +Inspiratory Capacity 5 8 2 +Inspiratory Reserve Volume 6 9 2 +Instillation, Drug 4 4 1 +Instinct 3 3 1 +Institutional Management Teams 3 3 1 +Institutional Practice 4 4 1 +Institutionalization 3 4 2 +Instructional Film and Video 2 4 2 +Insufflation 2 3 2 +Insular Cortex 8 8 1 +Insulator Elements 5 6 3 +Insulin 7 7 2 +Insulin Antagonists 3 6 2 +Insulin Antibodies 7 7 3 +Insulin Aspart 7 7 2 +Insulin Coma 5 7 2 +Insulin Detemir 7 7 2 +Insulin Glargine 7 7 2 +Insulin Infusion Systems 3 5 3 +Insulin Lispro 7 7 2 +Insulin Receptor Substrate Proteins 5 5 3 +Insulin Resistance 5 5 2 +Insulin Secretagogues 5 5 1 +Insulin Secretion 2 2 2 +Insulin, Isophane 7 7 2 +Insulin, Lente 7 7 2 +Insulin, Long-Acting 6 6 2 +Insulin, Regular, Human 8 8 2 +Insulin, Regular, Pork 8 8 2 +Insulin, Short-Acting 6 6 2 +Insulin, Ultralente 7 7 2 +Insulin-Like Growth Factor Binding Protein 1 5 5 1 +Insulin-Like Growth Factor Binding Protein 2 5 5 1 +Insulin-Like Growth Factor Binding Protein 3 5 5 1 +Insulin-Like Growth Factor Binding Protein 4 5 5 1 +Insulin-Like Growth Factor Binding Protein 5 5 5 1 +Insulin-Like Growth Factor Binding Protein 6 5 5 1 +Insulin-Like Growth Factor Binding Proteins 4 4 1 +Insulin-Like Growth Factor I 5 6 3 +Insulin-Like Growth Factor II 5 6 3 +Insulin-Like Peptides 3 4 3 +Insulin-Secreting Cells 3 4 5 +Insulinoma 5 6 6 +Insulins 5 5 2 +Insulysin 7 7 2 +Insurance 4 4 1 +Insurance Benefits 5 5 1 +Insurance Carriers 5 5 1 +Insurance Claim Reporting 5 5 1 +Insurance Claim Review 5 5 1 +Insurance Coverage 5 5 1 +Insurance Pools 5 5 1 +Insurance Selection Bias 5 5 1 +Insurance, Accident 6 6 1 +Insurance, Dental 6 6 1 +Insurance, Disability 5 5 1 +Insurance, Health 5 5 1 +Insurance, Health, Reimbursement 4 6 2 +Insurance, Hospitalization 6 6 1 +Insurance, Liability 5 5 1 +Insurance, Life 5 5 1 +Insurance, Long-Term Care 6 6 1 +Insurance, Major Medical 6 6 1 +Insurance, Medigap 6 6 1 +Insurance, Nursing Services 6 6 1 +Insurance, Pharmaceutical Services 6 6 1 +Insurance, Physician Services 6 6 1 +Insurance, Psychiatric 6 6 1 +Insurance, Surgical 6 6 1 +Insurance, Vision 6 6 1 +Integrase Inhibitors 5 5 1 +Integrases 4 4 1 +Integrated Advanced Information Management Systems 6 6 1 +Integrated Stress Response 3 3 1 +Integration Host Factors 3 4 3 +Integrative Medicine 3 3 1 +Integrative Oncology 3 3 1 +Integrin alpha Chains 7 7 1 +Integrin alpha1 8 8 1 +Integrin alpha1beta1 6 8 3 +Integrin alpha2 8 8 1 +Integrin alpha2beta1 6 8 8 +Integrin alpha3 8 8 1 +Integrin alpha3beta1 6 8 3 +Integrin alpha4 8 8 1 +Integrin alpha4beta1 6 8 7 +Integrin alpha5 8 8 1 +Integrin alpha5beta1 6 8 7 +Integrin alpha6 8 8 1 +Integrin alpha6beta1 6 8 6 +Integrin alpha6beta4 7 8 2 +Integrin alphaV 8 8 1 +Integrin alphaVbeta3 6 9 5 +Integrin alphaXbeta2 5 8 6 +Integrin beta Chains 7 7 1 +Integrin beta1 8 8 1 +Integrin beta3 8 8 1 +Integrin beta4 8 8 1 +Integrin-Binding Sialoprotein 4 5 4 +Integrins 6 6 1 +Integrons 8 8 1 +Integumentary System 1 1 1 +Integumentary System Physiological Phenomena 1 1 1 +Inteins 5 5 1 +Intellectual Disability 3 5 4 +Intellectual Property 4 5 2 +Intelligence 3 3 1 +Intelligence Tests 4 4 1 +Intelligent Systems 4 5 2 +Intense Pulsed Light Therapy 3 3 1 +Intensive Care Units 4 4 1 +Intensive Care Units, Neonatal 6 6 1 +Intensive Care Units, Pediatric 5 5 1 +Intensive Care, Neonatal 4 5 2 +Intention 3 3 2 +Intention to Treat Analysis 8 9 3 +Interactive Tutorial 4 4 1 +Interactive Ventilatory Support 4 4 2 +Interatrial Block 5 5 3 +Intercalating Agents 5 5 1 +Intercellular Adhesion Molecule-1 5 6 4 +Intercellular Adhesion Molecule-3 5 6 4 +Intercellular Junctions 5 5 1 +Intercellular Signaling Peptides and Proteins 2 3 3 +Intercostal Muscles 5 5 1 +Intercostal Nerves 6 6 1 +Interdepartmental Relations 4 4 1 +Interdisciplinary Communication 4 5 2 +Interdisciplinary Placement 2 2 1 +Interdisciplinary Research 4 4 1 +Interdisciplinary Studies 3 3 1 +Interferometry 2 2 1 +Interferon alpha-2 7 8 3 +Interferon beta-1a 7 8 3 +Interferon beta-1b 7 8 3 +Interferon gamma Receptor 8 8 1 +Interferon Inducers 5 5 1 +Interferon Lambda 5 6 3 +Interferon Regulatory Factor-1 5 6 7 +Interferon Regulatory Factor-2 5 6 5 +Interferon Regulatory Factor-3 4 6 6 +Interferon Regulatory Factor-4 5 6 5 +Interferon Regulatory Factor-7 4 6 6 +Interferon Regulatory Factor-8 5 6 5 +Interferon Regulatory Factors 4 5 5 +Interferon Type I 5 6 3 +Interferon-alpha 6 7 3 +Interferon-beta 6 7 3 +Interferon-gamma 5 7 6 +Interferon-gamma Release Tests 4 5 3 +Interferon-Induced Helicase, IFIH1 4 9 2 +Interferon-Stimulated Gene Factor 3 4 5 5 +Interferon-Stimulated Gene Factor 3, alpha Subunit 5 6 5 +Interferon-Stimulated Gene Factor 3, gamma Subunit 5 6 10 +Interferons 4 5 3 +Intergenerational Relations 4 5 2 +Interinstitutional Relations 4 4 1 +Interior Design and Furnishings 4 4 1 +Interleukin 1 Receptor Antagonist Protein 4 5 3 +Interleukin Inhibitors 6 6 1 +Interleukin Receptor Common gamma Subunit 9 10 6 +Interleukin-1 5 6 6 +Interleukin-1 Receptor Accessory Protein 9 9 1 +Interleukin-1 Receptor-Associated Kinases 5 8 2 +Interleukin-1 Receptor-Like 1 Protein 9 9 1 +Interleukin-10 5 6 3 +Interleukin-10 Receptor alpha Subunit 9 9 1 +Interleukin-10 Receptor beta Subunit 9 9 1 +Interleukin-11 5 6 3 +Interleukin-11 Receptor alpha Subunit 9 9 1 +Interleukin-12 5 6 3 +Interleukin-12 Receptor beta 1 Subunit 9 9 1 +Interleukin-12 Receptor beta 2 Subunit 9 9 1 +Interleukin-12 Subunit p35 6 7 3 +Interleukin-12 Subunit p40 6 7 6 +Interleukin-13 5 6 3 +Interleukin-13 Receptor alpha1 Subunit 10 10 2 +Interleukin-13 Receptor alpha2 Subunit 9 9 1 +Interleukin-15 5 6 3 +Interleukin-15 Receptor alpha Subunit 9 9 1 +Interleukin-16 5 6 3 +Interleukin-17 5 6 3 +Interleukin-18 5 6 3 +Interleukin-18 Receptor alpha Subunit 9 9 1 +Interleukin-18 Receptor beta Subunit 9 9 1 +Interleukin-1alpha 6 7 6 +Interleukin-1beta 6 7 6 +Interleukin-2 5 6 6 +Interleukin-2 Receptor alpha Subunit 9 9 1 +Interleukin-2 Receptor beta Subunit 9 9 2 +Interleukin-21 5 6 3 +Interleukin-21 Receptor alpha Subunit 9 9 1 +Interleukin-22 5 6 3 +Interleukin-23 5 6 3 +Interleukin-23 Subunit p19 6 7 3 +Interleukin-24 5 6 3 +Interleukin-27 5 6 3 +Interleukin-3 5 7 8 +Interleukin-3 Receptor alpha Subunit 9 9 1 +Interleukin-33 5 6 3 +Interleukin-4 5 6 3 +Interleukin-4 Receptor alpha Subunit 10 10 3 +Interleukin-5 5 6 3 +Interleukin-5 Receptor alpha Subunit 9 9 1 +Interleukin-6 5 6 3 +Interleukin-6 Inhibitors 7 7 1 +Interleukin-6 Receptor alpha Subunit 9 9 1 +Interleukin-7 5 6 3 +Interleukin-7 Receptor alpha Subunit 9 9 1 +Interleukin-8 5 7 8 +Interleukin-9 5 6 3 +Interleukins 4 5 3 +Interlibrary Loans 4 5 2 +Intermediate Back Muscles 5 5 1 +Intermediate Care Facilities 5 5 1 +Intermediate Filament Proteins 4 4 2 +Intermediate Filaments 7 7 1 +Intermediate-Conductance Calcium-Activated Potassium Channels 8 8 3 +Intermittent Claudication 3 5 2 +Intermittent Fasting 5 6 3 +Intermittent Pneumatic Compression Devices 2 2 1 +Intermittent Positive-Pressure Breathing 5 5 2 +Intermittent Positive-Pressure Ventilation 5 5 2 +Intermittent Renal Replacement Therapy 3 3 2 +Intermittent Urethral Catheterization 4 4 2 +Internal Capsule 3 6 2 +Internal Fixators 3 5 3 +Internal Hernia 4 4 1 +Internal Mammary-Coronary Artery Anastomosis 6 6 3 +Internal Medicine 3 3 1 +Internal Ribosome Entry Sites 6 7 3 +Internal-External Control 3 3 1 +International Agencies 3 3 1 +International Classification of Diseases 6 6 1 +International Classification of Functioning, Disability and Health 6 6 1 +International Cooperation 3 3 1 +International Council of Nurses 5 5 1 +International Educational Exchange 2 4 2 +International Health Regulations 5 6 2 +International Law 4 5 2 +International Normalized Ratio 5 6 2 +International Planned Parenthood Federation 4 5 2 +International System of Units 3 3 1 +Internationality 2 2 1 +Internet 5 5 1 +Internet Access 3 6 2 +Internet Addiction Disorder 7 7 1 +Internet of Things 6 6 1 +Internet Use 3 6 2 +Internet-Based Intervention 6 6 1 +Interneurons 3 3 2 +Internship and Residency 5 5 2 +Internship, Nonmedical 3 3 1 +Interoception 4 4 1 +Interosseous Membrane 3 3 1 +Interpeduncular Nucleus 9 9 1 +Interpersonal Psychotherapy 3 3 1 +Interpersonal Relations 3 3 1 +Interphase 3 3 1 +Interpleural Analgesia 3 3 1 +Interprofessional Education 3 3 1 +Interprofessional Relations 4 4 1 +Interrenal Gland 2 2 1 +Interrupted Time Series Analysis 5 6 3 +Intersectional Framework 3 5 3 +Intersectoral Collaboration 2 2 1 +Intersex Persons 4 4 1 +Interspersed Repetitive Sequences 5 6 3 +Interstitial Cells of Cajal 3 3 1 +Intertrigo 4 4 2 +Intervertebral Disc 4 5 3 +Intervertebral Disc Chemolysis 3 3 1 +Intervertebral Disc Degeneration 4 4 1 +Intervertebral Disc Displacement 4 4 2 +Interview 3 4 3 +Interview, Psychological 3 3 1 +Interviews as Topic 4 5 4 +Intestinal Absorption 4 7 5 +Intestinal Atresia 3 4 3 +Intestinal Barrier Function 3 3 2 +Intestinal Diseases 3 3 1 +Intestinal Diseases, Parasitic 3 4 2 +Intestinal Elimination 3 5 3 +Intestinal Failure 4 4 1 +Intestinal Fistula 3 5 3 +Intestinal Mucosa 4 4 2 +Intestinal Neoplasms 4 5 4 +Intestinal Obstruction 4 4 1 +Intestinal Perforation 4 4 1 +Intestinal Polyposis 4 4 1 +Intestinal Polyps 4 4 1 +Intestinal Pseudo-Obstruction 6 6 1 +Intestinal Reabsorption 3 8 6 +Intestinal Secretions 3 3 1 +Intestinal Volvulus 4 5 2 +Intestine, Large 4 4 2 +Intestine, Small 4 4 1 +Intestines 3 3 1 +Intimate Partner Violence 5 5 2 +Intra-Abdominal Fat 6 6 1 +Intra-Abdominal Hypertension 4 4 2 +Intra-Aortic Balloon Pumping 4 4 1 +Intra-Articular Fractures 3 3 1 +Intraabdominal Infections 2 2 1 +Intracameral Injection 6 6 1 +Intracellular Calcium-Sensing Proteins 4 5 3 +Intracellular Fluid 3 4 3 +Intracellular Membranes 4 4 2 +Intracellular Signaling Peptides and Proteins 3 3 2 +Intracellular Space 3 3 2 +Intracranial Aneurysm 4 6 3 +Intracranial Arterial Diseases 4 5 2 +Intracranial Arteriosclerosis 5 6 3 +Intracranial Arteriovenous Malformations 4 6 9 +Intracranial Embolism 5 6 3 +Intracranial Embolism and Thrombosis 4 5 3 +Intracranial Hemorrhage, Hypertensive 5 6 2 +Intracranial Hemorrhage, Traumatic 4 6 4 +Intracranial Hemorrhages 4 5 3 +Intracranial Hypertension 4 4 1 +Intracranial Hypotension 4 4 1 +Intracranial Pressure 4 4 1 +Intracranial Thrombosis 5 6 3 +Intractable Pain 5 5 3 +Intradermal Tests 5 6 3 +Intraepithelial Lymphocytes 8 9 6 +Intralaminar Thalamic Nuclei 8 8 1 +Intramolecular Lyases 4 4 1 +Intramolecular Oxidoreductases 4 4 1 +Intramolecular Transferases 4 4 1 +Intramuscular Absorption 3 6 4 +Intranuclear Inclusion Bodies 4 4 1 +Intranuclear Space 6 6 1 +Intraocular Lymphoma 4 5 4 +Intraocular Pressure 2 2 1 +Intraoperative Awareness 4 4 1 +Intraoperative Care 3 5 3 +Intraoperative Complications 3 3 1 +Intraoperative Neurophysiological Monitoring 3 5 3 +Intraoperative Period 3 5 2 +Intrauterine Device Expulsion 4 4 1 +Intrauterine Device Migration 3 4 2 +Intrauterine Devices 4 4 1 +Intrauterine Devices, Copper 6 6 1 +Intrauterine Devices, Medicated 5 5 1 +Intravital Microscopy 3 5 2 +Intravitreal Injections 6 6 1 +Intrinsic Factor 5 5 2 +Intrinsically Disordered Proteins 3 3 1 +Introduced Species 4 6 3 +Introductory Journal Article 3 3 1 +Introns 6 7 2 +Introversion, Psychological 4 4 2 +Intubation 2 2 2 +Intubation, Gastrointestinal 3 3 2 +Intubation, Intratracheal 3 3 3 +Intuition 4 4 1 +Intussusception 5 5 1 +Inuit 7 7 2 +Inula 8 8 1 +Inulin 4 6 4 +Invasive Fungal Infections 4 4 1 +Invasive Pulmonary Aspergillosis 5 6 4 +Inventions 3 3 1 +Inventories, Hospital 6 6 2 +Inventors 3 3 1 +Invertebrate Hormones 3 3 1 +Invertebrates 3 3 1 +Inverted Repeat Sequences 6 7 2 +Investigational New Drug Application 4 4 2 +Investigative Techniques 1 1 1 +Investments 3 3 1 +Involuntary Commitment 4 4 1 +Involuntary Fertility Control 5 5 1 +Involuntary Treatment 4 4 1 +Involuntary Treatment, Psychiatric 5 5 1 +Iodamide 7 9 2 +Iodates 3 5 2 +Iodide Peroxidase 5 5 1 +Iodides 3 5 2 +Iodine 4 4 1 +Iodine Compounds 2 2 1 +Iodine Isotopes 3 5 2 +Iodine Radioisotopes 4 6 3 +Iodipamide 7 9 2 +Iodized Oil 4 5 2 +Iodoacetamide 4 6 4 +Iodoacetates 5 5 2 +Iodoacetic Acid 6 6 2 +Iodobenzenes 5 6 2 +Iodobenzoates 5 7 2 +Iodocyanopindolol 7 7 3 +Iodohippuric Acid 5 9 4 +Iodophors 3 3 1 +Iodoproteins 3 3 1 +Iodopyracet 6 6 1 +Iodopyridones 5 5 1 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4 +Kalanchoe 10 10 1 +Kalinin 6 6 4 +Kallidin 5 6 5 +Kallikrein-Kinin System 3 5 5 +Kallikreins 3 7 4 +Kallmann Syndrome 3 7 7 +Kalopanax 8 8 1 +Kanamycin 4 4 1 +Kanamycin Kinase 6 6 1 +Kanamycin Resistance 4 7 3 +Kangai-1 Protein 5 5 5 +Kangaroo-Mother Care Method 4 5 3 +Kansas 6 6 1 +Kaolin 5 7 4 +Kaplan-Meier Estimate 5 6 3 +Kaposi Varicelliform Eruption 5 6 3 +Kappapapillomavirus 5 5 2 +Karaya Gum 4 5 3 +Karnofsky Performance Status 9 10 3 +Karoshi Death 5 6 2 +Kartagener Syndrome 3 6 12 +Karwinskia 10 10 1 +Karyometry 4 6 3 +Karyopherins 4 6 3 +Karyotype 4 4 1 +Karyotyping 4 6 4 +Kasabach-Merritt Syndrome 5 5 3 +Kashin-Beck Disease 5 5 1 +Kassinin 5 6 7 +Katanin 5 7 5 +KATP Channels 8 8 3 +Kava 9 9 1 +Kazakhstan 4 4 3 +Kazal Motifs 8 8 1 +KB Cells 4 6 3 +Kcnj10 Channel 8 8 3 +Kcnj11 Channel 8 8 2 +KCNQ Potassium Channels 9 9 3 +KCNQ1 Potassium Channel 10 10 3 +KCNQ2 Potassium Channel 10 10 3 +KCNQ3 Potassium Channel 10 10 3 +Kearns-Sayre Syndrome 4 7 10 +Kefir 3 6 12 +Kelch Repeat 6 9 4 +Kelch-Like ECH-Associated Protein 1 5 5 3 +Kell Blood-Group System 5 5 2 +Keloid 4 5 3 +Kelp 4 4 1 +Kentucky 6 6 2 +Kenya 5 5 1 +Keratan Sulfate 4 4 1 +Keratectomy 2 2 1 +Keratectomy, Subepithelial, Laser-Assisted 4 5 4 +Keratin-1 6 7 3 +Keratin-10 6 7 3 +Keratin-12 6 7 3 +Keratin-13 6 7 3 +Keratin-14 6 7 3 +Keratin-15 7 7 1 +Keratin-16 6 7 3 +Keratin-17 6 7 3 +Keratin-18 6 7 3 +Keratin-19 6 7 3 +Keratin-2 6 7 3 +Keratin-20 6 7 3 +Keratin-3 6 7 3 +Keratin-4 6 7 3 +Keratin-5 6 7 3 +Keratin-6 6 7 3 +Keratin-7 6 7 3 +Keratin-8 6 7 3 +Keratin-9 6 7 3 +Keratinocytes 3 3 2 +Keratins 4 5 3 +Keratins, Hair-Specific 5 6 3 +Keratins, Type I 5 6 3 +Keratins, Type II 5 6 3 +Keratitis 3 3 1 +Keratitis, Dendritic 5 7 5 +Keratitis, Herpetic 4 6 5 +Keratoacanthoma 3 3 1 +Keratoconjunctivitis 4 4 2 +Keratoconjunctivitis Sicca 4 5 3 +Keratoconjunctivitis, Infectious 2 5 6 +Keratoconus 3 3 1 +Keratoderma, Palmoplantar 4 4 3 +Keratoderma, Palmoplantar, Diffuse 5 5 3 +Keratoderma, Palmoplantar, 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Factor 4 5 5 2 +Kruppel-Like Factor 6 5 6 4 +Kruppel-Like Transcription Factors 4 4 2 +Krypton 4 4 2 +Krypton Radioisotopes 4 4 1 +Ku Autoantigen 4 7 7 +Kunzea 8 8 1 +Kupffer Cells 4 5 5 +Kuru 4 5 3 +Kuwait 5 5 1 +Kv Channel-Interacting Proteins 5 7 4 +Kv1.1 Potassium Channel 9 9 4 +Kv1.2 Potassium Channel 9 9 4 +Kv1.3 Potassium Channel 9 9 3 +Kv1.4 Potassium Channel 9 9 3 +Kv1.5 Potassium Channel 9 9 6 +Kv1.6 Potassium Channel 9 9 3 +Kveim Test 6 7 3 +Kwashiorkor 5 5 1 +Kyasanur Forest Disease 4 6 4 +Kymography 3 3 1 +Kynuramine 4 4 2 +Kynurenic Acid 4 6 2 +Kynurenine 3 3 1 +Kynurenine 3-Monooxygenase 6 6 1 +Kyphoplasty 5 5 2 +Kyphosis 5 5 1 +Kyrgyzstan 4 4 3 +L Cells 4 4 2 +L Forms 3 3 2 +L-Amino Acid Oxidase 6 6 1 +L-Aminoadipate-Semialdehyde Dehydrogenase 6 6 1 +L-Gulonolactone Oxidase 7 7 1 +L-Iditol 2-Dehydrogenase 7 7 1 +L-Lactate Dehydrogenase 6 6 2 +L-Lactate Dehydrogenase (Cytochrome) 6 6 1 +L-Lysine 6-Transaminase 6 6 1 +L-Selectin 5 7 10 +L-Serine Dehydratase 6 6 1 +La 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Thalamic Nuclei 8 8 1 +Lateral Ventricles 5 5 1 +Latex 4 6 7 +Latex Fixation Tests 6 7 3 +Latex Hypersensitivity 3 3 1 +Lathyrism 4 4 1 +Lathyrus 8 8 1 +Laticauda 7 9 3 +Latilactobacillus sakei 5 7 3 +Latin America 3 3 1 +Latvia 5 5 1 +Laughter 5 5 1 +Laughter Therapy 4 4 1 +Laundering 3 3 1 +Laundry Service, Hospital 6 6 2 +Lauraceae 8 8 1 +Laurales 7 7 1 +Laurates 4 4 1 +Laurence-Moon Syndrome 4 5 2 +Laurencia 3 3 1 +Lauric Acids 3 3 1 +Laurus 9 9 1 +Lavandula 9 9 1 +Law Enforcement 4 4 1 +Lawrencium 4 6 6 +Lawsonia Bacteria 3 4 2 +Lawsonia Plant 10 10 1 +Lawyers 3 3 1 +Laxatives 5 5 1 +Layer-by-Layer Nanoparticles 4 4 1 +LDL-Receptor Related Protein-Associated Protein 4 5 2 +LDL-Receptor Related Proteins 3 7 2 +Lead 4 4 2 +Lead Poisoning 4 4 1 +Lead Poisoning, Nervous System 4 5 2 +Lead Poisoning, Nervous System, Adult 5 6 2 +Lead Poisoning, Nervous System, Childhood 5 6 2 +Lead Radioisotopes 4 4 1 +Leadership 3 4 2 +Learning 3 4 2 +Learning Curve 5 5 1 +Learning Disabilities 3 6 4 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Implantation, Intraocular 4 4 1 +Lens Nucleus, Crystalline 5 5 1 +Lens Plant 8 8 1 +Lens Subluxation 3 3 1 +Lens, Crystalline 4 4 1 +Lenses 3 3 1 +Lenses, Intraocular 3 4 2 +Lentigo 6 6 1 +Lentinan 5 5 1 +Lentinula 5 5 1 +Lentivirus 4 4 1 +Lentivirus Infections 5 5 1 +Lentiviruses, Bovine 5 5 1 +Lentiviruses, Equine 5 5 1 +Lentiviruses, Feline 5 5 1 +Lentiviruses, Ovine-Caprine 5 5 1 +Lentiviruses, Primate 5 5 1 +Leontopithecus 12 12 1 +Leonurus 9 9 1 +LEOPARD Syndrome 4 7 8 +Leper Colonies 3 3 1 +Lepidium 8 8 1 +Lepidium sativum 9 9 1 +Lepidoptera 9 9 1 +Lepisma 6 6 1 +Leporipoxvirus 4 5 3 +Lepromin 4 5 2 +Leprostatic Agents 6 6 1 +Leprosy 8 8 1 +Leprosy, Borderline 10 10 1 +Leprosy, Lepromatous 10 10 1 +Leprosy, Multibacillary 9 9 1 +Leprosy, Paucibacillary 9 9 1 +Leprosy, Tuberculoid 10 10 1 +Leptin 4 5 5 +Leptophos 5 5 3 +Leptospermum 8 8 1 +Leptosphaeria 4 4 1 +Leptospira 4 6 2 +Leptospira interrogans 5 7 2 +Leptospira interrogans serovar australis 6 8 2 +Leptospira interrogans serovar autumnalis 6 8 2 +Leptospira interrogans serovar canicola 6 8 2 +Leptospira interrogans serovar hebdomadis 6 8 2 +Leptospira interrogans serovar icterohaemorrhagiae 6 8 2 +Leptospira interrogans serovar pomona 6 8 2 +Leptospiraceae 3 5 2 +Leptospirosis 6 6 1 +Leptothrix 5 5 1 +Leptotrichia 3 5 2 +Leriche Syndrome 4 4 2 +Lesch-Nyhan Syndrome 5 6 9 +Lesotho 5 5 1 +Lespedeza 8 8 1 +Lesser Pelvis 4 4 1 +Lethal Dose 50 3 5 4 +Lethargy 3 5 3 +Letrozole 3 5 2 +Letter 2 3 3 +Leucanthemum 8 8 1 +Leucine 4 4 2 +Leucine Dehydrogenase 6 6 1 +Leucine Transaminase 6 6 1 +Leucine Zippers 8 8 1 +Leucine-Responsive Regulatory Protein 4 4 2 +Leucine-Rich Repeat Proteins 3 3 1 +Leucine-Rich Repeat Serine-Threonine Protein Kinase-2 4 8 3 +Leucine-tRNA Ligase 6 6 1 +Leucogenenol 3 5 2 +Leucomycins 5 5 1 +Leuconostoc 5 5 2 +Leuconostoc mesenteroides 6 6 2 +Leuconostocaceae 4 4 2 +Leucovorin 5 9 2 +Leucyl Aminopeptidase 7 7 3 +Leukapheresis 4 6 5 +Leukemia 3 3 2 +Leukemia Inhibitory Factor 4 5 3 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Myelomonocytic, Acute 5 5 2 +Leukemia, Myelomonocytic, Chronic 5 5 4 +Leukemia, Myelomonocytic, Juvenile 5 5 3 +Leukemia, Neutrophilic, Chronic 5 5 2 +Leukemia, Plasma Cell 4 5 4 +Leukemia, Prolymphocytic 5 5 4 +Leukemia, Prolymphocytic, B-Cell 6 6 8 +Leukemia, Prolymphocytic, T-Cell 6 6 8 +Leukemia, Promyelocytic, Acute 6 6 2 +Leukemia, Radiation-Induced 3 5 5 +Leukemia, T-Cell 5 5 4 +Leukemia-Lymphoma, Adult T-Cell 6 6 4 +Leukemic Infiltration 4 5 2 +Leukemoid Reaction 5 5 2 +Leukoaraiosis 3 3 1 +Leukocidins 5 5 1 +Leukocyte Adherence Inhibition Test 4 5 3 +Leukocyte Common Antigens 6 9 3 +Leukocyte Count 4 7 7 +Leukocyte Disorders 3 3 1 +Leukocyte Elastase 8 8 2 +Leukocyte Immunoglobulin-like Receptor B1 6 6 1 +Leukocyte L1 Antigen Complex 4 6 3 +Leukocyte Migration-Inhibitory Factors 3 6 4 +Leukocyte Reduction Procedures 3 5 3 +Leukocyte Rolling 5 5 1 +Leukocyte Transfusion 5 5 1 +Leukocyte-Adhesion Deficiency Syndrome 4 4 2 +Leukocytes 3 4 3 +Leukocytes, Mononuclear 4 5 3 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8 8 1 +Levonorgestrel 8 8 1 +Levopropoxyphene 5 5 1 +Levorphanol 4 5 4 +Levulinic Acids 4 4 1 +Lewis Acids 2 3 2 +Lewis Bases 2 3 2 +Lewis Blood Group Antigens 5 5 2 +Lewis X Antigen 5 6 8 +Lewy Bodies 4 4 1 +Lewy Body Disease 4 6 5 +LexA Repressor Protein 4 7 3 +Leydig Cell Tumor 5 7 9 +Leydig Cells 3 5 4 +Li-Fraumeni Syndrome 3 4 3 +Liability, Legal 4 5 2 +Liberia 5 5 1 +Liberibacter 5 5 1 +Libido 4 4 1 +Libocedrus 8 8 1 +Librarians 3 3 1 +Libraries 3 4 2 +Libraries, Dental 5 6 2 +Libraries, Digital 5 7 3 +Libraries, Hospital 5 6 4 +Libraries, Medical 5 6 2 +Libraries, Nursing 5 6 2 +Libraries, Special 4 5 2 +Library Administration 3 3 1 +Library Associations 3 3 1 +Library Automation 3 3 1 +Library Collection Development 3 3 1 +Library Materials 3 3 1 +Library Schools 3 3 3 +Library Science 2 2 1 +Library Services 3 4 2 +Library Surveys 3 3 1 +Library Technical Services 3 4 2 +Libya 4 4 1 +Lice Infestations 5 5 2 +Licensed Practical Nurses 4 5 2 +Licensure 4 4 2 +Licensure, Dental 5 5 2 +Licensure, Hospital 5 5 2 +Licensure, Medical 5 5 2 +Licensure, Nursing 5 5 2 +Licensure, Pharmacy 5 5 2 +Lichen Nitidus 5 5 1 +Lichen Planus 5 5 1 +Lichen Planus, Oral 3 6 2 +Lichen Sclerosus et Atrophicus 5 5 1 +Lichenoid Eruptions 4 4 1 +Lichens 2 3 2 +Liddle Syndrome 4 7 4 +Lidocaine 5 6 2 +Lidocaine, Prilocaine Drug Combination 3 7 5 +Lidoflazine 4 4 1 +Lie Detection 3 5 3 +Liechtenstein 3 3 1 +Life 3 3 1 +Life Change Events 4 4 1 +Life Course Perspective 3 3 1 +Life Cycle Stages 2 5 2 +Life Expectancy 4 6 4 +Life History Traits 2 3 3 +Life Style 3 3 1 +Life Support Care 3 4 2 +Life Support Systems 4 4 1 +Life Tables 5 7 5 +Lifting 3 3 1 +Ligaments 2 3 2 +Ligaments, Articular 3 4 3 +Ligamentum Flavum 4 5 3 +Ligand-Gated Ion Channels 6 6 3 +Ligands 4 4 1 +Ligase Chain Reaction 4 4 1 +Ligases 3 3 1 +Ligation 2 2 1 +Light 3 5 4 +Light Coagulation 3 3 3 +Light Pollution 4 4 1 +Light Signal Transduction 3 4 2 +Light-Curing of Dental Adhesives 3 3 1 +Light-Harvesting Protein Complexes 5 7 4 +Lighting 4 4 1 +Lightning 4 6 3 +Lightning Injuries 3 3 1 +Ligilactobacillus salivarius 5 7 3 +Lignans 7 7 1 +Lignin 2 6 5 +Ligularia 8 8 1 +Ligusticum 8 8 1 +Ligustrum 9 9 1 +Likelihood Functions 4 6 7 +Liliaceae 9 9 1 +Liliales 8 8 1 +Lilianae 7 7 1 +Lilium 10 10 1 +LIM Domain Proteins 3 3 1 +Lim Kinases 4 8 3 +LIM-Homeodomain Proteins 4 4 2 +Limb Buds 2 2 1 +Limb Deformities, Congenital 3 4 2 +Limb Salvage 3 4 3 +Limbal Stem Cell Deficiency 3 3 1 +Limbal Stem Cells 4 4 1 +Limbic Encephalitis 4 5 7 +Limbic Lobe 5 8 2 +Limbic System 4 4 1 +Limbus Corneae 5 5 1 +Limit of Detection 5 6 3 +Limited English Proficiency 4 4 1 +Limnology 3 3 1 +Limonene 6 8 3 +Limonene Hydroxylases 5 8 3 +Limonins 5 5 1 +Limosilactobacillus fermentum 5 7 3 +Limosilactobacillus reuteri 5 7 3 +Limulus Test 3 6 5 +Linaceae 9 9 1 +Linagliptin 5 5 2 +Linaria 9 9 1 +Lincomycin 4 5 2 +Lincosamides 3 4 2 +Lindera 9 9 1 +Linear Energy Transfer 3 4 3 +Linear IgA Bullous Dermatosis 3 4 2 +Linear Models 4 6 7 +Linezolid 4 6 3 +Lingual Frenum 4 5 2 +Lingual Goiter 4 5 3 +Lingual Nerve 7 7 1 +Lingual Nerve Injuries 5 7 5 +Lingual Thyroid 4 4 2 +Linguistics 3 3 1 +Liniments 3 3 1 +Linitis Plastica 7 7 1 +Linkage Disequilibrium 3 3 1 +Linoleic Acid 6 6 2 +Linoleic Acids 5 5 2 +Linoleic Acids, Conjugated 6 6 1 +Linolenic Acids 5 5 1 +Linoleoyl-CoA Desaturase 7 7 1 +Linseed Oil 4 5 3 +Linuron 5 7 2 +Lions 11 11 1 +Lip 3 5 2 +Lip Augmentation 3 3 1 +Lip Diseases 3 3 1 +Lip Neoplasms 4 5 3 +Lipase 6 6 1 +Lipectomy 3 5 4 +Lipedema 3 3 1 +Lipid A 3 6 4 +Lipid Accumulation Product 6 8 2 +Lipid Bilayers 3 5 2 +Lipid Droplet Associated Proteins 4 4 1 +Lipid Droplets 7 7 1 +Lipid Metabolism 2 2 1 +Lipid Metabolism Disorders 3 3 1 +Lipid Metabolism, Inborn Errors 4 4 3 +Lipid Mobilization 4 4 1 +Lipid Peroxidation 3 4 2 +Lipid Peroxides 2 7 5 +Lipid Regulating Agents 4 4 1 +Lipid-Linked Proteins 4 4 1 +Lipidomics 5 6 3 +Lipidoses 5 5 3 +Lipids 1 1 1 +Lipoabdominoplasty 4 6 4 +Lipoblastoma 6 6 1 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+Liver Abscess, Amebic 4 6 5 +Liver Abscess, Pyogenic 4 6 2 +Liver Circulation 5 5 1 +Liver Cirrhosis 3 4 2 +Liver Cirrhosis, Alcoholic 4 6 4 +Liver Cirrhosis, Biliary 4 6 4 +Liver Cirrhosis, Experimental 4 5 3 +Liver Diseases 2 2 1 +Liver Diseases, Alcoholic 3 5 2 +Liver Diseases, Parasitic 3 3 2 +Liver Extracts 3 3 1 +Liver Failure 4 4 1 +Liver Failure, Acute 5 5 1 +Liver Function Tests 4 4 1 +Liver Glycogen 5 6 2 +Liver Neoplasms 3 4 3 +Liver Neoplasms, Experimental 3 5 5 +Liver Regeneration 3 3 2 +Liver Transplantation 3 5 4 +Liver X Receptors 4 4 2 +Liver, Artificial 4 4 1 +Liver-Specific Organic Anion Transporter 1 6 9 4 +Livestock 5 5 1 +Living Donors 3 3 1 +Living Wills 5 7 4 +Lizards 6 6 1 +LLC-PK1 Cells 3 4 2 +Loa 9 9 1 +Lobbying 3 3 1 +Lobelia 8 8 1 +Lobeline 3 4 2 +Lobesia botrana 11 11 1 +Lobomycosis 4 5 3 +Lobosea 3 3 1 +Local Area Networks 5 5 1 +Local Field Potential Measurement 4 4 2 +Local Government 3 4 2 +Local Lymph Node Assay 5 6 3 +Location Directories and Signs 4 4 1 +Locked-In Syndrome 3 6 3 +Locomotion 3 4 2 +Locus Coeruleus 6 9 2 +Locus Control Region 5 6 3 +Locusta migratoria 8 8 1 +Lod Score 3 3 1 +Loeys-Dietz Syndrome 3 5 5 +Lofepramine 5 5 1 +Loganiaceae 8 8 1 +Logic 3 3 1 +Logical Observation Identifiers Names and Codes 6 6 1 +Logistic Models 4 7 10 +Logotherapy 3 3 1 +Loiasis 8 8 1 +Loligo 7 7 1 +Lolium 8 8 1 +Loma 7 7 1 +Lomustine 4 5 2 +London 3 5 2 +Loneliness 3 5 2 +Long Interspersed Nucleotide Elements 7 8 3 +Long QT Syndrome 4 5 4 +Long Short Term Memory 4 7 2 +Long Term Adverse Effects 3 3 1 +Long-Acting Reversible Contraception 4 4 1 +Long-Acting Thyroid Stimulator 9 9 6 +Long-Chain-3-Hydroxyacyl-CoA Dehydrogenase 7 7 1 +Long-Chain-Fatty-Acid-CoA Ligase 6 6 1 +Long-Term Care 3 4 2 +Long-Term Potentiation 4 4 1 +Long-Term Synaptic Depression 4 4 1 +Longevity 2 4 2 +Longitudinal Ligaments 4 5 3 +Longitudinal Studies 6 7 3 +Lonicera 9 9 1 +Loop of Henle 6 6 1 +Loose Anagen Hair Syndrome 4 6 2 +Loperamide 4 4 1 +Lopinavir 5 5 1 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+Lung Neoplasms 3 5 3 +Lung Transplantation 4 4 2 +Lung Volume Measurements 5 5 1 +Lung, Hyperlucent 3 3 1 +Lupanes 6 6 1 +Lupinus 8 8 1 +Lupus Coagulation Inhibitor 3 9 4 +Lupus Erythematosus, Cutaneous 3 3 2 +Lupus Erythematosus, Discoid 4 4 2 +Lupus Erythematosus, Systemic 3 3 2 +Lupus Nephritis 4 8 5 +Lupus Vasculitis, Central Nervous System 4 6 14 +Lupus Vulgaris 5 6 3 +Lurasidone Hydrochloride 4 5 3 +Luria-Nebraska Neuropsychological Battery 4 4 1 +Luteal Cells 3 7 4 +Luteal Phase 4 4 1 +Lutein 5 10 4 +Luteinization 5 5 1 +Luteinizing Hormone 6 7 3 +Luteinizing Hormone, beta Subunit 7 8 3 +Lutembacher Syndrome 6 7 3 +Luteolin 8 8 2 +Luteolysis 5 5 1 +Luteolytic Agents 5 7 6 +Luteoma 4 8 8 +Luteoviridae 3 4 2 +Luteovirus 4 5 2 +Lutetium 4 5 3 +Lutheran Blood-Group System 5 5 2 +Luxembourg 3 3 1 +Lyases 3 3 1 +Lychnis 10 10 1 +Lycium 9 9 1 +Lycopene 4 9 4 +Lycopodiaceae 5 5 1 +Lycopodium 6 6 1 +Lycopus 9 9 1 +Lycoris 10 10 1 +Lye 4 6 2 +Lymantria dispar 11 11 1 +Lyme Disease 4 7 3 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6 9 3 +Lymphocyte Subsets 6 7 3 +Lymphocyte Transfusion 6 6 1 +Lymphocytes 5 6 3 +Lymphocytes, Null 6 7 3 +Lymphocytes, Tumor-Infiltrating 6 7 3 +Lymphocytic Choriomeningitis 5 6 5 +Lymphocytic choriomeningitis virus 7 7 1 +Lymphocytosis 5 5 1 +Lymphogranuloma Venereum 5 7 5 +Lymphography 5 5 1 +Lymphohistiocytosis, Hemophagocytic 5 5 1 +Lymphoid Enhancer-Binding Factor 1 5 7 4 +Lymphoid Progenitor Cells 4 5 3 +Lymphoid Tissue 2 4 2 +Lymphokines 4 5 3 +Lymphoma 3 4 3 +Lymphoma, AIDS-Related 6 7 3 +Lymphoma, B-Cell 5 6 3 +Lymphoma, B-Cell, Marginal Zone 6 7 3 +Lymphoma, Extranodal NK-T-Cell 6 6 1 +Lymphoma, Follicular 5 6 3 +Lymphoma, Large B-Cell, Diffuse 6 7 3 +Lymphoma, Large-Cell, Anaplastic 6 7 3 +Lymphoma, Large-Cell, Immunoblastic 5 6 3 +Lymphoma, Mantle-Cell 5 6 3 +Lymphoma, Non-Hodgkin 4 5 3 +Lymphoma, Primary Cutaneous Anaplastic Large Cell 7 8 3 +Lymphoma, Primary Effusion 6 7 3 +Lymphoma, T-Cell 5 6 3 +Lymphoma, T-Cell, Cutaneous 6 7 3 +Lymphoma, T-Cell, Peripheral 6 7 3 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Membrane Protein 2 6 6 3 +Lysosomal-Associated Membrane Protein 3 6 6 3 +Lysosomes 8 8 1 +Lysostaphin 7 7 2 +Lyssavirus 6 6 1 +Lytechinus 6 6 1 +Lythraceae 9 9 1 +Lythrum 10 10 1 +M Cells 3 4 2 +M Phase Cell Cycle Checkpoints 4 6 3 +Maackia 8 8 1 +Macaca 12 12 1 +Macaca arctoides 13 13 1 +Macaca fascicularis 13 13 1 +Macaca fuscata 13 13 1 +Macaca mulatta 13 13 1 +Macaca nemestrina 13 13 1 +Macaca radiata 13 13 1 +Macadamia 8 8 1 +Macau 3 5 2 +Machado-Joseph Disease 6 7 6 +Machiavellianism 3 3 1 +Machine Learning 4 5 2 +Machine Learning Algorithms 3 4 2 +Maclura 10 10 1 +Macroautophagy 3 3 1 +Macrocyclic Compounds 2 2 1 +Macrocystis 5 5 1 +Macroglobulins 5 5 2 +Macroglossia 4 4 1 +Macrolides 3 4 3 +Macromolecular Substances 1 1 1 +Macronucleus 5 8 2 +Macrophage Activation 3 3 1 +Macrophage Activation Syndrome 4 4 1 +Macrophage Colony-Stimulating Factor 5 7 5 +Macrophage Inflammatory Proteins 4 6 5 +Macrophage Migration-Inhibitory Factors 4 6 4 +Macrophage-1 Antigen 7 8 3 +Macrophage-Activating Factors 5 6 3 +Macrophages 3 4 5 +Macrophages, Alveolar 4 5 5 +Macrophages, Peritoneal 4 5 5 +Macropodidae 7 7 1 +Macrostomia 4 5 3 +Macula Lutea 4 4 1 +Macular Degeneration 4 4 1 +Macular Edema 5 5 1 +Macular Pigment 5 5 1 +Mad2 Proteins 4 4 2 +Madagascar 4 5 2 +Madhuca 9 9 1 +Madin Darby Canine Kidney Cells 3 4 2 +MADS Domain Proteins 4 5 2 +Madurella 4 5 2 +Maesa 9 9 1 +Maf Transcription Factors 5 5 2 +Maf Transcription Factors, Large 6 6 2 +Maf Transcription Factors, Small 6 6 6 +MafB Transcription Factor 7 7 2 +Mafenide 5 7 4 +MafF Transcription Factor 7 7 6 +MafG Transcription Factor 7 7 6 +MafK Transcription Factor 7 7 6 +Magainins 4 6 3 +Maggot Debridement Therapy 2 3 2 +Magic 4 6 2 +Magnaporthe 4 4 1 +Magnesium 4 4 3 +Magnesium Chloride 3 5 2 +Magnesium Compounds 2 2 1 +Magnesium Deficiency 5 5 1 +Magnesium Hydroxide 3 6 3 +Magnesium Oxide 3 4 2 +Magnesium Silicates 3 6 3 +Magnesium Sulfate 3 6 2 +Magnetic Field Therapy 2 2 1 +Magnetic Fields 3 3 1 +Magnetic Iron Oxide Nanoparticles 6 6 1 +Magnetic Particle Imaging 5 5 1 +Magnetic Phenomena 2 2 1 +Magnetic Resonance Angiography 5 6 2 +Magnetic Resonance Imaging 5 5 1 +Magnetic Resonance Imaging, Cine 6 6 1 +Magnetic Resonance Imaging, Interventional 3 3 1 +Magnetic Resonance Myelography 5 7 5 +Magnetic Resonance Spectroscopy 4 4 1 +Magnetics 3 3 1 +Magnetite Nanoparticles 7 7 1 +Magnetocardiography 3 5 3 +Magnetoencephalography 3 4 3 +Magnetometry 2 2 1 +Magnetosomes 2 7 2 +Magnetospirillum 6 6 2 +Magnets 3 3 1 +Magnolia 8 8 1 +Magnoliaceae 7 7 1 +Magnoliopsida 6 6 1 +Mahonia 8 8 1 +Maianthemum 10 10 1 +Maillard Reaction 3 3 1 +Maine 6 6 1 +Mainstreaming, Education 3 4 2 +Maintenance 2 2 1 +Maintenance and Engineering, Hospital 3 6 3 +Maintenance Chemotherapy 3 3 1 +Maize streak virus 4 4 2 +Major Depressive Disorder 4 4 1 +Major Histocompatibility Complex 3 6 3 +Major Vault Protein 7 7 2 +Malabsorption Syndromes 3 4 2 +Malacoplakia 3 3 1 +Malaria 4 4 2 +Malaria Vaccines 5 5 1 +Malaria, Avian 3 5 3 +Malaria, Cerebral 5 6 5 +Malaria, Falciparum 5 5 2 +Malaria, Vivax 5 5 2 +Malassezia 4 4 3 +Malate Dehydrogenase 6 6 1 +Malate Dehydrogenase (NADP+) 6 6 1 +Malate Synthase 5 5 1 +Malates 4 5 2 +Malathion 5 5 3 +Malawi 5 5 1 +Malaysia 4 4 1 +Maldives 5 5 1 +Male Urogenital Diseases 2 2 1 +Maleates 5 5 1 +Maleic Anhydrides 3 4 2 +Maleic Hydrazide 4 4 1 +Maleimides 3 6 3 +Malformations of Cortical Development 3 4 2 +Malformations of Cortical Development, Group I 4 5 2 +Malformations of Cortical Development, Group II 4 5 2 +Malformations of Cortical Development, Group III 4 5 2 +Mali 5 5 1 +Malignant Atrophic Papulosis 4 4 3 +Malignant Carcinoid Syndrome 7 7 3 +Malignant Catarrh 3 5 2 +Malignant Hyperthermia 4 5 3 +Malingering 4 4 1 +Malleus 5 5 1 +Mallory Bodies 4 4 1 +Mallory-Weiss Syndrome 5 5 1 +Mallotus Plant 10 10 1 +Malnutrition 3 3 1 +Malocclusion 3 3 1 +Malocclusion, Angle Class I 4 4 1 +Malocclusion, Angle Class II 4 4 1 +Malocclusion, Angle Class III 4 4 1 +Malonate-Semialdehyde Dehydrogenase (Acetylating) 6 6 1 +Malonates 5 5 1 +Malondialdehyde 3 3 1 +Malonyl Coenzyme A 5 9 4 +Malpighiaceae 9 9 1 +Malpighiales 8 8 1 +Malpighian Tubules 2 2 1 +Malpractice 4 5 2 +Malta 4 5 2 +Maltose 4 5 3 +Maltose-Binding Proteins 6 6 1 +Malus 10 10 1 +Malva 10 10 1 +Malvaceae 9 9 1 +Malvales 8 8 1 +Mamastrovirus 5 5 1 +Mammaglobin A 4 4 2 +Mammaglobin B 4 4 1 +Mammalian orthoreovirus 3 7 7 1 +Mammals 5 5 1 +Mammaplasty 3 3 2 +Mammary Analogue Secretory Carcinoma 5 5 1 +Mammary Arteries 5 5 1 +Mammary Glands, Animal 2 3 2 +Mammary Glands, Human 3 3 2 +Mammary Neoplasms, Animal 2 3 2 +Mammary Neoplasms, Experimental 3 5 3 +Mammary Tumor Virus, Mouse 5 5 2 +Mammea 9 9 1 +Mammillary Bodies 7 8 2 +Mammography 5 5 1 +Mammoths 9 9 1 +Man-Machine Systems 3 4 3 +Managed Care Programs 4 6 2 +Managed Competition 6 6 1 +Management Audit 3 3 1 +Management Information Systems 3 3 1 +Management Quality Circles 4 4 1 +Management Service Organizations 4 4 1 +Mandatory Programs 3 4 3 +Mandatory Reporting 4 7 5 +Mandatory Testing 4 5 4 +Mandatory Vaccination 4 5 2 +Mandelic Acids 4 4 2 +Mandible 3 7 2 +Mandibular Advancement 3 3 3 +Mandibular Canal 8 8 1 +Mandibular Condyle 4 8 2 +Mandibular Diseases 3 3 2 +Mandibular Fractures 5 7 3 +Mandibular Injuries 6 6 2 +Mandibular Neoplasms 4 6 6 +Mandibular Nerve 6 6 1 +Mandibular Nerve Injuries 5 7 5 +Mandibular Osteotomy 4 4 1 +Mandibular Prosthesis 4 4 1 +Mandibular Prosthesis Implantation 4 4 3 +Mandibular Reconstruction 4 4 1 +Mandibulofacial Dysostosis 4 6 4 +Mandragora 9 9 1 +Mandrillus 12 12 1 +Manduca 11 11 1 +Maneb 3 7 3 +Manganese 4 4 3 +Manganese Compounds 2 2 1 +Manganese Poisoning 4 4 2 +Mangifera 8 8 1 +Mania 3 5 3 +Manifest Anxiety Scale 5 5 1 +Manihot 10 10 1 +Manikins 7 8 2 +Manilkara 9 9 1 +Manipulation, Chiropractic 4 4 1 +Manipulation, Orthopedic 3 5 3 +Manipulation, Osteopathic 4 5 3 +Manipulation, Spinal 4 5 2 +Manitoba 5 5 1 +Mannans 3 3 1 +Mannheimia 5 5 2 +Mannheimia haemolytica 6 6 2 +Mannich Bases 3 3 2 +Mannitol 3 4 2 +Mannitol Dehydrogenases 7 7 1 +Mannitol Phosphates 3 5 3 +Mannoheptulose 5 5 2 +Mannomustine 6 6 1 +Mannose 5 5 1 +Mannose Receptor 5 5 3 +Mannose-6-Phosphate Isomerase 6 6 1 +Mannose-Binding Lectin 5 6 2 +Mannose-Binding Lectins 4 4 1 +Mannose-Binding Protein-Associated Serine Proteases 7 8 3 +Mannosephosphates 4 4 1 +Mannosidase Deficiency Diseases 5 5 4 +Mannosidases 5 5 1 +Mannosides 3 3 1 +Mannosyl-Glycoprotein Endo-beta-N-Acetylglucosaminidase 6 6 1 +Mannosyltransferases 6 6 1 +Manometry 2 2 1 +Mansonella 9 9 1 +Mansonelliasis 8 8 1 +Mantodea 8 8 1 +Manual Communication 4 7 4 +Manual Lymphatic Drainage 3 7 4 +Manuals as Topic 5 6 2 +Manubrium 6 6 1 +Manufactured Materials 2 2 1 +Manufacturing and Industrial Facilities 2 2 1 +Manufacturing Industry 3 3 1 +Manure 2 2 1 +Manuscript 2 2 1 +Manuscript, Medical 3 3 1 +Manuscripts as Topic 5 5 1 +Manuscripts, Medical as Topic 6 6 1 +Maori People 4 6 2 +Map 3 3 2 +MAP Kinase Kinase 1 6 9 4 +MAP Kinase Kinase 2 6 9 4 +MAP Kinase Kinase 3 6 9 4 +MAP Kinase Kinase 4 6 9 4 +MAP Kinase Kinase 5 6 9 4 +MAP Kinase Kinase 6 6 9 4 +MAP Kinase Kinase 7 6 9 4 +MAP Kinase Kinase Kinase 1 6 9 2 +MAP Kinase Kinase Kinase 2 6 9 2 +MAP Kinase Kinase Kinase 3 6 9 2 +MAP Kinase Kinase Kinase 4 6 9 2 +MAP Kinase Kinase Kinase 5 6 9 2 +MAP Kinase Kinase Kinase 7 6 9 2 +MAP Kinase Kinase Kinases 5 8 2 +MAP Kinase Signaling System 3 4 3 +MAP-Kinase-Activated Kinase 2 5 8 2 +Maple Syrup Urine Disease 5 6 6 +Maprotiline 4 7 2 +Maps as Topic 5 6 2 +Marantaceae 9 9 1 +Marasmius 5 5 1 +Marathon Running 4 7 4 +Maraviroc 5 7 2 +Marburg Virus Disease 4 6 3 +Marburgvirus 6 6 1 +Marchantia 6 6 1 +Marchiafava-Bignami Disease 3 5 2 +Mardivirus 5 5 1 +Marek Disease 3 5 5 +Marek Disease Vaccines 6 6 1 +Marfan Syndrome 3 5 7 +Margarine 4 5 5 +Margins of Excision 2 3 2 +Marijuana Abuse 3 3 2 +Marijuana Smoking 4 5 2 +Marijuana Use 3 3 2 +Marine Biology 4 6 3 +Marine Toxins 3 3 1 +Marinobacter 5 5 1 +Marinomonas 5 5 1 +Marital Status 4 6 5 +Marital Therapy 6 6 1 +Marketing 3 3 1 +Marketing of Health Services 4 4 3 +Markov Chains 3 6 7 +Marmota 9 9 1 +Marriage 5 7 5 +Marrubium 9 9 1 +Mars 6 6 1 +Marsdenia 9 9 1 +Marsileaceae 7 7 1 +Marsupialia 6 6 1 +MART-1 Antigen 5 5 2 +Martial Arts 5 5 1 +Martinique 4 5 2 +MARVEL Domain Containing 2 Protein 5 5 2 +MARVEL Domain-Containing Proteins 4 4 1 +Maryland 6 6 2 +Masculinity 4 6 3 +Masked Hypertension 4 4 1 +Masked Mycotoxins 4 4 1 +Masks 3 5 5 +Masochism 3 3 1 +Mason-Pfizer monkey virus 5 5 4 +Masoprocol 8 8 2 +Mass Behavior 4 4 1 +Mass Casualty Incidents 4 6 3 +Mass Chest X-Ray 4 8 8 +Mass Drug Administration 4 5 3 +Mass Gatherings 4 5 2 +Mass Media 4 4 1 +Mass Screening 3 7 6 +Mass Shooting Events 5 7 7 +Mass Spectrometry 3 3 1 +Mass Vaccination 5 8 6 +Massachusetts 6 6 1 +Massage 5 6 3 +Masseter Muscle 3 5 2 +Massive Hepatic Necrosis 4 6 2 +Mast Cell Activation Disorders 2 2 1 +Mast Cell Activation Syndrome 3 3 1 +Mast Cell Stabilizers 5 5 3 +Mast Cells 3 3 2 +Mast-Cell Sarcoma 4 6 2 +Mastadenovirus 4 4 1 +Mastectomy 2 2 1 +Mastectomy, Extended Radical 4 4 1 +Mastectomy, Modified Radical 4 4 1 +Mastectomy, Radical 3 3 1 +Mastectomy, Segmental 3 3 1 +Mastectomy, Simple 3 3 1 +Mastectomy, Subcutaneous 3 3 1 +Mastic Resin 5 5 1 +Mastication 4 5 2 +Masticatory Muscles 2 4 2 +Mastitis 4 5 2 +Mastitis, Bovine 3 3 1 +Mastocytoma 4 6 2 +Mastocytoma, Skin 5 7 6 +Mastocytosis 3 5 2 +Mastocytosis, Cutaneous 4 6 4 +Mastocytosis, Systemic 4 6 2 +Mastodons 9 9 1 +Mastodynia 5 5 3 +Mastoid 6 6 1 +Mastoidectomy 4 4 1 +Mastoiditis 4 5 3 +Masturbation 4 4 1 +Matched-Pair Analysis 4 5 6 +Materia Medica 2 2 1 +Material Safety Data Sheets 5 7 2 +Materials Management, Hospital 5 5 2 +Materials Science 2 3 2 +Materials Testing 2 2 1 +Maternal Age 3 5 3 +Maternal Behavior 5 5 1 +Maternal Death 5 5 2 +Maternal Deprivation 5 5 1 +Maternal Exposure 5 5 1 +Maternal Health 4 4 1 +Maternal Health Services 4 4 2 +Maternal Inheritance 4 4 1 +Maternal Mortality 5 7 4 +Maternal Nutritional Physiological Phenomena 4 4 1 +Maternal Serum Screening Tests 5 5 1 +Maternal Welfare 4 4 1 +Maternal-Child Health Centers 4 4 1 +Maternal-Child Health Services 5 5 2 +Maternal-Child Nursing 4 4 2 +Maternal-Fetal Exchange 5 5 1 +Maternal-Fetal Relations 6 7 2 +Mathematical Computing 3 3 1 +Mathematical Concepts 1 1 1 +Mathematics 2 2 1 +Mating Factor 3 3 2 +Mating Preference, Animal 6 6 1 +Matricaria 8 8 1 +Matrilin Proteins 5 5 1 +Matrines 4 4 1 +Matrix Attachment Region Binding Proteins 4 4 1 +Matrix Attachment Regions 4 5 2 +Matrix Bands 4 4 2 +Matrix Gla Protein 5 5 2 +Matrix Metalloproteinase 1 5 9 5 +Matrix Metalloproteinase 10 5 9 3 +Matrix Metalloproteinase 11 5 9 3 +Matrix Metalloproteinase 12 5 9 3 +Matrix Metalloproteinase 13 5 9 5 +Matrix Metalloproteinase 14 9 9 2 +Matrix Metalloproteinase 15 9 9 2 +Matrix Metalloproteinase 16 9 9 2 +Matrix Metalloproteinase 17 6 9 6 +Matrix Metalloproteinase 2 5 9 7 +Matrix Metalloproteinase 20 5 9 3 +Matrix Metalloproteinase 3 5 9 3 +Matrix Metalloproteinase 7 5 9 3 +Matrix Metalloproteinase 8 5 9 5 +Matrix Metalloproteinase 9 5 9 7 +Matrix Metalloproteinase Inhibitors 6 6 1 +Matrix Metalloproteinases 7 7 2 +Matrix Metalloproteinases, Membrane-Associated 4 8 3 +Matrix Metalloproteinases, Secreted 4 8 3 +Maturation-Promoting Factor 5 10 3 +Mauritania 5 5 1 +Mauritius 4 5 2 +Maus Elberfeld virus 8 8 1 +Maxilla 3 7 2 +Maxillary Artery 4 4 1 +Maxillary Diseases 3 3 2 +Maxillary Fractures 5 7 3 +Maxillary Neoplasms 4 6 6 +Maxillary Nerve 6 6 1 +Maxillary Osteotomy 4 4 1 +Maxillary Sinus 4 4 1 +Maxillary Sinus Neoplasms 5 7 7 +Maxillary Sinusitis 4 5 4 +Maxillofacial Abnormalities 3 5 4 +Maxillofacial Development 6 9 2 +Maxillofacial Injuries 5 5 2 +Maxillofacial Prosthesis 3 3 1 +Maxillofacial Prosthesis Implantation 3 3 3 +Maximal Expiratory Flow Rate 5 7 2 +Maximal Expiratory Flow-Volume Curves 5 7 2 +Maximal Midexpiratory Flow Rate 5 7 2 +Maximal Respiratory Pressures 5 5 1 +Maximal Voluntary Ventilation 4 6 2 +Maximum Allowable Concentration 5 6 2 +Maximum Tolerated Dose 3 4 2 +May-Thurner Syndrome 4 5 3 +Maytansine 4 4 2 +Maytenus 10 10 1 +Maze Learning 5 5 1 +Maze Procedure 3 4 3 +Mazindol 5 5 1 +MCF-7 Cells 5 5 1 +MDA-MB-231 Cells 5 5 2 +MDS1 and EVI1 Complex Locus Protein 4 6 3 +Meals 3 4 2 +Mean Platelet Volume 3 6 3 +Meaningful Use 4 4 1 +Measles 7 7 1 +Measles Vaccine 5 5 1 +Measles virus 8 8 1 +Measles-Mumps-Rubella Vaccine 5 6 4 +Meat 3 4 2 +Meat Products 4 5 2 +Meat Proteins 4 6 5 +Meat Substitutes 3 4 2 +Meat-Packing Industry 5 5 1 +Mebendazole 5 5 2 +Mecamylamine 5 7 2 +Mechanical Phenomena 2 2 1 +Mechanical Tests 3 3 1 +Mechanical Thrombolysis 2 5 2 +Mechanics 3 3 1 +Mechanistic Target of Rapamycin Complex 1 3 9 3 +Mechanistic Target of Rapamycin Complex 2 3 9 3 +Mechanoreceptors 4 5 3 +Mechanotransduction, Cellular 3 4 3 +Mechlorethamine 6 6 1 +Meckel Diverticulum 3 6 6 +Meclizine 4 7 2 +Meclofenamic Acid 8 10 2 +Meclofenoxate 5 7 2 +Meconium 3 3 2 +Meconium Aspiration Syndrome 3 5 5 +Meconium Ileus 5 5 1 +Medazepam 6 6 1 +Medecins Sans Frontieres 4 5 2 +Medetomidine 5 5 1 +Media Exposure 3 5 3 +Medial Collateral Ligament, Knee 5 6 3 +Medial Forebrain Bundle 3 3 1 +Medial Tibial Stress Syndrome 3 4 3 +Median Arcuate Ligament Syndrome 3 5 4 +Median Eminence 4 9 6 +Median Nerve 6 6 1 +Median Neuropathy 5 5 1 +Mediastinal Cyst 3 4 2 +Mediastinal Diseases 3 3 1 +Mediastinal Emphysema 4 4 2 +Mediastinal Neoplasms 4 4 2 +Mediastinitis 4 4 1 +Mediastinoscopes 4 4 2 +Mediastinoscopy 3 5 3 +Mediastinum 5 5 1 +Mediation Analysis 5 6 3 +Mediator Complex 3 5 5 +Mediator Complex Subunit 1 4 6 10 +Medicago 8 8 1 +Medicago sativa 9 9 1 +Medicago truncatula 9 9 1 +Medicaid 4 7 2 +Medical Assistance 6 6 1 +Medical Audit 4 5 2 +Medical Chaperones 3 4 2 +Medical Countermeasures 4 4 2 +Medical Debt 4 5 2 +Medical Device Legislation 5 5 1 +Medical Device Recalls 5 5 1 +Medical Errors 3 3 1 +Medical Futility 3 3 1 +Medical History Taking 3 3 1 +Medical Identity Theft 6 6 1 +Medical Illustration 2 6 4 +Medical Indigency 3 3 2 +Medical Informatics 3 3 1 +Medical Informatics Applications 4 4 1 +Medical Informatics Computing 4 4 1 +Medical Interpreting 3 3 1 +Medical Laboratory Personnel 3 4 3 +Medical Laboratory Science 3 3 2 +Medical Marijuana 2 2 1 +Medical Missions 4 4 1 +Medical Neglect 4 5 2 +Medical Office Buildings 3 3 1 +Medical Oncology 4 4 1 +Medical Order Entry Systems 5 7 4 +Medical Overuse 4 5 2 +Medical Receptionists 5 6 2 +Medical Record Administrators 4 5 2 +Medical Record Linkage 5 7 4 +Medical Records 4 6 4 +Medical Records Department, Hospital 6 6 2 +Medical Records Systems, Computerized 5 7 5 +Medical Records, Problem-Oriented 5 7 4 +Medical Savings Accounts 4 6 4 +Medical Secretaries 4 5 2 +Medical Staff 3 4 2 +Medical Staff Privileges 5 6 3 +Medical Staff, Hospital 4 5 4 +Medical Subject Headings 7 7 1 +Medical Tourism 3 3 1 +Medical Waste 3 5 2 +Medical Waste Disposal 4 7 3 +Medical Writing 5 5 1 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+Meglutol 6 6 1 +Meibomian Gland Dysfunction 3 3 1 +Meibomian Glands 4 4 2 +Meibomitis 4 4 1 +Meige Syndrome 5 5 2 +Meigs Syndrome 4 8 7 +Meiosis 4 5 2 +Meiotic Prophase I 5 6 2 +Meiotic Recombination Protein SPO11 7 7 2 +Mekong Valley 4 4 1 +Melaleuca 8 8 1 +Melanesia 4 4 2 +Melanins 3 6 2 +Melanocortins 6 7 6 +Melanocyte-Stimulating Hormones 4 8 8 +Melanocytes 3 3 2 +Melanogenesis 2 3 3 +Melanoma 4 6 5 +Melanoma, Amelanotic 5 7 3 +Melanoma, Experimental 3 7 5 +Melanoma-Specific Antigens 4 4 2 +Melanophores 4 4 1 +Melanopsin 6 6 2 +Melanosis 5 5 1 +Melanosomes 4 9 5 +Melanotrophs 3 11 7 +Melanthiaceae 9 9 1 +Melarsoprol 3 3 1 +MELAS Syndrome 5 6 9 +Melastomataceae 7 7 1 +Melatonin 3 6 2 +Melena 3 5 3 +Melengestrol Acetate 6 6 1 +Melia 8 8 1 +Melia azedarach 9 9 1 +Meliaceae 7 7 1 +Melibiose 4 5 2 +Melilotus 8 8 1 +Melinis repens 8 8 1 +Melioidosis 6 6 1 +Melissa 9 9 1 +Melitten 4 6 5 +Melkersson-Rosenthal Syndrome 3 4 3 +Melopsittacus 9 9 1 +Melorheostosis 6 6 1 +Meloxicam 4 5 4 +Melphalan 6 6 2 +Memantine 7 7 1 +Membrane Cofactor Protein 7 7 2 +Membrane Fluidity 2 3 3 +Membrane Fusion 2 2 1 +Membrane Fusion Proteins 4 4 1 +Membrane Glycoproteins 4 4 3 +Membrane Lipids 2 2 1 +Membrane Microdomains 5 5 1 +Membrane Potential, Mitochondrial 3 4 3 +Membrane Potentials 2 3 4 +Membrane Proteins 3 3 1 +Membrane Transport Modulators 4 4 1 +Membrane Transport Proteins 4 4 2 +Membranes 2 2 1 +Membranes, Artificial 2 4 3 +Memory 4 4 1 +Memory and Learning Tests 4 4 1 +Memory B Cells 5 9 8 +Memory Consolidation 6 6 1 +Memory Disorders 3 5 3 +Memory T Cells 7 9 6 +Memory, Episodic 5 5 1 +Memory, Long-Term 5 5 1 +Memory, Short-Term 5 5 1 +Men 2 2 1 +Men's Health 3 3 1 +Men's Health Services 3 3 1 +Menarche 4 5 2 +Mendelevium 4 6 5 +Mendelian Randomization Analysis 4 4 1 +Mengovirus 8 8 1 +Meniere Disease 5 5 1 +Meningeal Arteries 4 4 1 +Meningeal Carcinomatosis 5 6 2 +Meningeal Neoplasms 4 5 2 +Meninges 3 3 1 +Meningioma 4 6 4 +Meningism 3 4 2 +Meningitis 3 3 1 +Meningitis, Aseptic 4 4 1 +Meningitis, Bacterial 4 5 4 +Meningitis, Cryptococcal 5 6 5 +Meningitis, Escherichia coli 5 7 5 +Meningitis, Fungal 4 5 4 +Meningitis, Haemophilus 5 7 5 +Meningitis, Listeria 5 6 5 +Meningitis, Meningococcal 5 7 5 +Meningitis, Pneumococcal 5 7 5 +Meningitis, Viral 4 5 4 +Meningocele 4 5 3 +Meningococcal Infections 6 6 1 +Meningococcal Vaccines 5 5 1 +Meningoencephalitis 3 5 7 +Meningomyelocele 4 5 2 +Meniscectomy 3 3 1 +Menisci, Tibial 5 6 3 +Meniscus 4 5 2 +Menispermaceae 7 7 1 +Menispermum 8 8 1 +Menkes Kinky Hair Syndrome 4 6 10 +Menogaril 7 10 3 +Menopause 4 5 2 +Menopause, Premature 5 7 4 +Menorrhagia 4 7 4 +Menotropins 3 7 4 +Menstrual Cycle 3 3 1 +Menstrual Hygiene Products 3 3 1 +Menstruation 4 4 1 +Menstruation Disturbances 3 3 1 +Menstruation-Inducing Agents 5 7 5 +Mental Competency 2 5 4 +Mental Disorders 1 1 1 +Mental Fatigue 4 4 2 +Mental Foramen 4 8 2 +Mental Healing 4 4 2 +Mental Health 2 3 2 +Mental Health Associations 5 5 1 +Mental Health Recovery 4 4 1 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polyomavirus 6 6 2 +Merkel Cells 3 6 5 +Mermithoidea 8 8 1 +Meropenem 7 7 2 +Merozoite Surface Protein 1 4 5 3 +Merozoites 4 7 4 +MERRF Syndrome 5 8 9 +Mersalyl 5 5 1 +Mesalamine 7 10 6 +Mesangial Cells 4 6 3 +Mescaline 3 5 2 +Mesembryanthemum 10 10 1 +Mesencephalon 5 5 1 +Mesenchymal Stem Cell Transplantation 5 6 2 +Mesenchymal Stem Cells 4 4 2 +Mesenchymoma 4 4 1 +Mesenteric Arteries 4 4 1 +Mesenteric Artery, Inferior 5 5 1 +Mesenteric Artery, Superior 5 5 1 +Mesenteric Cyst 3 4 2 +Mesenteric Ischemia 3 4 3 +Mesenteric Lymphadenitis 3 4 2 +Mesenteric Vascular Occlusion 3 4 3 +Mesenteric Veins 5 5 1 +Mesentery 6 6 1 +Mesial Movement of Teeth 4 5 2 +Mesna 4 7 3 +Mesocestoides 7 7 1 +Mesocolon 7 7 1 +Mesocricetus 11 11 1 +Mesoderm 3 3 1 +Mesolimbic System 3 3 1 +Mesomycetozoea 2 2 1 +Mesomycetozoea Infections 3 3 1 +Mesonephroma 4 4 1 +Mesonephros 2 2 1 +Mesons 3 3 1 +Mesophyll Cells 2 4 2 +Mesopic Vision 3 5 3 +Mesoporphyrins 4 7 4 +Mesopotamia 4 4 1 +Mesorhizobium 5 5 1 +Mesoridazine 4 5 2 +Mesothelin 4 6 7 +Mesothelioma 5 5 2 +Mesothelioma, Cystic 6 6 2 +Mesothelioma, Malignant 4 6 7 +Mesotherapy 3 6 3 +Mesterolone 7 7 1 +Mestranol 8 8 2 +Mesylates 7 7 2 +meta-Aminobenzoates 6 8 2 +Meta-Analysis 2 4 3 +Meta-Analysis as Topic 4 5 4 +Metabolic Clearance Rate 2 4 5 +Metabolic Detoxication, Phase I 4 6 3 +Metabolic Detoxication, Phase II 4 6 3 +Metabolic Diseases 2 2 1 +Metabolic Engineering 4 5 3 +Metabolic Equivalent 3 4 2 +Metabolic Flux Analysis 3 5 3 +Metabolic Networks and Pathways 2 2 1 +Metabolic Reprogramming 3 4 3 +Metabolic Side Effects of Drugs and Substances 3 3 2 +Metabolic Syndrome 3 6 2 +Metabolism 1 1 1 +Metabolism, Inborn Errors 3 3 2 +Metabolome 2 2 1 +Metabolomics 4 5 3 +Metacarpal Bones 6 6 1 +Metacarpophalangeal Joint 5 5 1 +Metacarpus 5 5 1 +Metacercariae 5 8 2 +Metacognition 4 4 1 +Metadata 4 4 1 +Metagenome 4 4 1 +Metagenomics 6 6 1 +Metal Ceramic Alloys 3 6 5 +Metal Metabolism, Inborn Errors 4 4 2 +Metal Nanoparticles 5 5 1 +Metal Workers 3 3 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4 7 2 +Methyldimethylaminoazobenzene 5 5 1 +Methyldopa 5 10 4 +Methylene Blue 4 5 2 +Methylene Chloride 5 5 1 +Methylenebis(chloroaniline) 4 7 2 +Methylenetetrahydrofolate Dehydrogenase (NAD+) 5 5 1 +Methylenetetrahydrofolate Dehydrogenase (NADP) 5 5 1 +Methylenetetrahydrofolate Reductase (NADPH2) 4 5 2 +Methylergonovine 6 6 2 +Methylgalactosides 4 4 2 +Methylglucosides 4 4 2 +Methylglycosides 3 3 1 +Methylguanidine 4 4 1 +Methylhistamines 5 6 3 +Methylhistidines 5 5 1 +Methylhydrazines 3 3 1 +Methylmalonate-Semialdehyde Dehydrogenase (Acylating) 6 6 1 +Methylmalonic Acid 6 6 1 +Methylmalonyl-CoA Decarboxylase 6 6 1 +Methylmalonyl-CoA Mutase 5 5 1 +Methylmannosides 4 4 2 +Methylmercury Compounds 5 5 1 +Methylmethacrylate 7 10 4 +Methylmethacrylates 6 9 4 +Methylnitronitrosoguanidine 4 5 2 +Methylnitrosourea 4 5 2 +Methylobacillus 5 6 2 +Methylobacteriaceae 4 5 2 +Methylobacterium 5 6 2 +Methylobacterium extorquens 6 7 2 +Methylococcaceae 4 5 2 +Methylococcus 5 6 2 +Methylococcus 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Remodeling and Deacetylase Complex 4 6 3 +Mianserin 5 5 1 +Mibefradil 5 8 3 +Micafungin 3 5 3 +Mice 10 10 1 +Mice, 129 Strain 12 13 3 +Mice, Biozzi 12 12 1 +Mice, Congenic 7 11 2 +Mice, Hairless 7 12 3 +Mice, Inbred A 7 12 2 +Mice, Inbred AKR 7 12 2 +Mice, Inbred BALB C 7 12 2 +Mice, Inbred C3H 7 12 2 +Mice, Inbred C57BL 7 12 2 +Mice, Inbred CBA 7 12 2 +Mice, Inbred CFTR 7 12 2 +Mice, Inbred DBA 7 12 2 +Mice, Inbred ICR 7 12 2 +Mice, Inbred mdx 8 13 3 +Mice, Inbred MRL lpr 7 12 2 +Mice, Inbred NOD 7 12 2 +Mice, Inbred NZB 7 12 2 +Mice, Inbred SENCAR 7 12 2 +Mice, Inbred Strains 6 11 2 +Mice, Jimpy 12 12 1 +Mice, Knockout 6 12 3 +Mice, Knockout, ApoE 7 13 3 +Mice, Mutant Strains 11 11 1 +Mice, Neurologic Mutants 12 12 1 +Mice, Nude 12 12 1 +Mice, Obese 12 12 1 +Mice, Quaking 12 12 1 +Mice, SCID 12 12 1 +Mice, Transgenic 5 11 2 +Micelles 2 3 2 +Michigan 6 6 2 +Miconazole 5 5 1 +Micrasterias 6 6 1 +Micro-Electrical-Mechanical Systems 3 3 2 +Microaggression 5 5 1 +Microalgae 5 5 1 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Maintenance Complex Component 2 5 8 5 +Minichromosome Maintenance Complex Component 3 5 8 5 +Minichromosome Maintenance Complex Component 4 5 8 5 +Minichromosome Maintenance Complex Component 5 5 8 5 +Minichromosome Maintenance Complex Component 6 5 8 5 +Minichromosome Maintenance Complex Component 7 5 8 5 +Minichromosome Maintenance Complex Component 8 5 8 5 +Minichromosome Maintenance Complex Component 9 5 8 5 +Minichromosome Maintenance Proteins 4 7 5 +Minicomputers 5 5 1 +Minimal Clinically Important Difference 7 8 2 +Minimally Invasive Surgical Procedures 2 2 1 +Mining 5 5 1 +Minisatellite Repeats 6 7 3 +Mink 10 10 1 +Mink Cell Focus-Inducing Viruses 6 6 2 +Mink enteritis virus 7 7 1 +Mink Viral Enteritis 2 5 2 +Minke Whale 10 10 1 +Minnesota 6 6 2 +MINOCA 5 6 4 +Minocycline 5 8 2 +Minor Histocompatibility Antigens 5 5 2 +Minor Histocompatibility Loci 3 6 2 +Minor Lymphocyte Stimulatory Antigens 4 4 2 +Minor Lymphocyte Stimulatory Loci 3 6 2 +Minor Planets 5 5 1 +Minor Surgical 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4 3 +Molecular Epidemiology 3 7 7 +Molecular Farming 4 4 1 +Molecular Imaging 3 4 2 +Molecular Imprinting 3 5 3 +Molecular Mechanisms of Pharmacological Action 3 3 1 +Molecular Medicine 3 6 4 +Molecular Mimicry 2 4 3 +Molecular Motor Proteins 3 6 2 +Molecular Probe Techniques 2 2 1 +Molecular Probes 4 4 2 +Molecular Sequence Annotation 4 6 2 +Molecular Sequence Data 5 5 1 +Molecular Structure 2 3 2 +Molecular Targeted Therapy 3 3 1 +Molecular Typing 3 7 3 +Molecular Weight 2 2 1 +Molecularly Imprinted Polymers 3 5 3 +Moles 8 8 1 +Molindone 5 5 1 +Molineoidae 8 8 1 +Molluginaceae 9 9 1 +Mollusca 4 4 1 +Molluscacides 4 5 2 +Molluscipoxvirus 5 5 1 +Molluscum Contagiosum 4 5 3 +Molluscum contagiosum virus 6 6 1 +Mollusk Venoms 3 4 3 +Moloney murine leukemia virus 6 6 2 +Moloney murine sarcoma virus 4 6 3 +Molsidomine 5 7 2 +Molteno Implants 4 4 1 +Molting 5 5 1 +Molybdenum 4 4 3 +Molybdenum Cofactors 3 3 1 +Molybdoferredoxin 4 8 5 +Mometasone Furoate 7 7 1 +Mometasone Furoate, Formoterol 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2 1 +Monoiodotyrosine 4 6 2 +Monokines 4 5 3 +Monomeric Clathrin Assembly Proteins 6 6 1 +Monomeric GTP-Binding Proteins 5 7 3 +Monomethylhydrazine 4 4 1 +Mononegavirales 4 4 1 +Mononegavirales Infections 4 4 1 +Mononeuropathies 4 4 1 +Mononuclear Phagocyte System 3 3 1 +Monophenol Monooxygenase 7 7 1 +Monosaccharide Transport Proteins 5 5 2 +Monosaccharides 3 3 1 +Monosomy 4 6 3 +Monoterpene Aldehydes and Ketones 3 5 3 +Monoterpenes 4 4 1 +Monotremata 6 6 1 +Montana 6 6 1 +Montanoa 8 8 1 +Monte Carlo Method 4 5 4 +Monteggia's Fracture 4 5 4 +Montenegro 4 4 1 +Mood Disorders 2 2 1 +Moon 7 7 1 +Moorella 3 5 4 +Mopidamol 4 4 1 +Moraceae 9 9 1 +Moral Development 4 4 3 +Moral Obligations 5 5 2 +Moral Status 4 5 2 +Morale 3 3 1 +Morals 3 3 2 +Morantel 4 4 3 +Moraxella 5 6 2 +Moraxella bovis 6 7 2 +Moraxella catarrhalis 6 7 2 +Moraxellaceae 4 5 2 +Moraxellaceae Infections 5 5 1 +Morbidity 4 6 4 +Morbillivirus 7 7 1 +Morbillivirus Infections 6 6 1 +Morcellation 3 3 1 +Morganella 5 5 2 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General Medical Sciences (U.S.) 5 9 3 +National Institute of Mental Health (U.S.) 5 9 3 +National Institute of Neurological Disorders and Stroke (U.S.) 5 9 3 +National Institute of Nursing Research (U.S.) 5 9 3 +National Institute on Aging (U.S.) 5 9 3 +National Institute on Alcohol Abuse and Alcoholism (U.S.) 5 9 3 +National Institute on Deafness and Other Communication Disorders (U.S.) 5 9 3 +National Institute on Drug Abuse (U.S.) 5 9 3 +National Institutes of Health (U.S.) 4 8 3 +National Library of Medicine (U.S.) 5 9 5 +National Longitudinal Study of Adolescent Health 7 8 3 +National Practitioner Data Bank 5 8 2 +National Program of Cancer Registries 8 9 2 +National Socialism 3 3 1 +Native Hawaiian or Pacific Islander 4 6 4 +Native Polyacrylamide Gel Electrophoresis 5 5 2 +Natriuresis 4 4 1 +Natriuretic Agents 4 5 2 +Natriuretic Peptide, Brain 4 5 3 +Natriuretic Peptide, C-Type 5 5 2 +Natriuretic Peptides 4 4 2 +Natronobacterium 5 5 1 +Natronococcus 5 5 1 +Natural Childbirth 6 6 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Plexus Palsy 3 5 3 +Neonatal Nursing 5 5 4 +Neonatal Screening 3 8 8 +Neonatal Sepsis 3 6 3 +Neonatologists 5 6 2 +Neonatology 4 4 1 +Neonicotinoids 3 3 1 +Neoplasm Grading 3 3 1 +Neoplasm Invasiveness 3 4 2 +Neoplasm Metastasis 3 4 2 +Neoplasm Micrometastasis 4 5 2 +Neoplasm Proteins 3 3 1 +Neoplasm Recurrence, Local 3 4 2 +Neoplasm Regression, Spontaneous 3 4 3 +Neoplasm Seeding 4 5 2 +Neoplasm Staging 3 3 1 +Neoplasm Transplantation 2 2 1 +Neoplasm, Lymphatic Tissue 3 3 1 +Neoplasm, Residual 3 4 2 +Neoplasms 1 1 1 +Neoplasms by Histologic Type 2 2 1 +Neoplasms by Site 2 2 1 +Neoplasms, Adipose Tissue 4 4 1 +Neoplasms, Adnexal and Skin Appendage 4 4 1 +Neoplasms, Basal Cell 4 4 1 +Neoplasms, Bone Tissue 5 5 1 +Neoplasms, Complex and Mixed 3 3 1 +Neoplasms, Connective and Soft Tissue 3 3 1 +Neoplasms, Connective Tissue 3 4 2 +Neoplasms, Cystic, Mucinous, and Serous 4 4 1 +Neoplasms, Ductal, Lobular, and Medullary 4 4 1 +Neoplasms, Experimental 2 4 2 +Neoplasms, Fibroepithelial 4 6 2 +Neoplasms, Fibrous Tissue 5 5 1 +Neoplasms, Germ Cell and Embryonal 3 3 1 +Neoplasms, Glandular and Epithelial 3 3 1 +Neoplasms, Gonadal Tissue 3 3 1 +Neoplasms, Hormone-Dependent 2 2 1 +Neoplasms, Mesothelial 4 4 1 +Neoplasms, Multiple Primary 2 2 1 +Neoplasms, Muscle Tissue 4 4 1 +Neoplasms, Nerve Tissue 3 3 1 +Neoplasms, Neuroepithelial 4 5 3 +Neoplasms, Plasma Cell 3 3 1 +Neoplasms, Post-Traumatic 2 2 1 +Neoplasms, Radiation-Induced 2 5 3 +Neoplasms, Second Primary 2 2 1 +Neoplasms, Squamous Cell 4 4 1 +Neoplasms, Unknown Primary 4 5 2 +Neoplasms, Vascular Tissue 3 3 1 +Neoplastic Cells, Circulating 2 5 3 +Neoplastic Processes 2 3 2 +Neoplastic Stem Cells 3 3 1 +Neoplastic Syndromes, Hereditary 2 3 2 +Neoprene 4 7 4 +Neoptera 7 7 1 +Neopterin 4 7 2 +Neorickettsia 5 6 2 +Neorickettsia risticii 6 7 2 +Neorickettsia sennetsu 6 7 2 +Neosartorya 5 5 1 +Neospora 7 7 1 +Neostigmine 4 5 2 +Neostriatum 9 9 1 +Neotyphodium 4 5 2 +Neovascularization, Pathologic 4 4 2 +Neovascularization, Physiologic 3 4 2 +Nepal 5 5 1 +Nepeta 9 9 1 +Nephelometry and Turbidimetry 4 4 1 +Nephrectomy 4 4 1 +Nephritis 4 6 3 +Nephritis, Hereditary 3 7 8 +Nephritis, Interstitial 5 7 3 +Nephroblastoma Overexpressed Protein 4 6 5 +Nephrocalcinosis 4 6 4 +Nephrogenic Fibrosing Dermopathy 3 4 2 +Nephrolithiasis 4 6 6 +Nephrolithotomy, Percutaneous 4 5 2 +Nephrologists 4 5 2 +Nephrology 4 4 1 +Nephrology Nursing 4 4 2 +Nephroma, Mesoblastic 4 7 9 +Nephrons 4 4 1 +Nephropidae 7 7 1 +Nephrosclerosis 4 6 3 +Nephrosis 4 6 3 +Nephrosis, Lipoid 5 7 3 +Nephrostomy, Percutaneous 3 5 4 +Nephrotic Syndrome 5 7 3 +Nephrotomy 4 4 1 +Nephroureterectomy 5 5 1 +Nepovirus 4 6 2 +Neprilysin 4 7 4 +Neptune 6 6 1 +Neptunium 4 6 5 +Nerium 9 9 1 +Nerve Agents 3 5 2 +Nerve Block 4 4 2 +Nerve Compression Syndromes 4 4 1 +Nerve Conduction Studies 4 4 2 +Nerve Crush 4 4 1 +Nerve Degeneration 3 3 1 +Nerve Endings 3 3 1 +Nerve Expansion 4 4 1 +Nerve Fibers 3 3 2 +Nerve Fibers, Myelinated 3 4 3 +Nerve Fibers, Unmyelinated 4 4 2 +Nerve Growth Factor 4 5 4 +Nerve Growth Factors 3 4 4 +Nerve Net 2 2 1 +Nerve Regeneration 3 3 2 +Nerve Sheath Neoplasms 4 5 3 +Nerve Tissue 2 2 1 +Nerve Tissue Proteins 3 3 1 +Nerve Transfer 3 3 1 +Nervous System 1 1 1 +Nervous System Autoimmune Disease, Experimental 3 4 3 +Nervous System Diseases 1 1 1 +Nervous System Malformations 2 3 2 +Nervous System Neoplasms 2 3 2 +Nervous System Physiological Phenomena 2 2 1 +Nesidioblastosis 4 6 4 +Nested Genes 7 7 1 +Nestin 4 5 3 +Nesting Behavior 5 5 1 +Netherlands 3 3 1 +Netherlands Antilles 3 3 1 +Netherton Syndrome 4 6 7 +Netilmicin 6 6 1 +Netrin Receptors 5 5 1 +Netrin-1 4 6 6 +Netrins 3 5 6 +Netropsin 4 4 1 +Network Meta-Analysis 3 3 1 +Network Meta-Analysis as Topic 5 5 1 +Network Pharmacology 4 4 1 +Neurabins 4 5 3 +Neural Analyzers 3 3 1 +Neural Cell Adhesion Molecule L1 7 8 4 +Neural Cell Adhesion Molecules 6 7 4 +Neural Conduction 3 3 2 +Neural Crest 2 2 1 +Neural Inhibition 3 3 2 +Neural Networks, Computer 2 5 2 +Neural Pathways 2 2 1 +Neural Plate 2 2 1 +Neural Prostheses 5 6 2 +Neural Stem Cells 3 3 1 +Neural Tube 2 2 1 +Neural Tube Defects 3 4 2 +Neuralgia 4 5 4 +Neuralgia, Postherpetic 5 6 2 +Neuraminic Acids 3 5 4 +Neuraminidase 5 5 1 +Neurasthenia 3 3 1 +Neuregulin-1 5 6 4 +Neuregulins 4 5 4 +Neurexins 4 7 5 +Neurilemma 4 6 6 +Neurilemmoma 6 6 3 +Neurites 3 5 5 +Neuritis 4 4 1 +Neuritis, Autoimmune, Experimental 4 5 4 +Neuro-Oncological Ventral Antigen 4 5 3 +Neuroacanthocytosis 4 6 2 +Neuroanatomical Tract-Tracing Techniques 6 7 4 +Neuroanatomy 4 4 2 +Neuroanesthesia 3 3 1 +Neuroaspergillosis 4 5 4 +Neuroaxonal Dystrophies 4 4 1 +Neurobehavioral Manifestations 2 4 3 +Neurobiology 4 4 2 +Neuroblastoma 7 8 3 +Neurocalcin 5 7 4 +Neurocan 6 8 7 +Neurochemistry 4 4 3 +Neurocirculatory Asthenia 3 3 1 +Neurocognitive Disorders 2 2 1 +Neurocutaneous Syndromes 2 5 6 +Neurocysticercosis 5 7 4 +Neurocytoma 5 6 6 +Neurodegenerative Diseases 2 2 1 +Neurodermatitis 4 4 2 +Neurodevelopment 3 3 1 +Neurodevelopmental Disorders 2 2 1 +Neuroectodermal Tumor, Melanotic 5 5 2 +Neuroectodermal Tumors 4 4 2 +Neuroectodermal Tumors, Primitive 5 6 3 +Neuroectodermal Tumors, Primitive, Peripheral 6 7 3 +Neuroeffector Junction 3 7 3 +Neuroendocrine Cells 3 3 1 +Neuroendocrine Secretory Protein 7B2 4 4 2 +Neuroendocrine Tumors 5 5 2 +Neuroendocrinology 4 5 2 +Neuroendoscopes 4 4 2 +Neuroendoscopy 3 5 4 +Neuroepithelial Bodies 4 7 4 +Neuroepithelial Cells 3 6 7 +Neurofeedback 4 5 4 +Neurofibrillary Tangles 4 8 3 +Neurofibrils 3 7 3 +Neurofibroma 5 6 3 +Neurofibroma, Plexiform 6 7 3 +Neurofibromatoses 3 6 6 +Neurofibromatosis 1 4 7 7 +Neurofibromatosis 2 4 8 12 +Neurofibromin 1 5 7 3 +Neurofibromin 2 4 5 2 +Neurofibrosarcoma 6 7 5 +Neurofilament Proteins 4 5 3 +Neurogenesis 3 6 4 +Neurogenic Bowel 6 6 1 +Neurogenic Inflammation 3 4 2 +Neuroglia 2 2 2 +Neuroglobin 4 5 2 +Neurogranin 4 5 2 +Neuroimaging 2 4 3 +Neuroimmunomodulation 3 3 3 +Neuroinflammatory Diseases 2 4 2 +Neurokinin A 5 6 7 +Neurokinin B 5 6 7 +Neurokinin-1 Receptor Antagonists 5 5 2 +Neuroleptanalgesia 3 3 1 +Neuroleptic Malignant Syndrome 3 5 3 +Neuroligins 4 8 5 +Neurolinguistic Programming 3 5 7 +Neurologic Examination 4 4 2 +Neurologic Manifestations 2 3 2 +Neurological Rehabilitation 3 6 4 +Neurologists 4 5 2 +Neurology 3 3 1 +Neurolymphomatosis 3 3 1 +Neuroma 5 5 1 +Neuroma, Acoustic 3 7 9 +Neuromuscular Agents 5 5 1 +Neuromuscular Blockade 2 2 2 +Neuromuscular Blocking Agents 6 6 1 +Neuromuscular Depolarizing Agents 7 7 1 +Neuromuscular Diseases 2 2 1 +Neuromuscular Junction 4 8 3 +Neuromuscular Junction Diseases 3 3 1 +Neuromuscular Manifestations 3 4 2 +Neuromuscular Monitoring 4 4 1 +Neuromuscular Nondepolarizing Agents 7 7 1 +Neuromyelitis Optica 4 6 8 +Neuronal Apoptosis-Inhibitory Protein 4 7 4 +Neuronal Calcium-Sensor Proteins 4 6 4 +Neuronal Ceroid-Lipofuscinoses 4 6 5 +Neuronal Outgrowth 4 7 4 +Neuronal Plasticity 3 3 1 +Neuronal Tract-Tracers 4 5 2 +Neuronavigation 3 4 3 +Neurons 2 2 2 +Neurons, Afferent 3 3 2 +Neurons, Efferent 3 3 2 +Neuropathology 4 4 3 +Neuropeptide Y 4 5 2 +Neuropeptides 3 4 2 +Neuropharmacology 3 4 3 +Neurophysins 4 5 3 +Neurophysiological Monitoring 4 4 1 +Neurophysiology 4 4 2 +Neuropil 3 3 4 +Neuropil Threads 4 8 5 +Neuropilin-1 6 6 1 +Neuropilin-2 6 6 1 +Neuropilins 5 5 1 +Neuroprostanes 5 7 4 +Neuroprotection 3 3 1 +Neuroprotective Agents 5 5 2 +Neuropsychiatry 4 4 2 +Neuropsychological Tests 3 3 1 +Neuropsychology 4 5 2 +Neuroradiography 3 5 4 +Neuroschistosomiasis 4 6 5 +Neuroscience Nursing 4 4 2 +Neurosciences 3 3 1 +Neurosecretion 3 3 1 +Neurosecretory Systems 2 2 2 +Neuroserpin 4 4 2 +Neurospora 5 5 1 +Neurospora crassa 6 6 1 +Neurosteroids 4 5 3 +Neurosurgeons 5 6 2 +Neurosurgery 4 4 1 +Neurosurgical Procedures 2 2 1 +Neurosyphilis 4 8 5 +Neurotensin 4 5 2 +Neurothekeoma 5 6 2 +Neurotic Disorders 2 3 2 +Neuroticism 3 3 1 +Neurotology 4 5 2 +Neurotoxicity Syndromes 2 3 2 +Neurotoxins 4 4 1 +Neurotransmitter Agents 4 4 2 +Neurotransmitter Transport Proteins 5 5 2 +Neurotransmitter Uptake Inhibitors 5 5 3 +Neurotrophin 3 4 5 4 +Neurovascular Coupling 5 5 1 +Neurturin 5 6 4 +Neurulation 6 6 1 +Neutral Ceramidase 6 6 1 +Neutral Glycosphingolipids 4 5 3 +Neutral Red 5 5 1 +Neutralization Tests 5 6 3 +Neutron Activation Analysis 4 4 1 +Neutron Capture Therapy 4 4 1 +Neutron Diffraction 2 4 4 +Neutrons 4 4 1 +Neutropenia 6 6 2 +Neutrophil Activation 2 2 1 +Neutrophil Infiltration 2 2 1 +Neutrophils 3 6 6 +Nevada 6 6 1 +Nevi and Melanomas 3 3 1 +Nevirapine 4 4 1 +Nevus 4 4 1 +Nevus of Ota 6 6 1 +Nevus, Blue 6 6 1 +Nevus, Epithelioid and Spindle Cell 7 7 1 +Nevus, Halo 5 5 1 +Nevus, Intradermal 5 5 1 +Nevus, Pigmented 5 5 1 +Nevus, Sebaceous of Jadassohn 3 5 3 +Nevus, Spindle Cell 6 6 1 +New Brunswick 5 5 1 +New Caledonia 5 5 2 +New England 5 5 1 +New Guinea 3 3 1 +New Hampshire 6 6 1 +New Jersey 6 6 1 +New Mexico 6 6 1 +New Orleans 3 7 2 +New South Wales 4 5 2 +New York 6 6 3 +New York City 3 7 3 +New Zealand 4 4 3 +Newcastle Disease 3 7 2 +Newcastle disease virus 8 8 1 +Newfoundland and Labrador 5 5 1 +News 2 2 2 +Newspaper Article 2 2 1 +Newspapers as Topic 6 6 1 +NF-E2 Transcription Factor 5 5 4 +NF-E2 Transcription Factor, p45 Subunit 6 6 4 +NF-E2-Related Factor 1 5 5 2 +NF-E2-Related Factor 2 5 5 2 +NF-kappa B 3 4 4 +NF-kappa B p50 Subunit 5 5 3 +NF-kappa B p52 Subunit 5 5 5 +NF-KappaB Inhibitor alpha 5 5 4 +NF-kappaB-Inducing Kinase 5 8 2 +NFATC Transcription Factors 4 4 1 +NFI Transcription Factors 4 4 2 +NG-Nitroarginine Methyl Ester 5 5 2 +Niacin 4 5 2 +Niacinamide 4 5 2 +Nialamide 4 5 2 +Nicaragua 4 4 1 +Nicarbazin 4 8 5 +Nicardipine 5 5 1 +Nicergoline 5 5 2 +Niceritrol 4 5 2 +Nickel 4 4 3 +Niclofolan 4 8 3 +Niclosamide 5 6 3 +Nicolau Syndrome 5 5 2 +Nicorandil 3 6 3 +Nicotiana 9 9 1 +Nicotinamidase 5 5 1 +Nicotinamide Mononucleotide 4 4 1 +Nicotinamide N-Methyltransferase 6 6 1 +Nicotinamide Phosphoribosyltransferase 6 6 1 +Nicotinamide-Nucleotide Adenylyltransferase 5 6 3 +Nicotinate-Nucleotide Diphosphorylase (Carboxylating) 6 6 1 +Nicotine 4 4 2 +Nicotine Chewing Gum 3 6 5 +Nicotine Replacement Therapy 3 3 1 +Nicotinic Acids 3 4 2 +Nicotinic Agonists 7 7 2 +Nicotinic Antagonists 7 7 2 +Nicotinyl Alcohol 4 4 1 +Nictitating Membrane 2 2 1 +Nidovirales 4 4 1 +Nidovirales Infections 4 4 1 +Niemann-Pick C1 Protein 5 6 5 +Niemann-Pick Disease, Type A 6 9 10 +Niemann-Pick Disease, Type B 6 9 10 +Niemann-Pick Disease, Type C 6 9 10 +Niemann-Pick Diseases 5 8 10 +Nifedipine 5 5 1 +Niflumic Acid 4 10 4 +Nifuratel 4 6 3 +Nifurtimox 4 5 4 +Nigella 9 9 1 +Nigella damascena 10 10 1 +Nigella sativa 10 10 1 +Niger 5 5 1 +Nigeria 5 5 1 +Nigericin 4 5 6 +Night Blindness 3 3 1 +Night Care 3 4 2 +Night Eating Syndrome 3 3 1 +Night Terrors 5 5 2 +Night Vision 3 5 3 +NIH 3T3 Cells 5 5 2 +Nijmegen Breakage Syndrome 4 4 1 +Nikethamide 5 6 2 +NIMA-Interacting Peptidylprolyl Isomerase 6 6 1 +NIMA-Related Kinase 1 5 9 3 +NIMA-Related Kinases 4 8 3 +Nimaviridae 3 3 1 +Nimodipine 5 5 2 +Nimorazole 4 6 2 +Nimustine 4 5 2 +Ninhydrin 5 8 2 +Niobium 4 4 3 +Nipah Virus 8 8 1 +Nipecotic Acids 3 4 2 +Nipple Aspirate Fluid 4 4 1 +Nipple Discharge 3 3 1 +Nipples 3 3 1 +Nippostrongylus 9 9 1 +Niridazole 4 5 3 +Nisin 4 6 4 +Nisoldipine 5 5 1 +Nissl Bodies 3 10 5 +Nitella 6 6 1 +Nitracrine 6 6 1 +Nitrate Reductase 6 6 1 +Nitrate Reductase (NAD(P)H) 4 7 4 +Nitrate Reductase (NADH) 4 6 2 +Nitrate Reductase (NADPH) 4 7 4 +Nitrate Reductases 5 5 1 +Nitrate Transporters 4 7 3 +Nitrates 2 5 3 +Nitrazepam 7 7 1 +Nitrendipine 5 5 1 +Nitrergic Neurons 3 3 2 +Nitric Acid 3 4 2 +Nitric Oxide 4 5 5 +Nitric Oxide Donors 4 5 2 +Nitric Oxide Synthase 6 6 1 +Nitric Oxide Synthase Type I 7 7 1 +Nitric Oxide Synthase Type II 5 7 3 +Nitric Oxide Synthase Type III 7 7 1 +Nitrification 4 4 3 +Nitriles 2 2 1 +Nitrilotriacetic Acid 5 5 1 +Nitrite Reductase (NAD(P)H) 6 6 1 +Nitrite Reductases 5 5 1 +Nitrites 2 5 3 +Nitro Compounds 2 2 1 +Nitroanisole O-Demethylase 5 5 1 +Nitroarginine 5 5 2 +Nitrobacter 5 6 2 +Nitrobenzenes 3 6 2 +Nitrobenzoates 5 7 2 +Nitroblue Tetrazolium 6 6 1 +Nitrofurans 3 4 2 +Nitrofurantoin 4 5 2 +Nitrofurazone 4 5 2 +Nitrogen 3 3 2 +Nitrogen Compounds 2 2 1 +Nitrogen Cycle 3 3 3 +Nitrogen Dioxide 4 5 3 +Nitrogen Fixation 2 4 6 +Nitrogen Isotopes 3 4 3 +Nitrogen Mustard Compounds 5 5 1 +Nitrogen Oxides 3 4 3 +Nitrogen Radioisotopes 4 5 4 +Nitrogen-Fixing Bacteria 2 2 1 +Nitrogenase 4 4 1 +Nitrogenous Group Transferases 4 4 1 +Nitroglycerin 3 3 1 +Nitrohydroxyiodophenylacetate 4 8 3 +Nitroimidazoles 3 5 2 +Nitromifene 4 4 1 +Nitroparaffins 3 5 2 +Nitrophenols 3 7 2 +Nitrophenylgalactosides 4 4 1 +Nitroprusside 5 7 3 +Nitroquinolines 3 5 2 +Nitroreductases 4 4 1 +Nitrosamines 3 3 1 +Nitrosation 2 3 3 +Nitrosative Stress 3 4 2 +Nitroso Compounds 2 2 1 +Nitrosoguanidines 3 4 2 +Nitrosomethylurethane 3 6 2 +Nitrosomonadaceae 4 5 2 +Nitrosomonas 5 6 2 +Nitrosomonas europaea 6 7 2 +Nitrosourea Compounds 3 4 2 +Nitrous Acid 3 4 2 +Nitrous Oxide 4 5 3 +Nitrovin 4 5 2 +Nitroxinil 4 8 2 +Nivolumab 9 9 3 +Nizatidine 4 5 2 +NK Cell Lectin-Like Receptor Subfamily A 5 8 3 +NK Cell Lectin-Like Receptor Subfamily B 8 8 1 +NK Cell Lectin-Like Receptor Subfamily C 8 8 1 +NK Cell Lectin-Like Receptor Subfamily D 8 8 1 +NK Cell Lectin-Like Receptor Subfamily K 8 8 1 +NLR Family, Pyrin Domain-Containing 3 Protein 5 5 1 +NLR Proteins 4 4 1 +NM23 Nucleoside Diphosphate Kinases 7 7 1 +No-Observed-Adverse-Effect Level 3 4 2 +No-Reflow Phenomenon 4 4 1 +No-Show Patients 3 7 5 +Nobel Prize 3 3 1 +Nobelium 4 6 5 +Noble Gases 3 3 2 +Nocardia 5 5 1 +Nocardia asteroides 6 6 1 +Nocardia Infections 6 6 1 +Nocardiaceae 4 4 1 +Nocardioides 4 7 2 +Nocardiopsis 4 7 2 +Nocebo Effect 6 6 2 +Nociceptin 5 6 2 +Nociceptin Receptor 7 8 3 +Nociception 5 5 1 +Nociceptive Pain 5 5 3 +Nociceptors 4 5 3 +Nociplastic Pain 5 5 3 +Nocodazole 5 5 1 +Nocturia 5 5 1 +Nocturnal Enuresis 4 7 5 +Nocturnal Myoclonus Syndrome 4 5 2 +Nocturnal Paroxysmal Dystonia 4 4 2 +Nod Signaling Adaptor Proteins 5 5 4 +Nod1 Signaling Adaptor Protein 6 6 9 +Nod2 Signaling Adaptor Protein 6 6 9 +Nodal Protein 5 6 3 +Nodal Signaling Ligands 4 5 3 +Nodaviridae 3 4 2 +Nodding Syndrome 6 6 1 +Nodose Ganglion 4 6 6 +Nodularia 3 5 3 +Nogalamycin 6 9 3 +Noggin Protein 3 4 3 +Nogo Proteins 5 5 2 +Nogo Receptor 1 5 7 6 +Nogo Receptor 2 5 7 6 +Nogo Receptors 4 6 6 +Noise 3 5 4 +Noise, Occupational 4 5 2 +Noise, Transportation 4 5 2 +Noma 3 3 1 +Nomifensine 5 5 1 +Nomograms 2 6 6 +Non-alcoholic Fatty Liver Disease 4 4 1 +Non-Erosive Reflux Disease 7 7 1 +Non-Fibrillar Collagens 6 6 1 +Non-Filarial Lymphedema 4 4 1 +Non-Fungible Tokens 4 6 6 +Non-Medical Prescribing 2 5 3 +Non-Medical Public and Private Facilities 1 1 1 +Non-Melanoma Skin Neoplasms 4 5 3 +Non-Muscle Invasive Bladder Neoplasms 5 7 9 +Non-Neuronal Cholinergic System 3 4 3 +Non-Nutritive Sweeteners 7 8 3 +Non-Point Source Pollution 5 5 1 +Non-Radiographic Axial Spondyloarthritis 5 8 3 +Non-Randomized Controlled Trials as Topic 7 8 3 +Non-Smokers 2 2 1 +Non-ST Elevated Myocardial Infarction 5 6 4 +Nonachlazine 4 5 2 +Nonagenarians 6 6 1 +Noncommunicable Diseases 4 4 1 +Nondisjunction, Genetic 3 5 3 +Nonheme Iron Proteins 5 5 2 +Noninvasive Prenatal Testing 4 8 10 +Noninvasive Ventilation 4 4 2 +Nonlinear Dynamics 3 3 2 +Nonlinear Optical Microscopy 3 5 2 +Nonmuscle Myosin Type IIA 7 9 4 +Nonmuscle Myosin Type IIB 7 9 4 +Nonodontogenic Cysts 4 5 3 +Nonoxynol 4 6 4 +Nonprescription Drugs 2 2 1 +Nonsense Mediated mRNA Decay 3 4 4 +Nonsteroidal Anti-Androgens 4 7 2 +Nontherapeutic Human Experimentation 3 6 2 +Nontuberculous Mycobacteria 6 8 2 +Nonverbal Communication 3 4 2 +Noonan Syndrome 3 5 6 +Nootropic Agents 5 5 1 +Norandrostanes 5 5 1 +Norbornanes 4 6 2 +Nordazepam 8 8 1 +Nordefrin 5 10 2 +Nordic Walking 4 7 4 +Norepinephrine 4 9 5 +Norepinephrine Plasma Membrane Transport Proteins 6 8 6 +Norethandrolone 7 7 1 +Norethindrone 7 7 1 +Norethindrone Acetate 8 8 1 +Norethynodrel 7 7 1 +Norfenfluramine 6 6 1 +Norfloxacin 8 8 1 +Norgestrel 7 7 1 +Norgestrienone 7 7 1 +Norisoprenoids 5 9 3 +Norleucine 4 4 1 +Normal Distribution 3 6 4 +Normetanephrine 4 10 6 +Norovirus 5 5 1 +Norpregnadienes 6 6 1 +Norpregnanes 5 5 1 +Norpregnatrienes 6 6 1 +Norpregnenes 6 6 1 +Norprogesterones 7 7 1 +Norsteroids 4 4 1 +North African People 4 4 2 +North America 3 3 1 +North American People 3 3 1 +North Asian People 4 4 1 +North Carolina 6 6 2 +North Dakota 6 6 1 +North Sea 4 4 1 +Northern Ireland 4 4 1 +Northern Territory 4 5 2 +Northwest Territories 5 5 1 +Northwestern United States 5 5 1 +Nortriptyline 5 8 2 +Nortropanes 4 6 3 +Norwalk virus 6 6 1 +Norway 4 4 1 +Norwood Procedures 4 4 2 +Noscapine 4 5 2 +Nose 2 4 3 +Nose Deformities, Acquired 3 3 2 +Nose Diseases 2 2 2 +Nose Neoplasms 3 5 7 +Nosema 7 7 1 +Nostoc 3 5 2 +Nostoc commune 3 6 3 +Nostoc muscorum 3 6 3 +Nostrums 2 2 1 +Not-For-Profit Insurance Plans 6 6 1 +Notochord 2 2 1 +Notophthalmus 8 8 1 +Notophthalmus viridescens 9 9 1 +Nova Scotia 5 5 1 +Novirhabdovirus 6 6 1 +Novobiocin 3 7 3 +Noxae 3 3 1 +Noxythiolin 4 5 2 +Nuchal Cord 3 4 2 +Nuchal Translucency Measurement 6 6 2 +Nuclear Bodies 7 7 1 +Nuclear Cap-Binding Protein Complex 6 6 2 +Nuclear Energy 2 4 2 +Nuclear Envelope 5 6 4 +Nuclear Export Signals 4 6 2 +Nuclear Factor 45 Protein 5 6 2 +Nuclear Factor 90 Proteins 4 5 2 +Nuclear Family 5 7 6 +Nuclear Fission 3 5 2 +Nuclear Fusion 3 5 2 +Nuclear Lamina 6 8 4 +Nuclear Localization Signals 4 6 2 +Nuclear Magnetic Resonance, Biomolecular 5 5 1 +Nuclear Matrix 7 7 1 +Nuclear Matrix-Associated Proteins 4 4 1 +Nuclear Medicine 4 4 1 +Nuclear Medicine Department, Hospital 5 5 1 +Nuclear Microscopy 3 5 2 +Nuclear Pharmacy 3 3 1 +Nuclear Physics 3 3 1 +Nuclear Pore 7 7 1 +Nuclear Pore Complex Proteins 6 6 2 +Nuclear Power Plants 3 4 3 +Nuclear Proteins 3 3 1 +Nuclear Reactors 3 3 1 +Nuclear Receptor Co-Repressor 1 6 6 1 +Nuclear Receptor Co-Repressor 2 6 6 1 +Nuclear Receptor Coactivator 1 5 8 8 +Nuclear Receptor Coactivator 2 5 6 5 +Nuclear Receptor Coactivator 3 5 8 6 +Nuclear Receptor Coactivators 4 5 5 +Nuclear Receptor Interacting Protein 1 4 5 4 +Nuclear Receptor Subfamily 1, Group D, Member 1 5 5 3 +Nuclear Receptor Subfamily 1, Group F, Member 1 5 5 3 +Nuclear Receptor Subfamily 1, Group F, Member 2 5 5 3 +Nuclear Receptor Subfamily 1, Group F, Member 3 5 5 2 +Nuclear Receptor Subfamily 2, Group C, Member 1 5 5 2 +Nuclear Receptor Subfamily 2, Group C, Member 2 5 5 2 +Nuclear Receptor Subfamily 4, Group A, Member 1 5 5 2 +Nuclear Receptor Subfamily 4, Group A, Member 2 5 5 2 +Nuclear Receptor Subfamily 4, Group A, Member 3 5 5 2 +Nuclear Receptor Subfamily 6, Group A, Member 1 5 5 2 +Nuclear Respiratory Factor 1 5 5 2 +Nuclear Respiratory Factors 4 4 2 +Nuclear Speckles 8 8 1 +Nuclear Transfer Techniques 3 5 3 +Nuclear Warfare 6 6 1 +Nuclear Weapons 5 5 2 +Nuclease Protection Assays 3 3 1 +Nucleic Acid Amplification Techniques 3 3 1 +Nucleic Acid Conformation 3 5 2 +Nucleic Acid Denaturation 2 3 3 +Nucleic Acid Heteroduplexes 3 3 1 +Nucleic Acid Hybridization 3 3 2 +Nucleic Acid Precursors 2 2 1 +Nucleic Acid Probes 3 5 3 +Nucleic Acid Renaturation 3 3 1 +Nucleic Acid Synthesis Inhibitors 5 5 1 +Nucleic Acid-Based Vaccines 4 5 3 +Nucleic Acids 2 2 1 +Nucleic Acids, Nucleotides, and Nucleosides 1 1 1 +Nucleobase Transport Proteins 6 6 2 +Nucleobase, Nucleoside, Nucleotide, and Nucleic Acid Transport Proteins 5 5 2 +Nucleobindins 4 5 2 +Nucleocapsid 3 3 1 +Nucleocapsid Proteins 5 5 1 +Nucleocytoplasmic Transport Proteins 5 5 2 +Nucleolin 4 5 3 +Nucleolus Organizer Region 4 9 3 +Nucleons 3 3 1 +Nucleophosmin 4 4 1 +Nucleoplasmins 5 5 1 +Nucleopolyhedroviruses 4 4 2 +Nucleoproteins 3 3 1 +Nucleoside Deaminases 5 5 1 +Nucleoside Diphosphate Kinase D 4 8 2 +Nucleoside Diphosphate Sugars 4 4 3 +Nucleoside Q 5 7 3 +Nucleoside Transport Proteins 6 6 2 +Nucleoside-Diphosphate Kinase 6 6 1 +Nucleoside-Phosphate Kinase 6 6 1 +Nucleoside-Triphosphatase 5 5 1 +Nucleosides 2 3 2 +Nucleosome Assembly Protein 1 5 5 1 +Nucleosomes 5 10 3 +Nucleotidases 6 6 1 +Nucleotide Deaminases 5 5 1 +Nucleotide Mapping 3 6 3 +Nucleotide Motifs 4 6 4 +Nucleotide Transport Proteins 6 6 2 +Nucleotides 2 3 2 +Nucleotides, Cyclic 3 3 1 +Nucleotidyltransferases 5 5 1 +Nucleus Accumbens 10 10 1 +Nucleus Pulposus 5 6 3 +Nucleus Raphe Magnus 10 10 1 +Nucleus Raphe Obscurus 9 9 1 +Nucleus Raphe Pallidus 9 9 1 +Nudism 2 2 1 +Nudiviridae 3 3 2 +Nudix Hydrolases 6 6 1 +Numbers Needed To Treat 5 5 1 +Numerical Analysis, Computer-Assisted 4 4 1 +Numismatics 3 3 1 +Nunavut 5 5 1 +Nuns 4 4 1 +Nuphar 8 8 1 +Nurse Administrators 4 5 3 +Nurse Anesthetists 4 6 4 +Nurse Clinicians 5 6 2 +Nurse Midwives 5 6 2 +Nurse Practitioners 4 5 2 +Nurse Specialists 4 5 2 +Nurse's Role 4 6 2 +Nurse-Patient Relations 4 5 2 +Nurseries, Hospital 4 4 1 +Nurseries, Infant 3 5 3 +Nurses 3 4 2 +Nurses Improving Care for Health System Elders 3 3 1 +Nurses Instruction 2 2 1 +Nurses, Community Health 4 5 2 +Nurses, International 4 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+Obesity, Metabolically Benign 6 6 2 +Obesity, Morbid 6 6 2 +Obidoxime Chloride 5 5 2 +Object Attachment 4 4 1 +Observation 3 3 1 +Observational Studies as Topic 5 6 3 +Observational Studies, Veterinary as Topic 5 6 3 +Observational Study 3 3 1 +Observational Study, Veterinary 3 3 1 +Observer Variation 3 5 4 +Obsessive Behavior 4 4 1 +Obsessive-Compulsive Disorder 3 3 1 +Obstetric Labor Complications 4 4 1 +Obstetric Labor, Premature 5 5 1 +Obstetric Nursing 4 4 2 +Obstetric Surgical Procedures 2 2 1 +Obstetrical Forceps 4 4 1 +Obstetricians 4 5 2 +Obstetrics 4 4 1 +Obstetrics and Gynecology Department, Hospital 6 6 2 +Obturator Nerve 6 6 1 +Occipital Bone 5 5 1 +Occipital Lobe 8 8 1 +Occludin 5 5 2 +Occlusal Adjustment 4 4 1 +Occlusal Splints 5 5 1 +Occlusion Bodies, Viral 2 2 1 +Occlusion Body Matrix Proteins 5 5 1 +Occlusive Dressings 3 3 1 +Occult Blood 3 4 2 +Occultism 2 2 1 +Occupational Dentistry 3 3 1 +Occupational Diseases 1 1 1 +Occupational Exposure 5 5 1 +Occupational Groups 2 2 1 +Occupational Health 3 3 1 +Occupational Health Nursing 4 4 2 +Occupational Health Physicians 4 5 2 +Occupational Health Services 4 4 1 +Occupational Injuries 2 2 1 +Occupational Medicine 5 5 1 +Occupational Stress 2 5 3 +Occupational Therapists 3 4 2 +Occupational Therapy 3 6 3 +Occupational Therapy Department, Hospital 6 6 2 +Occupations 3 3 1 +Ocean Acidification 3 3 1 +Oceania 2 2 1 +Oceanians 3 3 1 +Oceanography 3 3 1 +Oceanospirillaceae 3 4 2 +Oceans and Seas 2 6 3 +Ochlerotatus 13 13 1 +Ochnaceae 7 7 1 +Ochratoxins 4 7 3 +Ochrobactrum 5 5 2 +Ochrobactrum anthropi 6 6 2 +Ochromonas 4 4 1 +Ochronosis 3 3 1 +Ochrosia 9 9 1 +Ocimum 9 9 1 +Ocimum basilicum 10 10 1 +Ocimum sanctum 10 10 1 +Ocotea 9 9 1 +Octamer Transcription Factor-1 6 6 2 +Octamer Transcription Factor-2 6 6 2 +Octamer Transcription Factor-3 6 6 2 +Octamer Transcription Factor-6 6 6 2 +Octamer Transcription Factors 5 5 2 +Octanes 5 5 1 +Octanols 3 4 2 +Octodon 8 8 1 +Octogenarians 6 6 1 +Octopamine 5 6 4 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+Ofloxacin 8 8 1 +Ohio 6 6 3 +Oil and Gas Fields 3 3 1 +Oil and Gas Industry 5 5 1 +Oils 2 2 1 +Oils, Volatile 3 3 1 +Ointment Bases 3 4 2 +Ointments 3 3 1 +Okadaic Acid 4 6 7 +Oklahoma 6 6 1 +Olacaceae 7 7 1 +Olanzapine 6 6 1 +Old Age Assistance 6 6 1 +Oldenlandia 9 9 1 +Olea 9 9 1 +Oleaceae 8 8 1 +Oleandomycin 4 4 1 +Oleanolic Acid 6 6 2 +Oleavirus 4 5 2 +Olecranon Fracture 4 5 4 +Olecranon Process 7 7 1 +Oleic Acid 6 6 1 +Oleic Acids 5 5 1 +Olfaction Disorders 4 5 2 +Olfactometry 2 4 2 +Olfactory Bulb 6 6 1 +Olfactory Cortex 7 9 2 +Olfactory Marker Protein 4 4 3 +Olfactory Mucosa 3 6 4 +Olfactory Nerve 5 5 1 +Olfactory Nerve Diseases 3 3 1 +Olfactory Nerve Injuries 4 5 4 +Olfactory Pathways 4 5 2 +Olfactory Perception 4 4 1 +Olfactory Receptor Neurons 4 7 7 +Olfactory Training 2 2 1 +Olfactory Tubercle 8 10 3 +Oligo-1,6-Glucosidase 5 5 1 +Oligochaeta 5 5 1 +Oligoclonal Bands 3 7 4 +Oligodendrocyte Precursor Cells 3 4 3 +Oligodendrocyte Transcription Factor 2 4 5 3 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Nursing 4 4 2 +Oncology Service, Hospital 6 6 2 +Oncolytic Virotherapy 3 3 1 +Oncolytic Viruses 2 2 1 +Oncorhynchus 9 9 2 +Oncorhynchus keta 10 10 1 +Oncorhynchus kisutch 10 10 1 +Oncorhynchus mykiss 10 10 1 +Oncostatin M 4 5 3 +Oncostatin M Receptor beta Subunit 9 9 1 +Ondansetron 5 6 3 +One Health 3 3 1 +One-Carbon Group Transferases 4 4 1 +One-Lung Ventilation 4 4 1 +Onecut Transcription Factors 4 5 2 +Onions 11 11 1 +Onium Compounds 2 2 1 +Online Social Networking 4 4 1 +Online Systems 6 6 1 +Only Child 4 5 2 +Ononis 8 8 1 +Onopordum 8 8 1 +Ontario 5 5 1 +Onycholysis 4 4 1 +Onychomycosis 4 6 4 +Onygenales 4 4 1 +Oocysts 3 6 4 +Oocyte Donation 4 4 2 +Oocyte Retrieval 4 4 3 +Oocytes 4 5 2 +Oogenesis 4 5 2 +Oogonia 4 5 2 +Oogonial Stem Cells 3 3 1 +Oomycetes 3 3 1 +Oophoritis 4 7 5 +Open Abdomen Techniques 4 4 1 +Open Access Publishing 3 3 1 +Open Bite 4 4 1 +Open Field Test 6 6 1 +Open Fracture Reduction 4 4 2 +Open Reading Frames 5 7 2 +Open Waste Burning 4 8 2 +Operating Room Information Systems 5 6 2 +Operating Room Nursing 5 5 2 +Operating Room Technicians 4 5 2 +Operating Rooms 4 4 1 +Operating Tables 3 3 2 +Operations Research 3 4 2 +Operative Blood Salvage 4 4 1 +Operative Time 4 6 2 +Operator Regions, Genetic 5 7 4 +Operon 5 6 2 +Ophiophagus hannah 7 9 3 +Ophiopogon 10 10 1 +Ophiostoma 5 5 1 +Ophiostomatales 4 4 1 +Ophthalmia Neonatorum 3 6 6 +Ophthalmia, Sympathetic 3 5 2 +Ophthalmic Artery 4 4 1 +Ophthalmic Assistants 5 6 2 +Ophthalmic Nerve 6 6 1 +Ophthalmic Solutions 4 5 3 +Ophthalmodynamometry 4 4 1 +Ophthalmologic Surgical Procedures 2 2 1 +Ophthalmologists 4 5 2 +Ophthalmology 4 4 1 +Ophthalmoplegia 3 5 4 +Ophthalmoplegia, Chronic Progressive External 4 6 8 +Ophthalmoplegic Migraine 3 7 7 +Ophthalmoscopes 3 3 1 +Ophthalmoscopy 4 4 1 +Opiate Alkaloids 3 3 1 +Opiate Overdose 5 6 4 +Opiate Substitution Treatment 3 3 1 +Opioid Epidemic 5 5 1 +Opioid Peptides 4 5 2 +Opioid-Induced Constipation 4 5 3 +Opioid-Related Disorders 4 4 2 +Opipramol 5 5 1 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Decarboxylase 6 6 1 +Orphan Drug Production 5 5 1 +Orphan Nuclear Receptors 4 4 3 +Orphanages 3 3 1 +Orphenadrine 4 7 2 +ortho-Aminobenzoates 6 8 2 +Orthobunyavirus 5 5 1 +Orthodontic Anchorage Procedures 3 3 1 +Orthodontic Appliance Design 3 3 3 +Orthodontic Appliances 3 3 1 +Orthodontic Appliances, Fixed 4 4 1 +Orthodontic Appliances, Functional 4 4 1 +Orthodontic Appliances, Removable 4 4 1 +Orthodontic Brackets 5 5 1 +Orthodontic Extrusion 4 4 1 +Orthodontic Friction 4 4 1 +Orthodontic Retainers 4 4 1 +Orthodontic Space Closure 4 4 1 +Orthodontic Wires 4 4 1 +Orthodontics 2 4 2 +Orthodontics, Corrective 3 3 1 +Orthodontics, Interceptive 3 3 1 +Orthodontics, Preventive 3 3 1 +Orthodontists 5 6 2 +Orthognathic Surgery 3 5 3 +Orthognathic Surgical Procedures 3 4 3 +Orthohantavirus 5 5 1 +Orthohepadnavirus 4 4 2 +Orthokeratologic Procedures 2 2 1 +Orthomolecular Therapy 3 3 2 +Orthomyxoviridae 4 4 1 +Orthomyxoviridae Infections 4 4 1 +Orthopedic Equipment 3 3 1 +Orthopedic Fixation Devices 4 4 2 +Orthopedic Nursing 4 4 2 +Orthopedic Procedures 2 2 2 +Orthopedic Surgeons 5 6 2 +Orthopedics 4 4 1 +Orthopoxvirus 5 5 1 +Orthopsychiatry 4 4 1 +Orthoptera 6 6 1 +Orthoptics 2 2 2 +Orthoreovirus 5 5 1 +Orthoreovirus, Avian 6 6 1 +Orthoreovirus, Mammalian 6 6 1 +Orthorexia Nervosa 3 3 1 +Orthosiphon 9 9 1 +Orthostatic Intolerance 4 4 2 +Orthotic Devices 4 4 1 +Orycteropodidae 8 8 1 +Oryza 8 8 1 +Oryzias 8 8 1 +Oscillatoria 3 5 2 +Oscillometry 2 2 1 +Oseltamivir 4 8 2 +Osmeriformes 6 6 1 +Osmium 4 4 3 +Osmium Compounds 2 2 1 +Osmium Tetroxide 3 4 2 +Osmolar Concentration 2 2 1 +Osmometry 3 3 1 +Osmoregulation 2 3 4 +Osmosis 2 4 4 +Osmotic Fragility 3 5 3 +Osmotic Pressure 3 4 3 +Osseointegration 5 5 2 +Ossicular Prosthesis 3 3 1 +Ossicular Replacement 3 4 2 +Ossification of Posterior Longitudinal Ligament 4 4 2 +Ossification, Heterotopic 3 3 1 +Osteitis 3 3 1 +Osteitis Deformans 3 3 1 +Osteitis Fibrosa Cystica 4 4 1 +Osteoarthritis 3 4 2 +Osteoarthritis, Hip 4 5 2 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2 +Osteosclerosis 5 5 1 +Osteotomy 3 3 1 +Osteotomy, Le Fort 3 4 3 +Osteotomy, Sagittal Split Ramus 3 4 3 +Ostertagia 9 9 1 +Ostertagiasis 8 8 1 +Ostomy 2 2 1 +Ostracism 5 5 2 +Ostrea 7 7 1 +Ostreidae 6 6 1 +Otitis 3 3 1 +Otitis Externa 4 4 1 +Otitis Media 4 4 1 +Otitis Media with Effusion 5 5 1 +Otitis Media, Suppurative 3 5 2 +Otoacoustic Emissions, Spontaneous 4 5 2 +Otolaryngologists 4 5 2 +Otolaryngology 4 4 1 +Otolithic Membrane 6 7 2 +Otologic Surgical Procedures 3 3 1 +Otomycosis 3 4 2 +Otorhinolaryngologic Diseases 1 1 1 +Otorhinolaryngologic Neoplasms 2 4 2 +Otorhinolaryngologic Surgical Procedures 2 2 1 +Otosclerosis 3 3 1 +Otoscopes 3 3 1 +Otoscopy 4 4 1 +Ototoxicity 3 5 5 +Otters 10 10 1 +Ottoman Empire 3 3 1 +Otx Transcription Factors 4 5 2 +Ouabain 5 8 2 +Out-of-Hospital Cardiac Arrest 4 4 1 +Outcome and Process Assessment, Health Care 3 4 2 +Outcome Assessment, Health Care 4 5 3 +Outcome Expectations 2 2 1 +Outliers, DRG 8 8 1 +Outline 2 2 1 +Outpatient Clinics, Hospital 4 6 3 +Outpatients 3 3 1 +Outsourced Services 5 5 1 +Oval Window, Ear 4 5 2 +Ovalbumin 4 6 5 +Ovarian Cysts 3 7 4 +Ovarian Diseases 3 6 3 +Ovarian Follicle 5 6 2 +Ovarian Function Tests 4 4 2 +Ovarian Hyperstimulation Syndrome 4 7 3 +Ovarian Neoplasms 3 7 7 +Ovarian Reserve 3 4 2 +Ovarian Torsion 4 7 4 +Ovariectomy 4 4 3 +Ovary 4 5 3 +Overall 2 3 2 +Overbite 5 5 1 +Overdiagnosis 2 3 3 +Overlapping Surgery 2 2 1 +Overlearning 4 4 1 +Overnutrition 3 3 1 +Overtraining Syndrome 4 4 1 +Overtreatment 5 6 2 +Overweight 4 7 5 +Oviducts 2 2 1 +Oviparity 3 3 1 +Oviposition 4 4 1 +Ovomucin 4 6 4 +Ovotesticular Disorders of Sex Development 4 6 5 +Ovoviviparity 3 3 1 +Ovulation 4 4 1 +Ovulation Detection 3 5 3 +Ovulation Induction 4 4 2 +Ovulation Inhibition 4 5 2 +Ovulation Prediction 3 5 3 +Ovule 6 6 1 +Ovum 2 4 3 +Ovum Transport 3 5 2 +Ownership 3 5 2 +OX40 Ligand 4 6 7 +Oxacillin 5 6 3 +Oxadiazoles 5 5 1 +Oxalates 5 5 1 +Oxalic Acid 6 6 1 +Oxalidaceae 7 7 1 +Oxaliplatin 3 3 1 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Depletion 3 6 2 +P Blood-Group System 5 5 2 +p-Aminoazobenzene 3 4 2 +p-Aminohippuric Acid 6 9 6 +p-Azobenzenearsonate 3 5 3 +p-Chloroamphetamine 6 6 1 +p-Chloromercuribenzoic Acid 7 9 5 +p-Dimethylaminoazobenzene 4 4 1 +p-Fluorophenylalanine 6 6 1 +p-Hydroxyamphetamine 6 6 1 +p-Hydroxynorephedrine 6 6 3 +p-Methoxy-N-methylphenethylamine 5 5 1 +P-Selectin 5 7 8 +P-type ATPases 5 6 3 +p120 GTPase Activating Protein 7 7 2 +p21-Activated Kinases 5 8 2 +p300-CBP Transcription Factors 4 8 2 +p300-CBP-Associated Factor 5 9 2 +p38 Mitogen-Activated Protein Kinases 6 9 2 +Pacemaker, Artificial 4 4 1 +Pachyonychia Congenita 4 5 6 +Pachyrhizus 8 8 1 +Pachysandra 8 8 1 +Pachytene Stage 6 7 4 +Pacific Island People 5 5 1 +Pacific Islands 3 4 3 +Pacific Ocean 3 3 1 +Pacific States 5 5 1 +Pacifiers 3 3 1 +Pacinian Corpuscles 5 6 3 +Paclitaxel 6 8 2 +Pactamycin 5 8 4 +Paecilomyces 4 4 1 +Paenibacillus 4 5 5 +Paenibacillus larvae 5 6 5 +Paenibacillus polymyxa 3 6 6 +Paeonia 7 7 1 +Paget Disease, Extramammary 5 6 2 +Paget's Disease, Mammary 6 7 2 +Pagetoid Reticulosis 8 9 3 +Pain 4 4 3 +Pain Clinics 5 7 3 +Pain Insensitivity, Congenital 3 4 2 +Pain Management 2 4 2 +Pain Measurement 5 5 1 +Pain Perception 4 4 1 +Pain Threshold 5 5 3 +Paint 3 3 1 +Paintings 3 3 1 +Pair Bond 6 6 1 +Paired Box Transcription Factors 4 4 2 +Paired-Associate Learning 5 5 1 +Pakistan 5 5 1 +Palaemonidae 7 7 1 +Palaeognathae 6 6 1 +Palaeoptera 7 7 1 +Palaquium 9 9 1 +Palatal Expansion Technique 4 4 1 +Palatal Muscles 4 4 2 +Palatal Neoplasms 4 6 6 +Palatal Obturators 4 4 2 +Palate 3 3 2 +Palate, Hard 4 7 3 +Palate, Soft 4 4 1 +Palatine Tonsil 3 5 4 +Palau 5 5 2 +Paleodontology 5 5 1 +Paleography 6 6 1 +Paleontology 3 4 2 +Paleopathology 5 5 1 +Palinuridae 7 7 1 +Paliperidone Palmitate 4 5 2 +Palivizumab 9 9 3 +Palladium 4 4 3 +Palliative Care 3 4 2 +Palliative Medicine 3 3 1 +Pallidotomy 3 3 1 +Pallister-Hall Syndrome 3 8 8 +Pallor 4 4 1 +Palm Oil 4 5 3 +Palmar Plate 4 6 4 +Palmitates 4 4 1 +Palmitic Acid 4 4 1 +Palmitic Acids 3 3 1 +Palmitoyl Coenzyme A 5 9 5 +Palmitoyl-CoA Hydrolase 6 6 1 +Palmitoylcarnitine 6 6 1 +Palonosetron 4 5 2 +Palpation 4 4 1 +Palyam Virus 6 6 1 +Pamidronate 5 5 1 +Pamphlets 5 5 1 +Pan American Health Organization 6 6 1 +Pan paniscus 11 11 1 +Pan troglodytes 11 11 1 +Panama 4 4 1 +Panama Canal Zone 3 5 2 +Panax 8 8 1 +Panax notoginseng 9 9 1 +Pancoast Syndrome 4 6 3 +Pancreas 2 2 1 +Pancreas Divisum 3 4 3 +Pancreas Transplantation 3 4 2 +Pancreas, Artificial 4 4 1 +Pancreas, Exocrine 3 3 2 +Pancreatectomy 3 3 1 +Pancreatic alpha-Amylases 7 7 1 +Pancreatic Cyst 3 3 2 +Pancreatic Diseases 2 2 1 +Pancreatic Ducts 3 3 1 +Pancreatic Elastase 7 7 2 +Pancreatic Extracts 3 3 1 +Pancreatic Fistula 3 5 3 +Pancreatic Function Tests 4 4 1 +Pancreatic Hormones 4 4 2 +Pancreatic Intraductal Neoplasms 4 5 6 +Pancreatic Juice 3 3 1 +Pancreatic Neoplasms 3 4 5 +Pancreatic Polypeptide 4 5 4 +Pancreatic Polypeptide-Secreting Cells 3 4 5 +Pancreatic Pseudocyst 4 4 2 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pantotrophus 6 6 2 +Paracrine Communication 3 3 1 +Paraduodenal Hernia 5 5 1 +Paraffin 3 3 1 +Paraffin Embedding 7 8 4 +Paraganglia, Chromaffin 3 3 1 +Paraganglia, Nonchromaffin 5 6 3 +Paraganglioma 6 6 2 +Paraganglioma, Extra-Adrenal 7 7 2 +Paragonimiasis 5 5 1 +Paragonimus 8 8 1 +Paragonimus westermani 9 9 1 +Paraguay 4 4 1 +Parahippocampal Gyrus 6 9 2 +Parainfluenza Vaccines 5 5 1 +Parainfluenza Virus 1, Human 8 8 1 +Parainfluenza Virus 2, Human 8 8 1 +Parainfluenza Virus 3, Bovine 8 8 1 +Parainfluenza Virus 3, Human 8 8 1 +Parainfluenza Virus 4, Human 8 8 1 +Parainfluenza Virus 5 8 8 1 +Parakeets 8 8 1 +Parakeratosis 4 4 1 +Paraldehyde 4 4 1 +Parallel Algorithms 3 4 2 +Paralyses, Familial Periodic 3 5 4 +Paralysis 3 4 2 +Paralysis, Hyperkalemic Periodic 4 6 4 +Paralysis, Obstetric 3 4 2 +Paramecium 7 7 1 +Paramecium aurelia 8 8 1 +Paramecium caudatum 8 8 1 +Paramecium tetraurelia 8 8 1 +Paramedicine 3 3 1 +Paramedics 4 5 3 +Paramethasone 5 7 2 +Parametritis 6 7 2 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Disease 3 6 3 +Pelvic Neoplasms 3 3 1 +Pelvic Organ Prolapse 4 4 1 +Pelvic Pain 5 5 3 +Pelvimetry 3 5 3 +Pelvis 3 3 1 +Pemetrexed 5 8 3 +Pemoline 5 5 1 +Pemphigoid Gestationis 4 4 2 +Pemphigoid, Benign Mucous Membrane 3 4 2 +Pemphigoid, Bullous 3 4 2 +Pemphigus 3 4 2 +Pemphigus, Benign Familial 4 4 3 +Pempidine 4 4 1 +Penaeidae 7 7 1 +Penbutolol 6 6 3 +Penetrance 3 3 2 +Penetrating Atherosclerotic Ulcer 3 5 3 +Penfluridol 4 4 1 +Penicillamine 4 4 2 +Penicillanic Acid 5 6 3 +Penicillic Acid 4 5 2 +Penicillin Amidase 5 5 1 +Penicillin G 5 6 3 +Penicillin G Benzathine 6 7 3 +Penicillin G Procaine 6 10 5 +Penicillin Resistance 5 8 3 +Penicillin V 5 6 3 +Penicillin-Binding Proteins 3 4 2 +Penicillinase 6 6 1 +Penicillins 4 5 3 +Penicillium 4 4 1 +Penicillium chrysogenum 5 5 1 +Peniculina 6 6 1 +Penile Diseases 4 4 2 +Penile Erection 4 4 1 +Penile Implantation 3 5 2 +Penile Induration 3 5 3 +Penile Neoplasms 4 5 7 +Penile Prosthesis 3 3 1 +Penile Transplantation 5 5 2 +Penis 4 4 1 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+Periapical Periodontitis 4 5 3 +Periapical Tissue 5 5 1 +Periaqueductal Gray 8 8 1 +Periarthritis 4 4 2 +Pericardial Effusion 3 3 1 +Pericardial Fluid 4 5 3 +Pericardial Window Techniques 3 4 3 +Pericardiectomy 4 4 2 +Pericardiocentesis 4 6 6 +Pericarditis 3 3 1 +Pericarditis, Constrictive 4 4 1 +Pericarditis, Tuberculous 4 10 4 +Pericardium 3 4 2 +Pericoronitis 5 5 1 +Pericytes 2 4 4 +Perilipin-1 6 6 1 +Perilipin-2 6 6 1 +Perilipin-3 6 6 1 +Perilipin-4 6 6 1 +Perilipin-5 6 6 1 +Perilipins 5 5 1 +Perilla 9 9 1 +Perilla frutescens 10 10 1 +Perilymph 5 5 1 +Perilymphatic Fistula 4 5 2 +Perimeningeal Infections 3 4 2 +Perimenopause 5 6 2 +Perinatal Care 3 5 4 +Perinatal Death 4 4 2 +Perinatal Mortality 6 8 4 +Perinatology 4 4 1 +Perindopril 5 5 1 +Perinephritis 4 6 3 +Perineum 2 2 1 +Perineuronal Nets 5 5 1 +Period Circadian Proteins 5 6 4 +Periodic Acid 3 4 2 +Periodic Acid-Schiff Reaction 5 7 6 +Periodical 2 2 1 +Periodical Index 2 2 1 +Periodicals as Topic 6 6 1 +Periodicity 3 3 2 +Periodization 4 7 2 +Periodontal Abscess 4 5 2 +Periodontal Atrophy 4 4 1 +Periodontal Attachment Loss 5 5 1 +Periodontal Cyst 4 6 4 +Periodontal Debridement 4 4 1 +Periodontal Diseases 3 3 1 +Periodontal Dressings 3 3 1 +Periodontal Index 3 8 6 +Periodontal Ligament 5 5 1 +Periodontal Pocket 5 5 1 +Periodontal Prosthesis 3 4 2 +Periodontal Splints 4 4 1 +Periodontics 2 4 2 +Periodontitis 4 4 1 +Periodontium 4 4 1 +Perioperative Care 2 4 3 +Perioperative Medicine 3 3 1 +Perioperative Nursing 4 5 4 +Perioperative Period 2 4 2 +Periosteum 4 4 1 +Periostin 5 5 1 +Periostitis 3 4 2 +Peripartum Cardiomyopathy 3 5 3 +Peripartum Period 3 3 1 +Peripheral Arterial Disease 4 6 2 +Peripheral Blood Stem Cell Transplantation 6 7 2 +Peripheral Blood Stem Cells 4 4 1 +Peripheral Nerve Injuries 3 4 3 +Peripheral Nerves 3 3 1 +Peripheral Nervous System 2 2 1 +Peripheral Nervous System Agents 4 4 1 +Peripheral Nervous System Diseases 3 3 1 +Peripheral Nervous System Neoplasms 3 4 3 +Peripheral Tolerance 5 5 1 +Peripheral Vascular Diseases 3 3 1 +Peripherins 5 5 1 +Periphyton 3 8 4 +Periplaneta 7 7 1 +Periplasm 4 4 1 +Periplasmic Binding Proteins 4 5 2 +Periplasmic Proteins 4 4 3 +Periploca 9 9 1 +Periprosthetic Fractures 3 3 1 +Perirhinal Cortex 9 9 1 +Perissodactyla 7 7 1 +Peristalsis 4 4 1 +Peritoneal Absorption 4 6 3 +Peritoneal Cavity 6 6 1 +Peritoneal Dialysis 4 4 2 +Peritoneal Dialysis, Continuous Ambulatory 4 5 4 +Peritoneal Diseases 2 2 1 +Peritoneal Fibrosis 3 4 2 +Peritoneal Lavage 3 3 1 +Peritoneal Neoplasms 3 4 4 +Peritoneal Stomata 2 6 2 +Peritoneovenous Shunt 3 4 3 +Peritoneum 4 5 2 +Peritonitis 3 3 2 +Peritonitis, Tuberculous 4 9 3 +Peritonsillar Abscess 4 5 5 +Perivascular Epithelioid Cell Neoplasms 4 4 1 +Periventricular Nodular Heterotopia 5 6 2 +Permafrost 4 6 5 +Permeability 2 2 1 +Permethrin 4 8 3 +Permissiveness 4 4 1 +Perna 7 7 1 +Peromyscus 11 11 1 +Peroneal Nerve 7 7 1 +Peroneal Neuropathies 5 5 1 +Peronospora 4 4 1 +Peroxidase 5 5 1 +Peroxidases 4 4 1 +Peroxidasin 5 6 2 +Peroxides 4 6 4 +Peroxins 4 4 1 +Peroxiredoxin III 4 6 2 +Peroxiredoxin VI 4 10 4 +Peroxiredoxins 5 5 1 +Peroxisomal Bifunctional Enzyme 4 8 4 +Peroxisomal Biogenesis Factor 2 4 5 2 +Peroxisomal Disorders 4 4 2 +Peroxisomal Multifunctional Protein-2 4 7 4 +Peroxisomal Targeting Signal 2 Receptor 5 5 1 +Peroxisomal Targeting Signals 4 6 2 +Peroxisome Proliferator-Activated Receptor Gamma Coactivator 1-alpha 5 6 7 +Peroxisome Proliferator-Activated Receptors 4 4 2 +Peroxisome Proliferators 5 5 1 +Peroxisome-Targeting Signal 1 Receptor 5 5 1 +Peroxisomes 8 10 2 +Peroxynitrous Acid 4 4 4 +Perphenazine 4 5 2 +Persea 9 9 1 +Persia 4 4 1 +Persian Gulf Syndrome 2 8 3 +Persistent Fetal Circulation Syndrome 3 4 2 +Persistent Hyperplastic Primary Vitreous 3 4 2 +Persistent Infection 2 4 2 +Persistent Left Superior Vena Cava 4 5 2 +Persistent Organic Pollutants 4 4 1 +Persistent Postural-Perceptual Dizziness 5 6 2 +Persistent Vegetative State 5 7 3 +Person-Centered Psychotherapy 3 3 1 +Personal Autonomy 2 6 5 +Personal Care Products 3 3 2 +Personal Construct Theory 3 3 1 +Personal Health Services 3 3 1 +Personal Narrative 3 4 3 +Personal Narratives as Topic 4 4 1 +Personal Protective Equipment 3 4 2 +Personal Satisfaction 3 3 1 +Personal Space 4 4 1 +Personal Trainers 3 5 2 +Personality 2 2 1 +Personality Assessment 2 2 1 +Personality Development 3 3 1 +Personality Disorders 2 2 1 +Personality Inventory 4 4 1 +Personality Tests 3 3 1 +Personally Identifiable Information 6 6 1 +Personhood 3 5 2 +Personnel Administration, Hospital 4 6 3 +Personnel Delegation 4 4 1 +Personnel Downsizing 4 4 2 +Personnel Loyalty 4 4 1 +Personnel Management 3 3 1 +Personnel Selection 4 4 1 +Personnel Staffing and Scheduling 3 4 2 +Personnel Staffing and Scheduling Information Systems 4 4 1 +Personnel Turnover 4 4 1 +Personnel, Hospital 3 4 2 +Persons 1 1 1 +Persons with Disabilities 2 2 1 +Persons with Hearing Disabilities 3 3 1 +Persons with Intellectual Disabilities 3 3 1 +Persons with Psychiatric Disorders 3 3 1 +Persons with Visual Disabilities 3 3 1 +Persuasive Communication 3 3 1 +Pertussis Toxin 5 7 3 +Pertussis Vaccine 5 5 1 +Peru 4 4 1 +Perylene 4 7 4 +Pessaries 3 3 1 +Pessimism 3 3 1 +Pest Control 5 5 1 +Pest Control, Biological 6 6 1 +Pestalotiopsis 4 5 2 +Peste-des-Petits-Ruminants 2 7 2 +Peste-des-petits-ruminants virus 8 8 1 +Pesticide Residues 4 5 3 +Pesticide Synergists 4 5 2 +Pesticides 3 4 2 +Pestivirus 5 5 1 +Pestivirus Infections 5 5 1 +Petasites 8 8 1 +Petrolatum 3 3 1 +Petroleum 3 5 2 +Petroleum Pollution 4 4 1 +Petromyzon 7 7 1 +Petrosal Sinus Sampling 4 6 5 +Petroselinum 8 8 1 +Petrosia 5 5 1 +Petrositis 4 5 3 +Petrous Bone 6 6 1 +Pets 5 5 1 +Petunia 9 9 1 +Peumus 9 9 1 +Peutz-Jeghers Syndrome 3 7 4 +Peyer's Patches 3 5 2 +Pfiesteria piscicida 4 4 1 +Phacoemulsification 3 5 2 +Phaeohyphomycosis 4 4 1 +Phaeophyceae 3 3 1 +Phage Therapy 3 3 1 +Phagocyte Bactericidal Dysfunction 3 4 2 +Phagocytes 2 3 2 +Phagocytosis 2 4 4 +Phagosomes 8 8 1 +Phakic Intraocular Lenses 5 5 1 +Phakopsora pachyrhizi 4 4 1 +Phalangeridae 7 7 1 +Phalaris 8 8 1 +Phalloidine 4 4 4 +Phalloplasty 3 3 1 +Phanerochaete 5 5 1 +Phantom Limb 4 6 5 +Phantoms, Imaging 2 2 1 +Pharmaceutic Aids 2 3 2 +Pharmaceutical Preparations 1 1 1 +Pharmaceutical Preparations, Dental 2 2 2 +Pharmaceutical Research 5 5 1 +Pharmaceutical Services 3 3 1 +Pharmaceutical Services, Online 4 4 1 +Pharmaceutical Solutions 3 4 3 +Pharmaceutical Vehicles 3 4 3 +Pharmacies 3 3 1 +Pharmacists 3 4 2 +Pharmacoepidemiology 3 5 3 +Pharmacogenetics 3 5 3 +Pharmacogenomic Testing 4 6 5 +Pharmacogenomic Variants 4 4 1 +Pharmacognosy 4 5 2 +Pharmacokinetics 2 3 2 +Pharmacologic Actions 2 2 1 +Pharmacological and Toxicological Phenomena 2 2 1 +Pharmacological Phenomena 3 3 1 +Pharmacology 2 3 2 +Pharmacology, Clinical 3 4 2 +Pharmacophore 4 4 1 +Pharmacopoeia 2 2 1 +Pharmacopoeia, Homeopathic 3 3 1 +Pharmacopoeias as Topic 7 7 1 +Pharmacopoeias, Homeopathic as Topic 8 8 1 +Pharmacovigilance 4 4 2 +Pharmacy 2 2 1 +Pharmacy Administration 3 3 1 +Pharmacy and Therapeutics Committee 4 5 2 +Pharmacy Research 3 5 2 +Pharmacy Residencies 4 4 2 +Pharmacy Service, Hospital 4 6 3 +Pharmacy Technicians 4 5 2 +Pharyngeal Diseases 2 2 2 +Pharyngeal Muscles 3 4 3 +Pharyngeal Neoplasms 3 5 4 +Pharyngectomy 3 3 1 +Pharyngitis 3 3 4 +Pharyngostomy 3 3 2 +Pharynx 2 3 3 +Phascolarctidae 7 7 1 +Phase Separation 2 2 1 +Phase Transition 2 2 2 +Phase Variation 3 3 1 +Phaseolus 8 8 1 +PHD Zinc Fingers 9 9 1 +Phellinus 4 4 1 +Phellodendron 8 8 1 +Phenacetin 5 6 2 +Phenalenes 3 6 2 +Phenanthrenes 3 6 2 +Phenanthridines 4 4 1 +Phenanthrolines 4 4 1 +Phenazines 4 4 1 +Phenazocine 6 6 2 +Phenazopyridine 5 5 1 +Phencyclidine 4 4 1 +Phencyclidine Abuse 3 3 2 +Phenelzine 3 3 1 +Phenethylamines 4 4 1 +Phenetidine 4 8 4 +Phenformin 5 5 1 +Phenindione 5 8 2 +Pheniramine 4 4 1 +Phenmetrazine 5 5 1 +Phenobarbital 6 6 1 +Phenol 7 7 1 +Phenolphthalein 8 8 1 +Phenolphthaleins 7 7 1 +Phenols 6 6 1 +Phenolsulfonphthalein 8 8 1 +Phenomics 5 5 2 +Phenoperidine 4 5 2 +Phenothiazines 3 4 2 +Phenotype 2 2 1 +Phenoxyacetates 5 6 2 +Phenoxybenzamine 4 4 1 +Phenoxypropanolamines 5 5 3 +Phenprocoumon 7 7 2 +Phentermine 6 6 1 +Phentolamine 5 5 1 +Phenyl Ethers 3 7 2 +Phenylacetates 4 4 1 +Phenylalanine 4 5 2 +Phenylalanine Ammonia-Lyase 6 6 1 +Phenylalanine Hydroxylase 6 6 1 +Phenylalanine-tRNA Ligase 6 6 1 +Phenylammonium Compounds 4 4 1 +Phenylbutazone 6 6 1 +Phenylbutyrates 4 4 1 +Phenylcarbamates 5 5 1 +Phenylenediamines 4 5 2 +Phenylephrine 5 5 2 +Phenylethanolamine N-Methyltransferase 6 6 1 +Phenylethyl Alcohol 4 4 1 +Phenylethylmalonamide 6 6 1 +Phenylglyoxal 4 4 1 +Phenylhydrazines 3 3 1 +Phenylisopropyladenosine 5 7 3 +Phenylketonuria, Maternal 4 7 7 +Phenylketonurias 5 6 6 +Phenylmercuric Acetate 5 5 1 +Phenylmercury Compounds 4 4 1 +Phenylmethylsulfonyl Fluoride 4 4 1 +Phenylphosphonothioic Acid, 2-Ethyl 2-(4-Nitrophenyl) Ester 5 5 3 +Phenylpropanolamine 5 5 3 +Phenylpropionates 4 4 1 +Phenylpyruvic Acids 4 5 2 +Phenylthiazolylthiourea 5 6 2 +Phenylthiohydantoin 8 8 1 +Phenylthiourea 4 5 2 +Phenylurea Compounds 4 6 2 +Phenytoin 7 7 1 +Pheochromocytoma 7 7 2 +Pheophytins 5 7 3 +Pheromones 2 2 1 +Pheromones, Human 3 3 1 +PHEX Phosphate Regulating Neutral Endopeptidase 5 7 5 +Phialophora 4 4 1 +Philadelphia 3 7 2 +Philadelphia Chromosome 5 8 6 +Philately 3 3 1 +Philippines 3 4 2 +Philodendron 10 10 1 +Philology 3 3 1 +Philology, Classical 4 4 1 +Philology, Oriental 4 4 1 +Philology, Romance 4 4 1 +Philosophy 2 2 1 +Philosophy, Dental 3 3 1 +Philosophy, Medical 3 3 1 +Philosophy, Nursing 3 3 1 +Phimosis 5 5 2 +Phlebitis 4 4 2 +Phlebography 5 6 2 +Phlebotomus 11 11 1 +Phlebotomus Fever 4 5 3 +Phlebotomy 3 6 4 +Phlebovirus 5 5 1 +Phleomycins 5 5 2 +Phleum 8 8 1 +Phloem 3 3 1 +Phlomis 9 9 1 +Phloretin 5 8 3 +Phlorhizin 3 3 1 +Phloroglucinol 7 7 1 +Phobia, School 4 4 1 +Phobia, Social 4 4 1 +Phobic Disorders 3 3 1 +Phoca 10 10 1 +Phocoena 9 9 1 +Phodopus 11 11 1 +Phoeniceae 8 8 1 +Pholiota 5 5 1 +Phoma 4 4 1 +Phomopsis 4 4 1 +Phonation 3 3 1 +Phonetics 4 4 1 +Phonocardiography 6 6 1 +Phonons 3 5 2 +Phonophoresis 4 4 1 +Phoradendron 8 8 1 +Phorate 5 5 3 +Phorbol 12,13-Dibutyrate 7 7 1 +Phorbol Esters 6 6 1 +Phorbols 5 5 1 +Phormidium 3 5 2 +Phosgene 3 3 1 +Phosmet 5 6 4 +Phosphamidon 4 4 1 +Phosphate Acetyltransferase 6 6 1 +Phosphate Transport Proteins 5 7 3 +Phosphate-Binding Proteins 4 4 1 +Phosphates 5 6 3 +Phosphatidate Phosphatase 6 6 1 +Phosphatidic Acids 5 5 1 +Phosphatidyl-N-Methylethanolamine N-Methyltransferase 6 6 1 +Phosphatidylcholine-Sterol O-Acyltransferase 5 5 1 +Phosphatidylcholines 7 7 1 +Phosphatidylethanolamine Binding Protein 4 4 3 +Phosphatidylethanolamine N-Methyltransferase 6 6 1 +Phosphatidylethanolamines 7 7 1 +Phosphatidylglycerols 7 7 1 +Phosphatidylinositol 3-Kinase 5 7 2 +Phosphatidylinositol 3-Kinases 4 6 2 +Phosphatidylinositol 4,5-Diphosphate 9 9 1 +Phosphatidylinositol Diacylglycerol-Lyase 5 8 2 +Phosphatidylinositol Phosphates 8 8 1 +Phosphatidylinositol-3,4,5-Trisphosphate 5-Phosphatases 7 7 1 +Phosphatidylinositol-4-Phosphate 3-Kinase 5 7 2 +Phosphatidylinositols 7 7 1 +Phosphatidylserines 7 7 1 +Phosphenes 3 5 3 +Phosphines 3 3 2 +Phosphinic Acids 3 5 3 +Phosphites 5 5 2 +Phosphoadenosine Phosphosulfate 5 7 3 +Phosphoamino Acids 3 3 1 +Phosphocreatine 4 4 2 +Phosphodiesterase 3 Inhibitors 6 6 1 +Phosphodiesterase 4 Inhibitors 6 6 1 +Phosphodiesterase 5 Inhibitors 6 6 1 +Phosphodiesterase I 6 6 1 +Phosphodiesterase Inhibitors 5 5 1 +Phosphoenolpyruvate 4 4 1 +Phosphoenolpyruvate Carboxykinase (ATP) 6 6 1 +Phosphoenolpyruvate Carboxykinase (GTP) 6 6 1 +Phosphoenolpyruvate Carboxylase 6 6 1 +Phosphoenolpyruvate Sugar Phosphotransferase System 4 6 3 +Phosphofructokinase-1 8 8 1 +Phosphofructokinase-1, Liver Type 9 9 1 +Phosphofructokinase-1, Muscle Type 9 9 1 +Phosphofructokinase-1, Type C 9 9 1 +Phosphofructokinase-2 4 8 3 +Phosphofructokinases 7 7 1 +Phosphoglucomutase 6 6 1 +Phosphogluconate Dehydrogenase 6 6 1 +Phosphoglycerate Dehydrogenase 6 6 1 +Phosphoglycerate Kinase 6 6 1 +Phosphoglycerate Mutase 6 6 1 +Phosphoinositide 5-Phosphatases 6 6 1 +Phosphoinositide Phosphatases 6 6 1 +Phosphoinositide Phospholipase C 4 8 2 +Phosphoinositide-3 Kinase Inhibitors 5 5 1 +Phospholamban 4 5 2 +Phospholipase A2 Inhibitors 5 5 1 +Phospholipase C beta 5 9 2 +Phospholipase C delta 5 9 2 +Phospholipase C gamma 5 9 2 +Phospholipase D 7 7 1 +Phospholipases 6 6 2 +Phospholipases A 7 7 1 +Phospholipases A1 8 8 1 +Phospholipases A2 8 8 1 +Phospholipases A2, Calcium-Independent 9 9 1 +Phospholipases A2, Cytosolic 9 9 1 +Phospholipases A2, Secretory 9 9 1 +Phospholipid Ethers 4 7 3 +Phospholipid Hydroperoxide Glutathione Peroxidase 5 6 2 +Phospholipid Transfer Proteins 4 4 2 +Phospholipids 3 3 1 +Phosphonoacetic Acid 4 5 2 +Phosphopeptides 3 3 1 +Phosphoprotein Phosphatases 4 6 2 +Phosphoproteins 3 3 1 +Phosphopyruvate Hydratase 6 6 1 +Phosphoramide Mustards 4 6 2 +Phosphoramides 3 7 3 +Phosphoranes 3 3 2 +Phosphoribosyl Pyrophosphate 4 4 1 +Phosphoribosylaminoimidazolecarboxamide Formyltransferase 6 6 1 +Phosphoribosylglycinamide Formyltransferase 6 6 1 +Phosphoric Acids 4 5 2 +Phosphoric Diester Hydrolases 5 5 1 +Phosphoric Monoester Hydrolases 5 5 1 +Phosphoric Triester Hydrolases 5 5 1 +Phosphorothioate Oligonucleotides 5 5 1 +Phosphorous Acids 4 5 2 +Phosphorus 3 3 1 +Phosphorus Acids 3 4 2 +Phosphorus Compounds 2 2 1 +Phosphorus Isotopes 3 4 2 +Phosphorus Metabolism Disorders 3 3 1 +Phosphorus Radioisotopes 4 5 3 +Phosphorus, Dietary 3 3 1 +Phosphorus-Oxygen Lyases 4 4 1 +Phosphorylase a 8 8 1 +Phosphorylase b 8 8 1 +Phosphorylase Kinase 7 7 1 +Phosphorylase Phosphatase 5 7 2 +Phosphorylases 7 7 1 +Phosphorylation 2 3 3 +Phosphorylcholine 5 6 2 +Phosphoserine 4 5 2 +Phosphothreonine 4 5 3 +Phosphotransferases 4 4 1 +Phosphotransferases (Alcohol Group Acceptor) 5 5 1 +Phosphotransferases (Carboxyl Group Acceptor) 5 5 1 +Phosphotransferases (Nitrogenous Group Acceptor) 5 5 1 +Phosphotransferases (Paired Acceptors) 5 5 1 +Phosphotransferases (Phosphate Group Acceptor) 5 5 1 +Phosphotransferases (Phosphomutases) 5 5 1 +Phosphotungstic Acid 3 6 3 +Phosphotyrosine 4 6 2 +Phosvitin 4 6 3 +Photic Stimulation 3 3 1 +Photinia 10 10 1 +Photoacoustic Techniques 2 3 2 +Photoaffinity Labels 6 6 1 +Photobacterium 5 5 2 +Photobiology 4 4 1 +Photobioreactors 3 5 2 +Photobleaching 3 3 1 +Photochemical Processes 2 2 1 +Photochemistry 4 4 1 +Photochemotherapy 3 3 3 +Photoelectron Spectroscopy 4 4 1 +Photofluorography 5 6 2 +Photogrammetry 5 5 1 +Photograph 2 2 1 +Photography 2 4 2 +Photography, Dental 3 5 3 +Photoinitiators, Dental 3 5 3 +Photolysis 3 3 1 +Photomechanical Print 3 3 1 +Photometry 3 3 1 +Photomicrography 3 5 3 +Photons 3 6 5 +Photoperiod 3 3 1 +Photopheresis 3 5 2 +Photophobia 3 6 3 +Photophosphorylation 3 5 10 +Photoplethysmography 5 5 1 +Photoreceptor Cells 4 5 6 +Photoreceptor Cells, Invertebrate 2 6 7 +Photoreceptor Cells, Vertebrate 5 6 6 +Photoreceptor Connecting Cilium 6 7 6 +Photoreceptors, Microbial 3 3 1 +Photoreceptors, Plant 4 4 1 +Photorefractive Keratectomy 4 5 4 +Photorhabdus 5 5 2 +Photosensitivity Disorders 3 3 1 +Photosensitizing Agents 5 5 2 +Photosynthesis 2 4 7 +Photosynthetic Reaction Center Complex Proteins 4 6 4 +Photosystem I Protein Complex 5 8 6 +Photosystem II Protein Complex 5 7 4 +Phototaxis 5 6 4 +Phototherapy 2 2 1 +Photothermal Therapy 3 3 2 +Phototrophic Processes 2 3 2 +Phototropins 5 5 1 +Phototropism 3 5 2 +Phrases 2 2 1 +Phrenic Nerve 6 6 1 +Phrenology 3 3 1 +Phthalazines 4 4 1 +Phthalic Acids 4 4 1 +Phthalic Anhydrides 3 5 2 +Phthalimides 3 5 3 +Phthiraptera 6 6 1 +Phthirus 8 8 1 +Phycobilins 4 6 3 +Phycobiliproteins 7 7 2 +Phycobilisomes 5 6 3 +Phycocyanin 3 8 4 +Phycodnaviridae 3 3 2 +Phycoerythrin 3 8 4 +Phycomyces 5 5 1 +Phyllachorales 4 4 1 +Phyllanthus 10 10 1 +Phyllanthus emblica 11 11 1 +Phyllobacteriaceae 4 4 1 +Phyllodes Tumor 5 5 1 +Phylogeny 3 3 3 +Phylogeography 4 6 2 +Physalaemin 4 6 9 +Physalis 9 9 1 +Physarida 5 5 1 +Physarum 6 6 1 +Physarum polycephalum 7 7 1 +Physiatrists 4 5 2 +Physical Abuse 5 5 2 +Physical and Rehabilitation Medicine 3 3 1 +Physical Appearance, Body 3 5 2 +Physical Chromosome Mapping 4 4 1 +Physical Conditioning, Animal 6 6 1 +Physical Conditioning, Human 3 6 2 +Physical Distancing 5 5 1 +Physical Education and Training 3 3 1 +Physical Endurance 3 6 2 +Physical Examination 3 3 1 +Physical Exertion 3 3 1 +Physical Fitness 3 6 3 +Physical Functional Performance 4 4 1 +Physical Phenomena 1 1 1 +Physical Stimulation 2 2 1 +Physical Therapist Assistants 4 5 2 +Physical Therapists 3 4 2 +Physical Therapy Department, Hospital 6 6 2 +Physical Therapy Modalities 2 3 2 +Physical Therapy Specialty 3 3 1 +Physician Assistants 4 5 2 +Physician Engagement 3 5 4 +Physician Executives 3 4 3 +Physician Impairment 6 7 2 +Physician Incentive Plans 4 4 1 +Physician Payment Review Commission 6 6 1 +Physician Self-Referral 4 7 4 +Physician's Role 6 6 1 +Physician-Nurse Relations 5 5 1 +Physician-Patient Relations 4 5 2 +Physicians 3 4 2 +Physicians' Offices 3 3 1 +Physicians, Family 4 5 2 +Physicians, Primary Care 4 5 2 +Physicians, Women 3 5 3 +Physics 2 2 1 +Physiognomy 2 2 1 +Physiological Effects of Drugs 3 3 1 +Physiological Phenomena 1 1 1 +Physiology 3 3 1 +Physiology, Comparative 4 4 1 +Physostigma 8 8 1 +Physostigmine 4 7 4 +Phytanic Acid 5 5 1 +Phytic Acid 4 6 3 +Phytoalexins 3 3 1 +Phytochelatins 5 5 1 +Phytochemicals 2 2 1 +Phytochrome 3 4 2 +Phytochrome A 4 8 4 +Phytochrome B 4 5 2 +Phytoestrogens 8 8 1 +Phytohemagglutinins 5 5 4 +Phytol 5 5 1 +Phytolacca 10 10 1 +Phytolacca americana 11 11 1 +Phytolacca dodecandra 11 11 1 +Phytolaccaceae 9 9 1 +Phytophthora 4 4 1 +Phytophthora infestans 5 5 1 +Phytoplankton 4 4 1 +Phytoplasma 5 5 1 +Phytoplasma Disease 3 3 1 +Phytosomes 3 5 4 +Phytosterols 3 6 3 +Phytotherapy 3 3 1 +Pia Mater 4 4 1 +Pica 3 4 2 +Picea 8 8 1 +Pichia 4 5 2 +Pichinde virus 7 7 1 +Picibanil 3 3 1 +Pick Disease of the Brain 6 7 2 +Picloram 4 5 2 +Picobirnavirus 3 4 2 +Picolines 4 4 1 +Picolinic Acids 3 4 2 +Picornavirales 4 4 1 +Picornaviridae 5 5 1 +Picornaviridae Infections 4 4 1 +Picrasma 8 8 1 +Picrates 4 8 2 +Picrorhiza 9 9 1 +Picrotoxin 3 7 6 +Picryl Chloride 5 5 3 +Pictorial Work 2 2 1 +Pictorial Works as Topic 3 3 1 +Piebaldism 5 6 6 +Piedra 4 4 2 +Pierre Robin Syndrome 4 7 6 +Piezosurgery 3 3 1 +Pigment Epithelium of Eye 3 3 2 +Pigment Epithelium-Derived Factor 4 5 7 +Pigmentation 2 4 2 +Pigmentation Disorders 3 3 2 +Pigments, Biological 2 2 1 +PII Nitrogen Regulatory Proteins 5 5 3 +Pili, Sex 2 4 2 +Pilocarpine 3 3 1 +Pilocarpus 8 8 1 +Piloerection 3 4 3 +Pilomatrixoma 5 5 1 +Pilonidal Sinus 3 3 1 +Pilot Projects 3 5 4 +Pilots 3 3 1 +Pima People 6 6 2 +Pimelic Acids 5 5 1 +Pimenta 8 8 1 +Pimozide 5 5 1 +Pimpinella 8 8 1 +Pinaceae 7 7 1 +Pinacidil 4 4 1 +Pinales 6 6 1 +Pinch Strength 5 6 2 +Pinctada 6 6 1 +Pindolol 6 6 3 +Pineal Gland 3 7 5 +Pinealectomy 3 3 1 +Pinealoma 5 6 6 +Pinellia 10 10 1 +Pinguecula 3 3 1 +Pinocytosis 3 3 1 +Pinta 4 7 5 +Pinus 8 8 1 +Pinus ponderosa 9 9 1 +Pinus sylvestris 9 9 1 +Pinus taeda 9 9 1 +Pioglitazone 5 6 2 +Pipe Smoking 4 4 1 +Pipecolic Acids 3 4 2 +Pipecuronium 4 4 1 +Pipemidic Acid 4 5 3 +Piper 8 8 1 +Piper betle 9 9 1 +Piper nigrum 9 9 1 +Piperaceae 7 7 1 +Piperacillin 7 8 3 +Piperacillin, Tazobactam Drug Combination 3 9 8 +Piperazine 4 4 1 +Piperazines 3 3 1 +Piperidines 3 3 1 +Piperidones 4 4 1 +Piperonyl Butoxide 5 5 2 +Piperoxan 4 4 3 +Pipidae 7 7 1 +Pipobroman 4 4 1 +Piracetam 4 6 3 +Pirenzepine 7 7 1 +Piribedil 4 4 1 +Piriform Cortex 8 10 2 +Piriformis Muscle Syndrome 5 6 5 +Pirinitramide 4 5 2 +Piromidic Acid 4 5 2 +Piromyces 5 5 1 +Piroplasmia 4 4 1 +Piroplasmida 5 5 1 +Piroxicam 4 4 2 +Piscirickettsia 5 5 1 +Piscirickettsiaceae 3 4 2 +Piscirickettsiaceae Infections 5 5 1 +Pisiform Bone 7 7 1 +Pistacia 8 8 1 +Pisum sativum 8 8 1 +Pit and Fissure Sealants 3 5 2 +Pitcairn Island 5 5 2 +Pitch Discrimination 5 6 2 +Pitch Perception 4 5 2 +Pitheciidae 10 10 1 +Pituitary ACTH Hypersecretion 4 7 2 +Pituitary Adenylate Cyclase-Activating Polypeptide 4 5 6 +Pituitary Apoplexy 3 6 4 +Pituitary Diseases 2 5 2 +Pituitary Function Tests 4 4 1 +Pituitary Gland 3 9 5 +Pituitary Gland, Anterior 4 10 5 +Pituitary Gland, Intermediate 4 10 5 +Pituitary Gland, Posterior 4 10 7 +Pituitary Hormone Release Inhibiting Hormones 5 6 4 +Pituitary Hormone-Releasing Hormones 5 6 4 +Pituitary Hormones 4 4 2 +Pituitary Hormones, Anterior 5 5 2 +Pituitary Hormones, Posterior 5 5 2 +Pituitary Irradiation 4 4 1 +Pituitary Neoplasms 3 8 8 +Pituitary-Adrenal Function Tests 4 4 1 +Pituitary-Adrenal System 3 3 1 +Pityriasis 4 4 1 +Pityriasis Lichenoides 5 5 3 +Pityriasis Rosea 5 5 1 +Pityriasis Rubra Pilaris 5 5 1 +Pivampicillin 7 8 3 +Piwi-Interacting RNA 5 7 3 +Pizotyline 4 4 2 +Place Cells 4 4 2 +Placebo Effect 5 5 2 +Placebos 2 2 2 +Placenta 2 2 1 +Placenta Accreta 5 5 2 +Placenta Diseases 4 4 1 +Placenta Growth Factor 4 6 2 +Placenta Previa 5 5 2 +Placenta, Retained 5 5 1 +Placental Circulation 4 4 1 +Placental Extracts 3 3 1 +Placental Function Tests 4 4 1 +Placental Hormones 4 4 2 +Placental Insufficiency 5 5 1 +Placental Lactogen 4 5 3 +Placentation 5 5 1 +Placozoa 4 4 1 +Plagiarism 3 3 1 +Plagiocephaly 4 5 2 +Plagiocephaly, Nonsynostotic 5 6 2 +Plague 3 7 2 +Plague Vaccine 5 5 1 +Plain Language Summaries 5 5 1 +Plakins 4 4 1 +Plakophilins 4 4 2 +Plakortis 5 5 1 +Planarians 7 7 1 +Planctomycetales 4 4 1 +Planctomycetes 3 3 1 +Planets 4 5 2 +Plankton 3 3 1 +Planktothrix 3 5 2 +Planning Techniques 3 3 1 +Planococcaceae 4 5 3 +Planococcus Bacteria 5 5 1 +Planococcus Insect 7 7 1 +Plant Bark 4 4 1 +Plant Breeding 4 4 1 +Plant Cells 2 2 1 +Plant Components, Aerial 2 2 1 +Plant Cone 3 3 1 +Plant Defense Against Herbivory 2 2 1 +Plant Development 2 3 2 +Plant Diseases 2 2 1 +Plant Dispersal 2 2 1 +Plant Dormancy 4 4 2 +Plant Epidermis 3 3 1 +Plant Extracts 2 4 2 +Plant Exudates 3 3 1 +Plant Growth Regulators 5 5 1 +Plant Gums 3 4 3 +Plant Immunity 2 3 2 +Plant Infertility 2 2 1 +Plant Leaves 3 3 1 +Plant Lectins 4 4 2 +Plant Mucilage 3 4 3 +Plant Necrosis and Chlorosis 3 3 1 +Plant Nectar 3 3 1 +Plant Oils 3 4 2 +Plant Pathology 5 5 2 +Plant Physiological Phenomena 1 1 1 +Plant Poisoning 3 3 1 +Plant Preparations 3 3 1 +Plant Proteins 3 3 1 +Plant Proteins, Dietary 4 5 4 +Plant Root Cap 4 4 1 +Plant Root Nodulation 2 2 1 +Plant Roots 2 2 1 +Plant Senescence 3 4 2 +Plant Shoots 3 3 1 +Plant Somatic Embryogenesis Techniques 2 4 2 +Plant Stems 3 3 1 +Plant Stomata 4 4 2 +Plant Structures 1 1 1 +Plant Systemic Acquired Resistance 3 4 2 +Plant Transpiration 2 2 1 +Plant Tubers 3 3 1 +Plant Tumor-Inducing Plasmids 4 4 1 +Plant Tumors 3 3 1 +Plant Vascular Bundle 2 2 1 +Plant Viral Movement Proteins 5 5 1 +Plant Viruses 2 2 1 +Plant Weeds 3 3 1 +Plant-based Milk 4 5 2 +Plantaginaceae 8 8 1 +Plantago 9 9 1 +Plantar Plate 4 6 7 +Plantibodies 7 7 3 +Plants 2 2 1 +Plants, Edible 3 3 1 +Plants, Genetically Modified 3 3 2 +Plants, Medicinal 3 3 1 +Plants, Toxic 3 3 1 +Plaque, Amyloid 3 3 1 +Plaque, Atherosclerotic 3 3 1 +Plasma 3 4 3 +Plasma Cell Granuloma, Pulmonary 3 3 1 +Plasma Cells 4 8 5 +Plasma Exchange 4 4 4 +Plasma Gases 3 3 1 +Plasma Kallikrein 8 8 2 +Plasma Membrane Calcium-Transporting ATPases 7 8 5 +Plasma Membrane Neurotransmitter Transport Proteins 6 6 2 +Plasma Skin Regeneration 3 3 1 +Plasma Substitutes 6 6 1 +Plasma Volume 4 5 2 +Plasmablastic Lymphoma 7 8 3 +Plasmacytoma 4 4 2 +Plasmalogens 8 8 1 +Plasmapheresis 3 3 3 +Plasmids 3 3 1 +Plasminogen 3 6 4 +Plasminogen Activator Inhibitor 1 4 5 4 +Plasminogen Activator Inhibitor 2 4 5 4 +Plasminogen Activators 5 7 3 +Plasminogen Inactivators 3 4 3 +Plasmodesmata 6 6 1 +Plasmodiophorida 4 4 1 +Plasmodium 5 5 1 +Plasmodium berghei 6 6 1 +Plasmodium chabaudi 6 6 1 +Plasmodium cynomolgi 6 6 1 +Plasmodium falciparum 6 6 1 +Plasmodium gallinaceum 6 6 1 +Plasmodium knowlesi 6 6 1 +Plasmodium malariae 6 6 1 +Plasmodium ovale 6 6 1 +Plasmodium vivax 6 6 1 +Plasmodium yoelii 6 6 1 +Plastic Embedding 7 8 4 +Plastic Surgery Procedures 2 2 1 +Plasticizers 3 3 1 +Plastics 3 5 3 +Plastids 7 7 1 +Plastination 3 3 2 +Plastocyanin 4 4 2 +Plastoquinol-Plastocyanin Reductase 4 9 6 +Plastoquinone 4 4 1 +Platelet Activating Factor 3 8 7 +Platelet Activation 4 4 1 +Platelet Adhesiveness 3 5 2 +Platelet Aggregation 4 5 2 +Platelet Aggregation Inhibitors 5 5 1 +Platelet Count 4 7 9 +Platelet Endothelial Cell Adhesion Molecule-1 5 6 6 +Platelet Factor 3 3 5 2 +Platelet Factor 4 3 7 7 +Platelet Function Tests 4 5 2 +Platelet Glycoprotein GPIb-IX Complex 6 7 4 +Platelet Glycoprotein GPIIb-IIIa Complex 6 8 5 +Platelet Membrane Glycoprotein IIb 8 8 1 +Platelet Membrane Glycoproteins 5 6 4 +Platelet Storage Pool Deficiency 4 4 3 +Platelet Transfusion 5 5 1 +Platelet-Derived Growth Factor 3 4 4 +Platelet-Rich Fibrin 5 6 3 +Platelet-Rich Plasma 4 5 3 +Plateletpheresis 4 6 4 +Platinum 4 4 3 +Platinum Compounds 2 2 1 +Platybasia 4 5 4 +Platycodon 8 8 1 +Platyhelminths 5 5 1 +Platypnea Orthodeoxia Syndrome 4 5 3 +Platypus 7 7 1 +Platyrrhini 9 9 1 +Play and Playthings 4 4 1 +Play Therapy 3 4 2 +Pleasure 3 4 2 +Pleasure-Pain Principle 4 4 1 +Pleckstrin Homology Domains 8 8 1 +Plectin 4 5 2 +Plectonema 3 5 3 +Plectranthus 9 9 1 +Plectrovirus 4 4 2 +Pleistophora 7 7 1 +Plesiomonas 5 5 2 +Plethysmography 4 4 1 +Plethysmography, Impedance 4 5 2 +Plethysmography, Whole Body 5 5 2 +Pleura 2 4 2 +Pleural Cavity 5 5 1 +Pleural Diseases 2 2 1 +Pleural Effusion 3 3 1 +Pleural Effusion, Malignant 4 6 4 +Pleural Neoplasms 3 5 3 +Pleurisy 3 3 3 +Pleurobranchaea 6 6 1 +Pleurodeles 8 8 1 +Pleurodesis 3 3 1 +Pleurodynia, Epidemic 7 7 1 +Pleuromutilins 5 5 1 +Pleuropneumonia 4 4 6 +Pleuropneumonia, Contagious 2 7 2 +Pleurotus 5 5 1 +Pliability 3 3 1 +Plicamycin 5 8 3 +Plocamium 3 3 1 +Ploidies 2 2 1 +Plum Pox Virus 5 6 3 +Plumbaginaceae 9 9 1 +Plummer-Vinson Syndrome 6 6 1 +Pluripotent Stem Cells 3 3 1 +Pluto 6 6 1 +Plutonium 4 6 5 +Plyometric Exercise 4 7 5 +Pneumatosis Cystoides Intestinalis 4 4 1 +Pneumocephalus 3 5 4 +Pneumococcal Infections 6 6 1 +Pneumococcal Vaccines 6 6 1 +Pneumoconiosis 2 4 3 +Pneumocystis 4 4 1 +Pneumocystis carinii 5 5 1 +Pneumocystis Infections 4 4 1 +Pneumoencephalography 4 6 5 +Pneumomediastinum, Diagnostic 4 4 1 +Pneumonectomy 2 4 2 +Pneumonia 3 3 3 +Pneumonia of Calves, Enzootic 4 5 7 +Pneumonia of Swine, Mycoplasmal 3 5 5 +Pneumonia, Aspiration 4 4 3 +Pneumonia, Atypical Interstitial, of Cattle 3 3 1 +Pneumonia, Bacterial 4 4 4 +Pneumonia, Lipid 5 5 3 +Pneumonia, Mycoplasma 5 7 5 +Pneumonia, Necrotizing 4 4 3 +Pneumonia, Pneumococcal 5 7 5 +Pneumonia, Pneumocystis 4 5 8 +Pneumonia, Progressive Interstitial, of Sheep 3 6 3 +Pneumonia, Rickettsial 4 6 5 +Pneumonia, Staphylococcal 5 6 5 +Pneumonia, Ventilator-Associated 4 7 5 +Pneumonia, Viral 3 4 4 +Pneumonolysis 5 5 1 +Pneumopericardium 3 3 1 +Pneumoperitoneum 3 3 1 +Pneumoperitoneum, Artificial 4 4 1 +Pneumoradiography 5 5 1 +Pneumorrhachis 4 4 1 +Pneumothorax 3 3 1 +Pneumothorax, Artificial 5 5 1 +Pneumovirinae 6 6 1 +Pneumovirus 7 7 1 +Pneumovirus Infections 6 6 1 +Poa 8 8 1 +Poaceae 7 7 1 +Podiatry 2 2 1 +Podocytes 3 7 3 +Podophyllin 5 5 1 +Podophyllotoxin 5 8 3 +Podophyllum 8 8 1 +Podophyllum peltatum 9 9 1 +Podoplanin 5 5 3 +Podosomes 4 4 2 +Podospora 5 5 1 +Podoviridae 4 4 2 +Poecilia 8 8 1 +POEMS Syndrome 4 5 4 +Poetry 2 2 1 +Poetry as Topic 3 3 1 +Pogostemon 9 9 1 +Point Mutation 4 4 1 +Point-of-Care Systems 3 6 3 +Point-of-Care Testing 4 4 1 +Poison Control Centers 3 4 2 +Poison Frogs 7 7 1 +Poisoning 2 2 1 +Poisons 3 4 2 +Poisson Distribution 3 6 4 +Pokeweed Mitogens 5 5 2 +pol Gene Products, Human Immunodeficiency Virus 5 8 5 +Pol1 Transcription Initiation Complex Proteins 5 5 2 +Poland 4 4 1 +Poland Syndrome 5 7 5 +Polar Bodies 5 6 2 +Polarography 3 3 2 +Police 5 5 1 +Policy 2 2 2 +Policy Making 3 3 1 +Polidocanol 4 6 4 +Poliomyelitis 3 6 6 +Poliomyelitis, Bulbar 4 7 6 +Poliovirus 8 8 1 +Poliovirus Vaccine, Inactivated 5 6 2 +Poliovirus Vaccine, Oral 6 6 1 +Poliovirus Vaccines 5 5 1 +Political Activism 3 3 1 +Political Systems 2 2 1 +Politicization 3 3 1 +Politics 2 2 1 +Pollen 6 6 1 +Pollen Tube 7 7 1 +Pollination 2 4 2 +Polo-Like Kinase 1 6 9 2 +Polo-like Kinases 4 8 3 +Polonium 4 5 4 +Poloxalene 4 6 4 +Poloxamer 4 6 4 +Poly (ADP-Ribose) Polymerase-1 8 8 1 +Poly A 5 5 1 +Poly A-U 6 6 2 +Poly Adenosine Diphosphate Ribose 5 7 3 +Poly ADP Ribosylation 6 8 4 +Poly C 5 5 1 +Poly dA-dT 5 5 1 +Poly G 5 5 1 +Poly I 5 5 1 +Poly I-C 6 6 2 +Poly T 5 5 1 +Poly U 5 5 1 +Poly(A)-Binding Protein I 6 6 2 +Poly(A)-Binding Protein II 6 6 2 +Poly(A)-Binding Proteins 5 5 2 +Poly(ADP-ribose) Polymerase Inhibitors 5 5 2 +Poly(ADP-ribose) Polymerases 7 7 1 +Poly-ADP-Ribose Binding Motif 8 8 1 +Poly-ADP-Ribose Binding Proteins 4 4 2 +Polyacetylene Polymer 3 6 4 +Polyacrylamides 6 8 3 +Polyadenylation 4 5 3 +Polyalthia 8 8 1 +Polyamine Oxidase 5 5 1 +Polyamines 3 3 1 +Polyanetholesulfonate 3 8 5 +Polyanhydrides 3 3 2 +Polyarteritis Nodosa 4 5 3 +Polybrominated Biphenyls 5 7 2 +Polycarbonates 3 3 1 +Polycarboxylate Cement 4 6 3 +Polychaeta 5 5 1 +Polychlorinated Biphenyls 3 7 3 +Polychlorinated Dibenzodioxins 4 4 2 +Polychloroterphenyl Compounds 5 7 2 +Polychondritis, Relapsing 3 4 2 +Polycomb Repressive Complex 1 4 6 5 +Polycomb Repressive Complex 2 4 9 5 +Polycomb-Group Proteins 3 5 4 +Polycyclic Aromatic Hydrocarbons 2 5 2 +Polycyclic Compounds 1 1 1 +Polycyclic Sesquiterpenes 2 5 2 +Polycystic Kidney Diseases 4 7 5 +Polycystic Kidney, Autosomal Dominant 5 8 5 +Polycystic Kidney, Autosomal Recessive 5 8 5 +Polycystic Ovary Syndrome 4 8 4 +Polycythemia 3 3 1 +Polycythemia Vera 5 5 4 +Polydactyly 4 5 2 +Polydeoxyribonucleotides 4 4 1 +Polydioxanone 3 6 5 +Polydipsia 3 3 2 +Polydipsia, Psychogenic 3 4 4 +Polydnaviridae 3 3 2 +Polyelectrolytes 3 3 2 +Polyendocrinopathies, Autoimmune 2 3 2 +Polyenes 5 5 1 +Polyesters 3 5 3 +Polyether Compounds 3 3 1 +Polyether Polyketides 4 4 4 +Polyether Toxins 4 5 5 +Polyethylene 5 7 4 +Polyethylene Glycols 3 5 4 +Polyethylene Terephthalates 4 6 3 +Polyethyleneimine 3 7 5 +Polyethylenes 4 6 4 +Polygala 9 9 1 +Polygalaceae 8 8 1 +Polygalacturonase 5 5 1 +Polygeline 3 5 4 +Polyglactin 910 4 6 3 +Polyglutamic Acid 3 5 3 +Polyglycolic Acid 4 6 3 +Polygonaceae 7 7 1 +Polygonatum 10 10 1 +Polygonum 8 8 1 +Polyhydramnios 4 4 1 +Polyhydroxyalkanoates 2 4 3 +Polyhydroxybutyrates 3 5 3 +Polyhydroxyethyl Methacrylate 4 9 8 +Polyisoprenyl Phosphate Monosaccharides 4 6 3 +Polyisoprenyl Phosphate Oligosaccharides 4 6 3 +Polyisoprenyl Phosphate Sugars 3 5 3 +Polyisoprenyl Phosphates 4 4 2 +Polyketide Synthases 4 5 2 +Polyketides 3 3 2 +Polylactic Acid-Polyglycolic Acid Copolymer 5 7 4 +Polylysine 3 5 3 +Polymenophorea 4 4 1 +Polymerase Chain Reaction 4 4 1 +Polymerization 2 2 1 +Polymers 2 4 3 +Polymethacrylic Acids 5 8 4 +Polymethyl Methacrylate 7 10 4 +Polymicrogyria 5 6 2 +Polymorphic Catecholaminergic Ventricular Tachycardia 6 6 3 +Polymorphism, Genetic 3 3 1 +Polymorphism, Restriction Fragment Length 4 4 1 +Polymorphism, Single Nucleotide 4 4 1 +Polymorphism, Single-Stranded Conformational 4 4 1 +Polymyalgia Rheumatica 3 4 3 +Polymyositis 4 5 2 +Polymyxin B 4 7 6 +Polymyxins 3 6 6 +Polynesia 4 4 2 +Polyneuropathies 4 4 1 +Polynucleotide 5'-Hydroxyl-Kinase 4 6 2 +Polynucleotide Adenylyltransferase 6 6 1 +Polynucleotide Ligases 4 4 1 +Polynucleotides 3 3 1 +Polyomaviridae 4 4 2 +Polyomavirus 5 5 2 +Polyomavirus Infections 4 4 1 +Polyoxometalates 4 5 3 +Polypeptide N-acetylgalactosaminyltransferase 7 7 1 +Polypharmacology 5 5 1 +Polypharmacy 3 7 3 +Polyphenols 7 7 1 +Polyphloretin Phosphate 3 9 6 +Polyphosphates 6 7 3 +Polyplacophora 5 5 1 +Polyploidy 3 5 3 +Polypodiaceae 7 7 1 +Polypodium 8 8 1 +Polypoidal Choroidal Vasculopathy 5 6 2 +Polyporaceae 5 5 1 +Polyporales 4 4 1 +Polyporus 6 6 1 +Polyprenols 3 4 2 +Polypropylenes 4 6 4 +Polyproteins 3 3 1 +Polyps 3 3 1 +Polypyrimidine Tract-Binding Protein 6 6 2 +Polyradiculoneuropathy 3 5 4 +Polyradiculoneuropathy, Chronic Inflammatory Demyelinating 4 6 5 +Polyradiculopathy 6 6 1 +Polyribonucleotide Nucleotidyltransferase 7 7 1 +Polyribonucleotides 4 4 1 +Polyribosomes 8 8 1 +Polysaccharide-Lyases 5 5 1 +Polysaccharides 2 2 1 +Polysaccharides, Bacterial 3 4 2 +Polysomnography 4 4 1 +Polysorbates 4 6 4 +Polystichum 8 8 1 +Polystyrenes 4 9 4 +Polytene Chromosomes 5 8 3 +Polytetrafluoroethylene 4 6 3 +Polythiazide 5 6 3 +Polyubiquitin 5 5 1 +Polyunsaturated Alkamides 3 5 3 +Polyurethanes 4 6 7 +Polyuria 4 6 4 +Polyvinyl Alcohol 3 7 5 +Polyvinyl Chloride 4 7 5 +Polyvinylpyridine N-Oxide 4 7 2 +Polyvinyls 4 6 5 +Polyynes 5 5 1 +Pomegranate 10 10 1 +Poncirus 8 8 1 +Ponds 3 5 3 +Pongamia 8 8 1 +Pongo 11 11 1 +Pongo abelii 12 12 1 +Pongo pygmaeus 12 12 1 +Pons 7 7 1 +Pontederiaceae 7 7 1 +Pontine Tegmentum 8 8 1 +Pooled Testing 4 4 2 +Popliteal Artery 4 4 1 +Popliteal Artery Aneurysm 4 4 1 +Popliteal Artery Entrapment Syndrome 4 4 1 +Popliteal Cyst 4 4 1 +Popliteal Vein 4 4 1 +Popular Culture 5 5 2 +Popular Work 2 2 1 +Population 2 2 1 +Population Characteristics 1 1 1 +Population Control 4 6 3 +Population Density 3 5 2 +Population Dynamics 3 5 3 +Population Forecast 3 3 2 +Population Groups 2 3 2 +Population Groups, US 4 4 1 +Population Growth 4 6 3 +Population Health 3 3 1 +Population Health Management 3 3 1 +Population Surveillance 4 7 4 +Populus 10 10 1 +Porcine epidemic diarrhea virus 8 8 1 +Porcine Postweaning Multisystemic Wasting Syndrome 3 5 2 +Porcine Reproductive and Respiratory Syndrome 3 6 2 +Porcine respiratory and reproductive syndrome virus 7 7 1 +Porcine Respiratory Coronavirus 10 10 1 +Porcupines 8 8 1 +Pore Forming Cytotoxic Proteins 4 4 1 +Porencephaly 5 6 4 +Porfiromycin 5 7 3 +Poria 6 6 1 +Porifera 4 4 1 +Porins 6 6 3 +Pork Meat 5 6 2 +Porokeratosis 4 4 3 +Poroma 6 6 2 +Porosity 3 3 1 +Porphobilinogen 5 5 1 +Porphobilinogen Synthase 6 6 1 +Porphyra 3 5 2 +Porphyria Cutanea Tarda 4 5 4 +Porphyria, Acute Intermittent 4 5 4 +Porphyria, Erythropoietic 4 4 3 +Porphyria, Hepatoerythropoietic 4 5 4 +Porphyria, Variegate 4 5 4 +Porphyrias 3 3 1 +Porphyrias, Hepatic 3 4 4 +Porphyridium 3 3 1 +Porphyrinogens 5 7 3 +Porphyrins 3 6 4 +Porphyromonas 5 6 2 +Porphyromonas endodontalis 6 7 2 +Porphyromonas gingivalis 6 7 2 +Porpoises 8 8 1 +Port-Wine Stain 4 4 2 +Portable X-Ray 2 2 1 +Portacaval Shunt, Surgical 4 6 2 +Portal Pressure 6 6 1 +Portal System 4 4 1 +Portal Vein 5 5 1 +Portasystemic Shunt, Surgical 3 5 2 +Portasystemic Shunt, Transjugular Intrahepatic 4 6 2 +Portion Size 5 5 1 +Portoenterostomy, Hepatic 3 4 2 +Portography 4 6 4 +Portrait 3 3 2 +Portraits as Topic 3 3 1 +Portugal 3 3 1 +Portulaca 10 10 1 +Portulacaceae 9 9 1 +Position-Specific Scoring Matrices 4 6 3 +Positive Regulatory Domain I-Binding Factor 1 5 7 3 +Positive Transcriptional Elongation Factor B 5 8 3 +Positive-Pressure Respiration 4 4 2 +Positive-Pressure Respiration, Intrinsic 4 4 1 +Positive-Strand RNA Viruses 3 3 1 +Positron Emission Tomography Computed Tomography 5 8 11 +Positron-Emission Tomography 6 7 5 +Post and Core Technique 5 5 2 +Post-Acute COVID-19 Syndrome 5 8 6 +Post-Cardiac Arrest Syndrome 3 5 4 +Post-Concussion Syndrome 5 6 3 +Post-Dural Puncture Headache 6 6 1 +Post-Exercise Hypotension 4 5 2 +Post-Exercise Recovery 3 6 4 +Post-Exercise Recovery Techniques 3 4 2 +Post-Exposure Prophylaxis 4 4 1 +Post-Infectious Disorders 5 5 1 +Post-Lyme Disease Syndrome 5 8 4 +Post-Synaptic Density 4 8 2 +Post-Traumatic Headache 6 6 1 +Postal Service 3 3 1 +Postanesthesia Nursing 5 5 2 +Postcard 2 2 1 +Postcards as Topic 2 2 1 +Postcholecystectomy Syndrome 3 4 2 +Postdoctoral Training 5 5 1 +Poster 3 3 1 +Posterior Capsular Rupture, Ocular 3 3 1 +Posterior Capsule of the Lens 6 6 1 +Posterior Capsulotomy 4 4 1 +Posterior Cerebellar Commissure 7 7 1 +Posterior Cerebral Artery 5 5 1 +Posterior Cervical Sympathetic Syndrome 4 4 1 +Posterior Cruciate Ligament 4 5 3 +Posterior Cruciate Ligament Reconstruction 3 4 3 +Posterior Eye Segment 3 3 1 +Posterior Horn Cells 4 5 3 +Posterior Leukoencephalopathy Syndrome 5 6 2 +Posterior Thalamic Nuclei 8 8 1 +Posterior Tibial Tendon Dysfunction 3 3 1 +Posters as Topic 6 6 1 +Postgastrectomy Syndromes 4 4 2 +Posthumous Conception 4 4 2 +Postmenopause 5 6 2 +Postmodernism 3 3 1 +Postmortem Changes 5 5 1 +Postmortem Imaging 3 6 4 +Postnatal Care 4 6 3 +Postoperative Care 3 5 3 +Postoperative Cognitive Complications 4 5 2 +Postoperative Complications 3 3 1 +Postoperative Hemorrhage 4 4 2 +Postoperative Nausea and Vomiting 4 5 3 +Postoperative Pain 4 5 4 +Postoperative Period 3 5 2 +Postpartum Hemorrhage 5 5 3 +Postpartum Period 3 3 1 +Postpartum Thyroiditis 4 5 3 +Postpericardiotomy Syndrome 3 4 2 +Postphlebitic Syndrome 4 5 2 +Postpoliomyelitis Syndrome 3 7 9 +Postprandial Period 3 3 1 +Postsynaptic Potential Summation 4 5 7 +Postthrombotic Syndrome 4 6 2 +Posttraumatic Growth, Psychological 2 3 2 +Postural Balance 3 5 4 +Postural Orthostatic Tachycardia Syndrome 5 5 1 +Posture 3 3 1 +Potamogetonaceae 9 9 1 +Potassium 4 4 4 +Potassium Acetate 3 6 2 +Potassium Channel Blockers 5 5 2 +Potassium Channels 6 6 3 +Potassium Channels, Calcium-Activated 7 7 3 +Potassium Channels, Inwardly Rectifying 7 7 3 +Potassium Channels, Sodium-Activated 7 7 3 +Potassium Channels, Tandem Pore Domain 7 7 3 +Potassium Channels, Voltage-Gated 7 7 3 +Potassium Chloride 3 5 2 +Potassium Citrate 7 7 1 +Potassium Compounds 2 2 1 +Potassium Cyanide 3 5 2 +Potassium Deficiency 5 5 1 +Potassium Dichromate 3 4 2 +Potassium Iodide 3 4 2 +Potassium Ionophores 4 6 2 +Potassium Isotopes 3 5 5 +Potassium Magnesium Aspartate 5 5 2 +Potassium Permanganate 3 3 2 +Potassium Radioisotopes 4 6 6 +Potassium, Dietary 3 3 1 +Potassium-Hydrogen Antiporters 6 7 3 +Potentially Inappropriate Medication List 3 4 2 +Potentilla 10 10 1 +Potentiometry 3 4 2 +Potexvirus 4 5 2 +Potoroidae 7 7 1 +Pott Puffy Tumor 3 5 3 +Potyviridae 3 4 2 +Potyvirus 4 5 3 +POU Domain Factors 4 4 2 +Pouchitis 6 6 3 +Poult Enteritis Mortality Syndrome 2 4 2 +Poultry 4 6 4 +Poultry Diseases 3 3 1 +Poultry Products 5 6 2 +Poultry Proteins 5 7 7 +Pouteria 9 9 1 +Poverty 3 5 3 +Poverty Areas 4 6 2 +Povidone 5 7 5 +Povidone-Iodine 4 8 6 +Powder Diffraction 4 4 1 +Powders 3 3 1 +Power Plant Operators 3 3 1 +Power Plants 2 3 2 +Power, Psychological 3 3 1 +Poxviridae 3 3 1 +Poxviridae Infections 4 4 1 +PPAR alpha 5 5 1 +PPAR delta 5 5 1 +PPAR gamma 5 5 1 +PPAR-beta 5 5 1 +PPAR-gamma Agonists 5 5 1 +PQQ Cofactor 3 6 2 +PR-SET Domains 8 8 1 +Practice Guideline 3 5 3 +Practice Guidelines as Topic 4 5 2 +Practice Management 4 4 1 +Practice Management, Dental 5 5 1 +Practice Management, Medical 5 5 1 +Practice Management, Veterinary 5 5 1 +Practice Patterns, Dentists' 3 4 2 +Practice Patterns, Nurses' 3 4 2 +Practice Patterns, Pharmacists' 3 4 2 +Practice Patterns, Physicians' 3 4 2 +Practice Valuation and Purchase 5 5 1 +Practice, Psychological 4 4 1 +Practolol 5 6 5 +Prader-Willi Syndrome 4 6 6 +Pradimicins and Benanomicins 4 8 6 +Pragmatic Clinical Trial 4 4 1 +Pragmatic Clinical Trials as Topic 6 6 1 +Prajmaline 6 9 3 +Pralidoxime Compounds 5 5 1 +Pramipexole 5 6 2 +Praseodymium 5 5 2 +Prasugrel Hydrochloride 4 4 3 +Pravastatin 4 7 2 +Prazepam 7 7 1 +Praziquantel 5 5 1 +Prazosin 5 5 1 +Pre-Analytical Phase 4 4 1 +Pre-B Cell Receptors 7 8 3 +Pre-B-Cell Leukemia Transcription Factor 1 4 6 3 +Pre-Eclampsia 5 5 1 +Pre-Excitation Syndromes 4 4 2 +Pre-Excitation, Mahaim-Type 5 5 2 +Pre-Exposure Prophylaxis 5 5 2 +Pre-Registration Publication 4 4 1 +Prealbumin 5 5 2 +Preanesthetic Medication 2 6 4 +Prebiotics 4 5 6 +Precancerous Conditions 2 2 1 +Preceptorship 3 3 1 +Precipitating Factors 5 5 2 +Precipitin Tests 4 6 5 +Precipitins 7 7 3 +Precision Medicine 2 4 2 +Preconception Care 3 5 4 +Preconception Injuries 2 2 1 +Precursor B-Cell Lymphoblastic Leukemia-Lymphoma 6 6 4 +Precursor Cell Lymphoblastic Leukemia-Lymphoma 5 5 4 +Precursor Cells, B-Lymphoid 5 7 3 +Precursor Cells, T-Lymphoid 5 7 3 +Precursor T-Cell Lymphoblastic Leukemia-Lymphoma 6 6 4 +Predatory Behavior 5 5 2 +Predatory Journals as Topic 7 7 1 +Prediabetic State 3 5 2 +Prediction Algorithms 3 4 2 +Prediction Methods, Machine 4 5 2 +Predictive Learning Models 4 6 3 +Predictive Value of Tests 5 6 3 +Prednimustine 7 8 2 +Prednisolone 7 7 1 +Prednisone 7 7 1 +Preexisting Condition Coverage 6 6 1 +Preferred Provider Organizations 5 7 2 +Prefrontal Cortex 9 9 1 +Pregabalin 5 7 2 +Pregnadienediols 6 6 1 +Pregnadienes 5 5 1 +Pregnadienetriols 6 6 1 +Pregnancy 4 4 1 +Pregnancy Complications 3 3 1 +Pregnancy Complications, Cardiovascular 2 4 2 +Pregnancy Complications, Hematologic 3 4 2 +Pregnancy Complications, Infectious 2 4 2 +Pregnancy Complications, Neoplastic 2 4 2 +Pregnancy Complications, Parasitic 3 5 2 +Pregnancy in Adolescence 5 5 1 +Pregnancy in Diabetics 4 4 1 +Pregnancy in Obesity 4 6 3 +Pregnancy Maintenance 5 5 1 +Pregnancy Outcome 3 5 2 +Pregnancy Proteins 3 3 1 +Pregnancy Rate 3 6 5 +Pregnancy Reduction, Multifetal 4 4 1 +Pregnancy Tests 3 4 3 +Pregnancy Tests, Immunologic 4 5 6 +Pregnancy Trimester, First 4 4 1 +Pregnancy Trimester, Second 4 4 1 +Pregnancy Trimester, Third 4 4 1 +Pregnancy Trimesters 3 3 1 +Pregnancy, Abdominal 5 5 1 +Pregnancy, Angular 5 5 1 +Pregnancy, Animal 5 5 1 +Pregnancy, Cornual 5 5 1 +Pregnancy, Ectopic 4 4 1 +Pregnancy, Heterotopic 5 5 1 +Pregnancy, High-Risk 5 5 1 +Pregnancy, Interstitial 6 6 1 +Pregnancy, Multiple 5 5 1 +Pregnancy, Ovarian 5 5 1 +Pregnancy, Prolonged 4 4 1 +Pregnancy, Quadruplet 6 6 1 +Pregnancy, Quintuplet 6 6 1 +Pregnancy, Triplet 6 6 1 +Pregnancy, Tubal 5 5 1 +Pregnancy, Twin 6 6 1 +Pregnancy, Unplanned 5 5 1 +Pregnancy, Unwanted 5 5 1 +Pregnancy-Associated alpha 2-Macroglobulins 4 7 2 +Pregnancy-Associated Plasma Protein-A 4 7 3 +Pregnancy-Related Death 6 8 4 +Pregnancy-Specific beta 1-Glycoproteins 4 4 1 +Pregnane X Receptor 5 5 2 +Pregnanediol 5 7 2 +Pregnanediones 5 5 1 +Pregnanes 4 4 1 +Pregnanetriol 5 9 2 +Pregnanolone 5 5 1 +Pregnant People 3 3 1 +Pregnatrienes 5 5 1 +Pregnenediones 6 6 1 +Pregnenes 5 5 1 +Pregnenolone 5 6 3 +Pregnenolone Carbonitrile 3 7 2 +Prehypertension 3 3 1 +Preimplantation Diagnosis 4 4 1 +Prejudice 3 4 2 +Prekallikrein 3 8 6 +Preleukemia 3 3 2 +Preliminary Data 4 5 3 +Premarital Examinations 3 3 1 +Premature Birth 6 6 1 +Premature Ejaculation 3 5 4 +Premature Rupture of Fetal Membranes 5 5 1 +Premedication 3 3 1 +Premenopause 5 6 2 +Premenstrual Dysphoric Disorder 4 5 2 +Premenstrual Syndrome 4 4 1 +Prenalterol 6 6 3 +Prenatal Care 3 5 3 +Prenatal Diagnosis 4 4 1 +Prenatal Education 6 7 2 +Prenatal Exposure Delayed Effects 5 5 1 +Prenatal Injuries 4 4 1 +Prenatal Nutritional Physiological Phenomena 5 5 2 +Prenylamine 5 5 1 +Prenylation 2 3 2 +Preoperative Care 3 5 3 +Preoperative Exercise 3 6 5 +Preoperative Period 3 5 2 +Preoptic Area 7 8 2 +Prepaid Health Plans 6 6 1 +Prephenate Dehydratase 6 6 2 +Prephenate Dehydrogenase 5 6 2 +Preprint 3 3 1 +Preprints as Topic 3 5 2 +Prepulse Inhibition 4 4 1 +Presbycusis 6 8 3 +Presbyopia 3 3 1 +Presbytini 12 12 1 +Prescription Drug Diversion 4 5 2 +Prescription Drug Misuse 4 4 2 +Prescription Drug Monitoring Programs 4 5 2 +Prescription Drug Overuse 5 5 2 +Prescription Drugs 2 2 1 +Prescription Fees 5 5 1 +Prescriptions 4 4 1 +Presenilin-1 5 5 1 +Presenilin-2 5 5 1 +Presenilins 4 4 1 +Presenteeism 4 5 2 +Preservation, Biological 2 2 2 +Preservatives, Pharmaceutical 3 4 2 +Pressoreceptors 5 6 4 +Pressure 3 3 1 +Pressure Ulcer 4 4 1 +Pressurized Intraperitoneal Aerosol Chemotherapy 3 4 2 +Presumed Consent 4 5 2 +Presynaptic Terminals 3 7 4 +Pretectal Region 7 7 1 +Prevalence 5 7 4 +Preventive Dentistry 2 3 2 +Preventive Health Services 3 3 1 +Preventive Medicine 4 4 1 +Preventive Psychiatry 5 5 3 +Prevotella 5 6 2 +Prevotella intermedia 6 7 2 +Prevotella melaninogenica 6 7 2 +Prevotella nigrescens 6 7 2 +Prevotella ruminicola 6 7 2 +Priapism 5 5 2 +Price List 3 3 1 +Price Transparency 5 5 2 +Prilocaine 4 5 2 +Primaquine 6 6 1 +Primary Care Nursing 4 4 1 +Primary Cell Culture 4 6 4 +Primary Dysautonomias 3 3 1 +Primary Graft Dysfunction 4 5 2 +Primary Health Care 4 4 1 +Primary Immunodeficiency Diseases 3 3 2 +Primary Myelofibrosis 5 5 1 +Primary Nursing 4 4 1 +Primary Ovarian Insufficiency 4 7 3 +Primary Prevention 4 4 2 +Primary Progressive Nonfluent Aphasia 5 10 8 +Primary Visual Cortex 10 10 2 +Primate Diseases 2 2 1 +Primate T-lymphotropic virus 1 5 5 2 +Primate T-lymphotropic virus 2 5 5 2 +Primate T-lymphotropic virus 3 5 5 2 +Primates 7 7 1 +Primed In Situ Labeling 5 8 6 +Primidone 7 7 1 +Primitive Streak 2 2 1 +Primula 7 7 1 +Primulaceae 8 8 1 +Prince Edward Island 3 5 2 +Principal Component Analysis 5 5 1 +Principle-Based Ethics 3 5 2 +Printers' Marks 7 7 1 +Printing 3 3 1 +Printing, Three-Dimensional 3 5 3 +Prion Diseases 3 4 3 +Prion Proteins 4 6 5 +Prions 3 3 1 +Prior Authorization 6 6 1 +Prisoner Dilemma 3 3 1 +Prisoners 2 2 1 +Prisoners of War 3 3 1 +Prisons 3 5 2 +Pristinamycin 5 5 2 +Privacy 4 6 3 +Private Facilities 2 2 1 +Private Practice 4 4 1 +Private Sector 2 4 2 +Privatization 4 4 1 +Pro-Opiomelanocortin 4 6 7 +Proactive Inhibition 4 5 2 +Proadifen 5 5 1 +Proanthocyanidins 5 7 3 +Probability 2 5 4 +Probability Learning 4 4 1 +Probability Theory 4 4 1 +Probenecid 4 5 2 +Probiotics 4 5 2 +Problem Behavior 4 4 2 +Problem Solving 4 4 2 +Problem-Based Learning 3 4 3 +Problems and Exercises 2 2 1 +Proboscidea Mammal 8 8 1 +Probucol 7 7 1 +Procainamide 4 9 5 +Procaine 7 9 2 +Procalcitonin 4 5 3 +Procarbazine 4 8 3 +Procaterol 5 6 3 +Procedural Pain 5 5 3 +Procedural Sedation 2 2 1 +Procedures and Techniques Utilization 4 5 4 +Process Assessment, Health Care 4 5 2 +Processing Bodies 8 10 2 +Processing Speed 3 4 3 +Prochlorococcus 6 6 1 +Prochloron 6 6 1 +Prochlorophytes 5 5 1 +Prochlorothrix 6 6 1 +Prochlorperazine 4 5 2 +Procollagen 4 6 2 +Procollagen N-Endopeptidase 7 7 2 +Procollagen-Lysine, 2-Oxoglutarate 5-Dioxygenase 6 6 1 +Procollagen-Proline Dioxygenase 7 7 2 +Procrastination 3 3 2 +Proctectomy 4 4 1 +Proctitis 4 5 2 +Proctocolectomy, Restorative 5 5 2 +Proctocolitis 5 6 5 +Proctoscopes 5 5 2 +Proctoscopy 5 7 4 +Procyclidine 4 4 1 +Procyonidae 9 9 1 +Prodigiosin 3 5 2 +Prodigiozan 4 4 1 +Prodromal Symptoms 2 3 2 +Prodrugs 2 2 1 +Product Labeling 4 4 1 +Product Line Management 4 5 2 +Product Packaging 3 3 1 +Product Recalls and Withdrawals 4 4 1 +Product Surveillance, Postmarketing 3 3 1 +Proestrus 4 4 1 +Professional Autonomy 4 4 1 +Professional Competence 3 3 1 +Professional Corporations 4 4 1 +Professional Impairment 5 6 2 +Professional Misconduct 3 5 2 +Professional Practice 3 3 1 +Professional Practice Gaps 3 4 2 +Professional Practice Location 4 4 1 +Professional Review Organizations 3 3 2 +Professional Role 5 5 1 +Professional Staff Committees 3 4 2 +Professional-Family Relations 4 4 1 +Professional-Patient Relations 3 4 2 +Professionalism 4 6 2 +Profilins 5 5 3 +Proflavine 6 6 1 +Progeria 4 4 3 +Progesterone 5 7 3 +Progesterone Congeners 5 5 1 +Progesterone Reductase 6 7 2 +Progesterone-Binding Globulin 6 6 2 +Progestins 6 6 1 +Proglucagon 4 5 4 +Proglumide 5 5 2 +Prognathism 4 7 8 +Prognosis 2 2 1 +Program 3 3 1 +Program Development 3 3 1 +Program Evaluation 3 4 3 +Programmed Cell Death 1 Ligand 2 Protein 4 5 5 +Programmed Cell Death 1 Receptor 4 7 4 +Programmed Instruction 2 2 1 +Programmed Instructions as Topic 4 5 2 +Programming Languages 4 4 1 +Programming, Linear 4 4 1 +Progranulins 3 4 5 +Progression-Free Survival 4 7 6 +Progressive Patient Care 4 4 1 +Proguanil 5 5 1 +Prohibitins 4 4 1 +Proinsulin 4 6 3 +Projection 3 3 1 +Projective Techniques 4 4 1 +Prokaryotic Cells 2 2 1 +Prokaryotic Initiation Factor-1 6 6 1 +Prokaryotic Initiation Factor-2 6 6 1 +Prokaryotic Initiation Factor-3 6 6 1 +Prokaryotic Initiation Factors 5 5 1 +Prolactin 6 6 3 +Prolactin Release-Inhibiting Factors 6 7 4 +Prolactin-Releasing Hormone 5 6 4 +Prolactinoma 4 7 5 +Prolamins 5 5 2 +Prolapse 3 3 1 +Prolidase Deficiency 4 5 4 +Proliferating Cell Nuclear Antigen 4 4 3 +Proline 5 5 1 +Proline Oxidase 5 6 2 +Proline-Directed Protein Kinases 5 8 2 +Proline-Rich Protein Domains 8 8 1 +Prolonged Grief Disorder 3 4 2 +Prolotherapy 3 3 1 +Prolyl Hydroxylases 6 6 2 +Prolyl Oligopeptidases 7 7 1 +Prolyl-Hydroxylase Inhibitors 5 5 1 +Promazine 4 5 2 +Promedol 5 6 2 +Promegestone 7 7 1 +Prometaphase 5 6 4 +Promethazine 4 5 3 +Promethium 4 5 4 +Prometryne 4 4 1 +Promoter Regions, Genetic 5 8 3 +Promyelocytic Leukemia Nuclear Bodies 8 8 1 +Promyelocytic Leukemia Protein 4 5 4 +Promyelocytic Leukemia Zinc Finger Protein 5 5 2 +Pronase 7 7 4 +Pronation 5 5 1 +Prone Position 4 4 1 +Pronephros 2 2 1 +Proof of Concept Study 4 4 1 +Propafenone 4 4 1 +Propaganda 3 3 1 +Propane 5 5 1 +Propanediol Dehydratase 6 6 1 +Propanidid 5 5 2 +Propanil 4 5 2 +Propanolamines 4 4 3 +Propanols 3 3 1 +Propantheline 4 5 3 +Propensity Score 5 6 3 +Properdin 6 6 3 +Prophages 3 3 1 +Prophase 5 6 4 +Prophylactic Mastectomy 3 3 2 +Prophylactic Surgical Procedures 2 2 1 +Propidium 5 5 1 +Propiolactone 3 3 1 +Propionates 4 4 2 +Propionibacteriaceae 4 6 2 +Propionibacterium 5 7 2 +Propionibacterium acnes 6 8 2 +Propionibacterium freudenreichii 6 8 2 +Propionic Acidemia 5 5 2 +Propionigenium 3 5 2 +Propionyl-Coenzyme A Carboxylase 6 6 1 +Propiophenones 3 3 1 +Proplast 5 7 3 +Propofol 7 7 1 +Propofol Infusion Syndrome 3 3 1 +Propolis 5 5 2 +Proportional Hazards Models 4 6 13 +Propoxur 6 6 1 +Propoxycaine 6 9 5 +Propranolol 4 7 5 +Proprioception 3 4 3 +Proprotein Convertase 1 6 7 3 +Proprotein Convertase 2 6 7 3 +Proprotein Convertase 5 6 7 3 +Proprotein Convertase 9 6 7 3 +Proprotein Convertases 5 5 1 +Propyl Gallate 6 9 4 +Propylamines 3 3 1 +Propylbenzilylcholine Mustard 5 6 3 +Propylene Glycol 5 5 1 +Propylene Glycols 4 4 1 +Propyliodone 6 6 1 +Propylthiouracil 7 7 1 +Prorenin Receptor 5 9 5 +Proscillaridin 4 7 2 +Prosencephalon 4 4 1 +Prosopagnosia 5 7 3 +Prosopis 8 8 1 +Prospective Payment Assessment Commission 7 7 1 +Prospective Payment System 6 6 1 +Prospective Studies 6 7 3 +Prospectus 3 3 1 +Prospero-Related Homeobox 1 Protein 5 5 1 +Prospidium 3 4 2 +Prostaglandin Antagonists 3 6 2 +Prostaglandin D2 7 7 2 +Prostaglandin Endoperoxides 5 7 6 +Prostaglandin Endoperoxides, Synthetic 4 7 3 +Prostaglandin H2 7 9 6 +Prostaglandin-E Synthases 5 5 1 +Prostaglandin-Endoperoxide Synthases 4 6 2 +Prostaglandins 5 5 2 +Prostaglandins A 6 6 2 +Prostaglandins A, Synthetic 4 7 3 +Prostaglandins B 6 6 2 +Prostaglandins D 6 6 2 +Prostaglandins E 6 6 2 +Prostaglandins E, Synthetic 4 7 3 +Prostaglandins F 6 6 2 +Prostaglandins F, Synthetic 4 7 3 +Prostaglandins G 6 8 6 +Prostaglandins H 6 8 6 +Prostaglandins I 6 6 2 +Prostaglandins, Synthetic 3 6 3 +Prostanoic Acids 3 3 1 +Prostasin 7 7 2 +Prostate 3 4 2 +Prostate-Specific Antigen 4 8 5 +Prostatectomy 5 5 1 +Prostatein 4 4 1 +Prostatic Diseases 4 4 2 +Prostatic Hyperplasia 5 5 2 +Prostatic Intraepithelial Neoplasia 6 6 1 +Prostatic Neoplasms 4 5 7 +Prostatic Neoplasms, Castration-Resistant 5 6 7 +Prostatic Secretory Proteins 5 5 1 +Prostatism 5 5 1 +Prostatitis 5 5 2 +Prostheses and Implants 2 2 1 +Prosthesis Coloring 4 4 2 +Prosthesis Design 3 3 2 +Prosthesis Failure 3 4 2 +Prosthesis Fitting 2 2 1 +Prosthesis Implantation 2 2 1 +Prosthesis Retention 3 3 1 +Prosthesis-Related Infections 2 4 2 +Prosthodontics 2 4 2 +Protactinium 4 6 5 +Protamine Kinase 6 9 2 +Protamines 4 4 2 +Proteaceae 7 7 1 +Protease Inhibitors 5 5 1 +Protease La 7 9 5 +Protease Nexins 4 4 2 +Proteasome Endopeptidase Complex 4 5 3 +Proteasome Inhibitors 6 6 1 +Protective Agents 3 4 2 +Protective Clothing 3 5 4 +Protective Devices 2 3 2 +Protective Factors 5 7 5 +Proteidae 7 7 1 +Protein Aggregates 2 2 1 +Protein Aggregation, Pathological 3 3 2 +Protein Array Analysis 3 4 2 +Protein Binding 2 3 2 +Protein Biosynthesis 3 4 3 +Protein C 3 5 6 +Protein C Deficiency 4 5 4 +Protein C Inhibitor 5 5 2 +Protein Carbamylation 3 7 6 +Protein Carbonylation 3 7 6 +Protein Conformation 5 5 1 +Protein Conformation, alpha-Helical 7 7 1 +Protein Conformation, beta-Strand 7 7 1 +Protein Corona 3 3 1 +Protein D-Aspartate-L-Isoaspartate Methyltransferase 8 8 1 +Protein Deficiency 5 5 1 +Protein Deglycase DJ-1 4 4 2 +Protein Degradation End Products 3 3 1 +Protein Denaturation 5 5 2 +Protein Disulfide Reductase (Glutathione) 5 5 1 +Protein Disulfide-Isomerases 6 6 1 +Protein Domains 7 7 2 +Protein Engineering 4 4 1 +Protein Folding 3 3 2 +Protein Footprinting 4 6 2 +Protein Glutamine gamma Glutamyltransferase 2 7 7 1 +Protein Hydrolysates 3 3 1 +Protein Inhibitors of Activated STAT 5 5 3 +Protein Interaction Domains and Motifs 8 8 1 +Protein Interaction Mapping 3 3 1 +Protein Interaction Maps 3 3 1 +Protein Isoforms 3 3 1 +Protein Kinase C 5 8 2 +Protein Kinase C beta 6 9 2 +Protein Kinase C zeta 6 9 2 +Protein Kinase C-alpha 6 9 2 +Protein Kinase C-delta 6 9 2 +Protein Kinase C-epsilon 6 9 2 +Protein Kinase C-lambda 6 9 2 +Protein Kinase C-theta 6 9 2 +Protein Kinase D2 5 8 2 +Protein Kinase Inhibitors 5 5 1 +Protein Kinases 6 6 1 +Protein Methyltransferases 6 6 1 +Protein Modification, Translational 3 5 4 +Protein Multimerization 3 3 1 +Protein O-Methyltransferase 7 7 1 +Protein Phosphatase 1 5 7 2 +Protein Phosphatase 2 5 7 2 +Protein Phosphatase 2C 5 7 2 +Protein Phosphatase Inhibitory Proteins 4 4 2 +Protein Precursors 3 3 1 +Protein Prenylation 3 7 6 +Protein Processing, Post-Translational 4 6 4 +Protein Refolding 4 4 2 +Protein Renaturation 5 5 2 +Protein S 3 4 4 +Protein S Deficiency 4 4 3 +Protein Serine-Threonine Kinases 4 7 2 +Protein Sorting Signals 3 5 2 +Protein Splicing 5 7 4 +Protein Stability 3 3 1 +Protein Structural Elements 6 6 1 +Protein Structure, Quaternary 6 6 1 +Protein Structure, Secondary 6 6 1 +Protein Structure, Tertiary 6 6 1 +Protein Subunit Vaccines 6 6 1 +Protein Subunits 3 3 1 +Protein Synthesis Inhibitors 5 5 1 +Protein Translocation Systems 3 3 1 +Protein Transport 3 3 1 +Protein Tyrosine Phosphatase, Non-Receptor Type 1 6 8 2 +Protein Tyrosine Phosphatase, Non-Receptor Type 11 6 8 4 +Protein Tyrosine Phosphatase, Non-Receptor Type 12 6 8 2 +Protein Tyrosine Phosphatase, Non-Receptor Type 13 6 8 2 +Protein Tyrosine Phosphatase, Non-Receptor Type 2 6 8 2 +Protein Tyrosine Phosphatase, Non-Receptor Type 22 6 8 2 +Protein Tyrosine Phosphatase, Non-Receptor Type 3 6 8 2 +Protein Tyrosine Phosphatase, Non-Receptor Type 4 6 8 2 +Protein Tyrosine Phosphatase, Non-Receptor Type 6 6 8 4 +Protein Tyrosine Phosphatases 4 6 2 +Protein Tyrosine Phosphatases, Non-Receptor 5 7 2 +Protein Unfolding 4 4 2 +Protein-Arginine Deiminase Type 1 5 5 1 +Protein-Arginine Deiminase Type 2 5 5 1 +Protein-Arginine Deiminase Type 3 5 5 1 +Protein-Arginine Deiminase Type 4 5 5 1 +Protein-Arginine Deiminase Type 6 5 5 1 +Protein-Arginine Deiminases 4 4 1 +Protein-Arginine N-Methyltransferases 7 7 1 +Protein-Energy Malnutrition 6 6 1 +Protein-Losing Enteropathies 4 4 1 +Protein-Lysine 6-Oxidase 6 6 1 +Protein-Tyrosine Kinases 4 7 2 +Proteinase Inhibitory Proteins, Secretory 3 3 2 +Proteins 2 2 1 +Proteinuria 4 6 4 +Proteobacteria 2 2 1 +Proteogenomics 5 7 4 +Proteoglycan 4 4 5 3 +Proteoglycan Link Protein 4 5 4 +Proteoglycans 3 4 3 +Proteolipids 3 3 2 +Proteolysis 2 3 2 +Proteolysis Targeting Chimera 6 6 1 +Proteome 3 3 1 +Proteomics 4 6 4 +Proteostasis 2 3 2 +Proteostasis Deficiencies 3 3 1 +Proteotoxic Stress 2 3 2 +Protestantism 4 4 1 +Proteus 5 5 2 +Proteus Infections 6 6 1 +Proteus mirabilis 6 6 2 +Proteus penneri 6 6 2 +Proteus Syndrome 4 5 6 +Proteus vulgaris 6 6 2 +Prothionamide 4 5 2 +Prothrombin 3 5 4 +Prothrombin Time 3 6 3 +ProTides 3 3 2 +Proto-Oncogene Mas 6 7 3 +Proto-Oncogene Protein c-ets-1 5 7 3 +Proto-Oncogene Protein c-ets-2 5 7 3 +Proto-Oncogene Protein c-fli-1 5 7 3 +Proto-Oncogene Protein Spi-1 5 6 4 +Proto-Oncogene Proteins 5 5 1 +Proto-Oncogene Proteins A-raf 7 7 1 +Proto-Oncogene Proteins B-raf 7 10 3 +Proto-Oncogene Proteins c-abl 5 8 3 +Proto-Oncogene Proteins c-akt 5 8 3 +Proto-Oncogene Proteins c-bcl-2 5 6 3 +Proto-Oncogene Proteins c-bcl-6 4 6 3 +Proto-Oncogene Proteins c-bcr 5 8 5 +Proto-Oncogene Proteins c-cbl 6 6 2 +Proto-Oncogene Proteins c-crk 5 6 4 +Proto-Oncogene Proteins c-ets 4 6 3 +Proto-Oncogene Proteins c-fes 5 8 3 +Proto-Oncogene Proteins c-fos 4 6 4 +Proto-Oncogene Proteins c-fyn 6 9 3 +Proto-Oncogene Proteins c-hck 6 9 3 +Proto-Oncogene Proteins c-jun 4 6 4 +Proto-Oncogene Proteins c-kit 6 9 6 +Proto-Oncogene Proteins c-maf 6 7 3 +Proto-Oncogene Proteins c-mdm2 4 6 3 +Proto-Oncogene Proteins c-met 6 9 5 +Proto-Oncogene Proteins c-mos 6 9 3 +Proto-Oncogene Proteins c-myb 4 6 3 +Proto-Oncogene Proteins c-myc 4 6 4 +Proto-Oncogene Proteins c-pim-1 5 8 3 +Proto-Oncogene Proteins c-raf 7 10 3 +Proto-Oncogene Proteins c-rel 4 6 4 +Proto-Oncogene Proteins c-ret 5 9 7 +Proto-Oncogene Proteins c-sis 4 6 6 +Proto-Oncogene Proteins c-vav 5 7 6 +Proto-Oncogene Proteins c-yes 6 9 3 +Proto-Oncogene Proteins p21(ras) 6 9 4 +Proto-Oncogene Proteins pp60(c-src) 6 9 3 +Proto-Oncogenes 8 8 1 +Protocadherins 6 7 4 +Protocatechuate-3,4-Dioxygenase 6 6 1 +Protochlorophyllide 5 7 3 +Proton Ionophores 4 6 3 +Proton Magnetic Resonance Spectroscopy 5 5 1 +Proton Pump Inhibitors 5 5 1 +Proton Pumps 7 7 2 +Proton Therapy 4 4 1 +Proton-Coupled Folate Transporter 6 9 6 +Proton-Motive Force 2 3 2 +Proton-Phosphate Symporters 6 8 5 +Proton-Translocating ATPases 6 8 4 +Protons 4 6 4 +Protoplasts 2 2 1 +Protoporphyria, Erythropoietic 4 5 4 +Protoporphyrinogen Oxidase 4 5 2 +Protoporphyrins 4 7 4 +Prototheca 4 4 1 +Protoveratrines 5 5 2 +Protozoan Infections 3 3 1 +Protozoan Infections, Animal 3 4 3 +Protozoan Proteins 3 3 1 +Protozoan Vaccines 4 4 1 +Protriptyline 5 8 2 +Proventriculus 3 3 1 +Providencia 5 5 2 +Provider-Sponsored Organizations 4 7 4 +Proviruses 2 2 1 +Provitamins 6 7 3 +Proximal Femoral Fractures 4 6 4 +Proxy 3 3 1 +PrP 27-30 Protein 6 6 1 +PrPC Proteins 5 5 1 +PrPSc Proteins 5 5 1 +Prune Belly Syndrome 4 4 1 +Prunella 9 9 1 +Prunus 10 10 1 +Prunus africana 11 11 1 +Prunus armeniaca 11 11 1 +Prunus avium 11 11 1 +Prunus domestica 11 11 1 +Prunus dulcis 11 11 1 +Prunus persica 11 11 1 +Prurigo 3 3 1 +Pruritus 3 4 2 +Pruritus Ani 4 6 2 +Pruritus Vulvae 4 6 3 +Prussia 3 3 1 +Prussian Blue Reaction 5 7 8 +Psacalium 8 8 1 +Pseudallescheria 4 4 1 +Pseudarthrosis 4 4 1 +Pseudoalteromonas 5 5 2 +Pseudoautosomal Regions 5 6 3 +Pseudobulbar Affect 3 3 1 +Pseudobulbar Palsy 4 5 2 +Pseudocowpox Virus 6 6 1 +Pseudoephedrine 5 5 4 +Pseudogenes 6 6 1 +Pseudohypoaldosteronism 4 7 4 +Pseudohypoparathyroidism 4 5 5 +Pseudolymphoma 3 3 1 +Pseudomonadaceae 4 5 2 +Pseudomonas 5 6 2 +Pseudomonas aeruginosa 6 7 2 +Pseudomonas aeruginosa Exotoxin A 4 7 4 +Pseudomonas alcaligenes 6 7 2 +Pseudomonas chlororaphis 6 7 2 +Pseudomonas fluorescens 6 7 2 +Pseudomonas fragi 6 7 2 +Pseudomonas Infections 5 5 1 +Pseudomonas mendocina 6 7 2 +Pseudomonas oleovorans 6 7 2 +Pseudomonas Phages 3 3 1 +Pseudomonas pseudoalcaligenes 6 7 2 +Pseudomonas putida 6 7 2 +Pseudomonas stutzeri 6 7 2 +Pseudomonas syringae 6 7 2 +Pseudomonas Vaccines 5 5 1 +Pseudomyxoma Peritonei 6 7 2 +Pseudonocardia 4 7 2 +Pseudophakia 3 3 1 +Pseudopodia 4 4 1 +Pseudopregnancy 5 5 1 +Pseudopseudohypoparathyroidism 5 6 5 +Pseudorabies 2 5 5 +Pseudorabies Vaccines 5 5 1 +Pseudoscience 4 4 1 +Pseudothrombocytopenia 5 5 2 +Pseudotsuga 8 8 1 +Pseudotumor Cerebri 5 5 1 +Pseudouridine 5 6 3 +Pseudowintera 8 8 1 +Pseudoxanthoma Elasticum 3 5 7 +Psidium 8 8 1 +Psilocybe 5 5 1 +Psilocybin 4 7 4 +Psittaciformes 6 6 1 +Psittacosis 7 7 1 +Psittacula 9 9 1 +Psoas Abscess 4 4 1 +Psoas Muscles 4 4 1 +Psoralea 8 8 1 +Psoriasis 4 4 1 +Psoroptidae 8 8 1 +Psychiatric Aides 5 6 2 +Psychiatric Department, Hospital 6 6 2 +Psychiatric Nursing 4 4 2 +Psychiatric Rehabilitation 3 5 3 +Psychiatric Somatic Therapies 2 2 1 +Psychiatric Status Rating Scales 4 4 1 +Psychiatrists 4 5 2 +Psychiatry 3 3 2 +Psychiatry in Literature 4 4 1 +Psycho-Oncology 3 5 3 +Psychoacoustics 4 6 3 +Psychoanalysis 4 4 1 +Psychoanalytic Interpretation 2 2 1 +Psychoanalytic Theory 3 3 1 +Psychoanalytic Therapy 3 3 1 +Psychodidae 10 10 1 +Psychodrama 4 5 2 +Psychogenic Nonepileptic Seizures 4 5 2 +Psycholinguistics 2 4 3 +Psychological Distance 4 4 2 +Psychological Distress 3 3 1 +Psychological First Aid 4 4 1 +Psychological Growth 3 3 1 +Psychological Phenomena 1 1 1 +Psychological Safety 3 5 2 +Psychological Techniques 2 2 2 +Psychological Tests 2 2 1 +Psychological Theory 2 2 1 +Psychological Trauma 4 4 1 +Psychological Warfare 6 6 1 +Psychological Well-Being 3 7 4 +Psychologists 4 4 1 +Psychology 3 3 1 +Psychology, Adolescent 4 4 1 +Psychology, Applied 2 2 1 +Psychology, Child 4 4 1 +Psychology, Clinical 4 4 1 +Psychology, Comparative 4 4 1 +Psychology, Developmental 4 4 1 +Psychology, Educational 3 4 2 +Psychology, Experimental 4 4 1 +Psychology, Industrial 3 4 2 +Psychology, Medical 2 4 2 +Psychology, Military 3 3 1 +Psychology, Positive 4 4 1 +Psychology, Social 2 4 2 +Psychology, Sports 4 4 1 +Psychometrics 3 3 1 +Psychomotor Agitation 4 6 6 +Psychomotor Disorders 3 5 3 +Psychomotor Performance 2 3 3 +Psychoneuroimmunology 4 5 2 +Psychopathology 3 3 1 +Psychopharmacology 3 4 4 +Psychophysics 3 3 2 +Psychophysiologic Disorders 4 4 1 +Psychophysiology 2 4 3 +Psychoses, Alcoholic 4 5 4 +Psychoses, Substance-Induced 3 4 4 +Psychosexual Development 4 4 2 +Psychosine 4 5 5 +Psychosocial Deprivation 3 6 3 +Psychosocial Functioning 2 6 3 +Psychosocial Intervention 3 3 1 +Psychosocial Support Systems 6 6 1 +Psychosomatic Medicine 4 4 1 +Psychosurgery 3 3 2 +Psychotherapeutic Processes 3 3 1 +Psychotherapists 4 4 1 +Psychotherapy 2 2 1 +Psychotherapy, Brief 3 3 1 +Psychotherapy, Group 4 4 1 +Psychotherapy, Multiple 3 3 1 +Psychotherapy, Psychodynamic 3 3 1 +Psychotherapy, Rational-Emotive 3 3 1 +Psychotic Disorders 3 3 1 +Psychotria 9 9 1 +Psychotropic Drugs 5 5 1 +Psychrobacter 5 6 2 +Psyllium 5 5 1 +PTB-Associated Splicing Factor 4 6 3 +PTEN Phosphohydrolase 4 6 3 +PTEN-Induced Putative Kinase 5 8 2 +Pteridaceae 7 7 1 +Pteridines 4 4 1 +Pteridium 8 8 1 +Pterins 3 5 2 +Pteris 8 8 1 +Pterocarpans 4 8 4 +Pterocarpus 8 8 1 +Pteroylpolyglutamic Acids 7 7 1 +Pterygium 3 3 1 +Pterygoid Muscles 3 5 2 +Pterygopalatine Fossa 5 5 1 +Pterygota 6 6 1 +Puberty 3 4 2 +Puberty Inhibitors 3 6 2 +Puberty Suppression 3 5 3 +Puberty, Delayed 3 3 1 +Puberty, Precocious 3 3 1 +Pubic Bone 6 6 1 +Pubic Symphysis 4 4 1 +Pubic Symphysis Diastasis 4 5 3 +Public Assistance 5 5 1 +Public Expenditures 3 3 1 +Public Facilities 2 2 1 +Public Health 2 3 3 +Public Health Administration 3 3 1 +Public Health Dentistry 2 4 2 +Public Health Informatics 3 3 1 +Public Health Infrastructure 2 2 1 +Public Health Nursing 4 4 1 +Public Health Practice 3 3 1 +Public Health Surveillance 5 8 4 +Public Health Systems Research 2 4 2 +Public Housing 3 5 3 +Public Nondiscrimination Policies 5 5 1 +Public Opinion 4 4 1 +Public Policy 4 5 3 +Public Relations 3 3 1 +Public Reporting of Healthcare Data 3 3 1 +Public Sector 2 4 2 +Public Service Announcement 2 2 1 +Public Service Announcements as Topic 3 3 1 +Public-Private Sector Partnerships 3 3 2 +Publication Bias 3 3 1 +Publication Components 1 1 1 +Publication Formats 1 1 1 +Publications 4 4 1 +Published Erratum 2 2 2 +Publishing 2 2 1 +PubMed 5 8 4 +Puccinia 4 4 1 +Pudendal Nerve 6 6 1 +Pudendal Neuralgia 5 6 3 +Pueraria 8 8 1 +Puerperal Disorders 4 4 1 +Puerperal Infection 3 5 3 +Puerto Rico 4 5 2 +Pulicaria 8 8 1 +Pulmonaria 8 8 1 +Pulmonary Adenomatosis, Ovine 3 6 4 +Pulmonary Alveolar Proteinosis 3 3 1 +Pulmonary Alveoli 3 3 1 +Pulmonary Arterial Hypertension 4 4 1 +Pulmonary Artery 4 4 1 +Pulmonary Aspergillosis 4 5 3 +Pulmonary Atelectasis 3 3 1 +Pulmonary Atresia 4 5 3 +Pulmonary Blastoma 4 6 2 +Pulmonary Circulation 3 4 2 +Pulmonary Diffusing Capacity 3 6 2 +Pulmonary Disease, Chronic Obstructive 4 5 2 +Pulmonary Edema 3 3 1 +Pulmonary Elimination 3 5 3 +Pulmonary Embolism 3 5 2 +Pulmonary Emphysema 5 6 2 +Pulmonary Eosinophilia 3 6 2 +Pulmonary Fibrosis 4 4 2 +Pulmonary Gas Exchange 4 5 3 +Pulmonary Heart Disease 3 3 1 +Pulmonary Infarction 4 6 4 +Pulmonary Medicine 4 4 1 +Pulmonary Sclerosing Hemangioma 4 6 3 +Pulmonary Stretch Receptors 5 6 3 +Pulmonary Subvalvular Stenosis 5 5 2 +Pulmonary Surfactant-Associated Protein A 4 6 2 +Pulmonary Surfactant-Associated Protein B 4 4 2 +Pulmonary Surfactant-Associated Protein C 4 4 3 +Pulmonary Surfactant-Associated Protein D 4 6 2 +Pulmonary Surfactant-Associated Proteins 3 3 1 +Pulmonary Surfactants 5 5 1 +Pulmonary Surgical Procedures 3 3 1 +Pulmonary Valve 4 4 1 +Pulmonary Valve Insufficiency 4 4 1 +Pulmonary Valve Stenosis 4 4 2 +Pulmonary Veins 4 4 1 +Pulmonary Veno-Occlusive Disease 3 3 2 +Pulmonary Ventilation 3 5 2 +Pulmonary Wedge Pressure 5 5 1 +Pulmonologists 4 5 2 +Pulp Capping and Pulpectomy Agents 3 5 3 +Pulpectomy 3 3 1 +Pulpitis 4 4 1 +Pulpotomy 3 3 1 +Pulsatile Flow 3 5 2 +Pulsatilla 9 9 1 +Pulse 4 4 1 +Pulse Radiolysis 4 4 1 +Pulse Therapy, Drug 4 4 1 +Pulse Wave Analysis 4 4 1 +Pulsed Radiofrequency Treatment 3 5 4 +Pulvinar 9 9 1 +Pulvinus 4 4 1 +Puma 10 10 1 +Punched-Card Systems 4 4 1 +Punctal Plugs 3 3 1 +Punctures 2 2 2 +Punishment 4 5 2 +Pupa 3 6 2 +Pupil 5 5 2 +Pupil Disorders 2 4 3 +Purchasing, Hospital 6 6 2 +Pure Autonomic Failure 4 4 1 +Purine Nucleosides 3 5 2 +Purine Nucleotides 3 5 2 +Purine-Nucleoside Phosphorylase 6 6 1 +Purine-Pyrimidine Metabolism, Inborn Errors 4 4 2 +Purinergic Agents 5 5 2 +Purinergic Agonists 6 6 2 +Purinergic Antagonists 6 6 2 +Purinergic P1 Receptor Agonists 7 7 2 +Purinergic P1 Receptor Antagonists 7 7 2 +Purinergic P2 Receptor Agonists 7 7 2 +Purinergic P2 Receptor Antagonists 7 7 2 +Purinergic P2X Receptor Agonists 8 8 2 +Purinergic P2X Receptor Antagonists 8 8 2 +Purinergic P2Y Receptor Agonists 8 8 2 +Purinergic P2Y Receptor Antagonists 8 8 2 +Purines 4 4 1 +Purinones 5 5 1 +Purkinje Cells 3 9 3 +Purkinje Fibers 4 4 1 +Puromycin 4 7 4 +Puromycin Aminonucleoside 5 8 7 +Purple Membrane 2 4 2 +Purpura 4 4 3 +Purpura Fulminans 5 5 3 +Purpura, Hyperglobulinemic 4 5 5 +Purpura, Thrombocytopenic 2 6 6 +Purpura, Thrombocytopenic, Idiopathic 3 7 8 +Purpura, Thrombotic Thrombocytopenic 4 7 6 +Pursuit, Smooth 3 3 1 +Putamen 10 10 1 +Putaminal Hemorrhage 6 8 6 +Putrescine 5 5 2 +Puumala virus 6 6 1 +PUVA Therapy 4 4 1 +Pycnodysostosis 3 5 4 +Pycnoporus 6 6 1 +Pyelectasis 3 5 3 +Pyelitis 5 7 3 +Pyelocystitis 5 8 6 +Pyelonephritis 6 8 6 +Pyelonephritis, Xanthogranulomatous 7 9 6 +Pyloric Antrum 5 5 1 +Pyloric Stenosis 5 5 1 +Pyloric Stenosis, Hypertrophic 6 6 1 +Pyloromyotomy 3 5 3 +Pylorus 5 5 1 +Pyocins 5 5 1 +Pyocyanine 3 5 2 +Pyoderma 3 3 1 +Pyoderma Gangrenosum 4 4 3 +Pyometra 5 6 2 +Pyomyositis 3 5 3 +Pyonephrosis 5 7 3 +Pyracantha 10 10 1 +Pyramidal Cells 3 3 2 +Pyramidal Tracts 4 4 2 +Pyran Copolymer 3 5 4 +Pyranocoumarins 4 6 3 +Pyrans 3 3 1 +Pyrantel 4 4 3 +Pyrantel Pamoate 5 5 3 +Pyrantel Tartrate 5 5 3 +Pyrazinamide 4 4 1 +Pyrazines 3 3 1 +Pyrazoles 4 4 1 +Pyrazolones 5 5 1 +Pyrenes 3 6 2 +Pyrethrins 6 6 1 +Pyricularia grisea 4 4 2 +Pyridazines 3 3 1 +Pyridines 3 3 1 +Pyridinium Compounds 4 4 1 +Pyridinolcarbamate 6 6 1 +Pyridones 4 4 1 +Pyridostigmine Bromide 5 5 1 +Pyridoxal 6 6 1 +Pyridoxal Kinase 6 6 1 +Pyridoxal Phosphate 3 7 2 +Pyridoxamine 6 6 1 +Pyridoxaminephosphate Oxidase 5 5 1 +Pyridoxic Acid 4 5 3 +Pyridoxine 6 6 1 +Pyriform Sinus 4 4 1 +Pyrilamine 5 5 1 +Pyrimethamine 4 4 1 +Pyrimidine Dimers 3 5 4 +Pyrimidine Nucleosides 3 4 2 +Pyrimidine Nucleotides 3 4 2 +Pyrimidine Phosphorylases 6 6 1 +Pyrimidines 3 3 1 +Pyrimidinones 4 4 1 +Pyrin 4 4 2 +Pyrin Domain 10 10 1 +Pyrithiamine 4 5 2 +Pyrithioxin 4 4 1 +Pyrobaculum 5 5 1 +Pyrococcus 5 5 1 +Pyrococcus abyssi 6 6 1 +Pyrococcus furiosus 6 6 1 +Pyrococcus horikoshii 6 6 1 +Pyrodictiaceae 4 4 1 +Pyrogallol 7 7 1 +Pyrogens 4 4 1 +Pyroglobulins 7 7 3 +Pyroglutamate Hydrolase 5 5 1 +Pyroglutamyl-Peptidase I 7 7 1 +Pyroglyphidae 8 8 1 +Pyrola 9 9 1 +Pyrolaceae 7 7 1 +Pyrolysis 2 2 1 +Pyrones 4 4 1 +Pyronine 5 5 1 +Pyrophosphatases 5 5 1 +Pyroptosis 5 5 1 +Pyrroles 4 4 1 +Pyrrolidines 3 3 1 +Pyrrolidinones 4 4 1 +Pyrrolidonecarboxylic Acid 5 5 3 +Pyrroline Carboxylate Reductases 5 5 1 +Pyrrolizidine Alkaloids 3 4 2 +Pyrrolnitrin 5 5 1 +Pyrroloiminoquinones 4 6 3 +Pyrularia 8 8 1 +Pyrus 10 10 1 +Pyruvaldehyde 4 4 1 +Pyruvate Carboxylase 5 5 1 +Pyruvate Carboxylase Deficiency Disease 4 6 7 +Pyruvate Decarboxylase 6 6 1 +Pyruvate Dehydrogenase (Lipoamide) 5 6 3 +Pyruvate Dehydrogenase (Lipoamide)-Phosphatase 5 7 2 +Pyruvate Dehydrogenase Acetyl-Transferring Kinase 5 8 2 +Pyruvate Dehydrogenase Complex 4 4 2 +Pyruvate Dehydrogenase Complex Deficiency Disease 4 6 10 +Pyruvate Kinase 6 6 1 +Pyruvate Metabolism, Inborn Errors 5 5 2 +Pyruvate Oxidase 6 6 1 +Pyruvate Synthase 6 6 1 +Pyruvate, Orthophosphate Dikinase 6 6 1 +Pyruvates 4 4 1 +Pyruvic Acid 5 5 1 +Pyrvinium Compounds 6 6 1 +Pythiosis 2 4 2 +Pythium 4 4 1 +Pyuria 3 6 4 +Q beta Replicase 7 7 1 +Q Fever 5 5 1 +Q-SNARE Proteins 6 6 2 +Q-Sort 3 3 1 +Qa-SNARE Proteins 7 7 2 +Qatar 5 5 1 +Qb-SNARE Proteins 7 7 2 +Qc-SNARE Proteins 7 7 2 +Qi 3 8 2 +Qigong 5 5 2 +Quackery 4 4 1 +Quadriceps Muscle 4 4 1 +Quadricuspid Aortic Valve 4 5 4 +Quadriplegia 4 5 2 +Quadruplets 3 3 1 +Quail 7 7 1 +Qualitative Research 5 5 1 +Quality Assurance, Health Care 2 3 2 +Quality Control 3 3 1 +Quality Improvement 3 3 2 +Quality Indicators, Health Care 3 3 2 +Quality of Health Care 2 2 2 +Quality of Life 2 6 3 +Quality-Adjusted Life Years 5 7 5 +Quantitative Light-Induced Fluorescence 3 6 2 +Quantitative Phase Imaging 4 6 2 +Quantitative Structure-Activity Relationship 4 5 2 +Quantitative Trait Loci 6 6 1 +Quantitative Trait, Heritable 3 3 1 +Quantum Dots 2 5 2 +Quantum Mechanics 5 5 1 +Quantum Theory 4 4 1 +Quarantine 5 5 1 +Quartz 4 5 3 +Quartz Crystal Microbalance Techniques 3 3 2 +Quasispecies 3 3 1 +Quassia 8 8 1 +Quassins 5 5 1 +Quaternary Ammonium Compounds 3 4 3 +Quaternary Prevention 3 4 4 +Quebec 5 5 1 +Queensland 4 5 2 +Quercetin 8 8 2 +Quercus 10 10 1 +Quetiapine Fumarate 5 6 2 +Quick Diagnosis Units 4 4 1 +Quillaja 10 10 1 +Quillaja Saponins 4 4 1 +Quinacrine 6 6 1 +Quinacrine Mustard 6 7 2 +Quinaldines 5 5 1 +Quinapril 6 6 1 +Quinazolines 4 4 1 +Quinazolinones 5 5 1 +Quinestrol 8 8 2 +Quinic Acid 4 5 2 +Quinidine 4 5 3 +Quinine 4 5 3 +Quinolines 4 4 1 +Quinolinic Acid 5 5 1 +Quinolinic Acids 4 4 1 +Quinolinium Compounds 5 5 1 +Quinolizidine Alkaloids 3 6 2 +Quinolizidines 5 5 1 +Quinolizines 4 4 1 +Quinolones 5 5 1 +Quinone Reductases 5 5 1 +Quinones 2 2 1 +Quinoxalines 4 4 1 +Quinpirole 4 5 2 +Quintuplets 3 3 1 +Quinuclidines 3 3 1 +Quinuclidinyl Benzilate 4 7 3 +Quipazine 4 5 2 +Quisqualic Acid 3 6 2 +Quorum Sensing 3 3 2 +R Factors 4 4 1 +R-Loop Structures 4 6 2 +R-SNARE Proteins 6 6 2 +R-Spondins 6 6 3 +rab GTP-Binding Proteins 6 8 3 +rab1 GTP-Binding Proteins 7 9 3 +rab11 GTP-Binding Proteins 7 9 3 +rab2 GTP-Binding Protein 7 9 3 +rab27 GTP-Binding Proteins 7 9 3 +rab3 GTP-Binding Proteins 7 9 3 +rab3A GTP-Binding Protein 8 10 3 +rab4 GTP-Binding Proteins 7 9 3 +rab5 GTP-Binding Proteins 7 9 3 +rab7 GTP-Binding Proteins 7 9 3 +Rabbits 8 8 1 +Rabeprazole 5 6 3 +Rabies 6 6 1 +Rabies Vaccines 5 5 1 +Rabies virus 7 7 1 +Rabphilin-3A 4 5 5 +rac GTP-Binding Proteins 7 9 3 +rac1 GTP-Binding Protein 8 10 3 +RAC2 GTP-Binding Protein 8 10 3 +Raccoon Dogs 10 10 1 +Raccoons 10 10 1 +Race Factors 4 4 1 +Race Relations 5 5 1 +Racemases and Epimerases 4 4 1 +Racemethionine 5 5 4 +Racepinephrine 5 10 5 +Racial Groups 5 5 1 +Racism 4 6 4 +Raclopride 4 8 5 +Racquet Sports 5 5 1 +Rad51 Recombinase 4 5 3 +Rad52 DNA Repair and Recombination Protein 4 4 1 +Radar 5 5 1 +Radial Artery 4 4 1 +Radial Basis Function Networks 3 6 2 +Radial Head and Neck Fractures 4 5 4 +Radial Nerve 6 6 1 +Radial Neuropathy 5 5 1 +Radiation 2 2 1 +Radiation Chimera 3 4 2 +Radiation Dosage 3 4 3 +Radiation Dose Hypofractionation 5 5 1 +Radiation Dosimeters 3 3 1 +Radiation Effects 3 4 2 +Radiation Equipment and Supplies 2 2 1 +Radiation Exposure 3 5 2 +Radiation Fibrosis Syndrome 3 7 5 +Radiation Genetics 4 5 2 +Radiation Genomics 4 6 3 +Radiation Hybrid Mapping 5 5 1 +Radiation Injuries 2 6 4 +Radiation Injuries, Experimental 3 7 5 +Radiation Leukemia Virus 6 6 2 +Radiation Monitoring 3 5 3 +Radiation Oncologists 5 6 4 +Radiation Oncology 4 5 2 +Radiation Pneumonitis 3 5 4 +Radiation Protection 4 4 1 +Radiation Tolerance 2 2 2 +Radiation, Ionizing 3 3 1 +Radiation, Nonionizing 3 3 1 +Radiation-Protective Agents 4 5 2 +Radiation-Sensitizing Agents 4 4 1 +Radicular Cyst 4 7 5 +Radiculopathy 4 4 1 +Radiesthesia 4 4 1 +Radio 5 6 4 +Radio Frequency Identification Device 2 4 2 +Radio Waves 4 5 3 +Radioactive Fallout 3 4 2 +Radioactive Hazard Release 4 4 1 +Radioactive Pollutants 2 2 1 +Radioactive Tracers 4 5 2 +Radioactive Waste 3 6 5 +Radioactivity 2 2 1 +Radioallergosorbent Test 5 6 7 +Radiobiology 4 4 1 +Radiochemistry 4 4 1 +Radiodermatitis 3 5 3 +Radioembolization, Therapeutic 4 4 2 +Radiofrequency Ablation 3 3 2 +Radiofrequency Therapy 2 2 1 +Radiographic Image Enhancement 5 6 3 +Radiographic Image Interpretation, Computer-Assisted 4 8 4 +Radiographic Magnification 5 5 1 +Radiography 4 4 1 +Radiography, Abdominal 5 5 1 +Radiography, Bitewing 4 6 2 +Radiography, Dental 3 5 2 +Radiography, Dental, Digital 4 7 4 +Radiography, Dual-Energy Scanned Projection 5 7 4 +Radiography, Interventional 3 5 2 +Radiography, Panoramic 4 6 2 +Radiography, Thoracic 5 5 1 +Radioimmunoassay 4 4 3 +Radioimmunodetection 3 5 3 +Radioimmunoprecipitation Assay 4 6 8 +Radioimmunosorbent Test 5 5 4 +Radioimmunotherapy 3 5 3 +Radioisotope Dilution Technique 3 4 2 +Radioisotope Renography 5 6 3 +Radioisotope Teletherapy 4 4 1 +Radioisotopes 3 3 1 +Radioligand Assay 3 4 4 +Radiologic and Imaging Nursing 4 4 1 +Radiologic Health 3 3 1 +Radiologists 4 5 2 +Radiology 3 3 1 +Radiology Department, Hospital 6 6 2 +Radiology Information Systems 4 4 1 +Radiology, Interventional 4 4 1 +Radiometric Dating 3 3 1 +Radiometry 2 2 1 +Radiomics 4 4 1 +Radionuclide Angiography 5 5 3 +Radionuclide Generators 3 3 1 +Radionuclide Imaging 4 4 2 +Radionuclide Ventriculography 5 6 5 +Radiopharmaceuticals 4 5 3 +Radiostereometric Analysis 5 6 2 +Radiosurgery 3 4 3 +Radiotherapy 2 2 1 +Radiotherapy Dosage 3 3 1 +Radiotherapy Planning, Computer-Assisted 3 8 2 +Radiotherapy Setup Errors 3 4 2 +Radiotherapy, Adjuvant 3 3 2 +Radiotherapy, Computer-Assisted 3 7 2 +Radiotherapy, Conformal 4 8 2 +Radiotherapy, High-Energy 3 3 1 +Radiotherapy, Image-Guided 3 3 1 +Radiotherapy, Intensity-Modulated 5 9 2 +Radium 4 5 6 +Radius 6 6 1 +Radius Fractures 3 4 2 +Radon 4 5 4 +Radon Daughters 5 6 4 +raf Kinases 6 9 3 +Raffinose 5 5 1 +Rafoxanide 5 6 3 +Rage 4 4 1 +Rahnella 4 5 2 +Railroads 3 3 1 +Rain 3 6 5 +Rainforest 5 6 2 +ral GTP-Binding Proteins 6 8 3 +ral Guanine Nucleotide Exchange Factor 6 6 2 +Raloxifene Hydrochloride 9 9 1 +Ralstonia 6 6 1 +Ralstonia pickettii 7 7 1 +Ralstonia solanacearum 7 7 1 +Raltegravir Potassium 5 5 1 +Ramipril 4 4 1 +Ramucirumab 9 9 3 +ran GTP-Binding Protein 4 8 6 +Rana catesbeiana 8 8 1 +Rana clamitans 8 8 1 +Rana esculenta 8 8 1 +Rana pipiens 8 8 1 +Rana ridibunda 8 8 1 +Rana temporaria 8 8 1 +Ranavirus 4 4 1 +Random Allocation 4 5 4 +Random Amplified Polymorphic DNA Technique 3 5 2 +Random Forest 3 4 2 +Randomized Controlled Trial 5 5 1 +Randomized Controlled Trial, Veterinary 4 4 1 +Randomized Controlled Trials as Topic 7 8 3 +Range of Motion, Articular 3 4 2 +Ranibizumab 9 9 3 +Ranidae 7 7 1 +Ranitidine 4 4 1 +RANK Ligand 5 6 3 +Ranolazine 4 6 3 +Ranula 3 3 2 +Ranunculaceae 8 8 1 +Ranunculales 7 7 1 +Ranunculus 9 9 1 +Ranvier's Nodes 4 6 7 +rap GTP-Binding Proteins 6 8 3 +rap1 GTP-Binding Proteins 7 9 3 +Rapamycin-Insensitive Companion of mTOR Protein 4 10 6 +Rape 5 5 3 +Rapeseed Oil 4 5 2 +Raphanus 8 8 1 +Raphe Nuclei 8 9 3 +Rapid Diagnostic Tests 3 5 3 +Rapid On-site Evaluation 5 5 1 +Rapid Sequence Induction and Intubation 4 4 3 +Rappaport 5 5 1 +Raptors 6 6 1 +Rare Books 6 6 1 +Rare Diseases 4 4 1 +ras GTPase-Activating Proteins 6 6 2 +ras Guanine Nucleotide Exchange Factors 6 6 2 +Ras Homolog Enriched in Brain Protein 5 8 4 +ras Proteins 6 8 3 +ras-GRF1 7 7 2 +Rat-Bite Fever 5 5 1 +Rate Setting and Review 4 5 2 +Ratibida 8 8 1 +Rationalization 3 3 1 +Rats 10 10 1 +Rats, Brattleboro 12 12 1 +Rats, Gunn 12 12 1 +Rats, Hairless 12 12 1 +Rats, Inbred ACI 7 12 2 +Rats, Inbred BB 7 12 2 +Rats, Inbred BN 7 12 2 +Rats, Inbred BUF 7 12 2 +Rats, Inbred Dahl 7 12 2 +Rats, Inbred F344 7 12 2 +Rats, Inbred LEC 7 12 2 +Rats, Inbred Lew 7 12 2 +Rats, Inbred OLETF 7 12 2 +Rats, Inbred SHR 7 12 2 +Rats, Inbred Strains 6 11 2 +Rats, Inbred WF 7 12 2 +Rats, Inbred WKY 7 12 2 +Rats, Long-Evans 11 11 1 +Rats, Mutant Strains 11 11 1 +Rats, Nude 12 12 1 +Rats, Sprague-Dawley 11 11 1 +Rats, Transgenic 5 11 2 +Rats, Wistar 11 11 1 +Rats, Zucker 12 12 1 +Rauscher Virus 6 6 2 +Rauwolfia 9 9 1 +RAW 264.7 Cells 4 5 2 +Raw Foods 3 4 2 +Raynaud Disease 4 6 3 +Razoxane 5 5 1 +Re-Epithelialization 2 4 3 +Re-Irradiation 3 3 2 +RE1-Silencing Transcription Factor 5 5 2 +Reaction Time 3 3 4 +Reactive Attachment Disorder 3 3 1 +Reactive Inhibition 4 5 2 +Reactive Nitrogen Species 3 3 4 +Reactive Oxygen Species 3 3 3 +Reading 4 4 1 +Reading Frames 4 4 1 +Reagent Kits, Diagnostic 2 5 3 +Reagent Strips 3 6 3 +Reagins 7 7 3 +Real-Time Polymerase Chain Reaction 5 5 1 +Reality Testing 5 5 2 +Reality Therapy 3 3 1 +Reassortant Viruses 2 2 1 +Reboxetine 5 5 1 +Rec A Recombinases 4 6 2 +Receptor Activator of Nuclear Factor-kappa B 8 8 1 +Receptor Activity-Modifying Protein 1 5 6 2 +Receptor Activity-Modifying Protein 2 5 6 2 +Receptor Activity-Modifying Protein 3 5 6 2 +Receptor Activity-Modifying Proteins 4 5 2 +Receptor Aggregation 2 2 1 +Receptor Cross-Talk 2 2 1 +Receptor for Advanced Glycation End Products 3 5 2 +Receptor Protein-Tyrosine Kinases 5 8 3 +Receptor Tyrosine Kinase-like Orphan Receptors 6 9 3 +Receptor, Adenosine A1 8 8 2 +Receptor, Adenosine A2A 9 9 2 +Receptor, Adenosine A2B 9 9 2 +Receptor, Adenosine A3 8 8 2 +Receptor, Anaphylatoxin C5a 6 7 2 +Receptor, Angiotensin, Type 1 7 7 2 +Receptor, Angiotensin, Type 2 7 7 2 +Receptor, Bradykinin B1 7 8 3 +Receptor, Bradykinin B2 7 8 3 +Receptor, Cannabinoid, CB1 7 7 1 +Receptor, Cannabinoid, CB2 7 7 1 +Receptor, Cholecystokinin A 7 8 4 +Receptor, Cholecystokinin B 7 8 4 +Receptor, Ciliary Neurotrophic Factor 8 8 1 +Receptor, Endothelin A 7 7 2 +Receptor, Endothelin B 7 7 2 +Receptor, EphA1 7 10 3 +Receptor, EphA2 7 10 3 +Receptor, EphA3 7 10 3 +Receptor, EphA4 7 10 3 +Receptor, EphA5 7 10 3 +Receptor, EphA6 7 10 3 +Receptor, EphA7 7 10 3 +Receptor, EphA8 7 10 3 +Receptor, EphB1 7 10 3 +Receptor, EphB2 7 10 3 +Receptor, EphB3 7 10 3 +Receptor, EphB4 7 10 3 +Receptor, EphB5 7 10 3 +Receptor, EphB6 7 7 1 +Receptor, ErbB-3 4 10 6 +Receptor, ErbB-4 4 10 6 +Receptor, Farnesoid X-Activated 4 4 1 +Receptor, Fibroblast Growth Factor, Type 1 6 9 4 +Receptor, Fibroblast Growth Factor, Type 2 6 9 4 +Receptor, Fibroblast Growth Factor, Type 3 6 9 5 +Receptor, Fibroblast Growth Factor, Type 4 6 9 4 +Receptor, Fibroblast Growth Factor, Type 5 8 8 1 +Receptor, Galanin, Type 1 7 7 1 +Receptor, Galanin, Type 2 7 7 1 +Receptor, Galanin, Type 3 7 7 1 +Receptor, IGF Type 1 6 9 4 +Receptor, IGF Type 2 8 8 1 +Receptor, Insulin 6 9 4 +Receptor, Interferon alpha-beta 8 8 1 +Receptor, Macrophage Colony-Stimulating Factor 6 9 6 +Receptor, Melanocortin, Type 1 7 9 4 +Receptor, Melanocortin, Type 2 7 9 4 +Receptor, Melanocortin, Type 3 7 9 4 +Receptor, Melanocortin, Type 4 7 9 4 +Receptor, Melatonin, MT1 7 7 1 +Receptor, Melatonin, MT2 7 7 1 +Receptor, Metabotropic Glutamate 5 7 9 2 +Receptor, Muscarinic M1 7 8 2 +Receptor, Muscarinic M2 7 8 2 +Receptor, Muscarinic M3 7 8 2 +Receptor, Muscarinic M4 7 8 2 +Receptor, Muscarinic M5 7 8 2 +Receptor, Nerve Growth Factor 8 8 1 +Receptor, Notch1 5 6 2 +Receptor, Notch2 5 6 2 +Receptor, Notch3 5 6 2 +Receptor, Notch4 5 6 3 +Receptor, PAR-1 7 8 7 +Receptor, PAR-2 6 6 2 +Receptor, Parathyroid Hormone, Type 1 7 7 2 +Receptor, Parathyroid Hormone, Type 2 7 7 2 +Receptor, Platelet-Derived Growth Factor alpha 7 10 4 +Receptor, Platelet-Derived Growth Factor beta 7 10 4 +Receptor, Serotonin, 5-HT1A 8 8 3 +Receptor, Serotonin, 5-HT1B 8 8 4 +Receptor, Serotonin, 5-HT1D 8 8 4 +Receptor, Serotonin, 5-HT1F 8 8 3 +Receptor, Serotonin, 5-HT2A 8 8 3 +Receptor, Serotonin, 5-HT2B 8 8 3 +Receptor, Serotonin, 5-HT2C 8 8 3 +Receptor, TIE-1 7 10 3 +Receptor, TIE-2 7 10 3 +Receptor, Transforming Growth Factor-beta Type I 5 8 4 +Receptor, Transforming Growth Factor-beta Type II 5 8 4 +Receptor, trkA 6 9 4 +Receptor, trkB 6 9 4 +Receptor, trkC 6 9 4 +Receptor-CD3 Complex, Antigen, T-Cell 6 8 3 +Receptor-Interacting Protein Serine-Threonine Kinase 2 6 9 7 +Receptor-Interacting Protein Serine-Threonine Kinases 5 8 7 +Receptor-Like Protein Tyrosine Phosphatases 4 7 3 +Receptor-Like Protein Tyrosine Phosphatases, Class 1 5 8 3 +Receptor-Like Protein Tyrosine Phosphatases, Class 2 6 8 2 +Receptor-Like Protein Tyrosine Phosphatases, Class 3 6 8 2 +Receptor-Like Protein Tyrosine Phosphatases, Class 4 6 8 2 +Receptor-Like Protein Tyrosine Phosphatases, Class 5 6 8 2 +Receptor-Like Protein Tyrosine Phosphatases, Class 7 6 8 2 +Receptor-Like Protein Tyrosine Phosphatases, Class 8 6 8 2 +Receptors for Activated C Kinase 4 4 1 +Receptors, Adenosine A2 8 8 2 +Receptors, Adipokine 5 5 1 +Receptors, Adiponectin 6 6 1 +Receptors, Adrenergic 7 7 3 +Receptors, Adrenergic, alpha 8 8 3 +Receptors, Adrenergic, alpha-1 9 9 3 +Receptors, Adrenergic, alpha-2 9 9 3 +Receptors, Adrenergic, beta 8 8 3 +Receptors, Adrenergic, beta-1 9 9 3 +Receptors, Adrenergic, beta-2 9 9 3 +Receptors, Adrenergic, beta-3 9 9 3 +Receptors, Adrenomedullin 6 6 2 +Receptors, Albumin 6 6 1 +Receptors, Amino Acid 6 6 1 +Receptors, AMPA 8 9 4 +Receptors, Androgen 5 5 1 +Receptors, Angiotensin 6 6 2 +Receptors, Antigen 6 6 1 +Receptors, Antigen, B-Cell 6 7 3 +Receptors, Antigen, T-Cell 7 7 1 +Receptors, Antigen, T-Cell, alpha-beta 8 8 1 +Receptors, Antigen, T-Cell, gamma-delta 8 8 1 +Receptors, Artificial 4 5 3 +Receptors, Aryl Hydrocarbon 4 5 3 +Receptors, Atrial Natriuretic Factor 6 7 3 +Receptors, Autocrine Motility Factor 5 6 2 +Receptors, Biogenic Amine 5 5 1 +Receptors, Bombesin 6 7 3 +Receptors, Bradykinin 6 7 3 +Receptors, Calcitonin 6 6 2 +Receptors, Calcitonin Gene-Related Peptide 6 7 3 +Receptors, Calcitriol 4 4 1 +Receptors, Calcium-Sensing 6 6 1 +Receptors, Cannabinoid 6 6 1 +Receptors, Catecholamine 6 6 3 +Receptors, CCR 7 8 2 +Receptors, CCR1 8 9 2 +Receptors, CCR10 8 9 2 +Receptors, CCR2 8 9 2 +Receptors, CCR3 8 9 2 +Receptors, CCR4 8 9 2 +Receptors, CCR5 7 9 3 +Receptors, CCR6 8 9 2 +Receptors, CCR7 8 9 2 +Receptors, CCR8 8 9 2 +Receptors, Cell Surface 4 4 1 +Receptors, Chemokine 6 7 2 +Receptors, Chimeric Antigen 5 8 3 +Receptors, Cholecystokinin 6 7 4 +Receptors, Cholinergic 6 6 1 +Receptors, Collagen 5 5 1 +Receptors, Colony-Stimulating Factor 7 7 2 +Receptors, Complement 6 6 1 +Receptors, Complement 3b 7 7 1 +Receptors, Complement 3d 6 7 2 +Receptors, Concanavalin A 7 7 1 +Receptors, Coronavirus 6 6 1 +Receptors, Corticotropin 7 7 3 +Receptors, Corticotropin-Releasing Hormone 6 7 4 +Receptors, CXCR 7 8 2 +Receptors, CXCR3 8 9 2 +Receptors, CXCR4 7 9 3 +Receptors, CXCR5 8 9 2 +Receptors, CXCR6 6 9 3 +Receptors, Cyclic AMP 7 7 2 +Receptors, Cytoadhesin 7 7 1 +Receptors, Cytokine 6 6 1 +Receptors, Cytoplasmic and Nuclear 3 3 1 +Receptors, Death Domain 5 5 1 +Receptors, Dopamine 7 7 3 +Receptors, Dopamine D1 8 8 3 +Receptors, Dopamine D2 8 8 3 +Receptors, Dopamine D3 9 9 3 +Receptors, Dopamine D4 9 9 3 +Receptors, Dopamine D5 9 9 3 +Receptors, Drug 3 3 1 +Receptors, Ectodysplasin 8 8 1 +Receptors, Eicosanoid 6 6 1 +Receptors, Endothelin 6 6 2 +Receptors, Enterotoxin 6 7 3 +Receptors, Eph Family 6 9 3 +Receptors, Epoprostenol 8 8 1 +Receptors, Erythropoietin 8 8 2 +Receptors, Estradiol 6 6 1 +Receptors, Estrogen 5 5 2 +Receptors, Fc 6 6 1 +Receptors, Fibrinogen 7 7 1 +Receptors, Fibroblast Growth Factor 7 7 1 +Receptors, Fibronectin 7 7 1 +Receptors, Formyl Peptide 6 6 3 +Receptors, FSH 6 8 4 +Receptors, G-Protein-Coupled 5 5 1 +Receptors, GABA 7 7 1 +Receptors, GABA-A 6 8 8 +Receptors, GABA-B 6 8 2 +Receptors, Galanin 6 7 3 +Receptors, Gastrointestinal Hormone 6 6 1 +Receptors, Ghrelin 6 6 1 +Receptors, Glucagon 6 7 2 +Receptors, Glucocorticoid 5 5 1 +Receptors, Glutamate 7 7 1 +Receptors, Glycine 6 8 8 +Receptors, Gonadotropin 7 7 1 +Receptors, Granulocyte Colony-Stimulating Factor 8 8 2 +Receptors, Granulocyte-Macrophage Colony-Stimulating Factor 8 8 2 +Receptors, Growth Factor 6 6 1 +Receptors, Guanylate Cyclase-Coupled 5 6 2 +Receptors, Histamine 6 6 2 +Receptors, Histamine H1 6 7 3 +Receptors, Histamine H2 6 7 3 +Receptors, Histamine H3 7 7 2 +Receptors, Histamine H4 6 7 3 +Receptors, HIV 6 6 1 +Receptors, IgE 7 7 1 +Receptors, IgG 7 7 1 +Receptors, Immunologic 5 5 1 +Receptors, Interferon 7 7 1 +Receptors, Interleukin 7 7 1 +Receptors, Interleukin-1 8 8 1 +Receptors, Interleukin-1 Type I 9 9 1 +Receptors, Interleukin-1 Type II 9 9 1 +Receptors, Interleukin-10 8 8 1 +Receptors, Interleukin-11 8 8 1 +Receptors, Interleukin-12 8 8 1 +Receptors, Interleukin-13 8 8 1 +Receptors, Interleukin-15 8 8 1 +Receptors, Interleukin-16 8 8 1 +Receptors, Interleukin-17 8 8 1 +Receptors, Interleukin-18 8 8 1 +Receptors, Interleukin-2 8 8 1 +Receptors, Interleukin-21 8 8 1 +Receptors, Interleukin-3 8 8 3 +Receptors, Interleukin-4 8 8 1 +Receptors, Interleukin-4, Type I 9 9 1 +Receptors, Interleukin-4, Type II 9 9 2 +Receptors, Interleukin-5 8 8 1 +Receptors, Interleukin-6 8 8 1 +Receptors, Interleukin-7 8 8 1 +Receptors, Interleukin-8 8 9 3 +Receptors, Interleukin-8A 9 10 3 +Receptors, Interleukin-8B 9 10 3 +Receptors, Interleukin-9 8 8 1 +Receptors, Invertebrate Peptide 6 6 1 +Receptors, Ionotropic Glutamate 7 8 4 +Receptors, Islet Amyloid Polypeptide 6 6 1 +Receptors, KIR 7 7 1 +Receptors, KIR2DL1 8 8 1 +Receptors, KIR2DL2 8 8 1 +Receptors, KIR2DL3 8 8 1 +Receptors, KIR2DL4 8 8 1 +Receptors, KIR2DL5 8 8 1 +Receptors, KIR3DL1 8 8 1 +Receptors, KIR3DL2 8 8 1 +Receptors, KIR3DS1 8 8 1 +Receptors, Kisspeptin-1 6 6 2 +Receptors, Laminin 6 6 1 +Receptors, LDL 6 6 1 +Receptors, Leptin 6 6 1 +Receptors, Leukocyte-Adhesion 7 7 1 +Receptors, Leukotriene 7 7 1 +Receptors, Leukotriene B4 8 8 1 +Receptors, LH 6 8 4 +Receptors, LHRH 6 7 4 +Receptors, Lipoprotein 5 5 1 +Receptors, Lipoxin 7 7 1 +Receptors, Lymphocyte Homing 5 6 5 +Receptors, Lysophosphatidic Acid 7 7 1 +Receptors, Lysophospholipid 6 6 1 +Receptors, Lysosphingolipid 7 7 1 +Receptors, Mating Factor 7 7 1 +Receptors, Melanocortin 6 8 4 +Receptors, Melatonin 4 6 2 +Receptors, Metabotropic Glutamate 6 8 2 +Receptors, Mineralocorticoid 5 5 1 +Receptors, Mitogen 6 6 1 +Receptors, Muscarinic 6 7 2 +Receptors, N-Acetylglucosamine 4 5 2 +Receptors, N-Methyl-D-Aspartate 8 9 4 +Receptors, Natural Cytotoxicity Triggering 7 7 1 +Receptors, Natural Killer Cell 6 6 1 +Receptors, Nerve Growth Factor 7 7 1 +Receptors, Neurokinin-1 7 8 3 +Receptors, Neurokinin-2 7 8 3 +Receptors, Neurokinin-3 7 8 3 +Receptors, Neuropeptide 6 6 2 +Receptors, Neuropeptide Y 6 7 3 +Receptors, Neurotensin 6 7 3 +Receptors, Neurotransmitter 5 5 1 +Receptors, Nicotinic 6 8 5 +Receptors, NK Cell Lectin-Like 7 7 1 +Receptors, Notch 4 5 2 +Receptors, Odorant 6 6 1 +Receptors, Oncostatin M 7 7 1 +Receptors, Oncostatin M, Type II 8 8 1 +Receptors, Opioid 6 7 3 +Receptors, Opioid, delta 7 8 3 +Receptors, Opioid, kappa 7 8 3 +Receptors, Opioid, mu 7 8 3 +Receptors, OSM-LIF 7 7 1 +Receptors, OX40 4 8 2 +Receptors, Oxidized LDL 7 7 1 +Receptors, Oxytocin 6 7 4 +Receptors, Pancreatic Hormone 6 6 1 +Receptors, Parathyroid Hormone 6 6 2 +Receptors, Pattern Recognition 6 6 1 +Receptors, Peptide 5 5 1 +Receptors, Phencyclidine 4 4 1 +Receptors, Pheromone 6 6 1 +Receptors, Phospholipase A2 5 5 1 +Receptors, Pituitary Adenylate Cyclase-Activating Polypeptide 6 6 1 +Receptors, Pituitary Adenylate Cyclase-Activating Polypeptide, Type I 7 7 1 +Receptors, Pituitary Hormone 6 6 1 +Receptors, Pituitary Hormone-Regulating Hormone 6 6 1 +Receptors, Platelet-Derived Growth Factor 6 9 4 +Receptors, Polymeric Immunoglobulin 7 7 1 +Receptors, Presynaptic 6 6 1 +Receptors, Progesterone 5 5 1 +Receptors, Prolactin 7 8 3 +Receptors, Prostaglandin 7 7 1 +Receptors, Prostaglandin E 8 8 1 +Receptors, Prostaglandin E, EP1 Subtype 9 9 1 +Receptors, Prostaglandin E, EP2 Subtype 9 9 1 +Receptors, Prostaglandin E, EP3 Subtype 9 9 1 +Receptors, Prostaglandin E, EP4 Subtype 9 9 1 +Receptors, Proteinase-Activated 5 5 1 +Receptors, Purinergic 6 6 2 +Receptors, Purinergic P1 7 7 2 +Receptors, Purinergic P2 7 7 2 +Receptors, Purinergic P2X 7 8 5 +Receptors, Purinergic P2X1 8 9 5 +Receptors, Purinergic P2X2 8 9 5 +Receptors, Purinergic P2X3 8 9 5 +Receptors, Purinergic P2X4 8 9 5 +Receptors, Purinergic P2X5 6 9 7 +Receptors, Purinergic P2X7 8 9 5 +Receptors, Purinergic P2Y 8 8 2 +Receptors, Purinergic P2Y1 9 9 2 +Receptors, Purinergic P2Y12 9 9 2 +Receptors, Purinergic P2Y2 9 9 2 +Receptors, Retinoic Acid 4 4 2 +Receptors, Scavenger 6 7 2 +Receptors, Serotonin 6 6 3 +Receptors, Serotonin, 5-HT1 7 7 3 +Receptors, Serotonin, 5-HT2 7 7 3 +Receptors, Serotonin, 5-HT3 6 8 6 +Receptors, Serotonin, 5-HT4 7 7 3 +Receptors, sigma 7 8 3 +Receptors, Somatomedin 7 7 1 +Receptors, Somatostatin 6 7 5 +Receptors, Somatotropin 7 7 2 +Receptors, Steroid 4 4 2 +Receptors, Tachykinin 6 7 3 +Receptors, Thrombin 6 7 7 +Receptors, Thrombopoietin 7 7 1 +Receptors, Thromboxane 7 7 1 +Receptors, Thromboxane A2, Prostaglandin H2 8 8 2 +Receptors, Thyroid Hormone 4 6 2 +Receptors, Thyrotropin 7 7 3 +Receptors, Thyrotropin-Releasing Hormone 7 7 3 +Receptors, TIE 6 9 3 +Receptors, TNF-Related Apoptosis-Inducing Ligand 6 8 2 +Receptors, Transferrin 5 5 2 +Receptors, Transforming Growth Factor beta 7 7 2 +Receptors, Tumor Necrosis Factor 7 7 1 +Receptors, Tumor Necrosis Factor, Member 10c 9 9 1 +Receptors, Tumor Necrosis Factor, Member 14 6 8 2 +Receptors, Tumor Necrosis Factor, Member 25 6 8 2 +Receptors, Tumor Necrosis Factor, Member 6b 9 9 1 +Receptors, Tumor Necrosis Factor, Type I 6 8 2 +Receptors, Tumor Necrosis Factor, Type II 8 8 1 +Receptors, Urokinase Plasminogen Activator 5 6 5 +Receptors, Vascular Endothelial Growth Factor 6 9 4 +Receptors, Vasoactive Intestinal Peptide 7 7 4 +Receptors, Vasoactive Intestinal Peptide, Type II 8 8 1 +Receptors, Vasoactive Intestinal Polypeptide, Type I 8 8 1 +Receptors, Vasopressin 6 7 4 +Receptors, Very Late Antigen 7 7 1 +Receptors, Virus 5 5 1 +Receptors, Vitronectin 8 8 1 +Receptors, Wnt 5 5 1 +Recidivism 4 4 2 +Recipient Vessels 3 3 3 +Recognition, Psychology 5 5 1 +Recombinant Fusion Proteins 4 4 1 +Recombinant Proteins 3 3 1 +Recombinases 3 3 1 +Recombination, Genetic 2 2 1 +Recombinational DNA Repair 3 4 3 +Recommended Dietary Allowances 5 8 4 +Records 3 5 5 +Recoverin 4 7 5 +Recovery of Function 2 2 1 +Recovery Room 5 5 1 +RecQ Helicases 5 7 2 +Recreation 3 3 1 +Recreation Therapy 3 6 2 +Recreational Drug Use 3 3 1 +Recruitment Detection, Audiologic 5 5 1 +Recruitment, Neurophysiological 4 4 2 +Rectal Absorption 4 7 4 +Rectal Diseases 4 4 1 +Rectal Fistula 4 6 4 +Rectal Neoplasms 6 7 5 +Rectal Prolapse 5 5 2 +Rectocele 4 5 2 +Rectovaginal Fistula 5 7 7 +Rectum 5 5 2 +Rectus Abdominis 5 5 1 +Recurrence 4 4 1 +Recurrent Laryngeal Nerve 7 7 4 +Recurrent Laryngeal Nerve Injuries 4 7 5 +Recurrent Neural Networks 3 6 2 +Recycling 6 8 2 +Red Cross 4 5 2 +Red Fluorescent Protein 4 4 1 +Red Light 4 6 4 +Red Meat 4 5 2 +Red Nucleus 8 8 1 +Red-Cell Aplasia, Pure 4 4 1 +Reduced Folate Carrier Protein 6 9 9 +Reducing Agents 5 5 1 +Reduviidae 8 8 1 +Reed-Sternberg Cells 2 2 1 +Reelin Protein 5 7 6 +Refeeding Syndrome 4 4 1 +Reference Books 6 6 1 +Reference Books, Medical 7 7 1 +Reference Standards 3 3 1 +Reference Values 3 3 1 +Referral and Consultation 4 4 1 +Referred Pain 5 5 3 +Reflex 3 5 4 +Reflex Sympathetic Dystrophy 4 5 2 +Reflex, Abdominal 4 6 3 +Reflex, Abnormal 3 6 5 +Reflex, Acoustic 3 6 4 +Reflex, Babinski 4 6 3 +Reflex, Monosynaptic 4 4 1 +Reflex, Oculocardiac 4 4 1 +Reflex, Pupillary 4 6 3 +Reflex, Righting 3 6 4 +Reflex, Startle 4 6 4 +Reflex, Stretch 4 6 3 +Reflex, Trigeminocardiac 4 4 1 +Reflex, Vestibulo-Ocular 3 4 2 +Reflexotherapy 3 3 1 +Refraction, Ocular 2 5 3 +Refractive Errors 2 2 1 +Refractive Surgical Procedures 3 3 1 +Refractometry 3 4 2 +Refractory Period, Electrophysiological 3 4 4 +Refractory Period, Psychological 4 4 3 +Refrigeration 3 3 2 +Refsum Disease 4 6 11 +Refsum Disease, Infantile 5 6 6 +Refugee Camps 3 3 1 +Refugees 2 2 1 +Refugium 3 4 2 +Refusal to Participate 6 6 1 +Refusal to Treat 4 5 2 +Refuse Disposal 6 7 2 +Regeneration 2 2 1 +Regenerative Endodontics 3 5 2 +Regenerative Medicine 3 3 1 +Regional Blood Flow 4 4 1 +Regional Health Planning 3 3 1 +Regional Medical Programs 4 4 1 +Registries 4 6 4 +Regression Analysis 4 5 3 +Regression, Psychology 3 3 1 +Regulated Cell Death 3 3 1 +Regulatory Elements, Transcriptional 7 7 1 +Regulatory Factor X Transcription Factors 5 5 2 +Regulatory Factor X1 6 6 2 +Regulatory Sequences, Nucleic Acid 4 5 2 +Regulatory Sequences, Ribonucleic Acid 5 6 3 +Regulatory-Associated Protein of mTOR 4 10 6 +Regulon 5 5 1 +Rehabilitation 2 5 4 +Rehabilitation Centers 3 3 1 +Rehabilitation Nursing 4 4 2 +Rehabilitation of Speech and Language Disorders 3 6 2 +Rehabilitation Research 5 5 1 +Rehabilitation, Vocational 3 6 3 +Rehmannia 8 8 1 +Rehydration Solutions 3 3 1 +Reiki 4 4 2 +Reimbursement Mechanisms 5 5 1 +Reimbursement, Disproportionate Share 6 6 1 +Reimbursement, Incentive 6 6 1 +Reindeer 10 10 1 +Reinfection 5 5 1 +Reinforcement Machine Learning 5 6 2 +Reinforcement Schedule 5 5 1 +Reinforcement, Psychology 4 4 1 +Reinforcement, Social 5 5 1 +Reinforcement, Verbal 5 5 1 +Reinjuries 2 2 1 +Reishi 7 7 1 +Rejection, Psychology 4 4 1 +Rejuvenation 2 2 1 +Relapsing Fever 4 7 2 +Relational Autonomy 5 7 2 +Relative Biological Effectiveness 5 5 1 +Relative Energy Deficiency in Sport 3 3 1 +Relative Value Scales 4 6 2 +Relaxation 3 3 1 +Relaxation Therapy 4 4 2 +Relaxin 4 5 3 +Relief Work 4 4 2 +Religion 2 2 1 +Religion and Medicine 3 3 1 +Religion and Psychology 2 3 2 +Religion and Science 3 3 1 +Religion and Sex 3 3 1 +Religious Missions 3 4 2 +Religious Personnel 3 3 1 +Religious Philosophies 3 3 1 +REM Sleep Behavior Disorder 5 5 2 +REM Sleep Parasomnias 4 4 2 +Remedial Teaching 3 4 2 +Remifentanil 4 5 2 +Reminder Systems 3 6 2 +Remission Induction 2 2 1 +Remission, Spontaneous 2 5 2 +Remote Consultation 5 6 3 +Remote Patient Monitoring 5 6 2 +Remote Sensing Technology 3 6 3 +Remoxipride 4 8 5 +Remuneration 4 4 1 +Remyelination 4 4 2 +Renal Agents 5 5 1 +Renal Aminoacidurias 4 7 4 +Renal Artery 4 4 1 +Renal Artery Obstruction 4 6 4 +Renal Blood Flow, Effective 4 5 2 +Renal Circulation 3 4 2 +Renal Colic 3 5 4 +Renal Dialysis 3 3 2 +Renal Elimination 3 5 3 +Renal Insufficiency 4 6 3 +Renal Insufficiency, Chronic 5 7 4 +Renal Nutcracker Syndrome 4 6 3 +Renal Plasma Flow 4 5 2 +Renal Plasma Flow, Effective 4 5 2 +Renal Reabsorption 4 6 4 +Renal Replacement Therapy 2 2 1 +Renal Tubular Transport, Inborn Errors 3 6 6 +Renal Veins 4 4 1 +Renewable Energy 4 4 1 +Renibacterium 5 5 2 +Renilla 6 6 1 +Renin 6 7 3 +Renin Inhibitors 6 6 1 +Renin-Angiotensin System 2 4 2 +Renshaw Cells 4 4 2 +Reoperation 2 2 1 +Reoviridae 4 4 1 +Reoviridae Infections 4 4 1 +Reperfusion 3 3 2 +Reperfusion Injury 3 4 2 +Repetition Priming 5 5 1 +Repetitive Sequences, Amino Acid 5 7 2 +Repetitive Sequences, Nucleic Acid 4 5 2 +Replantation 3 3 1 +Replica Techniques 5 6 4 +Replication Origin 6 6 2 +Replication Protein A 4 4 1 +Replication Protein C 4 7 4 +Replicon 5 5 1 +Replisomes 4 8 2 +Representation Machine Learning 5 6 2 +Repression, Psychology 3 3 1 +Repression-Sensitization 4 4 1 +Repressor Proteins 4 4 2 +Reproducibility of Results 3 5 4 +Reproduction 3 3 1 +Reproduction, Asexual 4 4 1 +Reproductive and Urinary Physiological Phenomena 1 1 1 +Reproductive Behavior 3 4 2 +Reproductive Control Agents 4 4 2 +Reproductive Health 3 3 1 +Reproductive Health Services 3 3 1 +Reproductive History 4 4 3 +Reproductive Isolation 3 3 2 +Reproductive Medicine 3 3 1 +Reproductive Physiological Phenomena 2 2 1 +Reproductive Rights 4 5 2 +Reproductive Techniques 2 2 2 +Reproductive Techniques, Assisted 3 3 2 +Reproductive Tract Infections 2 5 5 +Reptiles 5 5 1 +Reptilian Proteins 3 3 1 +Republic of Belarus 4 4 1 +Republic of Korea 5 5 1 +Republic of North Macedonia 4 4 1 +Rescue Work 4 4 1 +Research 3 3 1 +Research Design 3 4 2 +Research Embryo Creation 3 3 1 +Research Personnel 3 3 1 +Research Report 4 5 2 +Research Subjects 2 2 1 +Research Support as Topic 4 4 1 +Research Support, American Recovery and Reinvestment Act 3 3 1 +Research Support, N.I.H., Extramural 4 4 1 +Research Support, N.I.H., Intramural 4 4 1 +Research Support, Non-U.S. Gov't 2 2 1 +Research Support, U.S. Gov't, Non-P.H.S. 3 3 1 +Research Support, U.S. Gov't, P.H.S. 3 3 1 +Research Support, U.S. Government 2 2 1 +Researcher-Subject Relations 5 5 1 +Resedaceae 8 8 1 +Reserpine 6 9 3 +Residence Characteristics 3 5 2 +Residential Facilities 2 3 2 +Residential Segregation 5 7 3 +Residential Treatment 4 4 1 +Residual Volume 5 8 2 +Resilience, Psychological 2 2 1 +Resin Cements 4 7 5 +Resins, Plant 4 4 2 +Resins, Synthetic 3 6 5 +Resistance Training 4 7 5 +Resistant Starch 4 6 6 +Resistin 4 5 5 +Resonance Frequency Analysis 3 3 1 +Resorcinols 7 7 1 +Resource Allocation 3 3 1 +Resource Guide 2 2 1 +Resource-Limited Settings 4 4 2 +Respect 3 3 1 +Respiration 3 3 1 +Respiration Disorders 2 2 1 +Respiration, Artificial 3 4 3 +Respiratory Aerosols and Droplets 3 3 2 +Respiratory Aspiration 3 3 2 +Respiratory Aspiration of Gastric Contents 4 8 3 +Respiratory Burst 3 3 2 +Respiratory Care Units 5 5 1 +Respiratory Center 6 6 1 +Respiratory Dead Space 3 3 1 +Respiratory Distress Syndrome 3 3 2 +Respiratory Distress Syndrome, Newborn 4 4 3 +Respiratory Function Tests 4 4 1 +Respiratory Hypersensitivity 2 4 2 +Respiratory Insufficiency 3 3 1 +Respiratory Mechanics 4 4 1 +Respiratory Mucosa 2 4 2 +Respiratory Muscles 4 4 1 +Respiratory Paralysis 4 5 3 +Respiratory Physiological Phenomena 2 2 1 +Respiratory Protective Devices 3 5 3 +Respiratory Rate 4 5 2 +Respiratory Sinus Arrhythmia 5 5 1 +Respiratory Sounds 3 4 3 +Respiratory Syncytial Virus Infections 7 7 1 +Respiratory Syncytial Virus Vaccines 5 5 1 +Respiratory Syncytial Virus, Bovine 9 9 1 +Respiratory Syncytial Virus, Human 9 9 1 +Respiratory Syncytial Viruses 8 8 1 +Respiratory System 1 1 1 +Respiratory System Abnormalities 2 3 2 +Respiratory System Agents 4 4 1 +Respiratory Therapists 4 4 1 +Respiratory Therapy 2 2 1 +Respiratory Therapy Department, Hospital 6 6 2 +Respiratory Tract Absorption 3 6 4 +Respiratory Tract Diseases 1 1 1 +Respiratory Tract Fistula 2 4 2 +Respiratory Tract Infections 2 2 2 +Respiratory Tract Neoplasms 2 4 2 +Respiratory Transport 3 4 2 +Respiratory-Gated Imaging Techniques 4 4 2 +Respirovirus 7 7 1 +Respirovirus Infections 6 6 1 +Respite Care 5 6 3 +Response Elements 6 9 6 +Response Evaluation Criteria in Solid Tumors 4 7 3 +Rest 4 4 1 +Restaurants 2 5 2 +Resting Phase, Cell Cycle 4 4 1 +Restless Legs Syndrome 2 5 5 +Restraint, Physical 3 3 2 +Restriction Mapping 3 5 2 +Resuscitation 3 3 1 +Resuscitation Orders 4 5 5 +Resveratrol 8 9 2 +Rete Testis 5 5 1 +Retention in Care 4 5 2 +Retention, Psychology 5 5 1 +Reticular Formation 5 5 1 +Reticulin 4 4 2 +Reticulocyte Count 5 8 7 +Reticulocytes 3 5 5 +Reticulocytosis 3 3 2 +Reticuloendotheliosis virus 6 6 2 +Reticuloendotheliosis Viruses, Avian 5 5 2 +Reticuloendotheliosis, Avian 3 3 2 +Reticulum 3 3 1 +Retina 3 3 1 +Retinal Arterial Macroaneurysm 4 5 3 +Retinal Artery 4 4 2 +Retinal Artery Occlusion 3 4 2 +Retinal Bipolar Cells 4 5 5 +Retinal Cone Photoreceptor Cells 6 7 6 +Retinal Degeneration 3 3 2 +Retinal Dehydrogenase 4 7 3 +Retinal Detachment 3 3 1 +Retinal Diseases 2 2 1 +Retinal Drusen 4 4 1 +Retinal Dysplasia 3 4 5 +Retinal Dystrophies 4 4 1 +Retinal Ganglion Cells 5 5 3 +Retinal Hemorrhage 3 5 3 +Retinal Horizontal Cells 5 5 3 +Retinal Necrosis Syndrome, Acute 4 4 1 +Retinal Neoplasms 3 4 3 +Retinal Neovascularization 3 5 2 +Retinal Neurons 4 4 3 +Retinal Perforations 3 3 1 +Retinal Photoreceptor Cell Inner Segment 6 7 6 +Retinal Photoreceptor Cell Outer Segment 6 7 6 +Retinal Pigment Epithelium 4 4 2 +Retinal Pigments 3 3 1 +Retinal Rod Photoreceptor Cells 6 7 6 +Retinal Telangiectasis 3 4 2 +Retinal Vasculitis 3 4 2 +Retinal Vein 4 4 2 +Retinal Vein Occlusion 3 6 3 +Retinal Vessels 3 3 1 +Retinaldehyde 3 10 6 +Retinitis 3 3 1 +Retinitis Pigmentosa 3 5 3 +Retinoblastoma 3 6 7 +Retinoblastoma Binding Proteins 4 5 4 +Retinoblastoma Protein 4 5 4 +Retinoblastoma-Binding Protein 1 5 6 4 +Retinoblastoma-Binding Protein 2 5 8 6 +Retinoblastoma-Binding Protein 4 5 7 13 +Retinoblastoma-Binding Protein 7 5 7 12 +Retinoblastoma-Like Protein p107 4 5 3 +Retinoblastoma-Like Protein p130 4 5 3 +Retinoic Acid 4-Hydroxylase 5 8 3 +Retinoic Acid Receptor alpha 5 5 2 +Retinoic Acid Receptor gamma 5 5 2 +Retinoid Isomerohydrolase 4 5 2 +Retinoid X Receptor alpha 6 6 1 +Retinoid X Receptor beta 6 6 1 +Retinoid X Receptor gamma 6 6 1 +Retinoid X Receptors 5 5 2 +Retinoids 4 9 4 +Retinol O-Fatty-Acyltransferase 5 5 1 +Retinol-Binding Proteins 4 4 1 +Retinol-Binding Proteins, Cellular 5 5 1 +Retinol-Binding Proteins, Interstitial 4 5 2 +Retinol-Binding Proteins, Plasma 4 6 5 +Retinopathy of Prematurity 3 4 2 +Retinoschisis 4 4 1 +Retinoscopes 4 4 1 +Retinoscopy 5 5 1 +Retinyl Esters 4 10 5 +Retirement 2 2 1 +Retortamonadidae 2 2 1 +Retracted Publication 2 2 1 +Retraction Notice 2 2 2 +Retraction of Publication as Topic 3 3 1 +Retreatment 2 2 1 +Retrobulbar Hemorrhage 3 4 2 +Retrocaval Ureter 3 5 4 +Retrocochlear Diseases 3 3 1 +Retroelements 4 7 4 +Retrognathia 4 7 7 +Retrograde Degeneration 4 4 1 +Retrograde Ejaculation 5 5 3 +Retrograde Obturation 5 5 1 +Retroperitoneal Fibrosis 4 4 1 +Retroperitoneal Neoplasms 4 4 1 +Retroperitoneal Space 5 5 1 +Retropharyngeal Abscess 4 4 5 +Retropneumoperitoneum 2 3 2 +Retrospective Moral Judgment 4 6 2 +Retrospective Studies 6 7 6 +Retroviridae 3 3 2 +Retroviridae Infections 4 4 1 +Retroviridae Proteins 4 4 1 +Retroviridae Proteins, Oncogenic 5 6 3 +Retroviruses, Simian 4 4 2 +Rett Syndrome 5 6 3 +Return of Individual Research Results 4 4 1 +Return of Spontaneous Circulation 4 4 1 +Return to School 2 2 1 +Return to Sport 5 5 1 +Return to Work 3 4 2 +Reunion 4 5 2 +rev Gene Products, Human Immunodeficiency Virus 6 7 4 +Reversal Learning 4 4 1 +Reverse Genetics 3 3 1 +Reverse Transcriptase Inhibitors 6 6 2 +Reverse Transcriptase Polymerase Chain Reaction 5 5 1 +Reverse Transcription 4 4 2 +Reverse Vaccinology 4 4 1 +Review 2 3 2 +Review Literature as Topic 5 5 1 +Reward 5 5 1 +Rewarming 2 2 1 +Reye Syndrome 4 5 3 +Reynoutria 8 8 1 +RGS Proteins 6 6 2 +Rh Isoimmunization 3 4 3 +Rh-Hr Blood-Group System 5 5 2 +Rhabdiasoidea 8 8 1 +Rhabditida 7 7 1 +Rhabditida Infections 6 6 1 +Rhabditoidea 8 8 1 +Rhabdoid Tumor 4 4 1 +Rhabdomyolysis 3 3 1 +Rhabdomyoma 6 6 1 +Rhabdomyosarcoma 6 6 2 +Rhabdomyosarcoma, Alveolar 7 7 2 +Rhabdomyosarcoma, Embryonal 7 7 2 +Rhabdoviridae 5 5 1 +Rhabdoviridae Infections 5 5 1 +Rhadinovirus 5 5 3 +Rhamnaceae 9 9 1 +Rhamnogalacturonans 4 6 3 +Rhamnose 3 6 3 +Rhamnus 10 10 1 +Rheiformes 7 7 1 +Rhenium 4 4 3 +Rheology 2 3 2 +Rheum 8 8 1 +Rheumatic Diseases 2 3 2 +Rheumatic Fever 3 6 4 +Rheumatic Heart Disease 3 7 2 +Rheumatic Nodule 4 5 2 +Rheumatoid Factor 8 8 3 +Rheumatoid Nodule 4 5 3 +Rheumatoid Vasculitis 4 5 5 +Rheumatologists 4 5 2 +Rheumatology 4 4 1 +Rhinitis 3 3 4 +Rhinitis, Allergic 3 5 4 +Rhinitis, Allergic, Perennial 4 6 4 +Rhinitis, Allergic, Seasonal 4 6 4 +Rhinitis, Atrophic 4 4 2 +Rhinitis, Vasomotor 4 4 2 +Rhinomanometry 3 4 2 +Rhinometry, Acoustic 4 4 1 +Rhinophyma 4 4 2 +Rhinoplasty 3 4 3 +Rhinorrhea 4 4 1 +Rhinoscleroma 3 7 8 +Rhinosinusitis 4 5 8 +Rhinosporidiosis 4 4 1 +Rhinosporidium 3 3 1 +Rhinovirus 6 6 1 +Rhipicephalus 9 9 1 +Rhipicephalus sanguineus 10 10 1 +Rhizaria 2 2 1 +Rhizobiaceae 4 5 2 +Rhizobium 3 6 3 +Rhizobium etli 4 7 3 +Rhizobium leguminosarum 4 7 3 +Rhizobium phaseoli 4 7 3 +Rhizobium tropici 4 7 3 +Rhizoctonia 4 4 1 +Rhizome 3 4 2 +Rhizomucor 5 5 1 +Rhizophoraceae 9 9 1 +Rhizopus 5 5 1 +Rhizopus oryzae 6 6 1 +Rhizosphere 3 5 3 +Rhizotomy 4 4 1 +Rho Factor 4 4 1 +rho GTP-Binding Proteins 6 8 3 +rho Guanine Nucleotide Dissociation Inhibitor alpha 7 7 2 +rho Guanine Nucleotide Dissociation Inhibitor beta 6 7 4 +rho Guanine Nucleotide Dissociation Inhibitor gamma 7 7 2 +Rho Guanine Nucleotide Exchange Factors 6 6 2 +Rho(D) Immune Globulin 9 9 3 +rho-Associated Kinases 5 8 2 +rho-Specific Guanine Nucleotide Dissociation Inhibitors 6 6 2 +rhoA GTP-Binding Protein 7 9 3 +rhoB GTP-Binding Protein 7 9 3 +rhoC GTP-Binding Protein 7 9 3 +Rhodamine 123 6 6 1 +Rhodamines 5 5 1 +Rhodanine 4 5 2 +Rhode Island 6 6 1 +Rhodiola 10 10 1 +Rhodium 4 4 3 +Rhodnius 10 10 1 +Rhodobacter 3 5 2 +Rhodobacter capsulatus 4 6 2 +Rhodobacter sphaeroides 4 6 2 +Rhodobacteraceae 4 4 1 +Rhodococcus 5 5 1 +Rhodococcus equi 6 6 1 +Rhodocyclaceae 4 4 1 +Rhododendron 9 9 1 +Rhodomicrobium 3 5 2 +Rhodophyta 2 2 1 +Rhodopseudomonas 5 6 2 +Rhodopsin 6 6 2 +Rhodopsins, Microbial 4 4 1 +Rhodospirillaceae 5 5 2 +Rhodospirillales 4 4 1 +Rhodospirillum 6 6 2 +Rhodospirillum centenum 7 7 2 +Rhodospirillum rubrum 7 7 2 +Rhodothermus 4 5 2 +Rhodotorula 4 4 2 +Rhodovulum 3 5 2 +Rhombencephalon 5 5 1 +Rhus 8 8 1 +Rhytidoplasty 3 4 2 +Rib Cage 4 4 1 +Rib Fractures 3 3 2 +Ribavirin 4 4 1 +Ribes 10 10 1 +Ribitol 3 4 2 +Riboflavin 4 6 4 +Riboflavin Deficiency 7 7 1 +Riboflavin Synthase 5 5 1 +Ribonuclease H 7 7 2 +Ribonuclease H, Human Immunodeficiency Virus 8 10 8 +Ribonuclease III 7 7 1 +Ribonuclease P 4 7 4 +Ribonuclease T1 7 7 2 +Ribonuclease, Pancreatic 7 7 2 +Ribonucleases 5 5 1 +Ribonucleoprotein, U1 Small Nuclear 6 7 4 +Ribonucleoprotein, U2 Small Nuclear 6 7 4 +Ribonucleoprotein, U4-U6 Small Nuclear 7 7 2 +Ribonucleoprotein, U5 Small Nuclear 6 7 4 +Ribonucleoprotein, U7 Small Nuclear 7 7 2 +Ribonucleoproteins 5 5 2 +Ribonucleoproteins, Small Cytoplasmic 6 6 2 +Ribonucleoproteins, Small Nuclear 6 6 2 +Ribonucleoproteins, Small Nucleolar 7 7 2 +Ribonucleoside Diphosphate Reductase 5 5 1 +Ribonucleosides 3 3 1 +Ribonucleotide Reductases 4 4 1 +Ribonucleotides 3 3 1 +Ribose 5 5 1 +Ribose-Phosphate Pyrophosphokinase 6 6 1 +Ribosemonophosphates 4 4 1 +Ribosomal Protein L10 9 9 1 +Ribosomal Protein L3 4 4 1 +Ribosomal Protein S6 4 4 1 +Ribosomal Protein S6 Kinases 5 8 2 +Ribosomal Protein S6 Kinases, 70-kDa 6 9 2 +Ribosomal Protein S6 Kinases, 90-kDa 6 9 2 +Ribosomal Protein S9 4 4 1 +Ribosomal Proteins 3 3 1 +Ribosome Inactivating Proteins 4 6 2 +Ribosome Inactivating Proteins, Type 1 5 7 2 +Ribosome Inactivating Proteins, Type 2 5 7 3 +Ribosome Profiling 4 5 3 +Ribosome Shunting 4 6 4 +Ribosome Subunits 8 8 1 +Ribosome Subunits, Large 9 9 1 +Ribosome Subunits, Large, Archaeal 10 10 1 +Ribosome Subunits, Large, Bacterial 2 10 2 +Ribosome Subunits, Large, Eukaryotic 10 10 1 +Ribosome Subunits, Small 9 9 1 +Ribosome Subunits, Small, Archaeal 10 10 1 +Ribosome Subunits, Small, Bacterial 2 10 2 +Ribosome Subunits, Small, Eukaryotic 10 10 1 +Ribosomes 7 7 1 +Ribostamycin 5 5 1 +Riboswitch 5 7 4 +Ribotyping 4 7 3 +Ribs 5 5 1 +Ribulose-Bisphosphate Carboxylase 5 6 2 +Ribulosephosphates 4 4 1 +Rice Bran Oil 4 5 4 +Ricin 4 8 4 +Ricinoleic Acids 5 6 2 +Ricinus 10 10 1 +Ricinus communis 11 11 1 +Rickets 4 7 4 +Rickets, Hypophosphatemic 5 8 5 +Rickettsia 7 7 1 +Rickettsia akari 8 8 1 +Rickettsia conorii 8 8 1 +Rickettsia felis 8 8 1 +Rickettsia Infections 4 6 2 +Rickettsia prowazekii 8 8 1 +Rickettsia rickettsii 8 8 1 +Rickettsia typhi 8 8 1 +Rickettsiaceae 5 5 1 +Rickettsiaceae Infections 3 5 2 +Rickettsial Vaccines 5 5 1 +Rickettsiales 3 4 2 +Rickettsieae 6 6 1 +Riemerella 5 6 2 +Rifabutin 5 5 2 +Rifampin 5 5 2 +Rifamycins 4 4 2 +Rifaximin 5 5 2 +Rift Valley Fever 4 6 8 +Rift Valley fever virus 6 6 1 +Right to Die 5 6 2 +Right to Health 4 5 3 +Right to Work 4 5 2 +Rigor Mortis 6 6 1 +Rilmenidine 5 5 1 +Rilpivirine 3 4 2 +Riluzole 4 6 3 +Rimantadine 6 7 2 +Rimonabant 4 5 2 +Rinderpest 2 7 2 +Rinderpest virus 8 8 1 +Ring Chromosomes 4 5 4 +RING Finger Domains 9 9 2 +Ringer's Lactate 5 5 1 +Ringer's Solution 4 4 1 +Rioprostil 5 8 3 +Riot Control Agents, Chemical 3 4 2 +Riots 5 5 1 +Risedronic Acid 4 5 2 +Risk 3 6 4 +Risk Adjustment 4 8 3 +Risk Assessment 4 7 5 +Risk Evaluation and Mitigation 4 5 2 +Risk Factors 5 7 5 +Risk Management 3 4 2 +Risk Reduction Behavior 3 3 1 +Risk Sharing, Financial 4 5 2 +Risk-Taking 3 3 1 +Risperidone 5 5 1 +Ristocetin 4 4 2 +Ritanserin 4 5 2 +Ritodrine 5 5 4 +Ritonavir 4 5 2 +Rituximab 9 9 3 +Rivaroxaban 4 5 3 +Rivastigmine 6 6 1 +Rivers 3 5 3 +RNA 3 3 1 +RNA 3' End Processing 3 4 3 +RNA 3' Polyadenylation Signals 6 7 3 +RNA 5' Terminal Oligopyrimidine Sequence 6 7 3 +RNA Cap Analogs 6 8 4 +RNA Cap-Binding Proteins 5 5 2 +RNA Caps 5 7 4 +RNA Cleavage 2 3 2 +RNA Damage 2 2 1 +RNA Editing 3 4 3 +RNA Folding 3 6 4 +RNA Helicases 7 7 1 +RNA Interference 5 5 1 +RNA Isoforms 4 4 1 +RNA Ligase (ATP) 5 5 1 +RNA Methylation 3 5 6 +RNA Nucleotidyltransferases 6 6 1 +RNA Phages 3 3 1 +RNA Polymerase I 8 8 1 +RNA Polymerase II 8 8 1 +RNA Polymerase III 8 8 1 +RNA Polymerase Sigma 54 4 8 2 +RNA Precursors 3 4 2 +RNA Probes 4 6 3 +RNA Processing, Post-Transcriptional 2 3 3 +RNA Recognition Motif 9 9 1 +RNA Recognition Motif Proteins 5 5 2 +RNA Replication 2 4 3 +RNA Splice Sites 5 7 4 +RNA Splicing 3 4 3 +RNA Splicing Factors 5 5 2 +RNA Stability 3 3 1 +RNA Transport 3 3 1 +RNA Virus Infections 3 3 1 +RNA Viruses 2 2 1 +RNA, Algal 4 4 1 +RNA, Antisense 3 7 4 +RNA, Archaeal 4 4 1 +RNA, Bacterial 4 4 1 +RNA, Catalytic 3 5 2 +RNA, Chloroplast 5 5 1 +RNA, Circular 4 4 1 +RNA, Competitive Endogenous 4 4 1 +RNA, Complementary 4 7 3 +RNA, Double-Stranded 4 6 3 +RNA, Fungal 4 4 1 +RNA, Guide, CRISPR-Cas Systems 6 6 1 +RNA, Guide, Kinetoplastida 6 6 1 +RNA, Helminth 4 4 1 +RNA, Heterogeneous Nuclear 5 5 1 +RNA, Long Noncoding 5 5 1 +RNA, Messenger 4 4 1 +RNA, Messenger, Stored 5 5 1 +RNA, Mitochondrial 4 4 1 +RNA, Neoplasm 4 4 1 +RNA, Nuclear 4 4 1 +RNA, Plant 4 4 1 +RNA, Protozoan 4 4 1 +RNA, Ribosomal 4 4 1 +RNA, Ribosomal, 16S 5 5 1 +RNA, Ribosomal, 18S 5 5 1 +RNA, Ribosomal, 23S 5 5 1 +RNA, Ribosomal, 28S 5 5 1 +RNA, Ribosomal, 5.8S 5 5 1 +RNA, Ribosomal, 5S 5 5 1 +RNA, Ribosomal, Self-Splicing 4 7 3 +RNA, Satellite 4 4 1 +RNA, Small Cytoplasmic 6 6 1 +RNA, Small Interfering 4 6 3 +RNA, Small Nuclear 5 6 2 +RNA, Small Nucleolar 6 7 2 +RNA, Small Untranslated 5 5 1 +RNA, Spliced Leader 6 6 1 +RNA, Transfer 4 4 1 +RNA, Transfer, Ala 6 6 1 +RNA, Transfer, Amino Acid-Specific 5 5 1 +RNA, Transfer, Amino Acyl 3 5 2 +RNA, Transfer, Arg 6 6 1 +RNA, Transfer, Asn 6 6 1 +RNA, Transfer, Asp 6 6 1 +RNA, Transfer, Cys 6 6 1 +RNA, Transfer, Gln 6 6 1 +RNA, Transfer, Glu 6 6 1 +RNA, Transfer, Gly 6 6 1 +RNA, Transfer, His 6 6 1 +RNA, Transfer, Ile 6 6 1 +RNA, Transfer, Leu 6 6 1 +RNA, Transfer, Lys 6 6 1 +RNA, Transfer, Met 6 6 1 +RNA, Transfer, Phe 6 6 1 +RNA, Transfer, Pro 6 6 1 +RNA, Transfer, Ser 6 6 1 +RNA, Transfer, Thr 6 6 1 +RNA, Transfer, Trp 6 6 1 +RNA, Transfer, Tyr 6 6 1 +RNA, Transfer, Val 6 6 1 +RNA, Untranslated 4 4 1 +RNA, Viral 4 4 1 +RNA, Z-Form 4 6 2 +RNA-Binding Motifs 8 8 1 +RNA-Binding Protein EWS 4 7 8 +RNA-Binding Protein FUS 6 7 5 +RNA-Binding Proteins 4 4 2 +RNA-Dependent RNA Polymerase 7 7 1 +RNA-Directed DNA Polymerase 6 8 4 +RNA-Induced Silencing Complex 6 7 4 +RNA-Seq 4 5 3 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Syndrome, Congenital 4 7 2 +Rubella Vaccine 5 5 1 +Rubella virus 6 6 1 +Rubia 9 9 1 +Rubiaceae 8 8 1 +Rubidium 4 4 4 +Rubidium Radioisotopes 4 4 1 +Rubinstein-Taybi Syndrome 4 5 7 +Rubivirus 5 5 1 +Rubivirus Infections 5 5 1 +Rubredoxins 5 8 3 +Rubulavirus 7 7 1 +Rubulavirus Infections 6 6 1 +Rubus 10 10 1 +Rudbeckia 8 8 1 +Rudiviridae 3 3 2 +Rugby 5 5 1 +Rumen 3 3 1 +Rumex 8 8 1 +Ruminants 8 8 1 +Rumination Syndrome 3 3 2 +Rumination, Cognitive 4 4 1 +Rumination, Digestive 4 4 1 +Ruminiclostridium cellulolyticum 4 5 4 +Ruminococcus 4 4 2 +Runaway Behavior 4 4 1 +Running 3 6 4 +RUNX1 Translocation Partner 1 Protein 6 6 2 +Rupicapra 10 10 1 +Rupture 2 2 1 +Rupture, Spontaneous 3 3 1 +Rural Health 4 4 1 +Rural Health Services 3 3 1 +Rural Nursing 4 4 2 +Rural Population 3 3 1 +Ruscus 10 10 1 +Russia 4 4 2 +Russia (Pre-1917) 3 3 1 +Russian-Japanese War 5 6 2 +Ruta 8 8 1 +Rutaceae 7 7 1 +Rutamycin 5 5 1 +Ruthenium 4 4 3 +Ruthenium Compounds 2 2 1 +Ruthenium Radioisotopes 4 4 1 +Ruthenium Red 3 3 3 +Rutin 8 8 2 +Rwanda 5 5 1 +RxNorm 6 6 1 +Ryania 10 10 1 +Ryanodine 3 5 3 +Ryanodine Receptor Calcium Release Channel 5 7 4 +S Phase 3 4 3 +S Phase Cell Cycle Checkpoints 4 5 2 +S-Adenosylhomocysteine 5 7 5 +S-Adenosylmethionine 5 7 5 +S-Nitroso-N-Acetylpenicillamine 5 5 4 +S-Nitrosoglutathione 5 5 3 +S-Nitrosothiols 4 4 2 +S-Phase Kinase-Associated Proteins 4 4 1 +S100 Calcium Binding Protein A10 5 7 3 +S100 Calcium Binding Protein A6 4 6 2 +S100 Calcium Binding Protein A7 6 6 1 +S100 Calcium Binding Protein beta Subunit 5 6 2 +S100 Calcium Binding Protein G 6 6 2 +S100 Calcium-Binding Protein A4 6 6 1 +S100 Proteins 4 5 2 +S100A12 Protein 6 6 1 +Saccades 3 3 1 +Saccharin 4 6 3 +Saccharomyces 4 5 2 +Saccharomyces boulardii 5 6 2 +Saccharomyces cerevisiae 5 6 2 +Saccharomyces cerevisiae Proteins 4 4 1 +Saccharomycetales 4 4 1 +Saccharomycopsis 4 5 2 +Saccharopine Dehydrogenases 5 5 1 +Saccharopolyspora 4 5 4 +Saccharum 8 8 1 +Saccule and Utricle 4 5 2 +Sacrococcygeal Region 4 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8 2 +Saposins 4 4 1 +Sapotaceae 8 8 1 +Sapovirus 5 5 1 +Saprolegnia 4 4 1 +Saquinavir 5 5 2 +Saralasin 6 6 2 +Sarcina 4 5 2 +Sarcocystidae 6 6 1 +Sarcocystis 7 7 1 +Sarcocystosis 5 5 1 +Sarcoglycanopathies 3 7 5 +Sarcoglycans 5 6 3 +Sarcoidosis 4 4 2 +Sarcoidosis, Pulmonary 4 5 3 +Sarcolemma 4 4 1 +Sarcoma 4 4 1 +Sarcoma 180 4 6 2 +Sarcoma 37 4 6 2 +Sarcoma Virus, Woolly Monkey 5 5 4 +Sarcoma Viruses, Feline 3 5 3 +Sarcoma Viruses, Murine 3 5 3 +Sarcoma, Alveolar Soft Part 5 5 2 +Sarcoma, Avian 3 6 5 +Sarcoma, Clear Cell 5 5 2 +Sarcoma, Endometrial Stromal 4 9 6 +Sarcoma, Ewing 6 7 2 +Sarcoma, Experimental 3 5 3 +Sarcoma, Kaposi 4 5 3 +Sarcoma, Myeloid 5 5 3 +Sarcoma, Small Cell 5 5 2 +Sarcoma, Synovial 5 5 2 +Sarcoma, Yoshida 4 6 2 +Sarcomeres 5 7 4 +Sarcopenia 5 6 3 +Sarcophagidae 10 10 1 +Sarcoplasmic Reticulum 6 9 2 +Sarcoplasmic Reticulum Calcium-Transporting ATPases 7 8 5 +Sarcoptes scabiei 9 9 1 +Sarcoptidae 8 8 1 +Sarcosine 5 5 1 +Sarcosine Dehydrogenase 6 6 1 +Sarcosine Oxidase 4 6 2 +Sargassum 4 4 1 +Sarin 5 5 1 +Sarraceniaceae 8 8 1 +SARS-CoV-2 9 9 1 +Sasa 8 8 1 +Saskatchewan 5 5 1 +Sassafras 9 9 1 +Satellite Cells, Perineuronal 3 3 3 +Satellite Cells, Skeletal Muscle 5 5 1 +Satellite Communications 5 5 1 +Satellite Imagery 4 7 3 +Satellite Viruses 3 3 1 +Satiation 3 3 1 +Satiety Response 4 4 1 +Satureja 9 9 1 +Saturn 6 6 1 +Saudi Arabia 5 5 1 +Saururaceae 7 7 1 +Saussurea 8 8 1 +Saxifragaceae 9 9 1 +Saxifragales 7 7 1 +Saxitoxin 4 5 6 +Scabies 5 6 2 +Scaffold Protein ILK 4 4 2 +Scala Tympani 5 5 1 +Scala Vestibuli 5 5 1 +Scalp 3 3 1 +Scalp Dermatoses 3 3 1 +Scandentia 7 7 1 +Scandinavian and Nordic Countries 3 3 1 +Scandinavians and Nordic People 4 4 1 +Scandium 4 4 3 +Scanning Laser Polarimetry 4 4 1 +Scapegoating 4 4 3 +Scapharca 7 7 1 +Scaphoid Bone 7 7 1 +Scapula 5 5 1 +Scarlet Fever 6 6 1 +Scattering, Radiation 2 3 2 +Scattering, Small Angle 3 4 2 +Scavenger Receptors, Class A 7 8 2 +Scavenger Receptors, Class B 7 8 2 +Scavenger Receptors, 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+Sequence Homology, Nucleic Acid 3 4 2 +Sequence Inversion 3 4 2 +Sequence Tagged Sites 5 5 1 +Sequestering Agents 3 4 2 +Sequestosome-1 Protein 4 5 4 +Sequiviridae 3 4 2 +Sequivirus 4 6 2 +Sequoia 8 8 1 +Sequoiadendron 8 8 1 +Serbia 4 4 1 +Serenoa 8 8 1 +Serial Extraction 4 4 3 +Serial Infection Interval 4 4 1 +Serial Learning 5 5 1 +Serial Passage 4 5 2 +Serial Publications 5 5 1 +Sericins 5 5 2 +Serine 4 4 1 +Serine C-Palmitoyltransferase 5 5 1 +Serine Endopeptidases 6 6 2 +Serine O-Acetyltransferase 6 6 1 +Serine Peptidase Inhibitor Kazal-Type 5 5 5 2 +Serine Peptidase Inhibitors, Kazal Type 4 7 3 +Serine Proteases 5 5 1 +Serine Proteinase Inhibitors 6 6 1 +Serine Racemase 6 6 1 +Serine-Arginine Splicing Factors 6 6 2 +Serine-Threonine Kinase 3 5 8 2 +Serine-tRNA Ligase 6 6 1 +Serine-Type D-Ala-D-Ala Carboxypeptidase 6 7 2 +Sermon 3 3 1 +Sermorelin 7 8 4 +Seroconversion 2 2 1 +Seroepidemiologic Studies 5 6 3 +Serogroup 3 3 1 +Serologic Tests 4 5 3 +Serology 2 2 1 +Seroma 4 4 1 +Serositis 4 4 1 +Serotonergic Neurons 3 3 2 +Serotonin 4 6 3 +Serotonin 5-HT1 Receptor Agonists 7 7 2 +Serotonin 5-HT1 Receptor Antagonists 7 7 2 +Serotonin 5-HT2 Receptor Agonists 7 7 2 +Serotonin 5-HT2 Receptor Antagonists 7 7 2 +Serotonin 5-HT3 Receptor Agonists 7 7 2 +Serotonin 5-HT3 Receptor Antagonists 7 7 2 +Serotonin 5-HT4 Receptor Agonists 7 7 2 +Serotonin 5-HT4 Receptor Antagonists 7 7 2 +Serotonin Agents 5 5 2 +Serotonin and Noradrenaline Reuptake Inhibitors 6 6 3 +Serotonin Antagonists 6 6 2 +Serotonin Plasma Membrane Transport Proteins 6 7 6 +Serotonin Receptor Agonists 6 6 2 +Serotonin Syndrome 3 3 1 +Serotyping 4 7 5 +Serous Membrane 3 3 1 +Serpin E2 5 5 4 +Serpins 3 7 3 +Serrate-Jagged Proteins 3 5 4 +Serratia 5 5 2 +Serratia Infections 6 6 1 +Serratia liquefaciens 6 6 2 +Serratia marcescens 6 6 2 +Sertoli Cell Tumor 5 7 9 +Sertoli Cell-Only Syndrome 5 5 3 +Sertoli Cells 3 5 3 +Sertoli-Leydig Cell Tumor 4 8 16 +Sertraline 4 8 3 +Serum 3 4 2 +Serum Albumin 4 4 2 +Serum Albumin, Bovine 5 5 2 +Serum Albumin, Human 5 5 2 +Serum Albumin, Radio-Iodinated 6 6 2 +Serum Amyloid A Protein 5 5 2 +Serum Amyloid P-Component 4 6 4 +Serum Bactericidal Antibody Assay 5 6 3 +Serum Bactericidal Test 5 6 3 +Serum Globulins 4 4 2 +Serum Response Element 7 10 6 +Serum Response Factor 5 6 2 +Serum Sickness 5 5 2 +Serum-Glucocorticoid Regulated Kinases 4 8 3 +Service Animals 5 5 1 +Serving Size 5 5 1 +Sesame Oil 4 6 6 +Sesamoid Bones 4 4 1 +Sesamum 9 9 1 +Sesbania 8 8 1 +Sesquiterpenes 4 4 1 +Sesquiterpenes, Eudesmane 3 6 2 +Sesquiterpenes, Germacrane 6 6 1 +Sesquiterpenes, Guaiane 4 7 3 +Sesterterpenes 4 4 1 +Sestrins 5 5 1 +Set, Psychology 4 4 1 +Setaria Nematode 9 9 1 +Setaria Plant 8 8 1 +Setariasis 4 8 4 +Sevelamer 4 4 1 +Seven in Absentia Proteins 6 6 1 +Severe Acute Malnutrition 4 4 1 +Severe Acute Respiratory Syndrome 3 7 3 +Severe acute respiratory syndrome-related coronavirus 8 8 1 +Severe Combined Immunodeficiency 3 4 4 +Severe Dengue 5 7 4 +Severe Fever with Thrombocytopenia Syndrome 4 5 2 +Severity of Illness Index 8 9 3 +Seveso Accidental Release 5 5 2 +Sevoflurane 4 5 2 +Sewage 4 4 1 +Sex 3 3 1 +Sex Attractants 3 3 1 +Sex Characteristics 3 3 1 +Sex Chromatin 5 11 4 +Sex Chromosome Aberrations 4 5 2 +Sex Chromosome Disorders 4 4 2 +Sex Chromosome Disorders of Sex Development 4 6 7 +Sex Chromosomes 4 4 2 +Sex Cord-Gonadal Stromal Tumors 4 4 1 +Sex Counseling 4 5 5 +Sex Determination Analysis 3 4 3 +Sex Determination by Skeleton 4 5 4 +Sex Determination Processes 2 6 4 +Sex Differences 2 2 1 +Sex Differentiation 4 6 3 +Sex Distribution 3 5 3 +Sex Education 4 4 1 +Sex Factors 4 4 2 +Sex Hormone-Binding Globulin 4 6 3 +Sex Manuals 6 7 2 +Sex Offenses 4 4 1 +Sex Preselection 4 4 1 +Sex Ratio 2 6 4 +Sex Work 4 4 2 +Sex Workers 2 2 1 +Sex-Determining Region Y Protein 5 7 4 +Sexism 4 5 3 +Sexology 3 3 1 +Sexual Abstinence 4 4 1 +Sexual and Gender Disorders 3 3 1 +Sexual and Gender Minorities 3 3 1 +Sexual Arousal 4 4 2 +Sexual Behavior 3 3 1 +Sexual Behavior, Animal 5 5 1 +Sexual Development 3 3 2 +Sexual Dysfunction, Physiological 3 3 1 +Sexual Dysfunctions, Psychological 2 2 1 +Sexual Harassment 4 4 2 +Sexual Health 3 3 1 +Sexual Infantilism 4 7 6 +Sexual Maturation 4 4 2 +Sexual Partners 2 2 1 +Sexual Selection 3 3 1 +Sexual Trauma 4 4 1 +Sexuality 3 4 2 +Sexually Transmitted Diseases 2 5 4 +Sexually Transmitted Diseases, Bacterial 3 6 5 +Sexually Transmitted Diseases, Viral 3 6 5 +Seychelles 4 5 2 +Sezary Syndrome 4 8 5 +Sf9 Cells 4 4 1 +SH2 Domain-Containing Protein Tyrosine Phosphatases 5 7 2 +Shab Potassium Channels 9 9 6 +Shadowing Technique, Histology 6 7 4 +Shaken Baby Syndrome 5 5 2 +Shaker Superfamily of Potassium Channels 8 8 3 +Shal Potassium Channels 9 9 3 +Shallots 11 11 1 +Shamanism 4 6 3 +Shame 4 4 1 +Shape Memory Alloys 3 5 7 +Shared Governance, Nursing 3 5 4 +Shared Medical Appointments 3 6 3 +Shared Paranoid Disorder 4 4 1 +Sharks 7 7 1 +Shaw Potassium Channels 9 9 3 +Shc Signaling Adaptor Proteins 5 5 3 +Shear Strength 3 3 1 +Sheep 9 9 1 +Sheep Diseases 2 2 1 +Sheep, Bighorn 10 10 1 +Sheep, Domestic 10 10 1 +Shellfish 5 6 2 +Shellfish Hypersensitivity 5 5 1 +Shellfish Poisoning 4 4 1 +Shellfish Proteins 5 7 7 +Sheltered Workshops 2 4 2 +Shelterin Complex 3 10 6 +Shewanella 4 5 2 +Shewanella putrefaciens 5 6 2 +Shift Work Schedule 4 5 2 +Shiga Toxin 5 9 3 +Shiga Toxin 1 5 9 4 +Shiga Toxin 2 5 9 4 +Shiga Toxins 4 8 3 +Shiga-Toxigenic Escherichia coli 7 7 2 +Shigella 5 5 2 +Shigella boydii 6 6 2 +Shigella dysenteriae 6 6 2 +Shigella flexneri 6 6 2 +Shigella sonnei 6 6 2 +Shigella Vaccines 5 5 1 +Shiitake Mushrooms 6 6 1 +Shikimic Acid 4 8 3 +Ships 3 3 1 +Shivering 5 6 3 +Shock 3 3 1 +Shock, Cardiogenic 4 6 5 +Shock, Hemorrhagic 4 4 2 +Shock, Septic 3 6 3 +Shock, Surgical 4 4 2 +Shock, Traumatic 2 4 2 +Shoes 4 4 1 +Short Bowel Syndrome 4 5 2 +Short Chain Dehydrogenase-Reductases 6 6 1 +Short Interspersed Nucleotide Elements 7 8 3 +Short Rib-Polydactyly Syndrome 4 6 4 +Short Stature Homeobox Protein 5 5 1 +Short-Wave Therapy 3 4 2 +Shorthand 6 6 1 +Shotgun Sequencing 3 3 1 +Shoulder 4 4 1 +Shoulder Dislocation 3 4 3 +Shoulder Dystocia 6 6 1 +Shoulder Fractures 3 3 2 +Shoulder Impingement Syndrome 3 3 2 +Shoulder Injuries 2 2 1 +Shoulder Joint 4 4 1 +Shoulder Pain 4 6 4 +Shoulder Prosthesis 4 4 1 +Showdomycin 4 4 1 +Shrews 8 8 1 +Shwachman-Diamond Syndrome 4 6 4 +Shwartzman Phenomenon 4 5 3 +Shy-Drager Syndrome 4 6 4 +Shyness 4 4 1 +Siadenovirus 4 4 1 +Sialadenitis 4 4 1 +Sialic Acid Binding Ig-like Lectin 1 5 5 1 +Sialic Acid Binding Ig-like Lectin 2 5 6 5 +Sialic Acid Binding Ig-like Lectin 3 5 6 3 +Sialic Acid Binding Immunoglobulin-like Lectins 4 4 1 +Sialic Acid Storage Disease 6 7 6 +Sialic Acids 4 6 4 +Sialoglycoproteins 4 4 3 +Sialography 4 6 3 +Sialometaplasia, Necrotizing 4 4 1 +Sialomucins 6 6 2 +Sialorrhea 4 4 1 +Sialyl Lewis X Antigen 6 7 8 +Sialyltransferases 5 5 1 +Siberia 5 5 1 +Sibling Relations 5 5 1 +Siblings 2 5 3 +Sicily 4 5 3 +Sick Building Syndrome 3 4 2 +Sick Leave 5 5 2 +Sick Role 5 5 1 +Sick Sinus Syndrome 5 5 5 +Sickle Cell Trait 5 7 4 +Sickness Impact Profile 7 8 3 +Sida Plant 10 10 1 +Side-Population Cells 3 3 1 +Sideritis 9 9 1 +Siderophores 6 7 2 +Siderosis 3 5 3 +Sierra Leone 5 5 1 +Sigesbeckia 8 8 1 +Sigma Factor 4 4 1 +Sigma-1 Receptor 8 9 3 +Sigmodontinae 10 10 1 +Sigmoid Diseases 5 5 1 +Sigmoid Neoplasms 6 8 6 +Sigmoidoscopes 6 6 2 +Sigmoidoscopy 6 8 4 +Sign Language 5 8 5 +Signal Detection, Psychological 3 5 6 +Signal Processing, Computer-Assisted 3 3 1 +Signal Recognition Particle 7 7 2 +Signal Transduction 2 3 2 +Signal-To-Noise Ratio 3 6 7 +Signaling Lymphocytic Activation Molecule Associated Protein 5 5 3 +Signaling Lymphocytic Activation Molecule Family 5 6 4 +Signaling Lymphocytic Activation Molecule Family Member 1 6 7 4 +Signs and Symptoms 2 2 1 +Signs and Symptoms, Digestive 3 3 1 +Signs and Symptoms, Respiratory 3 3 1 +Sikkim 6 6 1 +Silage 3 6 4 +Silanes 3 3 1 +Sildenafil Citrate 4 5 4 +Silencer Elements, Transcriptional 5 6 3 +Silene 10 10 1 +Silent Information Regulator Proteins, Saccharomyces cerevisiae 4 5 2 +Silent Mutation 4 4 1 +Silica Gel 4 5 3 +Silicate Cement 4 6 2 +Silicates 3 5 2 +Silicic Acid 4 4 2 +Silicon 4 4 1 +Silicon Compounds 2 2 1 +Silicon Dioxide 3 4 3 +Silicone Elastomers 4 7 6 +Silicone Gels 5 7 4 +Silicone Oils 5 7 4 +Silicones 4 6 4 +Silicosis 3 5 3 +Silicotuberculosis 4 9 7 +Silk 4 4 2 +Silo Filler's Disease 3 5 2 +Silorane Resins 4 7 5 +Siloxanes 3 5 5 +Silver 4 4 3 +Silver Compounds 2 2 1 +Silver Nitrate 3 5 2 +Silver Proteins 3 3 1 +Silver Staining 6 7 4 +Silver Sulfadiazine 6 8 6 +Silver-Russell Syndrome 4 4 6 +Silybin 9 9 2 +Silybum marianum 8 8 1 +Silymarin 8 8 2 +Simarouba 8 8 1 +Simaroubaceae 7 7 1 +Simazine 4 4 1 +Simbu virus 6 6 1 +Simendan 4 4 2 +Simeprevir 4 5 2 +Simethicone 6 8 4 +Simian Acquired Immunodeficiency Syndrome 4 6 3 +Simian foamy virus 5 5 1 +Simian Immunodeficiency Virus 5 6 2 +Simian T-lymphotropic virus 1 5 6 4 +Simian T-lymphotropic virus 2 5 6 4 +Simian T-lymphotropic virus 3 5 6 3 +Simian virus 40 6 6 2 +Simplexvirus 5 5 1 +Simplified Acute Physiology Score 9 10 3 +Simulation Training 3 3 1 +Simuliidae 12 12 1 +Simvastatin 5 8 2 +Sin Nombre virus 6 6 1 +Sin3 Histone Deacetylase and Corepressor Complex 4 6 3 +Sinapis 8 8 1 +Sincalide 4 5 2 +Sindbis Virus 6 6 1 +Singapore 4 4 1 +Singing 4 4 1 +Single Embryo Transfer 5 5 2 +Single Molecule Imaging 3 4 2 +Single Parent 3 8 8 +Single Person 2 7 6 +Single Photon Emission Computed Tomography Computed Tomography 5 8 11 +Single Umbilical Artery 4 5 2 +Single-Balloon Enteroscopy 6 8 4 +Single-Blind Method 4 5 3 +Single-Case Studies as Topic 6 7 3 +Single-Cell Analysis 3 3 1 +Single-Cell Gene Expression Analysis 4 4 2 +Single-Chain Antibodies 7 9 5 +Single-Domain Antibodies 7 9 4 +Single-Parent Family 5 7 6 +Single-Payer System 4 6 3 +Single-Strand Specific DNA and RNA Endonucleases 7 7 4 +Single-Use Internal Condom 4 4 1 +Singlet Oxygen 4 4 2 +Sinistral Portal Hypertension 4 4 1 +Sino-Japanese War 5 6 2 +Sino-Nasal Outcome Test 5 8 7 +Sinoatrial Block 5 5 3 +Sinoatrial Node 4 4 1 +Sinomenium 8 8 1 +Sinorhizobium 5 6 2 +Sinorhizobium fredii 6 7 2 +Sinorhizobium meliloti 6 7 2 +Sint Maarten 4 4 1 +Sinus Arrest, Cardiac 5 5 2 +Sinus Floor Augmentation 3 4 3 +Sinus of Valsalva 5 5 1 +Sinus Pericranii 4 5 3 +Sinus Thrombosis, Intracranial 6 7 3 +Sinusitis 3 4 4 +Siphonaptera 9 9 1 +Siphoviridae 3 4 3 +Sirenia 8 8 1 +Sirolimus 4 4 1 +Sirtuin 1 5 8 2 +Sirtuin 2 5 8 3 +Sirtuin 3 4 8 4 +Sirtuins 4 7 3 +Sisomicin 5 5 1 +Sister Chromatid Exchange 4 4 1 +Sister Mary Joseph's Nodule 4 4 1 +Sitagliptin Phosphate 4 5 2 +Sitagliptin Phosphate, Metformin Hydrochloride Drug Combination 3 6 4 +Site-Specific DNA-Methyltransferase (Adenine-Specific) 7 7 1 +Site-Specific DNA-Methyltransferase (Cytosine-N4-Specific) 8 8 1 +Sitosterols 4 7 4 +Sitting Position 4 4 1 +Situs Inversus 3 3 1 +Size Perception 4 5 2 +Sizofiran 5 5 1 +Sjogren's Syndrome 4 5 6 +Sjogren-Larsson Syndrome 4 6 9 +Skates, Fish 7 7 2 +Skating 5 5 1 +Skatole 5 5 1 +Skeletal Muscle Enlargement 5 8 2 +Skeletal Muscle Myosins 7 9 4 +Skeletal Muscle Ventricle 3 3 2 +Skeleton 2 2 1 +Skiing 6 6 1 +Skilled Nursing Facilities 5 5 1 +Skin 2 2 1 +Skin Abnormalities 3 3 2 +Skin Absorption 3 6 4 +Skin Aging 3 3 1 +Skin and Connective Tissue Diseases 1 1 1 +Skin Care 3 3 1 +Skin Cream 4 4 2 +Skin Diseases 2 2 1 +Skin Diseases, Bacterial 3 4 3 +Skin Diseases, Eczematous 3 3 1 +Skin Diseases, Genetic 3 3 2 +Skin Diseases, Infectious 2 3 2 +Skin Diseases, Metabolic 3 3 2 +Skin Diseases, Papulosquamous 3 3 1 +Skin Diseases, Parasitic 3 4 2 +Skin Diseases, Vascular 3 3 1 +Skin Diseases, Vesiculobullous 3 3 1 +Skin Diseases, Viral 3 4 2 +Skin Irritancy Tests 4 4 1 +Skin Lightening Preparations 4 4 2 +Skin Manifestations 3 3 1 +Skin Microbiome 3 8 3 +Skin Neoplasms 3 3 2 +Skin Physiological Phenomena 2 2 1 +Skin Pigmentation 3 6 5 +Skin Temperature 3 3 2 +Skin Test End-Point Titration 6 7 3 +Skin Tests 4 5 3 +Skin Transplantation 4 5 3 +Skin Ulcer 3 3 1 +Skin Window Technique 4 6 9 +Skin, Artificial 4 4 1 +Skinfold Thickness 4 6 3 +SKP Cullin F-Box Protein Ligases 6 6 1 +Skull 4 4 1 +Skull Base 3 5 2 +Skull Base Neoplasms 5 5 2 +Skull Fracture, Basilar 4 5 3 +Skull Fracture, Depressed 4 5 3 +Skull Fractures 3 4 3 +Skull Neoplasms 4 4 2 +SLC31 Proteins 6 8 4 +SLC4A Proteins 5 6 5 +Sleep 3 3 2 +Sleep Aids, Pharmaceutical 6 7 2 +Sleep Apnea Syndromes 4 5 2 +Sleep Apnea, Central 5 6 2 +Sleep Apnea, Obstructive 5 6 2 +Sleep Arousal Disorders 4 4 2 +Sleep Bruxism 4 4 3 +Sleep Deprivation 4 5 4 +Sleep Disorders, Circadian Rhythm 2 4 4 +Sleep Disorders, Intrinsic 4 4 2 +Sleep Duration 4 4 2 +Sleep Hygiene 4 4 2 +Sleep Initiation and Maintenance Disorders 5 5 2 +Sleep Latency 4 4 2 +Sleep Medicine Specialty 4 4 1 +Sleep Paralysis 5 5 2 +Sleep Phase Chronotherapy 4 4 1 +Sleep Quality 5 5 1 +Sleep Stages 4 4 2 +Sleep Wake Disorders 2 4 3 +Sleep, REM 5 5 2 +Sleep, Slow-Wave 5 5 2 +Sleep-Wake Transition Disorders 4 4 2 +Sleepiness 3 5 2 +Slipped Capital Femoral Epiphyses 4 5 2 +Slit Homolog 2 Protein 3 4 4 +Slit Lamp 4 4 1 +Slit Lamp Microscopy 4 4 1 +Slit Ventricle Syndrome 4 6 2 +Sloths 8 8 1 +Slovakia 4 4 1 +Slovenia 4 4 1 +Slow Virus Diseases 3 3 1 +Sluggish Cognitive Tempo 4 4 1 +Smad Proteins 4 5 5 +Smad Proteins, Inhibitory 6 6 3 +Smad Proteins, Receptor-Regulated 4 6 6 +Smad1 Protein 5 7 6 +Smad2 Protein 5 7 5 +Smad3 Protein 5 7 6 +Smad4 Protein 5 6 6 +Smad5 Protein 5 7 6 +Smad6 Protein 5 7 4 +Smad7 Protein 7 7 3 +Smad8 Protein 5 7 6 +Small Business 3 3 1 +Small Cell Lung Carcinoma 5 8 3 +Small Fiber Neuropathy 4 4 1 +Small Leucine-Rich Proteoglycans 4 5 4 +Small Molecule Libraries 4 4 1 +Small Ubiquitin-Related Modifier Proteins 4 4 1 +Small-Area Analysis 5 6 3 +Small-Conductance Calcium-Activated Potassium Channels 8 8 3 +Smallpox 5 5 1 +Smallpox Vaccine 5 5 1 +SMARCB1 Protein 4 5 4 +Smart Glasses 4 6 2 +Smart Materials 2 4 4 +Smartphone 7 7 2 +Smear Layer 4 4 1 +Smegma 3 3 1 +Smegmamorpha 6 6 1 +Smell 4 4 2 +Smilacaceae 9 9 1 +Smilax 10 10 1 +Smiling 6 6 1 +Smith-Lemli-Opitz Syndrome 4 5 7 +Smith-Magenis Syndrome 3 4 4 +SMN Complex Proteins 4 5 3 +Smog 3 3 1 +Smoke 3 3 1 +Smoke Inhalation Injury 4 4 1 +Smoke-Free Policy 7 8 3 +Smokers 2 2 1 +Smoking 3 3 1 +Smoking Cessation 4 4 1 +Smoking Cessation Agents 4 4 1 +Smoking Devices 3 3 1 +Smoking Pipes 4 4 1 +Smoking Prevention 5 8 5 +Smoking Reduction 4 4 2 +Smoking Water Pipes 5 5 1 +Smoking, Non-Tobacco Products 4 4 1 +Smoldering Multiple Myeloma 3 5 5 +Smooth Muscle Myosins 7 9 4 +Smooth Muscle Tumor 5 5 1 +Smoothened Receptor 7 7 2 +Snacks 4 5 2 +Snail Family Transcription Factors 4 4 1 +Snails 6 6 1 +Snake Bites 3 4 2 +Snake Venoms 3 4 2 +Snakes 6 6 1 +SNARE Proteins 5 5 2 +Sneddon Syndrome 4 5 3 +Sneezing 3 4 2 +Snoring 5 5 1 +Snow 4 6 4 +Snow Sports 5 5 1 +snRNP Core Proteins 4 7 5 +Soaps 3 5 2 +Soccer 5 5 1 +Social Adjustment 4 4 1 +Social Alienation 5 5 1 +Social Behavior 3 3 1 +Social Behavior Disorders 4 4 1 +Social Capital 4 4 1 +Social Change 3 5 3 +Social Class 3 5 2 +Social Cognition 5 5 1 +Social Cohesion 4 7 5 +Social Communication Disorder 4 4 1 +Social Comparison 5 5 1 +Social Conditions 3 4 2 +Social Conformity 4 4 1 +Social Control Policies 3 4 3 +Social Control, Formal 2 3 2 +Social Control, Informal 3 3 1 +Social Defeat 4 4 1 +Social Deprivation 5 5 1 +Social Desirability 4 4 1 +Social Determinants of Health 3 4 3 +Social Discrimination 4 4 1 +Social Dominance 4 4 1 +Social Environment 4 4 1 +Social Evolution 4 4 1 +Social Facilitation 4 4 1 +Social Factors 5 5 1 +Social Genomics 6 6 2 +Social Group 3 4 2 +Social Identification 4 4 1 +Social Inclusion 4 4 1 +Social Integration 3 4 2 +Social Interaction 4 4 1 +Social Isolation 4 4 2 +Social Justice 4 6 4 +Social Learning 5 5 1 +Social Marginalization 4 4 2 +Social Marketing 4 5 2 +Social Media 4 6 2 +Social Medicine 3 3 1 +Social Mobility 4 6 2 +Social Network Analysis 4 4 1 +Social Networking 3 3 1 +Social Norms 3 4 2 +Social Participation 3 3 1 +Social Perception 4 4 1 +Social Planning 3 3 1 +Social Prescribing 6 6 1 +Social Problems 3 3 1 +Social Responsibility 4 4 2 +Social Sciences 1 3 2 +Social Security 5 6 2 +Social Segregation 4 4 1 +Social Skills 4 4 2 +Social Status 4 4 1 +Social Stigma 4 4 1 +Social Structure 3 3 1 +Social Support 5 5 1 +Social Theory 2 2 1 +Social Validity, Research 4 4 1 +Social Values 3 3 1 +Social Vulnerability 4 4 1 +Social Welfare 3 3 1 +Social Work 3 3 2 +Social Work Department, Hospital 6 6 2 +Social Work, Psychiatric 3 4 4 +Social Workers 3 3 1 +Socialism 3 3 1 +Socialization 4 4 1 +Societies 3 3 1 +Societies, Dental 4 4 1 +Societies, Hospital 4 4 1 +Societies, Medical 4 4 1 +Societies, Nursing 4 4 1 +Societies, Pharmaceutical 4 4 1 +Societies, Scientific 4 4 1 +Societies, Veterinary 4 4 1 +Sociobiology 3 4 2 +Sociodemographic Factors 4 4 1 +Socioeconomic Disparities in Health 5 5 2 +Socioeconomic Factors 2 4 2 +Socioenvironmental Therapy 3 3 1 +Sociological Factors 3 3 1 +Sociology 2 4 2 +Sociology, Medical 2 5 3 +Sociometric Techniques 3 3 1 +Sodium 4 4 4 +Sodium Acetate 6 6 1 +Sodium Azide 3 4 2 +Sodium Benzoate 6 8 2 +Sodium Bicarbonate 3 6 2 +Sodium Channel Agonists 5 5 1 +Sodium Channel Blockers 5 5 2 +Sodium Channels 6 6 3 +Sodium Chloride 3 5 2 +Sodium Chloride Symporter Inhibitors 5 6 2 +Sodium Chloride Symporters 7 7 1 +Sodium Chloride, Dietary 4 4 2 +Sodium Cholate 8 8 2 +Sodium Citrate 7 7 1 +Sodium Compounds 2 2 1 +Sodium Cyanide 3 5 2 +Sodium Dodecyl Sulfate 4 7 3 +Sodium Fluoride 3 5 4 +Sodium Glutamate 6 6 2 +Sodium Hydroxide 3 6 3 +Sodium Hypochlorite 3 5 3 +Sodium Iodide 3 4 2 +Sodium Ionophores 4 6 2 +Sodium Isotopes 3 5 5 +Sodium Lactate 5 5 1 +Sodium Morrhuate 3 3 1 +Sodium Nitrite 3 5 2 +Sodium Oxybate 5 6 2 +Sodium Pertechnetate Tc 99m 3 3 1 +Sodium Potassium Chloride Symporter Inhibitors 5 6 2 +Sodium Radioisotopes 4 6 6 +Sodium Salicylate 7 10 4 +Sodium Selenite 3 4 2 +Sodium Sulfate Cotransporter 7 8 8 +Sodium Tetradecyl Sulfate 3 7 3 +Sodium, Dietary 3 3 1 +Sodium-Bicarbonate Symporters 6 7 7 +Sodium-Calcium Exchanger 7 7 2 +Sodium-Coupled Vitamin C Transporters 6 9 6 +Sodium-Glucose Transport Proteins 6 7 4 +Sodium-Glucose Transporter 1 6 8 6 +Sodium-Glucose Transporter 2 6 8 6 +Sodium-Glucose Transporter 2 Inhibitors 4 5 2 +Sodium-Hydrogen Exchanger 1 7 8 5 +Sodium-Hydrogen Exchanger 3 7 8 5 +Sodium-Hydrogen Exchangers 6 7 5 +Sodium-Iodide Symporters 7 7 2 +Sodium-Phosphate Cotransporter Proteins 6 8 5 +Sodium-Phosphate Cotransporter Proteins, Type I 7 9 5 +Sodium-Phosphate Cotransporter Proteins, Type II 6 9 7 +Sodium-Phosphate Cotransporter Proteins, Type IIa 7 10 7 +Sodium-Phosphate Cotransporter Proteins, Type IIb 7 10 7 +Sodium-Phosphate Cotransporter Proteins, Type IIc 7 10 7 +Sodium-Phosphate Cotransporter Proteins, Type III 6 9 7 +Sodium-Potassium-Chloride Symporters 6 8 4 +Sodium-Potassium-Exchanging ATPase 6 7 7 +Sofosbuvir 6 7 3 +Soft Computing 3 3 1 +Soft Tissue Infections 2 2 1 +Soft Tissue Injuries 2 2 1 +Soft Tissue Neoplasms 3 3 1 +Software 3 3 1 +Software Design 4 4 1 +Software Validation 4 4 1 +Soil 2 4 4 +Soil Erosion 3 3 2 +Soil Microbiology 4 6 2 +Soil Pollutants 4 4 1 +Soil Pollutants, Radioactive 3 5 2 +Solanaceae 8 8 1 +Solanaceous Alkaloids 3 3 1 +Solanales 7 7 1 +Solanine 4 7 5 +Solanum 9 9 1 +Solanum glaucophyllum 10 10 1 +Solanum lycopersicum 10 10 1 +Solanum melongena 10 10 1 +Solanum nigrum 10 10 1 +Solanum tuberosum 10 10 1 +Solar Activity 3 3 1 +Solar Energy 3 5 2 +Solar System 4 4 1 +Solid Phase Extraction 4 4 1 +Solid Phase Microextraction 5 5 1 +Solid Waste 3 5 2 +Solid-Phase Synthesis Techniques 3 5 2 +Solidago 8 8 1 +Solifenacin Succinate 4 6 2 +Solitary Fibrous Tumor, Pleural 6 7 2 +Solitary Fibrous Tumors 6 6 1 +Solitary Kidney 3 5 4 +Solitary Nucleus 8 8 1 +Solitary Pulmonary Nodule 3 3 1 +Solubility 2 2 1 +Soluble Guanylyl Cyclase 5 6 2 +Soluble N-Ethylmaleimide-Sensitive Factor Attachment Proteins 5 5 1 +Solute Carrier Family 11, Member 2 7 7 2 +Solute Carrier Family 12 7 7 4 +Solute Carrier Family 12, Member 1 7 9 4 +Solute Carrier Family 12, Member 2 7 9 4 +Solute Carrier Family 12, Member 3 7 8 3 +Solute Carrier Family 12, Member 4 7 9 4 +Solute Carrier Family 22 Member 5 7 8 4 +Solute Carrier Family 44, Member 2 Protein 5 6 4 +Solute Carrier Organic Anion Transporter Family Member 1B3 6 9 4 +Solute Carrier Proteins 5 5 2 +Solutions 2 2 1 +Solvents 3 3 1 +Somalia 5 5 1 +Soman 5 5 1 +Somatic Hypermutation, Immunoglobulin 3 3 2 +Somatoform Disorders 2 2 1 +Somatomedins 4 5 3 +Somatosensory Cortex 9 9 2 +Somatosensory Disorders 4 5 2 +Somatostatin 5 7 6 +Somatostatin-28 6 8 6 +Somatostatin-Secreting Cells 3 6 7 +Somatostatinoma 5 7 7 +Somatotrophs 3 11 7 +Somatotypes 4 6 2 +Somites 4 4 1 +Somnambulism 5 5 2 +Son of Sevenless Protein, Drosophila 8 8 2 +Son of Sevenless Proteins 7 7 2 +Sonchus 8 8 1 +Songbirds 7 7 1 +Sonication 2 2 1 +Soot 4 4 1 +Sophora 8 8 1 +Sophora flavescens 9 9 1 +Sophora japonica 9 9 1 +Sorafenib 5 7 4 +Sorangium 5 5 1 +Sorbic Acid 4 4 1 +Sorbitol 3 4 2 +Sorbose 5 5 2 +Sorbus 10 10 1 +Sordariales 4 4 1 +Sorghum 8 8 1 +Sorption Detoxification 2 2 1 +Sortilin 6 6 1 +Sorting Nexins 6 6 1 +SOS Response, Genetics 3 4 2 +SOS1 Protein 8 8 2 +Sotalol 5 5 2 +Sotos Syndrome 4 4 3 +Sound 4 4 1 +Sound Localization 4 5 2 +Sound Recordings 3 6 3 +Sound Spectrography 2 2 1 +South Africa 5 5 1 +South America 3 3 1 +South American People 3 3 1 +South Asian People 5 5 1 +South Australia 4 5 2 +South Carolina 6 6 2 +South Dakota 6 6 1 +South Sudan 5 5 1 +Southeast Asian People 4 4 1 +Southeastern United States 5 5 1 +Southern African People 5 5 1 +Southwestern United States 5 5 1 +SOX Transcription Factors 4 6 4 +SOX9 Transcription Factor 6 8 4 +SOXB1 Transcription Factors 5 7 4 +SOXB2 Transcription Factors 5 7 4 +SOXC Transcription Factors 5 7 4 +SOXD Transcription Factors 5 7 4 +SOXE Transcription Factors 5 7 4 +SOXF Transcription Factors 5 7 4 +Soy Foods 3 6 4 +Soy Milk 5 7 4 +Soybean Oil 4 6 6 +Soybean Proteins 4 7 5 +Sp Transcription Factors 5 5 2 +Sp1 Transcription Factor 6 6 2 +Sp2 Transcription Factor 6 6 2 +Sp3 Transcription Factor 6 6 2 +Sp4 Transcription Factor 6 6 2 +Sp7 Transcription Factor 6 6 2 +Space Flight 4 4 1 +Space Maintenance, Orthodontic 4 4 1 +Space Motion Sickness 4 4 1 +Space Perception 4 4 1 +Space Research 4 4 1 +Space Simulation 4 4 1 +Space Suits 4 6 4 +Space-Time Clustering 4 7 4 +Spacecraft 5 5 1 +Spain 3 3 1 +Spalax 9 9 1 +Spanish-American War, 1898 5 6 2 +Sparganosis 6 6 1 +Sparganum 8 8 2 +Sparrows 8 8 1 +Sparsomycin 5 5 1 +Sparteine 4 4 1 +Spartium 8 8 1 +Spasm 4 5 2 +Spasms, Infantile 6 6 2 +Spastic Paraplegia, Hereditary 4 6 5 +Spastin 5 7 5 +Spatial Analysis 4 5 3 +Spatial Behavior 3 3 1 +Spatial Learning 4 5 3 +Spatial Memory 5 5 1 +Spatial Navigation 3 4 2 +Spatial Processing 4 4 1 +Spatial Regression 5 6 6 +Spatial Transcriptomics 4 4 1 +Spatio-Temporal Analysis 5 6 3 +Specialization 2 2 1 +Specialized Pro-Resolving Mediators 4 4 1 +Specialties, Dental 3 3 1 +Specialties, Nursing 3 3 1 +Specialties, Surgical 3 3 1 +Specialty Boards 5 5 2 +Specialty Uses of Chemicals 2 2 1 +Species Specificity 2 2 1 +Specific Gravity 2 2 1 +Specific Language Disorder 5 8 5 +Specific Learning Disorder 4 7 4 +Specific Pathogen-Free Organisms 3 3 1 +Specimen Handling 3 4 2 +Spectinomycin 4 4 3 +Spectral Karyotyping 5 7 6 +Spectrin 4 4 2 +Spectrometry, Fluorescence 4 6 2 +Spectrometry, Gamma 3 4 2 +Spectrometry, Mass, Electrospray Ionization 4 4 1 +Spectrometry, Mass, Fast Atom Bombardment 4 4 1 +Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization 4 4 1 +Spectrometry, Mass, Secondary Ion 4 4 1 +Spectrometry, X-Ray Emission 3 4 2 +Spectrophotometry 4 4 2 +Spectrophotometry, Atomic 5 5 2 +Spectrophotometry, Infrared 5 5 2 +Spectrophotometry, Ultraviolet 5 5 2 +Spectroscopy, Electron Energy-Loss 4 4 1 +Spectroscopy, Fourier Transform Infrared 6 6 2 +Spectroscopy, Mossbauer 5 5 1 +Spectroscopy, Near-Infrared 4 4 2 +Spectrum Analysis 3 3 1 +Spectrum Analysis, Raman 4 4 2 +Speech 3 5 3 +Speech Acoustics 3 4 2 +Speech Articulation Tests 4 4 1 +Speech Discrimination Tests 7 7 1 +Speech Disorders 6 7 2 +Speech Intelligibility 4 6 2 +Speech Perception 4 5 2 +Speech Production Measurement 3 3 1 +Speech Reception Threshold Test 7 7 1 +Speech Recognition Software 4 4 1 +Speech Sound Disorder 4 4 1 +Speech Therapy 4 7 2 +Speech, Alaryngeal 4 7 2 +Speech, Esophageal 5 8 2 +Speech-Language Pathology 3 3 1 +Speleotherapy 3 3 1 +Sperm Agglutination 4 4 1 +Sperm Banks 4 4 1 +Sperm Capacitation 5 5 1 +Sperm Count 3 6 6 +Sperm Head 4 5 2 +Sperm Immobilizing Agents 5 7 4 +Sperm Injections, Intracytoplasmic 5 5 2 +Sperm Maturation 5 6 2 +Sperm Midpiece 4 5 2 +Sperm Motility 3 5 3 +Sperm Proteins 4 4 1 +Sperm Retrieval 4 4 3 +Sperm Tail 4 5 3 +Sperm Transport 3 5 2 +Sperm Whale 9 9 1 +Sperm-Ovum Interactions 5 5 1 +Spermatic Cord 4 4 1 +Spermatic Cord Torsion 4 4 2 +Spermatids 4 5 2 +Spermatocele 4 4 3 +Spermatocidal Agents 5 7 4 +Spermatocytes 4 5 2 +Spermatogenesis 4 5 2 +Spermatogenesis-Blocking Agents 5 8 4 +Spermatogonia 4 5 2 +Spermatozoa 3 4 2 +Spermidine 4 6 2 +Spermidine Synthase 5 5 1 +Spermine 4 7 2 +Spermine Synthase 5 5 1 +Sphaerotilus 6 6 2 +Sphagnopsida 6 6 1 +Spheniscidae 6 6 1 +Sphenoid Bone 5 5 1 +Sphenoid Sinus 4 4 1 +Sphenoid Sinusitis 4 5 4 +Sphenopalatine Ganglion Block 6 6 2 +Sphenostylis 8 8 1 +Spherocytes 5 6 3 +Spherocytosis, Hereditary 4 6 2 +Spheroids, Cellular 3 3 1 +Spheroplasts 2 3 3 +Sphincter of Oddi 6 7 3 +Sphincter of Oddi Dysfunction 6 6 1 +Sphincterotomy 3 3 1 +Sphincterotomy, Endoscopic 4 6 4 +Sphincterotomy, Transduodenal 4 4 2 +Sphingobacterium 4 5 2 +Sphingolipid Activator Proteins 3 3 1 +Sphingolipidoses 6 7 9 +Sphingolipids 3 3 1 +Sphingomonadaceae 4 4 1 +Sphingomonas 5 5 2 +Sphingomyelin Phosphodiesterase 6 6 1 +Sphingomyelins 4 6 4 +Sphingosine 4 4 3 +Sphingosine 1 Phosphate Receptor Modulators 4 6 2 +Sphingosine Kinase 6 6 1 +Sphingosine N-Acyltransferase 5 5 1 +Sphingosine Phosphorylcholine Receptors 8 8 1 +Sphingosine-1-Phosphate Receptors 8 8 1 +Sphygmomanometers 3 3 1 +Spices 4 5 2 +Spider Bites 3 4 2 +Spider Venoms 4 5 2 +Spiders 6 6 1 +Spike Glycoprotein, Coronavirus 6 7 2 +Spin Labels 3 3 1 +Spin Trapping 3 3 1 +Spina Bifida Cystica 5 6 2 +Spina Bifida Occulta 5 6 2 +Spinacia oleracea 8 8 1 +Spinal Canal 5 5 1 +Spinal Cord 3 3 1 +Spinal Cord Compression 3 4 2 +Spinal Cord Diseases 3 3 1 +Spinal Cord Dorsal Horn 4 4 1 +Spinal Cord Injuries 2 4 3 +Spinal Cord Ischemia 4 5 2 +Spinal Cord Lateral Horn 4 4 1 +Spinal Cord Neoplasms 4 5 3 +Spinal Cord Regeneration 4 4 2 +Spinal Cord Stimulation 3 5 3 +Spinal Cord Vascular Diseases 3 4 2 +Spinal Cord Ventral Horn 4 4 1 +Spinal Curvatures 4 4 1 +Spinal Diseases 3 3 1 +Spinal Dysraphism 4 5 2 +Spinal Fractures 3 4 2 +Spinal Fusion 4 4 1 +Spinal Injuries 3 3 1 +Spinal Muscular Atrophies of Childhood 4 5 5 +Spinal Neoplasms 4 4 3 +Spinal Nerve Roots 5 5 1 +Spinal Nerves 4 4 1 +Spinal Osteochondrosis 4 4 2 +Spinal Osteophytosis 4 4 1 +Spinal Puncture 3 6 6 +Spinal Stenosis 4 4 1 +Spindle Apparatus 7 7 1 +Spindle Pole Bodies 8 9 2 +Spindle Poles 8 8 1 +Spine 4 4 1 +Spinocerebellar Ataxias 5 6 6 +Spinocerebellar Degenerations 4 5 4 +Spinocerebellar Tracts 4 4 1 +Spinothalamic Tracts 4 4 2 +Spiperone 3 5 3 +Spiraea 10 10 1 +Spiral Cone-Beam Computed Tomography 8 8 4 +Spiral Ganglion 4 7 4 +Spiral Lamina 5 5 1 +Spiral Ligament of Cochlea 5 5 1 +Spiramycin 6 6 1 +Spirillaceae 3 4 2 +Spirillum 4 5 2 +Spirit Possession 4 4 1 +Spiritual Therapies 3 3 1 +Spiritualism 4 4 1 +Spirituality 3 4 2 +Spiro Compounds 2 4 2 +Spirochaeta 4 6 2 +Spirochaetaceae 3 5 2 +Spirochaetales 2 2 1 +Spirochaetales Infections 5 5 1 +Spirogyra 6 6 1 +Spirometra 7 7 1 +Spirometry 5 5 1 +Spironolactone 3 6 2 +Spirooxindoles 3 8 5 +Spiroplasma 6 6 1 +Spiroplasma citri 7 7 1 +Spiroplasmataceae 5 5 1 +Spirostans 5 5 1 +Spirulina 3 3 1 +Spirurida 7 7 1 +Spirurida Infections 6 6 1 +Spirurina 8 8 1 +Spiruroidea 9 9 1 +Spisula 6 6 1 +Splanchnic Circulation 4 4 1 +Splanchnic Nerves 5 5 3 +Spleen 3 5 2 +Spleen Focus-Forming Viruses 6 6 2 +Splenectomy 2 2 1 +Splenic Artery 4 4 1 +Splenic Diseases 3 3 1 +Splenic Infarction 3 5 4 +Splenic Neoplasms 3 4 2 +Splenic Rupture 3 4 3 +Splenic Vein 5 5 1 +Splenomegaly 4 4 1 +Splenorenal Shunt, Surgical 4 6 2 +Splenosis 4 5 3 +Spliceosomes 7 7 1 +Splicing Factor U2AF 6 6 2 +Splints 6 6 2 +Split-Brain Procedure 3 3 1 +Spodoptera 11 11 1 +Spondylarthritis 4 5 2 +Spondylarthropathies 5 6 2 +Spondylitis 3 4 3 +Spondylitis, Ankylosing 5 8 3 +Spondylolisthesis 6 6 1 +Spondylolysis 5 5 1 +Spondylosis 4 4 1 +Spontaneous Combustion 2 4 2 +Spontaneous Perforation 3 3 1 +Sporadotrichina 6 6 1 +Sporangia 2 2 2 +Spores 2 2 2 +Spores, Bacterial 3 3 2 +Spores, Fungal 3 3 3 +Spores, Protozoan 3 3 2 +Sporidesmins 4 5 2 +Sporosarcina 5 6 6 +Sporothrix 4 4 1 +Sporotrichosis 4 5 3 +Sporozoites 4 7 4 +Sports 4 4 1 +Sports and Recreational Facilities 2 2 1 +Sports Equipment 3 3 1 +Sports for Persons with Disabilities 5 5 1 +Sports Medicine 3 3 1 +Sports Nutritional Physiological Phenomena 4 4 1 +Sports Nutritional Sciences 3 4 2 +Spotted Fever Group Rickettsiosis 4 7 2 +Spouse Abuse 6 6 4 +Spouses 2 5 3 +Sprains and Strains 2 2 1 +Spray Drying 3 3 2 +Sprue, Tropical 4 5 2 +Spumavirus 4 4 1 +Sputum 3 3 1 +Squalene 5 6 2 +Squalene Monooxygenase 6 6 1 +Squalus 9 9 1 +Squalus acanthias 10 10 1 +Squamous Cell Carcinoma of Head and Neck 4 6 2 +Squamous Intraepithelial Lesions 3 3 1 +Squamous Intraepithelial Lesions of the Cervix 4 8 4 +Src Homology 2 Domain-Containing, Transforming Protein 1 6 6 3 +Src Homology 2 Domain-Containing, Transforming Protein 2 6 6 3 +Src Homology 2 Domain-Containing, Transforming Protein 3 6 6 3 +src Homology Domains 9 9 1 +src-Family Kinases 5 8 2 +Sri Lanka 4 5 2 +SRS-A 6 7 3 +SS-A Antigen 4 6 3 +SS-B Antigen 4 6 3 +SSPE Virus 3 9 2 +ST Elevation Myocardial Infarction 5 6 4 +Stachybotrys 4 4 1 +Stachys 9 9 1 +Staff Development 3 4 2 +Stage-Specific Embryonic Antigens 4 5 2 +Staghorn Calculi 6 8 10 +Staining and Labeling 5 6 4 +Stainless Steel 4 6 4 +Stair Climbing 4 7 3 +Stakeholder Participation 2 3 2 +Stalking 5 5 2 +Standard of Care 3 4 2 +Standardized Nursing Terminology 5 6 2 +Standing Orders 3 6 2 +Standing Position 4 4 1 +Stanford-Binet Test 5 5 1 +Stanozolol 6 6 1 +Stapedius 4 4 2 +Stapes 5 5 1 +Stapes Mobilization 5 5 1 +Stapes Surgery 4 4 1 +Staphylococcaceae 4 5 6 +Staphylococcal Food Poisoning 4 6 2 +Staphylococcal Infections 5 5 1 +Staphylococcal Protein A 4 5 3 +Staphylococcal Scalded Skin Syndrome 5 7 4 +Staphylococcal Skin Infections 4 6 4 +Staphylococcal Toxoid 5 5 1 +Staphylococcal Vaccines 5 5 1 +Staphylococcus 5 6 6 +Staphylococcus aureus 6 7 6 +Staphylococcus capitis 6 7 6 +Staphylococcus epidermidis 6 7 6 +Staphylococcus haemolyticus 6 7 6 +Staphylococcus hominis 6 7 6 +Staphylococcus hyicus 6 7 6 +Staphylococcus intermedius 6 7 6 +Staphylococcus lugdunensis 6 7 6 +Staphylococcus Phages 3 3 1 +Staphylococcus saprophyticus 6 7 6 +Starch 3 5 3 +Starch Phosphorylase 8 8 1 +Starch Synthase 7 7 1 +Starfish 5 5 1 +Stargardt Disease 3 5 3 +Starlings 8 8 1 +Stars, Celestial 4 4 1 +Starvation 4 4 1 +STAT Transcription Factors 4 5 4 +STAT1 Transcription Factor 5 7 9 +STAT2 Transcription Factor 5 7 9 +STAT3 Transcription Factor 5 6 4 +STAT4 Transcription Factor 5 6 4 +STAT5 Transcription Factor 5 6 4 +STAT6 Transcription Factor 5 6 4 +State Dentistry 2 2 1 +State Government 3 4 2 +State Health Planning and Development Agencies 4 4 1 +State Health Plans 4 4 1 +State Medicine 2 4 2 +Stathmin 4 6 3 +Static Electricity 5 5 1 +Statistical Distributions 2 5 4 +Statistics 2 2 1 +Statistics as Topic 3 4 4 +Statistics, Nonparametric 4 5 3 +Status Asthmaticus 4 6 3 +Status Epilepticus 4 5 2 +Staurosporine 4 7 5 +Stavudine 5 6 4 +Steam 3 7 5 +Steam Bath 3 3 2 +Stearates 4 4 1 +Stearic Acids 3 3 1 +Stearoyl-CoA Desaturase 7 7 1 +Steatitis 2 7 2 +Steatocystoma Multiplex 5 6 5 +Steatorrhea 4 5 2 +Steel 3 5 4 +Stellaria 10 10 1 +Stellate Ganglion 5 6 3 +Stem Cell Factor 5 6 3 +Stem Cell Niche 3 3 1 +Stem Cell Research 3 5 2 +Stem Cell Transplantation 4 5 2 +Stem Cells 2 2 1 +Stemona Alkaloids 3 3 1 +Stemonaceae 7 7 1 +Stenella 9 9 1 +Stenosis, Pulmonary Artery 4 4 1 +Stenosis, Pulmonary Vein 3 3 1 +Stenotrophomonas 5 6 2 +Stenotrophomonas maltophilia 6 7 2 +Stents 3 3 1 +Stephania 8 8 1 +Stephania tetrandra 9 9 1 +Sterculia 10 10 1 +Stereocilia 4 4 1 +Stereognosis 5 6 2 +Stereoisomerism 4 4 1 +Stereolithography 4 6 3 +Stereotaxic Techniques 2 3 2 +Stereotyped Behavior 3 3 1 +Stereotypic Movement Disorder 3 3 1 +Stereotyping 3 4 2 +Sterigmatocystin 4 6 2 +Sterile Alpha Motif 8 9 2 +Sterilization 6 6 1 +Sterilization Reversal 4 4 1 +Sterilization, Involuntary 4 5 2 +Sterilization, Reproductive 3 4 2 +Sterilization, Tubal 4 4 2 +Sterilizing Immunity 3 3 1 +Sternoclavicular Joint 4 4 1 +Sternocostal Joints 4 4 1 +Sternotomy 3 3 1 +Sternum 5 5 1 +Steroid 11-beta-Hydroxylase 5 8 6 +Steroid 12-alpha-Hydroxylase 5 8 6 +Steroid 16-alpha-Hydroxylase 5 8 3 +Steroid 17-alpha-Hydroxylase 5 8 6 +Steroid 21-Hydroxylase 5 8 6 +Steroid Hydroxylases 4 7 3 +Steroid Isomerases 6 6 1 +Steroid Metabolism, Inborn Errors 4 4 2 +Steroid Synthesis Inhibitors 5 6 2 +Steroidogenic Acute Regulatory Protein 5 6 2 +Steroidogenic Factor 1 4 4 1 +Steroids 3 3 1 +Steroids, Brominated 4 4 1 +Steroids, Chlorinated 4 4 1 +Steroids, Fluorinated 4 4 1 +Steroids, Heterocyclic 4 4 1 +Sterol 14-Demethylase 5 8 6 +Sterol Esterase 6 6 1 +Sterol O-Acyltransferase 5 5 1 +Sterol O-Acyltransferase 2 6 6 1 +Sterol Regulatory Element Binding Protein 1 7 7 4 +Sterol Regulatory Element Binding Protein 2 7 7 4 +Sterol Regulatory Element Binding Proteins 6 6 4 +Sterols 3 5 2 +Steryl-Sulfatase 7 7 1 +Stethoscopes 3 3 1 +Stevens-Johnson Syndrome 4 5 6 +Stevia 8 8 1 +Stichopus 6 6 1 +Stiff-Person Syndrome 3 4 4 +Stifle 3 3 1 +Stigmasterol 4 8 4 +Stigmatella 5 5 1 +Stigmatella aurantiaca 6 6 1 +Stilbamidines 3 8 2 +Stilbenes 7 7 1 +Stilbestrols 8 8 1 +Still's Disease, Adult-Onset 4 5 4 +Stillbirth 5 6 3 +Stimulants, Historical 4 4 1 +Stimulation, Chemical 4 4 1 +Stimuli Responsive Polymers 3 5 7 +STING Protein 4 4 1 +Stochastic Processes 2 5 4 +Stockings, Compression 4 4 1 +Stomach 4 4 1 +Stomach Diseases 3 3 1 +Stomach Neoplasms 4 5 4 +Stomach Rupture 3 4 3 +Stomach Ulcer 5 6 2 +Stomach Volvulus 4 4 1 +Stomach, Avian 2 2 1 +Stomach, Ruminant 2 2 1 +Stomatitis 3 3 1 +Stomatitis, Aphthous 4 4 1 +Stomatitis, Denture 4 4 1 +Stomatitis, Herpetic 4 6 2 +Stomatognathic Diseases 1 1 1 +Stomatognathic System 1 1 1 +Stomatognathic System Abnormalities 2 3 2 +Strabismus 3 4 2 +Stramenopiles 2 2 1 +Strategic Planning 4 4 1 +Strategic Stockpile 4 5 2 +Stratospheric Ozone 5 5 3 +Street Food 4 5 2 +Strelitziaceae 9 9 1 +Strepsirhini 8 8 1 +Streptavidin 4 4 1 +Streptobacillus 3 4 2 +Streptococcaceae 4 4 3 +Streptococcal Infections 5 5 1 +Streptococcal Vaccines 5 5 1 +Streptococcus 5 5 3 +Streptococcus agalactiae 6 6 3 +Streptococcus anginosus 8 8 3 +Streptococcus bovis 6 6 3 +Streptococcus constellatus 8 8 3 +Streptococcus equi 6 6 3 +Streptococcus gallolyticus 6 6 3 +Streptococcus gallolyticus subspecies gallolyticus 7 7 3 +Streptococcus gordonii 6 6 3 +Streptococcus iniae 6 6 3 +Streptococcus intermedius 8 8 3 +Streptococcus milleri Group 7 7 3 +Streptococcus mitis 7 7 3 +Streptococcus mutans 7 7 3 +Streptococcus oralis 7 7 3 +Streptococcus Phages 3 3 1 +Streptococcus pneumoniae 6 6 3 +Streptococcus pyogenes 6 6 3 +Streptococcus salivarius 6 6 3 +Streptococcus sanguis 7 7 3 +Streptococcus sobrinus 7 7 3 +Streptococcus suis 6 6 3 +Streptococcus thermophilus 6 6 3 +Streptodornase and Streptokinase 7 8 3 +Streptogramin A 6 6 4 +Streptogramin B 6 6 2 +Streptogramin Group A 5 5 2 +Streptogramin Group B 5 5 2 +Streptogramins 4 4 2 +Streptokinase 6 6 2 +Streptolysins 4 5 3 +Streptomyces 5 6 4 +Streptomyces antibioticus 6 7 4 +Streptomyces aureofaciens 6 7 4 +Streptomyces coelicolor 6 7 4 +Streptomyces griseus 6 7 4 +Streptomyces lividans 6 7 4 +Streptomyces rimosus 6 7 4 +Streptomycetaceae 4 5 4 +Streptomycin 4 4 1 +Streptonigrin 4 5 2 +Streptophyta 3 3 1 +Streptothricins 4 4 1 +Streptovaricin 6 6 2 +Streptozocin 4 5 3 +Stress Disorders, Post-Traumatic 4 4 1 +Stress Disorders, Traumatic 3 3 1 +Stress Disorders, Traumatic, Acute 4 4 1 +Stress Fibers 8 8 1 +Stress Granules 8 10 2 +Stress, Mechanical 3 3 1 +Stress, Physiological 2 2 1 +Stress, Psychological 3 4 2 +Stretchers 4 5 2 +Stria Vascularis 6 6 1 +Striae Distensae 4 4 1 +Striatonigral Degeneration 5 6 3 +Striga 9 9 1 +Strigiformes 7 7 1 +Strikes, Employee 4 4 2 +Strobilurins 6 6 1 +Stroboscopy 4 4 1 +Stroke 4 5 2 +Stroke Rehabilitation 4 7 4 +Stroke Volume 5 6 2 +Stroke, Lacunar 5 8 6 +Stromal Cells 3 3 1 +Stromal Interaction Molecule 1 5 6 2 +Stromal Interaction Molecule 2 5 6 2 +Stromal Interaction Molecules 4 5 2 +Stromal Vascular Fraction 5 5 2 +Strongyle Infections, Equine 3 7 5 +Strongylida 7 7 1 +Strongylida Infections 6 6 1 +Strongylocentrotus 6 6 1 +Strongylocentrotus purpuratus 7 7 1 +Strongyloidea 8 8 1 +Strongyloides 9 9 1 +Strongyloides ratti 10 10 1 +Strongyloides stercoralis 10 10 1 +Strongyloidiasis 7 7 1 +Strongylus 9 9 1 +Strontium 4 4 4 +Strontium Isotopes 3 5 5 +Strontium Radioisotopes 4 6 6 +Stroop Test 4 4 1 +Strophanthidin 8 8 1 +Strophanthins 4 7 2 +Strophanthus 9 9 1 +Structural Homology, Protein 2 6 3 +Structural Maintenance of Chromosome Protein 1 4 5 3 +Structure Collapse 3 3 1 +Structure-Activity Relationship 3 4 2 +Struma Ovarii 5 5 1 +Struthioniformes 7 7 1 +Struvite 3 7 3 +Strychnine 5 8 3 +Strychnos 9 9 1 +Strychnos nux-vomica 10 10 1 +Student Dropouts 3 4 2 +Student Health Services 3 3 1 +Student Run Clinic 3 3 1 +Students 2 2 1 +Students, Dental 4 4 1 +Students, Health Occupations 3 3 1 +Students, Medical 4 4 1 +Students, Nursing 4 4 1 +Students, Pharmacy 4 4 1 +Students, Premedical 4 4 1 +Students, Public Health 4 4 1 +Study Characteristics 1 1 1 +Study Guide 2 2 1 +Study Guides as Topic 3 3 1 +Stupor 6 7 2 +Sturge-Weber Syndrome 3 5 3 +Stuttering 7 8 2 +Styracaceae 8 8 1 +Styrax 9 9 1 +Styrene 8 8 1 +Styrenes 7 7 1 +Sub-Saharan African People 4 4 1 +Subacute Care 3 4 2 +Subacute Combined Degeneration 3 8 5 +Subacute Sclerosing Panencephalitis 4 8 9 +Subarachnoid Hemorrhage 5 6 3 +Subarachnoid Hemorrhage, Traumatic 5 7 7 +Subarachnoid Space 5 5 1 +Subcellular Fractions 3 3 1 +Subclavian Artery 4 4 1 +Subclavian Steal Syndrome 6 7 2 +Subclavian Vein 4 4 1 +Subcommissural Organ 4 7 3 +Subcutaneous Absorption 4 6 3 +Subcutaneous Emphysema 4 4 1 +Subcutaneous Fat 5 5 1 +Subcutaneous Fat, Abdominal 6 6 2 +Subcutaneous Tissue 3 3 1 +Subdural Effusion 4 5 3 +Subdural Space 5 5 1 +Suberites 5 5 1 +Subfornical Organ 4 4 2 +Subgenomic RNA 5 5 2 +Subgingival Curettage 3 3 1 +Subject Headings 6 6 1 +Subjective Stress 4 5 2 +Sublimation, Chemical 3 3 2 +Sublimation, Psychological 3 3 1 +Subliminal Stimulation 5 5 1 +Sublingual Gland 4 5 3 +Sublingual Gland Neoplasms 5 6 3 +Sublingual Immunotherapy 5 7 2 +Submandibular Gland 4 5 3 +Submandibular Gland Diseases 4 4 1 +Submandibular Gland Neoplasms 5 6 4 +Submarine Medicine 4 4 1 +Submitochondrial Particles 5 8 2 +Submucous Plexus 5 5 3 +Subphrenic Abscess 4 5 3 +Subrenal Capsule Assay 4 6 2 +Subretinal Fluid 3 3 1 +Substance Abuse Detection 2 5 2 +Substance Abuse Treatment Centers 4 5 2 +Substance Abuse, Intravenous 3 3 2 +Substance Abuse, Oral 3 3 2 +Substance P 5 6 7 +Substance Withdrawal Syndrome 3 3 2 +Substance-Related Disorders 2 2 2 +Substandard Drugs 2 2 1 +Substantia Gelatinosa 5 6 3 +Substantia Innominata 5 8 2 +Substantia Nigra 7 7 1 +Substrate Cycling 3 3 2 +Substrate Specificity 3 3 1 +Subtalar Joint 6 6 1 +Subthalamic Nucleus 7 7 1 +Subthalamus 6 6 1 +Subtilisin 8 8 2 +Subtilisins 7 7 2 +Subtraction Technique 4 4 1 +Subtractive Hybridization Techniques 4 4 2 +Suburban Health 4 4 1 +Suburban Health Services 3 3 1 +Suburban Population 3 3 1 +Suburethral Slings 3 3 1 +Succimer 4 6 2 +Succinate Cytochrome c Oxidoreductase 4 5 4 +Succinate Dehydrogenase 6 8 10 +Succinate-CoA Ligases 6 6 1 +Succinate-Semialdehyde Dehydrogenase 6 6 1 +Succinate-Semialdehyde Dehydrogenase (NADP+) 6 6 1 +Succinates 5 5 1 +Succinic Acid 6 6 1 +Succinic Anhydrides 3 6 2 +Succinimides 3 5 2 +Succinivibrionaceae 5 5 2 +Succinylcholine 5 6 3 +Succinyldiaminopimelate Transaminase 6 6 1 +Sucking Behavior 3 3 1 +Sucralfate 4 6 3 +Sucrase 6 6 1 +Sucrase-Isomaltase Complex 4 7 4 +Sucrose 4 5 2 +Suction 3 3 1 +Sudan 5 5 1 +Sudden Infant Death 5 5 2 +Sudden Unexpected Death in Epilepsy 5 5 2 +Sufentanil 5 5 1 +Sugammadex 5 8 2 +Sugar Acids 2 4 3 +Sugar Alcohol Dehydrogenases 6 6 1 +Sugar Alcohols 2 3 2 +Sugar Phosphates 2 2 1 +Sugar-Sweetened Beverages 3 4 2 +Sugars 2 2 1 +Suggestion 4 5 2 +Suicidal Ideation 5 6 2 +Suicide 4 5 2 +Suicide Prevention 4 6 3 +Suicide, Assisted 4 6 4 +Suicide, Attempted 5 6 2 +Suicide, Completed 5 6 2 +Suipoxvirus 5 5 1 +Sulbactam 5 6 3 +Sulbenicillin 6 7 3 +Sulfacetamide 5 6 3 +Sulfachlorpyridazine 5 7 6 +Sulfadiazine 5 7 6 +Sulfadimethoxine 5 7 6 +Sulfadoxine 5 7 6 +Sulfaguanidine 4 7 7 +Sulfalene 5 7 6 +Sulfamerazine 5 7 6 +Sulfameter 5 7 6 +Sulfamethazine 5 7 6 +Sulfamethizole 5 7 6 +Sulfamethoxazole 5 7 6 +Sulfamethoxypyridazine 5 7 6 +Sulfamonomethoxine 5 7 6 +Sulfamoxole 5 7 6 +Sulfanilamide 5 7 6 +Sulfanilamides 4 5 3 +Sulfanilic Acids 6 6 2 +Sulfaphenazole 5 7 7 +Sulfapyridine 5 7 6 +Sulfaquinoxaline 5 7 6 +Sulfasalazine 4 5 2 +Sulfatases 5 5 1 +Sulfate Adenylyltransferase 6 6 1 +Sulfate Transporters 6 7 4 +Sulfates 5 5 2 +Sulfathiazole 5 7 8 +Sulfathiazoles 4 6 5 +Sulfatidosis 7 8 9 +Sulfenic Acids 4 4 1 +Sulfhemoglobin 5 6 2 +Sulfhemoglobinemia 3 3 1 +Sulfhydryl Compounds 3 3 1 +Sulfhydryl Reagents 5 5 1 +Sulfides 3 5 3 +Sulfinic Acids 4 5 3 +Sulfinpyrazone 7 7 1 +Sulfisomidine 5 7 6 +Sulfisoxazole 5 7 6 +Sulfite Dehydrogenase 5 5 1 +Sulfite Oxidase 5 5 1 +Sulfite Reductase (Ferredoxin) 5 5 1 +Sulfite Reductase (NADPH) 5 5 1 +Sulfites 3 5 2 +Sulfobromophthalein 8 8 1 +Sulfoglycosphingolipids 5 6 3 +Sulfolobaceae 4 4 1 +Sulfolobales 3 3 1 +Sulfolobus 5 5 1 +Sulfolobus acidocaldarius 6 6 1 +Sulfolobus solfataricus 6 6 1 +Sulfonamides 3 4 2 +Sulfones 3 3 1 +Sulfonic Acids 4 5 3 +Sulfonium Compounds 3 3 2 +Sulfonylurea Compounds 4 4 2 +Sulfonylurea Receptors 4 9 5 +Sulfotransferases 5 5 1 +Sulfoxides 3 3 1 +Sulfur 4 4 1 +Sulfur Acids 3 4 3 +Sulfur Compounds 2 2 2 +Sulfur Dioxide 3 5 3 +Sulfur Group Transferases 4 4 1 +Sulfur Hexafluoride 3 5 3 +Sulfur Isotopes 3 5 2 +Sulfur Oxides 3 4 2 +Sulfur Radioisotopes 4 6 3 +Sulfur-Reducing Bacteria 2 2 1 +Sulfur-Sulfur Bond Isomerases 5 5 1 +Sulfuric Acid Esters 5 5 1 +Sulfuric Acids 4 5 3 +Sulfurtransferases 5 5 1 +Sulindac 4 7 2 +Suloctidil 5 5 4 +Sulpiride 4 8 3 +Sumatriptan 4 6 3 +SUMO-1 Protein 5 5 1 +Sumoylation 6 8 4 +Sun Protection Factor 3 4 2 +Sunbathing 4 4 1 +Sunburn 3 4 2 +SUNCT Syndrome 7 7 1 +Sunflower Oil 5 5 1 +Sunitinib 5 5 2 +Sunlight 5 6 6 +Sunscreening Agents 4 6 4 +Sunstroke 4 4 1 +Suntan 4 4 1 +Super Enhancers 6 9 3 +Superantigens 3 3 1 +Superconductivity 4 4 1 +Superego 4 4 2 +Superfetation 6 6 1 +Superficial Back Muscles 5 5 1 +Superficial Musculoaponeurotic System 3 4 2 +Superinfection 3 3 1 +Superior Cervical Ganglion 5 6 3 +Superior Colliculi 7 7 1 +Superior Mesenteric Artery Syndrome 6 6 1 +Superior Olivary Complex 8 9 2 +Superior Sagittal Sinus 5 5 1 +Superior Vena Cava Syndrome 3 3 1 +Superovulation 5 5 3 +Superoxide Dismutase 4 4 1 +Superoxide Dismutase 2 5 5 1 +Superoxide Dismutase-1 5 5 1 +Superoxides 5 7 4 +Superstitions 5 5 1 +Supervised Machine Learning 5 6 2 +Supination 5 5 1 +Supine Position 4 4 1 +Support of Research 1 1 1 +Support Vector Machine 6 7 2 +Suppositories 3 3 1 +Suppression, Genetic 3 4 2 +Suppressor Factors, Immunologic 5 6 3 +Suppressor of Cytokine Signaling 1 Protein 6 6 3 +Suppressor of Cytokine Signaling 3 Protein 6 6 3 +Suppressor of Cytokine Signaling Proteins 5 5 3 +Suppuration 2 4 2 +Suprachiasmatic Nucleus 7 8 2 +Suprachiasmatic Nucleus Neurons 3 9 4 +Supraglottitis 3 3 4 +Supranuclear Palsy, Progressive 4 6 7 +Supraoptic Nucleus 7 8 2 +Supratentorial Neoplasms 5 6 3 +Supreme Court Decisions 5 5 1 +Suprofen 5 5 1 +Sural Nerve 8 8 1 +Suramin 5 8 3 +Suregada 10 10 1 +Surface Plasmon Resonance 3 4 2 +Surface Properties 2 2 1 +Surface Tension 3 3 1 +Surface-Active Agents 3 3 1 +Surge Capacity 4 4 1 +Surgeons 4 5 2 +Surgery Department, Hospital 6 6 2 +Surgery, Computer-Assisted 2 2 1 +Surgery, Oral 2 4 2 +Surgery, Plastic 4 4 1 +Surgery, Veterinary 3 3 1 +Surgical Attire 3 3 2 +Surgical Clearance 3 3 1 +Surgical Drapes 3 4 2 +Surgical Equipment 2 2 1 +Surgical Fixation Devices 3 3 1 +Surgical Flaps 3 3 2 +Surgical Instruments 3 3 1 +Surgical Mesh 3 3 1 +Surgical Navigation Systems 3 3 1 +Surgical Oncology 4 5 2 +Surgical Procedures, Operative 1 1 1 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4 5 6 +Thyroxine-Binding Proteins 4 4 4 +Thysanoptera 6 6 1 +Tiagabine 4 5 2 +Tiapamil Hydrochloride 4 5 2 +Tiapride Hydrochloride 4 8 3 +Tibet 5 5 1 +Tibia 6 6 1 +Tibial Arteries 4 4 1 +Tibial Fractures 3 3 2 +Tibial Meniscus Injuries 3 3 1 +Tibial Nerve 7 7 1 +Tibial Neuropathy 5 5 1 +Tibial Plateau Fractures 4 5 4 +Tibiofemoral Joint 5 5 1 +Tic Disorders 3 4 2 +Ticagrelor 5 7 3 +Ticarcillin 5 6 3 +Tick Bites 3 4 2 +Tick Control 6 6 1 +Tick Infestations 5 5 1 +Tick Paralysis 6 6 1 +Tick Toxicoses 5 5 1 +Tick-Borne Diseases 3 3 1 +Ticks 7 7 1 +Ticlopidine 5 5 4 +Ticrynafen 4 7 4 +Tics 4 5 2 +Tidal Volume 6 9 2 +Tidal Waves 3 6 4 +Tietze's Syndrome 3 4 2 +Tigecycline 5 8 2 +Tigers 11 11 1 +Tight Junction Proteins 4 4 1 +Tight Junctions 6 6 1 +Tilapia 8 8 1 +Tiletamine 4 7 4 +Tilia 10 10 1 +Tiliaceae 7 7 1 +Tilidine 5 5 1 +Tillandsia 8 8 1 +Tilorone 4 7 2 +Tilt-Table Test 4 4 1 +Timbre Perception 4 5 2 +Time 2 2 1 +Time and Motion Studies 5 5 2 +Time Factors 3 3 1 +Time Management 3 5 4 +Time Out, Healthcare 3 4 2 +Time Perception 4 4 1 +Time Pressure 4 5 2 +Time-Lapse Imaging 3 5 3 +Time-to-Pregnancy 4 4 1 +Time-to-Treatment 3 4 2 +Timolol 5 6 6 +Timor-Leste 4 4 1 +Tin 4 4 2 +Tin Compounds 2 2 1 +Tin Fluorides 3 5 4 +Tin Polyphosphates 3 8 3 +Tin Radioisotopes 4 4 1 +Tinea 4 5 3 +Tinea Capitis 4 6 4 +Tinea Cruris 5 6 5 +Tinea Favosa 5 7 4 +Tinea Pedis 5 6 5 +Tinea Versicolor 4 5 3 +Tinidazole 4 6 2 +Tinnitus 4 6 3 +Tinospora 8 8 1 +Tinzaparin 6 6 1 +Tiopronin 4 5 3 +Tiotropium Bromide 5 7 4 +Tirapazamine 4 4 1 +Tirofiban 6 6 1 +Tirzepatide 5 6 5 +Tissue Adhesions 5 5 1 +Tissue Adhesives 2 4 4 +Tissue and Organ Harvesting 3 3 1 +Tissue and Organ Procurement 3 3 1 +Tissue Array Analysis 4 4 1 +Tissue Banks 4 4 1 +Tissue Conditioning, Dental 3 3 1 +Tissue Culture Techniques 4 4 1 +Tissue Distribution 3 4 2 +Tissue Donors 2 2 1 +Tissue Embedding 6 7 4 +Tissue Engineering 5 5 1 +Tissue Expansion 3 3 1 +Tissue Expansion Devices 3 3 2 +Tissue Extracts 2 2 1 +Tissue Fixation 6 7 4 +Tissue Inhibitor of Metalloproteinase-1 5 5 1 +Tissue Inhibitor of Metalloproteinase-2 5 5 1 +Tissue Inhibitor of Metalloproteinase-3 5 5 1 +Tissue Inhibitor of Metalloproteinase-4 5 5 1 +Tissue Inhibitor of Metalloproteinases 4 4 1 +Tissue Kallikreins 4 8 3 +Tissue Plasminogen Activator 3 7 4 +Tissue Polypeptide Antigen 3 4 3 +Tissue Preservation 3 6 6 +Tissue Scaffolds 3 3 2 +Tissue Survival 2 2 1 +Tissue Therapy, Historical 4 4 2 +Tissue Transplantation 3 4 2 +Tissues 1 1 1 +Titanium 4 4 3 +Tithonia 8 8 1 +Titrimetry 3 3 1 +TNF Receptor-Associated Death Domain Protein 6 6 8 +TNF Receptor-Associated Factor 1 6 6 3 +TNF Receptor-Associated Factor 2 6 6 3 +TNF Receptor-Associated Factor 3 6 6 3 +TNF Receptor-Associated Factor 4 6 6 3 +TNF Receptor-Associated Factor 5 6 6 3 +TNF Receptor-Associated Factor 6 6 6 3 +TNF-Related Apoptosis-Inducing Ligand 5 6 3 +Tobacco Control 6 7 3 +Tobacco Industry 4 4 1 +Tobacco mosaic satellite virus 4 4 1 +Tobacco Mosaic Virus 5 5 2 +Tobacco necrosis satellite virus 4 4 1 +Tobacco Products 4 4 1 +Tobacco Smoke Pollution 4 5 2 +Tobacco Smoking 4 4 2 +Tobacco Use 3 3 1 +Tobacco Use Cessation 4 4 1 +Tobacco Use Cessation Devices 2 2 1 +Tobacco Use Disorder 3 3 2 +Tobacco, Smokeless 5 5 1 +Tobacco, Waterpipe 5 5 1 +Tobamovirus 4 4 2 +Tobramycin 6 6 1 +Tobramycin, Dexamethasone Drug Combination 3 8 4 +Tocainide 4 5 2 +Tocolysis 3 3 1 +Tocolytic Agents 5 5 2 +Tocopherols 6 6 2 +Tocotrienols 6 6 2 +Todralazine 3 5 2 +Toe Joint 5 5 1 +Toe Phalanges 6 6 1 +Toes 6 6 1 +Togaviridae 4 4 1 +Togaviridae Infections 4 4 1 +Togo 5 5 1 +Toilet Facilities 2 7 3 +Toilet Training 3 3 1 +Token Economy 6 6 1 +Tokyo 3 5 2 +Tolazamide 5 7 5 +Tolazoline 6 6 1 +Tolbutamide 5 7 5 +Tolcapone 4 8 4 +Toll-Like Receptor 1 8 8 1 +Toll-Like Receptor 10 8 8 1 +Toll-Like Receptor 2 8 8 1 +Toll-Like Receptor 3 8 8 1 +Toll-Like Receptor 4 8 8 1 +Toll-Like Receptor 5 8 8 1 +Toll-Like Receptor 6 8 8 1 +Toll-Like Receptor 7 8 8 1 +Toll-Like Receptor 8 8 8 1 +Toll-Like Receptor 9 4 8 2 +Toll-Like Receptor Agonists 4 6 2 +Toll-Like Receptors 7 7 1 +Tolloid-Like Metalloproteinases 3 9 3 +Tolmetin 5 5 1 +Tolnaftate 4 7 4 +Tolonium Chloride 4 5 2 +Tolosa-Hunt Syndrome 3 5 3 +Tolperisone 4 4 2 +Tolterodine Tartrate 6 8 5 +Toluene 6 6 1 +Toluene 2,4-Diisocyanate 3 7 2 +Toluidines 4 7 2 +Tolvaptan 5 5 1 +Tomatine 3 4 2 +Tombusviridae 3 4 2 +Tombusvirus 4 5 2 +Tomography 4 4 1 +Tomography Scanners, X-Ray Computed 2 2 1 +Tomography, Emission-Computed 5 6 5 +Tomography, Emission-Computed, Single-Photon 6 7 5 +Tomography, Optical 3 5 3 +Tomography, Optical Coherence 4 6 3 +Tomography, Spiral Computed 6 8 5 +Tomography, X-Ray 5 5 2 +Tomography, X-Ray Computed 5 7 5 +Tonga 5 5 2 +Tongue 3 4 2 +Tongue Diseases 3 3 1 +Tongue Habits 4 4 1 +Tongue Neoplasms 4 5 3 +Tongue, Fissured 4 4 1 +Tongue, Hairy 4 4 1 +Tonic Pupil 3 5 3 +Tonometry, Ocular 4 4 1 +Tonsillar Neoplasms 5 7 4 +Tonsillectomy 3 3 1 +Tonsillitis 4 4 4 +Tool Use Behavior 4 4 1 +Toona 8 8 1 +Tooth 4 4 1 +Tooth Abnormalities 3 4 3 +Tooth Abrasion 4 4 1 +Tooth Ankylosis 3 3 1 +Tooth Apex 6 6 1 +Tooth Attrition 4 4 1 +Tooth Avulsion 3 4 2 +Tooth Bleaching 3 3 1 +Tooth Bleaching Agents 5 5 1 +Tooth Calcification 3 5 2 +Tooth Cervix 5 5 1 +Tooth Components 4 4 1 +Tooth Crown 5 5 1 +Tooth Demineralization 3 3 1 +Tooth Discoloration 3 3 1 +Tooth Diseases 2 2 1 +Tooth Erosion 4 4 2 +Tooth Eruption 3 3 1 +Tooth Eruption, Ectopic 3 3 1 +Tooth Exfoliation 3 3 1 +Tooth Extraction 3 3 2 +Tooth Fractures 3 4 2 +Tooth Germ 5 5 1 +Tooth Injuries 2 3 2 +Tooth Loss 3 4 2 +Tooth Migration 3 4 2 +Tooth Mobility 3 4 2 +Tooth Movement Techniques 4 4 1 +Tooth Permeability 3 3 1 +Tooth Preparation 2 2 1 +Tooth Preparation, Prosthodontic 3 3 1 +Tooth Remineralization 2 2 1 +Tooth Replantation 3 4 4 +Tooth Resorption 3 3 2 +Tooth Root 5 5 1 +Tooth Socket 4 8 3 +Tooth Wear 3 3 1 +Tooth, Artificial 4 4 2 +Tooth, Deciduous 5 5 1 +Tooth, Impacted 3 3 1 +Tooth, Nonvital 4 4 1 +Tooth, Supernumerary 4 5 3 +Tooth, Unerupted 3 5 2 +Toothache 3 6 2 +Toothbrushing 4 4 1 +Toothpastes 3 5 2 +Topiramate 6 6 2 +Topography, Medical 4 5 2 +Topoisomerase I Inhibitors 6 6 2 +Topoisomerase II Inhibitors 6 6 2 +Topoisomerase Inhibitors 5 5 2 +Topotecan 4 4 1 +TOR Serine-Threonine Kinases 5 8 2 +Toremifene 9 9 1 +Tornadoes 5 5 2 +Torovirus 6 6 1 +Torovirus Infections 6 6 1 +Torpedo 7 7 2 +Torpor 4 5 3 +Torque 4 4 1 +Torque teno virus 4 4 1 +Torsades de Pointes 6 6 3 +Torsemide 4 5 3 +Torsion Abnormality 3 3 1 +Torsion, Mechanical 3 3 1 +Torso 2 2 1 +Torticollis 6 6 1 +Torture 5 5 2 +Torulaspora 4 5 2 +Tospovirus 3 5 2 +Tosyl Compounds 4 7 2 +Tosylarginine Methyl Ester 5 7 4 +Tosyllysine Chloromethyl Ketone 4 7 6 +Tosylphenylalanyl Chloromethyl Ketone 4 7 6 +Total Disc Replacement 4 5 3 +Total Lung Capacity 3 6 2 +Total Quality Management 3 4 3 +Totipotent Stem Cells 3 3 1 +Totiviridae 4 4 1 +Totivirus 5 5 1 +Touch 4 4 2 +Touch Perception 4 4 1 +Tourette Syndrome 4 5 5 +Tourism 3 3 1 +Tourniquets 2 2 1 +Toxaphene 5 7 3 +Toxascariasis 7 7 1 +Toxascaris 9 9 1 +Toxemia 2 2 1 +Toxic Actions 2 2 1 +Toxic Optic Neuropathy 3 6 8 +Toxicity Tests 2 2 1 +Toxicity Tests, Acute 3 3 1 +Toxicity Tests, Chronic 3 3 1 +Toxicity Tests, Subacute 3 3 1 +Toxicity Tests, Subchronic 3 3 1 +Toxicodendron 8 8 1 +Toxicogenetics 3 5 3 +Toxicokinetics 2 3 2 +Toxicological Phenomena 3 3 1 +Toxicology 2 3 2 +Toxiferine 4 6 5 +Toxin-Antitoxin Systems 2 2 1 +Toxins, Biological 2 2 1 +Toxocara 9 9 1 +Toxocara canis 10 10 1 +Toxocariasis 4 7 4 +Toxoids 4 4 1 +Toxoplasma 7 7 1 +Toxoplasmosis 5 5 1 +Toxoplasmosis, Animal 4 6 4 +Toxoplasmosis, Cerebral 4 6 8 +Toxoplasmosis, Congenital 3 6 4 +Toxoplasmosis, Ocular 4 6 3 +Toyocamycin 4 4 1 +Trabectedin 4 6 2 +Trabecular Meshwork 4 4 1 +Trabeculectomy 4 4 1 +Trace Amine-Associated Receptors 6 6 1 +Trace Elements 3 6 4 +Trachea 2 2 1 +Tracheal Diseases 2 2 1 +Tracheal Neoplasms 3 5 4 +Tracheal Stenosis 3 3 1 +Tracheitis 3 3 3 +Trachelectomy 5 5 1 +Tracheobronchomalacia 3 4 5 +Tracheobronchomegaly 3 4 4 +Tracheoesophageal Fistula 3 6 5 +Tracheomalacia 4 5 4 +Tracheophyta 5 5 1 +Tracheostomy 3 3 4 +Tracheostomy Decannulation 3 4 5 +Tracheotomy 3 3 2 +Trachoma 3 7 6 +Track and Field 5 5 1 +Traction 3 3 1 +Tradescantia 8 8 1 +Traditional Medicine Practitioners 3 4 2 +Traditional Pulse Diagnosis 3 3 1 +Traffic-Related Pollution 4 4 1 +Tragacanth 4 5 3 +Trager duck spleen necrosis virus 6 6 1 +Tragopogon 8 8 1 +Trail Making Test 4 4 1 +Trained Immunity 4 4 1 +Training Support 4 4 1 +Tramadol 5 6 3 +Trametes 6 6 1 +Tranexamic Acid 5 5 1 +Tranquilizing Agents 5 6 3 +Trans Fatty Acids 4 4 1 +trans-1,4-Bis(2-chlorobenzaminomethyl)cyclohexane Dihydrochloride 7 7 1 +Trans-Activation Responsive RNA-Binding Protein 5 5 2 +Trans-Activators 4 5 3 +Trans-Cinnamate 4-Monooxygenase 4 6 2 +trans-Golgi Network 8 8 1 +Trans-Splicing 4 5 3 +Transactinide Series Elements 4 5 2 +Transactional Analysis 4 4 1 +Transaldolase 5 5 1 +Transaminases 5 5 1 +Transanal Endoscopic Microsurgery 3 8 5 +Transanal Endoscopic Surgery 5 7 4 +Transcatheter Aortic Valve Replacement 4 5 5 +Transcatheter Mitral Valve Replacement 4 5 5 +Transcaucasia 3 3 1 +Transcellular Cell Migration 3 4 5 +Transcobalamins 4 5 3 +Transcortin 4 6 7 +Transcranial Direct Current Stimulation 3 4 3 +Transcranial Magnetic Stimulation 3 3 1 +Transcription Activator-Like Effector Nucleases 5 7 2 +Transcription Activator-Like Effectors 4 5 4 +Transcription Elongation, Genetic 4 4 2 +Transcription Factor 3 6 6 4 +Transcription Factor 4 6 6 2 +Transcription Factor 7-Like 1 Protein 5 7 4 +Transcription Factor 7-Like 2 Protein 5 7 4 +Transcription Factor AP-1 5 5 2 +Transcription Factor AP-2 4 4 2 +Transcription Factor Brn-3 5 5 2 +Transcription Factor Brn-3A 6 6 2 +Transcription Factor Brn-3B 6 6 2 +Transcription Factor Brn-3C 6 6 2 +Transcription Factor CHOP 5 6 3 +Transcription Factor DP1 4 4 1 +Transcription Factor HES-1 5 5 2 +Transcription Factor MTF-1 4 4 2 +Transcription Factor Pit-1 5 5 2 +Transcription Factor RelA 5 5 3 +Transcription Factor RelB 5 5 3 +Transcription Factor TFIIA 6 6 2 +Transcription Factor TFIIB 6 6 2 +Transcription Factor TFIID 6 6 2 +Transcription Factor TFIIH 6 6 2 +Transcription Factor TFIIIA 6 6 2 +Transcription Factor TFIIIB 6 6 2 +Transcription Factor TFIIIC 6 6 2 +Transcription Factors 3 3 1 +Transcription Factors, General 4 4 2 +Transcription Factors, TFII 5 5 2 +Transcription Factors, TFIII 5 5 2 +Transcription Initiation Site 8 8 1 +Transcription Initiation, Genetic 4 4 2 +Transcription Termination, Genetic 4 4 2 +Transcription, Genetic 3 3 2 +Transcriptional Activation 3 3 1 +Transcriptional Coactivator with PDZ-Binding Motif Proteins 5 5 3 +Transcriptional Elongation Factors 4 4 1 +Transcriptional Regulator ERG 5 5 2 +Transcriptome 3 4 3 +Transcultural Nursing 4 4 1 +Transcutaneous Electric Nerve Stimulation 3 5 4 +Transcytosis 3 3 3 +Transdermal Patch 2 2 1 +Transducers 3 3 1 +Transducers, Pressure 4 4 1 +Transducin 5 8 4 +Transduction, Genetic 3 4 2 +Transendothelial and Transepithelial Migration 3 5 2 +Transfection 3 4 2 +Transfer Agreement 4 4 1 +Transfer Factor 5 6 3 +Transfer Machine Learning 5 6 2 +Transfer RNA Aminoacylation 4 5 6 +Transfer, Psychology 4 4 1 +Transferases 3 3 1 +Transferases (Other Substituted Phosphate Groups) 5 5 1 +Transference, Psychology 4 4 1 +Transferrin 5 6 5 +Transferrin-Binding Protein A 6 7 3 +Transferrin-Binding Protein B 6 7 3 +Transferrin-Binding Proteins 4 4 1 +Transferrins 5 5 2 +Transformation, Bacterial 3 5 4 +Transformation, Genetic 3 3 1 +Transforming Growth Factor alpha 4 5 6 +Transforming Growth Factor beta 4 5 6 +Transforming Growth Factor beta1 5 6 6 +Transforming Growth Factor beta2 5 6 6 +Transforming Growth Factor beta3 5 6 5 +Transforming Growth Factors 3 4 3 +Transfusion Medicine 5 5 1 +Transfusion Reaction 2 3 2 +Transfusion-Related Acute Lung Injury 3 5 3 +Transgender Persons 2 4 2 +Transgenes 6 6 1 +Transglutaminases 6 6 1 +Transient Receptor Potential Channels 6 6 2 +Transient Tachypnea of the Newborn 4 5 5 +Transients and Migrants 2 2 1 +Transillumination 3 5 2 +Transistors, Electronic 4 4 1 +Transition Elements 3 3 1 +Transition Temperature 4 4 1 +Transition to Adult Care 4 6 3 +Transitional Care 3 6 5 +Transketolase 5 5 1 +Translating 4 4 1 +Translational Research, Biomedical 5 5 1 +Translational Science, Biomedical 3 3 1 +Translations 5 5 1 +Translesion DNA Synthesis 4 5 2 +Translocation, Genetic 3 5 3 +Transmembrane Activator and CAML Interactor Protein 8 8 1 +Transmissible gastroenteritis virus 9 9 1 +Transmyocardial Laser Revascularization 5 5 2 +Transplant Donor Site 2 2 1 +Transplant Recipients 2 2 1 +Transplantation 2 2 1 +Transplantation Chimera 3 3 1 +Transplantation Conditioning 4 6 2 +Transplantation Immunology 2 2 1 +Transplantation Tolerance 4 4 1 +Transplantation, Autologous 3 3 1 +Transplantation, Haploidentical 4 4 1 +Transplantation, Heterologous 3 3 1 +Transplantation, Heterotopic 3 3 1 +Transplantation, Homologous 3 3 1 +Transplantation, Isogeneic 4 4 1 +Transplants 2 2 1 +Transport Vesicles 8 8 1 +Transportation 2 2 1 +Transportation Facilities 2 2 1 +Transportation of Patients 3 5 4 +Transposases 5 6 2 +Transposition of Great Vessels 4 5 3 +Transposon Resolvases 4 6 2 +Transsexualism 5 6 5 +Transtheoretical Model 3 5 3 +Transtympanic Micropressure Treatment 4 4 1 +Transurethral Resection of Bladder 4 4 1 +Transurethral Resection of Prostate 6 6 1 +Transverse Sinuses 5 5 1 +Transvestism 3 3 1 +Tranylcypromine 4 4 1 +Trapezium Bone 7 7 1 +Trapezoid Body 9 9 1 +Trapezoid Bone 7 7 1 +Trapidil 4 5 2 +Trastuzumab 9 9 3 +Trauma and Stressor Related Disorders 2 2 1 +Trauma Centers 5 7 3 +Trauma Nursing 4 4 2 +Trauma Severity Indices 2 7 5 +Trauma, Nervous System 2 2 2 +Traumatology 5 5 1 +Travel 2 2 1 +Travel Medicine 3 3 1 +Travel Nursing 5 5 1 +Travel-Related Illness 3 3 2 +Travoprost 6 9 3 +Trazodone 4 5 2 +Treatment Adherence and Compliance 4 4 3 +Treatment Delay 4 5 2 +Treatment Effect Heterogeneity 7 7 1 +Treatment Expectations 3 7 4 +Treatment Failure 4 7 3 +Treatment Interruption 5 5 1 +Treatment Outcome 3 6 3 +Treatment Refusal 5 6 5 +Treatment Switching 2 4 4 +Trees 3 3 1 +Trefoil Factor-1 4 4 1 +Trefoil Factor-2 4 4 1 +Trefoil Factor-3 4 4 1 +Trefoil Factors 3 3 1 +Trehalase 6 6 1 +Trehalose 4 5 3 +Trema 10 10 1 +Trematoda 6 6 1 +Trematode Infections 4 4 1 +Tremor 4 5 2 +Tremorine 4 4 1 +Trenbolone Acetate 6 6 1 +Trench Fever 7 7 1 +Trephining 4 4 1 +Treponema 4 6 2 +Treponema denticola 5 7 2 +Treponema Immobilization Test 6 7 3 +Treponema pallidum 5 7 2 +Treponemal Infections 5 6 2 +Tretinoin 5 11 5 +Tretoquinol 6 6 1 +Triacetin 4 4 1 +Triacetoneamine-N-Oxyl 4 5 3 +Triage 4 4 1 +Trial of Labor 6 6 1 +Trialkyltin Compounds 4 4 1 +Triallate 4 6 2 +Triamcinolone 5 6 2 +Triamcinolone Acetonide 6 7 2 +Triamterene 5 5 1 +Triangular Fibrocartilage 4 6 3 +Triatoma 10 10 1 +Triatominae 9 9 1 +Triazenes 2 2 1 +Triazines 3 3 1 +Triaziquone 5 5 1 +Triazolam 6 6 1 +Triazoles 4 4 1 +Tribolium 10 10 1 +Tribulus 8 8 1 +Tricarboxylic Acids 4 4 1 +Trichechus 9 9 1 +Trichechus inunguis 10 10 1 +Trichechus manatus 10 10 1 +Trichiasis 3 3 1 +Trichinella 9 9 1 +Trichinella spiralis 10 10 1 +Trichinellosis 7 7 1 +Trichlorfon 4 4 1 +Trichlormethiazide 7 8 3 +Trichloroacetic Acid 6 6 2 +Trichloroepoxypropane 5 5 2 +Trichloroethanes 5 5 1 +Trichloroethylene 5 5 1 +Trichoderma 4 4 1 +Trichodermin 4 7 3 +Trichodesmium 3 5 3 +Tricholoma 5 5 1 +Trichomes 3 4 2 +Trichomonadida 3 3 1 +Trichomonas 4 4 1 +Trichomonas Infections 4 4 1 +Trichomonas vaginalis 5 5 1 +Trichomonas Vaginitis 5 7 3 +Trichophytin 4 5 2 +Trichophyton 4 4 1 +Trichosanthes 8 8 1 +Trichosanthin 6 6 1 +Trichosporon 4 4 2 +Trichosporonosis 4 6 4 +Trichostomatida 5 5 1 +Trichostomatina 6 6 1 +Trichostrongyloidea 8 8 1 +Trichostrongyloidiasis 7 7 1 +Trichostrongylosis 8 8 1 +Trichostrongylus 9 9 1 +Trichosurus 8 8 1 +Trichothecenes 3 5 3 +Trichothecenes, Type A 4 6 3 +Trichothecenes, Type B 4 6 3 +Trichothecenes, Type C 4 6 3 +Trichothiodystrophy Syndromes 4 4 5 +Trichotillomania 3 4 2 +Trichuriasis 7 7 1 +Trichuris 9 9 1 +Trichuroidea 8 8 1 +Triclabendazole 5 5 1 +Triclosan 4 8 2 +Tricuspid Atresia 4 5 4 +Tricuspid Valve 4 4 1 +Tricuspid Valve Insufficiency 4 4 1 +Tricuspid Valve Prolapse 5 5 1 +Tricuspid Valve Stenosis 4 4 1 +Trientine 6 6 1 +Triethylenemelamine 4 5 2 +Triethylenephosphoramide 5 5 1 +Triethyltin Compounds 5 5 1 +Trifluoperazine 4 5 2 +Trifluoroacetic Acid 6 6 2 +Trifluoroethanol 4 4 1 +Trifluperidol 4 4 1 +Triflupromazine 4 5 2 +Trifluralin 5 7 2 +Trifluridine 5 6 3 +Trifolium 8 8 1 +Trigeminal Autonomic Cephalalgias 6 6 1 +Trigeminal Caudal Nucleus 7 9 2 +Trigeminal Ganglion 4 6 3 +Trigeminal Motor Nucleus 6 9 2 +Trigeminal Nerve 5 5 1 +Trigeminal Nerve Diseases 5 5 2 +Trigeminal Nerve Injuries 4 6 5 +Trigeminal Neuralgia 6 6 2 +Trigeminal Nuclei 5 5 1 +Trigeminal Nucleus, Spinal 6 9 3 +Trigger Finger Disorder 5 5 1 +Trigger Points 2 2 1 +Triggering Receptor Expressed on Myeloid Cells-1 5 6 4 +Triglycerides 3 3 1 +Trigonella 8 8 1 +Trihalomethanes 4 4 1 +Trihexosylceramides 4 7 4 +Trihexyphenidyl 4 4 1 +Triiodobenzoic Acids 6 8 2 +Triiodothyronine 5 7 2 +Triiodothyronine, Reverse 5 7 2 +Trillium 10 10 1 +Trilogy of Fallot 4 5 3 +TRIM21 Protein 4 6 2 +Trimebutine 6 9 4 +Trimecaine 5 6 2 +Trimedoxime 5 5 2 +Trimeprazine 4 5 2 +Trimeresurus 8 10 3 +Trimetazidine 4 4 1 +Trimethadione 5 5 1 +Trimethaphan 5 5 1 +Trimethoprim 4 4 1 +Trimethoprim Resistance 4 7 3 +Trimethoprim, Sulfamethoxazole Drug Combination 3 8 8 +Trimethyl Ammonium Compounds 4 4 1 +Trimethylsilyl Compounds 3 3 1 +Trimethyltin Compounds 5 5 1 +Trimetrexate 5 5 1 +Trimipramine 5 5 1 +Trinidad and Tobago 4 5 2 +Trinitrobenzenes 4 7 2 +Trinitrobenzenesulfonic Acid 5 8 3 +Trinitrotoluene 7 7 1 +Trinucleotide Repeat Expansion 4 9 8 +Trinucleotide Repeats 7 8 3 +Triolein 4 4 2 +Triose Sugar Alcohols 3 4 2 +Triose-Phosphate Isomerase 6 6 1 +Trioses 4 4 1 +Trioxsalen 5 7 3 +Triparanol 6 6 1 +Tripartite Motif Proteins 3 3 1 +Tripartite Motif-Containing Protein 28 4 6 4 +Tripelennamine 5 6 2 +Tripeptidyl-Peptidase 1 6 7 3 +Triphenylmethyl Compounds 6 6 1 +Triple Negative Breast Neoplasms 4 5 2 +Triplets 3 3 1 +Tripleurospermum 8 8 1 +Triploidy 4 6 3 +Triprolidine 4 4 1 +Tripterygium 10 10 1 +Triptorelin Pamoate 5 8 5 +Triquetrum Bone 7 7 1 +Trisaccharides 4 4 1 +Trismus 5 6 2 +Trisomy 4 6 5 +Trisomy 13 Syndrome 4 5 7 +Trisomy 18 Syndrome 4 5 6 +Tristetraprolin 4 4 3 +Triterpenes 4 4 1 +Triticale 8 8 1 +Triticum 8 8 1 +Tritium 4 4 3 +Tritolyl Phosphates 4 8 2 +Tritonia Sea Slug 6 6 1 +Tritrichomonas 4 4 1 +Tritrichomonas foetus 5 5 1 +Triturus 8 8 1 +Triumfetta 10 10 1 +tRNA Methyltransferases 6 6 1 +Trochlear Nerve 5 5 1 +Trochlear Nerve Diseases 3 3 1 +Trochlear Nerve Injuries 4 5 3 +Troglitazone 5 6 4 +Troglotrematidae 7 7 1 +Trogocytosis 2 2 2 +Troleandomycin 5 5 1 +Trombiculiasis 6 6 1 +Trombiculidae 8 8 1 +Tromethamine 5 5 1 +Tropaeolaceae 7 7 1 +Tropaeolum 8 8 1 +Tropanes 3 5 4 +Tropheryma 4 4 1 +Trophoblastic Neoplasms 3 5 3 +Trophoblastic Tumor, Placental Site 5 7 4 +Trophoblasts 3 4 3 +Trophozoites 3 6 4 +Tropical Climate 5 6 2 +Tropical Medicine 3 3 1 +Tropicamide 4 4 1 +Tropisetron 5 5 1 +Tropism 2 4 2 +Tropocollagen 6 6 1 +Tropoelastin 4 6 2 +Tropolone 7 7 1 +Tropomodulin 5 6 3 +Tropomyosin 5 5 3 +Troponin 3 5 4 +Troponin C 4 6 5 +Troponin I 4 6 4 +Troponin T 4 6 4 +Trout 8 8 1 +TRPA1 Cation Channel 7 7 2 +TRPC Cation Channels 7 7 2 +TRPC6 Cation Channel 7 8 5 +TRPM Cation Channels 7 7 2 +TRPP Cation Channels 7 7 4 +TRPV Cation Channels 7 7 1 +Truck Drivers 3 3 1 +Truncated Hemoglobins 6 6 1 +Truncus Arteriosus 4 4 2 +Truncus Arteriosus, Persistent 6 7 3 +Trusses 3 3 1 +Trust 4 4 1 +Trustees 4 4 2 +Truth Disclosure 5 7 2 +Trypan Blue 3 8 4 +Trypanocidal Agents 7 7 1 +Trypanosoma 5 5 1 +Trypanosoma brucei brucei 6 6 1 +Trypanosoma brucei gambiense 6 6 1 +Trypanosoma brucei rhodesiense 6 6 1 +Trypanosoma congolense 6 6 1 +Trypanosoma cruzi 6 6 1 +Trypanosoma lewisi 6 6 1 +Trypanosoma rangeli 6 6 1 +Trypanosoma vivax 6 6 1 +Trypanosomatina 4 4 1 +Trypanosomiasis 5 5 1 +Trypanosomiasis, African 3 6 2 +Trypanosomiasis, Bovine 3 6 5 +Trypsin 7 7 2 +Trypsin Inhibitor, Bowman-Birk Soybean 5 5 1 +Trypsin Inhibitor, Kazal Pancreatic 5 5 3 +Trypsin Inhibitor, Kunitz Soybean 5 5 1 +Trypsin Inhibitors 7 7 1 +Trypsinogen 3 5 2 +Tryptamines 5 5 2 +Tryptases 7 7 2 +Tryptophan 4 5 2 +Tryptophan Hydroxylase 6 6 1 +Tryptophan Oxygenase 6 6 1 +Tryptophan Synthase 4 6 2 +Tryptophan Transaminase 6 6 1 +Tryptophan-tRNA Ligase 6 6 1 +Tryptophanase 5 5 1 +Tsetse Flies 11 11 1 +Tsg101 Protein 4 6 2 +Tsuga 8 8 1 +Tsunamis 3 3 1 +Tuber Cinereum 7 8 2 +Tubercidin 4 6 3 +Tuberculin 4 5 2 +Tuberculin Test 5 6 3 +Tuberculoma 8 8 1 +Tuberculoma, Intracranial 5 10 5 +Tuberculosis 7 7 1 +Tuberculosis Disease 8 8 1 +Tuberculosis Societies 5 5 1 +Tuberculosis Vaccines 5 5 1 +Tuberculosis, Avian 3 8 2 +Tuberculosis, Bovine 3 8 2 +Tuberculosis, Cardiovascular 3 9 3 +Tuberculosis, Central Nervous System 4 9 4 +Tuberculosis, Cutaneous 4 9 4 +Tuberculosis, Endocrine 2 9 2 +Tuberculosis, Extrapulmonary 8 8 1 +Tuberculosis, Female Genital 3 10 5 +Tuberculosis, Gastrointestinal 3 9 2 +Tuberculosis, Hepatic 3 9 2 +Tuberculosis, Laryngeal 3 9 5 +Tuberculosis, Lymph Node 9 9 1 +Tuberculosis, Male Genital 3 10 5 +Tuberculosis, Meningeal 5 10 8 +Tuberculosis, Miliary 9 9 1 +Tuberculosis, Multidrug-Resistant 8 8 1 +Tuberculosis, Ocular 4 5 3 +Tuberculosis, Oral 3 9 2 +Tuberculosis, Osteoarticular 3 9 3 +Tuberculosis, Pleural 3 9 4 +Tuberculosis, Pulmonary 3 8 4 +Tuberculosis, Renal 3 10 7 +Tuberculosis, Spinal 4 10 4 +Tuberculosis, Splenic 4 9 2 +Tuberculosis, Urogenital 2 9 4 +Tuberous Sclerosis 3 6 9 +Tuberous Sclerosis Complex 1 Protein 4 5 3 +Tuberous Sclerosis Complex 2 Protein 4 5 3 +Tubocurarine 4 6 5 +Tubular Sweat Gland Adenomas 6 6 2 +Tubulin 4 5 3 +Tubulin Modulators 6 6 1 +Tubulina 5 5 1 +Tudor Domain 9 9 1 +Tuft Cells 3 6 4 +Tuftsin 4 8 8 +Tularemia 4 5 2 +Tulipa 10 10 1 +Tumor Burden 4 4 1 +Tumor Cells, Cultured 3 3 1 +Tumor Escape 2 2 1 +Tumor Hypoxia 4 4 2 +Tumor Lysis Syndrome 4 4 2 +Tumor Microenvironment 3 3 1 +Tumor Necrosis Factor alpha-Induced Protein 3 4 7 5 +Tumor Necrosis Factor Decoy Receptors 8 8 1 +Tumor Necrosis Factor Inhibitors 5 5 1 +Tumor Necrosis Factor Ligand Superfamily Member 13 5 6 3 +Tumor Necrosis Factor Ligand Superfamily Member 14 5 6 3 +Tumor Necrosis Factor Ligand Superfamily Member 15 5 6 3 +Tumor Necrosis Factor Receptor Superfamily, Member 7 5 8 3 +Tumor Necrosis Factor Receptor Superfamily, Member 9 8 8 1 +Tumor Necrosis Factor Receptor-Associated Peptides and Proteins 5 5 3 +Tumor Necrosis Factor-alpha 4 6 9 +Tumor Necrosis Factors 4 5 3 +Tumor Protein p73 4 5 4 +Tumor Protein, Translationally-Controlled 1 4 4 3 +Tumor Stem Cell Assay 4 6 7 +Tumor Suppressor p53-Binding Protein 1 4 5 3 +Tumor Suppressor Protein p14ARF 4 5 2 +Tumor Suppressor Protein p53 4 5 5 +Tumor Suppressor Proteins 4 4 1 +Tumor Virus Infections 3 3 1 +Tumor-Associated Macrophages 4 5 5 +Tuna 7 7 1 +Tundra 4 5 2 +Tunga 10 10 1 +Tungiasis 6 6 1 +Tungrovirus 4 4 2 +Tungsten 4 4 3 +Tungsten Compounds 2 2 1 +Tunica Intima 3 3 1 +Tunica Media 3 3 1 +Tunicamycin 4 5 2 +Tunisia 4 4 1 +Tupaia 9 9 1 +Tupaiidae 8 8 1 +Turbellaria 6 6 1 +Turbinates 3 6 2 +Turkey 5 5 1 +Turkeys 7 7 2 +Turkmenistan 4 4 2 +Turner Syndrome 4 7 15 +Turnera 8 8 1 +Turpentine 4 5 2 +Turtles 6 6 1 +Tussilago 8 8 1 +TWEAK Receptor 8 8 1 +Twin Studies as Topic 4 5 3 +Twin Study 2 2 1 +Twin-Arginine-Translocation System 4 4 1 +Twinning, Dizygotic 7 7 1 +Twinning, Embryonic 6 6 1 +Twinning, Monozygotic 7 7 1 +Twins 3 3 1 +Twins, Conjoined 4 4 1 +Twins, Dizygotic 4 4 1 +Twins, Monozygotic 4 4 1 +Twist Transcription Factors 5 5 2 +Twist-Related Protein 1 6 6 2 +Twist-Related Protein 2 6 6 2 +Two-Dimensional Difference Gel Electrophoresis 5 5 2 +Two-Hybrid System Techniques 3 4 3 +Two-Pore Channels 6 6 3 +TYK2 Kinase 6 9 2 +Tylenchida 7 7 1 +Tylenchoidea 8 8 1 +Tylophora 9 9 1 +Tylosin 4 4 1 +Tymoviridae 3 4 2 +Tymovirus 4 5 3 +Tympanic Membrane 4 4 1 +Tympanic Membrane Perforation 2 3 2 +Tympanocentesis 4 6 4 +Tympanoplasty 4 4 1 +Tympanosclerosis 3 3 1 +Type A Personality 4 4 1 +Type B Personality 4 4 1 +Type C Phospholipases 7 7 1 +Type D Personality 4 4 1 +Type I Secretion Systems 5 5 1 +Type II Secretion Systems 5 5 1 +Type III Secretion Systems 5 5 1 +Type IV Secretion Systems 5 5 1 +Type V Secretion Systems 5 5 1 +Type VI Secretion Systems 5 5 1 +Type VII Secretion Systems 5 5 1 +Typhaceae 7 7 1 +Typhlitis 3 6 3 +Typhoid Fever 7 7 1 +Typhoid-Paratyphoid Vaccines 6 6 1 +Typhus, Endemic Flea-Borne 5 7 2 +Typhus, Epidemic Louse-Borne 5 7 2 +Tyramine 5 5 1 +Tyrocidine 5 5 2 +Tyropanoate 6 7 2 +Tyrosine 5 5 1 +Tyrosine 3-Monooxygenase 6 6 2 +Tyrosine Decarboxylase 6 6 1 +Tyrosine Kinase Inhibitors 6 6 1 +Tyrosine Phenol-Lyase 5 5 1 +Tyrosine Transaminase 6 6 1 +Tyrosine-tRNA Ligase 6 6 1 +Tyrosinemias 5 6 6 +Tyrothricin 4 4 2 +Tyrphostins 3 7 3 +U937 Cells 5 6 5 +Ubiquinone 3 4 2 +Ubiquitin 4 4 1 +Ubiquitin C 5 5 1 +Ubiquitin Thiolesterase 4 6 3 +Ubiquitin-Activating Enzymes 5 5 1 +Ubiquitin-Conjugating Enzyme UBC9 6 6 1 +Ubiquitin-Conjugating Enzymes 5 5 1 +Ubiquitin-Protein Ligase Complexes 4 4 1 +Ubiquitin-Protein Ligases 5 5 1 +Ubiquitin-Specific Peptidase 7 5 7 2 +Ubiquitin-Specific Proteases 4 6 2 +Ubiquitinated Proteins 3 3 1 +Ubiquitination 5 7 4 +Ubiquitins 3 3 1 +UDP Xylose-Protein Xylosyltransferase 6 6 1 +UDP-Galactose Translocators 6 7 4 +UDP-Glucuronosyltransferase 1A9 7 7 1 +UDPglucose 4-Epimerase 6 6 1 +UDPglucose-Hexose-1-Phosphate Uridylyltransferase 6 6 1 +Uganda 5 5 1 +UGT1A1 Enzyme 7 7 1 +UK Biobank 5 8 2 +Ukraine 4 4 1 +Ulcer 3 3 1 +Ulex 8 8 1 +Ulmaceae 9 9 1 +Ulmus 10 10 1 +Ulna 6 6 1 +Ulna Fractures 3 4 2 +Ulnar Artery 4 4 1 +Ulnar Collateral Ligament Reconstruction 3 3 3 +Ulnar Nerve 6 6 1 +Ulnar Nerve Compression Syndromes 4 6 3 +Ulnar Neuropathies 5 5 1 +Ultimobranchial Body 2 2 1 +Ultracentrifugation 3 3 2 +Ultradian Rhythm 4 4 1 +Ultrafiltration 3 4 4 +Ultrasonic Surgical Procedures 2 2 1 +Ultrasonic Therapy 4 4 1 +Ultrasonic Waves 5 5 1 +Ultrasonics 4 4 1 +Ultrasonography 4 4 1 +Ultrasonography, Carotid Arteries 5 5 1 +Ultrasonography, Doppler 5 5 1 +Ultrasonography, Doppler, Color 7 7 1 +Ultrasonography, Doppler, Duplex 6 6 1 +Ultrasonography, Doppler, Pulsed 6 6 1 +Ultrasonography, Doppler, Transcranial 5 7 6 +Ultrasonography, Interventional 3 5 2 +Ultrasonography, Mammary 4 5 2 +Ultrasonography, Prenatal 5 5 2 +Ultrasound, High-Intensity Focused, Transrectal 4 6 3 +Ultraviolet Rays 4 7 8 +Ultraviolet Therapy 3 3 1 +Ulva 4 4 1 +Umbelliferones 6 6 2 +Umbellularia 9 9 1 +Umbilical Arteries 4 4 2 +Umbilical Cord 3 3 1 +Umbilical Cord Clamping 4 4 1 +Umbilical Veins 4 5 2 +Umbilicus 4 4 1 +Umbridae 7 7 1 +Unaccompanied Minors 3 3 1 +Uncaria 9 9 1 +Uncertainty 3 6 5 +Uncinate Fasciculus 9 9 2 +Uncompensated Care 3 4 2 +Unconscious, Psychology 4 4 2 +Unconsciousness 5 6 2 +Uncoupling Agents 5 5 1 +Uncoupling Protein 1 6 7 4 +Uncoupling Protein 2 6 7 4 +Uncoupling Protein 3 6 7 4 +Undaria 4 4 1 +Undecylenic Acids 5 5 1 +Underachievement 4 4 1 +Underage Drinking 4 5 3 +Undertreatment 4 5 2 +Undiagnosed Diseases 4 4 1 +Undifferentiated Connective Tissue Diseases 3 3 2 +Undocumented Immigrants 3 3 1 +Unedited Footage 2 2 1 +Unemployment 4 4 1 +UNESCO 5 5 1 +Unfolded Protein Response 3 7 5 +Unified Medical Language System 6 6 1 +Unilamellar Liposomes 4 6 3 +Unilateral Breast Neoplasms 4 5 2 +Unio 7 7 1 +Union List 2 2 1 +Unionidae 6 6 1 +Uniparental Disomy 5 5 2 +United Arab Emirates 5 5 1 +United Kingdom 3 3 1 +United Nations 4 4 1 +United States 4 4 1 +United States Agency for Healthcare Research and Quality 7 8 2 +United States Agency for International Development 5 6 2 +United States Department of Agriculture 5 6 2 +United States Department of Defense 5 6 2 +United States Department of Homeland Security 5 6 2 +United States Department of Veterans Affairs 5 6 2 +United States Dept. of Health and Human Services 5 6 2 +United States Environmental Protection Agency 5 6 2 +United States Federal Trade Commission 5 6 2 +United States Food and Drug Administration 7 8 2 +United States Government Agencies 4 5 2 +United States Health Resources and Services Administration 7 8 2 +United States Indian Health Service 7 8 2 +United States National Aeronautics and Space Administration 5 6 2 +United States Occupational Safety and Health Administration 5 6 2 +United States Office of Economic Opportunity 5 6 2 +United States Office of National Drug Control Policy 5 6 2 +United States Office of Research Integrity 7 8 2 +United States Office of Technology Assessment 5 6 2 +United States Public Health Service 6 7 2 +United States Social Security Administration 5 6 2 +United States Substance Abuse and Mental Health Services Administration 7 8 2 +United States Virgin Islands 4 5 2 +Unithiol 5 5 1 +Univentricular Heart 4 5 3 +Universal Design 3 5 3 +Universal Health Care 2 5 3 +Universal Health Insurance 6 6 1 +Universal Precautions 5 5 1 +Universities 3 3 2 +Unmanned Aerial Devices 5 5 1 +Unnecessary Procedures 5 6 2 +Unpublished Work 2 2 1 +Unrelated Donors 3 3 1 +Unsafe Sex 4 4 1 +Unsupervised Machine Learning 5 6 2 +Untranslated Regions 5 7 4 +Unvaccinated Persons 2 2 1 +Unverricht-Lundborg Syndrome 4 8 4 +Up-Regulation 3 4 3 +Upper Extremity 3 3 1 +Upper Extremity Deep Vein Thrombosis 6 6 1 +Upper Extremity Deformities, Congenital 4 5 2 +Upper Gastrointestinal Tract 3 3 1 +Upstream Stimulatory Factors 5 5 2 +Urachal Cyst 3 3 1 +Urachus 2 2 1 +Uracil 5 5 1 +Uracil Mustard 6 6 2 +Uracil Nucleotides 4 5 3 +Uracil-DNA Glycosidase 5 5 1 +Uranium 4 6 5 +Uranium Compounds 2 2 1 +Uranus 6 6 1 +Uranyl Nitrate 3 5 2 +Urate Oxidase 4 4 1 +Urban Health 4 4 1 +Urban Health Services 3 3 1 +Urban Population 3 3 1 +Urban Renewal 4 4 1 +Urbanization 5 5 1 +Urea 3 3 1 +Urea Cycle Disorders, Inborn 5 6 6 +Urea Transporters 6 6 2 +Ureaplasma 6 6 1 +Ureaplasma Infections 6 6 1 +Ureaplasma urealyticum 7 7 1 +Urease 5 5 1 +Uremia 4 6 3 +Uremic Toxins 3 3 1 +Ureohydrolases 4 4 1 +Ureter 3 3 1 +Ureteral Calculi 5 7 10 +Ureteral Diseases 3 5 3 +Ureteral Neoplasms 4 6 8 +Ureteral Obstruction 4 6 3 +Ureterocele 4 6 3 +Ureterolithiasis 4 6 6 +Ureteroscopes 4 4 2 +Ureteroscopy 4 5 4 +Ureterostomy 3 5 2 +Urethane 5 5 1 +Urethra 3 5 2 +Urethral Diseases 3 5 3 +Urethral Neoplasms 4 6 8 +Urethral Obstruction 4 6 3 +Urethral Stricture 5 7 3 +Urethritis 4 6 3 +Uric Acid 4 7 2 +Uricosuric Agents 6 6 1 +Uridine 4 5 3 +Uridine Diphosphate 5 6 3 +Uridine Diphosphate Galactose 6 8 5 +Uridine Diphosphate Glucose 6 8 5 +Uridine Diphosphate Glucose Dehydrogenase 6 6 1 +Uridine Diphosphate Glucuronic Acid 6 8 5 +Uridine Diphosphate N-Acetylgalactosamine 6 8 5 +Uridine Diphosphate N-Acetylglucosamine 6 8 5 +Uridine Diphosphate N-Acetylmuramic Acid 6 8 5 +Uridine Diphosphate Sugars 5 7 5 +Uridine Diphosphate Xylose 6 8 5 +Uridine Kinase 6 6 1 +Uridine Monophosphate 5 6 3 +Uridine Phosphorylase 7 7 1 +Uridine Triphosphate 5 6 3 +Uridylate-Specific Endoribonucleases 8 8 1 +Urinalysis 4 5 3 +Urinary Bladder 3 3 1 +Urinary Bladder Calculi 4 7 7 +Urinary Bladder Diseases 3 5 3 +Urinary Bladder Fistula 4 6 7 +Urinary Bladder Neck Obstruction 4 7 6 +Urinary Bladder Neoplasms 4 6 8 +Urinary Bladder, Neurogenic 3 6 5 +Urinary Bladder, Overactive 4 6 4 +Urinary Bladder, Underactive 4 6 4 +Urinary Calculi 4 6 4 +Urinary Catheterization 3 4 3 +Urinary Catheters 3 3 1 +Urinary Diversion 4 4 1 +Urinary Fistula 3 5 4 +Urinary Incontinence 4 6 4 +Urinary Incontinence, Stress 5 7 4 +Urinary Incontinence, Urge 5 7 4 +Urinary Reservoirs, Continent 3 3 2 +Urinary Retention 4 6 3 +Urinary Sediment Analysis 5 6 3 +Urinary Sphincter, Artificial 3 4 2 +Urinary Tract 2 2 1 +Urinary Tract Infections 2 5 4 +Urinary Tract Physiological Phenomena 2 2 1 +Urination 3 3 1 +Urination Disorders 3 5 3 +Urine 3 3 1 +Urine Specimen Collection 4 5 2 +Urinoma 4 4 1 +Urobilin 4 7 4 +Urobilinogen 4 7 4 +Urocanate Hydratase 6 6 1 +Urocanic Acid 5 5 2 +Urochordata 5 5 2 +Urocortins 4 4 3 +Urodynamics 3 3 1 +Urofollitropin 7 8 3 +Urogenital Abnormalities 2 4 4 +Urogenital Diseases 1 1 1 +Urogenital Neoplasms 2 4 4 +Urogenital Surgical Procedures 2 2 1 +Urogenital System 1 1 1 +Urography 4 5 2 +Urokinase-Type Plasminogen Activator 6 7 3 +Urolithiasis 3 5 3 +Urologic Diseases 2 4 3 +Urologic Neoplasms 3 5 5 +Urologic Surgical Procedures 3 3 1 +Urologic Surgical Procedures, Male 4 4 1 +Urological Agents 5 5 1 +Urological Manifestations 3 3 1 +Urologists 4 5 2 +Urology 4 4 1 +Urology Department, Hospital 6 6 2 +Uromodulin 5 6 6 +Uronic Acids 3 5 4 +Uropathogenic Escherichia coli 8 8 2 +Uroplakin Ia 5 6 4 +Uroplakin Ib 5 6 4 +Uroplakin II 6 6 3 +Uroplakin III 6 6 3 +Uroplakins 5 5 3 +Uroporphyrinogen Decarboxylase 6 6 1 +Uroporphyrinogen III Synthetase 6 6 1 +Uroporphyrinogens 6 8 3 +Uroporphyrins 4 7 4 +Urotensins 4 5 3 +Urothelium 3 3 1 +Ursidae 9 9 1 +Ursodeoxycholic Acid 7 7 2 +Ursolic Acid 6 6 2 +Urtica dioica 10 10 1 +Urticaceae 9 9 1 +Urticaria 4 4 2 +Urticaria Pigmentosa 4 7 5 +Urticaria, Solar 4 7 4 +Uruguay 4 4 1 +User-Centered Design 3 4 3 +User-Computer Interface 4 4 1 +Usher Syndromes 5 8 10 +Usnea 5 5 2 +USSR 3 3 2 +Ustekinumab 9 9 3 +Ustilaginales 4 4 1 +Ustilago 5 5 1 +Utah 6 6 1 +Uterine Artery 4 4 1 +Uterine Artery Embolization 4 4 3 +Uterine Balloon Tamponade 4 5 4 +Uterine Cervical Diseases 5 6 2 +Uterine Cervical Dysplasia 3 7 3 +Uterine Cervical Erosion 6 7 2 +Uterine Cervical Incompetence 6 7 3 +Uterine Cervical Neoplasms 5 7 7 +Uterine Cervicitis 6 7 2 +Uterine Contraction 4 6 2 +Uterine Didelphys 7 7 1 +Uterine Diseases 4 5 2 +Uterine Duplication Anomalies 3 6 5 +Uterine Hemorrhage 4 6 3 +Uterine Inertia 6 6 1 +Uterine Inversion 5 6 3 +Uterine Monitoring 4 4 2 +Uterine Myomectomy 4 4 1 +Uterine Neoplasms 4 6 5 +Uterine Perforation 4 7 3 +Uterine Prolapse 5 6 3 +Uterine Retroversion 4 6 3 +Uterine Rupture 3 6 4 +Uteroglobin 4 4 1 +Uterus 4 4 1 +Utilization Review 3 3 2 +Utopias 3 3 1 +UTP-Glucose-1-Phosphate Uridylyltransferase 6 6 1 +UTP-Hexose-1-Phosphate Uridylyltransferase 6 6 1 +Utrophin 4 4 2 +Uukuniemi virus 6 6 1 +Uvaria 8 8 1 +Uvea 3 3 1 +Uveal Diseases 2 2 1 +Uveal Effusion Syndrome 4 5 2 +Uveal Melanoma 4 7 6 +Uveal Neoplasms 3 4 3 +Uveitis 3 3 1 +Uveitis, Anterior 5 5 1 +Uveitis, Intermediate 4 4 1 +Uveitis, Posterior 5 5 1 +Uveitis, Suppurative 3 5 10 +Uveomeningoencephalitic Syndrome 3 4 3 +Uveoparotid Fever 5 5 2 +Uvula 5 5 1 +Uzbekistan 4 4 3 +V(D)J Recombination 3 3 2 +V-Set Domain-Containing T-Cell Activation Inhibitor 1 4 5 4 +Vaccaria 10 10 1 +Vaccination 5 7 5 +Vaccination Coverage 4 7 2 +Vaccination Hesitancy 7 8 5 +Vaccination Refusal 6 7 5 +Vaccine Development 2 2 1 +Vaccine Efficacy 3 3 1 +Vaccine Excipients 3 6 2 +Vaccine Potency 3 3 2 +Vaccine-Preventable Diseases 2 2 1 +Vaccines 3 3 1 +Vaccines, Acellular 5 5 1 +Vaccines, Attenuated 4 4 1 +Vaccines, Combined 4 4 1 +Vaccines, Conjugate 4 5 2 +Vaccines, Contraceptive 4 4 1 +Vaccines, DNA 5 6 3 +Vaccines, Edible 4 5 4 +Vaccines, Inactivated 4 4 1 +Vaccines, Live, Unattenuated 4 4 1 +Vaccines, Marker 4 4 1 +Vaccines, Subunit 4 4 1 +Vaccines, Synthetic 3 4 3 +Vaccines, Virosome 4 5 3 +Vaccines, Virus-Like Particle 4 5 3 +Vaccinia 5 5 1 +Vaccinia virus 6 6 1 +Vaccinium 9 9 1 +Vaccinium macrocarpon 10 10 1 +Vaccinium myrtillus 10 10 1 +Vaccinium vitis-idaea 10 10 1 +Vaccinology 3 3 1 +Vacuolar Proton-Translocating ATPases 7 9 4 +Vacuolar Sorting Protein VPS15 5 9 5 +Vacuoles 8 8 1 +Vacuum 4 6 3 +Vacuum Curettage 4 5 2 +Vacuum Extraction, Obstetrical 5 5 1 +Vagina 4 4 1 +Vaginal Absorption 4 6 3 +Vaginal Birth after Cesarean 4 4 1 +Vaginal Creams, Foams, and Jellies 3 3 2 +Vaginal Discharge 5 6 2 +Vaginal Diseases 4 5 2 +Vaginal Douching 3 3 1 +Vaginal Fistula 4 6 3 +Vaginal Neoplasms 4 6 5 +Vaginal Smears 3 7 7 +Vaginismus 3 6 6 +Vaginitis 5 6 2 +Vaginosis, Bacterial 4 7 3 +Vagotomy 6 6 1 +Vagotomy, Proximal Gastric 7 7 1 +Vagotomy, Truncal 7 7 1 +Vagus Nerve 5 5 4 +Vagus Nerve Diseases 3 3 1 +Vagus Nerve Injuries 4 5 4 +Vagus Nerve Stimulation 3 3 1 +Valacyclovir 9 9 1 +Valerates 4 4 2 +Valerian 9 9 1 +Valerianaceae 8 8 1 +Valerianella 9 9 1 +Valganciclovir 10 10 1 +Validation Studies as Topic 3 5 2 +Validation Study 2 2 1 +Valine 4 4 2 +Valine Dehydrogenase (NADP+) 6 6 1 +Valine-tRNA Ligase 6 6 1 +Valinomycin 5 5 2 +Valosin Containing Protein 4 7 4 +Valproic Acid 6 6 2 +Valsalva Maneuver 3 5 4 +Valsartan 5 5 3 +Value of Life 4 4 1 +Value-Based Health Care 3 4 2 +Value-Based Health Insurance 4 6 2 +Value-Based Purchasing 6 6 1 +Vanadates 3 5 2 +Vanadium 4 4 3 +Vanadium Compounds 2 2 1 +Vancomycin 4 4 2 +Vancomycin Resistance 4 7 3 +Vancomycin-Resistant Enterococci 6 6 2 +Vancomycin-Resistant Staphylococcus aureus 7 8 6 +Vanilla 10 10 1 +Vanillic Acid 6 9 4 +Vanilmandelic Acid 5 5 2 +Vanuatu 5 5 2 +Vaping 4 4 1 +Vapor Pressure 4 4 1 +Vardenafil Dihydrochloride 4 5 2 +Varenicline 5 5 2 +Variant Surface Glycoproteins, Trypanosoma 4 5 6 +Varicella Zoster Virus Infection 5 5 1 +Varicellovirus 5 5 1 +Varicocele 3 4 3 +Varicose Ulcer 4 5 2 +Varicose Veins 3 3 1 +Variola virus 6 6 1 +Varroidae 8 8 1 +Vas Deferens 4 4 1 +Vasa Nervorum 3 3 1 +Vasa Previa 5 5 1 +Vasa Vasorum 3 3 1 +Vascular Access Devices 3 3 1 +Vascular Calcification 5 5 1 +Vascular Capacitance 4 4 1 +Vascular Cell Adhesion Molecule-1 5 6 4 +Vascular Closure Devices 4 4 1 +Vascular Depression 4 5 3 +Vascular Diseases 2 2 1 +Vascular Endothelial Growth Factor A 5 6 3 +Vascular Endothelial Growth Factor B 5 6 3 +Vascular Endothelial Growth Factor C 5 6 3 +Vascular Endothelial Growth Factor D 5 6 3 +Vascular Endothelial Growth Factor Receptor-1 7 10 4 +Vascular Endothelial Growth Factor Receptor-2 7 10 4 +Vascular Endothelial Growth Factor Receptor-3 7 10 4 +Vascular Endothelial Growth Factor, Endocrine-Gland-Derived 5 6 3 +Vascular Endothelial Growth Factors 4 5 3 +Vascular Fistula 3 4 3 +Vascular Grafting 4 4 1 +Vascular Headaches 4 6 3 +Vascular Health 3 3 1 +Vascular Malformations 3 4 2 +Vascular Neoplasms 3 4 2 +Vascular Patency 3 3 1 +Vascular Remodeling 3 4 4 +Vascular Resistance 4 4 1 +Vascular Ring 4 5 3 +Vascular Stiffness 3 3 1 +Vascular Surgical Procedures 3 3 1 +Vascular System Injuries 2 3 2 +Vascularized Composite Allotransplantation 4 4 2 +Vasculitis 3 3 1 +Vasculitis, Central Nervous System 3 5 5 +Vasculitis, Leukocytoclastic, Cutaneous 4 4 3 +Vasectomy 4 5 2 +Vaso-Occlusive Crises 5 7 4 +Vasoactive Intestinal Peptide 4 5 5 +Vasoconstriction 4 4 1 +Vasoconstrictor Agents 5 5 1 +Vasodilation 4 4 1 +Vasodilator Agents 5 5 1 +Vasodilator-Stimulated Phosphoprotein 5 6 5 +Vasomotor System 5 5 1 +Vasopeptidase Inhibitors 7 7 1 +Vasoplegia 3 4 2 +Vasopressins 4 6 5 +Vasospasm, Intracranial 4 5 2 +Vasotocin 6 6 2 +Vasovasostomy 3 5 3 +Vatican City 3 3 1 +Vault Ribonucleoprotein Particles 6 6 2 +VDJ Exons 7 8 2 +VDJ Recombinases 4 6 2 +Vector Borne Diseases 2 2 1 +Vectorcardiography 5 6 2 +Vecuronium Bromide 6 6 1 +Vegans 3 3 1 +Vegetable Products 4 5 2 +Vegetables 3 4 4 +Vegetarians 2 2 1 +Vehicle Emissions 2 2 1 +Veillonella 6 6 1 +Veillonellaceae 3 5 2 +Vein of Galen Malformations 5 7 9 +Veins 3 3 1 +Velopharyngeal Insufficiency 3 5 5 +Velopharyngeal Sphincter 4 5 3 +Vemurafenib 4 5 3 +Vena Cava Filters 4 4 1 +Vena Cava, Inferior 5 5 1 +Vena Cava, Superior 5 5 1 +Venae Cavae 4 4 1 +Venereal Tumors, Veterinary 2 4 2 +Venereology 3 3 1 +Venezuela 4 4 1 +Venlafaxine Hydrochloride 5 8 4 +Venom Hypersensitivity 4 4 1 +Venombin A 7 7 2 +Venomous Snakes 5 7 2 +Venoms 2 3 3 +Venous Cutdown 4 4 1 +Venous Insufficiency 3 3 1 +Venous Pressure 5 5 1 +Venous Thromboembolism 5 5 1 +Venous Thrombosis 5 5 1 +Venous Valves 4 4 1 +Ventilation 4 4 1 +Ventilation-Perfusion Ratio 3 6 2 +Ventilation-Perfusion Scan 5 5 3 +Ventilator Weaning 4 4 2 +Ventilator-Induced Lung Injury 4 4 1 +Ventilators, Mechanical 2 2 1 +Ventilators, Negative-Pressure 3 3 1 +Ventral Striatum 9 9 1 +Ventral Tegmental Area 8 8 1 +Ventral Thalamic Nuclei 8 8 1 +Ventricular Dysfunction 3 3 1 +Ventricular Dysfunction, Left 4 4 1 +Ventricular Dysfunction, Right 4 4 1 +Ventricular Fibrillation 4 4 2 +Ventricular Flutter 4 4 2 +Ventricular Function 3 3 1 +Ventricular Function, Left 4 4 1 +Ventricular Function, Right 4 4 1 +Ventricular Myosins 8 10 4 +Ventricular Outflow Obstruction 3 3 1 +Ventricular Outflow Obstruction, Left 4 4 1 +Ventricular Outflow Obstruction, Right 4 4 1 +Ventricular Premature Complexes 5 5 3 +Ventricular Pressure 4 4 2 +Ventricular Remodeling 3 4 2 +Ventricular Septal Rupture 5 5 1 +Ventricular Septum 4 4 1 +Ventriculography, First-Pass 6 7 5 +Ventriculoperitoneal Shunt 4 4 2 +Ventriculostomy 4 4 2 +Ventromedial Hypothalamic Nucleus 7 8 2 +Venturicidins 3 3 1 +Venules 4 4 2 +Venus 6 6 1 +Verapamil 5 5 1 +Veratridine 5 5 2 +Veratrine 5 5 2 +Veratrum 10 10 1 +Veratrum Alkaloids 3 3 1 +Verbal Behavior 4 4 1 +Verbal Learning 4 4 1 +Verbascum 9 9 1 +Verbena 9 9 1 +Verbenaceae 8 8 1 +Verbesina 8 8 1 +Vermilingua 8 8 1 +Vermont 6 6 1 +Vernalization 3 4 2 +Vernamycin B 5 5 2 +Vernix Caseosa 3 3 2 +Vernonia 8 8 1 +Vero Cells 3 4 2 +Veronica 9 9 1 +Verrucomicrobia 3 3 1 +Versicans 5 7 5 +Version, Fetal 4 4 1 +Vertebral Artery 4 4 1 +Vertebral Artery Dissection 4 6 5 +Vertebral Body 5 5 1 +Vertebrates 4 4 1 +Vertebrobasilar Insufficiency 5 6 2 +Vertebroplasty 4 4 2 +Verteporfin 5 7 3 +Vertical Dimension 4 4 1 +Verticillium 4 4 1 +Vertigo 3 5 3 +Vesicle-Associated Membrane Protein 1 7 7 2 +Vesicle-Associated Membrane Protein 2 7 7 2 +Vesicle-Associated Membrane Protein 3 7 7 2 +Vesico-Ureteral Reflux 4 6 3 +Vesicovaginal Fistula 5 7 7 +Vesicular Acetylcholine Transport Proteins 8 9 4 +Vesicular Biogenic Amine Transport Proteins 7 8 4 +Vesicular Exanthema of Swine 3 5 2 +Vesicular exanthema of swine virus 6 6 1 +Vesicular Glutamate Transport Protein 1 8 9 4 +Vesicular Glutamate Transport Protein 2 8 9 4 +Vesicular Glutamate Transport Proteins 7 8 4 +Vesicular Inhibitory Amino Acid Transport Proteins 7 8 4 +Vesicular Monoamine Transport Proteins 8 9 4 +Vesicular Neurotransmitter Transport Proteins 6 7 4 +Vesicular Stomatitis 2 6 3 +Vesicular stomatitis Indiana virus 7 7 1 +Vesicular stomatitis New Jersey virus 7 7 1 +Vesicular Transport Proteins 4 4 1 +Vesiculovirus 6 6 1 +Vesivirus 5 5 1 +Vestibular Aqueduct 4 5 2 +Vestibular Diseases 4 4 1 +Vestibular Evoked Myogenic Potentials 6 6 1 +Vestibular Function Tests 4 4 1 +Vestibular Migraine 6 7 2 +Vestibular Nerve 3 6 2 +Vestibular Neuronitis 4 5 2 +Vestibular Nuclei 3 8 2 +Vestibular Nucleus, Lateral 4 9 2 +Vestibular System 2 2 1 +Vestibule, Labyrinth 3 4 2 +Vestibulocochlear Nerve 5 5 1 +Vestibulocochlear Nerve Diseases 3 4 2 +Vestibulocochlear Nerve Injuries 4 5 5 +Vestibulocochlear Physiological Phenomena 2 2 1 +Vestibuloplasty 4 4 2 +Veterans 2 2 1 +Veterans Disability Claims 5 5 1 +Veterans Health 3 3 1 +Veterans Health Services 3 3 2 +Veterinarians 3 4 2 +Veterinary Drugs 2 2 1 +Veterinary Medicine 2 2 1 +Veterinary Service, Military 3 3 1 +Veterinary Sports Medicine 3 4 2 +Vibration 3 3 1 +Vibrio 5 5 2 +Vibrio alginolyticus 6 6 2 +Vibrio cholerae 6 6 2 +Vibrio cholerae non-O1 7 7 2 +Vibrio cholerae O1 7 7 2 +Vibrio cholerae O139 7 7 2 +Vibrio Infections 5 5 1 +Vibrio mimicus 6 6 2 +Vibrio parahaemolyticus 6 6 2 +Vibrio vulnificus 6 6 2 +Vibrionaceae 4 4 2 +Vibrissae 2 2 1 +Viburnum 9 9 1 +Vicia 8 8 1 +Vicia faba 9 9 1 +Vicia sativa 9 9 1 +Victoria 4 5 2 +Vidarabine 4 7 3 +Vidarabine Phosphate 4 7 3 +Video Games 4 5 2 +Video Recording 3 3 1 +Video-Assisted Surgery 4 5 2 +Video-Assisted Techniques and Procedures 2 2 1 +Video-Audio Media 2 3 2 +Videoconferencing 5 5 1 +Videodisc Recording 4 7 6 +Videotape Recording 4 7 7 +Vietnam 4 4 1 +Vietnam Conflict 5 6 2 +vif Gene Products, Human Immunodeficiency Virus 6 6 3 +Vigabatrin 5 7 2 +Vigna 8 8 1 +Vilazodone Hydrochloride 4 5 3 +Vildagliptin 3 4 2 +Viloxazine 5 5 1 +Vimentin 5 5 2 +Vinblastine 6 9 3 +Vinca 9 9 1 +Vinca Alkaloids 5 8 3 +Vincamine 6 9 3 +Vincetoxicum 9 9 1 +Vincristine 6 9 3 +Vinculin 4 4 1 +Vindesine 6 9 3 +Vinorelbine 6 9 3 +Vinyl Chloride 5 6 2 +Vinyl Compounds 5 5 1 +Viola 10 10 1 +Violaceae 9 9 1 +Violence 4 4 2 +Viologens 5 5 1 +Viomycin 4 4 2 +Viper Venoms 4 5 2 +Vipera 8 10 3 +Viperidae 6 8 3 +Viperin Protein 4 4 1 +Viperinae 7 9 3 +Vipoma 5 7 7 +Viral Core Proteins 6 6 1 +Viral Envelope 3 3 1 +Viral Envelope Proteins 5 5 4 +Viral Fusion Protein Inhibitors 4 6 2 +Viral Fusion Proteins 5 6 2 +Viral Genome Packaging 5 5 1 +Viral Hepatitis Vaccines 5 5 1 +Viral Interference 3 3 1 +Viral Load 3 5 3 +Viral Matrix Proteins 6 6 1 +Viral Nonstructural Proteins 4 4 1 +Viral Packaging Sequence 5 6 3 +Viral Papain-like Proteases 6 7 3 +Viral Plaque Assay 5 6 2 +Viral Protease Inhibitors 6 6 2 +Viral Proteases 5 5 2 +Viral Proteins 3 3 1 +Viral Pseudotyping 3 5 3 +Viral Regulatory and Accessory Proteins 4 4 1 +Viral Replicase Complex Proteins 4 5 2 +Viral Replication Compartments 4 5 2 +Viral Structural Proteins 4 4 1 +Viral Structures 1 1 1 +Viral Tail Proteins 5 5 1 +Viral Transcription 4 4 1 +Viral Tropism 3 3 2 +Viral Vaccines 4 4 1 +Viral Zoonoses 3 3 3 +Viremia 3 6 2 +Virgibacillus 6 6 3 +Virginia 6 6 2 +Virginiamycin 5 5 2 +Viridans Streptococci 6 6 3 +Viridiplantae 2 2 1 +Virilism 3 3 1 +Virion 2 2 1 +Viroids 2 2 1 +Virology 5 5 1 +Virome 3 8 3 +Virophages 3 3 1 +Viroporin Proteins 6 6 1 +Virosomes 3 5 4 +Virtual Reality 3 4 2 +Virtual Reality Exposure Therapy 5 5 1 +Virtues 4 4 2 +Virulence 2 2 1 +Virulence Factors 3 3 1 +Virulence Factors, Bordetella 4 4 2 +Virus Activation 4 4 1 +Virus Assembly 4 4 1 +Virus Attachment 3 3 1 +Virus Cultivation 4 5 2 +Virus Diseases 2 2 1 +Virus Inactivation 3 5 3 +Virus Integration 2 3 2 +Virus Internalization 3 3 1 +Virus Latency 3 3 1 +Virus Physiological Phenomena 2 2 1 +Virus Release 3 3 1 +Virus Replication 3 3 1 +Virus Shedding 2 2 1 +Virus Uncoating 3 3 1 +Viruses 1 1 1 +Viruses, Unclassified 2 2 1 +Viscaceae 8 8 1 +Viscera 2 2 1 +Visceral Afferents 4 4 1 +Visceral Pain 6 6 1 +Visceral Prolapse 3 5 2 +Viscoelastic Substances 3 3 1 +Viscosity 2 2 1 +Viscosupplementation 3 6 2 +Viscosupplements 4 5 3 +Viscum 8 8 1 +Viscum album 9 9 1 +Visible Human Projects 5 8 4 +Vision Disorders 2 5 3 +Vision Disparity 2 6 3 +Vision Screening 5 8 5 +Vision Tests 4 4 1 +Vision, Binocular 5 5 1 +Vision, Entoptic 3 5 3 +Vision, Low 3 6 3 +Vision, Monocular 5 5 1 +Vision, Ocular 2 5 5 +Visitors to Patients 2 2 1 +Visna 3 6 3 +Visna-maedi virus 6 6 1 +Visual Acuity 2 5 3 +Visual Analog Scale 3 3 1 +Visual Cortex 9 9 2 +Visual Field Tests 5 5 1 +Visual Fields 2 5 2 +Visual Pathways 4 4 1 +Visual Perception 4 4 1 +Visual Prosthesis 3 3 1 +Vitaceae 7 7 1 +Vital Capacity 4 7 2 +Vital Signs 4 4 1 +Vital Statistics 3 5 4 +Vitalism 3 3 1 +Vitallium 4 7 6 +Vitamin A 5 10 5 +Vitamin A Deficiency 6 6 1 +Vitamin B 12 5 7 3 +Vitamin B 12 Deficiency 7 7 1 +Vitamin B 6 5 5 1 +Vitamin B 6 Deficiency 7 7 1 +Vitamin B Complex 6 6 1 +Vitamin B Deficiency 6 6 1 +Vitamin D 5 5 1 +Vitamin D Deficiency 6 6 1 +Vitamin D Response Element 7 10 6 +Vitamin D-Binding Protein 4 4 1 +Vitamin D3 24-Hydroxylase 5 8 3 +Vitamin E 5 5 2 +Vitamin E Deficiency 6 6 1 +Vitamin K 5 8 3 +Vitamin K 1 4 9 4 +Vitamin K 2 4 9 4 +Vitamin K 3 4 9 4 +Vitamin K Deficiency 4 6 3 +Vitamin K Deficiency Bleeding 3 7 5 +Vitamin K Epoxide Reductases 6 6 1 +Vitamin U 5 5 2 +Vitamins 5 6 3 +Vitelliform Macular Dystrophy 4 5 2 +Vitelline Duct 2 2 1 +Vitelline Membrane 3 3 1 +Vitellins 4 4 1 +Vitellogenesis 5 6 2 +Vitellogenins 4 5 3 +Vitex 9 9 1 +Vitiligo 5 5 1 +Vitis 8 8 1 +Vitrectomy 3 3 1 +Vitreoretinal Surgery 3 3 1 +Vitreoretinopathy, Proliferative 3 3 1 +Vitreoscilla 4 5 2 +Vitreous Body 4 4 1 +Vitreous Detachment 2 2 1 +Vitreous Hemorrhage 3 5 2 +Vitrification 3 3 1 +Vitronectin 4 5 4 +Vittaforma 7 7 1 +Viverridae 9 9 1 +Viviparity, Nonmammalian 3 3 1 +Vivisection 3 3 1 +Voacanga 9 9 1 +Vocabulary 4 4 1 +Vocabulary, Controlled 5 5 1 +Vocal Cord Dysfunction 3 3 3 +Vocal Cord Paralysis 3 5 5 +Vocal Cords 4 4 1 +Vocalization, Animal 5 5 1 +Vocational Education 3 3 1 +Vocational Guidance 4 4 2 +Voice 3 3 1 +Voice Disorders 3 4 4 +Voice Quality 4 4 1 +Voice Recognition 5 6 3 +Voice Training 4 7 2 +Volatile Organic Compounds 2 2 1 +Volatilization 3 3 2 +Volcanic Eruptions 3 3 1 +Volition 3 3 1 +Volleyball 5 5 1 +Voltage-Dependent Anion Channel 1 5 8 5 +Voltage-Dependent Anion Channel 2 5 8 5 +Voltage-Dependent Anion Channels 7 7 3 +Voltage-Gated Sodium Channel Agonists 6 6 1 +Voltage-Gated Sodium Channel beta Subunits 5 8 4 +Voltage-Gated Sodium Channel beta-1 Subunit 6 9 4 +Voltage-Gated Sodium Channel beta-2 Subunit 6 9 4 +Voltage-Gated Sodium Channel beta-3 Subunit 6 9 4 +Voltage-Gated Sodium Channel beta-4 Subunit 6 9 4 +Voltage-Gated Sodium Channel Blockers 6 6 2 +Voltage-Gated Sodium Channels 4 7 4 +Voltage-Sensitive Dye Imaging 4 4 1 +Volume Electron Microscopy 4 6 3 +Voluntary Health Agencies 4 4 1 +Voluntary Programs 3 3 1 +Volunteers 2 2 1 +Volvariella 5 5 1 +Volvocida 4 4 1 +Volvox 4 4 1 +Vomer 4 6 2 +Vomeronasal Organ 3 3 1 +Vomiting 4 4 1 +Vomiting, Anticipatory 5 5 1 +von Ebner Glands 4 5 3 +von Hippel-Lindau Disease 3 5 4 +Von Hippel-Lindau Tumor Suppressor Protein 5 6 2 +von Willebrand Disease, Type 1 5 6 4 +von Willebrand Disease, Type 2 5 6 4 +von Willebrand Disease, Type 3 5 6 4 +von Willebrand Diseases 4 5 5 +von Willebrand Factor 3 5 2 +Voriconazole 5 5 1 +Vorinostat 4 5 4 +Vortioxetine 4 4 1 +Voting 3 3 1 +Voyeurism 3 3 1 +vpr Gene Products, Human Immunodeficiency Virus 6 7 2 +Vulnerable Populations 3 3 1 +Vulva 4 4 1 +Vulvar Diseases 4 5 2 +Vulvar Lichen Sclerosus 5 6 2 +Vulvar Neoplasms 4 6 5 +Vulvar Vestibulitis 6 7 2 +Vulvectomy 4 4 1 +Vulvitis 5 6 2 +Vulvodynia 5 6 2 +Vulvovaginitis 6 7 4 +Waardenburg Syndrome 4 4 1 +WAGR Syndrome 4 8 24 +Waikavirus 4 6 2 +Waist Circumference 5 7 3 +Waist-Height Ratio 5 7 2 +Waist-Hip Ratio 4 6 3 +Waiting Lists 4 4 1 +Waiting Rooms 2 3 2 +Wakefulness 4 4 2 +Wakefulness-Promoting Agents 6 6 1 +Waldenstrom Macroglobulinemia 4 5 6 +Wales 4 4 1 +Walk Test 6 6 1 +Walker-Warburg Syndrome 3 8 4 +Walkers 4 4 1 +Walking 3 6 4 +Walking Speed 5 7 2 +Wallerian Degeneration 4 4 1 +Walruses 9 9 1 +Wandering Behavior 4 5 2 +Wandering Spleen 4 4 1 +WAP Four-Disulfide Core Domain Protein 2 4 4 1 +War Crimes 4 6 2 +War Exposure 6 6 2 +War-Related Injuries 2 7 2 +Warburg Effect, Oncologic 3 6 8 +Warfare 5 5 1 +Warfare and Armed Conflicts 4 4 1 +Warfarin 7 7 2 +Warm Ischemia 3 3 1 +Warm-Up Exercise 3 6 2 +Warts 4 5 4 +Wasabia 8 8 1 +Washington 6 6 2 +Wasp Venoms 4 5 2 +Wasps 10 10 1 +Waste Disposal Facilities 6 8 2 +Waste Disposal, Fluid 7 7 2 +Waste Management 5 7 2 +Waste Products 2 4 2 +Wastewater 3 5 2 +Wastewater-Based Epidemiological Monitoring 4 5 2 +Wasting Disease, Chronic 2 5 5 +Wasting Syndrome 3 3 2 +Watchful Waiting 5 5 1 +Water 4 6 3 +Water Cycle 3 3 2 +Water Decolorization 7 9 2 +Water Deprivation 3 3 1 +Water Insecurity 4 5 2 +Water Intoxication 3 4 2 +Water Loss, Insensible 4 5 3 +Water Microbiology 4 6 2 +Water Movements 3 5 3 +Water Pipe Smoking 5 5 1 +Water Pollutants 4 4 1 +Water Pollutants, Chemical 5 5 1 +Water Pollutants, Radioactive 3 5 2 +Water Pollution 4 4 1 +Water Pollution, Chemical 5 5 1 +Water Pollution, Radioactive 4 5 2 +Water Purification 6 8 2 +Water Quality 5 6 2 +Water Resources 3 5 3 +Water Softening 5 5 1 +Water Sports 5 5 1 +Water Supply 4 4 1 +Water Wells 5 5 1 +Water-Electrolyte Balance 3 4 3 +Water-Electrolyte Imbalance 3 3 1 +Waterborne Diseases 2 2 1 +Waterhouse-Friderichsen Syndrome 4 8 10 +Wavelet Analysis 2 4 3 +Waxes 2 2 1 +WD40 Repeats 6 9 4 +Weaning 4 6 2 +Weapons 3 3 1 +Weapons of Mass Destruction 4 4 1 +Wearable Electronic Devices 3 3 1 +Weather 4 5 3 +Web Archive 3 3 1 +Web Archives as Topic 6 6 1 +Web Browser 3 4 2 +Webcast 2 4 2 +Webcasts as Topic 6 6 1 +Wechsler Memory Scale 5 6 2 +Wechsler Scales 5 5 1 +Wedelia 8 8 1 +Wedge Argument 4 6 2 +Weed Control 3 6 2 +Weevils 10 10 1 +Weibel-Palade Bodies 7 9 2 +Weight Cycling 6 8 4 +Weight Gain 5 7 2 +Weight Lifting 5 5 1 +Weight Loss 5 7 2 +Weight Perception 4 4 1 +Weight Prejudice 4 5 2 +Weight Reduction Programs 6 7 2 +Weight-Bearing 3 3 1 +Weightlessness 6 6 1 +Weightlessness Countermeasures 2 2 1 +Weightlessness Simulation 2 5 2 +Weights and Measures 2 2 1 +Weil Disease 7 7 1 +Weill-Marchesani Syndrome 3 5 5 +Weissella 5 5 2 +Welding 6 6 1 +Werner Syndrome 3 4 2 +Werner Syndrome Helicase 5 8 6 +Wernicke Area 9 9 2 +Wernicke Encephalopathy 4 8 5 +West African People 5 5 1 +West Asian People 4 4 1 +West Indies 3 4 2 +West Nile Fever 5 8 12 +West Nile virus 5 7 2 +West Nile Virus Vaccines 5 5 1 +West Virginia 6 6 2 +Western Australia 4 5 2 +Western World 6 6 1 +Wet Macular Degeneration 5 5 1 +Wetlands 4 5 2 +Wettability 3 3 2 +Wetting Agents 4 4 1 +Whale, Killer 9 9 1 +Whales 8 8 1 +Whales, Pilot 9 9 1 +Wharton Jelly 3 3 1 +Wheat Germ Agglutinin-Horseradish Peroxidase Conjugate 6 6 3 +Wheat Germ Agglutinins 5 5 2 +Wheat Hypersensitivity 5 5 1 +Wheelchairs 3 3 1 +Whey 3 6 5 +Whey Proteins 4 7 8 +Whiplash Injuries 3 3 1 +Whipple Disease 4 7 3 +Whistleblowing 4 7 2 +White 4 6 2 +White Coat Hypertension 4 4 1 +White Dot Syndromes 6 6 1 +White Heifer Disease 3 3 1 +White Matter 4 4 2 +White Muscle Disease 3 3 1 +White People 3 3 1 +White spot syndrome virus 1 4 4 1 +Whole Blood Coagulation Time 3 6 3 +Whole Body Imaging 2 4 2 +Whole Genome Sequencing 5 5 1 +Whole Grains 5 6 4 +Whole-Body Counting 3 3 1 +Whole-Body Irradiation 2 3 2 +Whooping Cough 3 6 3 +Widowhood 5 7 5 +Wigglesworthia 5 5 2 +Wikstroemia 8 8 1 +Wilderness 4 4 1 +Wilderness Medicine 3 3 1 +Wildfires 3 5 3 +Wildlife Trade 2 2 1 +Williams Syndrome 4 7 4 +Williopsis 4 5 2 +Wills 5 5 1 +Wilms Tumor 3 7 11 +Wind 5 7 6 +Wine 4 5 6 +Winged-Helix Transcription Factors 4 4 2 +Wings, Animal 3 3 1 +Winteraceae 7 7 1 +Wireless Technology 5 5 1 +Wisconsin 6 6 2 +Wisconsin Card Sorting Test 4 4 1 +Wiskott-Aldrich Syndrome 4 6 9 +Wiskott-Aldrich Syndrome Protein 6 6 2 +Wiskott-Aldrich Syndrome Protein Family 5 5 2 +Wiskott-Aldrich Syndrome Protein, Neuronal 6 6 2 +Wissler's Syndrome 3 5 3 +Wisteria 8 8 1 +Wit and Humor 2 2 1 +Wit and Humor as Topic 3 3 2 +Witchcraft 4 7 2 +Withania 9 9 1 +Withanolides 4 7 4 +Withholding Treatment 3 4 2 +WNK Lysine-Deficient Protein Kinase 1 5 8 4 +Wnt Proteins 3 4 2 +Wnt Signaling Pathway 3 4 2 +Wnt-5a Protein 4 6 3 +Wnt1 Protein 4 6 3 +Wnt2 Protein 4 6 3 +Wnt3 Protein 4 5 2 +Wnt3A Protein 4 5 2 +Wnt4 Protein 4 5 2 +Wolbachia 6 6 1 +Wolf-Hirschhorn Syndrome 4 4 3 +Wolff-Parkinson-White Syndrome 5 5 3 +Wolffian Ducts 2 2 1 +Wolfiporia 6 6 1 +Wolfram Syndrome 4 8 17 +Wolinella 5 6 2 +Wolman Disease 3 7 6 +Wolves 10 10 1 +Women 2 2 1 +Women's Health 3 3 1 +Women's Health Services 3 3 1 +Women's Rights 4 5 2 +Women, Working 3 3 1 +Wood 4 4 2 +Woodfordia 10 10 1 +Wool 2 3 2 +Wool Fiber 4 4 1 +Word Association Tests 4 4 1 +Word Processing 4 5 2 +Work 2 2 1 +Work Capacity Evaluation 4 4 1 +Work Engagement 3 4 2 +Work of Breathing 3 5 2 +Work Performance 3 3 1 +Work Schedule Tolerance 4 5 2 +Work Simplification 5 5 2 +Work-Life Balance 2 5 3 +Workers' Compensation 5 6 2 +Workflow 3 3 1 +Workforce 3 3 1 +Workforce Diversity 6 6 4 +Workhouses 4 6 2 +Working Conditions 5 5 2 +Working Dogs 5 5 1 +Working Poor 2 2 1 +Workload 4 5 2 +Workplace 4 4 2 +Workplace Violence 5 5 2 +World Health Organization 5 5 1 +World War I 5 6 2 +World War II 5 6 2 +Wortmannin 7 7 1 +Wound Closure Techniques 2 2 1 +Wound Healing 3 3 1 +Wound Infection 2 2 1 +Wounds and Injuries 1 1 1 +Wounds, Gunshot 3 3 1 +Wounds, Nonpenetrating 2 2 1 +Wounds, Penetrating 2 2 1 +Wounds, Stab 3 3 1 +Wrestling 5 5 1 +Wrist 4 4 1 +Wrist Fractures 3 4 2 +Wrist Injuries 3 3 1 +Wrist Joint 5 5 1 +Writing 4 4 1 +Wrongful Life 4 5 2 +WT1 Proteins 5 5 1 +Wuchereria 9 9 1 +Wuchereria bancrofti 10 10 1 +WW Domain-Containing Oxidoreductase 5 7 2 +WW Domains 9 9 1 +Wyoming 6 6 1 +X Chromosome 5 5 2 +X Chromosome Inactivation 5 5 1 +X-Box Binding Protein 1 5 5 2 +X-Linked Combined Immunodeficiency Diseases 4 5 4 +X-Linked Emery-Dreifuss Muscular Dystrophy 5 7 4 +X-Linked Inhibitor of Apoptosis Protein 6 7 3 +X-Linked Intellectual Disability 4 5 3 +X-linked Nuclear Protein 5 5 1 +X-Ray Absorption Spectroscopy 4 4 1 +X-Ray Diffraction 2 4 4 +X-Ray Film 2 2 1 +X-Ray Intensifying Screens 2 2 1 +X-Ray Microtomography 7 7 2 +X-ray Repair Cross Complementing Protein 1 4 5 3 +X-Ray Therapy 3 3 1 +X-Rays 4 5 3 +Xamoterol 5 6 4 +Xanthenes 4 4 1 +Xanthine 4 7 2 +Xanthine Dehydrogenase 5 5 1 +Xanthine Oxidase 5 5 1 +Xanthines 3 6 2 +Xanthinol Niacinate 4 8 3 +Xanthium 8 8 1 +Xanthobacter 5 5 2 +Xanthogranuloma, Juvenile 3 5 2 +Xanthomatosis 4 4 1 +Xanthomatosis, Cerebrotendinous 5 5 4 +Xanthomonadaceae 4 5 2 +Xanthomonas 5 6 2 +Xanthomonas axonopodis 6 6 1 +Xanthomonas campestris 6 7 2 +Xanthomonas vesicatoria 6 7 2 +Xanthones 5 5 1 +Xanthophylls 4 9 4 +Xanthopterin 4 6 2 +Xanthorhiza 9 9 1 +Xanthosoma 10 10 1 +Xanthurenates 3 3 1 +Xedar Receptor 9 9 1 +Xenarthra 7 7 1 +Xenobiotics 2 2 1 +Xenodiagnosis 3 5 3 +Xenograft Model Antitumor Assays 3 5 2 +Xenon 4 4 2 +Xenon Isotopes 3 5 3 +Xenon Radioisotopes 4 6 4 +Xenophobia 4 5 3 +Xenopsylla 10 10 1 +Xenopus 8 8 1 +Xenopus laevis 9 9 1 +Xenopus Proteins 4 4 1 +Xenorhabdus 5 5 2 +Xenotropic and Polytropic Retrovirus Receptor 6 6 2 +Xenotropic murine leukemia virus-related virus 5 5 1 +Xeroderma Pigmentosum 3 4 8 +Xeroderma Pigmentosum Group A Protein 4 5 3 +Xeroderma Pigmentosum Group D Protein 5 7 4 +Xeromammography 6 6 2 +Xerophthalmia 3 4 2 +Xeroradiography 5 5 1 +Xerostomia 4 4 1 +Xestospongia 5 5 1 +Xipamide 4 5 2 +Xipapillomavirus 5 5 2 +Xiphoid Bone 6 6 1 +Xylan Endo-1,3-beta-Xylosidase 6 6 1 +Xylans 3 3 1 +Xylariales 4 4 1 +Xylazine 4 4 2 +Xylella 5 6 2 +Xylem 3 3 1 +Xylenes 6 6 1 +Xylitol 3 4 2 +Xylophilus 5 5 1 +Xylopia 8 8 1 +Xylose 5 5 1 +Xylosidases 5 5 1 +Xylulose 5 5 2 +XYY Karyotype 5 6 5 +Y Chromosome 5 5 2 +Y-Box-Binding Protein 1 5 6 3 +Y-Family DNA Polymerases 8 8 1 +Yaba monkey tumor virus 5 6 3 +Yang Deficiency 3 3 1 +YAP-Signaling Proteins 5 5 3 +Yarrowia 4 5 2 +Yatapoxvirus 4 5 3 +Yawning 3 3 1 +Yaws 4 7 5 +Yeast, Dried 4 5 2 +Yeasts 3 3 1 +Yellow Fever 4 6 4 +Yellow Fever Vaccine 5 5 1 +Yellow fever virus 6 6 1 +Yellow Nail Syndrome 3 4 4 +Yemen 5 5 1 +Yersinia 5 5 2 +Yersinia enterocolitica 6 6 2 +Yersinia Infections 6 6 1 +Yersinia pestis 6 6 2 +Yersinia pseudotuberculosis 6 6 2 +Yersinia pseudotuberculosis Infections 7 7 1 +Yersinia ruckeri 6 6 2 +Yin Deficiency 3 3 1 +Yin-Yang 3 8 2 +Yoga 4 4 4 +Yogurt 4 6 5 +Yohimbine 5 8 3 +Yolk Sac 3 4 3 +Young Adult 4 4 1 +Youth Sports 5 5 1 +Ytterbium 5 5 2 +Yttrium 4 4 3 +Yttrium Isotopes 3 5 4 +Yttrium Radioisotopes 4 6 5 +Yucca 10 10 1 +Yugoslavia 3 3 1 +Yukon Territory 5 5 1 +YY1 Transcription Factor 5 5 2 +Zalcitabine 5 7 4 +Zambia 5 5 1 +Zamiaceae 6 6 1 +Zanamivir 4 7 6 +Zantedeschia 10 10 1 +Zanthoxylum 8 8 1 +ZAP-70 Protein-Tyrosine Kinase 5 8 2 +Zea mays 8 8 1 +Zearalenone 3 8 3 +Zeatin 7 7 1 +Zeaxanthins 5 10 4 +Zebrafish 8 8 1 +Zebrafish Proteins 4 4 1 +Zein 6 6 2 +Zellweger Syndrome 3 6 11 +Zenker Diverticulum 5 7 2 +Zeolites 5 7 4 +Zeranol 4 9 2 +zeta Carotene 4 9 4 +zeta-Crystallins 5 7 2 +zeta-Globins 7 8 2 +Zidovudine 5 6 4 +Zigadenus 10 10 1 +Zika Virus 6 6 1 +Zika Virus Infection 4 6 3 +Zimbabwe 5 5 1 +Zimeldine 6 6 1 +Zinc 4 4 3 +Zinc Acetate 6 6 1 +Zinc Compounds 2 2 1 +Zinc Finger E-box Binding Homeobox 2 5 5 3 +Zinc Finger E-box-Binding Homeobox 1 5 5 3 +Zinc Finger Nucleases 5 7 3 +Zinc Finger Protein GLI1 5 6 3 +Zinc Finger Protein Gli2 5 5 2 +Zinc Finger Protein Gli3 5 5 4 +Zinc Fingers 8 8 1 +Zinc Isotopes 3 5 4 +Zinc Oxide 3 4 2 +Zinc Oxide-Eugenol Cement 4 6 2 +Zinc Phosphate Cement 4 6 2 +Zinc Radioisotopes 4 6 5 +Zinc Sulfate 3 6 2 +Zinc Transporter 8 6 7 4 +Zineb 3 7 3 +Zingiber officinale 10 10 1 +Zingiberaceae 9 9 1 +Zingiberales 8 8 1 +Zinostatin 6 8 2 +Ziram 5 7 2 +Zirconium 4 4 3 +Ziziphus 10 10 1 +Zn-Alpha-2-Glycoprotein 4 5 7 +Zolazepam 4 4 1 +Zoledronic Acid 5 5 2 +Zollinger-Ellison Syndrome 4 6 7 +Zolpidem 4 4 1 +Zona Fasciculata 5 5 1 +Zona Glomerulosa 5 5 1 +Zona Incerta 7 7 1 +Zona Pellucida 3 5 4 +Zona Pellucida Glycoproteins 4 5 5 +Zona Reticularis 5 5 1 +Zonisamide 4 5 3 +Zonula Occludens Proteins 5 5 1 +Zonula Occludens-1 Protein 6 6 1 +Zonula Occludens-2 Protein 6 6 1 +Zoogloea 5 5 2 +Zoology 4 4 1 +Zoonoses 2 2 2 +Zooplankton 4 4 1 +Zoster Sine Herpete 7 7 1 +Zosteraceae 9 9 1 +Zoxazolamine 5 5 1 +Zuclomiphene 9 9 1 +Zygapophyseal Joint 4 4 1 +Zygnematales 5 5 1 +Zygoma 6 6 1 +Zygomatic Fractures 4 6 3 +Zygomycosis 4 4 1 +Zygophyllaceae 7 7 1 +Zygophyllum 8 8 1 +Zygosaccharomyces 5 5 1 +Zygote 2 5 3 +Zygote Intrafallopian Transfer 4 4 2 +Zymomonas 4 5 2 +Zymosan 5 5 1 +Zyxin 4 4 3 From 178e8ddc07f5243818e6ecc85e58c79edf88c3bc Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 9 Jul 2026 15:14:50 +0200 Subject: [PATCH 147/205] include list of empty-keyword replacements to list of heuristically generated keywords --- .../preprocessing/other-scripts/preprocess.R | 6 ++ .../preprocessing/other-scripts/summarize.R | 25 +++++++- .../other-scripts/test/run_tests.R | 3 +- .../test/test_replace_keywords_routing.R | 58 +++++++++++++++++++ 4 files changed, 88 insertions(+), 4 deletions(-) create mode 100644 server/preprocessing/other-scripts/test/test_replace_keywords_routing.R diff --git a/server/preprocessing/other-scripts/preprocess.R b/server/preprocessing/other-scripts/preprocess.R index 23f8f06fb..d2c9a75c0 100644 --- a/server/preprocessing/other-scripts/preprocess.R +++ b/server/preprocessing/other-scripts/preprocess.R @@ -114,10 +114,16 @@ deduplicate_titles <- function(metadata, list_size) { replace_keywords_if_empty <- function(metadata, stops) { metadata$subject <- unlist(lapply(metadata$subject, function(x) {gsub(" +", " ", x)})) + # Flag papers whose `subject` we are about to SYNTHESISE from the title/abstract + # (they had no real keywords). The ranking treats a flagged paper's subject as a + # heuristic source (rank 2), not real keywords (rank 1) — see get_cluster_corpus. + # Set for all rows first so the column always exists downstream. + metadata$subject_is_heuristic <- FALSE missing_subjects = which(lapply(metadata$subject, function(x) {nchar(x)}) <= 1) if (length(missing_subjects) == 0) { return(metadata) } + metadata$subject_is_heuristic[missing_subjects] <- TRUE vplog$info(paste("vis_id:", .GlobalEnv$VIS_ID, "Documents without subjects:", length(missing_subjects))) candidates = mapply(paste, metadata$title) batch_size <- 1000 diff --git a/server/preprocessing/other-scripts/summarize.R b/server/preprocessing/other-scripts/summarize.R index 3730d275f..dccad3382 100644 --- a/server/preprocessing/other-scripts/summarize.R +++ b/server/preprocessing/other-scripts/summarize.R @@ -355,9 +355,19 @@ get_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, heuristic_col = HEUR_MIN2) { subjectlist = list() subject_dbg = list(); heuristic_dbg = list(); heuristic_min1_dbg = list() + replaced_subject_dbg = list() # subjects synthesised from titles (rank 2, not rank 1) for (k in seq(1, clusters$num_clusters)) { matches = which(unname(clusters$groups == k) == TRUE) subjects = metadata$subject[matches] + # subject_is_heuristic flags papers whose `subject` was synthesised from the + # title by replace_keywords_if_empty (they had no real keywords). Their tokens + # are routed to the HEURISTIC rank source (rank 2), not the cleaned/keyword + # source (rank 1). Absent on fixtures captured before this change -> all FALSE. + flagged = if (!is.null(metadata$subject_is_heuristic)) { + as.logical(metadata$subject_is_heuristic[matches]) + } else { + rep(FALSE, length(matches)) + } # Heuristic keywords are pre-generated "_"-joined n-grams (see # add_heuristic_keyword_fields): the corpus uses heuristic_col (min2 initial / # min1 fallback); the rank map always uses min1 (the superset), so any heuristic @@ -379,7 +389,11 @@ get_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, subjects = lapply(subjects, function(x){paste(unlist(x), collapse=";")}) subjects = mapply(paste, subjects, taxons, collapse=";") } - subject_dbg[[k]] = paste(unlist(subjects), collapse=";") + # Corpus is unchanged: it uses ALL subjects + heuristics (flagged or not), so + # tf-idf weights are identical. Only the RANK MAP splits them — flagged papers' + # subject tokens go to the heuristic rank source below, not the cleaned one. + subject_dbg[[k]] = paste(unlist(subjects[!flagged]), collapse=";") # rank 1 (real keywords) + replaced_subject_dbg[[k]] = paste(unlist(subjects[flagged]), collapse=";") # rank 2 (title-synthesised) heuristic_dbg[[k]] = paste(unlist(heuristics), collapse=";") heuristic_min1_dbg[[k]] = paste(unlist(heuristics_min1), collapse=";") all_subjects = paste(subjects, heuristics, collapse=" ") @@ -402,8 +416,13 @@ get_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, t <- gsub("^_+|_+$", "", t) # mirror the TDM tokenizer's edge-punctuation strip t[nzchar(t)] } - rank_sources <- list(cleaned = lapply(subject_dbg, split_tokens), - heuristic = lapply(heuristic_min1_dbg, split_tokens)) + # cleaned = real-keyword subject tokens (non-flagged papers). heuristic = the min1 + # title n-grams PLUS the flagged papers' synthesised subject tokens (title-derived, + # so they belong in rank 2). Both are already in the corpus, so no double-counting. + rank_sources <- list( + cleaned = lapply(subject_dbg, split_tokens), + heuristic = mapply(function(h, r) unique(c(split_tokens(h), split_tokens(r))), + heuristic_min1_dbg, replaced_subject_dbg, SIMPLIFY = FALSE)) nn_corpus <- VCorpus(VectorSource(subjectlist)) return(list(corpus = nn_corpus, rank_sources = rank_sources)) } diff --git a/server/preprocessing/other-scripts/test/run_tests.R b/server/preprocessing/other-scripts/test/run_tests.R index 943492cd7..48835a205 100644 --- a/server/preprocessing/other-scripts/test/run_tests.R +++ b/server/preprocessing/other-scripts/test/run_tests.R @@ -31,7 +31,8 @@ base_files <- c( # the pipeline image, run via test/run_tests.sh), skipped on a bare host. replay_files <- c( "test/test_replay_harness.R", - "test/test_replay_modes.R" + "test/test_replay_modes.R", + "test/test_replace_keywords_routing.R" ) default_files <- base_files if (requireNamespace("tm", quietly = TRUE)) { diff --git a/server/preprocessing/other-scripts/test/test_replace_keywords_routing.R b/server/preprocessing/other-scripts/test/test_replace_keywords_routing.R new file mode 100644 index 000000000..62772c569 --- /dev/null +++ b/server/preprocessing/other-scripts/test/test_replace_keywords_routing.R @@ -0,0 +1,58 @@ +# Regression for the replace_keywords_if_empty routing fix. +# +# replace_keywords_if_empty synthesises a `subject` from the title for papers with +# no real keywords, and flags them via `subject_is_heuristic`. Those synthesised +# "keywords" are title n-grams, so the ranking must route them to the HEURISTIC +# rank source (rank 2), NOT the cleaned/keyword source (rank 1) — otherwise title +# fragments outrank real keywords (the "Action changing, Action ethics" case). +# +# Runs inside the pipeline image (needs tm). + +if (!exists("get_cluster_corpus")) source("test/replay_harness.R") +if (!requireNamespace("testthat", quietly = TRUE)) { + if (!exists("test_that")) source("test/testthat_shim.R") +} else { + library(testthat) +} + +STOPS <- c("the", "for", "a", "in", "of", "and", "to") + +build_md <- function() { + md <- data.frame( + # paper 1: real author keywords, title unrelated to them (non-flagged) + # paper 2: keyword-less -> subject SYNTHESISED from its title (flagged) + title = c("Alpha beta gamma delta", "Taking action in a changing world"), + subject = c("neural networks; deep learning", "action changing; taking action"), + paper_abstract = c("", ""), + subject_is_heuristic = c(FALSE, TRUE), + stringsAsFactors = FALSE) + add_heuristic_keyword_fields(md, STOPS) +} + +test_that("a real keyword lands in rank 1 (cleaned), not rank 2", { + md <- build_md() + co <- get_cluster_corpus(list(groups = c(1, 1), num_clusters = 1), + md, STOPS, taxonomy_separator = NULL, heuristic_col = HEUR_MIN2) + cleaned <- co$rank_sources$cleaned[[1]]; heur <- co$rank_sources$heuristic[[1]] + expect_true("neural_networks" %in% cleaned) + expect_false("neural_networks" %in% heur) +}) + +test_that("a title-synthesised subject lands in rank 2 (heuristic), not rank 1", { + md <- build_md() + co <- get_cluster_corpus(list(groups = c(1, 1), num_clusters = 1), + md, STOPS, taxonomy_separator = NULL, heuristic_col = HEUR_MIN2) + cleaned <- co$rank_sources$cleaned[[1]]; heur <- co$rank_sources$heuristic[[1]] + expect_true("taking_action" %in% heur) + expect_false("taking_action" %in% cleaned) + expect_false("action_changing" %in% cleaned) +}) + +test_that("without the flag column, all subjects stay in rank 1 (backward compatible)", { + md <- build_md(); md$subject_is_heuristic <- NULL # simulate a pre-fix fixture + co <- get_cluster_corpus(list(groups = c(1, 1), num_clusters = 1), + md, STOPS, taxonomy_separator = NULL, heuristic_col = HEUR_MIN2) + cleaned <- co$rank_sources$cleaned[[1]] + expect_true("neural_networks" %in% cleaned) + expect_true("taking_action" %in% cleaned) # unflagged -> treated as keyword, as before +}) From 2519f8353d9865a2dbd22b345b21b85f321d165d Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 9 Jul 2026 15:15:03 +0200 Subject: [PATCH 148/205] update of contentprovider.json cache --- server/workers/common/common/contentproviders.json | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/server/workers/common/common/contentproviders.json b/server/workers/common/common/contentproviders.json index 700ec7e86..8fd96b5c8 100644 --- a/server/workers/common/common/contentproviders.json +++ b/server/workers/common/common/contentproviders.json @@ -1,4 +1,12 @@ [ + { + "name": "Adiyaman University Research and Academic Performance System", + "internal_name": "ftid14865" + }, + { + "name": "Journal Institutre", + "internal_name": "ftid14866" + }, { "name": "Universidad Americana", "internal_name": "ftid14848" From 78e62157e0843e031adb9f9d409b01ee5ee7a465 Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 9 Jul 2026 18:34:53 +0200 Subject: [PATCH 149/205] revert heuristic keyword threshold filtering concerning legacy ranking mode 0 --- server/preprocessing/other-scripts/ranking.R | 2 +- .../preprocessing/other-scripts/summarize.R | 271 ++++++++++++------ .../other-scripts/test/replay_harness.R | 19 +- .../test/test_replace_keywords_routing.R | 6 +- 4 files changed, 200 insertions(+), 98 deletions(-) diff --git a/server/preprocessing/other-scripts/ranking.R b/server/preprocessing/other-scripts/ranking.R index 42d499c9e..2b31a9cae 100644 --- a/server/preprocessing/other-scripts/ranking.R +++ b/server/preprocessing/other-scripts/ranking.R @@ -73,7 +73,7 @@ rank_of_terms <- function(terms, cleaned, heuristic, policy_spec) { # Waterfall selection over one cluster's ranked candidates. Walks the policy spec # top->down: a "topup" rank fills open label slots; an "exclusive" rank contributes -# only if the label is still empty. De-nesting runs WITHIN each rank (decision #6). +# only if the label is still empty. De-nesting runs WITHIN each rank. # terms_us : "_"-joined, weight-ordered survivors (post-prune). # term_ranks : aligned provenance-rank vector. # top_n : max label terms. diff --git a/server/preprocessing/other-scripts/summarize.R b/server/preprocessing/other-scripts/summarize.R index dccad3382..22e91f33d 100644 --- a/server/preprocessing/other-scripts/summarize.R +++ b/server/preprocessing/other-scripts/summarize.R @@ -1,12 +1,11 @@ library(stringr) vslog <- getLogger('vis.summarize') -# Metadata columns holding the heuristically generated (title n-gram) keywords, -# pre-binned by map-wide document frequency (see add_heuristic_keyword_fields: -# _min1 = n-grams appearing in >= 1 resource (all) -> used for the FALLBACK corpus -# _min2 = n-grams appearing in >= 2 resources -> used for the INITIAL corpus +# Metadata column holding the heuristically generated (title n-gram) keywords for +# each paper: the FULL per-paper n-gram set (DF >= 1). The document-frequency filter +# is PER-CLUSTER and applied downstream in get_cluster_corpus (§6.1), not here — so a +# title phrase must recur within its OWN cluster to enter that cluster's initial corpus. HEUR_MIN1 <- "keywords_rank_heuristically_generated_min1" -HEUR_MIN2 <- "keywords_rank_heuristically_generated_min2" # summarize.R # Cluster labelling for the overview visualisation: turns each cluster of papers @@ -112,26 +111,19 @@ paper_title_ngrams <- function(title, stops) { unique(grams[keep]) } -# Add the two heuristic-keyword metadata columns (HEUR_MIN1 / HEUR_MIN2) to the -# metadata data frame. Generates each paper's title n-grams, computes each -# n-gram's MAP-WIDE document frequency (number of distinct resources it appears -# in), and stores per paper, as "; "-joined "_"-n-gram strings: -# HEUR_MIN1 = all of the paper's n-grams (DF >= 1) -# HEUR_MIN2 = the paper's n-grams with document frequency >= 2 -# Computed once, early in labelling. +# Add the heuristic-keyword metadata column (HEUR_MIN1) to the metadata data frame: +# each paper's pruned title bi-/tri-grams, stored as a "; "-joined "_"-n-gram string. +# This is the FULL per-paper set (DF >= 1); the document-frequency filter is applied +# PER-CLUSTER downstream in get_cluster_corpus (§6.1). Computed once, early in labelling. add_heuristic_keyword_fields <- function(metadata, stops) { per_paper <- lapply(metadata$title, paper_title_ngrams, stops = stops) - df <- table(unlist(lapply(per_paper, unique))) # map-wide document frequency - min2_set <- names(df)[df >= 2] metadata[[HEUR_MIN1]] <- vapply(per_paper, function(g) paste(g, collapse = "; "), character(1)) - metadata[[HEUR_MIN2]] <- vapply(per_paper, - function(g) paste(g[g %in% min2_set], collapse = "; "), character(1)) metadata } # Last-resort label for a cluster that produced no tf-idf label (empty even after -# the min1 fallback): build one from the most frequent bi-/tri-grams of the +# the all-n-grams fallback): build one from the most frequent bi-/tri-grams of the # cluster's papers' titles + abstracts. Returns a single ", "-joined label string. # matches : row indices of the cluster's papers in `metadata`. # top_n : number of terms kept. @@ -192,70 +184,91 @@ create_cluster_labels <- function(clusters, metadata, weightingspec = weightingspec, top_n = top_n, stops = stops, taxonomy_separator = taxonomy_separator, params = params, service = service), "summarize_00_label_inputs") - # Additive rank columns on the metadata data frame: - # - keywords_rank_cleaned: the rank-1 source. Stage 1 = subject_cleaned verbatim - # (keywords + MeSH pooled); Stage 2 carves MeSH out into an upstream mesh column. - # - the two heuristic columns (min1/min2), pre-binned by map-wide document - # frequency. subject_cleaned (metadata$subject) is left untouched. - metadata <- add_heuristic_keyword_fields(metadata, stops) - metadata$keywords_rank_cleaned <- metadata$subject - cc <- params$custom_clustering - if (!(is.null(cc)) && (cc %in% names(metadata))) { - corpus_out <- get_custom_cluster_corpus(clusters, metadata, stops, taxonomy_separator, custom_clustering=cc) - fallback_corpus <- corpus_out$corpus # custom path: no heuristic min1/min2 split - } else { - # Initial corpus uses the min2 heuristic set (DF >= 2); the fallback corpus - # swaps in min1 (all n-grams). - corpus_out <- get_cluster_corpus(clusters, metadata, stops, taxonomy_separator, heuristic_col = HEUR_MIN2) - fallback_corpus <- get_cluster_corpus(clusters, metadata, stops, taxonomy_separator, heuristic_col = HEUR_MIN1)$corpus - } - # get_*_cluster_corpus returns the corpus plus per-cluster rank sources (the - # separated keyword/heuristic tokens used only for rank lookup). rank_sources - # is NULL on the custom-clustering path, so ranked modes fall back to legacy there. - nn_corpus <- corpus_out$corpus - rank_sources <- corpus_out$rank_sources - dump_data(nn_corpus, "summarize_04_corpus") - # Resolve the ranking mode BEFORE the TDM: the local frequency bound is - # mode-dependent c(2, Inf) for Mode 0 (byte-identical legacy), c(1, Inf) - # for Modes 1-3 so low-frequency real keywords survive into the ranking. The - # corpus text and weighting are otherwise identical across modes. + # Resolve the ranking mode BEFORE building the corpus. Mode 0 is gated to the + # verbatim legacy path (get_cluster_corpus_legacy + zero-sum fill_empty_clusters_ + # legacy), so it stays BYTE-IDENTICAL to the pre-ranking pipeline; Modes 1-3 take + # the per-cluster DF-filtered corpus + rank-aware selection. mode <- ranking_mode(service) - local_bound <- if (identical(mode, "0")) c(2, Inf) else c(1, Inf) + cc <- params$custom_clustering vslog$debug(paste("create_cluster_labels: ranking mode", mode, "for service", - if (is.null(service)) "(none)" else service, - "; local bound", local_bound[1])) - nn_tfidf <- TermDocumentMatrix(nn_corpus, control = list( - tokenize = SplitTokenizer, - weighting = function(x) weightSMART(x, spec="ntn"), - bounds = list(local = local_bound), - tolower = TRUE - )) - tfidf_top <- apply(nn_tfidf, 2, function(x) {x2 <- sort(x, TRUE);x2[x2>0]}) - vslog$debug(paste("create_cluster_labels: tf-idf matrix", nTerms(nn_tfidf), "terms x", - nDocs(nn_tfidf), "clusters")) - - # Ranking-mode wedge (ranking.R): Mode 0 is the unchanged legacy selection; - # Modes 1-3 apply rank-aware selection over the same global ranking, partitioned - # by rank_sources. Initial labels come from the min2 (DF >= 2) corpus. - tfidf_top_names <- select_cluster_label_names(tfidf_top, top_n, stops, mode = mode, - rank_sources = rank_sources) - - # min1 fallback: any cluster whose label came out EMPTY is re-labelled from the - # min1 corpus (all title n-grams, bound 1). The trigger is "empty label", NOT - # "zero tf-idf sum": with the min2 DF filter a cluster can have a tiny tf-idf that - # prunes away to nothing, which the old zero-sum check missed, dropping it - # straight to the abstract-frequency fallback instead of the intended min1 rescue. - # The title/abstract-frequency fallback below remains the true last resort. - empty_label <- which(!vapply(tfidf_top_names, - function(x) { s <- if (length(x)) x[[1]] else ""; nzchar(s) }, - logical(1))) - if (length(empty_label) > 0) { - vslog$debug(paste("create_cluster_labels: min1 fallback for", length(empty_label), - "clusters with an empty min2 label")) - fallback_top <- fill_empty_clusters(fallback_corpus) - fallback_names <- select_cluster_label_names(fallback_top, top_n, stops, mode = mode, - rank_sources = rank_sources) - tfidf_top_names[empty_label] <- fallback_names[empty_label] + if (is.null(service)) "(none)" else service)) + + if (identical(mode, "0")) { + # ---- Mode 0: legacy no-ranking path (inline title n-grams, no DF filter) ---- + if (!(is.null(cc)) && (cc %in% names(metadata))) { + nn_corpus <- get_custom_cluster_corpus(clusters, metadata, stops, taxonomy_separator, custom_clustering=cc)$corpus + } else { + nn_corpus <- get_cluster_corpus_legacy(clusters, metadata, stops, taxonomy_separator) + } + dump_data(nn_corpus, "summarize_04_corpus") + nn_tfidf <- TermDocumentMatrix(nn_corpus, control = list( + tokenize = SplitTokenizer, + weighting = function(x) weightSMART(x, spec="ntn"), + bounds = list(local = c(2, Inf)), + tolower = TRUE + )) + tfidf_top <- apply(nn_tfidf, 2, function(x) {x2 <- sort(x, TRUE);x2[x2>0]}) + # Legacy fallback: clusters whose tf-idf summed to zero are re-filled from the + # SAME corpus at bound c(1, Inf). + empty_tfidf <- which(apply(nn_tfidf, 2, sum) == 0) + tfidf_top[c(empty_tfidf)] <- fill_empty_clusters_legacy(nn_tfidf, nn_corpus)[c(empty_tfidf)] + tfidf_top_names <- get_top_names(tfidf_top, top_n, stops) + } else { + # ---- Modes 1-3: DF-filtered corpus + rank-aware selection ------------------ + # Additive rank columns on the metadata data frame: + # - keywords_rank_cleaned: the rank-1 source (Stage 1 = subject_cleaned verbatim). + # - the heuristic column (full per-paper n-gram set); the per-cluster DF filter is + # applied inside get_cluster_corpus. subject_cleaned (metadata$subject) untouched. + metadata <- add_heuristic_keyword_fields(metadata, stops) + metadata$keywords_rank_cleaned <- metadata$subject + if (!(is.null(cc)) && (cc %in% names(metadata))) { + corpus_out <- get_custom_cluster_corpus(clusters, metadata, stops, taxonomy_separator, custom_clustering=cc) + fallback_corpus <- corpus_out$corpus # custom path: no per-cluster heuristic filter + } else { + # Initial corpus applies the per-cluster DF>=2 heuristic filter; the fallback + # corpus keeps all n-grams (percluster_filter = FALSE). + corpus_out <- get_cluster_corpus(clusters, metadata, stops, taxonomy_separator, percluster_filter = TRUE) + fallback_corpus <- get_cluster_corpus(clusters, metadata, stops, taxonomy_separator, percluster_filter = FALSE)$corpus + } + # get_*_cluster_corpus returns the corpus plus per-cluster rank sources (the + # separated keyword/heuristic tokens used only for rank lookup). rank_sources + # is NULL on the custom-clustering path, so ranked modes fall back to legacy there. + nn_corpus <- corpus_out$corpus + rank_sources <- corpus_out$rank_sources + dump_data(nn_corpus, "summarize_04_corpus") + # Local bound c(1, Inf) so low-frequency real keywords survive into the ranking. + nn_tfidf <- TermDocumentMatrix(nn_corpus, control = list( + tokenize = SplitTokenizer, + weighting = function(x) weightSMART(x, spec="ntn"), + bounds = list(local = c(1, Inf)), + tolower = TRUE + )) + tfidf_top <- apply(nn_tfidf, 2, function(x) {x2 <- sort(x, TRUE);x2[x2>0]}) + vslog$debug(paste("create_cluster_labels: tf-idf matrix", nTerms(nn_tfidf), "terms x", + nDocs(nn_tfidf), "clusters")) + + # Rank-aware selection (ranking.R) over the global ranking, partitioned by + # rank_sources. Initial labels come from the per-cluster-filtered corpus. + tfidf_top_names <- select_cluster_label_names(tfidf_top, top_n, stops, mode = mode, + rank_sources = rank_sources) + + # Fallback: any cluster whose label came out EMPTY is re-labelled from the + # all-n-grams corpus (bound 1). The trigger is "empty label", NOT "zero tf-idf + # sum": with the DF filter a cluster can have a tiny tf-idf that prunes away to + # nothing, which the old zero-sum check missed, dropping it straight to the + # abstract-frequency fallback instead of the intended all-n-grams rescue. + # The title/abstract-frequency fallback below remains the true last resort. + empty_label <- which(!vapply(tfidf_top_names, + function(x) { s <- if (length(x)) x[[1]] else ""; nzchar(s) }, + logical(1))) + if (length(empty_label) > 0) { + vslog$debug(paste("create_cluster_labels: all-n-grams fallback for", length(empty_label), + "clusters with an empty initial label")) + fallback_top <- fill_empty_clusters(fallback_corpus) + fallback_names <- select_cluster_label_names(fallback_top, top_n, stops, mode = mode, + rank_sources = rank_sources) + tfidf_top_names[empty_label] <- fallback_names[empty_label] + } } dump_data(tfidf_top_names, "summarize_05_tfidf_top_names") clusters$cluster_labels = "" @@ -263,7 +276,7 @@ create_cluster_labels <- function(clusters, metadata, matches = which(unname(clusters$groups == k) == TRUE) summary = tfidf_top_names[[k]] if (summary == "") { - # No tf-idf label survived even the min1 fallback: last-resort label built + # No tf-idf label survived even the all-n-grams fallback: last-resort label built # from the papers' titles + abstracts (see title_abstract_fallback_label). vslog$debug(paste("create_cluster_labels: title/abstract fallback for cluster", k, "with", length(matches), "papers")) @@ -343,6 +356,67 @@ get_custom_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separa return(list(corpus = nn_corpus, rank_sources = NULL)) } +# --- Mode 0 (legacy) corpus + fallback ------------------------------------- +# Verbatim from feat/keyword-label-improvements: Mode 0 is gated to this path so +# it stays BYTE-IDENTICAL to the pre-ranking pipeline (docs §7). It generates the +# title n-grams INLINE (get_title_ngrams, unchanged) rather than reading the +# per-paper heuristic column, and has no rank_sources. The team did not accept +# a changed Mode-0 baseline, so the DF filter (§6.1) is confined to Modes 1-3. +get_cluster_corpus_legacy <- function(clusters, metadata, stops, taxonomy_separator, + add_title_ngrams = T, custom_clustering=NULL) { + subjectlist = list() + for (k in seq(1, clusters$num_clusters)) { + matches = which(unname(clusters$groups == k) == TRUE) + titles = metadata$title[matches] + subjects = metadata$subject[matches] + titles = lapply(titles, function(x) {gsub("[^[:alnum:]-]", " ", x)}) + titles = lapply(titles, gsub, pattern="\\s+", replacement=" ") + title_ngrams <- get_title_ngrams(titles, stops, c(2, 3)) + batch_size <- 1000 + total_length <- length(stops) + for (i in seq(1, total_length, batch_size)) { + titles = lapply(titles, function(x) {removeWords(x, stops[i:min(i+batch_size -1, total_length)])}) + } + subjects = mapply(gsub, subjects, pattern = "; ", replacement=";") + subjects = mapply(gsub, subjects, pattern=" ", replacement="_") + titles = mapply(gsub, titles, pattern=" ", replacement=";") + + if (!is.null(taxonomy_separator)) { + subjects = mapply(function(x){strsplit(x, ";")}, subjects) + taxons = lapply(subjects, function(y){Filter(function(x){grepl(taxonomy_separator, x)}, y)}) + subjects = lapply(subjects, function(y){Filter(function(x){!grepl(taxonomy_separator, x)}, y)}) + taxons = lapply(taxons, function(x){lapply(strsplit(x, taxonomy_separator), function(y){tail(y,1)})}) + taxons = lapply(taxons, function(x){paste(unlist(x), collapse=";")}) + subjects = lapply(subjects, function(x){paste(unlist(x), collapse=";")}) + subjects = mapply(paste, subjects, taxons, collapse=";") + } + if (add_title_ngrams == T) { + all_subjects = paste(subjects, title_ngrams, collapse=" ") + } else { + all_subjects = paste(subjects, collapse=" ") + } + all_subjects <- str_replace_all(all_subjects, "\\?+_\\?+|\\?+|\\?+ ", "") + all_subjects <- str_replace_all(all_subjects, ";+", ";") + all_subjects <- str_replace_all(all_subjects, " ?; ?", ";") + all_subjects <- str_replace_all(all_subjects, " +", ";") + subjectlist = c(subjectlist, all_subjects) + } + nn_corpus <- VCorpus(VectorSource(subjectlist)) + return(nn_corpus) +} + +# Legacy (Mode 0) fallback: rebuild the tf-idf of the SAME corpus with local bound +# c(1, Inf) for clusters whose initial tf-idf summed to zero. Verbatim legacy +# 2-arg signature — distinct from the Modes 1-3 fill_empty_clusters (all-n-grams corpus). +fill_empty_clusters_legacy <- function(nn_tfidf, nn_corpus){ + replacement_nn_tfidf <- TermDocumentMatrix(nn_corpus, control = list(tokenize = SplitTokenizer, + weighting = function(x) weightSMART(x, spec="ntn"), + bounds = list(local = c(1, Inf)) + )) + replacement_tfidf_top <- apply(replacement_nn_tfidf, 2, function(x) {x2 <- sort(x, TRUE);x2[x2>0]}) + return(replacement_tfidf_top) +} + # Build the per-cluster corpus used for tf-idf labelling. Per cluster: combines # the papers' `subject` keywords with bi-/tri-grams from their titles. Taxonomy # subjects (containing taxonomy_separator) are reduced to their last path segment. @@ -352,9 +426,9 @@ get_custom_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separa # lowercased) used by the ranked selection. add_title_ngrams toggles the title # n-gram contribution. get_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, - heuristic_col = HEUR_MIN2) { + percluster_filter = TRUE) { subjectlist = list() - subject_dbg = list(); heuristic_dbg = list(); heuristic_min1_dbg = list() + subject_dbg = list(); heuristic_dbg = list(); heuristic_all_dbg = list() replaced_subject_dbg = list() # subjects synthesised from titles (rank 2, not rank 1) for (k in seq(1, clusters$num_clusters)) { matches = which(unname(clusters$groups == k) == TRUE) @@ -368,17 +442,28 @@ get_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, } else { rep(FALSE, length(matches)) } - # Heuristic keywords are pre-generated "_"-joined n-grams (see - # add_heuristic_keyword_fields): the corpus uses heuristic_col (min2 initial / - # min1 fallback); the rank map always uses min1 (the superset), so any heuristic - # term resolves to rank 2 regardless of which pass produced it. - heuristics = as.character(metadata[[heuristic_col]][matches]) - heuristics_min1 = as.character(metadata[[HEUR_MIN1]][matches]) + # Heuristic keywords are pre-generated "_"-joined n-grams (add_heuristic_keyword_ + # fields gives the FULL per-paper set). PER-CLUSTER DF FILTER (§6.1): for the + # INITIAL corpus (percluster_filter=TRUE) a title n-gram is kept only if it recurs + # in >= 2 papers of THIS cluster — local corroboration, so a cluster-local singleton + # can't become a label just because the same n-gram appears once in another cluster. + # The FALLBACK corpus (percluster_filter=FALSE) keeps all n-grams. The rank map + # always uses the full set, so any heuristic term still resolves to rank 2. + heuristics_all = as.character(metadata[[HEUR_MIN1]][matches]) + if (percluster_filter) { + per_paper = lapply(strsplit(heuristics_all, "; "), function(x) x[nzchar(x)]) + cluster_df = table(unlist(lapply(per_paper, unique))) # per-cluster document frequency + recurring = names(cluster_df)[cluster_df >= 2] + heuristics = vapply(per_paper, + function(g) paste(g[g %in% recurring], collapse = "; "), character(1)) + } else { + heuristics = heuristics_all + } subjects = mapply(gsub, subjects, pattern = "; ", replacement=";") subjects = mapply(gsub, subjects, pattern=" ", replacement="_") heuristics = gsub("; ", ";", heuristics) - heuristics_min1 = gsub("; ", ";", heuristics_min1) + heuristics_all = gsub("; ", ";", heuristics_all) if (!is.null(taxonomy_separator)) { subjects = mapply(function(x){strsplit(x, ";")}, subjects) @@ -395,7 +480,7 @@ get_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, subject_dbg[[k]] = paste(unlist(subjects[!flagged]), collapse=";") # rank 1 (real keywords) replaced_subject_dbg[[k]] = paste(unlist(subjects[flagged]), collapse=";") # rank 2 (title-synthesised) heuristic_dbg[[k]] = paste(unlist(heuristics), collapse=";") - heuristic_min1_dbg[[k]] = paste(unlist(heuristics_min1), collapse=";") + heuristic_all_dbg[[k]] = paste(unlist(heuristics_all), collapse=";") all_subjects = paste(subjects, heuristics, collapse=" ") all_subjects <- normalize_corpus_tokens(all_subjects) subjectlist = c(subjectlist, all_subjects) @@ -410,19 +495,19 @@ get_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, # split by provenance and run through the SAME normalize_corpus_tokens() + # lowercasing + edge-strip as the TDM terms — so every rank token matches a tf-idf # term. cleaned = keywords_rank_cleaned (subject_cleaned); - # heuristic = the min1 n-gram set. Used only for rank lookup, never fed to the TDM. + # heuristic = the FULL n-gram set. Used only for rank lookup, never fed to the TDM. split_tokens <- function(s) { t <- tolower(unlist(strsplit(normalize_corpus_tokens(s), ";"))) t <- gsub("^_+|_+$", "", t) # mirror the TDM tokenizer's edge-punctuation strip t[nzchar(t)] } - # cleaned = real-keyword subject tokens (non-flagged papers). heuristic = the min1 + # cleaned = real-keyword subject tokens (non-flagged papers). heuristic = the FULL # title n-grams PLUS the flagged papers' synthesised subject tokens (title-derived, # so they belong in rank 2). Both are already in the corpus, so no double-counting. rank_sources <- list( cleaned = lapply(subject_dbg, split_tokens), heuristic = mapply(function(h, r) unique(c(split_tokens(h), split_tokens(r))), - heuristic_min1_dbg, replaced_subject_dbg, SIMPLIFY = FALSE)) + heuristic_all_dbg, replaced_subject_dbg, SIMPLIFY = FALSE)) nn_corpus <- VCorpus(VectorSource(subjectlist)) return(list(corpus = nn_corpus, rank_sources = rank_sources)) } diff --git a/server/preprocessing/other-scripts/test/replay_harness.R b/server/preprocessing/other-scripts/test/replay_harness.R index ca96ce9fd..4ee5131a0 100644 --- a/server/preprocessing/other-scripts/test/replay_harness.R +++ b/server/preprocessing/other-scripts/test/replay_harness.R @@ -25,6 +25,23 @@ if (!exists("ranking_mode")) source("ranking.R") REPLAY_DIR <- "test/replay" +# Fixtures captured BEFORE the replace_keywords_if_empty routing fix lack the +# `subject_is_heuristic` flag, so get_cluster_corpus falls back to treating every +# subject as a real keyword (rank 1) — including subjects that were synthesised from +# titles for keyword-less papers. That mis-labels complete-fallback clusters as +# rank-1 situations. These fixtures still carry `subject_orig` (the subject BEFORE +# synthesis), so reconstruct the flag the same way preprocess.R does: a paper whose +# ORIGINAL subject was empty had its subject synthesised and must route to the +# heuristic (rank-2) source. No-op once fixtures are captured post-fix (flag present) +# and harmless in Mode 0 (the flag only affects rank sources, not the tf-idf corpus). +backfill_subject_is_heuristic <- function(metadata) { + if ("subject_is_heuristic" %in% names(metadata)) return(metadata) + if (!"subject_orig" %in% names(metadata)) return(metadata) + so <- metadata$subject_orig + metadata$subject_is_heuristic <- nchar(ifelse(is.na(so), "", so)) <= 1 + metadata +} + # Reduce clusters$cluster_labels (one entry per paper) to a deterministic, # cluster-keyed vector: one label per cluster id, ordered by id. labels_per_cluster <- function(clusters) { @@ -52,7 +69,7 @@ replay_labels <- function(bundle, mode = "0") { clusters <- create_cluster_labels( clusters = bundle$clusters, - metadata = bundle$metadata, + metadata = backfill_subject_is_heuristic(bundle$metadata), type_counts = bundle$type_counts, weightingspec = bundle$weightingspec, top_n = bundle$top_n, diff --git a/server/preprocessing/other-scripts/test/test_replace_keywords_routing.R b/server/preprocessing/other-scripts/test/test_replace_keywords_routing.R index 62772c569..abacf4fb4 100644 --- a/server/preprocessing/other-scripts/test/test_replace_keywords_routing.R +++ b/server/preprocessing/other-scripts/test/test_replace_keywords_routing.R @@ -32,7 +32,7 @@ build_md <- function() { test_that("a real keyword lands in rank 1 (cleaned), not rank 2", { md <- build_md() co <- get_cluster_corpus(list(groups = c(1, 1), num_clusters = 1), - md, STOPS, taxonomy_separator = NULL, heuristic_col = HEUR_MIN2) + md, STOPS, taxonomy_separator = NULL, percluster_filter = TRUE) cleaned <- co$rank_sources$cleaned[[1]]; heur <- co$rank_sources$heuristic[[1]] expect_true("neural_networks" %in% cleaned) expect_false("neural_networks" %in% heur) @@ -41,7 +41,7 @@ test_that("a real keyword lands in rank 1 (cleaned), not rank 2", { test_that("a title-synthesised subject lands in rank 2 (heuristic), not rank 1", { md <- build_md() co <- get_cluster_corpus(list(groups = c(1, 1), num_clusters = 1), - md, STOPS, taxonomy_separator = NULL, heuristic_col = HEUR_MIN2) + md, STOPS, taxonomy_separator = NULL, percluster_filter = TRUE) cleaned <- co$rank_sources$cleaned[[1]]; heur <- co$rank_sources$heuristic[[1]] expect_true("taking_action" %in% heur) expect_false("taking_action" %in% cleaned) @@ -51,7 +51,7 @@ test_that("a title-synthesised subject lands in rank 2 (heuristic), not rank 1", test_that("without the flag column, all subjects stay in rank 1 (backward compatible)", { md <- build_md(); md$subject_is_heuristic <- NULL # simulate a pre-fix fixture co <- get_cluster_corpus(list(groups = c(1, 1), num_clusters = 1), - md, STOPS, taxonomy_separator = NULL, heuristic_col = HEUR_MIN2) + md, STOPS, taxonomy_separator = NULL, percluster_filter = TRUE) cleaned <- co$rank_sources$cleaned[[1]] expect_true("neural_networks" %in% cleaned) expect_true("taking_action" %in% cleaned) # unflagged -> treated as keyword, as before From 3e891e65155712e1e0b7cefaeb71362989e6d5cb Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 9 Jul 2026 18:37:57 +0200 Subject: [PATCH 150/205] revert heuristic keyword threshold filtering concerning legacy ranking mode 0 --- server/preprocessing/other-scripts/ranking.R | 2 +- server/preprocessing/other-scripts/summarize.R | 11 +++++------ .../other-scripts/test/test_ranking_select.R | 2 +- 3 files changed, 7 insertions(+), 8 deletions(-) diff --git a/server/preprocessing/other-scripts/ranking.R b/server/preprocessing/other-scripts/ranking.R index 2b31a9cae..c3f554e91 100644 --- a/server/preprocessing/other-scripts/ranking.R +++ b/server/preprocessing/other-scripts/ranking.R @@ -141,7 +141,7 @@ select_cluster_label_names <- function(tfidf_top, top_n, stops, mode = "0", weights <- tfidf_top[[k]] nms <- names(weights) if (is.null(nms) || length(nms) == 0) { out[[k]] <- ""; next } - # Shared prune head (§3 step 1): same well-formedness filter as legacy, on the + # Shared prune head: same well-formedness filter as legacy, on the # global weight-ordered list. another_prune_ngrams preserves order. pruned <- unlist(another_prune_ngrams(nms, stops)) if (length(pruned) == 0) { out[[k]] <- ""; next } diff --git a/server/preprocessing/other-scripts/summarize.R b/server/preprocessing/other-scripts/summarize.R index 22e91f33d..0b195cf1d 100644 --- a/server/preprocessing/other-scripts/summarize.R +++ b/server/preprocessing/other-scripts/summarize.R @@ -3,7 +3,7 @@ vslog <- getLogger('vis.summarize') # Metadata column holding the heuristically generated (title n-gram) keywords for # each paper: the FULL per-paper n-gram set (DF >= 1). The document-frequency filter -# is PER-CLUSTER and applied downstream in get_cluster_corpus (§6.1), not here — so a +# is PER-CLUSTER and applied downstream in get_cluster_corpus, not here — so a # title phrase must recur within its OWN cluster to enter that cluster's initial corpus. HEUR_MIN1 <- "keywords_rank_heuristically_generated_min1" @@ -114,7 +114,7 @@ paper_title_ngrams <- function(title, stops) { # Add the heuristic-keyword metadata column (HEUR_MIN1) to the metadata data frame: # each paper's pruned title bi-/tri-grams, stored as a "; "-joined "_"-n-gram string. # This is the FULL per-paper set (DF >= 1); the document-frequency filter is applied -# PER-CLUSTER downstream in get_cluster_corpus (§6.1). Computed once, early in labelling. +# PER-CLUSTER downstream in get_cluster_corpus. Computed once, early in labelling. add_heuristic_keyword_fields <- function(metadata, stops) { per_paper <- lapply(metadata$title, paper_title_ngrams, stops = stops) metadata[[HEUR_MIN1]] <- vapply(per_paper, @@ -358,10 +358,9 @@ get_custom_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separa # --- Mode 0 (legacy) corpus + fallback ------------------------------------- # Verbatim from feat/keyword-label-improvements: Mode 0 is gated to this path so -# it stays BYTE-IDENTICAL to the pre-ranking pipeline (docs §7). It generates the +# it stays BYTE-IDENTICAL to the pre-ranking pipeline. It generates the # title n-grams INLINE (get_title_ngrams, unchanged) rather than reading the -# per-paper heuristic column, and has no rank_sources. The team did not accept -# a changed Mode-0 baseline, so the DF filter (§6.1) is confined to Modes 1-3. +# per-paper heuristic column, and has no rank_sources. get_cluster_corpus_legacy <- function(clusters, metadata, stops, taxonomy_separator, add_title_ngrams = T, custom_clustering=NULL) { subjectlist = list() @@ -443,7 +442,7 @@ get_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, rep(FALSE, length(matches)) } # Heuristic keywords are pre-generated "_"-joined n-grams (add_heuristic_keyword_ - # fields gives the FULL per-paper set). PER-CLUSTER DF FILTER (§6.1): for the + # fields gives the FULL per-paper set). PER-CLUSTER DF FILTER: for the # INITIAL corpus (percluster_filter=TRUE) a title n-gram is kept only if it recurs # in >= 2 papers of THIS cluster — local corroboration, so a cluster-local singleton # can't become a label just because the same n-gram appears once in another cluster. diff --git a/server/preprocessing/other-scripts/test/test_ranking_select.R b/server/preprocessing/other-scripts/test/test_ranking_select.R index 5ecfa72e5..e97e024ac 100644 --- a/server/preprocessing/other-scripts/test/test_ranking_select.R +++ b/server/preprocessing/other-scripts/test/test_ranking_select.R @@ -49,7 +49,7 @@ test_that("select_by_rank: rank 1 fills up to top_n", { test_that("select_by_rank: exclusive rank 2 is skipped while rank 1 is non-empty", { lab <- select_by_rank(c("k1", "h1", "h2"), c(1L, 2L, 2L), 3, spec1, id_denest) - expect_equal(lab, "k1") # < top_n is acceptable; no backfill (§15-0) + expect_equal(lab, "k1") # < top_n is acceptable; no backfill }) test_that("select_by_rank: falls to rank 2 only when rank 1 is empty", { From 1c8a4fad255d00e79bfceaa8c230dd11d51c6903 Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 9 Jul 2026 18:44:17 +0200 Subject: [PATCH 151/205] add additional test fixtures for area labeling tests --- .../replay/base_cancer_fallback.expected.rds | Bin 0 -> 630 bytes .../replay/base_cancer_fallback.inputs.rds | Bin 0 -> 128748 bytes .../test/replay/orcid_0204881x.expected.rds | Bin 0 -> 531 bytes .../test/replay/orcid_0204881x.inputs.rds | Bin 0 -> 62081 bytes .../test/replay/orcid_02979614.expected.rds | Bin 0 -> 338 bytes .../test/replay/orcid_02979614.inputs.rds | Bin 0 -> 21070 bytes .../test/replay/orcid_22336926.expected.rds | Bin 0 -> 728 bytes .../test/replay/orcid_22336926.inputs.rds | Bin 0 -> 133196 bytes .../test/replay/orcid_24414043.expected.rds | Bin 0 -> 694 bytes .../test/replay/orcid_24414043.inputs.rds | Bin 0 -> 65744 bytes .../test/replay/orcid_28976075.expected.rds | Bin 0 -> 627 bytes 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.../openaire_fight_ncov.expected.mode1.rds | Bin 0 -> 420 bytes .../replay/orcid_0204881x.expected.mode1.rds | Bin 0 -> 523 bytes .../replay/orcid_02979614.expected.mode1.rds | Bin 0 -> 351 bytes .../replay/orcid_22336926.expected.mode1.rds | Bin 0 -> 722 bytes .../replay/orcid_24414043.expected.mode1.rds | Bin 0 -> 660 bytes .../replay/orcid_28976075.expected.mode1.rds | Bin 0 -> 640 bytes .../replay/orcid_39246636.expected.mode1.rds | Bin 0 -> 321 bytes .../replay/orcid_42216275.expected.mode1.rds | Bin 0 -> 652 bytes .../replay/orcid_45050517.expected.mode1.rds | Bin 0 -> 561 bytes .../replay/orcid_49712944.expected.mode1.rds | Bin 0 -> 577 bytes .../replay/orcid_5116955x.expected.mode1.rds | Bin 0 -> 617 bytes .../orcid_5116955x_r2.expected.mode1.rds | Bin 0 -> 579 bytes .../replay/orcid_58498137.expected.mode1.rds | Bin 0 -> 312 bytes .../replay/orcid_60114382.expected.mode1.rds | Bin 0 -> 710 bytes .../replay/orcid_89117832.expected.mode1.rds | Bin 0 -> 282 bytes .../replay/orcid_90626039.expected.mode1.rds | Bin 0 -> 604 bytes .../replay/orcid_92873770.expected.mode1.rds | Bin 0 -> 509 bytes .../replay/orcid_96127791.expected.mode1.rds | Bin 0 -> 726 bytes .../orcid_96127791_r2.expected.mode1.rds | Bin 0 -> 779 bytes .../replay/orcid_98436798.expected.mode1.rds | Bin 0 -> 686 bytes .../pubmed_cancer_datasets.expected.mode1.rds | Bin 0 -> 594 bytes .../pubmed_climate_change.expected.mode1.rds | Bin 0 -> 594 bytes ...bmed_climate_change_all.expected.mode1.rds | Bin 0 -> 517 bytes .../replay/pubmed_disease.expected.mode1.rds | Bin 0 -> 631 bytes .../pubmed_disease_all.expected.mode1.rds | Bin 0 -> 695 bytes .../pubmed_infection.expected.mode1.rds | Bin 0 -> 611 bytes .../pubmed_infection_all.expected.mode1.rds | Bin 0 -> 628 bytes ...bmed_infection_datasets.expected.mode1.rds | Bin 0 -> 635 bytes .../pubmed_space_travel.expected.mode1.rds | Bin 0 -> 324 bytes ...pubmed_space_travel_all.expected.mode1.rds | Bin 0 -> 653 bytes .../replay/pubmed_species.expected.mode1.rds | Bin 0 -> 628 bytes .../pubmed_species_all.expected.mode1.rds | Bin 0 -> 645 bytes .../replay/pubmed_vienna.expected.mode1.rds | Bin 0 -> 615 bytes .../pubmed_vienna_all.expected.mode1.rds | Bin 0 -> 723 bytes .../replay/pubmed_work.expected.mode1.rds | Bin 0 -> 628 bytes .../replay/pubmed_work_all.expected.mode1.rds | Bin 0 -> 625 bytes .../other-scripts/test/replay_harness.R | 14 +++++--- .../other-scripts/test/test_replay_modes.R | 33 +++++++++++------- 42 files changed, 41 insertions(+), 22 deletions(-) create mode 100644 server/preprocessing/other-scripts/test/replay/base_cancer_fallback.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/base_cancer_research.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/base_digital_education.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/openaire_fight_ncov.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/orcid_0204881x.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/orcid_02979614.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/orcid_22336926.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/orcid_24414043.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/orcid_28976075.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/orcid_39246636.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/orcid_42216275.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/orcid_45050517.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/orcid_49712944.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/orcid_5116955x.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/orcid_5116955x_r2.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/orcid_58498137.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/orcid_60114382.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/orcid_89117832.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/orcid_90626039.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/orcid_92873770.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/orcid_96127791.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/orcid_96127791_r2.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/orcid_98436798.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/pubmed_cancer_datasets.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/pubmed_climate_change.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/pubmed_climate_change_all.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/pubmed_disease.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/pubmed_disease_all.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/pubmed_infection.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/pubmed_infection_all.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/pubmed_infection_datasets.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/pubmed_space_travel.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/pubmed_space_travel_all.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/pubmed_species.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/pubmed_species_all.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/pubmed_vienna.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/pubmed_vienna_all.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/pubmed_work.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/pubmed_work_all.expected.mode1.rds diff --git a/server/preprocessing/other-scripts/test/mine_cases.R b/server/preprocessing/other-scripts/test/mine_cases.R index 8da906132..64ba7ccce 100644 --- a/server/preprocessing/other-scripts/test/mine_cases.R +++ b/server/preprocessing/other-scripts/test/mine_cases.R @@ -26,16 +26,16 @@ is_nested <- function(a, b) { # Recompute the Mode-1 per-cluster candidates exactly as create_cluster_labels does. cluster_candidates <- function(bundle) { - md <- add_heuristic_keyword_fields(bundle$metadata, bundle$stops) + md <- add_heuristic_keyword_fields(backfill_subject_is_heuristic(bundle$metadata), bundle$stops) md$keywords_rank_cleaned <- md$subject - co <- get_cluster_corpus(bundle$clusters, md, bundle$stops, bundle$taxonomy_separator, heuristic_col = HEUR_MIN2) + co <- get_cluster_corpus(bundle$clusters, md, bundle$stops, bundle$taxonomy_separator, percluster_filter = TRUE) tdm <- TermDocumentMatrix(co$corpus, control = list( tokenize = SplitTokenizer, weighting = function(x) weightSMART(x, spec = "ntn"), bounds = list(local = c(1, Inf)), tolower = TRUE)) tt <- apply(tdm, 2, function(x) { x2 <- sort(x, TRUE); x2[x2 > 0] }) empty <- which(apply(tdm, 2, sum) == 0) if (length(empty)) { - fb <- get_cluster_corpus(bundle$clusters, md, bundle$stops, bundle$taxonomy_separator, heuristic_col = 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z-`9J~?ARvvbe%7X`~lzG$sLaQo%I(FOx8mjAnqd`ATn3}M|g~QGUA?%*on6od2jv# LIo7^$JOls$H=!wJ literal 0 HcmV?d00001 diff --git a/server/preprocessing/other-scripts/test/replay_harness.R b/server/preprocessing/other-scripts/test/replay_harness.R index 4ee5131a0..94b8e7652 100644 --- a/server/preprocessing/other-scripts/test/replay_harness.R +++ b/server/preprocessing/other-scripts/test/replay_harness.R @@ -82,13 +82,19 @@ replay_labels <- function(bundle, mode = "0") { # File helpers. Fixtures: test/replay/.inputs.rds # expected Mode-0 out: test/replay/.expected.rds +# expected Mode-N out: test/replay/.expected.modeN.rds (N = 1,2,3) +# Mode 0 keeps the bare ".expected.rds" name for backward compatibility with the +# baselines committed before ranked-mode baselining existed. fixture_files <- function() list.files(REPLAY_DIR, pattern = "\\.inputs\\.rds$", full.names = TRUE) fixture_name <- function(path) sub("\\.inputs\\.rds$", "", basename(path)) -expected_file <- function(name) file.path(REPLAY_DIR, paste0(name, ".expected.rds")) -read_expected <- function(name) readRDS(expected_file(name)) -write_expected <- function(name, labels) { +expected_file <- function(name, mode = "0") { + suffix <- if (identical(mode, "0")) ".expected.rds" else paste0(".expected.mode", mode, ".rds") + file.path(REPLAY_DIR, paste0(name, suffix)) +} +read_expected <- function(name, mode = "0") readRDS(expected_file(name, mode)) +write_expected <- function(name, labels, mode = "0") { dir.create(REPLAY_DIR, showWarnings = FALSE, recursive = TRUE) - saveRDS(labels, expected_file(name)) + saveRDS(labels, expected_file(name, mode)) } # A small, self-contained input bundle for validating the harness without any diff --git a/server/preprocessing/other-scripts/test/test_replay_modes.R b/server/preprocessing/other-scripts/test/test_replay_modes.R index de6ab2ca7..0d98ebbd0 100644 --- a/server/preprocessing/other-scripts/test/test_replay_modes.R +++ b/server/preprocessing/other-scripts/test/test_replay_modes.R @@ -1,8 +1,10 @@ # Replay regression over real fixtures (replay_harness.R). # -# For every fixture bundle in test/replay/*.inputs.rds: -# - replay under Mode 0 and assert the labels match the stored expected output; +# For every fixture bundle in test/replay/*.inputs.rds, and every mode in +# REPLAY_MODES: +# - replay under that mode and assert the labels match the stored expected output; # - if no expected output exists yet, record it (bootstrap) and pass. +# Mode 0 baselines are .expected.rds; Mode N are .expected.modeN.rds. # Fixtures are created from real maps — see test/replay/README.md. # # Runs inside the pipeline image (needs tm). Skips cleanly when no fixtures exist, @@ -15,6 +17,10 @@ if (!requireNamespace("testthat", quietly = TRUE)) { library(testthat) } +# Modes with committed baselines. Mode 0 is the byte-identical legacy baseline; +# Mode 1 pins the first ranked mode. Add "2"/"3" here as those modes land. +REPLAY_MODES <- c("0", "1") + fixtures <- fixture_files() if (length(fixtures) == 0) { @@ -22,15 +28,18 @@ if (length(fixtures) == 0) { } else { for (fx in fixtures) { name <- fixture_name(fx) - test_that(paste0("Mode-0 labels are stable for fixture '", name, "'"), { - labels <- replay_labels(fx, mode = "0") - if (!file.exists(expected_file(name))) { - write_expected(name, labels) - cat(" (recorded expected output for '", name, "')\n", sep = "") - expect_true(TRUE) - } else { - expect_equal(labels, read_expected(name)) - } - }) + for (m in REPLAY_MODES) { + test_that(sprintf("Mode-%s labels are stable for fixture '%s'", m, name), { + labels <- replay_labels(fx, mode = m) + if (!file.exists(expected_file(name, m))) { + write_expected(name, labels, m) + cat(" (recorded Mode-", m, " expected output for '", name, "')\n", sep = "") + expect_true(TRUE) + } else { + expect_equal(labels, read_expected(name, m)) + } + }) + gc(verbose = FALSE) # keep peak memory bounded across 39 fixtures x N modes + } } } From f4148f86c510c8c007d51916d64fac844e6082bd Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 10 Jul 2026 15:31:12 +0200 Subject: [PATCH 153/205] implement global heuristic keyword threshold instead of per-cluster; reduces situations in where lower-quality fallback is needed --- .../preprocessing/other-scripts/summarize.R | 103 +++++++++--------- .../other-scripts/test/mine_cases.R | 4 +- .../base_cancer_fallback.expected.mode1.rds | Bin 543 -> 557 bytes .../base_cancer_research.expected.mode1.rds | Bin 551 -> 567 bytes .../base_digital_education.expected.mode1.rds | Bin 644 -> 649 bytes .../replay/orcid_0204881x.expected.mode1.rds | Bin 523 -> 540 bytes .../replay/orcid_02979614.expected.mode1.rds | Bin 351 -> 334 bytes .../replay/orcid_22336926.expected.mode1.rds | Bin 722 -> 723 bytes .../replay/orcid_24414043.expected.mode1.rds | Bin 660 -> 646 bytes .../replay/orcid_28976075.expected.mode1.rds | Bin 640 -> 638 bytes .../replay/orcid_49712944.expected.mode1.rds | Bin 577 -> 577 bytes .../replay/orcid_5116955x.expected.mode1.rds | Bin 617 -> 609 bytes .../orcid_5116955x_r2.expected.mode1.rds | Bin 579 -> 576 bytes .../replay/orcid_60114382.expected.mode1.rds | Bin 710 -> 630 bytes .../replay/orcid_90626039.expected.mode1.rds | Bin 604 -> 644 bytes .../replay/orcid_92873770.expected.mode1.rds | Bin 509 -> 538 bytes .../replay/orcid_96127791.expected.mode1.rds | Bin 726 -> 736 bytes .../orcid_96127791_r2.expected.mode1.rds | Bin 779 -> 785 bytes .../replay/orcid_98436798.expected.mode1.rds | Bin 686 -> 686 bytes .../pubmed_cancer_datasets.expected.mode1.rds | Bin 594 -> 567 bytes .../pubmed_climate_change.expected.mode1.rds | Bin 594 -> 566 bytes .../replay/pubmed_disease.expected.mode1.rds | Bin 631 -> 626 bytes .../pubmed_disease_all.expected.mode1.rds | Bin 695 -> 708 bytes .../pubmed_infection.expected.mode1.rds | Bin 611 -> 618 bytes .../pubmed_infection_all.expected.mode1.rds | Bin 628 -> 629 bytes ...bmed_infection_datasets.expected.mode1.rds | Bin 635 -> 635 bytes ...pubmed_space_travel_all.expected.mode1.rds | Bin 653 -> 646 bytes .../replay/pubmed_species.expected.mode1.rds | Bin 628 -> 614 bytes .../replay/pubmed_vienna.expected.mode1.rds | Bin 615 -> 591 bytes .../pubmed_vienna_all.expected.mode1.rds | Bin 723 -> 732 bytes .../replay/pubmed_work.expected.mode1.rds | Bin 628 -> 622 bytes .../replay/pubmed_work_all.expected.mode1.rds | Bin 625 -> 635 bytes .../test/test_replace_keywords_routing.R | 6 +- 33 files changed, 56 insertions(+), 57 deletions(-) diff --git a/server/preprocessing/other-scripts/summarize.R b/server/preprocessing/other-scripts/summarize.R index 0b195cf1d..274967ae2 100644 --- a/server/preprocessing/other-scripts/summarize.R +++ b/server/preprocessing/other-scripts/summarize.R @@ -1,11 +1,13 @@ library(stringr) vslog <- getLogger('vis.summarize') -# Metadata column holding the heuristically generated (title n-gram) keywords for -# each paper: the FULL per-paper n-gram set (DF >= 1). The document-frequency filter -# is PER-CLUSTER and applied downstream in get_cluster_corpus, not here — so a -# title phrase must recur within its OWN cluster to enter that cluster's initial corpus. +# Metadata columns holding the heuristically generated (title n-gram) keywords, +# pre-binned by MAP-WIDE document frequency (see add_heuristic_keyword_fields): +# _min1 = n-grams appearing in >= 1 resource (all) -> used for the FALLBACK corpus +# _min2 = n-grams appearing in >= 2 resources -> used for the INITIAL corpus +# The DF filter is GLOBAL (map-wide), applied to Modes 1-3 only; Mode 0 is legacy. HEUR_MIN1 <- "keywords_rank_heuristically_generated_min1" +HEUR_MIN2 <- "keywords_rank_heuristically_generated_min2" # summarize.R # Cluster labelling for the overview visualisation: turns each cluster of papers @@ -111,19 +113,26 @@ paper_title_ngrams <- function(title, stops) { unique(grams[keep]) } -# Add the heuristic-keyword metadata column (HEUR_MIN1) to the metadata data frame: -# each paper's pruned title bi-/tri-grams, stored as a "; "-joined "_"-n-gram string. -# This is the FULL per-paper set (DF >= 1); the document-frequency filter is applied -# PER-CLUSTER downstream in get_cluster_corpus. Computed once, early in labelling. +# Add the two heuristic-keyword metadata columns (HEUR_MIN1 / HEUR_MIN2) to the +# metadata data frame. Generates each paper's title n-grams, computes each n-gram's +# MAP-WIDE document frequency (number of distinct resources it appears in), and stores +# per paper, as "; "-joined "_"-n-gram strings: +# HEUR_MIN1 = all of the paper's n-grams (DF >= 1) +# HEUR_MIN2 = the paper's n-grams with map-wide document frequency >= 2 +# Computed once, early in labelling. add_heuristic_keyword_fields <- function(metadata, stops) { per_paper <- lapply(metadata$title, paper_title_ngrams, stops = stops) + df <- table(unlist(lapply(per_paper, unique))) # map-wide document frequency + min2_set <- names(df)[df >= 2] metadata[[HEUR_MIN1]] <- vapply(per_paper, function(g) paste(g, collapse = "; "), character(1)) + metadata[[HEUR_MIN2]] <- vapply(per_paper, + function(g) paste(g[g %in% min2_set], collapse = "; "), character(1)) metadata } # Last-resort label for a cluster that produced no tf-idf label (empty even after -# the all-n-grams fallback): build one from the most frequent bi-/tri-grams of the +# the min1 fallback): build one from the most frequent bi-/tri-grams of the # cluster's papers' titles + abstracts. Returns a single ", "-joined label string. # matches : row indices of the cluster's papers in `metadata`. # top_n : number of terms kept. @@ -187,7 +196,7 @@ create_cluster_labels <- function(clusters, metadata, # Resolve the ranking mode BEFORE building the corpus. Mode 0 is gated to the # verbatim legacy path (get_cluster_corpus_legacy + zero-sum fill_empty_clusters_ # legacy), so it stays BYTE-IDENTICAL to the pre-ranking pipeline; Modes 1-3 take - # the per-cluster DF-filtered corpus + rank-aware selection. + # the map-wide DF-filtered (min2/min1) corpus + rank-aware selection. mode <- ranking_mode(service) cc <- params$custom_clustering vslog$debug(paste("create_cluster_labels: ranking mode", mode, "for service", @@ -217,18 +226,19 @@ create_cluster_labels <- function(clusters, metadata, # ---- Modes 1-3: DF-filtered corpus + rank-aware selection ------------------ # Additive rank columns on the metadata data frame: # - keywords_rank_cleaned: the rank-1 source (Stage 1 = subject_cleaned verbatim). - # - the heuristic column (full per-paper n-gram set); the per-cluster DF filter is - # applied inside get_cluster_corpus. subject_cleaned (metadata$subject) untouched. + # - the two heuristic columns (min1/min2), pre-binned by MAP-WIDE document + # frequency (add_heuristic_keyword_fields). subject_cleaned (metadata$subject) + # is left untouched. metadata <- add_heuristic_keyword_fields(metadata, stops) metadata$keywords_rank_cleaned <- metadata$subject if (!(is.null(cc)) && (cc %in% names(metadata))) { corpus_out <- get_custom_cluster_corpus(clusters, metadata, stops, taxonomy_separator, custom_clustering=cc) - fallback_corpus <- corpus_out$corpus # custom path: no per-cluster heuristic filter + fallback_corpus <- corpus_out$corpus # custom path: no heuristic min1/min2 split } else { - # Initial corpus applies the per-cluster DF>=2 heuristic filter; the fallback - # corpus keeps all n-grams (percluster_filter = FALSE). - corpus_out <- get_cluster_corpus(clusters, metadata, stops, taxonomy_separator, percluster_filter = TRUE) - fallback_corpus <- get_cluster_corpus(clusters, metadata, stops, taxonomy_separator, percluster_filter = FALSE)$corpus + # Initial corpus uses the map-wide min2 heuristic set (DF >= 2); the fallback + # corpus swaps in min1 (all n-grams). + corpus_out <- get_cluster_corpus(clusters, metadata, stops, taxonomy_separator, heuristic_col = HEUR_MIN2) + fallback_corpus <- get_cluster_corpus(clusters, metadata, stops, taxonomy_separator, heuristic_col = HEUR_MIN1)$corpus } # get_*_cluster_corpus returns the corpus plus per-cluster rank sources (the # separated keyword/heuristic tokens used only for rank lookup). rank_sources @@ -248,22 +258,22 @@ create_cluster_labels <- function(clusters, metadata, nDocs(nn_tfidf), "clusters")) # Rank-aware selection (ranking.R) over the global ranking, partitioned by - # rank_sources. Initial labels come from the per-cluster-filtered corpus. + # rank_sources. Initial labels come from the map-wide min2 (DF >= 2) corpus. tfidf_top_names <- select_cluster_label_names(tfidf_top, top_n, stops, mode = mode, rank_sources = rank_sources) - # Fallback: any cluster whose label came out EMPTY is re-labelled from the - # all-n-grams corpus (bound 1). The trigger is "empty label", NOT "zero tf-idf - # sum": with the DF filter a cluster can have a tiny tf-idf that prunes away to - # nothing, which the old zero-sum check missed, dropping it straight to the - # abstract-frequency fallback instead of the intended all-n-grams rescue. + # min1 fallback: any cluster whose label came out EMPTY is re-labelled from the + # min1 corpus (all title n-grams, bound 1). The trigger is "empty label", NOT + # "zero tf-idf sum": with the DF filter a cluster can have a tiny tf-idf that prunes + # away to nothing, which the old zero-sum check missed, dropping it straight to the + # abstract-frequency fallback instead of the intended min1 rescue. # The title/abstract-frequency fallback below remains the true last resort. empty_label <- which(!vapply(tfidf_top_names, function(x) { s <- if (length(x)) x[[1]] else ""; nzchar(s) }, logical(1))) if (length(empty_label) > 0) { - vslog$debug(paste("create_cluster_labels: all-n-grams fallback for", length(empty_label), - "clusters with an empty initial label")) + vslog$debug(paste("create_cluster_labels: min1 fallback for", length(empty_label), + "clusters with an empty min2 label")) fallback_top <- fill_empty_clusters(fallback_corpus) fallback_names <- select_cluster_label_names(fallback_top, top_n, stops, mode = mode, rank_sources = rank_sources) @@ -276,7 +286,7 @@ create_cluster_labels <- function(clusters, metadata, matches = which(unname(clusters$groups == k) == TRUE) summary = tfidf_top_names[[k]] if (summary == "") { - # No tf-idf label survived even the all-n-grams fallback: last-resort label built + # No tf-idf label survived even the min1 fallback: last-resort label built # from the papers' titles + abstracts (see title_abstract_fallback_label). vslog$debug(paste("create_cluster_labels: title/abstract fallback for cluster", k, "with", length(matches), "papers")) @@ -360,7 +370,7 @@ get_custom_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separa # Verbatim from feat/keyword-label-improvements: Mode 0 is gated to this path so # it stays BYTE-IDENTICAL to the pre-ranking pipeline. It generates the # title n-grams INLINE (get_title_ngrams, unchanged) rather than reading the -# per-paper heuristic column, and has no rank_sources. +# DF-filtered min1/min2 columns, and has no rank_sources. get_cluster_corpus_legacy <- function(clusters, metadata, stops, taxonomy_separator, add_title_ngrams = T, custom_clustering=NULL) { subjectlist = list() @@ -406,7 +416,7 @@ get_cluster_corpus_legacy <- function(clusters, metadata, stops, taxonomy_separa # Legacy (Mode 0) fallback: rebuild the tf-idf of the SAME corpus with local bound # c(1, Inf) for clusters whose initial tf-idf summed to zero. Verbatim legacy -# 2-arg signature — distinct from the Modes 1-3 fill_empty_clusters (all-n-grams corpus). +# 2-arg signature — distinct from the Modes 1-3 fill_empty_clusters (min1 corpus). fill_empty_clusters_legacy <- function(nn_tfidf, nn_corpus){ replacement_nn_tfidf <- TermDocumentMatrix(nn_corpus, control = list(tokenize = SplitTokenizer, weighting = function(x) weightSMART(x, spec="ntn"), @@ -425,9 +435,9 @@ fill_empty_clusters_legacy <- function(nn_tfidf, nn_corpus){ # lowercased) used by the ranked selection. add_title_ngrams toggles the title # n-gram contribution. get_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, - percluster_filter = TRUE) { + heuristic_col = HEUR_MIN2) { subjectlist = list() - subject_dbg = list(); heuristic_dbg = list(); heuristic_all_dbg = list() + subject_dbg = list(); heuristic_dbg = list(); heuristic_min1_dbg = list() replaced_subject_dbg = list() # subjects synthesised from titles (rank 2, not rank 1) for (k in seq(1, clusters$num_clusters)) { matches = which(unname(clusters$groups == k) == TRUE) @@ -441,28 +451,17 @@ get_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, } else { rep(FALSE, length(matches)) } - # Heuristic keywords are pre-generated "_"-joined n-grams (add_heuristic_keyword_ - # fields gives the FULL per-paper set). PER-CLUSTER DF FILTER: for the - # INITIAL corpus (percluster_filter=TRUE) a title n-gram is kept only if it recurs - # in >= 2 papers of THIS cluster — local corroboration, so a cluster-local singleton - # can't become a label just because the same n-gram appears once in another cluster. - # The FALLBACK corpus (percluster_filter=FALSE) keeps all n-grams. The rank map - # always uses the full set, so any heuristic term still resolves to rank 2. - heuristics_all = as.character(metadata[[HEUR_MIN1]][matches]) - if (percluster_filter) { - per_paper = lapply(strsplit(heuristics_all, "; "), function(x) x[nzchar(x)]) - cluster_df = table(unlist(lapply(per_paper, unique))) # per-cluster document frequency - recurring = names(cluster_df)[cluster_df >= 2] - heuristics = vapply(per_paper, - function(g) paste(g[g %in% recurring], collapse = "; "), character(1)) - } else { - heuristics = heuristics_all - } + # Heuristic keywords are pre-generated "_"-joined n-grams (see + # add_heuristic_keyword_fields): the corpus uses heuristic_col (map-wide min2 + # initial / min1 fallback); the rank map always uses min1 (the superset), so any + # heuristic term resolves to rank 2 regardless of which pass produced it. + heuristics = as.character(metadata[[heuristic_col]][matches]) + heuristics_min1 = as.character(metadata[[HEUR_MIN1]][matches]) subjects = mapply(gsub, subjects, pattern = "; ", replacement=";") subjects = mapply(gsub, subjects, pattern=" ", replacement="_") heuristics = gsub("; ", ";", heuristics) - heuristics_all = gsub("; ", ";", heuristics_all) + heuristics_min1 = gsub("; ", ";", heuristics_min1) if (!is.null(taxonomy_separator)) { subjects = mapply(function(x){strsplit(x, ";")}, subjects) @@ -479,7 +478,7 @@ get_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, subject_dbg[[k]] = paste(unlist(subjects[!flagged]), collapse=";") # rank 1 (real keywords) replaced_subject_dbg[[k]] = paste(unlist(subjects[flagged]), collapse=";") # rank 2 (title-synthesised) heuristic_dbg[[k]] = paste(unlist(heuristics), collapse=";") - heuristic_all_dbg[[k]] = paste(unlist(heuristics_all), collapse=";") + heuristic_min1_dbg[[k]] = paste(unlist(heuristics_min1), collapse=";") all_subjects = paste(subjects, heuristics, collapse=" ") all_subjects <- normalize_corpus_tokens(all_subjects) subjectlist = c(subjectlist, all_subjects) @@ -494,19 +493,19 @@ get_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, # split by provenance and run through the SAME normalize_corpus_tokens() + # lowercasing + edge-strip as the TDM terms — so every rank token matches a tf-idf # term. cleaned = keywords_rank_cleaned (subject_cleaned); - # heuristic = the FULL n-gram set. Used only for rank lookup, never fed to the TDM. + # heuristic = the min1 n-gram set. Used only for rank lookup, never fed to the TDM. split_tokens <- function(s) { t <- tolower(unlist(strsplit(normalize_corpus_tokens(s), ";"))) t <- gsub("^_+|_+$", "", t) # mirror the TDM tokenizer's edge-punctuation strip t[nzchar(t)] } - # cleaned = real-keyword subject tokens (non-flagged papers). heuristic = the FULL + # cleaned = real-keyword subject tokens (non-flagged papers). heuristic = the min1 # title n-grams PLUS the flagged papers' synthesised subject tokens (title-derived, # so they belong in rank 2). Both are already in the corpus, so no double-counting. rank_sources <- list( cleaned = lapply(subject_dbg, split_tokens), heuristic = mapply(function(h, r) unique(c(split_tokens(h), split_tokens(r))), - heuristic_all_dbg, replaced_subject_dbg, SIMPLIFY = FALSE)) + heuristic_min1_dbg, replaced_subject_dbg, SIMPLIFY = FALSE)) nn_corpus <- VCorpus(VectorSource(subjectlist)) return(list(corpus = nn_corpus, rank_sources = rank_sources)) } diff --git a/server/preprocessing/other-scripts/test/mine_cases.R b/server/preprocessing/other-scripts/test/mine_cases.R index 64ba7ccce..b64e513d1 100644 --- a/server/preprocessing/other-scripts/test/mine_cases.R +++ b/server/preprocessing/other-scripts/test/mine_cases.R @@ -28,14 +28,14 @@ is_nested <- function(a, b) { cluster_candidates <- function(bundle) { md <- add_heuristic_keyword_fields(backfill_subject_is_heuristic(bundle$metadata), bundle$stops) md$keywords_rank_cleaned <- md$subject - co <- get_cluster_corpus(bundle$clusters, md, bundle$stops, bundle$taxonomy_separator, percluster_filter = TRUE) + co <- get_cluster_corpus(bundle$clusters, md, bundle$stops, bundle$taxonomy_separator, heuristic_col = HEUR_MIN2) tdm <- TermDocumentMatrix(co$corpus, control = list( tokenize = SplitTokenizer, weighting = function(x) weightSMART(x, spec = "ntn"), bounds = list(local = c(1, Inf)), tolower = TRUE)) tt <- apply(tdm, 2, function(x) { x2 <- sort(x, TRUE); x2[x2 > 0] }) empty <- which(apply(tdm, 2, sum) == 0) if (length(empty)) { - fb <- get_cluster_corpus(bundle$clusters, md, bundle$stops, bundle$taxonomy_separator, percluster_filter = FALSE)$corpus + fb <- get_cluster_corpus(bundle$clusters, md, bundle$stops, bundle$taxonomy_separator, heuristic_col = HEUR_MIN1)$corpus tt[empty] <- fill_empty_clusters(fb)[empty] } diff --git a/server/preprocessing/other-scripts/test/replay/base_cancer_fallback.expected.mode1.rds b/server/preprocessing/other-scripts/test/replay/base_cancer_fallback.expected.mode1.rds index 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zu6vRqXncYf`8dl*Ra^SS>s=W(7L6H8MX|R7*u4?H?{r52hd+qBFAgiJNrb8&m_c^; z-`9J~?ARvvbe%7X`~lzG$sLaQo%I(FOx8mjAnqd`ATn3}M|g~QGUA?%*on6od2jv# LIo7^$JOls$H=!wJ diff --git a/server/preprocessing/other-scripts/test/test_replace_keywords_routing.R b/server/preprocessing/other-scripts/test/test_replace_keywords_routing.R index abacf4fb4..62772c569 100644 --- a/server/preprocessing/other-scripts/test/test_replace_keywords_routing.R +++ b/server/preprocessing/other-scripts/test/test_replace_keywords_routing.R @@ -32,7 +32,7 @@ build_md <- function() { test_that("a real keyword lands in rank 1 (cleaned), not rank 2", { md <- build_md() co <- get_cluster_corpus(list(groups = c(1, 1), num_clusters = 1), - md, STOPS, taxonomy_separator = NULL, percluster_filter = TRUE) + md, STOPS, taxonomy_separator = NULL, heuristic_col = HEUR_MIN2) cleaned <- co$rank_sources$cleaned[[1]]; heur <- co$rank_sources$heuristic[[1]] expect_true("neural_networks" %in% cleaned) expect_false("neural_networks" %in% heur) @@ -41,7 +41,7 @@ test_that("a real keyword lands in rank 1 (cleaned), not rank 2", { test_that("a title-synthesised subject lands in rank 2 (heuristic), not rank 1", { md <- build_md() co <- get_cluster_corpus(list(groups = c(1, 1), num_clusters = 1), - md, STOPS, taxonomy_separator = NULL, percluster_filter = TRUE) + md, STOPS, taxonomy_separator = NULL, heuristic_col = HEUR_MIN2) cleaned <- co$rank_sources$cleaned[[1]]; heur <- co$rank_sources$heuristic[[1]] expect_true("taking_action" %in% heur) expect_false("taking_action" %in% cleaned) @@ -51,7 +51,7 @@ test_that("a title-synthesised subject lands in rank 2 (heuristic), not rank 1", test_that("without the flag column, all subjects stay in rank 1 (backward compatible)", { md <- build_md(); md$subject_is_heuristic <- NULL # simulate a pre-fix fixture co <- get_cluster_corpus(list(groups = c(1, 1), num_clusters = 1), - md, STOPS, taxonomy_separator = NULL, percluster_filter = TRUE) + md, STOPS, taxonomy_separator = NULL, heuristic_col = HEUR_MIN2) cleaned <- co$rank_sources$cleaned[[1]] expect_true("neural_networks" %in% cleaned) expect_true("taking_action" %in% cleaned) # unflagged -> treated as keyword, as before From 280c891ae0871eaf167caf81f7f2613ab001b594 Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 10 Jul 2026 15:31:24 +0200 Subject: [PATCH 154/205] expand stopwords list --- server/preprocessing/resources/additional_stopwords.txt | 3 +++ 1 file changed, 3 insertions(+) diff --git a/server/preprocessing/resources/additional_stopwords.txt b/server/preprocessing/resources/additional_stopwords.txt index 2af358387..aeca85321 100644 --- a/server/preprocessing/resources/additional_stopwords.txt +++ b/server/preprocessing/resources/additional_stopwords.txt @@ -600,3 +600,6 @@ psy relig anthro-se socio +science +medicine +animals From 8107fc68b4987a7b246ff671a7ada5e35e032acb Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 10 Jul 2026 15:52:27 +0200 Subject: [PATCH 155/205] update of contentprovider.json cache --- .../common/common/contentproviders.json | 88 +++++++++++++++---- 1 file changed, 72 insertions(+), 16 deletions(-) diff --git a/server/workers/common/common/contentproviders.json b/server/workers/common/common/contentproviders.json index 8fd96b5c8..c9446122c 100644 --- a/server/workers/common/common/contentproviders.json +++ b/server/workers/common/common/contentproviders.json @@ -1,4 +1,76 @@ [ + { + "name": "Scientific Journals Online Andrzej Frycz Modrzewski Krakow University", + "internal_name": "ftid14868" + }, + { + "name": "Scholastic Research Publication (SRP)", + "internal_name": "ftid14881" + }, + { + "name": "E-Journal Yayasan Pustaka Karya Mandiri", + "internal_name": "ftid14893" + }, + { + "name": "Libyan Journal of Medical and Applied Sciences (LJMAS)", + "internal_name": "ftid14898" + }, + { + "name": "Egertu Digital Library", + "internal_name": "ftid14901" + }, + { + "name": "Fondo documental digital Melilla-Rif", + "internal_name": "ftid14902" + }, + { + "name": "EduFacturing Open AM Knowledge Base", + "internal_name": "ftid14903" + }, + { + "name": "IASRD Open Access Journals", + "internal_name": "ftid14904" + }, + { + "name": "Revista Fórum Trabalhista - RFT", + "internal_name": "ftid14905" + }, + { + "name": "Omni Journal", + "internal_name": "ftid14906" + }, + { + "name": "True Parents Legacy Archive", + "internal_name": "ftid14907" + }, + { + "name": "Journal of Educational Impact", + "internal_name": "ftid14908" + }, + { + "name": "Rumah Jurnal Digital Edukasi Nusantara", + "internal_name": "ftid14909" + }, + { + "name": "Institute of Advanced Technology and Green Innovation (INATGI)", + "internal_name": "ftid14910" + }, + { + "name": "Journal of Health Synapse (JHS)", + "internal_name": "ftid14911" + }, + { + "name": "Journal of Biomedical & Space Sciences (JBSS)", + "internal_name": "ftid14912" + }, + { + "name": "Revista Esmat", + "internal_name": "ftid14913" + }, + { + "name": "Journal of Computer Science, Engineering & Applied Mathematics (JCSEAM)", + "internal_name": "ftid14914" + }, { "name": "Adiyaman University Research and Academic Performance System", "internal_name": "ftid14865" @@ -8167,10 +8239,6 @@ "name": "Médiations et médiatisations", "internal_name": "ftjrmediations" }, - { - "name": "Österreichische Zeitschrift für Politikwissenschaft", - "internal_name": "ftjoezp" - }, { "name": "Pathfinder - A Canadian Journal for Information Science Students and Early Career Professionals", "internal_name": "ftjpathfinder" @@ -31263,10 +31331,6 @@ "name": "IZGOnZeit - Onlinezeitschrift des Interdisziplinären Zentrums für Geschlechterforschung", "internal_name": "ftjizgonzeit" }, - { - "name": "Informatics Journals (Informatics Publishing Ltd.)", - "internal_name": "ftinformaticsojs" - }, { "name": "Известия Национальной академии наук Беларуси. Серия химических наук", "internal_name": "ftjpnasbcs" @@ -46639,10 +46703,6 @@ "name": "Smithsonian Institution: Digital Repository", "internal_name": "ftsmithonian" }, - { - "name": "Deutsche Bodenkundliche Gesellschaft: DBGPrints-Archiv", - "internal_name": "ftdbg" - }, { "name": "Тверской государственный университе", "internal_name": "fttverstateuniv" @@ -48535,10 +48595,6 @@ "name": "HighWire Press (Stanford University)", "internal_name": "fthighwire" }, - { - "name": "Informatik an der Universität Stuttgart: Veröffentlichungen", - "internal_name": "ftunivstucsa" - }, { "name": "Papyrus Projekt Gießen (Justus-Liebig Universität, JLU)", "internal_name": "ftubgiessdig" From a0c4dc847585fc61b6bff7c22394c33a9afb3a9a Mon Sep 17 00:00:00 2001 From: chreman Date: Sat, 11 Jul 2026 11:46:02 +0200 Subject: [PATCH 156/205] added area label exclusion list, a more targeted approach than stopwords; only applied to TF-IDF label candidates --- server/preprocessing/other-scripts/ranking.R | 18 ++++++ .../preprocessing/other-scripts/summarize.R | 21 ++++-- .../other-scripts/test/orcid_review_list.R | 60 ++++++++++++++++++ .../base_cancer_fallback.expected.mode1.rds | Bin 557 -> 553 bytes .../replay/base_cancer_fallback.expected.rds | Bin 630 -> 639 bytes .../base_cancer_research.expected.mode1.rds | Bin 567 -> 569 bytes .../replay/base_cancer_research.expected.rds | Bin 601 -> 608 bytes .../replay/orcid_39246636.expected.mode1.rds | Bin 321 -> 338 bytes .../test/replay/orcid_39246636.expected.rds | Bin 330 -> 327 bytes .../replay/orcid_42216275.expected.mode1.rds | Bin 652 -> 657 bytes .../test/replay/orcid_42216275.expected.rds | Bin 655 -> 656 bytes .../replay/orcid_45050517.expected.mode1.rds | Bin 561 -> 562 bytes .../test/replay/orcid_45050517.expected.rds | Bin 519 -> 522 bytes .../replay/orcid_5116955x.expected.mode1.rds | Bin 609 -> 618 bytes .../test/replay/orcid_5116955x.expected.rds | Bin 622 -> 627 bytes .../orcid_5116955x_r2.expected.mode1.rds | Bin 576 -> 580 bytes .../replay/orcid_5116955x_r2.expected.rds | Bin 657 -> 656 bytes .../replay/orcid_90626039.expected.mode1.rds | Bin 644 -> 654 bytes .../test/replay/orcid_90626039.expected.rds | Bin 697 -> 701 bytes .../replay/orcid_96127791_r2.expected.rds | Bin 779 -> 788 bytes .../other-scripts/test/test_ranking_select.R | 28 ++++++++ server/preprocessing/other-scripts/utils.R | 15 +++++ .../resources/additional_stopwords.txt | 3 - .../resources/label_exclusions.txt | 4 ++ 24 files changed, 141 insertions(+), 8 deletions(-) create mode 100644 server/preprocessing/other-scripts/test/orcid_review_list.R create mode 100644 server/preprocessing/resources/label_exclusions.txt diff --git a/server/preprocessing/other-scripts/ranking.R b/server/preprocessing/other-scripts/ranking.R index c3f554e91..fdce8749a 100644 --- a/server/preprocessing/other-scripts/ranking.R +++ b/server/preprocessing/other-scripts/ranking.R @@ -106,6 +106,24 @@ format_label <- function(terms) { paste(cap, collapse = ", ") } +# Area-label exclusion filter (post-tf-idf, PRE-ranking). Drops excluded terms from +# each cluster's tf-idf candidate list so they can never become a label AND never +# consume a top-n slot. Matching is WHOLE-TERM, case-insensitive, exact: a candidate +# term is normalised (underscores -> spaces, lowercased, trimmed) and dropped only if +# the ENTIRE term equals an excluded term — no substring / partial / nested-n-gram +# match, so multi-word terms ("Animal models", "Sports medicine") are untouched. +# tfidf_top : per-cluster named, descending tf-idf term lists. +# exclusions : lowercase character vector (see get_label_exclusions). Empty = no-op. +drop_excluded_terms <- function(tfidf_top, exclusions) { + if (!length(exclusions)) return(tfidf_top) + ex <- tolower(trimws(exclusions)) + lapply(tfidf_top, function(w) { + if (is.null(w) || length(w) == 0 || is.null(names(w))) return(w) + norm <- trimws(tolower(gsub("_", " ", names(w)))) + w[!(norm %in% ex)] + }) +} + # Selection wedge: turn the per-cluster ranked tf-idf term lists into display # labels according to `mode`. # - Mode 0 (or no rank_sources, or an unimplemented mode) -> the unchanged legacy diff --git a/server/preprocessing/other-scripts/summarize.R b/server/preprocessing/other-scripts/summarize.R index 274967ae2..a9eb65b98 100644 --- a/server/preprocessing/other-scripts/summarize.R +++ b/server/preprocessing/other-scripts/summarize.R @@ -134,9 +134,11 @@ add_heuristic_keyword_fields <- function(metadata, stops) { # Last-resort label for a cluster that produced no tf-idf label (empty even after # the min1 fallback): build one from the most frequent bi-/tri-grams of the # cluster's papers' titles + abstracts. Returns a single ", "-joined label string. -# matches : row indices of the cluster's papers in `metadata`. -# top_n : number of terms kept. -title_abstract_fallback_label <- function(matches, metadata, stops, top_n = 3) { +# matches : row indices of the cluster's papers in `metadata`. +# top_n : number of terms kept. +# label_exclusions : curated area-label exclusion list (whole-term, case-insensitive) +# applied here too so listed terms never survive as a last resort. +title_abstract_fallback_label <- function(matches, metadata, stops, top_n = 3, label_exclusions = character(0)) { batch_size <- 1000 total_length <- length(stops) candidates = mapply(paste, metadata$title[matches], metadata$paper_abstract[matches]) @@ -152,6 +154,10 @@ title_abstract_fallback_label <- function(matches, metadata, stops, top_n = 3) { candidates = unlist(lapply(candidates, str_split, " "), recursive = F) candidates = unlist(lapply(candidates, function(x) {another_prune_ngrams(x, stops)})) top_ngrams = sort(table(strsplit(paste(candidates, collapse=" "), " ")), decreasing = T) + if (length(label_exclusions)) { # whole-term exclusion (see drop_excluded_terms) + norm <- trimws(tolower(gsub("_", " ", names(top_ngrams)))) + top_ngrams <- top_ngrams[!(norm %in% tolower(trimws(label_exclusions)))] + } summary <- filter_out_nested_ngrams(names(top_ngrams), top_n) summary = lapply(summary, FUN = function(x) {paste(unlist(x), collapse="; ")}) summary = gsub("_", " ", summary) @@ -199,6 +205,10 @@ create_cluster_labels <- function(clusters, metadata, # the map-wide DF-filtered (min2/min1) corpus + rank-aware selection. mode <- ranking_mode(service) cc <- params$custom_clustering + # Curated area-label exclusion list, applied post-tf-idf / pre-ranking at every + # candidate-producing tier (initial, fallback, title/abstract) so listed generic + # terms can never become a label — in all modes. See get_label_exclusions. + label_exclusions <- get_label_exclusions() vslog$debug(paste("create_cluster_labels: ranking mode", mode, "for service", if (is.null(service)) "(none)" else service)) @@ -254,6 +264,7 @@ create_cluster_labels <- function(clusters, metadata, tolower = TRUE )) tfidf_top <- apply(nn_tfidf, 2, function(x) {x2 <- sort(x, TRUE);x2[x2>0]}) + tfidf_top <- drop_excluded_terms(tfidf_top, label_exclusions) # post-tf-idf, pre-ranking vslog$debug(paste("create_cluster_labels: tf-idf matrix", nTerms(nn_tfidf), "terms x", nDocs(nn_tfidf), "clusters")) @@ -274,7 +285,7 @@ create_cluster_labels <- function(clusters, metadata, if (length(empty_label) > 0) { vslog$debug(paste("create_cluster_labels: min1 fallback for", length(empty_label), "clusters with an empty min2 label")) - fallback_top <- fill_empty_clusters(fallback_corpus) + fallback_top <- drop_excluded_terms(fill_empty_clusters(fallback_corpus), label_exclusions) fallback_names <- select_cluster_label_names(fallback_top, top_n, stops, mode = mode, rank_sources = rank_sources) tfidf_top_names[empty_label] <- fallback_names[empty_label] @@ -290,7 +301,7 @@ create_cluster_labels <- function(clusters, metadata, # from the papers' titles + abstracts (see title_abstract_fallback_label). vslog$debug(paste("create_cluster_labels: title/abstract fallback for cluster", k, "with", length(matches), "papers")) - summary <- title_abstract_fallback_label(matches, metadata, stops, top_n) + summary <- title_abstract_fallback_label(matches, metadata, stops, top_n, label_exclusions) } clusters$cluster_labels[c(matches)] = summary } diff --git a/server/preprocessing/other-scripts/test/orcid_review_list.R b/server/preprocessing/other-scripts/test/orcid_review_list.R new file mode 100644 index 000000000..e67998a8e --- /dev/null +++ b/server/preprocessing/other-scripts/test/orcid_review_list.R @@ -0,0 +1,60 @@ +#!/usr/bin/env Rscript +# Generate a Mode-1 ORCID review list: 1-2 example clusters per ORCID fixture. +# +# For every orcid_*.inputs.rds bundle, replay Mode-1 selection and pick up to two +# clusters worth eyeballing on the dev server, preferring the situations that +# actually exercise the ranking (rank-1 exclusivity, rank-2 fallback, de-nesting) +# over plain single-rank labels. Prints the reconstructed ORCID suffix so the map +# is findable. +# +# R_PROFILE_USER=/dev/null Rscript test/orcid_review_list.R + +suppressWarnings(suppressMessages(source("test/mine_cases.R"))) + +MAX_PER_ORCID <- 2 + +# orcid_5116955x -> "....-5116-955X"; orcid_96127791 -> "....-9612-7791" +orcid_suffix <- function(name) { + s <- toupper(sub("^orcid_", "", sub("_r2$", "", name))) + paste0("....-", substr(s, 1, 4), "-", substr(s, 5, 8)) +} + +# Rank a cluster by how interesting it is to review (higher = pick first). +cluster_score <- function(c) { + denest <- FALSE + sel <- if (length(c$r1) > 0) c$r1 else c$r2 + top <- head(sel, 6) + for (i in seq_along(top)) for (j in seq_along(top)) + if (i != j && is_nested(top[i], top[j])) denest <- TRUE + fallback <- length(c$r1) == 0 && length(c$r2) > 0 + exclusive <- length(c$r1) > 0 && length(c$r2) > 0 + tag <- c(if (denest) "DE-NEST", if (fallback) "RANK2-FALL", + if (exclusive) "RANK1-ONLY") + score <- 3 * denest + 2 * fallback + 1 * exclusive + list(score = score, tag = if (length(tag)) paste(tag, collapse = ",") else "PLAIN") +} + +fixtures <- sort(grep("orcid_", fixture_files(), value = TRUE)) + +for (path in fixtures) { + name <- fixture_name(path) + cand <- cluster_candidates(readRDS(path)) + + rows <- list() + for (k in seq_along(cand)) { + c <- cand[[k]]; if (is.null(c) || !nzchar(c$label)) next + sc <- cluster_score(c) + rows[[length(rows) + 1]] <- list(k = k, c = c, score = sc$score, tag = sc$tag) + } + cat(sprintf("\n=== %s (%s) [%d labelled clusters] ===\n", + name, orcid_suffix(name), length(rows))) + if (!length(rows)) { cat(" (no labelled clusters)\n"); next } + + ord <- order(-vapply(rows, function(r) r$score, numeric(1))) + picks <- head(rows[ord], MAX_PER_ORCID) + for (r in picks) { + cat(sprintf(" cluster %-2d [%s] -> %s\n", r$k, r$tag, r$c$label)) + cat(" rank1:", fmt(r$c$r1, 6), "\n") + cat(" rank2:", fmt(r$c$r2, 6), "\n") + } +} diff --git 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v } +test_that("drop_excluded_terms removes whole-term, case-insensitive exact matches", { + tt <- list(c(humans = 5, animals = 4, medicine = 3, neoplasms = 2)) + out <- drop_excluded_terms(tt, c("humans", "animals", "science", "medicine")) + expect_equal(names(out[[1]]), "neoplasms") + expect_equal(unname(out[[1]]), 2) +}) + +test_that("drop_excluded_terms is case-insensitive and normalises underscores", { + tt <- list(c(Humans = 5, Sports_Medicine = 4)) # underscore n-gram + out <- drop_excluded_terms(tt, c("humans", "medicine")) + expect_equal(names(out[[1]]), "Sports_Medicine") # whole term != "medicine" -> kept +}) + +test_that("drop_excluded_terms does NOT do partial / nested matches", { + tt <- list(c(medicine = 3, `sports medicine` = 2, `animal models` = 1)) + out <- drop_excluded_terms(tt, c("medicine", "animals")) + expect_equal(sort(names(out[[1]])), sort(c("animal models", "sports medicine"))) +}) + +test_that("drop_excluded_terms is a no-op with empty exclusions or empty cluster", { + tt <- list(c(a = 1, b = 2), numeric(0)) + expect_equal(drop_excluded_terms(tt, character(0)), tt) + expect_equal(length(drop_excluded_terms(tt, c("a"))[[2]]), 0) +}) diff --git a/server/preprocessing/other-scripts/utils.R b/server/preprocessing/other-scripts/utils.R index d880c1aaa..0bde5766a 100644 --- a/server/preprocessing/other-scripts/utils.R +++ b/server/preprocessing/other-scripts/utils.R @@ -52,6 +52,21 @@ get_stopwords <- function(languages) { return(stopwords) } +# Curated area-label exclusion list: terms that must never appear as an area label +# (generic MeSH check-tags / classification words, e.g. "Humans", "Animals", +# "Science", "Medicine"). Unlike stopwords this is applied ONLY at label selection +# (post-tf-idf, pre-ranking) as a whole-term, case-insensitive exact match — it does +# NOT touch n-gram generation or the corpus. Returns a lowercase character vector +# (empty if the resource file is absent, so it is a no-op by default). +get_label_exclusions <- function() { + path <- if (dir.exists("../resources")) "../resources/label_exclusions.txt" else + if (dir.exists("./resources")) "./resources/label_exclusions.txt" else + "../../resources/label_exclusions.txt" + if (!file.exists(path)) return(character(0)) + x <- tolower(trimws(readLines(path, warn = FALSE))) + x[nzchar(x)] +} + # Configure logging at the given level. Sets the *root logger level* so that # records at `loglevel` and above are actually emitted -- the previous version diff --git a/server/preprocessing/resources/additional_stopwords.txt b/server/preprocessing/resources/additional_stopwords.txt index aeca85321..2af358387 100644 --- a/server/preprocessing/resources/additional_stopwords.txt +++ b/server/preprocessing/resources/additional_stopwords.txt @@ -600,6 +600,3 @@ psy relig anthro-se socio -science -medicine -animals diff --git a/server/preprocessing/resources/label_exclusions.txt b/server/preprocessing/resources/label_exclusions.txt new file mode 100644 index 000000000..1bc3ae42c --- /dev/null +++ b/server/preprocessing/resources/label_exclusions.txt @@ -0,0 +1,4 @@ +humans +animals +science +medicine From 372fd4f6d00fb41f0f71b2fc5a93a70df634abcc Mon Sep 17 00:00:00 2001 From: chreman Date: Sun, 12 Jul 2026 14:36:38 +0200 Subject: [PATCH 157/205] update test fixtures for ranked area labeling --- .../preprocessing/other-scripts/summarize.R | 7 ++- .../replay/base_cancer_fallback.expected.rds | Bin 639 -> 630 bytes .../replay/base_cancer_research.expected.rds | Bin 608 -> 601 bytes .../test/replay/orcid_39246636.expected.rds | Bin 327 -> 330 bytes .../test/replay/orcid_42216275.expected.rds | Bin 656 -> 655 bytes .../test/replay/orcid_45050517.expected.rds | Bin 522 -> 519 bytes .../test/replay/orcid_5116955x.expected.rds | Bin 627 -> 622 bytes .../replay/orcid_5116955x_r2.expected.rds | Bin 656 -> 657 bytes .../test/replay/orcid_90626039.expected.rds | Bin 701 -> 697 bytes .../replay/orcid_96127791_r2.expected.rds | Bin 788 -> 779 bytes .../other-scripts/test/test_fallback.R | 44 ++++++------------ 11 files changed, 18 insertions(+), 33 deletions(-) diff --git a/server/preprocessing/other-scripts/summarize.R b/server/preprocessing/other-scripts/summarize.R index 6a1bf03f7..eaba3446a 100644 --- a/server/preprocessing/other-scripts/summarize.R +++ b/server/preprocessing/other-scripts/summarize.R @@ -214,8 +214,10 @@ create_cluster_labels <- function(clusters, metadata, cc <- params$custom_clustering # Curated area-label exclusion list, applied post-tf-idf / pre-ranking at every # candidate-producing tier (initial, fallback, title/abstract) so listed generic - # terms can never become a label — in all modes. See get_label_exclusions. - label_exclusions <- get_label_exclusions() + # terms can never become a label. Scoped to Modes 1-3 ONLY: Mode 0 stays + # byte-identical to the legacy pipeline, so it gets no exclusion (empty list -> + # drop_excluded_terms is a no-op). See get_label_exclusions. + label_exclusions <- if (identical(mode, "0")) character(0) else get_label_exclusions() vslog$debug(paste("create_cluster_labels: ranking mode", mode, "for service", if (is.null(service)) "(none)" else service)) @@ -238,6 +240,7 @@ create_cluster_labels <- function(clusters, metadata, # SAME corpus at bound c(1, Inf). empty_tfidf <- which(apply(nn_tfidf, 2, sum) == 0) tfidf_top[c(empty_tfidf)] <- fill_empty_clusters_legacy(nn_tfidf, nn_corpus)[c(empty_tfidf)] + # NB: no label-exclusion filter here — Mode 0 is byte-identical to legacy. tfidf_top_names <- get_top_names(tfidf_top, top_n, stops) } else { # ---- Modes 1-3: DF-filtered corpus + rank-aware selection ------------------ diff --git a/server/preprocessing/other-scripts/test/replay/base_cancer_fallback.expected.rds b/server/preprocessing/other-scripts/test/replay/base_cancer_fallback.expected.rds index 2af6610dc265efbfc2ec333d08df51ea6bb5a8ce..f3f630eff3ec4c946cbab36970f962890fcde797 100644 GIT binary patch literal 630 zcmV-+0*U<}iwFP!0000016@>0Z`42x4t?=vIdDdELL9O;UgARAZC9;O5u!!nhGr+; z#9}gz=CQDUnx9bdO?I|wIao;_W-t!*J}U!)qMJ)s&<~^V^Zx^ z5Apms)6S4r!FVFauC3J>X&XDWpiZw?e65arTr5$a3lBTPP0m(<2-sg`NLEJ{H(lsJjWU}!Pc4V&$CFx>WFszmu?4a& z-kEq(r^(Aer~%bwcdgFd(rZ8@6H!XZdH)4Og`y!HyIOtY@Jku1L&!}Q!ne~kX{dtM zcqKlR^iQ)+TT8B`pkx@t$K*INq2-zMLyBX1)~WB46BIE%l{h0-+I4|>ah7`RkaWTn zYc;1{+iivKjy=SSH#(rYS{-9Fty7Ef2qkORNY7zpQz52ii~Qz@{cpWiy*ALJ(`_#8 zC`B<{?8He5%BDQzqFoQqmU&)$-Dds`he7+&-32w!#*SPVYPbRL6d;u86g6ohUO{_3 zBWU+WrAVGp!}-Z&hC~CH&DeiwvBb{`APv}$p0iw;5k!{V$c^b68Z!6k-55-76H=gF z-H{OLu|zVR8%I!jVYhAjaLx-a3oJkkYVZ@dBjrd2 zB=y_fnBN`T&Fb8e$|Uc{-CshpJpECQ=@M73#G{95ZwF}i3hKVoJq5h{h21`wG_)kZ zH9xWAxVQfu@8#Q6d{0MzRponseakl~{+;nhft<_-JAmDXJ%DAb{3Ce`ds6bAmE0|E QRdBEW0dK2l)^h{^0Iz~Jc>n+a literal 639 zcmV-_0)YJ=iwFP!0000017%d(Zqz^w4ZU%vd;uEq#6$Ltn|OikwyRdC2+<<(hGr+; z#9}gz=CWmfnop=Wlbx+vc(9Uq>~rj6PkubAs%p2|J%H`wwc7uFIiI|*s-5TfIIQ-n zhj@ONX=li*U_6my*VgKkw2hrwP^VWczE)EodC7*gRqP^J%Pk?PAiTIpensnmIH&D8 zFy78dhvcaz7faOV!o$vRle1MI0`^xKlGTyLO%^&(qs%7GQ_CUx@uXHI*@%l?Y=Nwc zcP5_HN%ArfYCv__U8^&<^coP!M3ho;-hBa4p=e0Qu2$bT{8Gm15OR}+@XcgR8mgc* zUWpGS{o}0D){<)}C>aLvF*%M*Xn7|6km8u0b?W=%1VxNbCC-SIc3ogz9y7&S&8gRx za;ilOCL+&~3@D}F623k1kTTw=f?8{Jgh91VEyg1>pj{(9hml)_n3^rm>nZyyy;i+8 zP^Z&vE_9lrm@anWBn4$t9(hr+hi9wQy!g7!HZ&Xt?Mtuc)Icdaa$&^b2EbE*Y^GDx zq>YpX?bVE+ehYc+WaLrHbJ?`>9$9wsP72ng*Usd^EaDB^nSo}NV&jUG`4|V{%4|@R1So!Dj Z81|&(JuA6e+^XPS{{?oQh=Yd&001WkJ81v_ diff --git a/server/preprocessing/other-scripts/test/replay/base_cancer_research.expected.rds b/server/preprocessing/other-scripts/test/replay/base_cancer_research.expected.rds index afe64284034022a5055a58ecf562e31d6b018e73..502ff901dda4a9739c6dab4dc783f8839976de55 100644 GIT binary patch literal 601 zcmV-f0;c^RiwFP!0000016@?fZWJ*PHQCr1?nubu033R5EV+ebl7UDhNF+$ykjGuq zZQ-TmWng}qPl$Ln?j*yZ)ShzHdsVN>^Zfy1Y@2QG!FF+EyWcM7(|yLap5n)Z?Xdg! 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The min1 fallback must catch this (trigger = -# "empty label", not "zero tf-idf sum"); otherwise the cluster drops straight to -# the abstract-frequency fallback and surfaces affiliation/email boilerplate. +# base_cancer_fallback is a captured real map where cluster 15 once regressed to +# affiliation/email boilerplate ("People Republic ChinaEmail, …") instead of its +# reference label. This pins that one cluster so the boilerplate regression can't +# recur. # -# base_cancer_fallback is the captured real map that exhibited this (cluster 15: -# the reference label is "Breast cancer survivors, Cancer recurrence fear, -# Disease-free survival", but the bug produced "People Republic ChinaEmail, …"). +# NB: the broader min2 -> min1 "empty-label" fallback invariant that used to live +# here was Modes-1-3-specific (that trigger is the DF-filter's; Mode 0 uses the +# legacy zero-sum fallback) and no longer mapped to Mode 0's path, so it was reduced +# to this concrete guard. # # Runs inside the pipeline image (needs tm). -if (!exists("mode0_fallback_breakdown")) source("test/replay_harness.R") +if (!exists("replay_labels")) source("test/replay_harness.R") if (!requireNamespace("testthat", quietly = TRUE)) { if (!exists("test_that")) source("test/testthat_shim.R") } else { @@ -23,28 +24,9 @@ FX <- file.path(REPLAY_DIR, "base_cancer_fallback.inputs.rds") if (!file.exists(FX)) { cat(" (base_cancer_fallback fixture missing — skipping fallback regression)\n") } else { - bundle <- readRDS(FX) - - # Invariant: whenever the min2 label is empty but the min1 label is not, the - # FINAL Mode-0 label must be the min1 label (the fallback fired before the - # abstract-frequency last resort). - test_that("min1 fallback fires on empty labels, not just zero tf-idf sum", { - bd <- mode0_fallback_breakdown(bundle) - final <- replay_labels(FX, mode = "0") - n <- bundle$clusters$num_clusters - checked <- 0 - for (k in seq_len(n)) { - if (!nzchar(bd$min2[k]) && nzchar(bd$min1[k])) { - checked <- checked + 1 - expect_equal(unname(final[[as.character(k)]]), unname(bd$min1[k])) - } - } - expect_true(checked > 0) # the fixture must actually exercise the fallback - }) - - # Concrete anchor: the cluster that motivated the fix reproduces its reference - # label rather than the abstract-fallback boilerplate. - test_that("base_cancer_fallback cluster 15 gets its reference (min1) label", { + # The cluster that motivated the fix reproduces its reference label rather than + # the abstract-fallback boilerplate. + test_that("base_cancer_fallback cluster 15 gets its reference label, not boilerplate", { final <- replay_labels(FX, mode = "0") expect_equal(final[["15"]], "Breast cancer survivors, Cancer recurrence fear, Disease-free survival") From 7c534b91172f2e3144d2351d3338f8f68cb9defa Mon Sep 17 00:00:00 2001 From: chreman Date: Mon, 13 Jul 2026 01:03:02 +0200 Subject: [PATCH 158/205] add ranking mode 3; including tests and separate mesh_fields extractor --- server/preprocessing/other-scripts/base.R | 4 ++ .../preprocessing/other-scripts/mesh_fields.R | 67 +++++++++++++++++++ server/preprocessing/other-scripts/ranking.R | 22 ++++-- .../other-scripts/test/replay_harness.R | 6 +- .../other-scripts/test/run_tests.R | 3 +- .../other-scripts/test/test_mesh_fields.R | 52 ++++++++++++++ .../other-scripts/test/test_mode2.R | 35 ++++++---- .../other-scripts/test/test_ranking_select.R | 48 +++++++++++++ .../other-scripts/test/test_replay_modes.R | 5 +- server/workers/orcid/src/orcid_service.py | 6 +- 10 files changed, 224 insertions(+), 24 deletions(-) create mode 100644 server/preprocessing/other-scripts/mesh_fields.R create mode 100644 server/preprocessing/other-scripts/test/test_mesh_fields.R diff --git a/server/preprocessing/other-scripts/base.R b/server/preprocessing/other-scripts/base.R index 4437298cf..9e560250b 100644 --- a/server/preprocessing/other-scripts/base.R +++ b/server/preprocessing/other-scripts/base.R @@ -3,6 +3,7 @@ library(stringr) library(dplyr) source('preprocess.R') source('subject_cleaning.R') +source('mesh_fields.R') # add_mesh_rank_fields(): MeSH specific/generic columns (ranking Modes 2/3) # get_papers # @@ -289,6 +290,9 @@ etl <- function(res, repo, non_public) { subject_cleaned = gsub("\\s+", " ", subject_cleaned) # clean up keyword separation subject_cleaned = stringi::stri_trim(subject_cleaned) # clean up keyword separation metadata$subject = subject_cleaned + # Additive MeSH rank-provenance columns for ranking Modes 2/3 (derived from the + # raw [MeSH]-marked subject_orig; subject/subject_orig untouched). Shared module. + metadata = add_mesh_rank_fields(metadata) metadata$authors = check_metadata(res$dccreator) diff --git a/server/preprocessing/other-scripts/mesh_fields.R b/server/preprocessing/other-scripts/mesh_fields.R new file mode 100644 index 000000000..03815c998 --- /dev/null +++ b/server/preprocessing/other-scripts/mesh_fields.R @@ -0,0 +1,67 @@ +# mesh_fields.R +# Shared, reusable production of the MeSH rank-provenance metadata columns used by +# ranking Modes 2/3 (docs/area_title_ranking_plan.md §11). Data integrations +# (base.R, and later pubmed/openaire) `source()` this file and call +# add_mesh_rank_fields(metadata) once they have populated `subject_orig` (the RAW, +# still-[MeSH]-marked subject). It adds two ADDITIVE columns and leaves +# `subject` / `subject_orig` untouched: +# +# keywords_rank_mesh_specific : "; "-joined descriptors classified "specific" +# keywords_rank_mesh_generic : "; "-joined descriptors classified "generic" +# +# Form handling (verified against real BASE data): +# * CLASSIFY on the qualifier-stripped ORIGINAL form. BASE presents official MeSH +# descriptors, including legitimate comma-terms ("History, 21st Century") that +# de-inversion would mangle and break the mesh_tree_depth lookup — so we do NOT +# de-invert before classifying (mesh_classification.R keys the official forms). +# * STORE the cleaned form = strip_qualifier(deinvert(original)), i.e. the SAME +# transform base.R applies to build `subject_cleaned`, so the ranking's membership +# test (get_cluster_corpus: mesh token must be a subject token) actually lands. +# +# Pure of pipeline state; depends only on the shared classifier + cleaning helpers. + +if (!exists("classify_mesh")) source("mesh_classification.R") +if (!exists("deinvert_mesh_term")) source("subject_cleaning.R") + +# NB: keep these two names in sync with summarize.R (the consumer). +if (!exists("KW_MESH_SPECIFIC")) KW_MESH_SPECIFIC <- "keywords_rank_mesh_specific" +if (!exists("KW_MESH_GENERIC")) KW_MESH_GENERIC <- "keywords_rank_mesh_generic" + +MESH_MARKER_RE <- "\\s*(\\[MeSH\\]|\\(mesh\\))\\s*" + +# The [MeSH]/(mesh)-marked descriptors of one raw subject string, in ORIGINAL form +# (marker removed, otherwise untouched). "" / NA -> character(0). +marked_mesh_descriptors <- function(subject_orig) { + if (is.null(subject_orig) || is.na(subject_orig) || !nzchar(subject_orig)) return(character(0)) + kws <- trimws(strsplit(subject_orig, ";", fixed = TRUE)[[1]]) + kws <- kws[grepl("\\[MeSH\\]|\\(mesh\\)", kws, ignore.case = TRUE)] + desc <- trimws(gsub(MESH_MARKER_RE, "", kws, ignore.case = TRUE, perl = TRUE)) + desc[nzchar(desc)] +} + +# Add keywords_rank_mesh_specific / _generic to `metadata`, derived from +# metadata$subject_orig. If subject_orig is absent the columns are added empty, so +# Modes 2/3 degrade cleanly to Mode-1 behaviour (get_cluster_corpus: has_mesh = FALSE). +add_mesh_rank_fields <- function(metadata) { + so <- metadata$subject_orig + if (is.null(so)) { + metadata[[KW_MESH_SPECIFIC]] <- rep("", nrow(metadata)) + metadata[[KW_MESH_GENERIC]] <- rep("", nrow(metadata)) + return(metadata) + } + spec <- character(length(so)); gen <- character(length(so)) + for (i in seq_along(so)) { + orig <- marked_mesh_descriptors(so[i]) + if (!length(orig)) { spec[i] <- ""; gen[i] <- ""; next } + # classify: qualifier-stripped ORIGINAL (comma-inversion preserved) -> tsv match. + classify_form <- trimws(strip_mesh_qualifier(orig)) + # store: same transform as subject_cleaned (deinvert THEN strip qualifier). + stored_form <- trimws(strip_mesh_qualifier(vapply(orig, deinvert_mesh_term, character(1), USE.NAMES = FALSE))) + cls <- classify_mesh(classify_form) # "generic" / "specific", vectorised + spec[i] <- paste(unique(stored_form[cls == "specific" & nzchar(stored_form)]), collapse = "; ") + gen[i] <- paste(unique(stored_form[cls == "generic" & nzchar(stored_form)]), collapse = "; ") + } + metadata[[KW_MESH_SPECIFIC]] <- spec + metadata[[KW_MESH_GENERIC]] <- gen + metadata +} diff --git a/server/preprocessing/other-scripts/ranking.R b/server/preprocessing/other-scripts/ranking.R index e15fe7c21..cab48a07d 100644 --- a/server/preprocessing/other-scripts/ranking.R +++ b/server/preprocessing/other-scripts/ranking.R @@ -62,9 +62,9 @@ rank_spec <- function(mode) { list(rank = 2L, sources = c("mesh_generic"), policy = "exclusive"), # generic MeSH list(rank = 3L, sources = c("heuristic"), policy = "exclusive")), "3" = list( - list(rank = 1L, sources = c("cleaned_ex_mesh"), policy = "topup"), # keywords - list(rank = 2L, sources = c("mesh_specific"), policy = "topup"), # specific MeSH tops up (decision #1) - list(rank = 3L, sources = c("mesh_generic"), policy = "exclusive"), # generic MeSH + list(rank = 1L, sources = c("cleaned_ex_mesh"), policy = "topup"), # keywords + list(rank = 2L, sources = c("mesh_specific"), policy = "topup", cross_denest = TRUE), # specific MeSH tops up + cross-rank de-nest vs keywords (decisions #1/#6) + list(rank = 3L, sources = c("mesh_generic"), policy = "exclusive"), # generic MeSH list(rank = 4L, sources = c("heuristic"), policy = "exclusive")), NULL) } @@ -102,14 +102,26 @@ select_by_rank <- function(terms_us, term_ranks, top_n, policy_spec, label <- character(0) for (step in policy_spec) { if (identical(step$policy, "exclusive") && length(label) > 0) next + # A cross-rank step (Mode 3 rank 2) may still REPLACE a nested term even when no + # slots are open, so it must not be short-circuited by the open-slot guard. + cross <- isTRUE(step$cross_denest) && length(label) > 0 open <- top_n - length(label) - if (open <= 0) break + if (open <= 0 && !cross) break sel <- terms_us[term_ranks == step$rank] # weight-ordered if (length(sel) == 0) next sel <- trimws(gsub("_", " ", sel)) sel <- unlist(denest_fn(sel, top_n)) # de-nest within rank if (length(sel) == 0) next - label <- c(label, head(sel, open)) + if (cross) { + # Cross-rank de-nesting (Mode 3, rank 1 <-> rank 2 only, decision #6): de-nest + # this rank's candidates TOGETHER with the label built so far. String nesting + # (filter_out_nested_ngrams): a specific-MeSH term that CONTAINS a selected + # keyword replaces it in place (backfill, e.g. "cancer" -> "breast cancer"); a + # specific-MeSH term nested INSIDE a keyword is dropped; keyword order is kept. + label <- unlist(denest_fn(c(label, sel), top_n)) + } else { + label <- c(label, head(sel, open)) + } } label } diff --git a/server/preprocessing/other-scripts/test/replay_harness.R b/server/preprocessing/other-scripts/test/replay_harness.R index 93b7f927b..3302156fa 100644 --- a/server/preprocessing/other-scripts/test/replay_harness.R +++ b/server/preprocessing/other-scripts/test/replay_harness.R @@ -22,6 +22,7 @@ suppressWarnings(suppressMessages({ if (!exists("dump_data")) source("utils.R") if (!exists("create_cluster_labels")) source("summarize.R") if (!exists("ranking_mode")) source("ranking.R") +if (!exists("add_mesh_rank_fields")) source("mesh_fields.R") REPLAY_DIR <- "test/replay" @@ -67,9 +68,12 @@ replay_labels <- function(bundle, mode = "0") { if (length(rk)) Sys.unsetenv(rk) Sys.setenv(RANKING_MODE = mode, LOGLEVEL = "INFO") # INFO => dump_data() no-ops + # Fixtures captured before the MeSH-column feature carry no keywords_rank_mesh_* + # columns; derive them from subject_orig (as base.R now does) so Modes 2/3 replay + # meaningfully. Harmless for Modes 0/1 (they don't read the columns). Idempotent. clusters <- create_cluster_labels( clusters = bundle$clusters, - metadata = backfill_subject_is_heuristic(bundle$metadata), + metadata = add_mesh_rank_fields(backfill_subject_is_heuristic(bundle$metadata)), type_counts = bundle$type_counts, weightingspec = bundle$weightingspec, top_n = bundle$top_n, diff --git a/server/preprocessing/other-scripts/test/run_tests.R b/server/preprocessing/other-scripts/test/run_tests.R index 826cb120a..fdc4ac5c5 100644 --- a/server/preprocessing/other-scripts/test/run_tests.R +++ b/server/preprocessing/other-scripts/test/run_tests.R @@ -26,7 +26,8 @@ base_files <- c( "test/test_ranking_config.R", "test/test_ranking_wedge.R", "test/test_ranking_select.R", - "test/test_mesh_classification.R" + "test/test_mesh_classification.R", + "test/test_mesh_fields.R" ) # Replay tests need the tm stack — included only when tm is available (i.e. inside # the pipeline image, run via test/run_tests.sh), skipped on a bare host. diff --git a/server/preprocessing/other-scripts/test/test_mesh_fields.R b/server/preprocessing/other-scripts/test/test_mesh_fields.R new file mode 100644 index 000000000..67d37418c --- /dev/null +++ b/server/preprocessing/other-scripts/test/test_mesh_fields.R @@ -0,0 +1,52 @@ +# Unit tests for add_mesh_rank_fields (mesh_fields.R): production of the MeSH +# specific/generic rank-provenance columns from the raw [MeSH]-marked subject_orig. +# Needs the classifier resource files (mesh_tree_depth.tsv / mesh_check_tags.txt), so +# it runs inside the pipeline image; pure base R otherwise. + +if (!exists("add_mesh_rank_fields")) source("mesh_fields.R") +if (!requireNamespace("testthat", quietly = TRUE)) { + if (!exists("test_that")) source("test/testthat_shim.R") +} else { + library(testthat) +} + +md <- data.frame( + subject_orig = c( + "Neoplasms [MeSH]; Humans [MeSH]; Breast Neoplasms [MeSH]; Cancer research", + "History, 21st Century [MeSH]; Autistic Disorder/genetics [MeSH]", + "just; plain; keywords", + NA_character_), + stringsAsFactors = FALSE) +out <- add_mesh_rank_fields(md) +splitc <- function(s) strsplit(s, "; ", fixed = TRUE)[[1]] + +test_that("adds the two additive columns without dropping rows", { + expect_true(all(c(KW_MESH_SPECIFIC, KW_MESH_GENERIC) %in% names(out))) + expect_equal(nrow(out), 4) +}) + +test_that("check-tags and shallow MeSH -> generic; deeper MeSH -> specific", { + gen <- splitc(out[[KW_MESH_GENERIC]][1]) + spec <- splitc(out[[KW_MESH_SPECIFIC]][1]) + expect_true("Humans" %in% gen) # check tag -> generic + expect_true("Neoplasms" %in% gen) # tree depth <= 2 -> generic + expect_true("Breast Neoplasms" %in% spec) # deeper -> specific +}) + +test_that("stored form is de-inverted (comma-terms) and qualifier-stripped", { + all2 <- c(splitc(out[[KW_MESH_GENERIC]][2]), splitc(out[[KW_MESH_SPECIFIC]][2])) + expect_true("21st Century History" %in% all2) # "History, 21st Century" de-inverted + expect_true("Autistic Disorder" %in% all2) # "/genetics" qualifier stripped +}) + +test_that("non-MeSH keywords and NA subjects yield empty columns", { + expect_equal(out[[KW_MESH_SPECIFIC]][3], "") # plain keywords -> no mesh + expect_equal(out[[KW_MESH_GENERIC]][3], "") + expect_equal(out[[KW_MESH_SPECIFIC]][4], "") # NA subject_orig +}) + +test_that("absent subject_orig -> empty columns (Modes 2/3 degrade to Mode 1)", { + o2 <- add_mesh_rank_fields(data.frame(title = c("a", "b"), stringsAsFactors = FALSE)) + expect_true(all(o2[[KW_MESH_SPECIFIC]] == "")) + expect_true(all(o2[[KW_MESH_GENERIC]] == "")) +}) diff --git a/server/preprocessing/other-scripts/test/test_mode2.R b/server/preprocessing/other-scripts/test/test_mode2.R index 71a867700..809f38379 100644 --- a/server/preprocessing/other-scripts/test/test_mode2.R +++ b/server/preprocessing/other-scripts/test/test_mode2.R @@ -1,13 +1,10 @@ -# Mode-2 robustness regression. +# Mode-2 robustness + differential regression. # -# Mode 2 needs the MeSH rank columns (keywords_rank_mesh_specific/generic) which -# are produced upstream by the BASE client. When they are absent (any current -# fixture, and any integration without MeSH), Mode 2 must **degrade to Mode-1 -# behaviour**: rank 1 = keywords (cleaned_ex_mesh == cleaned), the empty MeSH ranks -# are skipped, heuristic is the last rank. This test pins that on a real fixture. -# -# Once a MeSH-bearing fixture exists (BASE client re-run), a differential Mode-2 -# case (generic MeSH demoted) will be added here. +# Mode 2 needs the MeSH rank columns (keywords_rank_mesh_specific/generic). The +# replay harness now derives them from subject_orig (exactly as base.R does), so a +# MeSH-bearing fixture exercises the real specific/generic split, while a MeSH-free +# input must degrade cleanly to Mode-1 output (rank 1 = keywords == cleaned_ex_mesh, +# the empty MeSH ranks skipped, heuristic last). # # Runs inside the pipeline image (needs tm). @@ -18,14 +15,26 @@ if (!requireNamespace("testthat", quietly = TRUE)) { library(testthat) } -FX <- file.path(REPLAY_DIR, "base_cancer_research.inputs.rds") +# Degradation: the synthetic bundle has no subject_orig / no [MeSH] markers, so the +# MeSH columns come out empty and Mode 2 must equal Mode 1. +test_that("Mode 2 degrades to Mode 1 when no MeSH is present", { + b <- build_synthetic_bundle() + expect_equal(unname(unlist(replay_labels(b, mode = "2"))), + unname(unlist(replay_labels(b, mode = "1")))) +}) +# Differential: on a MeSH-bearing fixture the split is active — generic MeSH +# (e.g. "Neoplasms", "Europe") is demoted to the exclusive generic rank, so Mode 2 +# diverges from Mode 1. +FX <- file.path(REPLAY_DIR, "base_cancer_research.inputs.rds") if (!file.exists(FX)) { - cat(" (base_cancer_research fixture missing — skipping Mode-2 robustness test)\n") + cat(" (base_cancer_research fixture missing — skipping Mode-2 differential test)\n") } else { - test_that("Mode 2 degrades to Mode 1 when no MeSH columns are present", { + test_that("Mode 2 activates the MeSH split on a MeSH-bearing map (differs from Mode 1)", { m1 <- replay_labels(FX, mode = "1") m2 <- replay_labels(FX, mode = "2") - expect_equal(unname(unlist(m2)), unname(unlist(m1))) + expect_false(identical(unname(unlist(m2)), unname(unlist(m1)))) # split is active + # the cluster whose Mode-1 label led with generic MeSH no longer does under Mode 2. + expect_false(grepl("Neoplasms|Europe", m2[["3"]])) }) } diff --git a/server/preprocessing/other-scripts/test/test_ranking_select.R b/server/preprocessing/other-scripts/test/test_ranking_select.R index 6542d1cf2..19f92bdb8 100644 --- a/server/preprocessing/other-scripts/test/test_ranking_select.R +++ b/server/preprocessing/other-scripts/test/test_ranking_select.R @@ -132,3 +132,51 @@ test_that("drop_excluded_terms is a no-op with empty exclusions or empty cluster expect_equal(drop_excluded_terms(tt, character(0)), tt) expect_equal(length(drop_excluded_terms(tt, c("a"))[[2]]), 0) }) + +# --- Mode-3 cross-rank de-nesting (rank 1 keywords <-> rank 2 specific MeSH) -- +spec3 <- rank_spec("3") +# Faithful string-nesting de-nester mirroring filter_out_nested_ngrams: substring +# nesting, replace a nested term IN PLACE with the more specific (containing) one, +# preserve order, truncate to n. (The real fn needs stringi; this keeps the pure.) +str_denest <- function(x, n) { + out <- character(0) + for (t in x) { + if (!nzchar(t)) next + if (length(out)) { + contains <- vapply(out, function(o) grepl(o, t, fixed = TRUE), logical(1)) # existing inside t + within <- vapply(out, function(o) grepl(t, o, fixed = TRUE), logical(1)) # t inside existing + if (any(within)) next + if (any(contains)) { out[which(contains)] <- t; next } + } + out <- c(out, t) + } + head(unique(out), n) +} + +test_that("spec3 rank 2 (specific MeSH) is flagged for cross-rank de-nesting", { + expect_true(isTRUE(spec3[[2]]$cross_denest)) + expect_null(rank_spec("1")[[2]]$cross_denest) # Mode 1 does NOT cross-denest +}) + +test_that("Mode 3: specific MeSH backfills a nested keyword (cancer -> breast cancer)", { + expect_equal(select_by_rank(c("cancer", "breast_cancer"), c(1L, 2L), 3, spec3, str_denest), + "breast cancer") +}) + +test_that("Mode 3: a keyword more specific than the MeSH is kept, the MeSH dropped", { + expect_equal(select_by_rank(c("breast_cancer", "cancer"), c(1L, 2L), 3, spec3, str_denest), + "breast cancer") +}) + +test_that("Mode 3: non-nested keyword + specific MeSH both appear (top-up)", { + expect_equal(sort(select_by_rank(c("diet", "breast_cancer"), c(1L, 2L), 3, spec3, str_denest)), + sort(c("diet", "breast cancer"))) +}) + +test_that("Mode 3: backfill still fires when rank 1 already filled top_n", { + lab <- select_by_rank(c("cancer", "diet", "female", "breast_cancer"), + c(1L, 1L, 1L, 2L), 3, spec3, str_denest) + expect_true("breast cancer" %in% lab) + expect_false("cancer" %in% lab) + expect_equal(length(lab), 3) +}) diff --git a/server/preprocessing/other-scripts/test/test_replay_modes.R b/server/preprocessing/other-scripts/test/test_replay_modes.R index 0d98ebbd0..fb8033a5b 100644 --- a/server/preprocessing/other-scripts/test/test_replay_modes.R +++ b/server/preprocessing/other-scripts/test/test_replay_modes.R @@ -18,8 +18,9 @@ if (!requireNamespace("testthat", quietly = TRUE)) { } # Modes with committed baselines. Mode 0 is the byte-identical legacy baseline; -# Mode 1 pins the first ranked mode. Add "2"/"3" here as those modes land. -REPLAY_MODES <- c("0", "1") +# Modes 1-3 pin the ranked modes (2/3 use the MeSH split, derived from subject_orig +# by the harness for fixtures captured before that feature). +REPLAY_MODES <- c("0", "1", "2", "3") fixtures <- fixture_files() diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 5ddf4fdbd..52da6197f 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -283,7 +283,9 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra original_columns = metadata.columns.to_list() required_fields = ['id', 'identifier', 'relevance', 'relation', 'title', 'subtitle', 'doi', - 'paper_abstract', 'link', 'subject', 'oa_state', 'subject_orig', 'published_in', + 'paper_abstract', 'link', 'subject', 'oa_state', 'subject_orig', + 'keywords_rank_mesh_specific', 'keywords_rank_mesh_generic', # ranking Modes 2/3 (from BASE enrichment) + 'published_in', 'year', 'authors', 'url', 'resulttype', 'type', 'typenorm', 'lang', 'language', 'content_provider', 'coverage', 'is_duplicate', 'has_dataset', 'sanitized_authors', 'relations', 'annotations', 'repo', 'source', 'volume', 'issue', 'page', 'issn', @@ -467,7 +469,7 @@ def custom_merge(existing_value, new_value): self._log_dataframe(enriched_metadata.sort_values(by='title'), params, '_enriched') # temporal solution, for some reason if we have some undefined data, dataprocessing is failing - enriched_metadata = enriched_metadata.reindex(columns=list(set(original_columns + ['oa_state', 'subject', 'subject_orig', 'paper_abstract', 'link', 'relation']))) + enriched_metadata = enriched_metadata.reindex(columns=list(set(original_columns + ['oa_state', 'subject', 'subject_orig', 'paper_abstract', 'link', 'relation', 'keywords_rank_mesh_specific', 'keywords_rank_mesh_generic']))) return enriched_metadata From 9096250b5c7bfa6d41661c58ab893854578363b8 Mon Sep 17 00:00:00 2001 From: chreman Date: Mon, 13 Jul 2026 01:15:54 +0200 Subject: [PATCH 159/205] mode 3 replay fixtures --- .../other-scripts/test/mine_mode3.R | 52 ++++++++++++++++++ .../base_cancer_fallback.expected.mode2.rds | Bin 0 -> 576 bytes .../base_cancer_fallback.expected.mode3.rds | Bin 0 -> 560 bytes .../base_cancer_research.expected.mode2.rds | Bin 0 -> 590 bytes .../base_cancer_research.expected.mode3.rds | Bin 0 -> 584 bytes .../base_digital_education.expected.mode2.rds | Bin 0 -> 649 bytes .../base_digital_education.expected.mode3.rds | Bin 0 -> 644 bytes .../openaire_fight_ncov.expected.mode2.rds | Bin 0 -> 420 bytes .../openaire_fight_ncov.expected.mode3.rds | Bin 0 -> 420 bytes .../replay/orcid_0204881x.expected.mode2.rds | Bin 0 -> 540 bytes .../replay/orcid_0204881x.expected.mode3.rds | Bin 0 -> 540 bytes .../replay/orcid_02979614.expected.mode2.rds | Bin 0 -> 334 bytes .../replay/orcid_02979614.expected.mode3.rds | Bin 0 -> 334 bytes .../replay/orcid_22336926.expected.mode2.rds 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server/preprocessing/other-scripts/test/replay/pubmed_vienna.expected.mode3.rds create mode 100644 server/preprocessing/other-scripts/test/replay/pubmed_vienna_all.expected.mode2.rds create mode 100644 server/preprocessing/other-scripts/test/replay/pubmed_vienna_all.expected.mode3.rds create mode 100644 server/preprocessing/other-scripts/test/replay/pubmed_work.expected.mode2.rds create mode 100644 server/preprocessing/other-scripts/test/replay/pubmed_work.expected.mode3.rds create mode 100644 server/preprocessing/other-scripts/test/replay/pubmed_work_all.expected.mode2.rds create mode 100644 server/preprocessing/other-scripts/test/replay/pubmed_work_all.expected.mode3.rds diff --git a/server/preprocessing/other-scripts/test/mine_mode3.R b/server/preprocessing/other-scripts/test/mine_mode3.R new file mode 100644 index 000000000..f911fe76b --- /dev/null +++ b/server/preprocessing/other-scripts/test/mine_mode3.R @@ -0,0 +1,52 @@ +#!/usr/bin/env Rscript +# Mine the replay fixtures for clusters where the MeSH-aware ranking (Modes 2/3) +# actually fires, and classify WHY. For each cluster whose Mode-3 label differs from +# Mode-1 it prints the m1/m2/m3 labels, the per-cluster MeSH rank sources, and tags: +# GENERIC-DEMOTE : an m1 label term that is a GENERIC MeSH token is gone in m3 +# SPECIFIC-TOPUP : an m3 label term is a SPECIFIC MeSH token not in the m1 label +# CROSS-BACKFILL : an m3 specific-MeSH term string-contains an m1 keyword (replaced) +# MODE3-DISTINCT : m3 differs from m2 (the specific-MeSH-own-rank / cross-denest split) +# +# R_PROFILE_USER=/dev/null Rscript test/mine_mode3.R [fixture.inputs.rds ...] + +suppressWarnings(suppressMessages(source("test/replay_harness.R"))) + +norm <- function(x) gsub(" ", "_", tolower(trimws(x))) +terms <- function(lbl) { t <- trimws(strsplit(as.character(lbl), ",")[[1]]); t[nzchar(t)] } + +# Per-cluster rank sources (MeSH columns derived from subject_orig, as base.R does). +cluster_sources <- function(b) { + md <- add_mesh_rank_fields(backfill_subject_is_heuristic(b$metadata)) + md$keywords_rank_cleaned <- md$subject + get_cluster_corpus(b$clusters, md, b$stops, b$taxonomy_separator, heuristic_col = HEUR_MIN2)$rank_sources +} + +mine <- function(p) { + b <- readRDS(p); name <- fixture_name(p) + rs <- cluster_sources(b) + m1 <- replay_labels(b, "1"); m2 <- replay_labels(b, "2"); m3 <- replay_labels(b, "3") + for (k in names(m1)) { + if (identical(unname(m1[k]), unname(m3[k]))) next # Mode 3 not applied here + ci <- as.integer(k) + spec <- rs$mesh_specific[[ci]]; gen <- rs$mesh_generic[[ci]] + t1 <- terms(m1[k]); t3 <- terms(m3[k]) + tags <- character(0) + if (any(norm(setdiff(t1, t3)) %in% gen)) tags <- c(tags, "GENERIC-DEMOTE") + if (any(norm(setdiff(t3, t1)) %in% spec)) tags <- c(tags, "SPECIFIC-TOPUP") + for (a in t3[norm(t3) %in% spec]) for (bt in t1) + if (a != bt && grepl(bt, a, fixed = TRUE)) tags <- c(tags, "CROSS-BACKFILL") + if (!identical(unname(m2[k]), unname(m3[k]))) tags <- c(tags, "MODE3-DISTINCT") + tags <- unique(tags); if (!length(tags)) tags <- "OTHER" + cat(sprintf("\n[%s] %s cl%s\n", paste(tags, collapse = ","), name, k)) + cat(" m1:", m1[k], "\n m2:", m2[k], "\n m3:", m3[k], "\n") + if (length(spec)) cat(" specific MeSH:", paste(head(gsub("_", " ", spec), 8), collapse = " | "), "\n") + if (length(gen)) cat(" generic MeSH:", paste(head(gsub("_", " ", gen), 8), collapse = " | "), "\n") + } + gc(FALSE) +} + +args <- commandArgs(trailingOnly = TRUE) +files <- if (length(args)) args else grep( + "base_cancer_fallback|base_cancer_research|orcid_5116955x\\.|orcid_96127791\\.|orcid_42216275|orcid_45050517|orcid_39246636|orcid_89117832|orcid_22336926|orcid_90626039", + fixture_files(), value = TRUE) +for (f in files) mine(f) diff --git a/server/preprocessing/other-scripts/test/replay/base_cancer_fallback.expected.mode2.rds b/server/preprocessing/other-scripts/test/replay/base_cancer_fallback.expected.mode2.rds new file mode 100644 index 0000000000000000000000000000000000000000..a2e03e3bd59ca6084330341030c931682131e3ba GIT binary patch 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+++ b/server/preprocessing/other-scripts/mesh_fields.R @@ -1,6 +1,6 @@ # mesh_fields.R # Shared, reusable production of the MeSH rank-provenance metadata columns used by -# ranking Modes 2/3 (docs/area_title_ranking_plan.md §11). Data integrations +# ranking Modes 2/3 . Data integrations # (base.R, and later pubmed/openaire) `source()` this file and call # add_mesh_rank_fields(metadata) once they have populated `subject_orig` (the RAW, # still-[MeSH]-marked subject). It adds two ADDITIVE columns and leaves From d5d68c4b4a1b7f0f6ed2b65604bfe1b4b9ffda59 Mon Sep 17 00:00:00 2001 From: chreman Date: Mon, 13 Jul 2026 13:05:50 +0200 Subject: [PATCH 161/205] mode 3 replay fixtures --- .../other-scripts/test/test_mode1_selection.R | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/server/preprocessing/other-scripts/test/test_mode1_selection.R b/server/preprocessing/other-scripts/test/test_mode1_selection.R index 68c7d2252..5c4a25793 100644 --- a/server/preprocessing/other-scripts/test/test_mode1_selection.R +++ b/server/preprocessing/other-scripts/test/test_mode1_selection.R @@ -2,11 +2,11 @@ # # These pin the Stage-1 ranking behaviour end-to-end (corpus + rank map + waterfall) # on frozen real data, complementing the pure-unit tests in test_ranking_select.R: -# G1 §15-3 guard: no candidate term drifts out of the rank map (unknown == 0). +# G1 guard: no candidate term drifts out of the rank map (unknown == 0). # G2 de-nesting: no area label contains a term nested inside another. # C1 exclusivity: with rank 1 present, the label is drawn ONLY from rank 1. # C2 fallback: with rank 1 empty, the label falls back to rank 2 (non-empty). -# Runs inside the pipeline image (needs tm). See docs §6, §15. +# Runs inside the pipeline image (needs tm). if (!exists("mode1_cluster_breakdown")) source("test/replay_harness.R") if (!requireNamespace("testthat", quietly = TRUE)) { @@ -40,8 +40,8 @@ if (length(fixtures) == 0) { cat(" (no fixtures in test/replay — skipping Mode-1 selection tests)\n") } else { - # G1 — §15-3: every pruned tf-idf term resolves to a rank (no drift). - test_that("Mode 1: zero unknown-rank terms across all fixtures (§15-3 guard)", { + # G1: every pruned tf-idf term resolves to a rank (no drift). + test_that("Mode 1: zero unknown-rank terms across all fixtures", { total <- 0 for (fx in fixtures) total <- total + breakdown(fx)$unknown_total expect_equal(total, 0) From c276fa1ff5eacd5614c942329363d483c34138c9 Mon Sep 17 00:00:00 2001 From: chreman Date: Mon, 13 Jul 2026 18:26:15 +0200 Subject: [PATCH 162/205] metadata handling bugfix --- server/workers/orcid/src/orcid_service.py | 22 +++++++++++++++++----- 1 file changed, 17 insertions(+), 5 deletions(-) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 52da6197f..5640ce189 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -413,12 +413,17 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra # Select and rename relevant fields from base_metadata, including subject_orig fields_to_merge = { - 'oa_state': 'oa_state_base', - 'subject': 'subject_base', + 'oa_state': 'oa_state_base', + 'subject': 'subject_base', 'subject_orig': 'subject_orig_base', # Include subject_orig - 'paper_abstract': 'paper_abstract_base', + 'paper_abstract': 'paper_abstract_base', 'link': 'link_base', - 'relation': 'relation_base' + 'relation': 'relation_base', + # ranking Modes 2/3: carry the MeSH rank columns produced by the BASE + # client through the enrichment merge (otherwise they are dropped here and + # only re-created empty by the reindex below -> Modes 2/3 degrade to Mode 1). + 'keywords_rank_mesh_specific': 'keywords_rank_mesh_specific_base', + 'keywords_rank_mesh_generic': 'keywords_rank_mesh_generic_base' } # Rename base metadata columns to avoid conflicts with original metadata @@ -462,8 +467,15 @@ def custom_merge(existing_value, new_value): enriched_metadata['relation'] = enriched_metadata.apply( lambda row: custom_merge(row['relation'], row['relation_base']), axis=1 ) + # ORCID records have no MeSH of their own, so take the BASE-derived columns. + enriched_metadata['keywords_rank_mesh_specific'] = enriched_metadata.apply( + lambda row: custom_merge(row.get('keywords_rank_mesh_specific'), row.get('keywords_rank_mesh_specific_base')), axis=1 + ) + enriched_metadata['keywords_rank_mesh_generic'] = enriched_metadata.apply( + lambda row: custom_merge(row.get('keywords_rank_mesh_generic'), row.get('keywords_rank_mesh_generic_base')), axis=1 + ) - enriched_metadata.drop(columns=['paper_abstract_base', 'subject_orig_base', 'subject_base', 'oa_state_base', 'link_base', 'relation_base'], inplace=True) + enriched_metadata.drop(columns=['paper_abstract_base', 'subject_orig_base', 'subject_base', 'oa_state_base', 'link_base', 'relation_base', 'keywords_rank_mesh_specific_base', 'keywords_rank_mesh_generic_base'], inplace=True) if self.logger.isEnabledFor(logging.DEBUG): self._log_dataframe(enriched_metadata.sort_values(by='title'), params, '_enriched') From 4fb437161be037aa313ae2d341c37a6c4a966470 Mon Sep 17 00:00:00 2001 From: chreman Date: Mon, 13 Jul 2026 18:56:38 +0200 Subject: [PATCH 163/205] metadata handling bugfix --- server/workers/common/common/enrichment.py | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/server/workers/common/common/enrichment.py b/server/workers/common/common/enrichment.py index 632068fbc..67e32e712 100644 --- a/server/workers/common/common/enrichment.py +++ b/server/workers/common/common/enrichment.py @@ -79,6 +79,8 @@ def enrich_anchor_using_duplicates(df, duplicate_groups): has_link = 'link' in df.columns has_additional_dois = 'additional_dois' in df.columns has_doi = 'doi' in df.columns + has_mesh_specific = 'keywords_rank_mesh_specific' in df.columns + has_mesh_generic = 'keywords_rank_mesh_generic' in df.columns is_all_columns_are_missing = (not has_subject_orig and not has_subject and not has_paper_abstract and not has_oa_state and not has_link and not has_additional_dois) @@ -112,6 +114,8 @@ def enrich_anchor_using_duplicates(df, duplicate_groups): oa_state_acc = {'best_value': None, 'best_priority': float('inf')} all_links = set() additional_dois_acc = {} + mesh_specific_acc = {'all_keywords': set(), 'best_value': None, 'best_count': 0} + mesh_generic_acc = {'all_keywords': set(), 'best_value': None, 'best_count': 0} for element_idx in idx: if has_subject_orig: @@ -139,6 +143,15 @@ def enrich_anchor_using_duplicates(df, duplicate_groups): additional_dois_value = group_data.loc[element_idx, 'additional_dois'] if has_additional_dois else None process_additional_dois_element(doi_value, additional_dois_value, additional_dois_acc) + # MeSH rank columns (ranking Modes 2/3): merge them exactly like subject_orig, + # so a duplicate carrying MeSH is not lost when its subject_orig is absorbed + # into the anchor. Without this the anchor gets [MeSH]-marked subject_orig but + # EMPTY MeSH columns, and Modes 2/3 silently degrade to Mode 1. + if has_mesh_specific: + process_subject_element(group_data.loc[element_idx, 'keywords_rank_mesh_specific'], mesh_specific_acc) + if has_mesh_generic: + process_subject_element(group_data.loc[element_idx, 'keywords_rank_mesh_generic'], mesh_generic_acc) + if has_subject_orig: apply_subject_improvements(df, anchor_idx, subject_orig_acc, 'subject_orig') @@ -157,6 +170,11 @@ def enrich_anchor_using_duplicates(df, duplicate_groups): if has_additional_dois: apply_additional_dois_improvements(df, anchor_idx, additional_dois_acc) + if has_mesh_specific: + apply_subject_improvements(df, anchor_idx, mesh_specific_acc, 'keywords_rank_mesh_specific') + if has_mesh_generic: + apply_subject_improvements(df, anchor_idx, mesh_generic_acc, 'keywords_rank_mesh_generic') + # _log_anchor_state('AFTER', df, anchor_idx) return df From 7379cd0a8e04df0fb4111e307c0304be4560508b Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 14 Jul 2026 14:20:46 +0200 Subject: [PATCH 164/205] unit tests for metadata handling esp. of new columns --- .../workers/common/tests/test_enrichment.py | 265 ++++++++++++++++++ .../common/tests/test_process_oa_state.py | 76 +++++ 2 files changed, 341 insertions(+) create mode 100644 server/workers/common/tests/test_enrichment.py create mode 100644 server/workers/common/tests/test_process_oa_state.py diff --git a/server/workers/common/tests/test_enrichment.py b/server/workers/common/tests/test_enrichment.py new file mode 100644 index 000000000..6b96a7ff4 --- /dev/null +++ b/server/workers/common/tests/test_enrichment.py @@ -0,0 +1,265 @@ +"""Integration tests for PDF link enrichment in common.enrichment. + +These run the full `enrich_anchor_using_duplicates` function on small, +hand-built DataFrames and assert on the `pdf_link_candidates_from_duplicates` +column. They cover: + + * happy path: duplicates contribute new candidates + * https is preferred over http during dedup + * the anchor's own link is filtered out + * empty / single-element groups don't write a candidates value + * multiple candidates are joined and sorted + +Run from the package directory: + + cd server/workers/common && pytest tests/test_enrichment.py +""" + +import pandas as pd +import pytest + +from common.enrichment import enrich_anchor_using_duplicates + + +def _make_df(rows): + """Build a DataFrame with the columns enrichment expects. + + `rows` is a list of dicts. Any missing column defaults to '' so the test + can stay focused on the link logic. + """ + columns = ["id", "is_anchor", "link", "subject_orig", "subject", + "paper_abstract", "oa_state", "doi", "title", + "keywords_rank_mesh_specific", "keywords_rank_mesh_generic", + "pdf_link_candidates_from_duplicates"] + defaults = {c: "" for c in columns} + defaults["is_anchor"] = False + full_rows = [{**defaults, **r} for r in rows] + df = pd.DataFrame(full_rows) + # Match the indexing the base worker uses — string ids as the index. + df.index = df["id"] + return df + + +def _groups_for(df): + """All rows go into a single duplicate group keyed by their first id.""" + return pd.Series({df["id"].iloc[0]: df.index.tolist()}) + + +def test_anchor_gains_candidates_from_duplicate_links(): + df = _make_df([ + {"id": "a", "is_anchor": True, "link": "https://doi.org/10.1/anchor"}, + {"id": "b", "is_anchor": False, "link": "https://repo.example.org/paper/42"}, + {"id": "c", "is_anchor": False, "link": "https://pubmed.ncbi.nlm.nih.gov/12345"}, + ]) + + result = enrich_anchor_using_duplicates(df, _groups_for(df)) + candidates = result.loc["a", "pdf_link_candidates_from_duplicates"] + + assert candidates # non-empty + parts = [p.strip() for p in candidates.split(";")] + assert "https://repo.example.org/paper/42" in parts + assert "https://pubmed.ncbi.nlm.nih.gov/12345" in parts + # Anchor's own link is excluded. + assert "https://doi.org/10.1/anchor" not in parts + + +def test_anchor_gains_mesh_from_duplicate(): + # Regression: the anchor absorbs subject_orig from duplicates (incl. [MeSH]) but + # historically kept its own (empty) MeSH columns -> [MeSH]-marked subject_orig with + # empty MeSH columns, so ranking Modes 2/3 degraded to Mode 1. The MeSH columns must + # merge like subject_orig. + df = _make_df([ + {"id": "a", "is_anchor": True, "subject_orig": "Economics", + "keywords_rank_mesh_specific": "", "keywords_rank_mesh_generic": ""}, + {"id": "b", "is_anchor": False, "subject_orig": "Cooperative Behavior [MeSH]; Humans [MeSH]", + "keywords_rank_mesh_specific": "Cooperative Behavior", "keywords_rank_mesh_generic": "Humans"}, + ]) + + result = enrich_anchor_using_duplicates(df, _groups_for(df)) + + assert "Cooperative Behavior" in result.loc["a", "keywords_rank_mesh_specific"] + assert "Humans" in result.loc["a", "keywords_rank_mesh_generic"] + # ...and stays consistent with the subject_orig the anchor absorbed. + assert "Cooperative Behavior [MeSH]" in result.loc["a", "subject_orig"] + + +def test_https_preferred_over_http_in_candidates(): + df = _make_df([ + {"id": "a", "is_anchor": True, "link": "https://doi.org/10.1/anchor"}, + {"id": "b", "is_anchor": False, "link": "http://repo.example.org/paper/42"}, + {"id": "c", "is_anchor": False, "link": "https://repo.example.org/paper/42"}, + ]) + + result = enrich_anchor_using_duplicates(df, _groups_for(df)) + candidates = result.loc["a", "pdf_link_candidates_from_duplicates"] + parts = [p.strip() for p in candidates.split(";")] + + assert "https://repo.example.org/paper/42" in parts + assert "http://repo.example.org/paper/42" not in parts + + +def test_candidates_are_sorted_and_semicolon_joined(): + df = _make_df([ + {"id": "a", "is_anchor": True, "link": "https://doi.org/10.1/anchor"}, + {"id": "b", "is_anchor": False, "link": "https://z.example.org/x"}, + {"id": "c", "is_anchor": False, "link": "https://a.example.org/x"}, + {"id": "d", "is_anchor": False, "link": "https://m.example.org/x"}, + ]) + + result = enrich_anchor_using_duplicates(df, _groups_for(df)) + candidates = result.loc["a", "pdf_link_candidates_from_duplicates"] + parts = [p.strip() for p in candidates.split(";")] + + assert parts == sorted(parts) + # Exactly the 3 duplicate URLs, anchor link excluded. + assert len(parts) == 3 + + +def test_semicolon_separated_link_field_is_split_per_url(): + df = _make_df([ + {"id": "a", "is_anchor": True, "link": "https://doi.org/10.1/anchor"}, + {"id": "b", "is_anchor": False, + "link": "https://repo.example.org/a; https://pubmed.ncbi.nlm.nih.gov/9"}, + ]) + + result = enrich_anchor_using_duplicates(df, _groups_for(df)) + candidates = result.loc["a", "pdf_link_candidates_from_duplicates"] + parts = [p.strip() for p in candidates.split(";")] + + assert "https://repo.example.org/a" in parts + assert "https://pubmed.ncbi.nlm.nih.gov/9" in parts + + +def test_single_member_group_writes_no_candidates(): + df = _make_df([ + {"id": "a", "is_anchor": True, "link": "https://doi.org/10.1/anchor"}, + ]) + + result = enrich_anchor_using_duplicates(df, _groups_for(df)) + # Field stays at its initialized empty value. + assert result.loc["a", "pdf_link_candidates_from_duplicates"] == "" + + +def test_group_with_no_links_writes_no_candidates(): + df = _make_df([ + {"id": "a", "is_anchor": True, "link": ""}, + {"id": "b", "is_anchor": False, "link": ""}, + {"id": "c", "is_anchor": False, "link": ""}, + ]) + + result = enrich_anchor_using_duplicates(df, _groups_for(df)) + assert result.loc["a", "pdf_link_candidates_from_duplicates"] == "" + + +def test_group_without_anchor_is_left_untouched(): + df = _make_df([ + {"id": "a", "is_anchor": False, "link": "https://x.example.org/1"}, + {"id": "b", "is_anchor": False, "link": "https://y.example.org/2"}, + ]) + + result = enrich_anchor_using_duplicates(df, _groups_for(df)) + assert result.loc["a", "pdf_link_candidates_from_duplicates"] == "" + assert result.loc["b", "pdf_link_candidates_from_duplicates"] == "" + + +# --------------------------------------------------------------------------- +# additional_dois propagation. When the anchor absorbs a duplicate group, every +# DOI the group represents must be folded into the anchor's additional_dois, +# otherwise the dropped duplicate's DOI becomes unmatchable downstream (e.g. the +# ORCID worker explodes additional_dois and merges on doi_merge). Regression for +# the zenodo concept/version case (5771603 anchor absorbing 5833952). +# --------------------------------------------------------------------------- + + +def test_anchor_absorbs_duplicate_doi_into_additional_dois(): + df = _make_df([ + {"id": "a", "is_anchor": True, "doi": "https://doi.org/10.5281/zenodo.5771603", + "additional_dois": ["https://doi.org/10.5281/zenodo.5771603"], "oa_state": "1"}, + {"id": "b", "is_anchor": False, "doi": "https://doi.org/10.5281/zenodo.5833952", + "additional_dois": ["https://doi.org/10.5281/zenodo.5833952"], "oa_state": "2"}, + ]) + + result = enrich_anchor_using_duplicates(df, _groups_for(df)) + additional = result.loc["a", "additional_dois"] + + # Contract: a one-element list holding a "; "-joined string (base.R normalize_dois). + assert isinstance(additional, list) and len(additional) == 1 + dois = [d.strip() for d in additional[0].split(";")] + assert "https://doi.org/10.5281/zenodo.5771603" in dois + assert "https://doi.org/10.5281/zenodo.5833952" in dois + + +def test_additional_dois_are_deduped_case_insensitively_and_sorted(): + df = _make_df([ + {"id": "a", "is_anchor": True, "doi": "https://doi.org/10.1/AAA", + "additional_dois": ["https://doi.org/10.1/AAA"], "oa_state": "1"}, + {"id": "b", "is_anchor": False, "doi": "https://dx.doi.org/10.1/aaa", + "additional_dois": ["https://doi.org/10.1/bbb"], "oa_state": "2"}, + ]) + + result = enrich_anchor_using_duplicates(df, _groups_for(df)) + dois = [d.strip() for d in result.loc["a", "additional_dois"][0].split(";")] + + # 10.1/AAA and 10.1/aaa collapse to one entry; output is sorted. + assert len(dois) == 2 + assert dois == sorted(dois) + + +def test_single_member_group_leaves_additional_dois_untouched(): + df = _make_df([ + {"id": "a", "is_anchor": True, "doi": "https://doi.org/10.1/only", + "additional_dois": ["https://doi.org/10.1/only"], "oa_state": "1"}, + ]) + + result = enrich_anchor_using_duplicates(df, _groups_for(df)) + # Singleton groups are skipped, so the field is left exactly as provided. + assert result.loc["a", "additional_dois"] == ["https://doi.org/10.1/only"] + + +# --------------------------------------------------------------------------- +# Known-issue tests. These document current behaviour and would need to flip +# if the filter in `apply_link_improvements` is hardened (see review notes in +# the enrichment.py logging discussion). +# --------------------------------------------------------------------------- + + +@pytest.mark.xfail( + reason="Known issue: the anchor's own link can re-enter the candidate " + "set when only the protocol differs, because the post-filter " + "uses exact string comparison.", + strict=True, +) +def test_anchor_link_protocol_upgrade_is_excluded(): + df = _make_df([ + {"id": "a", "is_anchor": True, "link": "http://repo.example.org/paper/42"}, + {"id": "b", "is_anchor": False, "link": "https://repo.example.org/paper/42"}, + ]) + + result = enrich_anchor_using_duplicates(df, _groups_for(df)) + candidates = result.loc["a", "pdf_link_candidates_from_duplicates"] + parts = [p.strip() for p in candidates.split(";") if p.strip()] + + # Both URLs resolve to the same target; an https upgrade of the anchor's + # own URL should not count as new candidate info. + assert "https://repo.example.org/paper/42" not in parts + + +@pytest.mark.xfail( + reason="Known issue: when the anchor link is itself multi-valued " + "(semicolon-joined), each component is not filtered individually.", + strict=True, +) +def test_multi_valued_anchor_link_components_are_filtered(): + df = _make_df([ + {"id": "a", "is_anchor": True, + "link": "https://repo.example.org/a; https://repo.example.org/b"}, + {"id": "b", "is_anchor": False, "link": "https://repo.example.org/a"}, + {"id": "c", "is_anchor": False, "link": "https://pubmed.ncbi.nlm.nih.gov/9"}, + ]) + + result = enrich_anchor_using_duplicates(df, _groups_for(df)) + candidates = result.loc["a", "pdf_link_candidates_from_duplicates"] + parts = [p.strip() for p in candidates.split(";") if p.strip()] + + assert "https://repo.example.org/a" not in parts + assert "https://pubmed.ncbi.nlm.nih.gov/9" in parts diff --git a/server/workers/common/tests/test_process_oa_state.py b/server/workers/common/tests/test_process_oa_state.py new file mode 100644 index 000000000..55865290b --- /dev/null +++ b/server/workers/common/tests/test_process_oa_state.py @@ -0,0 +1,76 @@ +"""Unit tests for `process_oa_state_element` in common.enrichment. + +These exercise the priority-pick logic directly, without running the full +`enrich_anchor_using_duplicates` pipeline. Priority is "1" (yes) > +"0" (no) > "2" (unknown). + +Run from the package directory: + + cd server/workers/common && pytest tests/test_process_oa_state.py +""" + +import numpy as np +import pandas as pd +import pytest + +from common.enrichment import process_oa_state_element + + +def _fresh_acc(): + return {"best_value": None, "best_priority": float("inf")} + + +def test_yes_beats_unknown(): + acc = _fresh_acc() + process_oa_state_element("2", acc) + process_oa_state_element("1", acc) + assert acc["best_value"] == "1" + + +def test_yes_beats_no(): + acc = _fresh_acc() + process_oa_state_element("0", acc) + process_oa_state_element("1", acc) + assert acc["best_value"] == "1" + + +def test_no_beats_unknown(): + acc = _fresh_acc() + process_oa_state_element("2", acc) + process_oa_state_element("0", acc) + assert acc["best_value"] == "0" + + +def test_better_existing_is_kept(): + acc = _fresh_acc() + process_oa_state_element("1", acc) + process_oa_state_element("2", acc) + assert acc["best_value"] == "1" + + +def test_nan_is_ignored(): + acc = _fresh_acc() + process_oa_state_element(np.nan, acc) + assert acc["best_value"] is None + assert acc["best_priority"] == float("inf") + + +def test_unknown_string_does_not_win_against_known(): + acc = _fresh_acc() + process_oa_state_element("2", acc) + process_oa_state_element("garbage", acc) + assert acc["best_value"] == "2" + + +@pytest.mark.parametrize("value", ["1", 1, 1.0]) +def test_yes_value_wins_regardless_of_numeric_type(value): + """`oa_state` arrives as a string in BASE but other workers (e.g. orcid) + cast it to int, and pandas may upcast to float after a left-join that + introduces NaN. The priority pick should treat all three as 'yes'.""" + acc = _fresh_acc() + process_oa_state_element("2", acc) + process_oa_state_element(value, acc) + assert acc["best_value"] == value, ( + f"value={value!r} (type={type(value).__name__}) was not recognised " + f"as oa_state=yes; accumulator ended at {acc!r}" + ) From 4bc33fb57915d6819477c675792e465c5f097bb6 Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 17 Jul 2026 13:39:59 +0200 Subject: [PATCH 165/205] update of contentprovider.json cache --- .../common/common/contentproviders.json | 2416 ++--------------- 1 file changed, 298 insertions(+), 2118 deletions(-) diff --git a/server/workers/common/common/contentproviders.json b/server/workers/common/common/contentproviders.json index ee7f175a8..5055ed2de 100644 --- a/server/workers/common/common/contentproviders.json +++ b/server/workers/common/common/contentproviders.json @@ -1,4 +1,300 @@ [ + { + "name": "Research Parks Publishing", + "internal_name": "ftid14944" + }, + { + "name": "Digital Social Sciences", + "internal_name": "ftid14916" + }, + { + "name": "Portal de revistas ESAN", + "internal_name": "ftid14917" + }, + { + "name": "Horizontes Amazónicos", + "internal_name": "ftid14918" + }, + { + "name": "Publicera", + "internal_name": "ftid14919" + }, + { + "name": "FE Gulf Publishers", + "internal_name": "ftid14920" + }, + { + "name": "Asian Journal of Public Health and Nursing", + "internal_name": "ftid14921" + }, + { + "name": "Journals Nusanara", + "internal_name": "ftid14922" + }, + { + "name": "Journal of Applied Pharmacology and Toxicology", + "internal_name": "ftid14923" + }, + { + "name": "Lembaga KITA Open Journal Systems", + "internal_name": "ftid14924" + }, + { + "name": "Haliç Yayınevi", + "internal_name": "ftid14915" + }, + { + "name": "Scientific Journals Online Andrzej Frycz Modrzewski Krakow University", + "internal_name": "ftid14868" + }, + { + "name": "Scholastic Research Publication (SRP)", + "internal_name": "ftid14881" + }, + { + "name": "E-Journal Yayasan Pustaka Karya Mandiri", + "internal_name": "ftid14893" + }, + { + "name": "Libyan Journal of Medical and Applied Sciences (LJMAS)", + "internal_name": "ftid14898" + }, + { + "name": "Egertu Digital Library", + "internal_name": "ftid14901" + }, + { + "name": "Fondo documental digital Melilla-Rif", + "internal_name": "ftid14902" + }, + { + "name": "EduFacturing Open AM Knowledge Base", + "internal_name": "ftid14903" + }, + { + "name": "IASRD Open Access Journals", + "internal_name": "ftid14904" + }, + { + "name": "Revista Fórum Trabalhista - RFT", + "internal_name": "ftid14905" + }, + { + "name": "Omni Journal", + "internal_name": "ftid14906" + }, + { + "name": "True Parents Legacy Archive", + "internal_name": "ftid14907" + }, + { + "name": "Journal of Educational Impact", + "internal_name": "ftid14908" + }, + { + "name": "Rumah Jurnal Digital Edukasi Nusantara", + "internal_name": "ftid14909" + }, + { + "name": "Institute of Advanced Technology and Green Innovation (INATGI)", + "internal_name": "ftid14910" + }, + { + "name": "Journal of Health Synapse (JHS)", + "internal_name": "ftid14911" + }, + { + "name": "Journal of Biomedical & Space Sciences (JBSS)", + "internal_name": "ftid14912" + }, + { + "name": "Revista Esmat", + "internal_name": "ftid14913" + }, + { + "name": "Journal of Computer Science, Engineering & Applied Mathematics (JCSEAM)", + "internal_name": "ftid14914" + }, + { + "name": "Adiyaman University Research and Academic Performance System", + "internal_name": "ftid14865" + }, + { + "name": "Journal Institutre", + "internal_name": "ftid14866" + }, + { + "name": "Universidad Americana", + "internal_name": "ftid14848" + }, + { + "name": "International Journal of Research Welfare Society", + "internal_name": "ftid14870" + }, + { + "name": "Biblioteca Digital Editora Poisson", + "internal_name": "ftid14876" + }, + { + "name": "Tourismnomic: Journal of Tourism and Economic", + "internal_name": "ftid14882" + }, + { + "name": "Dar El-Mahara Center", + "internal_name": "ftid14883" + }, + { + "name": "Revista Científica Prospherus", + "internal_name": "ftid14884" + }, + { + "name": "Social Lens", + "internal_name": "ftid14885" + }, + { + "name": "Acta Peruana de Ciencias Sociales y Humanidades", + "internal_name": "ftid14886" + }, + { + "name": "Horizons Intermediary Journal of Business Research (HIJBR)", + "internal_name": "ftid14887" + }, + { + "name": "EduLearn Revista Multidisciplinaria", + "internal_name": "ftid14888" + }, + { + "name": "Scripta Scientia", + "internal_name": "ftid14889" + }, + { + "name": "Journal of the Epidemiology Foundation of India", + "internal_name": "ftid14890" + }, + { + "name": "International Journal of Ayurveda & Modern Sciences (IJAMS)", + "internal_name": "ftid14891" + }, + { + "name": "Revista de Epidemiologia e Saúde Pública (RESP)", + "internal_name": "ftid14892" + }, + { + "name": "Subset Journal", + "internal_name": "ftid14894" + }, + { + "name": "PT. Global Pustaka Ilmiah", + "internal_name": "ftid14895" + }, + { + "name": "Nutrition and Metabolism Journal: Clinical and Experimental", + "internal_name": "ftid14896" + }, + { + "name": "Jurnal Kajian Pembaruan Hukum (JKPH)", + "internal_name": "ftid14900" + }, + { + "name": "International Journal of Eco-Innovation in Science and Engineering (IJEISE)", + "internal_name": "ftid14836" + }, + { + "name": "Asosiasi Transformasi Digital dan Bisnis Indonesia", + "internal_name": "ftid14841" + }, + { + "name": "Jurnal Online Fakultas Tarbiyah dan Keguruan UIN SMH Banten", + "internal_name": "ftid14842" + }, + { + "name": "Espacio ECP", + "internal_name": "ftid14857" + }, + { + "name": "SAP Multidisciplinary Open", + "internal_name": "ftid14858" + }, + { + "name": "Global Journals", + "internal_name": "ftid14859" + }, + { + "name": "Pelabuhan Jurnal IAI TABAH", + "internal_name": "ftid14860" + }, + { + "name": "Global Health Synapse", + "internal_name": "ftid14861" + }, + { + "name": "Incaper em Revista", + "internal_name": "ftid14862" + }, + { + "name": "International Journal of Economics and Project Management", + "internal_name": "ftid14863" + }, + { + "name": "Legal Research & Analysis", + "internal_name": "ftid14869" + }, + { + "name": "Insitut Muslim Cendekia", + "internal_name": "ftid14871" + }, + { + "name": "Margulan Readings", + "internal_name": "ftid14872" + }, + { + "name": "Civilization Research: Journal of Islamic Studies", + "internal_name": "ftid14873" + }, + { + "name": "Stanzaleaf International Journal of Multidisciplinary Studies", + "internal_name": "ftid14874" + }, + { + "name": "Edelweispublishing", + "internal_name": "ftid14875" + }, + { + "name": "Journal of Education and Development Lab", + "internal_name": "ftid14877" + }, + { + "name": "Journal of South Asian Issues (JSAI)", + "internal_name": "ftid14878" + }, + { + "name": "International Journal of Politics and International Relations (IJPIR)", + "internal_name": "ftid14879" + }, + { + "name": "Bielefeld University Press (BiUP)", + "internal_name": "crid14897" + }, + { + "name": "ACCScience Publishing", + "internal_name": "crid14899" + }, + { + "name": "Repositorio de la Universidad Industrial de Santander", + "internal_name": "ftid14824" + }, + { + "name": "AgEcon Frontiers", + "internal_name": "ftid14829" + }, + { + "name": "Institut Binamadani Indonesia (INBI): Scientific Ejournal", + "internal_name": "ftid14831" + }, + { + "name": "Mandailing Journal of Education and Sciences", + "internal_name": "ftid14834" + }, { "name": "Institutional Repository DAU", "internal_name": "ftid14799" @@ -1547,10 +1843,6 @@ "name": "Bulletin of Scientific Research in English Education", "internal_name": "ftid14458" }, - { - "name": "Scientific and Innovative Therapy", - "internal_name": "ftid14433" - }, { "name": "Qainar Journal of Social Science", "internal_name": "ftid14445" @@ -1868,7 +2160,7 @@ "internal_name": "ftid14416" }, { - "name": "Helmut-Schmidt-Universität Hamburg: Volltextserver der HSU", + "name": "openHSU - Helmut-Schmidt-Universität Hamburg / Universität der Bundeswehr", "internal_name": "fthsunivhamburg" }, { @@ -2827,10 +3119,6 @@ "name": "Repositorio Institucional CONACYT", "internal_name": "ftid14072" }, - { - "name": "Jurnal Yayasan Pendidikan Intan Cendekia", - "internal_name": "ftid14077" - }, { "name": "Revista Disserata", "internal_name": "ftid14078" @@ -3131,10 +3419,6 @@ "name": "Rumah Jurnal", "internal_name": "ftid14064" }, - { - "name": "UNEJ", - "internal_name": "ftid14068" - }, { "name": "Journals of Delitekno Media Mandiri", "internal_name": "ftid14070" @@ -3431,10 +3715,6 @@ "name": "Journal Micro Economic Sharia", "internal_name": "ftid13980" }, - { - "name": "Novelty Edukasi Islam", - "internal_name": "ftid13981" - }, { "name": "Bornov", "internal_name": "ftid13982" @@ -3835,10 +4115,6 @@ "name": "E-Journal Meja Ilmiah", "internal_name": "ftmejailmiah" }, - { - "name": "E-Jurnal ISBI Bandung (Institut Seni Budaya Indonesia)", - "internal_name": "ftisbibandung2" - }, { "name": "Firmana Research Center: OJS", "internal_name": "ftfrcojs" @@ -4599,10 +4875,6 @@ "name": "Simbiosis - Rrevista de Educación y Psicología", "internal_name": "ftjsimbiosis" }, - { - "name": "International Journal of Medicine and Occupational Health and Safety Sciences (IJMOHSS)", - "internal_name": "ftjijmohss" - }, { "name": "Review of Artificial Intelligence in Education", "internal_name": "ftjraie" @@ -4619,10 +4891,6 @@ "name": "Convergência Lusíada", "internal_name": "ftjrcl" }, - { - "name": "Journal of Artificial Intelligence Research (JAIR)", - "internal_name": "ftjair" - }, { "name": "Science Journal of University of Zakho (SJUOZ)", "internal_name": "ftjsjuoz" @@ -4907,10 +5175,6 @@ "name": "Jurnal Tekstil - Jurnal Keilmuan dan Aplikasi Bidang Tekstil dan Manajemen Industri (JUTE)", "internal_name": "ftjkabtmi" }, - { - "name": "Izzatuna - Jurnal Ilmu Al-Qur'an dan Tafsir", - "internal_name": "ftjizzatuna" - }, { "name": "Aggiornamento - Jurnal Filsafat Teologi Kontekstual", "internal_name": "ftjaggiornamento" @@ -5019,10 +5283,6 @@ "name": "The Indian Society of Agricultural Engineers: OJS", "internal_name": "ftindiansaeojs" }, - { - "name": "International Journal of Magistravitae Management (IJOMM)", - "internal_name": "ftjijomm" - }, { "name": "Multidisciplinary Journal of Horseed International University (MJHIU)", "internal_name": "ftjmjhiu" @@ -5163,10 +5423,6 @@ "name": "E-Jurnal Poltekkes Kemenkes Tasikmalaya", "internal_name": "ftpoltasikmalaya" }, - { - "name": "UNIT: Urdarbrønnen", - "internal_name": "ftunivbrage" - }, { "name": "Journal Of Fisheries Agribusiness", "internal_name": "ftungorosvjijfa" @@ -5975,10 +6231,6 @@ "name": "Revista Centro Universitário Paulistano", "internal_name": "ftcntrupaulistan" }, - { - "name": "Jurnal Farmasi Sandi Karsa (JFS)", - "internal_name": "ftjfsk" - }, { "name": "Science China Press", "internal_name": "crsciencechinapr" @@ -6155,10 +6407,6 @@ "name": "Journal of Veterinary Physiology and Pathology (JVPP)", "internal_name": "ftjvpp" }, - { - "name": "Portal de Publicações UNIFIMES (Universitário de Mineiros)", - "internal_name": "ftunifimesojs" - }, { "name": "GEMA - Jurnal Gentiaras Manajemen dan Akuntansi", "internal_name": "ftjgema" @@ -6319,10 +6567,6 @@ "name": "IAES International Journal of Robotics and Automation (IJRA)", "internal_name": "ftjijra" }, - { - "name": "Jurnal Rekayasa Elektro Sriwijaya (JRES)", - "internal_name": "ftjrekayasaes" - }, { "name": "Journal of Digital Social Research", "internal_name": "ftjodsr" @@ -6691,10 +6935,6 @@ "name": "Adi Buana Repository (Universitas PGRI Adi Buana Surabaya)", "internal_name": "ftupgriadibuasur" }, - { - "name": "Digilib IKIP PGRI Pontianak", - "internal_name": "ftikippgripntiak" - }, { "name": "eprint UIN Raden Fatah Palembang", "internal_name": "ftuinradenfatah" @@ -6727,10 +6967,6 @@ "name": "RICL - Repository of Institute of Comparative Law", "internal_name": "ftinsclawbelgrad" }, - { - "name": "UMM Electronic Theses and Dissertations Repository ( Universitas Muhammadiyah Malang)", - "internal_name": "ftunivmmalangdis" - }, { "name": "Portail HAL UHA (Université de Haute-Alsace)", "internal_name": "ftunihautealsace" @@ -7367,10 +7603,6 @@ "name": "Repositorio Institucional de la Universidad Nacional de Música", "internal_name": "ftunivnmusica" }, - { - "name": "IPBPub - Institute of Physics Belgrade Publication Repository", - "internal_name": "ftinsphysicsblgr" - }, { "name": "CLARIN-PL digital repository (Common Language Resources & Technology Infrastructure)", "internal_name": "ftclarinpl" @@ -7699,10 +7931,6 @@ "name": "International Journal of Recent Advances in Multidisciplinary Topics", "internal_name": "ftjijramt" }, - { - "name": "Journal of Humanities and Social Science Research", - "internal_name": "ftjhssr" - }, { "name": "International Journal of Engineering Business and Social Science (IJEBSS)", "internal_name": "ftjijebss" @@ -7759,10 +7987,6 @@ "name": "Portal de Revistas ESDEG (Escuela Superior de Guerra \"General Rafael Reyes Prieto\")", "internal_name": "ftescsdguerra" }, - { - "name": "Data & Metadata", - "internal_name": "ftjmetadata" - }, { "name": "Jurnal Nafatimah Gresik Pustaka", "internal_name": "ftnafatimahppust" @@ -7875,10 +8099,6 @@ "name": "Journal Sport Academy (JSA)", "internal_name": "ftjsportacad" }, - { - "name": "Jurnal Kesehatan “Love That Renewed”", - "internal_name": "ftjkhjltr" - }, { "name": "Journal Siddiq Institute", "internal_name": "ftsiddiqinst" @@ -7999,10 +8219,6 @@ "name": "Médiations et médiatisations", "internal_name": "ftjrmediations" }, - { - "name": "Österreichische Zeitschrift für Politikwissenschaft", - "internal_name": "ftjoezp" - }, { "name": "Pathfinder - A Canadian Journal for Information Science Students and Early Career Professionals", "internal_name": "ftjpathfinder" @@ -8039,10 +8255,6 @@ "name": "Bulletin of Biological and Allied Sciences Research", "internal_name": "ftjbbasr" }, - { - "name": "Journal of Peace & Diplomacy (JPD)", - "internal_name": "ftjjpd" - }, { "name": "Boletines de la Academia Nacional de Historia del Ecuador", "internal_name": "ftjbanh" @@ -8063,10 +8275,6 @@ "name": "Wordly Knowledge", "internal_name": "ftwordlyknowlg" }, - { - "name": "Medical Journal of South Punjab (MJSP)", - "internal_name": "ftjmjsp" - }, { "name": "AESS Publications (Asian Economic and Social Society)", "internal_name": "ftaesspublojs" @@ -8243,10 +8451,6 @@ "name": "Smart Cities and Regional Development (SCRD) Open Access Publishing", "internal_name": "ftscrdojs" }, - { - "name": "Eagora Science (GKA Ediciones)", - "internal_name": "fteagoraojs" - }, { "name": "Pakistan Journal of Clinical Psychology", "internal_name": "ftjpjcp" @@ -8259,10 +8463,6 @@ "name": "Educação Transversal: OJS", "internal_name": "fteducacaotransv" }, - { - "name": "Metaverse Basic and Applied Research", - "internal_name": "ftjmetaverse" - }, { "name": "Eurasian Journal of Economic and Business Studies (EJEBS)", "internal_name": "ftjejebs" @@ -8563,10 +8763,6 @@ "name": "Journal EKSTENSI (Eksplorasi Teknologi Enterprise & Sistem Informasi)", "internal_name": "ftjekstensi" }, - { - "name": "Directory Of Holyphang Hopa (Phang-Edu) Journal", - "internal_name": "ftphangedu" - }, { "name": "Journal SIKOMTIA (Sistem Komputer & Teknologi Intelegensi Artifisial)", "internal_name": "ftjsikomtia" @@ -8575,10 +8771,6 @@ "name": "IQTISHOD - Jurnal Pemikiran dan Hukum Ekonomi Syariah", "internal_name": "ftjiqtishod" }, - { - "name": "IPB Cirebon (Institut Pendidikan dan Bahasa Invada): OJS", - "internal_name": "ftinstpbcirebon" - }, { "name": "Melek IT - Information Technology Journal", "internal_name": "ftjmiitj" @@ -8783,10 +8975,6 @@ "name": "Open Journal System Universitas Harapan Medan (UnHar)", "internal_name": "ftunharapanmedan" }, - { - "name": "Jurnal Media Online Forum Pemuda Giat Publikasi ilmiah (FORDAGIPI)", - "internal_name": "ftfordagipiojs" - }, { "name": "Jurnal Pengabdian Masyarakat Singa Podium (JPMSIPO)", "internal_name": "ftjpmsipo" @@ -9239,10 +9427,6 @@ "name": "Asian Journal of Dental and Health Sciences (AJDHS)", "internal_name": "ftjajdhs" }, - { - "name": "University of Tampa Institutional Repository", - "internal_name": "ftunivtampa" - }, { "name": "Institutional Repository der FHNW (Fachhochschule Nordwestschweiz)", "internal_name": "ftfhnwschweiz" @@ -9303,10 +9487,6 @@ "name": "Infermia Journal", "internal_name": "ftinfermiajojs" }, - { - "name": "Portal de Periódicos Científicos do Instituto Federal do Piauí", - "internal_name": "ftifpiojs" - }, { "name": "Annales Henri Lebesgue", "internal_name": "ftjahl" @@ -9391,10 +9571,6 @@ "name": "Linköping Electronic Press Workshop and Conference Collection", "internal_name": "ftlinkoepunivocs" }, - { - "name": "Komunikan - Jurnal Komunikasi dan Dakwah", - "internal_name": "ftjkomunikan" - }, { "name": "湘南鎌倉医療大学学術情報リポジトリ", "internal_name": "ftshonankamaums" @@ -9415,10 +9591,6 @@ "name": "Journal of Multimedia Trend and Technology (JMTT)", "internal_name": "ftjmtt" }, - { - "name": "Jurnal Sains Manajemen, Bisnis dan Administrasi", - "internal_name": "ftjsmba" - }, { "name": "Jurnal Politeknik Negeri Ambon", "internal_name": "ftpolinambonojs" @@ -9487,10 +9659,6 @@ "name": "Langgam Journal - International Journal of Social Science Education, Art and Culture", "internal_name": "ftjlanggam" }, - { - "name": "E-Journal ILMAWA - Rumah Journal Institut Al-Ma'arif Way Kanan", - "internal_name": "ftstaimaarifojs" - }, { "name": "Repositorio Digital Institucional de la Universidad Nacional del Sur (RID-UNS)", "internal_name": "ftunivndelsur" @@ -9523,10 +9691,6 @@ "name": "RIA-UAEH (Repositorio Académico Digital Universidad Autónoma del Estado de Hidalgo)", "internal_name": "ftuniaehidalgoir" }, - { - "name": "DARIUS Repository (Dissertation Archive Repository Information Usage Service - Houston Baptist University)", - "internal_name": "fthbaptunidarius" - }, { "name": "Gexin Online Publications", "internal_name": "crgexinpubl" @@ -9615,10 +9779,6 @@ "name": "Editora Universitária da UFCG (Universidade Federal de Campina Grande)", "internal_name": "ftufcampinagromp" }, - { - "name": "LabCom Comunicacao e Artes (Universidade da Beira Interior)", - "internal_name": "ftunivbeirainojs" - }, { "name": "Запорізький національний університет наукових публікацій", "internal_name": "ftzaporizhzhianu" @@ -9739,10 +9899,6 @@ "name": "Masker Medika", "internal_name": "ftjmaskermedika" }, - { - "name": "JALHu - Jurnal Al-Mujaddid Humaniora", - "internal_name": "ftjalhu" - }, { "name": "The International Journal of Education Management and Sociology (IJEMS)", "internal_name": "ftjijems" @@ -9919,10 +10075,6 @@ "name": "EVSOS (Educación y Vida Sostenible)", "internal_name": "ftjevsos" }, - { - "name": "Portal de periódicos da UCSal (Universidade Católica do Salvador)", - "internal_name": "ftunivcsalvador" - }, { "name": "Portal de Periódicos do Centro Universitário Alfredo Nasser", "internal_name": "ftcentrouanasser" @@ -10047,10 +10199,6 @@ "name": "Jurnal Oase Nusantara", "internal_name": "ftjoase" }, - { - "name": "Bulletin of Community Service in Information System (BECERIS)", - "internal_name": "ftjbeceris" - }, { "name": "Jurnal Hukum Pidana & Kriminologi (JHPK)", "internal_name": "ftjhpk" @@ -10063,10 +10211,6 @@ "name": "Contemporary Journal on Business and Accounting (CjBA)", "internal_name": "ftjcjba" }, - { - "name": "LPPM STIKes Karya Kesehatan Kendari: Open Journal System", - "internal_name": "ftstikeskendari" - }, { "name": "Universitas Sebelas Maret: UNS Journal", "internal_name": "ftusebalasmaret2" @@ -10111,10 +10255,6 @@ "name": "Journal of Learning Improvement and Lesson Study", "internal_name": "ftjlils" }, - { - "name": "Universidad Nacional de Frontera (UNFS): Repositorio Instucional", - "internal_name": "ftunivnfrontera" - }, { "name": "FEPOL - Fondo Editorial Professionals On Line", "internal_name": "ftfepolomp" @@ -10307,10 +10447,6 @@ "name": "Portal de Revistas Institucionales Corporación Universitaria Remington", "internal_name": "ftcunivremington" }, - { - "name": "المجلات العلمية لجامعة جيجل", - "internal_name": "ftunivjijelojs" - }, { "name": "Journals of Hawassa University", "internal_name": "fthawassauniojs" @@ -10367,10 +10503,6 @@ "name": "Revista Ciencias y Humanidades (Centro de Estudios en Ciencias y Humanidades)", "internal_name": "ftjrcyh" }, - { - "name": "Publicaciones y Revistas de la Universidad Adventista de Bolivia", - "internal_name": "ftuadventistabol" - }, { "name": "Prometeo Conocimiento Científico", "internal_name": "ftjprometeo" @@ -10395,18 +10527,10 @@ "name": "QQRCenter: Journals (Qualitative Quantitative Research)", "internal_name": "ftqqrcenter" }, - { - "name": "Jurnal Online STIT Misbahul Ulum Gumawang", - "internal_name": "ftstitmugu" - }, { "name": "RCS Publishing by Lembaga Jurnal & Publikasi UM Buton", "internal_name": "ftrcspubl" }, - { - "name": "Kumpulan Jurnal Yayasan Pendidikan Ujung Pandang Makassar", - "internal_name": "ftypupmakassar" - }, { "name": "Ejournal Universitas Teknologi Digital Indonesia", "internal_name": "ftunitdindonesia" @@ -10759,10 +10883,6 @@ "name": "Platform of Georgian Academic Journals", "internal_name": "ftgeorgianopjou" }, - { - "name": "Urban - Jurnal Seni Urban dan Industri Budaya", - "internal_name": "ftjurban" - }, { "name": "E-Journal UKiP (Universitas Kristen Papua)", "internal_name": "ftunivkpapua" @@ -10783,10 +10903,6 @@ "name": "REUNIDO Portal de Revistas de a Universidad de Oviedo", "internal_name": "ftunivoviedoojs" }, - { - "name": "Arthatama (E-Jpournal)", - "internal_name": "ftjarthatama" - }, { "name": "E-Journal Portal Sekolah Tinggi Agama Islam Taruna Sura", "internal_name": "ftstaitarunasura" @@ -10931,10 +11047,6 @@ "name": "Revistats Científicas de la Universidade de Taubaté", "internal_name": "ftunivtaubateojs" }, - { - "name": "Jurnal STIKES MATARAM", - "internal_name": "ftstikesmatarojs" - }, { "name": "South East European Journal of Immunology", "internal_name": "ftjseejim" @@ -11015,10 +11127,6 @@ "name": "Portal de Revistas Scientificas de la Universidad Le Cordon Bleu", "internal_name": "ftunivlecbleuojs" }, - { - "name": "Jurnal Unika Santu Paulus Ruteng (Universitas Katolik Indonesia)", - "internal_name": "ftunivkrutojs" - }, { "name": "Open Journal Systems Jurnal Universitas Sang Bumi Ruwa Jurai Lampung", "internal_name": "ftunivsbrjlamojs" @@ -11067,10 +11175,6 @@ "name": "Research Review - Jurnal Ilmiah Multidisiplin", "internal_name": "ftjrrjim" }, - { - "name": "Open European Academy of Public Sciences (OEAPS)", - "internal_name": "ftopenacadpsojs" - }, { "name": "Aitías - Revista de Estudios Filosóficos de la UANL", "internal_name": "ftjaitias" @@ -11147,10 +11251,6 @@ "name": "Centrism Foundation: OJS", "internal_name": "ftcentfoundojs" }, - { - "name": "Gateway Digital Collections (UNC Greensboro - UNCG)", - "internal_name": "ftuncarolinagdc" - }, { "name": "Digital Smith (Johnson C. Smith University)", "internal_name": "ftjohncsmithuniv" @@ -11251,10 +11351,6 @@ "name": "Open Jurnal System Online Universitas Pembangunan Panca Budi", "internal_name": "ftunivppancabudi" }, - { - "name": "Jurnal Ilmiah Guru Madrasah (JIGM)", - "internal_name": "ftjigm" - }, { "name": "Hawari Publikasi: CV.Hawari", "internal_name": "fthawaripubl" @@ -11699,10 +11795,6 @@ "name": "Jurnal Pascasarjana Universitas Mataram", "internal_name": "ftunivmatajpojs" }, - { - "name": "Open Journal System (OJS) Cahaya Andakara", - "internal_name": "ftatoojs" - }, { "name": "Jurnal Riset Perkebunan (JRP)", "internal_name": "ftjjrp" @@ -12167,10 +12259,6 @@ "name": "Best Publication", "internal_name": "ftbestpublicatio" }, - { - "name": "Jurnal Online Politeknik Kesehatan Kartini Bali", - "internal_name": "ftpoltekkkb" - }, { "name": "International Journal of Engineering Technologies and Management Research", "internal_name": "ftjijetmr" @@ -12235,10 +12323,6 @@ "name": "Journal of Quranic and Social Studies (JQSS)", "internal_name": "ftjoqass" }, - { - "name": "Jurnal Pendidikan dan Teknologi Kesehatan", - "internal_name": "ftjptkesehatan" - }, { "name": "Datokarama English Education Journal", "internal_name": "ftjdeej" @@ -12255,10 +12339,6 @@ "name": "Sao Literasi Publisher: OJS", "internal_name": "ftsaolitpublojs" }, - { - "name": "Portal Eletrônico de Períódicos da Academia Nacional de Policia (ANP)", - "internal_name": "ftacadnpoliojs" - }, { "name": "Journal of Applied Health Sciences and Medicine", "internal_name": "ftjahsm" @@ -12335,10 +12415,6 @@ "name": "Lighthouse Publishing Company", "internal_name": "ftlighthousepubl" }, - { - "name": "Jurnal Maitreyawira", - "internal_name": "ftjmaitreyawira" - }, { "name": "Jurnal Fakultas Teknologi dan Manajemen Kesehatan (FTMK) Institut Ilmu Kesehatan (IIK) Bhakti Wiyata", "internal_name": "ftinstilmukeftmk" @@ -12363,10 +12439,6 @@ "name": "Theology and Philosophy of Education (TAPE)", "internal_name": "ftjtape" }, - { - "name": "Jurnal Sosio-Komunika (JSK)", - "internal_name": "ftjsosiokomunika" - }, { "name": "GPH International Journals", "internal_name": "ftgphojs" @@ -12411,10 +12483,6 @@ "name": "Repositorio Institucional de UNIBE (Universidad Iberoamericana)", "internal_name": "ftuniiberoameric" }, - { - "name": "Salud, Ciencia y Tecnología (SCT)", - "internal_name": "ftjsct" - }, { "name": "Maestro y Sociedad", "internal_name": "ftjmys" @@ -12511,10 +12579,6 @@ "name": "DRS Digital Library", "internal_name": "ftdesignresearch" }, - { - "name": "Payne Center Research Hub", - "internal_name": "ftpaynecentersj" - }, { "name": "Asian Journal of Hospital Pharmacy", "internal_name": "ftjajhp" @@ -12795,10 +12859,6 @@ "name": "OJS Seminar Indonesia Journal", "internal_name": "ftseminarindones" }, - { - "name": "Research in Technical and Vocational Education and Training", - "internal_name": "ftjrintvet" - }, { "name": "Takuana - Jurnal Pendidikan, Sains, dan Humaniora", "internal_name": "ftjtakuana" @@ -13211,10 +13271,6 @@ "name": "Journal of Architectural and Engineering Research", "internal_name": "ftjaer" }, - { - "name": "Tetkik - Türk-İslam Kültürü Dergisi", - "internal_name": "ftjtetkik" - }, { "name": "SIED-UNMdP: OJS (Universidad Nacional de Mar del Plata)", "internal_name": "ftunmardelplata" @@ -13263,14 +13319,6 @@ "name": "Український фізичний журнал (UJP)", "internal_name": "ftjujp" }, - { - "name": "ІДЕЇ. ФІЛОСОФСЬКИЙ ЧАСОПИС. СПЕЦІАЛЬНІ НАУКОВІ ВИПУСКИ", - "internal_name": "ftjipjssi" - }, - { - "name": "Наукові записки. Серія: Філологічні науки Центральноукраїнський державний педагогічний університет імені Володимира Винниченка", - "internal_name": "ftjcuspusps" - }, { "name": "Наукові записки. Серія: Педагогічні науки Центральноукраїнський державний педагогічний університет імені Володимира Винниченка", "internal_name": "ftjcuspups" @@ -13287,10 +13335,6 @@ "name": "Jurnal IAIN Pontianak", "internal_name": "ftiainpontianak2" }, - { - "name": "Дніпровський державний аграрно-економічний університет: OJS", - "internal_name": "ftdnipropetrsteu" - }, { "name": "Карантин і захист рослин", "internal_name": "ftjqpp" @@ -13299,30 +13343,6 @@ "name": "Захист і карантин рослин", "internal_name": "ftjppqd" }, - { - "name": "Актуальні проблеми держави і права", - "internal_name": "ftjapdb" - }, - { - "name": "Актуальні проблеми політики ", - "internal_name": "ftjcpop" - }, - { - "name": "Часопис цивілістики", - "internal_name": "ftjchc" - }, - { - "name": "Знання європейського права", - "internal_name": "ftjjes" - }, - { - "name": "Прикарпатський юридичний вісник", - "internal_name": "ftjpyuv" - }, - { - "name": "Юридичний вісник", - "internal_name": "ftjyuv" - }, { "name": "Вісник Дніпровського університету. Серія: Механіка", "internal_name": "ftjbdusm" @@ -13431,10 +13451,6 @@ "name": "Хімія, фізика та технологія поверхні", "internal_name": "ftjcpts" }, - { - "name": "ВІСНИК ОДЕСЬКОГО НАЦІОНАЛЬНОГО МОРСЬКОГО УНІВЕРСИТЕТУ ", - "internal_name": "ftjhonmu" - }, { "name": "Український Педагогічний журнал", "internal_name": "ftjuej" @@ -13647,10 +13663,6 @@ "name": "European International Journals - Next Scientists", "internal_name": "fteijojs" }, - { - "name": "Gnosis Carajá", - "internal_name": "ftjgc" - }, { "name": "Revista Ciencia & Sociedad (Universidad Autónoma Tomas Frias)", "internal_name": "ftjrcys" @@ -13739,10 +13751,6 @@ "name": "Innovation and Sustainability", "internal_name": "ftjins" }, - { - "name": "Інноватика у вихованні", - "internal_name": "ftjiiu" - }, { "name": "Вісник Економіки (ЗУНУ - Західноукраїнський національний університет)", "internal_name": "ftwestukrnatuniv" @@ -13923,14 +13931,6 @@ "name": "Економічний часопис Волинського національного університету імені Лесі Українки", "internal_name": "ftjechas" }, - { - "name": "Актуальні питання іноземної філології", - "internal_name": "ftjapiph" - }, - { - "name": "Науковий вісник Східноєвропейського національного університету імені Лесі Укаїнки", - "internal_name": "ftjrgf" - }, { "name": "Текст і образ - Актуальні проблеми історії мистецтва", "internal_name": "ftjtxim" @@ -14043,10 +14043,6 @@ "name": "Lembaga Riset dan Inovasi Al-Matani", "internal_name": "ftlembagaalmatan" }, - { - "name": "Fundamental and Applied Soil Science", - "internal_name": "ftjfass" - }, { "name": "Ejournal INSUD Lamongan (LP2M Institut Pesantren Sunan Drajat Lamongan Jawa Timur)", "internal_name": "ftinsudlamonga" @@ -14211,10 +14207,6 @@ "name": "Texas A&M University: A&M-Commerce Digital Commons", "internal_name": "fttexamucommerce" }, - { - "name": "Zorotic Online Library", - "internal_name": "ftzoroticohs" - }, { "name": "Akademia Morska w Szczecinie: Biblioteka Cyfrowa", "internal_name": "ftamszczecin" @@ -14499,10 +14491,6 @@ "name": "OJS YASPIM", "internal_name": "ftyaspimojs" }, - { - "name": "Rumah Jurnal Kopertais Wilayah 1 DKI Jakarta dan Banten", - "internal_name": "ftkopertwilayah" - }, { "name": "OJS ProSciences", "internal_name": "ftprosciences" @@ -14599,10 +14587,6 @@ "name": "Scientific Publications Rey Media Grafika", "internal_name": "ftrmgojs" }, - { - "name": "Jurnal Online Fakultas Ushuluddin dan Filsafat Universitas Islam Negeri Sunan Ampel Surabaya", - "internal_name": "ftiainsunanamfuf" - }, { "name": "Sustainability Science and Resources (SSR)", "internal_name": "ftjssr" @@ -14611,10 +14595,6 @@ "name": "Open Conference System Universitas Islam Malang Conference", "internal_name": "ftunivimalangocs" }, - { - "name": "Journal of Education, Science and Health - JESH (Revista de Educação, Ciência e Saúde)", - "internal_name": "ftjesh" - }, { "name": "STAR - Jurnal Sains dan Kesehatan Terapan", "internal_name": "ftjstar" @@ -14627,10 +14607,6 @@ "name": "Yayasan Penelitian dan Inovasi Sumatera (YPIS): OJS", "internal_name": "ftypisojs" }, - { - "name": "Jurnal Online Fakultas Ekonomi dan Bisnis UIN Sunan Ampel", - "internal_name": "ftiainsunanafebi" - }, { "name": "Jurnal Kiprah Pendidikan", "internal_name": "ftjkpd" @@ -14647,14 +14623,6 @@ "name": "Open Access Jakarta Journal of Health Sciences", "internal_name": "ftjoajjhs" }, - { - "name": "Jurnal Pendidikan Agama Islam", - "internal_name": "ftjpai" - }, - { - "name": "Universitasa Islam Negeri (UIN) Sunan Ampel Surabaya: OJS", - "internal_name": "ftiainsunanampps" - }, { "name": "CHIMIA - International Journal for Chemistry and Official Membership Journal of the Swiss Chemical Society (SCS) and its Divisions", "internal_name": "ftjchimia" @@ -14715,10 +14683,6 @@ "name": "Jurnal Adhyasta Pemilu (JAP)", "internal_name": "ftjjap" }, - { - "name": "International Journal of Advances in Agricultural Science and Technology (IJAAST)", - "internal_name": "ftjijaast" - }, { "name": "Revista Itacaiúnas", "internal_name": "ftjitacaiunas" @@ -14739,10 +14703,6 @@ "name": "Journal of Palembang Nursing Studies (JPNS)", "internal_name": "ftjpns" }, - { - "name": "Rumah Jurnal Sekolah Tinggi Agama Buddha (STAB) Dharma Widya", - "internal_name": "ftstabdharmawidy" - }, { "name": "Jurnal Politeknik Penerbangan Indonesia Curug", "internal_name": "ftppicurug" @@ -14767,10 +14727,6 @@ "name": "STIEKEN Blitar Repository", "internal_name": "ftstikenblitar" }, - { - "name": "Universidad Nacional San Luis Gonzaga: DSpace", - "internal_name": "ftunivslgonzaga" - }, { "name": "Gümüşhane Üniversitesi Kurumsal Akademik Arşiv Sistemi (DSpace@Gümüşhane)", "internal_name": "ftgumushaneuniv" @@ -14839,10 +14795,6 @@ "name": "Anagrafe della Ricerca d'Ateneo (Universitá degli studi Roma Tre)", "internal_name": "ftunivroma3iris" }, - { - "name": "International Archives of Medicine", - "internal_name": "ftjiam" - }, { "name": "CINECA IRIS Universitá Degli Studi di Sassari", "internal_name": "ftsassariuniiris" @@ -14855,10 +14807,6 @@ "name": "EleA@Unisa (Università degli Studi di Salerno)", "internal_name": "ftunisalernoiris" }, - { - "name": "Revista Odontología Pediátrica", - "internal_name": "ftjop" - }, { "name": "Unnur Repository (Universitas Nurtanio Bandung)", "internal_name": "ftunurtaniobandu" @@ -14883,10 +14831,6 @@ "name": "Jurnal Ekonomi & Bisnis (JEB)", "internal_name": "ftjeb" }, - { - "name": "NERO (Networking Engineering Research Operation)", - "internal_name": "ftjnero" - }, { "name": "INSPIRA Open Journal Systems", "internal_name": "ftinspirasi" @@ -14935,10 +14879,6 @@ "name": "Repository Universitas Muhammadiyah Bangka Belitung", "internal_name": "ftunmuhammadiyah" }, - { - "name": "CEIBS Research Online", - "internal_name": "ftceibschool" - }, { "name": "Open Publishing LMU (Ludwig-Maximilians-Universität München)", "internal_name": "ftlmumuenchenoph" @@ -15055,10 +14995,6 @@ "name": "Zeal Press", "internal_name": "ftzealpressojs" }, - { - "name": "STKIP Kie Raha Ternate - Open Journal System", - "internal_name": "ftstkipkierahate" - }, { "name": "Rumah Jurnal STAI DDI Kota Makassar", "internal_name": "ftstaiddimakassa" @@ -15067,10 +15003,6 @@ "name": "Sekigen Akademi Dergileri (SA)", "internal_name": "ftsekizgenacad" }, - { - "name": "Sekolah Tinggi Agama Islam (STAI) Binamadani Tangerang OJS", - "internal_name": "ftstaibinamadani" - }, { "name": "Markas Jurnal Sekolah Tinggi Agama Islam Publisistik Thawalib Jakarta", "internal_name": "ftstaipublisisti" @@ -15079,10 +15011,6 @@ "name": "CV.Eureka Murakabi Abad", "internal_name": "ftcveurekamuraka" }, - { - "name": "Jurnal Online Fakultas Adab dan Humaniora Universitas Islam Negeri Sunan Ampel Surabaya", - "internal_name": "ftiainsunanamfah" - }, { "name": "Universidad Nacional Agraria La Molina: Repositorio Institucional", "internal_name": "ftunivnalamolina" @@ -15179,10 +15107,6 @@ "name": "Journal of Government and Political Issues (JGPI)", "internal_name": "ftjgpi" }, - { - "name": "Revista de Enfermagem e Saúde Baseada em Evidência (RESBE)", - "internal_name": "ftjresbe" - }, { "name": "Jurnal Locus Media", "internal_name": "ftlocusmedia" @@ -15411,10 +15335,6 @@ "name": "Repository of Institute of Agricultural Economics (IAE)", "internal_name": "ftinsagriculecon" }, - { - "name": "Repository of Politeknik Negeri Banjarmasin", - "internal_name": "ftpoliteknbanjar" - }, { "name": "Central Asian Journal of Literature, Philosophy and Culture", "internal_name": "ftjcajlpc" @@ -15535,10 +15455,6 @@ "name": "Global Journal for Management and Administrative Sciences", "internal_name": "ftjgjmas" }, - { - "name": "International Journal of Minerals, Metallurgy and Materials", - "internal_name": "ftjijmmm" - }, { "name": "Scientific Journal of Astana IT University", "internal_name": "ftjsjaitu" @@ -15551,10 +15467,6 @@ "name": "CICERO Senter for klimaforskning", "internal_name": "ftcicerosfk" }, - { - "name": "Ascarya Solution Publisher", - "internal_name": "ftascaryasol" - }, { "name": "IVL Svenska Miljöinstitutet", "internal_name": "ftserinst" @@ -15575,10 +15487,6 @@ "name": "EUG institutional repository (Escuelas Universitarias Gimbernat)", "internal_name": "ftescunivgimbern" }, - { - "name": "Repositorio institucional Escuela de postgrados Fuerza Aérea Colombiana", - "internal_name": "ftescuelapfac" - }, { "name": "ELAKPI – Електронний архів наукових та освітніх матеріалів КПІ ім. Ігоря Сікорського", "internal_name": "ftkyivpinstitute" @@ -15607,10 +15515,6 @@ "name": "IMDEA Networks Institute Digital Repository", "internal_name": "ftimdeanetinst" }, - { - "name": "AMAD - „Archivum Medii Aevi Digitale - Interdisziplinäres Open-Access-Fachrepositorium und Wissenschaftsblog für Mittelalterforschung‟", - "internal_name": "ftamad" - }, { "name": "EIR NUOS - Репозитарій НУК (електронний інституційний репозитарій)", "internal_name": "ftnuosuniv" @@ -15767,10 +15671,6 @@ "name": "TU Delft Open Textbooks", "internal_name": "fttudelfttbo" }, - { - "name": "Ejournal Catuspata", - "internal_name": "ftcatuspataojs" - }, { "name": "Kadirli Uygulamalı Bilimler Fakültesi Dergisi", "internal_name": "ftjkubfd" @@ -15819,10 +15719,6 @@ "name": "Pakistan Journal of Health Sciences", "internal_name": "ftjtjas" }, - { - "name": "Jurnal Sekolah Tinggi Keguruan dan Ilmu Pendidikan (STKIP) Banten", - "internal_name": "ftstkipbantenojs" - }, { "name": "Jurnal Huriah (Journal of Educational Evaluation and Research)", "internal_name": "ftjhurriah" @@ -15859,10 +15755,6 @@ "name": "Avanti Publishers", "internal_name": "ftavanpublishers" }, - { - "name": "Fetus and Newborn", - "internal_name": "ftjfnb" - }, { "name": "Portal E-Jurnal Sekolah Tinggi Ilmu Syariah Abu Zairi Bondowoso", "internal_name": "ftstisabuzairibo" @@ -15871,10 +15763,6 @@ "name": "Journal IAIN Takengon", "internal_name": "ftiaintakengon" }, - { - "name": "Publikasi Jurnal Universitas Yapis Papua", - "internal_name": "ftuniyapispapua2" - }, { "name": "Seismo Verlag", "internal_name": "crseismoverlag" @@ -16003,10 +15891,6 @@ "name": "Jaringan Sistem Informasi Robotik - JSR", "internal_name": "ftjsir" }, - { - "name": "Current Research in Biochemistry and Molecular Biology (CRBMB, E-Journal)", - "internal_name": "ftjcrbmb" - }, { "name": "Revista Brasileira de Ciências Ambientais (RBCIAMB)", "internal_name": "ftjrbciamb" @@ -16027,10 +15911,6 @@ "name": "Revistas Científicas de la Universidad EAN", "internal_name": "ftuniveanojs" }, - { - "name": "Jurnal Ilmiah Nizamia", - "internal_name": "ftjinizamia" - }, { "name": "ASSAf Research Respository (Academy of Science of South Africa)", "internal_name": "ftassaf" @@ -16099,10 +15979,6 @@ "name": "E-Journal Universitas Muhammadiyah Cirebon", "internal_name": "ftunivmcirebojs" }, - { - "name": "e-Journal IAIN Pekalongan", - "internal_name": "ftiainpekalonojs" - }, { "name": "Le Matematiche (Dipartimento di Matematica e Informatica, Università degli Studi di Catania)", "internal_name": "ftjlm" @@ -16139,10 +16015,6 @@ "name": "TUScholarShare (Temple University)", "internal_name": "fttempleuniv" }, - { - "name": "Sekolah Tinggi Agama Islam Bumi Silampari (STAI Bumi Silampari)", - "internal_name": "ftstaibumsil" - }, { "name": "Universitaria Agustiniana Institutional Repository", "internal_name": "ftunivagustinia" @@ -16223,10 +16095,6 @@ "name": "Korpus 21", "internal_name": "ftjkorpus21" }, - { - "name": "Tumotowa", - "internal_name": "ftjtumotowa" - }, { "name": "Melo - Jurnal Studi Agama-Agama", "internal_name": "ftjmelo" @@ -16251,10 +16119,6 @@ "name": "Dialectical Literature And Education Journal", "internal_name": "ftjdlej" }, - { - "name": "Lensa", - "internal_name": "ftjoflensa" - }, { "name": "Quantum Journal of Social Sciences and Humanities (QJSSH)", "internal_name": "ftjqjssh" @@ -16279,10 +16143,6 @@ "name": "Rumah Jurnal Institut Agama Hindu Negeri Gde Pudja Mataram", "internal_name": "ftiaingdepumaojs" }, - { - "name": "Nuris Journal of Education and Islamic Studies", - "internal_name": "ftjnuris" - }, { "name": "Revista Académica Internacional de Educación Física", "internal_name": "ftjraief" @@ -16351,10 +16211,6 @@ "name": "World Journal of Current Medical and Pharmaceutical Research (WJCMPR)", "internal_name": "ftjwjcmpr" }, - { - "name": "Rumah Jurnal INKAFA (Institut Keislaman Abdullah Faqih) Gresik", - "internal_name": "ftinstkafagrojs" - }, { "name": "Revistas de Análisis Económico y Financiero", "internal_name": "ftjraef" @@ -16591,10 +16447,6 @@ "name": "Revista Científica ACERTTE (Administração, Contábeis, Economia, Turismo e Engenharia)", "internal_name": "ftjacertte" }, - { - "name": "SESAWI - Jurnal Teologi dan Pendidikan Kristen", - "internal_name": "ftjsesawi" - }, { "name": "Texere", "internal_name": "ftjtexere" @@ -16603,10 +16455,6 @@ "name": "Bulletin of the Academy of Sciences of Moldova. Medical Science", "internal_name": "ftjbulmed" }, - { - "name": "Jurnal STIKES Abdi Nusantara Jakarta", - "internal_name": "ftstikesanjojs" - }, { "name": "Cubic Journal", "internal_name": "ftjcubic" @@ -16679,18 +16527,10 @@ "name": "International Journal of Multidisciplinary - Applied Business and Education Research", "internal_name": "ftjijmaber" }, - { - "name": "E-Journal UNIWARA (Universitas PGRI Wiranegara)", - "internal_name": "ftunivpgriwiojs" - }, { "name": "Revista Científica Estudiantil Inmedsur", "internal_name": "ftjims" }, - { - "name": "Journal of Science and Research in Nursing (JSRN)", - "internal_name": "ftjsrn" - }, { "name": "Jurnal Universitas Primagraha", "internal_name": "ftunivprimagojs" @@ -16751,10 +16591,6 @@ "name": "Journal of Sustainability Science and Technology (JOSST)", "internal_name": "ftjosst" }, - { - "name": "Journal of Socio-Economics and Religious Studies (JSERS)", - "internal_name": "ftjsers" - }, { "name": "Global Clinical Research Journal (Glob Clin Res)", "internal_name": "ftjgcrj" @@ -16863,10 +16699,6 @@ "name": "Open Journal Systems STT Kadesi Yogyakarta", "internal_name": "ftsttkyogyojs" }, - { - "name": "Rumah Jurnal Humani", - "internal_name": "fthumaniiojs" - }, { "name": "Open Journal System of STKIP Muhammadiyah Barru", "internal_name": "ftstkipmuhammojs" @@ -16911,22 +16743,10 @@ "name": "Revista Educación y Sociedad", "internal_name": "ftjres" }, - { - "name": "Jurnal Ekonomi Rabbani", - "internal_name": "ftjerabbani" - }, { "name": "Portal de la Investigación Universidad de La Rioja", "internal_name": "ftunivriojair" }, - { - "name": "Jurnal Indah Sains dan Klinis", - "internal_name": "ftjisk" - }, - { - "name": "Literacy - Jurnal Ilmiah Sosial (JIS)", - "internal_name": "ftjis" - }, { "name": "Revistas Cientificas de la ESPAM MFL", "internal_name": "ftespammflojs" @@ -16971,10 +16791,6 @@ "name": "UNIMED - Revista Científica Estudiantil", "internal_name": "ftjunimed" }, - { - "name": "Journal LLDikti (Lembaga Layanan Pendidikan Tinggi) Wilayah XII", - "internal_name": "ftlldiktiwilxiia" - }, { "name": "OpenWorks @ MD Anderson (The University of Texas MD Anderson Cancer Center)", "internal_name": "ftutexasmdanders" @@ -17003,10 +16819,6 @@ "name": "International Journal of Educational Studies in Social Sciences (IJESSS)", "internal_name": "ftjijesss" }, - { - "name": "International Journal of Forensic Research & Criminal Justice (IJFRCJ)", - "internal_name": "ftjijfrcj" - }, { "name": "BIMIKI (Berkala Ilmiah Mahasiswa Ilmu Keperawatan Indonesia)", "internal_name": "ftjbimiki" @@ -17067,10 +16879,6 @@ "name": "Universidad, Ciencia y Tecnología", "internal_name": "ftjruct" }, - { - "name": "Company of Scientists Publishing (COSCIP - Company of Scientists & Physicians)", - "internal_name": "ftcoscipojs" - }, { "name": "DSpace@IIT Bombay (Indian Institute of Technology)", "internal_name": "ftiitbombay" @@ -17131,10 +16939,6 @@ "name": "RI-UCM - Repositorio Institucional-Universidad Catolica de Manizales", "internal_name": "ftunivcmanizales" }, - { - "name": "Pustaka Learning Center", - "internal_name": "ftpustalearncent" - }, { "name": "Jurnal UML (Online Journal System Universitas Muhammadiyah Lampung)", "internal_name": "ftunivmlampung" @@ -17491,18 +17295,10 @@ "name": "Jurnal SADE (Arsitektur, Planologi dan Teknik Sipil)", "internal_name": "ftjsade" }, - { - "name": "Journal of Learning and Instructional Studies", - "internal_name": "ftjjlis" - }, { "name": "E-Jurnal FKIP UMMY Solok", "internal_name": "ftunimmysolokfki" }, - { - "name": "مجلة الجامعة الأسمرية", - "internal_name": "ftjalasmaryauniv" - }, { "name": "Transnational Press London ", "internal_name": "crtransnatpress" @@ -17547,10 +17343,6 @@ "name": "Repositório do INPA", "internal_name": "ftinstnpamazon" }, - { - "name": "JIKSH: Jurnal Ilmiah Kesehatan Sandi Husada", - "internal_name": "ftjiksh" - }, { "name": "Progressive Law Review", "internal_name": "ftjplr" @@ -17623,10 +17415,6 @@ "name": "Review of Applied Management and Social Sciences (RAMSS)", "internal_name": "ftjramss" }, - { - "name": "Inter Faculty", - "internal_name": "ftjinterfaculty" - }, { "name": "Proceedings of Annual Conference for Muslim Scholars (AnCoMS)", "internal_name": "ftjancoms" @@ -17811,10 +17599,6 @@ "name": "al-Urwatul Wutsqo - Jurnal Ilmu Keislaman dan Pendidikan", "internal_name": "ftjauw" }, - { - "name": "Jurnal Arsitektur Archicentre", - "internal_name": "ftjaarchicentre" - }, { "name": "Biosight journal", "internal_name": "ftjbiosight" @@ -17847,10 +17631,6 @@ "name": "E - Journal Politeknik Negeri Samarinda", "internal_name": "ftpolteknsojs2" }, - { - "name": "Jurnal Ada Indonesia", - "internal_name": "ftadaindonesojs" - }, { "name": "Jurnal Keberlanjutan (Journal of Sustainability)", "internal_name": "ftsustainjourojs" @@ -17867,10 +17647,6 @@ "name": "Jurnal Jendela Pendidikan", "internal_name": "ftjjp" }, - { - "name": "Teoría y Práctica - Revista Peruana de Psicología CPsP-CDR-I", - "internal_name": "ftjrtyp" - }, { "name": "Revista Tecnología, Ciencia y Educación", "internal_name": "ftjtce" @@ -17887,10 +17663,6 @@ "name": "Jurnal Teknologi dan Rekayasa Sistem Komputer (TEKNOKOM)", "internal_name": "ftjteknokom" }, - { - "name": "Dirasat Nafsiat wa Tarbaweyat (Psychological & Educational Studies)", - "internal_name": "ftjdnwt" - }, { "name": "Acman - Accounting and Management Journal", "internal_name": "ftjacman" @@ -17927,10 +17699,6 @@ "name": "Jurnal Daring Fakultas Ilmu Sosial dan Ilmu Politik Universitas Jenderal Ahmad Yani", "internal_name": "ftujayanifisip" }, - { - "name": "Sekolah Tinggi Teologi (STT) Yerusalem Baru: Open Journal Systems", - "internal_name": "ftsttyerusalemba" - }, { "name": "Madrasah Jurnal - IDIA Prenduan", "internal_name": "ftmadrasahojs" @@ -17995,10 +17763,6 @@ "name": "Madrascience - Jurnal Pendidikan Islam, Sains, Sosial, dan Budaya", "internal_name": "ftjmadrascience" }, - { - "name": "International Research Journal of Advanced Science (IRJAS)", - "internal_name": "ftjirjas" - }, { "name": "UMK Electronic Journal (Universitas Muhammadiyah Kupang)", "internal_name": "ftunivmkupangojs" @@ -18007,10 +17771,6 @@ "name": "Journal of Community Service (JCS)", "internal_name": "ftjjcs" }, - { - "name": "e-Journal Universitas Indonesia Timur (UIT)", - "internal_name": "ftunivitimur" - }, { "name": "Karinosseff Muda Indonesia e-Journal System", "internal_name": "ftkarinosseffmi" @@ -18103,10 +17863,6 @@ "name": "JESS (Journal of Education on Social Science)", "internal_name": "ftjess" }, - { - "name": "Al Ashriyyah - Jurnal Studi Keislaman", - "internal_name": "ftjalashriyyah" - }, { "name": "Jurnal Pendidikan Kebutuhan Khusus (JPKK)", "internal_name": "ftjpkk" @@ -18151,10 +17907,6 @@ "name": "Vox Dei - Jurnal Teologi dan Pastoral", "internal_name": "ftjvoxdei" }, - { - "name": "Action Research Journal Indonesia (ARJI)", - "internal_name": "ftjarji" - }, { "name": "Indonesian Journal Of Education and Humanity (IJOEHM)", "internal_name": "ftjijoehm" @@ -18243,10 +17995,6 @@ "name": "Jurnal UNW Mataram", "internal_name": "ftunivnwmataram" }, - { - "name": "Jurnal Teknokes", - "internal_name": "ftjteknokes" - }, { "name": "Jurnal AL-AZHAR INDONESIA", "internal_name": "ftunivalazhar" @@ -18271,10 +18019,6 @@ "name": "Teunuleh Publisher", "internal_name": "ftteunulehpubl" }, - { - "name": "Journal of Regional Public Administration (JRPA)", - "internal_name": "ftjrpa" - }, { "name": "Jurnal Revolusi Indonesia (JRI)", "internal_name": "ftjrindonesia" @@ -18311,14 +18055,6 @@ "name": "Jurnal Ekologi, Masyarakat & Sains (EMS)", "internal_name": "ftjjems" }, - { - "name": "Jurnal Fundamental - Jurnal Ilmu Hukum", - "internal_name": "ftjfundamental" - }, - { - "name": "Jurnal Ilmiah Abdi Mas TPB Unram", - "internal_name": "ftjamtpb" - }, { "name": "FELT - Focus on ELT Journal", "internal_name": "ftjfelt" @@ -18375,10 +18111,6 @@ "name": "Repositorio Institucional Universidad Nacional de Colombia", "internal_name": "ftuncolombiair" }, - { - "name": "Ευρωπαϊκό πανεπιστήμιο Κύπρου (EUC): Πλημοχόη ιδρυματικό καταθετήριο", - "internal_name": "fteuropeancyuniv" - }, { "name": "Biblioteka Cyfrowa Uniwersytetu Jana Kochanowskiego", "internal_name": "ftjankochan" @@ -18387,10 +18119,6 @@ "name": "Digitale Sammlungen Hochschul- und Landesbibliothek RheinMain", "internal_name": "fthlbrheinmaindc" }, - { - "name": "Jurnal EDUKES (Jurnal Penelitian Edukasi Kesehatan)", - "internal_name": "ftjedukes" - }, { "name": "Eigen Mathematics Journal", "internal_name": "ftjemj" @@ -18399,10 +18127,6 @@ "name": "Jurnal Online Universitas Muara Bungo", "internal_name": "ftunivmuarabungo" }, - { - "name": "OJS Universitas Andi Jemma", - "internal_name": "ftunivandijemma" - }, { "name": "E-Jurnal UNES Padang", "internal_name": "ftlppmuekasakti" @@ -18491,18 +18215,10 @@ "name": "International Journal of Business and Social Science Research (IJBSSR)", "internal_name": "ftjijbssr" }, - { - "name": "Brazilian Journal of Policy and Development (BRJPD)", - "internal_name": "ftjbrjpd" - }, { "name": "Bosnian studies", "internal_name": "ftjbstudies" }, - { - "name": "Raudhah Proud To Be Professionals - Jurnal Tarbiyah Islamiyah", - "internal_name": "ftjraudhah" - }, { "name": "Bussecon International Academy", "internal_name": "ftbusseconint" @@ -18627,10 +18343,6 @@ "name": "LSE Law Review", "internal_name": "ftjlselr" }, - { - "name": "ADHAPER - Jurnal Hukum Acara Perdata", - "internal_name": "ftjadhaper" - }, { "name": "The Knowles Review of Economic History", "internal_name": "ftjkreh" @@ -18759,10 +18471,6 @@ "name": "Revista Científica Educ@ção (RCE)", "internal_name": "ftjrce" }, - { - "name": "OJS Sekolah Tinggi Ilmu Kesehatan Kesdam IX/Udayana", - "internal_name": "ftstikeskuojs" - }, { "name": "Journal of Science and Education (JSE)", "internal_name": "ftjse" @@ -18771,10 +18479,6 @@ "name": "Biological and Clinical Sciences Research Journal (BCSRJ)", "internal_name": "ftjbcsrj" }, - { - "name": "Jurnal STKIP Pembangunan Indonesia", - "internal_name": "ftstkippimojs" - }, { "name": "Open Journals Nigeria (OJN)", "internal_name": "ftojnigerojs" @@ -18991,10 +18695,6 @@ "name": "Universidad Autonóma de Aguascalientes DSpace", "internal_name": "ftunivaaguas" }, - { - "name": "香川県立保健医療大学リポジトリ", - "internal_name": "ftkagawapuniv" - }, { "name": "IJIIS - International Journal of Informatics and Information Systems", "internal_name": "ftjijiis" @@ -19011,10 +18711,6 @@ "name": "OJS Politeknik Cendana", "internal_name": "ftpoltekcenojs" }, - { - "name": "The University of Jordan: JU Journals Portal", - "internal_name": "ftunivjordojs" - }, { "name": "Portal de Revistas Académicas UC Temuco", "internal_name": "ftunivctemuojs" @@ -19323,10 +19019,6 @@ "name": "Banco de España Institutional Repository", "internal_name": "ftbancodeesp" }, - { - "name": "LAO Space - Laos Open Access Repository", - "internal_name": "ftlaospace" - }, { "name": "ADA Dataverse (Australian Data Archive)", "internal_name": "ftadadataverse" @@ -19407,10 +19099,6 @@ "name": "e-Journal Kementerian Sosial RI", "internal_name": "ftksosialriojs" }, - { - "name": "Sricommerce - Journal of Sriwijaya Community Service", - "internal_name": "ftjscs" - }, { "name": "EKSIBANK (Ekonomi Syariah dan Bisnis Perbankan)", "internal_name": "ftjeksisbank" @@ -19443,34 +19131,14 @@ "name": "ORFEE - HEP Vaud (Pädagogische Hochschule Waadt)", "internal_name": "fthepvaud" }, - { - "name": "Journal Vokasi UI (Universitas Indonesia)", - "internal_name": "ftuindvokasi" - }, - { - "name": "E-Journal STF Muhammadiyah Tangerang", - "internal_name": "ftstfmtojs" - }, { "name": "Revista de Derecho de la Universidad Nacional del Altiplano de Puno", "internal_name": "ftjrderecho" }, - { - "name": "rita_revista indexada de textos académicos", - "internal_name": "ftjridta" - }, - { - "name": "Jurnal Energi dan Teknologi Manufaktur (JETM)", - "internal_name": "ftjetm" - }, { "name": "International Journal of Modern Education Studies (IJONMES)", "internal_name": "ftjijonmes" }, - { - "name": "EDULEAD: Journal of Christian Education and Leadership", - "internal_name": "ftjedulead" - }, { "name": "Revista Peruana de Medicina Integrativa (RPMI)", "internal_name": "ftjrpmi" @@ -19511,10 +19179,6 @@ "name": "Sophist - Jurnal Sosial Politik Kajian Islam dan Tafsir", "internal_name": "ftjsophist" }, - { - "name": "Jurnal Online Fakultas Syariah dan Hukum (UIN Sunan Ampel Surabaya)", - "internal_name": "ftiainsunanfsh" - }, { "name": "Journal of Educational Research in Developing Areas", "internal_name": "ftjereda" @@ -19787,10 +19451,6 @@ "name": "Jurnal Farmasi Fakultas Kedokteran Universitas Mataram", "internal_name": "ftunivmataramfk" }, - { - "name": "Pusat Jurnal Kopertais Wilayah V Aceh", - "internal_name": "ftkopertaiswilay" - }, { "name": "Shared Science Publishers", "internal_name": "crsharedsp" @@ -19895,10 +19555,6 @@ "name": "EDUCARE - Journal of Primary Education (JPE)", "internal_name": "ftjeducare" }, - { - "name": "EmTHYMÓS - Revista de Estudios Empresariales", - "internal_name": "ftjemthymos" - }, { "name": "International Journal of Accounting, Finance, Auditing, Management and Economics (IJAFAME)", "internal_name": "ftjijafame" @@ -20051,10 +19707,6 @@ "name": "ASM Journals (American Society for Microbiology)", "internal_name": "crasmicro" }, - { - "name": "Scientific Research Initiative Journals", - "internal_name": "ftsciresearchini" - }, { "name": "E-Journal Fakultas Ekonomi UMI", "internal_name": "ftunimuslimindfe" @@ -20091,10 +19743,6 @@ "name": "Gardu Jurnal MU Pamekasan (LPM STAI Miftaul Ulum Pamekasan)", "internal_name": "ftstaimupamekesa" }, - { - "name": "Fundacao FAFIMAN: SEER (Sistema Eletrônico de Editoração de Revistas)", - "internal_name": "ftfundfafiman" - }, { "name": "Spizaetus - Jurnal Biologi dan Pendidikan Biologi", "internal_name": "ftjspizaetus" @@ -20167,10 +19815,6 @@ "name": "Universidad Autónoma de Bucaramanga (UNAB): Revistas", "internal_name": "ftuniabucaramang" }, - { - "name": "Journal of Contemporary Information Technology, Management, and Accounting", - "internal_name": "ftjcitma" - }, { "name": "Grouper - Jurnal Ilmiah Fakultas Perikanan Universitas Islam Lamongan", "internal_name": "ftjgrouper" @@ -20403,10 +20047,6 @@ "name": "Dépôt commun de l'Union africaine (UA)", "internal_name": "ftafricanunion" }, - { - "name": "Repositorio Universidad de Lambayeque", - "internal_name": "ftunivlambayeque" - }, { "name": "Repositorio institucional del INDECOPI", "internal_name": "ftinstdecopi" @@ -20415,10 +20055,6 @@ "name": "Repositorio Institucional de Bluefields Indian and Caribbean University", "internal_name": "ftbluefieldsicun" }, - { - "name": "Scientific Route OÜ", - "internal_name": "ftscientifroute" - }, { "name": "Repozitorijum Stomatološkog fakulteta, Univerziteta u Beogradu", "internal_name": "ftunivbelgradfdm" @@ -20463,10 +20099,6 @@ "name": "Center for the Journals of National Library of Indonesia", "internal_name": "ftnatlibraryind" }, - { - "name": "Sekolah Tinggi Pariwisata Mataram: OJS", - "internal_name": "ftstpmataramojs" - }, { "name": "Academia International Journals", "internal_name": "ftacadintjournal" @@ -20491,10 +20123,6 @@ "name": "Universidade Federal do Ceará (UFC): Portal de Periódico", "internal_name": "ftunivfcearaojs" }, - { - "name": "e-Journal STIKES Muhammadiyah Sidrap", - "internal_name": "ftstikesmsidrap" - }, { "name": "Akurasi - Jurnal Studi Akuntansi dan Keuangan", "internal_name": "ftjakurasi" @@ -20507,10 +20135,6 @@ "name": "Journal AHMER Institute", "internal_name": "ftahmarinstojs" }, - { - "name": "Jurnal Geografi Lingkungan Tropik (JGLITrop)", - "internal_name": "ftjglitrop" - }, { "name": "Asian Social Work Journal (ASWJ)", "internal_name": "ftjaswj" @@ -20699,10 +20323,6 @@ "name": "Ovid", "internal_name": "crovidcr" }, - { - "name": "Journal of Clinical and Cultural Psychiatry", - "internal_name": "ftjccp" - }, { "name": "AMPCo (Australasian Medical Publishing Company)", "internal_name": "craustralmedpubl" @@ -21155,10 +20775,6 @@ "name": "Lecturas", "internal_name": "ftjlecturas" }, - { - "name": "Jurnal Sistem Informasi dan Komputer", - "internal_name": "ftjsikom" - }, { "name": "PASCA", "internal_name": "ftjpasca" @@ -21211,10 +20827,6 @@ "name": "Kuras Institute Journal Collection", "internal_name": "ftkurasinstojs" }, - { - "name": "Jurnal IAI Bunga Bangsa Cirebon", - "internal_name": "ftiaibungabangsa" - }, { "name": "eScholarship Repository (University of California)", "internal_name": "crescholarship" @@ -21503,18 +21115,10 @@ "name": "Missionalia - Southern African Journal of Missiology", "internal_name": "ftjmissionalia" }, - { - "name": "Revistas Eletrônicas Unicruz", - "internal_name": "ftunivcruzojs" - }, { "name": "E-journal Universitas Widyagama Malang (V-3)", "internal_name": "ftuwidyagamamala" }, - { - "name": "SUST Journal Systems (Sudan University of Science and Technology)", - "internal_name": "ftsudanunivstojs" - }, { "name": "Physical Education of Students", "internal_name": "ftjpes" @@ -21599,14 +21203,6 @@ "name": "Revista Científica Multidisciplinaria Arbitrada \"YACHASUN\"", "internal_name": "ftjyachasun" }, - { - "name": "Jurnal STIkes Insan Cendekia Husada", - "internal_name": "ftstikesicchusad" - }, - { - "name": "Tatar Pasundan", - "internal_name": "ftjtpasundan" - }, { "name": "Open Research Library", "internal_name": "ftopenresearchl" @@ -21679,10 +21275,6 @@ "name": "CEDES Repositorio Digital", "internal_name": "ftcedesbuenosair" }, - { - "name": "Repositorio Universidad Técnica de Ambato (UTA)", - "internal_name": "ftunivtambato" - }, { "name": "Sakarya Üniversitesi Açık Erişim", "internal_name": "ftsakaryauniv" @@ -21755,10 +21347,6 @@ "name": "Carta Internacional", "internal_name": "ftjcartai" }, - { - "name": "Revista de Formación en Investigación", - "internal_name": "ftjrefi" - }, { "name": "Scalpelo", "internal_name": "ftjscalpelo" @@ -21807,10 +21395,6 @@ "name": "Ways to Improve Construction Efficiency", "internal_name": "ftjways" }, - { - "name": "JRBEE: Journal of Research in Business, Economics, and Education", - "internal_name": "ftjrbee" - }, { "name": "Newinera Publisher (Scientific Journal)", "internal_name": "ftnewineraojs" @@ -21835,10 +21419,6 @@ "name": "TERBITAN BERKALA ILMIAH ONLINE FAKULTAS ILMU BUDAYA UNIVERSITAS HALU OLEO", "internal_name": "ftunihaluoleofib" }, - { - "name": "eJournal STAI Syamsul 'Ulum", - "internal_name": "ftstaisyamsululu" - }, { "name": "Jurnal Wacana Kinerja", "internal_name": "ftjwacanakinerja" @@ -21887,18 +21467,10 @@ "name": "Jurnal Ilmu Kesehatan Bhakti Husada: Health Science Journal", "internal_name": "ftjstikku" }, - { - "name": "Jurnal Online Fakultas Tarbiyah dan Keguruan (UIN Sunan Ampel Surabaya)", - "internal_name": "ftiainsunanamftk" - }, { "name": "Jurnal Keterapian Fisik", "internal_name": "ftjketerapianfis" }, - { - "name": "Jurnal Fakultas Ekonomi Universitas Islam Lamongan", - "internal_name": "ftuniilamonganfe" - }, { "name": "Research at Solent University", "internal_name": "ftunivsolentcris" @@ -21971,10 +21543,6 @@ "name": "Asian Literature and Translation", "internal_name": "ftjalt" }, - { - "name": "Теология. Философия. Право", - "internal_name": "ftjtheophil" - }, { "name": "Scientia Generalis", "internal_name": "ftjscientiagener" @@ -22011,14 +21579,6 @@ "name": "Jurnal Magister Administrasi Pendidikan", "internal_name": "ftjmapojs" }, - { - "name": "INZAH Online Journal", - "internal_name": "ftinszainulhasan" - }, - { - "name": "Open Journal Systems Universidad de Las Tunas", - "internal_name": "ftunilastunasojs" - }, { "name": "Науковий погляд у майбутнє", "internal_name": "ftjslif" @@ -22063,10 +21623,6 @@ "name": "Revista Lusófona de Estudos Culturais", "internal_name": "ftjriec" }, - { - "name": "International Journal of Informatics and Computation", - "internal_name": "ftjijicom" - }, { "name": "Ejournal UIN Imam Bonjol Padang", "internal_name": "ftuinimambonjolp" @@ -22127,14 +21683,6 @@ "name": "Jurnal Maju Badan Penelitian dan Pengembangan Daerah Provinsi Sulawesi Barat", "internal_name": "ftsulawesibarat" }, - { - "name": "JURNAL ILMIAH STMIK Pelita Nusantara", - "internal_name": "ftstmikpelitanus" - }, - { - "name": "Journals STIE Putra Bangsa", - "internal_name": "ftstieputrabangs" - }, { "name": "Indonesian Mining Professionals Journal", "internal_name": "ftjimpj" @@ -22279,10 +21827,6 @@ "name": "Міжнародні відносини, суспільні комунікації та регіональні студії", "internal_name": "ftjirpcrs" }, - { - "name": "Jurnal Program Studi Universitas Pertahana", - "internal_name": "ftunipertahanan" - }, { "name": "SWORD - South West Open Research Deposit (Munster Technological University Research)", "internal_name": "ftcorkinsttechno" @@ -22291,10 +21835,6 @@ "name": "Social Law", "internal_name": "ftjsociallaw" }, - { - "name": "Jurnal Daring Universitas Winaya Mukti", - "internal_name": "ftuwinayamukti2" - }, { "name": "Indonesian Journal of Animal Science and Technology", "internal_name": "ftjitpi" @@ -22339,10 +21879,6 @@ "name": "RI FURG (Repositório da Universidade Federal do Rio Grande)", "internal_name": "ftunivfurg" }, - { - "name": "Jurnal Repositor", - "internal_name": "ftjrepositor" - }, { "name": "Universidad Zaragoza: Open Journal Systems", "internal_name": "ftunizaragozaojs" @@ -22399,10 +21935,6 @@ "name": "Brazilian Journal of Implantology and Health Sciences", "internal_name": "ftjbjihs" }, - { - "name": "Indonesian Trust Health Journal", - "internal_name": "ftjithj" - }, { "name": "Jurnal Socius", "internal_name": "ftjsocius" @@ -22507,10 +22039,6 @@ "name": "Journal of African Cultural Heritage Studies", "internal_name": "ftjachs" }, - { - "name": "The International Journal of Recirculating Aquaculture", - "internal_name": "ftjrasj" - }, { "name": "Applications of Modeling and Simulation", "internal_name": "ftjams" @@ -22647,10 +22175,6 @@ "name": "Anthurium: A Caribbean Studies Journal", "internal_name": "ftjanthurium" }, - { - "name": "READ: An Online Journal for Literacy Educators", - "internal_name": "ftjread" - }, { "name": "Journal of College Academic Support Programs", "internal_name": "ftjcasp" @@ -22703,10 +22227,6 @@ "name": "Administratio - Jurnal Ilmiah Administrasi Publik dan Pembangunan", "internal_name": "ftjadministratio" }, - { - "name": "Jurnal Universitas Islam As-Syafi'iyah", - "internal_name": "ftuniviasiojs" - }, { "name": "Aptikom Publisher", "internal_name": "ftaptikompublojs" @@ -22735,10 +22255,6 @@ "name": "JTIP : Jurnal Teknologi Informasi dan Pendidikan", "internal_name": "ftjtip" }, - { - "name": "Revistas - FASB", - "internal_name": "ftfasbojs" - }, { "name": "Metakom - Jurnal Kajian Komunikasi", "internal_name": "ftjmetakom" @@ -22851,10 +22367,6 @@ "name": "JURNAL STT KAO", "internal_name": "ftsttkaoojs" }, - { - "name": "Uzbekistan Research Online", - "internal_name": "ftuzbekistanro" - }, { "name": "EntreDiversidades. Revista de Ciencias Sociales y Humanidades", "internal_name": "ftjentred" @@ -22927,10 +22439,6 @@ "name": "Horizonte Médico", "internal_name": "ftjhorizontemedi" }, - { - "name": "Jurnal STKIP Weetebula", - "internal_name": "ftstkipweetebula" - }, { "name": "E-Journal Institut Agama Hindu Negeri Tampung Penyang Palangka Raya", "internal_name": "ftiahntpprojs" @@ -23015,22 +22523,10 @@ "name": "Northumbria University: Figshare", "internal_name": "ftnumbriaunifig" }, - { - "name": "UWU eRepository (Uva Wellassa University)", - "internal_name": "ftunivwellassa" - }, { "name": "Mental Health: Global Challenges Journal", "internal_name": "ftjmhgcj" }, - { - "name": "Jurnal Teknik Sipil", - "internal_name": "ftjprokons" - }, - { - "name": "Jurnal Riset dan Aplikasi: Akuntansi dan Manajeme", - "internal_name": "ftjraam" - }, { "name": "Czasopisma Uniwersytetu Opolskiego", "internal_name": "ftunivopolskiojs" @@ -23099,10 +22595,6 @@ "name": "Corporación Universitaria Latinoamericana Portal de Libros Electronicos", "internal_name": "ftcorpulatinoame" }, - { - "name": "CMRE Open Library (NATO STO Centre for Maritime Research and Experimentation)", - "internal_name": "ftcentremre" - }, { "name": "Al Amin: Jurnal Kajian Ilmu dan Budaya Islam", "internal_name": "ftjalamin" @@ -23131,10 +22623,6 @@ "name": "Purdue University Graduate School: Figshare", "internal_name": "ftpurdueunivport" }, - { - "name": "E-JOURNAL LPPM Sekolah Tinggi Teknologi Pagar Alam", - "internal_name": "ftlppmsttppagara" - }, { "name": "GSSRR.ORG: International Journals: Publishing Research Papers in all Fields", "internal_name": "ftgssrrojs" @@ -23155,18 +22643,10 @@ "name": "Acta Chimica Slovenica", "internal_name": "ftjacsi" }, - { - "name": "Rumah Jurnal IAI Dalwa (Institut Agama Islam Darullughah Wadda'wah Bangil Pasuruan)", - "internal_name": "ftinidalwaojs" - }, { "name": "Revista Brasileira de Medicina Veterinária", "internal_name": "ftjrbmv" }, - { - "name": "Neuroanatomy and Behaviour.", - "internal_name": "ftjnab" - }, { "name": "Formal Approaches to South Asian Languages (FASAL)", "internal_name": "ftjfasal" @@ -23211,22 +22691,10 @@ "name": "Repositorio Institucional Universidad Nacional Pedro Ruiz Gallo", "internal_name": "ftunivnprgallo" }, - { - "name": "Repositorio Académico Instituto Universitario Asociación Cristiana de Jóvenes", - "internal_name": "ftuacjmontevideo" - }, - { - "name": "Repositorio Universidad Privada Juan Pablo II", - "internal_name": "ftupjuanpabloii" - }, { "name": "Repositorio de la Facultad de Teología Pontificia y Civil de Lima", "internal_name": "ftftpclima" }, - { - "name": "UANCV Repositorio Digital (Universidad Andina Néstor Cáceres Velásque)", - "internal_name": "ftunivandinancv" - }, { "name": "Folkehelseinstituttet: Open Repository (Brage)", "internal_name": "ftfolkehelseins" @@ -23259,10 +22727,6 @@ "name": "Olimpianos - Journal of Olympic Studies", "internal_name": "ftjolimpianos" }, - { - "name": "Jurnal Kedokteran", - "internal_name": "ftjku" - }, { "name": "Jurnal Inovasi Matematika (Inomatika)", "internal_name": "ftjinomatika" @@ -23279,10 +22743,6 @@ "name": "Konteksty Pedagogiczne", "internal_name": "ftjkontekstp" }, - { - "name": "Yayasan Pembina Lembaga Pendidikan PGRI Sumbawa Barat", - "internal_name": "ftpgrisumbawa" - }, { "name": "Jurnal Kesehatan Madani Medika (JKMM)", "internal_name": "ftjkmm" @@ -23323,10 +22783,6 @@ "name": "Jurnal Admmirasi", "internal_name": "ftjadmmirasi" }, - { - "name": "Ilmu Gizi Indonesia", - "internal_name": "ftjilgi" - }, { "name": "Hatay Mustafa Kemal Üniversitesi Akademik Arşiv Sistemi (DSpace@Hatay)", "internal_name": "fthataykemaluniv" @@ -23379,14 +22835,6 @@ "name": "Repositorio Institucional de la Universidad Católica Trujillo Benedicto XVI", "internal_name": "ftunivctrujillo" }, - { - "name": "Каза́нский федера́льный университе́т Science Tatarstan", - "internal_name": "ftkazanunivojs" - }, - { - "name": "Jurnal Kebidanan Akademi Kebidanan Griya Husada Surabaya", - "internal_name": "ftjmidfiwery" - }, { "name": "Repository Poltekkesjogja", "internal_name": "ftpoltekkemenkes" @@ -23419,10 +22867,6 @@ "name": "Российский университет дружбы народов: Открытый репозиторий", "internal_name": "ftrudnuniv" }, - { - "name": "Portal de Revistas Científicas de la UAI (Universidad Abierta Interamericana)", - "internal_name": "ftuabiertaintame" - }, { "name": "Kunsthøgskolen i Oslo: KHIODA", "internal_name": "ftkhoslo" @@ -23451,10 +22895,6 @@ "name": "ADI Journal on Recent Innovation", "internal_name": "ftjajri" }, - { - "name": "Repositorio de la Universidad Politécnica Amazónica", - "internal_name": "ftunivpamazonica" - }, { "name": "Repositorio de la Universidad Privada Líder Peruana", "internal_name": "ftunivplperuana" @@ -23479,10 +22919,6 @@ "name": "theses.fr", "internal_name": "ftstarfr" }, - { - "name": "Repositorio Institucional de la Universidad Santo Domingo de Guzmán", - "internal_name": "ftunivsdguzman" - }, { "name": "Repositorio Institucional UTEC (Universidad de Ingeniería y Tecnología)", "internal_name": "ftunivteclima" @@ -23491,18 +22927,10 @@ "name": "Repositorio Institucional de la Universidad Nacional Federico Villarreal (UNFV)", "internal_name": "ftuninfvillareal" }, - { - "name": "Repositorio Institucional Digital de la Universidad Nacional de Piura", - "internal_name": "ftunivnpiura" - }, { "name": "Repositorio Institucional Universidad Nacional Autónoma de Chota", "internal_name": "ftunivnachota" }, - { - "name": "Universidad Nacional San Luis Gozaga de Ica: Repositorio Institucional Digital", - "internal_name": "ftunivnslgonzaga" - }, { "name": "Repositorio Institucional de la Universidad María Auxiliadora", "internal_name": "ftumauxiliadora" @@ -23535,10 +22963,6 @@ "name": "Havforskningsinstituttet: Brage IMR", "internal_name": "ftimr" }, - { - "name": "Ψηφιακή Βιβλιοθήκη Λεβαδείας", - "internal_name": "ftektdl" - }, { "name": "Open Gender Journal", "internal_name": "ftjogj" @@ -23607,10 +23031,6 @@ "name": "VID vitenskapelige høgskole: VID Open", "internal_name": "ftvid" }, - { - "name": "Nevsehir Haci Bektas Veli University Institutional Repository (DSpace@NEVU)", - "internal_name": "ftnevsehiruniv" - }, { "name": "IST Austria Research Explorer (Institute of Science and Technology)", "internal_name": "ftistaustriar" @@ -23835,10 +23255,6 @@ "name": "Universidad Interamericana para el Desarrollo: UNID DSpace", "internal_name": "ftuniinterameric" }, - { - "name": "Universidad Global del Cusco: Repositorio Institucional", - "internal_name": "ftunivgcusco" - }, { "name": "Universidad Peruana de Ciencias e Informática: UPCI Repositorio DSpace", "internal_name": "ftunivperuanaci" @@ -24011,10 +23427,6 @@ "name": "Publicacións periódicas da Real Academia Galega", "internal_name": "ftrealacadgalega" }, - { - "name": "European Center for Science Education and Research (EUSER): E-Journals", - "internal_name": "ftecenterserojs" - }, { "name": "Meliora - International Journal of Student Sustainability Research", "internal_name": "ftjmeliora" @@ -24059,10 +23471,6 @@ "name": "Goodwood Publishing: Journals", "internal_name": "ftgoodwoodpubojs" }, - { - "name": "Synthesis Publication", - "internal_name": "ftsynthesispubl" - }, { "name": "UNSIKA Journal Systems (Universitas Singaperbangsa Karawang)", "internal_name": "ftusingaperbangs" @@ -24079,14 +23487,6 @@ "name": "Direktori jurnal elektronik Politeknik Negeri Padang (PNP)", "internal_name": "ftpoliteknpaojs2" }, - { - "name": "Journals of Badan Penelitian dan Pengembangan Kesehatan", - "internal_name": "ftlitbangkemkes" - }, - { - "name": "Miracle Journal of Public Health (MJPH)", - "internal_name": "ftjmjph" - }, { "name": "Current - The Journal of Marine Education", "internal_name": "ftjcurrent" @@ -24179,10 +23579,6 @@ "name": "AIB studi (Associazione italiana bibliotech)", "internal_name": "ftjaibstudi" }, - { - "name": "CSU Online Journal System (Caraga State University)", - "internal_name": "ftcaragastateojs" - }, { "name": "Etude de la Population Africaine (UEPA)", "internal_name": "ftjaps" @@ -24287,10 +23683,6 @@ "name": "CWI's Institutional Repository (Centrum voor Wiskunde en Informatica)", "internal_name": "ftcwinl" }, - { - "name": "Water JPI Open Data & Open Access", - "internal_name": "ftwaterjpi" - }, { "name": "St. Petersburg College Collections", "internal_name": "ftstpetersburgco" @@ -24315,10 +23707,6 @@ "name": "Ecole Polytechnique Fédérale de Lausanne (EPFL): PLUME", "internal_name": "ftepflplume" }, - { - "name": "Rumah Jurnal Online - Fakultas Sains dan Teknologi UIN Sunan Ampel Surabaya", - "internal_name": "ftiainsunanamfst" - }, { "name": "Karatina University: Karuspace Repository", "internal_name": "ftkaratinauniv" @@ -24371,10 +23759,6 @@ "name": "Indonesia Prime", "internal_name": "ftjindonesiaprim" }, - { - "name": "International Journal of Aging Research", - "internal_name": "ftjijoar" - }, { "name": "Carácter - Revista Cientifica de la Universidad Del Pacifico", "internal_name": "ftjcaracter" @@ -24531,18 +23915,10 @@ "name": "Formação Docente – Revista Brasileira de Pesquisa sobre Formação de Professores", "internal_name": "ftjrfd" }, - { - "name": "OJS LP2M Sekolah Tinggi Islam Blambangan (STIB) Banyuwangi", - "internal_name": "ftstiblambanganb" - }, { "name": "e-Journal Institut Agama Islam Negeri Ambon", "internal_name": "ftiainambonojs" }, - { - "name": "Jurnal Akademi Kebidanan (Akbid) RSPAD Gatot Soebroto", - "internal_name": "ftjikebidanan" - }, { "name": "E-Jurnal Sekolah Tinggi Teknologi Industri Padang (STTIND)", "internal_name": "ftsttipadangojs" @@ -24583,10 +23959,6 @@ "name": "Universitas Merdeka Malang Repository", "internal_name": "ftumerdekamalang" }, - { - "name": "Repository Universitas Palangka Raya", - "internal_name": "ftupalangkaraya" - }, { "name": "Repositorio Institucional de la Universidad Católica de Colombia (RIUCaC)", "internal_name": "ftunivccatolica" @@ -24599,10 +23971,6 @@ "name": "Inta Digital (ID - Instituto Nacional de Tecnología Agropecuaria)", "internal_name": "ftargentinainta" }, - { - "name": "Al-Maiyyah - Media Transformasi Gender dalam Paradigma Sosial Keagamaan", - "internal_name": "ftjalmaiyyah" - }, { "name": "Abdimas Universal (Jurnal Pengabdian Kepada Masyarakat)", "internal_name": "ftjabdimas" @@ -24631,10 +23999,6 @@ "name": "鳴門教育大学学術研究コレクション", "internal_name": "ftnarutouniveduc" }, - { - "name": "Lembaga Penelitian dan Pengabdian kepada Masyarakat (LPPM) Universitas Putra Indonesia YPTK Padang: Open Journal Systems", - "internal_name": "ftupipadanglppm" - }, { "name": "AMPTA Open Journal Systems (Sekolah Tinggi Pariwisata AMPTA Yogyakarta)", "internal_name": "ftamptaojs" @@ -24707,10 +24071,6 @@ "name": "Revistas del Instituto Colombiano de Antropología e Historia (ICANH)", "internal_name": "fticanhbogota" }, - { - "name": "Scientific Journals of INIA (Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria)", - "internal_name": "ftiniamadrid" - }, { "name": "Research Data Unipd (Università degli Studi die Padova)", "internal_name": "ftunivpadovard" @@ -24919,10 +24279,6 @@ "name": "Jurnal Al-Fatih", "internal_name": "ftjalfaith" }, - { - "name": "Repositório Cientifico do LNEC (Laboratório Nacional de Engenharia Civil)", - "internal_name": "ftlnec" - }, { "name": "Portal de Revistas de la Universidad de Panamá", "internal_name": "ftunivpanamaojs" @@ -25015,10 +24371,6 @@ "name": "Open Journal published by Universitas Persada Indonesia YAI (Yayasan Administrasi Indonesia)", "internal_name": "ftunivpersadaojs" }, - { - "name": "International Healthcare Research Journal (IHRJ)", - "internal_name": "ftjihrj" - }, { "name": "Disparidades - Revista de Antropología", "internal_name": "ftjdra" @@ -25103,10 +24455,6 @@ "name": "Retratos de Assentamentos", "internal_name": "ftjrassentamento" }, - { - "name": "Revista Angolana de Ciências (RAC)", - "internal_name": "ftjrac" - }, { "name": "LITPAM Journal Center", "internal_name": "ftlitpamojs" @@ -25207,10 +24555,6 @@ "name": "Publicaciones seriadas de la Escuela Superior de Administración Pública (ESAP)", "internal_name": "ftesapojs" }, - { - "name": "АКТУАЛЬНІ ПРОБЛЕМИ СОЦІОЛОГІЇ, ПСИХОЛОГІЇ, ПЕДАГОГІКИ", - "internal_name": "ftjapspp" - }, { "name": "Jurnal Online Universitas Ibrahimy", "internal_name": "ftiniibrahimyojs" @@ -25283,10 +24627,6 @@ "name": "Phaidra Digital Collections (Permanent Hosting, Archiving and Indexing of Digital Resources and Assets - Università degli Studi di Padova)", "internal_name": "ftunivpadovadc" }, - { - "name": "Jurnal Sains Teknologi Akuakultur", - "internal_name": "ftjsta" - }, { "name": "Biruni University Institutional Repository (DSpace@Biruni)", "internal_name": "ftbiruniuniv" @@ -25319,10 +24659,6 @@ "name": "Tecnologí­a Educativa (Universidad de Holguín, Cuba)", "internal_name": "ftjtecedu" }, - { - "name": "Наукові видання Університету ДФС України (Національний університет державної фіскальної служби - НУДФСУ)", - "internal_name": "ftunivstsojs" - }, { "name": "Aksaray University Institutional Repository (DSpace@Aksaray)", "internal_name": "ftaksarayuniv" @@ -25415,10 +24751,6 @@ "name": "Всі періодичні видання ХНТУ (Херсонський національний технічний університет)", "internal_name": "ftkirovogradspuo" }, - { - "name": "University of Leicester Open Journals", - "internal_name": "ftleicesterunojs" - }, { "name": "UCLouvain: Open Journal Repository (Université catholique de Louvain)", "internal_name": "ftunivlouvainojs" @@ -25471,10 +24803,6 @@ "name": "E-Journal Universitas Islam Darul Ulum Lamongan", "internal_name": "ftuidarululumlam" }, - { - "name": "Jurnal IKIP PGRI Jember", - "internal_name": "ftikippgrijember" - }, { "name": "Bulletin of the New Zealand Society for Earthquake Engineering (NZSEE)", "internal_name": "ftjbnzsee" @@ -25555,10 +24883,6 @@ "name": "E-Journal Universitas Teknokrat Indonesia", "internal_name": "ftunivteknokrat" }, - { - "name": "Ejournal Institut Agama Islam Syarifuddin", - "internal_name": "ftiaisyarifuddin" - }, { "name": "Dalhousie University Libraries Journal Hosting Service", "internal_name": "ftdalhouseuniv" @@ -25659,10 +24983,6 @@ "name": "Jurnal Riset Hesti Medan Akper Kesdam I/BB Medan", "internal_name": "ftjurhesti" }, - { - "name": "富山市科学博物館リポジトリ", - "internal_name": "fttoyamasciencem" - }, { "name": "E-Jurnal Mikroskil (STMIK - STIE Mikroskil)", "internal_name": "ftstmikstiemikro" @@ -25671,10 +24991,6 @@ "name": "UFV Portal de Periodicos (Universidade Federal de Viçosa)", "internal_name": "ftunivfvicosaojs" }, - { - "name": "Людинознавчі студії. Серія \"Педагогіка\"", - "internal_name": "ftjlssp" - }, { "name": "Open Journals System Universitas Ngudi Waluyo", "internal_name": "ftuningudiwaluyo" @@ -25915,10 +25231,6 @@ "name": "Journal STTII Surabaya (Sekolah Tinggi Teologi Injili Indonesia Surabay)", "internal_name": "ftsttiisurabaya" }, - { - "name": "Syntax Literate - Jurnal Ilmiah Indonesia", - "internal_name": "ftjsl" - }, { "name": "Кібербезпека: освіта, наука, техніка", "internal_name": "ftjcybersecurity" @@ -25963,10 +25275,6 @@ "name": "Batman University Institutional Repository", "internal_name": "ftbatmanuniv" }, - { - "name": "Firat University Institutional Open Archives (DSpace@FIRAT)", - "internal_name": "ftfiratuniv" - }, { "name": "Вестник университета", "internal_name": "ftjvuniversiteta" @@ -26011,10 +25319,6 @@ "name": "Journal of Embodied Research (JER)", "internal_name": "ftjoer" }, - { - "name": "International Journal of Orthoplastic Surgery (IJOPS)", - "internal_name": "ftjijops" - }, { "name": "Scandinavian Journal of Work and Organizational Psychology (SJWOP)", "internal_name": "ftjsjwop" @@ -26119,10 +25423,6 @@ "name": "Expeditio - Repositorio Institucional Universidad de Bogotá Jorge Tadeo Lozano (UTADEO)", "internal_name": "ftunivbogotajtl" }, - { - "name": "Repositorio Institucional de la Universidad Seminario Evangélico de Lima (USEL)", - "internal_name": "ftunivselima" - }, { "name": "BCNROC - Repositori Obert de Coneixement de l'Ajuntament de Barcelona", "internal_name": "ftbcnrocbarcelon" @@ -26163,10 +25463,6 @@ "name": "E-Jurnal UMNAW (Universitas Muslim Nusantara Al Washliya)", "internal_name": "ftumnalwashliyah" }, - { - "name": "Jurnal Ilmiah Agropolitan Fakultas Pertanian Universitas Ichsan Gorontalo", - "internal_name": "ftuichsangoronta" - }, { "name": "International Journal of Applied Business Research", "internal_name": "ftjijabr" @@ -26183,10 +25479,6 @@ "name": "UMB Digital Archive (University of Maryland, Baltimore)", "internal_name": "ftumarylandhshsl" }, - { - "name": "Digitalni repozitorij je RIT Croatia", - "internal_name": "ftritcroatia" - }, { "name": "VTDK VB (Vilniaus technologijų ir dizaino kolegija virtualią biblioteką)", "internal_name": "ftvilniuscolltd" @@ -26207,10 +25499,6 @@ "name": "LillOA (HAL Lille Open Archive, Université de Lille)", "internal_name": "ftunivlille" }, - { - "name": "Istighna - Jurnal Pendidikan dan Pemikiran Islam", - "internal_name": "ftjistighna" - }, { "name": "SCIA - Scholarly Citation Index Analytics", "internal_name": "fthindex" @@ -26299,10 +25587,6 @@ "name": "Jurnal FKIP Universitas Mataram (Fakultas Keguruan Dan Ilmu Pendidikan)", "internal_name": "ftunimataramfkip" }, - { - "name": "Jurnal Kimia Terapan Indonesia (JKTI)", - "internal_name": "ftjinajac" - }, { "name": "Jurnal Psikologi Sosial (JPS)", "internal_name": "ftjjps" @@ -26503,10 +25787,6 @@ "name": "Hacettepe University Institutional Repository", "internal_name": "fthacettepeuniir" }, - { - "name": "Thesis Journal Repository (Kolegji AAB, Kosovo)", - "internal_name": "ftjthesis" - }, { "name": "MRU institucinė talpykla (Mykolo Romerio universitetas)", "internal_name": "ftmykolasromeris" @@ -26519,10 +25799,6 @@ "name": "E-QIEN - Jurnal Ekonomi dan Bisnis", "internal_name": "ftjeqien" }, - { - "name": "O que nos faz pensar (Cadernos do Departamento de Filosofia da PUC-Rio)", - "internal_name": "ftjoqnfp" - }, { "name": "UNY Journal (Journal Universitas Negeri Yogyakarta)", "internal_name": "ftyogyakartastun" @@ -26531,10 +25807,6 @@ "name": "Portal Jurnal Malahayati (Universitas Malahayati)", "internal_name": "ftunivmalahayati" }, - { - "name": "Jurnal Islaminomics (Journal of Islamic Economics, Business,and Finance)", - "internal_name": "ftjislaminomics" - }, { "name": "Науковий вісник Східноєвропейсього національного університету імені Лесі Українки. Серія: Біологічні науки", "internal_name": "ftjluuenusbbio" @@ -26563,10 +25835,6 @@ "name": "JEM - Jurnal Ekonomi dan Manajemen", "internal_name": "ftjedm" }, - { - "name": "Portal de Revistas - Universidad de Camagüey", - "internal_name": "ftunivcamaguey" - }, { "name": "Journal of Applied Agricultural Science and Technology (JAAST - Politeknik Pertanian Negeri Payakumbuh)", "internal_name": "ftjaast" @@ -26835,14 +26103,6 @@ "name": "OJS Sekolah Tinggi Alkitab Tiranus", "internal_name": "ftstatiranus" }, - { - "name": "Università Ca’ Foscari Venezia: Riviste on line", - "internal_name": "ftunivveneziaojs" - }, - { - "name": "Portal Publikasi Ilmiah Pusat Penelitian Arkeologi Nasional", - "internal_name": "ftpusatpenarknat" - }, { "name": "Gladius", "internal_name": "ftjgladius" @@ -26867,14 +26127,6 @@ "name": "Journal of Global Citizenship & Equity Education (JGCEE)", "internal_name": "ftjgcee" }, - { - "name": "Journal of Manufacturing Technologies (JMT - Warsaw University of Technology)", - "internal_name": "ftjmtwip" - }, - { - "name": "Jurnal CARING (Center of Research Publication in Midwifery and Nursing)", - "internal_name": "ftjcaring" - }, { "name": "Indonesian Journal of Educational Counseling (IJEC)", "internal_name": "ftjijec" @@ -26899,10 +26151,6 @@ "name": "Érudit - Dépôt de documents", "internal_name": "fteruditdepot" }, - { - "name": "Repositorio Institucional de la Universidad Michoacana de San Nicolás de Hidalgo (DSpace)", - "internal_name": "ftunivmichoacana" - }, { "name": "Scholarly Works @ SHSU (Sam Houston State University)", "internal_name": "ftsamhoustonsuni" @@ -27007,10 +26255,6 @@ "name": "Metallurgical and Materials Engineering", "internal_name": "ftjmme" }, - { - "name": "Revistas Científicas de la Universidad Técnica de Cotopaxi", - "internal_name": "ftunivtcotopaxi" - }, { "name": "İbn Haldun Çalışmaları Dergisi", "internal_name": "ftjihcd" @@ -27063,10 +26307,6 @@ "name": "Bennington College Digital Repository", "internal_name": "ftbenningtoncoll" }, - { - "name": "Repositorio Digital de CEDRO (Centro de Información y Educación para la Prevención del Abuso de Drogas)", - "internal_name": "ftcedro" - }, { "name": "Repositorio Institucional del CIEMAT (Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas)", "internal_name": "ftciemat" @@ -27095,10 +26335,6 @@ "name": "Universidad de Santander (UDES): Repositorio Digital", "internal_name": "ftunisantander" }, - { - "name": "Digital Repository Concordia University Irvine", - "internal_name": "ftconcordiauniir" - }, { "name": "Servicio Meteorológico Nacional: elabrigo Repositorio Institucional SMN", "internal_name": "ftsmnargentina" @@ -27139,10 +26375,6 @@ "name": "International Journal of Innovative Technology and Interdisciplinary Sciences (IJITIS)", "internal_name": "ftjijitis" }, - { - "name": "Indonesian Journal of Health Research (IJHR)", - "internal_name": "ftjijhr" - }, { "name": "Sistema Eletrônico de Periódicos - IFCH/Unicamp (Instituto de Filosofia e Ciências Humanas da Universidade Estadual de Campinas)", "internal_name": "ftuncampinasifch" @@ -27171,14 +26403,6 @@ "name": "Université Paris Seine: ComUE (HAL)", "internal_name": "ftunivparisseine" }, - { - "name": "Jurnal Ilmiah STIKES Citra Delima Bangka Belitung", - "internal_name": "ftjiscdbb" - }, - { - "name": "Journal Technology and Implementation Business (JTTB)", - "internal_name": "ftjttb" - }, { "name": "Jurnal LPMI UNVIC Sorong (Lembaga Penjaminan Mutu Internal, Universitas Victory)", "internal_name": "ftunivvsorong" @@ -27231,10 +26455,6 @@ "name": "Herausforderung Lehrer*innenbildung – Zeitschrift zur Konzeption, Gestaltung und Diskussion (HLZ)", "internal_name": "ftjhlz" }, - { - "name": "Jurnal STAHN MPU Kuturan Singaraja", - "internal_name": "ftstahnmpukutura" - }, { "name": "Emerita", "internal_name": "ftjemerita" @@ -27379,10 +26599,6 @@ "name": "Педагогічний дискурс", "internal_name": "ftjpd" }, - { - "name": "E-Journal Akademi Kebidanan Panca Bhakti Pontianak", - "internal_name": "ftakademikpb" - }, { "name": "e-Jurnal Poltekkes Tanjungkarang", "internal_name": "ftpoltekkestanju" @@ -27511,10 +26727,6 @@ "name": "Sineace - Sistema Nacional de Evaluación, Acreditación y Certificación de la Calidad Educativa: Repositorio Institucional", "internal_name": "ftsineace" }, - { - "name": "Revista Sociedad Colombiana de Oftalmología", - "internal_name": "ftjrsco" - }, { "name": "Magnolia press", "internal_name": "ftmagnoliapress" @@ -27687,10 +26899,6 @@ "name": "Summit Memory", "internal_name": "ftakronscplibdc" }, - { - "name": "Knowledge Press", - "internal_name": "ftknowledgepress" - }, { "name": "Известия вузов. Цветная металлургия", "internal_name": "ftjphsnm" @@ -27759,10 +26967,6 @@ "name": "Jurnal Penelitian Kelapa Sawit", "internal_name": "ftjpks" }, - { - "name": "Jurnal Sains dan Kesehatan (JSK)", - "internal_name": "ftjsk" - }, { "name": "Jurnal Ilmiah Universitas Islam Balitar", "internal_name": "ftunivibalitar" @@ -27863,10 +27067,6 @@ "name": "Universitas Putera Batam (UPB): Open Journal Systems", "internal_name": "ftuputerabatam" }, - { - "name": "Jurnal Kajian Wilayah (JKW)", - "internal_name": "ftjkw" - }, { "name": "Jurnal Universitas Lancang Kuning", "internal_name": "ftulancangkunojs" @@ -28103,10 +27303,6 @@ "name": "e-Jurnal STKIP-PGRI Lubuklinggau", "internal_name": "ftstkippgrilubuk" }, - { - "name": "e-Jurnal STIKes Bakti Tunas Husada Tasikmalaya", - "internal_name": "ftstikesbthtasik" - }, { "name": "Open Jurnal System Universitas Muhammadiyah Sumatera Barat", "internal_name": "ftunivmsumaterab" @@ -28115,10 +27311,6 @@ "name": "Portal de Periódicos Eletrônicos da UFRB (Universidade Federal do Recôncavo da Bahia)", "internal_name": "ftunivfrbahia" }, - { - "name": "International Review of Humanities Studies (IRHS)", - "internal_name": "ftjirhs" - }, { "name": "Al-Mishbah", "internal_name": "ftjalmisbah" @@ -28143,10 +27335,6 @@ "name": "Journal of Islamic Monetary Economics and Finance (JIMF)", "internal_name": "ftjimf" }, - { - "name": "Jurnal Balai Penelitian dan Pengembangan Agama Semarang", - "internal_name": "ftblasemarang" - }, { "name": "Jurnal Elektronik STKIP Citra Bakti", "internal_name": "ftstkipcitrabakt" @@ -28223,10 +27411,6 @@ "name": "Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance)", "internal_name": "ftjremef" }, - { - "name": "Revista Cultura Física y Deportes de Guantánamo (Universidad de Guantánamo)", - "internal_name": "ftjpcsg" - }, { "name": "Escuela Superior Politécnica del Litoral (ESPOL): Open Journal Systems", "internal_name": "ftespolojs" @@ -28375,14 +27559,6 @@ "name": "Anuario de la Escuela de Historia", "internal_name": "ftjaeh" }, - { - "name": "Marine Research in Indonesia (MRI)", - "internal_name": "ftjmarineri" - }, - { - "name": "Annual International Conference on Language and Literature", - "internal_name": "ftjaicll" - }, { "name": "Эпидемиология и Вакцинопрофилактика", "internal_name": "ftjepidemvac" @@ -28391,10 +27567,6 @@ "name": "The Journal of Social Media in Society", "internal_name": "ftjsms" }, - { - "name": "Revista Cubana de Finanzas y Precio", - "internal_name": "ftjrcfp" - }, { "name": "International Journal of Research in Counseling and Education (IJRiCE)", "internal_name": "ftjijrce" @@ -28415,10 +27587,6 @@ "name": "Jurnal Online Universitas Muhammadiyah Purwokerto", "internal_name": "ftunimpurwokerto" }, - { - "name": "Repozitorij Europske poslovne škole Zagreb", - "internal_name": "ftunivcollegeeem" - }, { "name": "EIZ - Ekonomski institut, Zagreb", "internal_name": "ftinsteconomzagr" @@ -28531,10 +27699,6 @@ "name": "Universitas Maritim Raja Ali Haji Pusat Jurnal Ilmiah", "internal_name": "ftunivmrah" }, - { - "name": "Portal Jurnal (Institut Pesantren KH Abdul Chalim Mojokerto)", - "internal_name": "ftinstkhac" - }, { "name": "Eagle Scholar University of Mary Washington", "internal_name": "ftunimarywashing" @@ -28695,10 +27859,6 @@ "name": "OIST Institutional Repository", "internal_name": "ftokinawainstst" }, - { - "name": "Jurnal STMIK Eresha (Sekolah Tinggi Manajemen Informatika dan Komputer)", - "internal_name": "ftstmikeresha" - }, { "name": "eJournal Badan Penelitan dan Pengembangan Kelautan dan Perikanan", "internal_name": "ftbalitbangkkp" @@ -28787,10 +27947,6 @@ "name": "Polyphōnía.Revista de Educación Inclusiva", "internal_name": "ftjpolyphonia" }, - { - "name": "MGI e-Journal System Portal (Marine Geological Institute of Indonesia)", - "internal_name": "ftmarinegeolinst" - }, { "name": "TU Delft Open Access Journals", "internal_name": "fttudelftspool" @@ -28891,10 +28047,6 @@ "name": "Journal of Social Sciences (JSS)", "internal_name": "ftjss" }, - { - "name": "Journal in Humanities (International Black Sea University)", - "internal_name": "ftjinhumanities" - }, { "name": "Journal of Education in Black Sea Region (International Black Sea University)", "internal_name": "ftjebs" @@ -29003,10 +28155,6 @@ "name": "F1000 Research: Figshare", "internal_name": "ftf1000researchp" }, - { - "name": "Repositorio Digital UEB (Universidad Estatal de Bolívar)", - "internal_name": "ftunivebolivar" - }, { "name": "Narotama University Repository", "internal_name": "ftunivnarotama" @@ -29139,18 +28287,10 @@ "name": "Jurnal Islam Nusantara (LTN-PBNU)", "internal_name": "ftjnu" }, - { - "name": "E-Jurnal Politeknik LP3I Medan", - "internal_name": "ftpolitpiiimedan" - }, { "name": "Warta Adhia - Jurnal Perhubungan Udara", "internal_name": "ftjwa" }, - { - "name": "Jurnal WalennaE", - "internal_name": "ftjwalennae" - }, { "name": "Jurnal Pendidikan (JP) : Riset dan Konseptual (Universitas Nahdlatul Ulama Blitar)", "internal_name": "ftjprk" @@ -29159,10 +28299,6 @@ "name": "Jurnal Ilmiah Universitas Batanghari Jambi (JIUBJ)", "internal_name": "ftjiubj" }, - { - "name": "Repositorio Digital del IPEN (Instituto Peruano de Energía Nuclear)", - "internal_name": "ftinsperuengnucl" - }, { "name": "ScholarWorks@UNIST (Ulsan National Institute of Science and Technology)", "internal_name": "ftuisanist" @@ -29179,10 +28315,6 @@ "name": "Newcastle University eTheses", "internal_name": "ftuninewcastleth" }, - { - "name": "Repositorio Institucional UDAFF (Universidad de Ayacucho Federico Froebel)", - "internal_name": "ftunivdaff" - }, { "name": "Universidad de Ciencias y Artes de América Latina (UCAL): DSpace", "internal_name": "ftunicienciasart" @@ -29251,10 +28383,6 @@ "name": "Institiúid Ard-Léinn Bhaile Átha Cliath", "internal_name": "ftdublininstadvs" }, - { - "name": "University of North Alabama: UNA Scholarly Repository", - "internal_name": "ftunivnalabama" - }, { "name": "Dartmouth Digital Commons (Dartmouth College)", "internal_name": "ftdartmouthcoll" @@ -29283,10 +28411,6 @@ "name": "e-Journal STAI Al Hidayah Bogor", "internal_name": "ftstaiaihidayahb" }, - { - "name": "Jurnal Ilmiah Terpadu - Universitas Bina Darma", - "internal_name": "ftunivbinadarma" - }, { "name": "Jurnal Belantara (Universitas Mataram)", "internal_name": "ftjbelantara" @@ -29311,10 +28435,6 @@ "name": "Digital Commons at Oberlin (Oberlin College)", "internal_name": "ftoberlincollege" }, - { - "name": "Medical University of South Carolina (MUSC): MEDICA", - "internal_name": "ftmedunisouthcar" - }, { "name": "Duquesne University: Gumberg Library Digital Collections", "internal_name": "ftduquesneunidc" @@ -29731,10 +28851,6 @@ "name": "Economía y Política", "internal_name": "ftjeyp" }, - { - "name": "Repositorio PUCESA (Pontificia Universidad Católica del Ecuador Sede Ambato)", - "internal_name": "ftpucecuadorsamb" - }, { "name": "Рукописна та книжкова спадщина України", "internal_name": "ftjrksu" @@ -29767,10 +28883,6 @@ "name": "Harper Adams University Repository (CREST)", "internal_name": "ftharperadamsuni" }, - { - "name": "Repositorio Institucional de la Universidad Regional Autónoma de Los Andes \"Uniandes\"", - "internal_name": "ftunivralosandes" - }, { "name": "Repositorio Digital UNACH (Universidad Nacional de Chimborazo)", "internal_name": "ftuninchimborazo" @@ -29883,10 +28995,6 @@ "name": "Spring Arbor University: White Library Digital Repository", "internal_name": "ftspringarboruni" }, - { - "name": "The Scholarship Repository of Florida Institute of Technology", - "internal_name": "ftfloridainsttec" - }, { "name": "Universidad ORT Uruguay: Repositorio académico digital", "internal_name": "ftunivorturuguay" @@ -29899,10 +29007,6 @@ "name": "岐阜市立女子短期大学リポジトリ", "internal_name": "ftgifucitywomens" }, - { - "name": "Pertanika Journal of Scholarly Research Reviews (PJSRR - Universiti Putra Malaysia, UPM)", - "internal_name": "ftjpjsrr" - }, { "name": "UNESUM-Ciencias (Universidad Estatal Del Sur De Manabi)", "internal_name": "ftuniesmanabiojs" @@ -29947,10 +29051,6 @@ "name": "Nottingham Research Data Management Repository (University of Nottingham)", "internal_name": "ftunottinghamrdm" }, - { - "name": "Journal of Tropical Pharmacy and Chemistry", - "internal_name": "ftjtpc" - }, { "name": "Research data at Essex (University of Essex)", "internal_name": "ftunivessexrd" @@ -30011,10 +29111,6 @@ "name": "UMT Journal Management System (UMTJSP - Universitas Muhammadiyah Tangerang)", "internal_name": "ftunivmtangerang" }, - { - "name": "E-Journal STMIK STIKOM Indonesia", - "internal_name": "ftstmikstikomind" - }, { "name": "USMA Digital Commons (United States Military Academy, West Point)", "internal_name": "ftusmilitaryacad" @@ -30043,10 +29139,6 @@ "name": "Digital Commons @ SIA (Sotheby's Institute of Art)", "internal_name": "ftsothebysinsart" }, - { - "name": "Jurnal Ners dan Kebidanan (Journal of Ners and Midwifery)", - "internal_name": "ftjnk" - }, { "name": "Jurnal Pertanian UMPAR (Universitas Muhammadiyah Parepare)", "internal_name": "ftjpuojs" @@ -30163,10 +29255,6 @@ "name": "Journal of Patan Academy of Health Sciences", "internal_name": "ftjpahs" }, - { - "name": "ISI Surakarta: Jurnal (Institut Seni Indonesia)", - "internal_name": "ftisisurakarta" - }, { "name": "E-Journal Unima Mapalus (Universitas Negeri Manago, Department of Chemistry)", "internal_name": "ftunivnmanadoojs" @@ -30223,10 +29311,6 @@ "name": "Mines de Saint-Etienne: Archives Ouvertes (HAL)", "internal_name": "ftecoleminesstet" }, - { - "name": "eprints Iran University of Medical Sciences", - "internal_name": "ftiranunivms" - }, { "name": "立正大学学術機関リポジトリ", "internal_name": "ftrisshouniv" @@ -30263,10 +29347,6 @@ "name": "AFTI Scholar (Air Force Institute of Technology)", "internal_name": "ftairforceinstec" }, - { - "name": "LETRAS (Revista de la Facultad de Letras y Ciencias Humanas - Ciudad Universitaria de la UNMSM)", - "internal_name": "ftjletras" - }, { "name": "Digital Commons at St. Mary's University, San Antonio", "internal_name": "ftstmarysuniv" @@ -30291,10 +29371,6 @@ "name": "Repositorio de la Escuela Superior de Guerra Naval (RIESUP)", "internal_name": "ftescsguerranava" }, - { - "name": "Universidad Privada Sergio Bernales (UPSB): Repositorio", - "internal_name": "ftunivpsbernales" - }, { "name": "Repositorio institucional de la Universidad San Ignacio de Loyola", "internal_name": "ftunisanigndloyo" @@ -30403,26 +29479,14 @@ "name": "Journal Online ISI Padangpanjang (Institut Seni Indonesia)", "internal_name": "ftisipadangpanja" }, - { - "name": "Revista Raites (Red de Investigación en Administración de la Innovación Tecnológica)", - "internal_name": "ftinsttcelaya" - }, { "name": "Repozytorium PL (Politechnika Łódzka)", "internal_name": "fttunivlodz" }, - { - "name": "中国科学院水生生物研究所机构知识库", - "internal_name": "ftchinacadsciihb" - }, { "name": "Universidad Arzobispo Loayza: DSpace", "internal_name": "ftunivaloayza" }, - { - "name": "Korea Consumer Agency: KCA Repository", - "internal_name": "ftkca" - }, { "name": "Universidad Nacional de Educacion Enrique Guzmán y Valle: Repositorio UNE", "internal_name": "ftunivneevalle" @@ -30503,14 +29567,6 @@ "name": "Universitas Islam Raden Rahmat (UNIRA) Malang: Journals", "internal_name": "ftuniramalang" }, - { - "name": "PSM Journals (Pakistan Science Mission)", - "internal_name": "ftpsmpublojs" - }, - { - "name": "Sangkhakala Berkala Arkeologi", - "internal_name": "ftjsba" - }, { "name": "Repositorio UARM (Universidad Antonio Ruiz de Montoya)", "internal_name": "ftunivaruizmonto" @@ -30527,10 +29583,6 @@ "name": "Repositorio de la Universidad Peruana del Centro (UPECEN)", "internal_name": "ftuniperuanacent" }, - { - "name": "Universidad Nacional de Huancavelica: Repositorio Institucional Digital", - "internal_name": "ftunivnhuancavel" - }, { "name": "Universidad Inca Garcilaso de la Vega: Repositorio Institucional", "internal_name": "ftunivigvega" @@ -30627,10 +29679,6 @@ "name": "Universidad Nacional de Ucayali: Repositorio Institucional UNU", "internal_name": "ftunivucayali" }, - { - "name": "Repositorio Institucional de la Universidad Nacional de Trujillo", - "internal_name": "ftunivntrujillo" - }, { "name": "Neumann Business School: Repositorio Institucional", "internal_name": "ftescuelapneuman" @@ -30651,10 +29699,6 @@ "name": "Digital Commons @ University at Buffalo School of Law", "internal_name": "ftunibuffaloslaw" }, - { - "name": "Institut Agama Islam Tribakti (IAIT) Kediri: e-Journal", - "internal_name": "ftiaitribaktiked" - }, { "name": "Wichita State University: Electronic Journals Hosted by University Libraries", "internal_name": "ftwichitastateun" @@ -30731,10 +29775,6 @@ "name": "International Journal of Innovation in Enterprise System (IJIES)", "internal_name": "ftjijies" }, - { - "name": "Health Psychology Bulletin", - "internal_name": "ftjehp" - }, { "name": "E-Journal STIE AAS Surakarta (Sekolah Tinggi Ilmu Ekonomi)", "internal_name": "ftstieasurakarta" @@ -30759,10 +29799,6 @@ "name": "Revista Colombiana de Nefrología", "internal_name": "ftjrcdf" }, - { - "name": "International Medical Publisher Journals (iMedPub)", - "internal_name": "ftimedpub" - }, { "name": "UNIDA Gontor Journals (Universitas Darussalam)", "internal_name": "ftunidagontorojs" @@ -30871,10 +29907,6 @@ "name": "SeaSpray Literary Journal (Texas Digital Library - TDL E-Journals)", "internal_name": "ftjseaspray" }, - { - "name": "Angelo State University Social Sciences Research Journal (Texas Digital Library - TDL E-Journals)", - "internal_name": "ftjssrj" - }, { "name": "Journal of the Texas Tech University Ethics Center (Texas Digital Library - TDL E-Journals)", "internal_name": "ftjttuec" @@ -30891,14 +29923,6 @@ "name": "MRJ - MyResearchJournals (MRI Publications Lucknow, Uttar Pradesh, India)", "internal_name": "ftmyresearchjour" }, - { - "name": "e-Journal Balitbangkumham (Balitbang Hukum Dan Ham)", - "internal_name": "ftbitbangkumham" - }, - { - "name": "Journal Online Sekolah Tinggi Agama Islam Negeri (STAIN) Kediri", - "internal_name": "ftstainkediriojs" - }, { "name": "Journal Cendekia Hukum (JCH - STIH Putri Maharaja Payakumbuh)", "internal_name": "ftjch" @@ -30935,30 +29959,14 @@ "name": "Jurnal On Line Institut Teknologi Dirgantara Adisutjipto", "internal_name": "ftsttadisutjipto" }, - { - "name": "Jurnal Unswagati Cirebon (Jurnal Universitas Swadaya Gunung Jati)", - "internal_name": "ftunivswagati" - }, { "name": "Portal Jurnal Ilmiah STKIP PGRI Banjarmasin (Sekolah Tinggi Keguruan Dan Ilmu Pendidikan Persatuan Guru Republik Indonesia)", "internal_name": "ftmathdidactic" }, - { - "name": "Biovelentia - Biological Research Journal", - "internal_name": "ftjbiovalentia" - }, { "name": "Jurnal Ilmu-Ilmu Peternakan (JIIP - Fakultas Peternakan Universitas Brawijaya)", "internal_name": "ftjiip" }, - { - "name": "Trisakti Open Journal Systems (Universitas Trisakti)", - "internal_name": "ftunitrisaktiojs" - }, - { - "name": "Jurnal Online Fakultas Psikologi dan Kesehatan (Universitas Islam Negeri Sunan Ampel Surabaya)", - "internal_name": "ftiainsunanamfpk" - }, { "name": "湘北短期大学リポジトリ", "internal_name": "ftshohokucollege" @@ -31039,10 +30047,6 @@ "name": "北九州工業高等専門学校機関リポジトリ", "internal_name": "ftnitkitakyushuc" }, - { - "name": "ePrints@TNMGRM (Tamil Nadu Dr. M.G.R. Medical University)", - "internal_name": "fttnmgrmedicalu" - }, { "name": "Digitální knihovna Filozofické fakulty Masarykovy univerzity", "internal_name": "ftmasarykufarts" @@ -31123,10 +30127,6 @@ "name": "IZGOnZeit - Onlinezeitschrift des Interdisziplinären Zentrums für Geschlechterforschung", "internal_name": "ftjizgonzeit" }, - { - "name": "Informatics Journals (Informatics Publishing Ltd.)", - "internal_name": "ftinformaticsojs" - }, { "name": "Известия Национальной академии наук Беларуси. Серия химических наук", "internal_name": "ftjpnasbcs" @@ -31159,10 +30159,6 @@ "name": "International Journal of artificial intelligence research (IJAIR)", "internal_name": "ftjijair" }, - { - "name": "Jurnal Ilmiah Universitas Tadulako", - "internal_name": "ftunivtadulakojs" - }, { "name": "UIN (Universitas Islam Negeri) Sunan Kalijaga, Yogyakarta: E-Journal Fakultas Dakwah dan Komunikasi", "internal_name": "ftuinsunkdakwah" @@ -31427,10 +30423,6 @@ "name": "Opin vísindi (Island)", "internal_name": "ftopinvisindi" }, - { - "name": "Одеський національний політехнічний університет (ОНПУ)", - "internal_name": "ftodessanpuniv" - }, { "name": "Електронний Інституційний репозитарій Таврійського державного агротехнологічного університету", "internal_name": "fttavriasatuniv" @@ -31543,14 +30535,6 @@ "name": "Вопросы статистики", "internal_name": "ftjvoprstat" }, - { - "name": "Karib - Nordic Journal for Caribbean Studies", - "internal_name": "ftjkarib" - }, - { - "name": "Romanian Journal of History and International Studies (RJHIS)", - "internal_name": "ftjrjhis" - }, { "name": "Open Journal Systems - Universitas Negeri Surabaya", "internal_name": "ftuninsurabaya2" @@ -31583,10 +30567,6 @@ "name": "LALR - Latin American Literary Review", "internal_name": "ftjlalr" }, - { - "name": "eGEMs (Generating Evidence & Methods to improve patient outcomes)", - "internal_name": "ftjegems" - }, { "name": "e-Journal UMAHA (Universitas Maarif Hasyim)", "internal_name": "ftunimaarifhasyi" @@ -31823,10 +30803,6 @@ "name": "Institutet för språk och folkminnen: Publikationer (DiVA)", "internal_name": "ftinstlfuppsala" }, - { - "name": "APU Digital Archives (Azusa Pacific University)", - "internal_name": "ftazusapacificun" - }, { "name": "MUShare - Collected Scholarship at Marian University Indianapolis", "internal_name": "ftmariancollege" @@ -31883,10 +30859,6 @@ "name": "DSpace@Baskent - Baskent University Institutional Repository", "internal_name": "ftbaskentuniv" }, - { - "name": "Recursos Educativos Abiertos – Universidad Austral de Chile (UACh)", - "internal_name": "ftunivaustralchi" - }, { "name": "Szent István Egyetem Archivum (SZIE)", "internal_name": "ftszentistvanuir" @@ -31991,10 +30963,6 @@ "name": "九州産業大学図書館学術リポジトリ", "internal_name": "ftkyushusangyoun" }, - { - "name": "中国科学院理论物理研究所机构知识库", - "internal_name": "ftchinacadscitp" - }, { "name": "Islamology", "internal_name": "ftjislamology" @@ -32095,10 +31063,6 @@ "name": "AV Notas - Revista de investigación musical", "internal_name": "ftjavnotas" }, - { - "name": "Профессиональное образование в современном мире", - "internal_name": "ftjprofed" - }, { "name": "Правоприменение", "internal_name": "ftjenforcement" @@ -32119,10 +31083,6 @@ "name": "Атеротромбоз", "internal_name": "ftjaterotromboz" }, - { - "name": "Universidad de San Carlos de Guatemala: Revistas Investigación y Postgrado", - "internal_name": "ftuscarlosgtrip" - }, { "name": "Clute Journals (Clute Institute)", "internal_name": "ftcluteinstojs" @@ -32175,10 +31135,6 @@ "name": "Jurnal Online Universitas Galuh", "internal_name": "ftunivgaluhojs" }, - { - "name": "Jurnal Online Universitas Pertahanan (Indonesian Defense University)", - "internal_name": "ftunivpertahanan" - }, { "name": "Technologies for Lightweight Structures", "internal_name": "ftjtls" @@ -32679,10 +31635,6 @@ "name": "Briliant: Jurnal Riset dan Konseptual (Jurnal Online UNU Blitar - Universitas Nahdlatul Ulama)", "internal_name": "ftunivnublitar" }, - { - "name": "Atom Indonesia", - "internal_name": "ftjatomindonesia" - }, { "name": "E-Journal STIESIA Surabaya (Sekolah Tinggi Ilmu Ekonomi Indonesia)", "internal_name": "ftstiesiaurabaya" @@ -32939,10 +31891,6 @@ "name": "Repositorio Institucional Digital de Acceso Abierto de la Universidad Nacional de Quilmes (RIDAA)", "internal_name": "ftunivnquilmes" }, - { - "name": "大原記念労働科学研究所", - "internal_name": "ftinstsciencelab" - }, { "name": "Nazarbayev University Repository", "internal_name": "ftnazarbayevuniv" @@ -33035,10 +31983,6 @@ "name": "Proceedings Published by the LSA (Linguistic Society of America)", "internal_name": "ftlingsocamerojs" }, - { - "name": "Industrial and Systems Engineering Review (ISER)", - "internal_name": "ftbinghamtonuojs" - }, { "name": "Praxis (Villanova University)", "internal_name": "ftjpraxis" @@ -33291,10 +32235,6 @@ "name": "Journal of the Motherhood Initiative for Research and Community Involvement (JMI - York University)", "internal_name": "ftjarm" }, - { - "name": "E-Journal of Indonesia (EJI)", - "internal_name": "ftejiojs" - }, { "name": "UNISEL OJS (Universiti Selangor)", "internal_name": "ftuniselangorojs" @@ -33327,10 +32267,6 @@ "name": "Assam Don Bosco University Journals", "internal_name": "ftdonboscouniojs" }, - { - "name": "Universitas Jember (UNEJ): Digital Repository", - "internal_name": "ftunivjember" - }, { "name": "Frontiers (Publisher)", "internal_name": "crfrontiers" @@ -33447,10 +32383,6 @@ "name": "e-Journal Universitas Indraprasta PGRI (Persatuan Guru Republik Indonesia)", "internal_name": "ftunindrapgriojs" }, - { - "name": "Journal on Faculty of Mathematics and Science Education (Fakultas Pendidikan Matematika dan Ilmu Pengetahuan Alam, FPMIPA - Universitas Pendidikan Indonesia, UPI)", - "internal_name": "ftupendidindfpmi" - }, { "name": "SEER - Universidade Feevale", "internal_name": "ftunivfeevaleojs" @@ -33487,30 +32419,14 @@ "name": "ScienceDirect (Elsevier)", "internal_name": "crelsevierbv" }, - { - "name": "Jurnal University of Jember", - "internal_name": "ftunivjemberojs" - }, { "name": "Deakin University: openjournals@Deakin", "internal_name": "ftdeakinunivojs" }, - { - "name": "Portal de Periódicos UNIBAVE (Centro Universitário Barriga Verde)", - "internal_name": "ftunibave" - }, { "name": "FAmagazine - Ricerche e progetti sull'architettura e la città", "internal_name": "ftjfamagazine" }, - { - "name": "International Journal of Curriculum and Instruction (World Council for Curriculum and Instruction - WCCI)", - "internal_name": "ftjijci" - }, - { - "name": "Open Journal System BPPT (Badan Pengkajian dan Penerapan Teknologi)", - "internal_name": "ftbpptojs" - }, { "name": "Sistema OJS UCBSP-Cochabamba (Universidad Católica Boliviana \"San Pablo\")", "internal_name": "ftucbspcochabamb" @@ -33643,10 +32559,6 @@ "name": "Univesity of Nairobi Journal Systems", "internal_name": "ftunivnairobiojs" }, - { - "name": "Repositorio Institucional del Grupo Educativo Universidad Privada de Ica (UPICA)", - "internal_name": "ftinivpica" - }, { "name": "International Journal of Librarianship", "internal_name": "ftjijol" @@ -33783,10 +32695,6 @@ "name": "Università degli Studi di Brescia: OPENBS - Open Archive UniBS", "internal_name": "ftunivbrescia" }, - { - "name": "The Indian Journal of Veterinary Science & Biotechnology (IJVSBT)", - "internal_name": "ftjijvsbt" - }, { "name": "Hamilton Digital Commons (Hamilton College)", "internal_name": "fthamiltoncoll" @@ -33875,10 +32783,6 @@ "name": "Revistas de Investigación UNAS (Universidad Nacional Agraria de la Selva - Tingo María, Perú)", "internal_name": "ftuninaselvaojs" }, - { - "name": "University of Twente Open Journals", - "internal_name": "ftunitwentesjojs" - }, { "name": "Научный вестник МГТУ ГА", "internal_name": "ftjmstuca" @@ -33967,10 +32871,6 @@ "name": "UNCG Hosted Online Journals (The University of North Carolina at Greensboro)", "internal_name": "ftuncarolinagojs" }, - { - "name": "Albert Einstein College of Medicine, Yeshiva University: Open Journal Systems", - "internal_name": "ftaeinsteincmed" - }, { "name": "Journal of Engineering Research (Kuwait University)", "internal_name": "ftjengresearch" @@ -34007,10 +32907,6 @@ "name": "Jurnal Elektronik Universitas Negeri Padang", "internal_name": "ftunivnpadangojs" }, - { - "name": "The University of the West Indies at Mona, Jamaica: UWI Journals", - "internal_name": "ftuniwestindmona" - }, { "name": "Revistas de la Universidad Científica del Perú (UCP)", "internal_name": "ftucientificperu" @@ -34099,10 +32995,6 @@ "name": "Atlantis: Critical Studies in Gender, Culture & Social Justice/Études critiques sur le genre, la culture, et la justice sociale", "internal_name": "ftmtstvincentuni" }, - { - "name": "مجلات الجامعة الإسلامية", - "internal_name": "ftislamicunigaza" - }, { "name": "HASP Journals (Heidelberg Asian Studies Publishing)", "internal_name": "ftubheicrossasia" @@ -34163,10 +33055,6 @@ "name": "Universidad San Martín de Porres (USMP): Portal Revistas Académicas", "internal_name": "ftusmarporresojs" }, - { - "name": "Florida Online Journals (FloridaOJ)", - "internal_name": "ftfloridaclaojs" - }, { "name": "Revistas académico científicas UTMACH (Universidad Técnica de Machala)", "internal_name": "ftunitmachalaojs" @@ -34187,10 +33075,6 @@ "name": "岐阜女子大学リポジトリ", "internal_name": "ftgifuwomensuniv" }, - { - "name": "Sistema de Publicaciones del Campus Virtual (Universidad de Extremadura - CVUEx)", - "internal_name": "ftuextremadojs" - }, { "name": "LaCRIS - University of Lapland Current Research System", "internal_name": "ftulaplandcdispu" @@ -34307,10 +33191,6 @@ "name": "Karl Polanyi Fonds (Karl Polanyi Institute of Political Economy - KPIPE)", "internal_name": "ftkripe" }, - { - "name": "Repositoro de Tesis - Universidad Catolica de Santa Maria (UCSM)", - "internal_name": "ftunicstmariadis" - }, { "name": "ICONARP - International Journal Of Architecture And Planning", "internal_name": "ftjiconarp" @@ -34371,18 +33251,10 @@ "name": "Травматология и ортопедия России", "internal_name": "ftjtor" }, - { - "name": "Digital Collections at Western Theological Seminary", - "internal_name": "ftjrr" - }, { "name": "Доклады Национальной академии наук Беларуси", "internal_name": "ftjdnasb" }, - { - "name": "Вісник Київського національного університету імені Тараса Шевченка. Соціологія", - "internal_name": "ftjbtsnuks" - }, { "name": "Цифровое пространство научных исследований", "internal_name": "ftcpniojs" @@ -34487,10 +33359,6 @@ "name": "福岡女学院学術機関リポジトリ", "internal_name": "ftfukuokajogakui" }, - { - "name": "广西民族大学机构知识库", - "internal_name": "ftguangxiuniv" - }, { "name": "Jurnal Konseling dan Pendidikan (JKP - Indonesian Institute for Counseling, Education and Therapy, IICET)", "internal_name": "ftjkdp" @@ -34499,10 +33367,6 @@ "name": "SciPlatform (Pakistan): Open Journal Systems", "internal_name": "ftsciplatformojs" }, - { - "name": "Rumah Jurnal Fakultas Ekonomi dan Bisnis Islam Universitas Islam Negeri Imam Bonjol Padang", - "internal_name": "ftianimambonjol" - }, { "name": "Jurnal Online Informatika (JOIN)", "internal_name": "ftjoin" @@ -34723,14 +33587,6 @@ "name": "University of Tartu: Datadoi Repositorium", "internal_name": "ftunivtartudata" }, - { - "name": "CaSA NaRA", - "internal_name": "ftcasa" - }, - { - "name": "Artvin Çoruh Üniversitesi Kurumsal Arşiv Sistemi (DSpace@Artvin)", - "internal_name": "ftartvintcollege" - }, { "name": "Niğde Ömer Halisdemir Üniversitesi Akademik Arşiv Sistemi (DSpace@ÖHÜ)", "internal_name": "ftnigdeuniv" @@ -34927,14 +33783,6 @@ "name": "Revistas Científicas Indexadas y Estudiantiles de la Universidad Pedagógica Nacional, Bogotá", "internal_name": "ftunipnbogotaojs" }, - { - "name": "Ciencia y Tecnología Agropecuaria", - "internal_name": "ftjccta" - }, - { - "name": "Mouth", - "internal_name": "ftjmouth" - }, { "name": "Revistas - Facultad de Humanidades UNMDP (Universidad Nacional de Mar del Plata)", "internal_name": "ftunivnmpojs" @@ -35047,10 +33895,6 @@ "name": "The Scientific Journal of Riga Technical University", "internal_name": "ftrigatunivojs" }, - { - "name": "Revista Gestão & Tecnologia", - "internal_name": "ftjrgt" - }, { "name": "Journal of European Psychology Students (JEPS)", "internal_name": "ftjeps" @@ -35191,10 +34035,6 @@ "name": "Performance Philosophy", "internal_name": "ftjperfphilosoph" }, - { - "name": "Scholar Science Journals (India)", - "internal_name": "ftssjournalsojs" - }, { "name": "Journal of Lumbini Medical College (JLMC)", "internal_name": "ftjlmc" @@ -35227,10 +34067,6 @@ "name": "Bartın Üniversitesi Kurumsal Akademik Arşivi (DSpace@Bartin)", "internal_name": "ftbartinuniv" }, - { - "name": "Харківський національний університет Повітряних Сил ім. І. Кожедуба: Архiв наукових видань", - "internal_name": "ftkozhuairforce" - }, { "name": "DePaul University Library Digital Collection", "internal_name": "ftdepaulunivdc" @@ -35347,10 +34183,6 @@ "name": "UIN (Universitas Islam Negeri) Sunan Kalijaga, Yogyakarta: E-Journal Fakultas Ilmu Sosial dan Humaniora", "internal_name": "ftuinsunankalish" }, - { - "name": "Federal University of Agriculture, Abeokuta: FUNAAB Journal", - "internal_name": "ftfuniagriabeoku" - }, { "name": "Saber UCV: OJS (Repositorio Institucional de la Universidad Central de Venezuela)", "internal_name": "ftucvenezuelaojs" @@ -35363,14 +34195,6 @@ "name": "GSTF Digital Library (GSTF-DL): Open Journal Systems (Global Science and Technology Forum)", "internal_name": "ftgstfojs" }, - { - "name": "Ars Boni et Aequi ( Facultad de Derecho y Comunicación Social de la Universidad Bernardo O’Higgins)", - "internal_name": "ftjaba" - }, - { - "name": "Андрология и генитальная хирургия", - "internal_name": "ftjaags" - }, { "name": "Лёд и Снег", "internal_name": "ftjias" @@ -35435,10 +34259,6 @@ "name": "CINEJ Cinema Journal (University of Pittsburgh)", "internal_name": "ftjcinej" }, - { - "name": "Lambung Mangkurat Law Journal", - "internal_name": "ftjlmlj" - }, { "name": "University of Maryland University College: UMUC Digital Repository", "internal_name": "ftumarylanducdc" @@ -35707,14 +34527,6 @@ "name": "UNM Digital Repository (The University of New Mexico)", "internal_name": "ftunvnewmexicoir" }, - { - "name": "Inquiry (Faculty of Business and Administration, International University of Sarajevo)", - "internal_name": "ftjinquiry" - }, - { - "name": "Бюллетень сибирской медицины", - "internal_name": "ftjbosm" - }, { "name": "RD&E Research Repository (Royal Devon and Exeter NHS Foundation Trust)", "internal_name": "ftrde" @@ -35795,10 +34607,6 @@ "name": "Periódicos Uniamérica (Foz do Iguaçu)", "internal_name": "ftuniamericaojs" }, - { - "name": "E-Journal System IAIN Bengkulu (Institut Agama Islam Negeri)", - "internal_name": "ftiainbengkuluoj" - }, { "name": "Український біофармацевтичний журнал", "internal_name": "ftjubphj" @@ -36015,10 +34823,6 @@ "name": "Universidad Nacional de Salta: Open Journal Systems", "internal_name": "ftuninsanjuanijs" }, - { - "name": "IJCU - International Journal of College and University", - "internal_name": "ftijcuojs" - }, { "name": "French-Ukrainian Journal of Chemistry", "internal_name": "ftjfruajc" @@ -36067,10 +34871,6 @@ "name": "Periodicals of Engineering and Natural Sciences (PEN - International University of Sarajevo)", "internal_name": "ftjpens" }, - { - "name": "Revistas Científicas USS (Universidad \"Señor de Sipán\")", - "internal_name": "ftunivssipanojs" - }, { "name": "Portal De Revistas Unicesar (Universidad Popular del Cesar)", "internal_name": "ftunivpcesarojs" @@ -36443,10 +35243,6 @@ "name": "Российский вестник перинатологии и педиатрии", "internal_name": "ftjrvpp" }, - { - "name": "e-Journal - STAI Muara Bulian (Sekolah Tinggi Agama Islam)", - "internal_name": "ftstaimuarabuojs" - }, { "name": "Медицинская генетика", "internal_name": "ftjmedgen" @@ -36487,10 +35283,6 @@ "name": "Université Toulouse III - Paul Sabatier: HAL-UPS", "internal_name": "ftutoulouse3hal" }, - { - "name": "المستودع الرقمي المؤسسي لجامعة نايف العربية للعلوم الأمنية", - "internal_name": "ftnaunivss" - }, { "name": "Jurnal Fakultas Ekonomi UM Metro (Universitas Muhammadiyah)", "internal_name": "ftunimmetrofeojs" @@ -37115,10 +35907,6 @@ "name": "大阪大学学術情報庫リポジトリ", "internal_name": "ftosakauniv" }, - { - "name": "Repositorio Institucional del Consorcio Ecuatoriano para el Desarrollo de Internet Avanzado (REDCEDIA)", - "internal_name": "ftcedia" - }, { "name": "Hasan Kalyoncu University Institutional Repository", "internal_name": "fthasankalyoncu" @@ -37351,10 +36139,6 @@ "name": "Universitas Diponegoro: Undip E-Journal System (UEJS) Portal", "internal_name": "ftundipojs2" }, - { - "name": "Revista de Ingeniería (Facultad de Ingeniería, Universidad de los Andes)", - "internal_name": "ftjrevistaing" - }, { "name": "IBICT - Portal de periódicos OJS (Instituto Brasileiro de Informação em Ciência e Tecnologia)", "internal_name": "ftibictojs" @@ -37731,10 +36515,6 @@ "name": "Carolina Law Scholarship Repository (University of North Carolina, School of Law)", "internal_name": "ftunincarolinasl" }, - { - "name": "國立臺北護理健康大學", - "internal_name": "ftntunivnhs" - }, { "name": "FHSU Scholars Repository (Fort Hays State University)", "internal_name": "ftforthaysstuniv" @@ -37775,10 +36555,6 @@ "name": "UNM Online Journal Systems (Universitas Negeri Makassar)", "internal_name": "ftunmakassarojs" }, - { - "name": "World Construction", - "internal_name": "ftjwc" - }, { "name": "Urban Transportation & Construction", "internal_name": "ftjutc" @@ -37803,10 +36579,6 @@ "name": "Opus - Hochschulschriftenserver der Hochschule für Musik (HfM) Detmold", "internal_name": "fthsmdetmold" }, - { - "name": "Ejournal of industrial system portal (Kementerian Perindustrian)", - "internal_name": "ftkemenperinojs" - }, { "name": "Journal of Universitas Airlangga", "internal_name": "ftunairlanggaojs" @@ -37879,10 +36651,6 @@ "name": "Universidad Complutense de Madrid (UCM): Revistas Científicas Complutenses", "internal_name": "ftunicmadridrev" }, - { - "name": "ISLAMICA: Jurnal Studi Keislaman (UIN Sunan Ampel Surabaya)", - "internal_name": "ftjislamica" - }, { "name": "Portal de Periódicos da Uniarp (Universidade Alto Vale do Rio do Peixe)", "internal_name": "ftunivarpojs" @@ -38031,10 +36799,6 @@ "name": "Westminster Papers in Communication and Culture (WPCC)", "internal_name": "ftjwpcc" }, - { - "name": "Widyariset (Pusbindiklat Peneliti-LIPI)", - "internal_name": "ftwidyariset" - }, { "name": "Фармация и фармакология", "internal_name": "ftjpharmpharm" @@ -38187,10 +36951,6 @@ "name": "Sanglap: Journal of Literary and Cultural Inquiry", "internal_name": "ftjsanglap" }, - { - "name": "STAIN Pamekasan Jurnal Online (Sekolah Tinggi Agama Islam Negeri)", - "internal_name": "ftstainpamekasan" - }, { "name": "PublicacionesDidácticas", "internal_name": "ftdidcticas" @@ -38251,18 +37011,10 @@ "name": "Open University of Tanzania Repository", "internal_name": "ftopenunivtanz" }, - { - "name": "日本大学リポジトリ", - "internal_name": "ftnihonuniv" - }, { "name": "Repositorio Institucional de la Universidad del Tolima (RIUT)", "internal_name": "ftunivtolima" }, - { - "name": "National Institute of Education, Singapore: NIE Digital Repository", - "internal_name": "ftninstesingap" - }, { "name": "Digital Commons@Humboldt State University (HSU)", "internal_name": "fthumboldtsudc" @@ -38703,10 +37455,6 @@ "name": "Université Sorbonne Nouvelle - Paris 3: HAL", "internal_name": "ftunivparis3" }, - { - "name": "Jurnal Penelitian Kehutanan Wallacea", - "internal_name": "ftjpkw" - }, { "name": "eJournal Sriwijaya University (UNSRI)", "internal_name": "ftunsriwijayaojs" @@ -38783,10 +37531,6 @@ "name": "Universidade Federal de São Paulo (UNIFESP): Repositório Institucional", "internal_name": "ftunivfsaopaulo" }, - { - "name": "Université Angers: Okina (Open Knowledge, INformation, Access)", - "internal_name": "ftunivangokina" - }, { "name": "Исследования и практика в медицине", "internal_name": "ftjrpmj" @@ -38871,10 +37615,6 @@ "name": "Сравнительная политика", "internal_name": "ftjcpr" }, - { - "name": "Наукові журнали Прикарпатського національного університету", - "internal_name": "ftvsprecarpath" - }, { "name": "Digital Commons @ Gardner-Webb University", "internal_name": "ftgardnerwebb" @@ -39275,10 +38015,6 @@ "name": "Вінницький національний технічний університет", "internal_name": "ftvinnytsiatuniv" }, - { - "name": "대학메인", - "internal_name": "ftulsancollege" - }, { "name": "University of Humanistic Studies Research Portal (UVH)", "internal_name": "ftunihumanistiek" @@ -39391,10 +38127,6 @@ "name": "The University of Melbourne: Course Work", "internal_name": "ftumelbournecw" }, - { - "name": "The American College of Financial Services: Institutional Repository", - "internal_name": "ftamericancoll" - }, { "name": "Xavier University Cincinnati: Exhibit", "internal_name": "ftxavieruniv" @@ -39419,10 +38151,6 @@ "name": "National Research Council Canada: NRC Publications Archive", "internal_name": "ftnrccanada" }, - { - "name": "Secretaría de Derechos Humanos para el Pasado Reciente (SDH): Colección digital PRENSA", - "internal_name": "ftprensasdh" - }, { "name": "Universidad Militar Nueva Granada: Repositorio Institucional UMNG", "internal_name": "ftunivmilnueva" @@ -39487,10 +38215,6 @@ "name": "E Jurnal STIE Pasundan Bandung (Sekolah Tinggi Ilmu Ekonomi)", "internal_name": "ftstiebandung" }, - { - "name": "Publicaciones Unisangil (Universitaria de San Gil)", - "internal_name": "ftunisangilojs" - }, { "name": "Uniwersytet im. Adama Mickiewicza w Poznaniu: PRESSto", "internal_name": "ftamickiewiczojs" @@ -39511,10 +38235,6 @@ "name": "Leeds Beckett University Repository", "internal_name": "ftleedsbeckettun" }, - { - "name": "คลังข้อมูลดิจิทัลด้านคุณธรรมความดี", - "internal_name": "ftmoralcenter" - }, { "name": "Вестник трансплантологии и искусственных органов", "internal_name": "ftjvtio" @@ -39543,10 +38263,6 @@ "name": "GAMS - Geisteswissenschaftliches Asset Management System (Zentrum für Informationsmodellierung, Universität Graz)", "internal_name": "ftunivgrazgams" }, - { - "name": "Электронный архив РГППУ (Российский государственный профессионально-педагогический университет в Екатеринбурге)", - "internal_name": "ftrsvpuniv" - }, { "name": "Nyugat-magyarországi Egyetem (NYME): Publicatio Repozitórium", "internal_name": "ftunivwesthu" @@ -39595,10 +38311,6 @@ "name": "RonPub - Research Online Publishing", "internal_name": "ftronpub" }, - { - "name": "Academy Publication Online", - "internal_name": "ftacadpublicatio" - }, { "name": "Пульмонология", "internal_name": "ftjpulmonology" @@ -39627,10 +38339,6 @@ "name": "Falmouth University Research Repository (FURR)", "internal_name": "ftfalmouthuniv" }, - { - "name": "University of Cambridge, Department of Earth Sciences: ESC Publications", - "internal_name": "ftucambridgeesc" - }, { "name": "University of Minnesota Law School Scholarship Repository", "internal_name": "ftuniminnesotals" @@ -39715,10 +38423,6 @@ "name": "Instituto Tecnológico de Costa Rica: Repositorio TEC", "internal_name": "ftinsttec" }, - { - "name": "Nemzeti Közszolgálati Egyetem: Ludovika Digitális Tudástár és Archívum", - "internal_name": "ftnationalunivps" - }, { "name": "University of Louisville: ThinkIR", "internal_name": "ftunivlouisvir" @@ -39863,10 +38567,6 @@ "name": "The Journal of Quality in Education (AMAQUEN Institute)", "internal_name": "ftjqe" }, - { - "name": "Uluslararası Akademik Yönetim Bilimleri Dergisi", - "internal_name": "ftjyonbil" - }, { "name": "Revistas Científicas Indexadas Universidad Surcolombiana", "internal_name": "ftunisurcolombia" @@ -39987,10 +38687,6 @@ "name": "Электронная библиотека Уральского государственного педагогического университета (объединенного вуза – УрГПУ и РГППУ)", "internal_name": "ftunivsfe" }, - { - "name": "Indonesian Center for Animal Research and Development : Scientific Journal of ICARD", - "internal_name": "fticardojs" - }, { "name": "Revista Mexicana de Estomatología", "internal_name": "ftjrme" @@ -40035,10 +38731,6 @@ "name": "Darwiniana, nueva serie", "internal_name": "ftjdarwiniana" }, - { - "name": "International Journal of Multifaceted and Multilingual Studies (IJMMS)", - "internal_name": "ftjijmms" - }, { "name": "Virginia Community College System: Digital Commons @ VCCS", "internal_name": "ftvirginiacomcol" @@ -40115,10 +38807,6 @@ "name": "Georgetown University: DigitalGeorgetown", "internal_name": "ftgeorgetownuniv" }, - { - "name": "Jurnal Infotel (Sekolah Tinggi Teknologi Telematika Telkom Purwokerto)", - "internal_name": "ftjinfotel" - }, { "name": "Management Dynamics in the Knowledge Economy", "internal_name": "ftjmdke" @@ -40171,10 +38859,6 @@ "name": "Portal Revista Científica y Literarias de la UNAN-León (Universidad Nacional Autónoma de Nicaragua)", "internal_name": "ftunanleonojs" }, - { - "name": "Riset Manajemen & Akuntansi", - "internal_name": "ftjrma" - }, { "name": "Pelita Perkebunan (Coffee and Cocoa Research Journal, CCRJ)", "internal_name": "ftjccrj" @@ -40235,14 +38919,6 @@ "name": "Research Archive of Indian Institute of Technology, Hyderabad (RAIITH)", "internal_name": "ftiith" }, - { - "name": "Bushehr University of Medical Sciences Repository", - "internal_name": "ftbushehrunivms" - }, - { - "name": "GISAP (Global International Scientific Analytical Project): Scientific Journal", - "internal_name": "ftgisapojs" - }, { "name": "Press Start (University of Glasgow)", "internal_name": "ftjpressstart" @@ -40299,18 +38975,10 @@ "name": "Kwantlen Polytechnic University: KORA (Kwantlen Open Resource Access)", "internal_name": "ftkwantlenpuniv" }, - { - "name": "中国科学院文献情报中心机构知识库", - "internal_name": "ftchinacadscinsl" - }, { "name": "Universidad de Medellin: Repositorio Institucional", "internal_name": "ftunivmedellin" }, - { - "name": "中国科学院生态环境研究中心机构知识库", - "internal_name": "ftchacadscircees" - }, { "name": "The Christie School of Oncology: Christie Research Publications Repository", "internal_name": "ftchristienhs" @@ -40447,10 +39115,6 @@ "name": "中国科学院半导体研究所机构知识库", "internal_name": "ftchinacadscsemi" }, - { - "name": "Repositorio Digital USFQ (Universidad San Francisco de Quito)", - "internal_name": "ftunivfquito" - }, { "name": "Kafa'ah: Journal of Gender Studies (Center for Gender and Child Studies, State Institute of Islamic Studies (IAIN) Imam Bonjol Padang)", "internal_name": "ftjkafaah" @@ -40583,18 +39247,10 @@ "name": "中国科学院广州能源研究所机构知识库", "internal_name": "ftchacadsciegiec" }, - { - "name": "中国科学院沈阳自动化研究所机构知识库", - "internal_name": "ftchacadsciensia" - }, { "name": "中国科学院心理研究所机构知识库", "internal_name": "ftchacscienpsych" }, - { - "name": "中国科学院新疆生态与地理研究所机构知识库", - "internal_name": "ftchacadscienegi" - }, { "name": "Portal de Periódicos UFSC (Universidade Federal de Santa Catarina)", "internal_name": "ftunivfscojs" @@ -40607,10 +39263,6 @@ "name": "Al-Ta’lim (Faculty of Islamic Education and Teacher Training IAIN Imam Bonjol Padang)", "internal_name": "ftinstagamaojs" }, - { - "name": "Open Journal Systems at University of Gothenburg", - "internal_name": "ftunivgoetebojs" - }, { "name": "Universidade Estadual Paulista São Paulo: Repositório Institucional UNESP", "internal_name": "ftunivespir" @@ -40691,10 +39343,6 @@ "name": "Lincoln Memorial University, Duncan School of Law: Digital Commons @ LMU-DSOL", "internal_name": "ftlincolnmemuni" }, - { - "name": "Київський університет імені Бориса Грінченка: Наукові доробки магістрантів", - "internal_name": "ftkunivbgojs" - }, { "name": "Revista de Gestão Social e Ambiental (RGSA - Centro Universitário da FEI)", "internal_name": "ftjrgsa" @@ -40867,10 +39515,6 @@ "name": "Open Access-Zeitschriften an der Universität Münster", "internal_name": "ftubmuensterojs" }, - { - "name": "Prifysgol Metropolitan Caerdydd", - "internal_name": "ftcardiffmetuniv" - }, { "name": "Universiti Utara Malaysia: UUM eTheses", "internal_name": "ftuniutaramalays" @@ -40931,10 +39575,6 @@ "name": "Université Mouloud Mammeri de Tizi Ouzou (UMMTO): Research Review of Sciences and Technologies", "internal_name": "ftunivmmtiziouzo" }, - { - "name": "The Open University of Sri Lanka: OUSL Digital Archive", - "internal_name": "ftopenusrilanka" - }, { "name": "Banco de la República Colombia: Publicaciones", "internal_name": "ftbancorepublica" @@ -40999,14 +39639,6 @@ "name": "The University of Vermont: ScholarWorks @ UVM", "internal_name": "ftunivermont" }, - { - "name": "University of California, Irvine: UCI Law Scholarly Commons", - "internal_name": "ftucirvineschool" - }, - { - "name": "Instituto Superior Miguel Torga (ISMT): Repositório Aberto", - "internal_name": "ftinstsmt" - }, { "name": "PennState: Digital Collections", "internal_name": "ftpennstateuncdm" @@ -41083,26 +39715,14 @@ "name": "Revistas URI - FW (Universidade Regional Integrada do Alto Uruguai e das Missões, campus de Frederico Westphalen/RS)", "internal_name": "ftunivrifwojs" }, - { - "name": "新世纪科学出版社", - "internal_name": "ftncspress" - }, { "name": "CBPC - Companhia Brasileira de Produção Científic: Portal de Periódicos da Sustenere Publishing", "internal_name": "ftsustenerepubl" }, - { - "name": "Faculdades Integradas Teresa D'Ávila: Publicações Fatea", - "internal_name": "ftfateaojs" - }, { "name": "Paavan Education Trust", "internal_name": "ftpaavanetrust" }, - { - "name": "Fundação de Economia e Estatística: Revistas Eletrônicas FEE - SEPLAG (Secretaria de Estado de Planejamento e Gestão)", - "internal_name": "ftfeeojs" - }, { "name": "International Journal of Electronics and Telecommunications (Warsaw University of Technology)", "internal_name": "ftjijet" @@ -41139,10 +39759,6 @@ "name": "Université Mohamed Khider, Biskra: Theses Repository", "internal_name": "ftunivbiskra" }, - { - "name": "Universidad Centroamericana (UCA), Nicaragua: Repositorio Institucional", - "internal_name": "ftunicamericana" - }, { "name": "Nationalmuseum (Stockholm): Publikationer (DiVA)", "internal_name": "ftnatmustockholm" @@ -41203,10 +39819,6 @@ "name": "Revista Internacional de Psicología (Instituto de la Familia Guatemala)", "internal_name": "ftjrip" }, - { - "name": "Revista Brasileira de Marketing (REMark - Universidade Nove de Julho - UNINOVE, São Paulo)", - "internal_name": "ftjbjm" - }, { "name": "Journal of Research in Marketing (JORM - Techmind Research, Canada)", "internal_name": "ftjorm" @@ -41251,10 +39863,6 @@ "name": "Pontifícia Universidade Católica de Goiás: Portal de Periódicos Eletrônicos da UCG", "internal_name": "ftunivcgoiasojs" }, - { - "name": "Vilniaus Gedimino Technikos Universitetas: VGTU Talpykla", - "internal_name": "ftvilniusgtuniv" - }, { "name": "Hochschule für Technik und Wirtschaft Dresden (HTWDD): Qucosa", "internal_name": "fthtwdresden" @@ -41331,10 +39939,6 @@ "name": "Universidade do Contestado: Periódicos UnC", "internal_name": "ftunivcontestado" }, - { - "name": "Université de Lille 1 - Sciences et Technologies: IRIS (Bibliothèque numérique en histoire des science)", - "internal_name": "ftunivlille1dc" - }, { "name": "Repozytorium Uniwersytetu w Białymstoku (RUB)", "internal_name": "ftunivbialystok" @@ -41415,10 +40019,6 @@ "name": "Naturhistoriska riksmuseet: Publikationer (DiVA)", "internal_name": "ftnrm" }, - { - "name": "Aisthema International Journal", - "internal_name": "ftjaisthema" - }, { "name": "Scientific Journals of Unnes (Universitas Negeri Semarang)", "internal_name": "ftunsemarangojs" @@ -41431,10 +40031,6 @@ "name": "Center for Scientific Publication: PPI (Pusat Publikasi Ilmiah - LPPM Institut Teknologi Sepuluh Nopember)", "internal_name": "ftitsojs" }, - { - "name": "Universidade de Uberab: Revistas Digitais Uniube", - "internal_name": "ftunivuberabaojs" - }, { "name": "Centro Universitário de Belo Horizonte: Portal de Revistas Eletrônicas do UniBH", "internal_name": "ftcubelohorizone" @@ -41563,10 +40159,6 @@ "name": "Servifapa (Instituto de Investigación y Formación Agraria y Pesquera, IFAPA)", "internal_name": "ftifapa" }, - { - "name": "Universidade do Estado do Rio de Janeiro (UERJ): Biblioteca Digital de Teses e Dissertações da UERJ", - "internal_name": "ftunieriodejan" - }, { "name": "Architexturez South Asia", "internal_name": "ftatsouthasia" @@ -41607,10 +40199,6 @@ "name": "Georgia College: Knowledge Box", "internal_name": "ftgeorgiacollege" }, - { - "name": "Australian International Academic Centre: AIAC Journals", - "internal_name": "ftaiacjournals" - }, { "name": "University of Malaya: UM Students' Repository", "internal_name": "ftunivmalayasr" @@ -41631,10 +40219,6 @@ "name": "Универзитет у Нишу", "internal_name": "ftunivnisojs" }, - { - "name": "MENAdoc (Virtuelle Fachbibliothek Vorderer Orient/Universitäts- und Landesbibliothek Sachsen-Anhalt, Halle)", - "internal_name": "ftulbhallemenado" - }, { "name": "Hamline University: DigitalCommons@Hamline", "internal_name": "fthamlineuniv" @@ -41663,70 +40247,10 @@ "name": "Kenyon College: Digital Kenyon - Research, Scholarship, and Creative Exchange", "internal_name": "ftkenyoncollege" }, - { - "name": "県立広島大学", - "internal_name": "ftprefunihiroshi" - }, - { - "name": "広島経済大学", - "internal_name": "fthirosunieconom" - }, - { - "name": "広島女学院大学", - "internal_name": "fthiroshjogakuin" - }, - { - "name": "広島国際大学", - "internal_name": "fthiroshimaintun" - }, - { - "name": "尾道市立大学", - "internal_name": "ftonochimicityun" - }, - { - "name": "比治山大学", - "internal_name": "fthijiyamauniv" - }, - { - "name": "日本赤十字広島看護大学", - "internal_name": "fthircollnursing" - }, - { - "name": "広島国際学院大学", - "internal_name": "fthiroshimakguni" - }, - { - "name": "呉工業高等専門学校", - "internal_name": "ftkurenct" - }, - { - "name": "福山市立大学", - "internal_name": "ftfukuyamasityun" - }, - { - "name": "Universidad del Norte, Colombia: Portal de Eventos", - "internal_name": "ftunivnorteojs2" - }, - { - "name": "広島市立大学機関リポジトリ", - "internal_name": "fthiroshimacityu" - }, - { - "name": "広島文教女子大学", - "internal_name": "fthiroshimabwuni" - }, { "name": "広島文化学園大学", "internal_name": "fthiroshimabguni" }, - { - "name": "海上保安大学校", - "internal_name": "ftjapcoastguarda" - }, - { - "name": "広島工業大学", - "internal_name": "fthiroshimait" - }, { "name": "Universität Leipzig: Historische Bestände und Nachlässe der UBL", "internal_name": "ftunivleipzigds" @@ -41795,10 +40319,6 @@ "name": "Central Electrochemical Research Institute, Karaikudi: IR@CECRI", "internal_name": "ftcsirurdipir" }, - { - "name": "Revues Scientifiques de l'université de Tlemcen", - "internal_name": "ftunivtlemcenojs" - }, { "name": "Rose-Hulman Institute of Technology: Rose-Hulman Scholar", "internal_name": "ftrosehulmaninst" @@ -41903,10 +40423,6 @@ "name": "Escuela Politécnica Nacional: Repositorio Digital EPN", "internal_name": "ftbieecepn" }, - { - "name": "КРАД - Наука Центральной Азии (Ассоциация Библиотечно-Информационный Консорциум)", - "internal_name": "ftbik" - }, { "name": "International Journal of Innovative Technology and Research (IJITR)", "internal_name": "ftjijitr" @@ -42067,10 +40583,6 @@ "name": "Revista Medicina (Facultad de Ciencias Médicas de la Unviersidad Católica de Santiago de Guayaquil)", "internal_name": "ftjrm" }, - { - "name": "Informatics in Primary Care (BCS, The Chartered Institute for IT)", - "internal_name": "ftjipc" - }, { "name": "Repositorio Digital de la Universidad Nacional de Córdoba (RD-UNC)", "internal_name": "ftunivncordoba" @@ -42111,10 +40623,6 @@ "name": "Magyar Tudományos Akadémia Könyvtára Magyar Tudományos Akadémia Könyvtár és Információs Központ)", "internal_name": "ftmtakorphanth" }, - { - "name": "Eastern University, Dhaka: Digital Library", - "internal_name": "fteasternudhaka" - }, { "name": "SSBFNET Center for Strategic Studies in Business and Finance: OJS (Society for the Study of Business & Finance)", "internal_name": "ftssbfnetojs" @@ -42131,10 +40639,6 @@ "name": "s u b \\ u r b a n. zeitschrift für kritische stadtforschung (Kritische Geographie Berlin e.V.)", "internal_name": "ftjzsu" }, - { - "name": "Academos Public Archive", - "internal_name": "ftacademospa" - }, { "name": "SciELO Brazil (Scientific Electronic Library Online)", "internal_name": "ftjscielo" @@ -42195,14 +40699,6 @@ "name": "University of West London: UWL Repository", "internal_name": "ftuniwestlondon" }, - { - "name": "Università degli Studi di Catania: Archivia", - "internal_name": "ftunivcatania" - }, - { - "name": "Muzeum Historii Polski: Dziennik Ustaw RP na Uchodźstwie", - "internal_name": "ftmuzhistoriipol" - }, { "name": "The University of Oxford Text Archive", "internal_name": "ftoxfordunita" @@ -42271,14 +40767,6 @@ "name": "Repositório Institucional da Universidade Federal de Lavras (RIUFLA)", "internal_name": "ftunivflavras" }, - { - "name": "Ferris State University: FIR (Ferris Institutional Repository)", - "internal_name": "ftferrisstateuni" - }, - { - "name": "Repositorio Digital del Instituto de Altos Estudios Nacionales", - "internal_name": "ftiaen" - }, { "name": "Repositório Institucional da Universidade Federal de Sergipe (RIUFS)", "internal_name": "ftunivfsergipe" @@ -42311,10 +40799,6 @@ "name": "University of Tulsa, College of Law: TU Law Digital Commons", "internal_name": "ftunitulsacollaw" }, - { - "name": "Blue Ocean Research Journals", - "internal_name": "ftbopublishers" - }, { "name": "Institut Teknologi Sepuluh Nopember (ITS): Publikasi Ilmiah Online Mahasiswa ITS (POMITS)", "internal_name": "ftitssurabayaojs" @@ -42351,10 +40835,6 @@ "name": "Universidad Nacional de La Plata, Facultad de Humanidades y Ciencias de la Educación (FaHCE-UNLP): Memoria Académica", "internal_name": "ftfahceunlp" }, - { - "name": "I-Revues (INIST-CNRS)", - "internal_name": "ftjirevues" - }, { "name": "Wyższa Szkoła Biznesu - National-Louis University, Nowy Sącz: Repozytorium WSB-NLU", "internal_name": "ftnatlouisuniwsb" @@ -42455,10 +40935,6 @@ "name": "Днепропетровский национальный университет железнодорожного транспорта: Електронний архів", "internal_name": "ftdnipropetrovsk" }, - { - "name": "Instituto Español de Oceanografía: e-IEO", - "internal_name": "ftieo" - }, { "name": "University of Chichester: EPrints Repository", "internal_name": "ftunivchichester" @@ -42687,10 +41163,6 @@ "name": "International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE)", "internal_name": "ftjijcrsee" }, - { - "name": "Open Access World", - "internal_name": "ftoaworld" - }, { "name": "Universidade Federal do Estado do Rio de Janeiro: Portal de Revistas da UNIRIO", "internal_name": "ftunivrioojs" @@ -42727,10 +41199,6 @@ "name": "PennState, The Dickinson School of Law: Penn State Law eLibrary", "internal_name": "ftpennstateuni" }, - { - "name": "International Journal of Mechanical Engineering and Computer Applications (IJMCA)", - "internal_name": "ftjijmca" - }, { "name": "Journal Academic Marketing Mysticism Online (JAMMO)", "internal_name": "ftjammo" @@ -42767,10 +41235,6 @@ "name": "Bates College: SCARAB (Scholarly Communication and Research at Bates)", "internal_name": "ftbatescollege" }, - { - "name": "Western Oregon University: Digital Commons@WOU", - "internal_name": "ftwesternoregon" - }, { "name": "Northwestern College Iowa: NWCommons", "internal_name": "ftnorthwestcoll" @@ -42815,10 +41279,6 @@ "name": "Embry-Riddle Aeronautical University: ERAU Scholarly Commons", "internal_name": "ftembryriddleaun" }, - { - "name": "Rhode Island College: DigitalCommons@RIC", - "internal_name": "ftrhodesislandco" - }, { "name": "Universidad Católica de Córdoba: Producción Académica UCC", "internal_name": "ftunivccordoba" @@ -42839,10 +41299,6 @@ "name": "Space and Culture, India", "internal_name": "ftjsc" }, - { - "name": "Socioeconomica", - "internal_name": "ftjsocioeconom" - }, { "name": "VFAST - Virtual Foundation for Advancement of Science and Technology (Pakistan)", "internal_name": "ftvfastojs" @@ -43131,10 +41587,6 @@ "name": "University of East Anglia: UEA Digital Repository", "internal_name": "ftuniveastangl" }, - { - "name": "Дигиталнa Народна и универзитетска библиотека Републике Српске", - "internal_name": "ftnusrpskadc" - }, { "name": "Ανοικτό Πανεπιστήμιο Κύπρου: Κυψέλη", "internal_name": "ftopenunivcyprus" @@ -43255,10 +41707,6 @@ "name": "FAUBA Digital (Facultad de Agronomía, Universidad de Buenos Aires - UBA)", "internal_name": "ftunibuenairesfa" }, - { - "name": "Chełmska Biblioteka Cyfrowa", - "internal_name": "ftchbc" - }, { "name": "WHO (World Health Organization): Institutional Repository for Information Sharing (IRIS)", "internal_name": "ftwhoiris" @@ -43271,10 +41719,6 @@ "name": "Наукові журнали Державного університету “Київський авіаційний інститут”", "internal_name": "ftnaviationunojs" }, - { - "name": "國立暨南國際大學", - "internal_name": "ftnchinanuniv" - }, { "name": "The World Bank: Open Knowledge Repository (OKR)", "internal_name": "ftworldbank" @@ -43435,10 +41879,6 @@ "name": "Institut für Deutsche Sprache: Publikationsserver", "internal_name": "ftinstdeusprache" }, - { - "name": "Vaal University of Technology: VUT DigiResearch", - "internal_name": "ftvaaluniv" - }, { "name": "歡迎光臨亞洲大學全球資訊網", "internal_name": "ftasiauniv" @@ -43483,10 +41923,6 @@ "name": "Faculdade de Educação Superior do Paraná: Open Journal Systems", "internal_name": "ftunivfespojs" }, - { - "name": "Northern Illinois University (NIU): Huskie Commons Repository", - "internal_name": "ftnorthillinuni" - }, { "name": "VTechWorks (VirginiaTech)", "internal_name": "ftvirginiatec" @@ -43499,10 +41935,6 @@ "name": "eNUFTIR - Національний Університет Харчових Технологій", "internal_name": "ftnunivfood" }, - { - "name": "北海道教育大学学術リポジトリ", - "internal_name": "fthokkaidouniedu" - }, { "name": "Репозиторий Белорусского национального технического университета", "internal_name": "ftbelarusntuni" @@ -43523,10 +41955,6 @@ "name": "University of Limpopo: Institutional Repository", "internal_name": "ftunivlimpopo" }, - { - "name": "La Salle University, Philadelphia: Digital Commons", - "internal_name": "ftlasalleuniv" - }, { "name": "Lancaster Theological Seminary: Digital Archive", "internal_name": "ftlancastersem" @@ -43579,10 +42007,6 @@ "name": "St George's University of London: Repository", "internal_name": "ftstgeorgesuniv" }, - { - "name": "Doğuş Üniversitesi Dergisi", - "internal_name": "ftjdud" - }, { "name": "Portal da Universidade Metodista de São Paulo", "internal_name": "ftunivmsaopojs" @@ -43663,10 +42087,6 @@ "name": "Biblioteka Cyfrowa Politechniki Koszalińskiej", "internal_name": "ftkoszalintuniv" }, - { - "name": "MDC - Memòria Digital de Cataluny", - "internal_name": "ftmdcatalunya" - }, { "name": "Bauhaus-Universität Weimar: Digitale Sammlung", "internal_name": "ftunivweimards" @@ -43699,10 +42119,6 @@ "name": "Revista Brasileira de Pesquisa em Turismo (RBTur)", "internal_name": "ftrbtur" }, - { - "name": "Coleccion de Tesis digitales de la UDLAP (Universidad de las Américas, Puebla)", - "internal_name": "ftunipueblatesis" - }, { "name": "The University of Adelaide: Digital Library", "internal_name": "ftunivadelaidedl" @@ -43727,10 +42143,6 @@ "name": "University of North Carolina: UNC Digital Collections", "internal_name": "ftuninorthcardc" }, - { - "name": "Universitas Surakarta: eJournal", - "internal_name": "ftunisurakarta" - }, { "name": "Clark University: Clark Digital Commons", "internal_name": "ftclarkuniv" @@ -43999,14 +42411,6 @@ "name": "Dolnośląska Biblioteka Cyfrowa", "internal_name": "ftdolnoslaskadl" }, - { - "name": "Université Pierre et Marie Curie, Paris (UPMC): Jubilothèque", - "internal_name": "ftupmcdc" - }, - { - "name": "育達商業科技大學機構典藏系統", - "internal_name": "ftyudauniv" - }, { "name": "RUJA - Repositorio de la Universidad de Jaén", "internal_name": "ftunivjaen" @@ -44059,10 +42463,6 @@ "name": "Illinois Mathematics and Science Academy: DigitalCommons@IMSA", "internal_name": "ftimsa" }, - { - "name": "國立體育大學", - "internal_name": "ftntsportuniv" - }, { "name": "Repositório Científico do Centro Hospitalar do Porto", "internal_name": "ftchporto" @@ -44175,10 +42575,6 @@ "name": "Electra (University of Patras)", "internal_name": "ftunivpatraspas" }, - { - "name": "Journal of Mobile, Embedded and Distributed Systems (Bucharest Academy of Economic Studies)", - "internal_name": "ftjmeds" - }, { "name": "Tasarım+Kuram Dergisi (Mimar Sinan Güzel Sanatlar Üniversitesi)", "internal_name": "ftjtk" @@ -44203,10 +42599,6 @@ "name": "専修大学学術機関リポジトリ", "internal_name": "ftsenshuuniv" }, - { - "name": "國立勤益科技大學", - "internal_name": "ftncyuniv" - }, { "name": "Rivista Internazionale di Filosofia e Psicologia (Università degli Studi di Bari)", "internal_name": "ftjrifp" @@ -44439,10 +42831,6 @@ "name": "Bogor Agricultural University: IPB Scientific Repository (Institut Pertanian Bogoar)", "internal_name": "ftbbaunivipb" }, - { - "name": "國立高雄師範大學機構典藏", - "internal_name": "ftnknuniv" - }, { "name": "Repozytorium Cyfrowe Instytutów Naukowych (RCIN)", "internal_name": "ftrcin" @@ -44451,10 +42839,6 @@ "name": "Universidad del Norte, Colombia: Repositorio Digital", "internal_name": "ftunivnorte" }, - { - "name": "National Science Foundation of Sri Lanka Digital Repository", - "internal_name": "ftnsfsrilanka" - }, { "name": "Universidade Municipal de São Caetano do Sul: Repositório Digital da USCS", "internal_name": "ftunivscs" @@ -44467,10 +42851,6 @@ "name": "Cyfrowa Ziemia Sieradzka", "internal_name": "ftsieradzdl" }, - { - "name": "RICABIB: the Digital Repository of Centro Atómico Bariloche and Instituto Balseiro", - "internal_name": "ftcabib" - }, { "name": "City University London: City Research Online", "internal_name": "ftcityunivlondon" @@ -44511,10 +42891,6 @@ "name": "PUBLISSO Fachrepositorium Lebenswissenschaften (ZB MED)", "internal_name": "ftzbmed" }, - { - "name": "University of Puget Sound: Sound Ideas", - "internal_name": "ftunivpugetsound" - }, { "name": "Jurnal Institut Seni Indonesia Denpasar", "internal_name": "ftisidenpasarojs" @@ -44559,10 +42935,6 @@ "name": "熊本大学学術リポジトリ", "internal_name": "ftkumamotouniv" }, - { - "name": "Banco Internacional de Objetos Educacionais (Ministry of Education - Brazil)", - "internal_name": "ftbioe" - }, { "name": "CyberTesis UACh (Tesis Electrónicas, Universidad Austral de Chile, Valdivia)", "internal_name": "ftunaustralchile" @@ -44579,10 +42951,6 @@ "name": "이화여자대학교", "internal_name": "ftewhawomensuniv" }, - { - "name": "奈良大学リポジトリ", - "internal_name": "ftnarauniv" - }, { "name": "SciELO España (Scientific Electronic Library Online)", "internal_name": "ftscielospain" @@ -44607,10 +42975,6 @@ "name": "East Carolina University, Joyner Library: Digital Collections", "internal_name": "fteastcarolinaun" }, - { - "name": "Anglia Ruskin University: Anglia Ruskin Research Online (ARRO)", - "internal_name": "ftarro" - }, { "name": "Institutional Knowledge (InK) at Singapore Management University", "internal_name": "ftsingaporemuniv" @@ -44707,10 +43071,6 @@ "name": "Repositório do Instituto Politécnico de Viseu", "internal_name": "ftipviseu" }, - { - "name": "逢甲大學全球資訊網", - "internal_name": "ftfengchiauniv" - }, { "name": "Електронний архів Харківського національного університету радіоелектроніки", "internal_name": "ftnunivtre" @@ -44739,10 +43099,6 @@ "name": "兵庫教育大学学術情報リポジトリ", "internal_name": "fthyokyouniv" }, - { - "name": "國立屏東大學", - "internal_name": "ftnationpuniedu" - }, { "name": "首都大学東京機関リポジトリ", "internal_name": "fttokyomuniv" @@ -44755,18 +43111,10 @@ "name": "國立東華大學", "internal_name": "ftndonghwauniv" }, - { - "name": "華夏機構典藏", - "internal_name": "fthwahsiait" - }, { "name": "National Changhua University of Education Institutional Repository", "internal_name": "ftchanghuauniedu" }, - { - "name": "輔英科技大學機構典藏", - "internal_name": "ftfooyinuniv" - }, { "name": "南華大學機構典藏", "internal_name": "ftnanhuauniv" @@ -44799,10 +43147,6 @@ "name": "OAPEN (Open Access Publishing in European Networks)", "internal_name": "ftoapen" }, - { - "name": "國北教大機構典藏", - "internal_name": "ftntaipeiuniv" - }, { "name": "中華信義神學院", "internal_name": "ftchinalutheran" @@ -44815,10 +43159,6 @@ "name": "修平科技大學", "internal_name": "fthsiupinginst" }, - { - "name": "元培科技大學", - "internal_name": "ftyuanpeiuniv" - }, { "name": "國立聯合大學", "internal_name": "ftnuniteduniv" @@ -44883,10 +43223,6 @@ "name": "Universidad de León: BULERIA", "internal_name": "ftunivleon" }, - { - "name": "Quantropy Eprints", - "internal_name": "ftquantropy" - }, { "name": "Corvinus University of Budapest: Research Archive", "internal_name": "ftcorvinusunivir" @@ -44931,10 +43267,6 @@ "name": "Revista de Estudios Bolivianos", "internal_name": "ftjbsj" }, - { - "name": "Trinity Western University: TWU Academic Journals", - "internal_name": "fttrinitywestern" - }, { "name": "Applied Medical Informatics (University of Medicine and Pharmacy Cluj-Napoca)", "internal_name": "ftjami" @@ -45011,10 +43343,6 @@ "name": "École de technologie supérieure, Montréal: Espace ÉTS", "internal_name": "ftecolets" }, - { - "name": "Politeknik Elektronika Negeri Surabaya (PENS): EEPIS Repository", - "internal_name": "ftitssurabaya" - }, { "name": "Repository Universitas Andalas", "internal_name": "ftunivandalas" @@ -45115,10 +43443,6 @@ "name": "Dominikańska biblioteka cyfrowa", "internal_name": "ftarmarium" }, - { - "name": "Zachodniopomorska Biblioteka Cyfrowa: ZBC Pomerania, Szczecin", - "internal_name": "ftzbcpomerania" - }, { "name": "Nülan - Portal de Promoción y Difusión Pública del Conocimiento Académico y Científic (Facultad de Ciencias Económicas y Sociales, Universidad Nacional de Mar del Plata - UNMDP)", "internal_name": "ftunivnmdpfceys" @@ -45139,10 +43463,6 @@ "name": "Cyfrowa Biblioteka Diecezjalna w Sandomierzu", "internal_name": "ftsandomierzdll" }, - { - "name": "Miejska Biblioteka Publiczna w Iławie: Biblioteka Cyfrowa", - "internal_name": "ftilawskadl" - }, { "name": "NLA Høgskolen, Bergen: NLA Brage", "internal_name": "ftnlahs" @@ -45203,18 +43523,10 @@ "name": "Özyeğin University: eResearch@Ozyegin", "internal_name": "ftozyeginuniv" }, - { - "name": "Instituto Tecnológico y de Estudios Superiores de Monterrey (ITESM): DAR (Desarrolla, Aprende y Reutiliza)", - "internal_name": "ftedemonterrey" - }, { "name": "Literacy in Composition Studies (LiCS)", "internal_name": "ftjlics" }, - { - "name": "Management In Health, MIH (National School of Public Health, Management, Romania)", - "internal_name": "ftjmih" - }, { "name": "GDZ - Göttinger Digitalisierungszentrum (Georg-August-Universität Götingen)", "internal_name": "ftgdzgoettingen" @@ -45291,10 +43603,6 @@ "name": "Repozytorium Cyfrowe Poloników (Instytut Badań nad Dziedzictwem Kulturowym Europy)", "internal_name": "ftdrpolonica" }, - { - "name": "Olivet Nazarene University: Digital Commons @ Olivet", - "internal_name": "ftolivetnazarene" - }, { "name": "Economia, Società e Istituzioni (University of Perugia)", "internal_name": "ftjrei" @@ -45375,10 +43683,6 @@ "name": "Pacific University Oregon: CommonKnowledge", "internal_name": "ftpacificuniv" }, - { - "name": "中華醫事科技大學", - "internal_name": "fthunghwauniv" - }, { "name": "Universitätsbibliothek Frankfurt/Main: Digitale Sammlungen", "internal_name": "ftunivffmds" @@ -45451,10 +43755,6 @@ "name": "文化大學機構典藏", "internal_name": "ftchineseculture" }, - { - "name": "Università degli Studi Roma Tre: DSpace@RomaTre", - "internal_name": "ftunivroma3" - }, { "name": "関西大学学術リポジトリ", "internal_name": "ftkansaiuniv" @@ -45479,10 +43779,6 @@ "name": "Journal of Literary Theory (JLT)", "internal_name": "ftjlt" }, - { - "name": "Electronic Communications of the EASST (European Association of Software Science and Technology)", - "internal_name": "ftjeseasst" - }, { "name": "International Journal of Spatial Data Infrastructures Research (Joint Research Centre of the European Commission)", "internal_name": "ftjijsdir" @@ -45499,10 +43795,6 @@ "name": "Copenhagen Business School: CBS Open Journals", "internal_name": "ftcbscopenhagojs" }, - { - "name": "Slavica Journals (Eurasia Academic Publishers)", - "internal_name": "ftjslavica" - }, { "name": "Universidad Nacional de La Plata: Portal del Revistas del UNLP", "internal_name": "ftuninlaplataojs" @@ -45539,10 +43831,6 @@ "name": "Electronic Letters on Computer Vision and Image Analysis (ELCVIA - Universitat Autònoma de Barcelona)", "internal_name": "ftjelcvia" }, - { - "name": "Revista Electrónica de la Autopsia", - "internal_name": "ftjera" - }, { "name": "Al-Qanṭara (Centro de Ciencias Humanas y Sociales - CSIC)", "internal_name": "ftjaq" @@ -45607,10 +43895,6 @@ "name": "Thailand Digital Journals", "internal_name": "ftthaijournals" }, - { - "name": "DRDO Publications (Online Publishing @ DESIDOC - Defence Research & Development Organization, India)", - "internal_name": "ftjdrdo" - }, { "name": "Srinakharinwirot University: SWU e-Journals System", "internal_name": "ftsrinakharinojs" @@ -45675,10 +43959,6 @@ "name": "The University of Kansas: Journals@KU", "internal_name": "ftjbi" }, - { - "name": "ASAGE - American Society for Aesthetics Graduate E-Journal", - "internal_name": "ftjasage" - }, { "name": "Australian Policy Online (Institute for Social Research, Swinburne University of Technology)", "internal_name": "ftapo" @@ -45691,10 +43971,6 @@ "name": "Universidad Andina Simón Bolívar, Sede Ecudaor: UASB-Digital", "internal_name": "ftuniandinasimon" }, - { - "name": "Hong Kong Baptist University: HKBU Institutional Repository", - "internal_name": "fthongkongbapti" - }, { "name": "University of Victoria (Canada): Journal Publishing Service", "internal_name": "ftunivictoriaojs" @@ -45903,10 +44179,6 @@ "name": "Ancient Asia - Journal of the Society of South Asian Archaeology", "internal_name": "ftjaa" }, - { - "name": "Universidade Estadual Paulista São Paulo (UNESP), Faculdade de Ciências Farmacêuticas: Portal de Periódicos Científicos", - "internal_name": "ftjunesp" - }, { "name": "Högskolan Väst, Trollhättan: Elektroniska publikationer (DiVA)", "internal_name": "ftunivwest" @@ -45951,10 +44223,6 @@ "name": "Unidade Local de Saúde Amadora / Sintra", "internal_name": "fthff" }, - { - "name": "南台,南台科大學機構典藏", - "internal_name": "ftsoutherntaiwan" - }, { "name": "Repositori Universitat Jaume I (Repositorio UJI)", "internal_name": "ftunivjaumeirep" @@ -46295,10 +44563,6 @@ "name": "Base Institutionnelle de Recherche de l'université Paris-Dauphine (BIRD)", "internal_name": "ftunivdauphine" }, - { - "name": "Rosalis - Bibliothèque numérique de Tolouse", - "internal_name": "ftbibltoulou" - }, { "name": "University of Windsor, Ontario: Open Journal Systems", "internal_name": "ftunivwindojs" @@ -46451,10 +44715,6 @@ "name": "University College Cork, Ireland: Cork Open Research Archive (CORA)", "internal_name": "ftunivcollcork" }, - { - "name": "Cineca: LEO - Letteratura Elettronica Online", - "internal_name": "ftcilealeo" - }, { "name": "Universidade de Lisboa: repositório.UL", "internal_name": "ftunivlisboa" @@ -46507,18 +44767,10 @@ "name": "UVaDOC - Repositorio Documental de la Universidad de Valladolid", "internal_name": "ftunivvalladolid" }, - { - "name": "台灣科技大學", - "internal_name": "ftntaiwanust" - }, { "name": "Smithsonian Institution: Digital Repository", "internal_name": "ftsmithonian" }, - { - "name": "Deutsche Bodenkundliche Gesellschaft: DBGPrints-Archiv", - "internal_name": "ftdbg" - }, { "name": "Тверской государственный университе", "internal_name": "fttverstateuniv" @@ -46555,10 +44807,6 @@ "name": "pedocs-Dokumentenserver (Fachportal Pädagogik/DIPF)", "internal_name": "ftdipf" }, - { - "name": "Ryerson University: RULA Digital Repository", - "internal_name": "ftryersonuniv" - }, { "name": "BDAE - Biblioteca Digital Ação Educativa", "internal_name": "ftbdae" @@ -46583,10 +44831,6 @@ "name": "Biblioteca Digital da Univates (BDU)", "internal_name": "ftunivates" }, - { - "name": "ITESO - Universidad Jesuita de Guadalajara: EduDoc", - "internal_name": "ftedudocdc" - }, { "name": "서비스", "internal_name": "ftstoai" @@ -46731,10 +44975,6 @@ "name": "Search4Dev (Digital documents by Dutch development organizations)", "internal_name": "ftdprn" }, - { - "name": "Volunteer Voices - Tennessee Electronic Library, The University of Tennessee", - "internal_name": "ftvolunteervoi" - }, { "name": "Tropicos.org (Missouri Botanical Garden)", "internal_name": "ftmbgarden" @@ -46743,10 +44983,6 @@ "name": "National University of Ireland (NUI), Galway: ARAN", "internal_name": "ftnuigalway" }, - { - "name": "Repositorio de la Facultad de Filosofía y Letras (FFyL), Universidad Nacional Autónoma de México (UNAM)", - "internal_name": "ftunivnamexi" - }, { "name": "Billington Library Digital Collections, Johnson County Community College (JCCC)", "internal_name": "ftjohnsoncolldl" @@ -46799,10 +45035,6 @@ "name": "Biblioteca Digital de la Comunidad de Madrid", "internal_name": "ftbvmadrid" }, - { - "name": "Florida State University: Publication of Archival Library & Museum Materials", - "internal_name": "ftfloridacla" - }, { "name": "Biblioteca Virtual del Patrimonio Bibliográfico", "internal_name": "ftmcubvpb" @@ -46839,10 +45071,6 @@ "name": "Digital Repository of University of Zaragoza (ZAGUAN)", "internal_name": "ftunivzaraaneto" }, - { - "name": "Biblioteca Digital de Castilla-La Mancha (BIDICAM)", - "internal_name": "ftbidicam" - }, { "name": "EMD - Euskal Memoria Digitala", "internal_name": "ftemd" @@ -47431,10 +45659,6 @@ "name": "Universidad Carlos III de Madrid: e-Archivo", "internal_name": "ftunivcarlosmadr" }, - { - "name": "Hirsla - Landspítali University Hospital research archive", - "internal_name": "ftlandspitaliuni" - }, { "name": "Biblioteca Digital do Instituto Politécnico de Bragança (IPB)", "internal_name": "ftipb" @@ -47463,10 +45687,6 @@ "name": "Oregon Historic Photograph Collections (Salem Public Library)", "internal_name": "ftsalemhist" }, - { - "name": "Cartoteca Digital (ICGC - Institut Cartogràfic i Geològic de Catalunya)", - "internal_name": "fticc" - }, { "name": "NWISRL Publications (Northwest Irrigation and Soils Reseach Laboratory, United Steates Department of Agriculture)", "internal_name": "ftnwisrl" @@ -47643,10 +45863,6 @@ "name": "旭川医科大学学術成果リポジトリ", "internal_name": "ftasahikawaa" }, - { - "name": "宇都宮大学 学術情報リポジトリ（UU-AIR)", - "internal_name": "ftutunomiya" - }, { "name": "Athabasca University: AUSpace", "internal_name": "ftathabasuniv" @@ -47823,10 +46039,6 @@ "name": "東京大学学術機関リポジトリ", "internal_name": "ftunivtokyo" }, - { - "name": "EPrints@IIT Delhi (Indian Institute of Technology Delhi)", - "internal_name": "ftiitdelhi" - }, { "name": "慶應義塾大学学術情報アーカイブ", "internal_name": "ftkeiouniv" @@ -47856,7 +46068,7 @@ "internal_name": "ftunivtorun" }, { - "name": "Universität Koblenz-Landau: Hochschulschriftenserver", + "name": "OPUS der Universität Koblenz", "internal_name": "ftunivkoblenzlan" }, { @@ -47967,10 +46179,6 @@ "name": "Dalarna University: Publikationer (DiVA)", "internal_name": "ftunivdalarna" }, - { - "name": "Mississippi State University: ETD Collection", - "internal_name": "ftmississippista" - }, { "name": "Deutsches Zentrum für Luft und Raumfahrt: elib - DLR electronic library", "internal_name": "ftdlr" @@ -48127,10 +46335,6 @@ "name": "Universitat Internacional de Catalunya: Tesis Doctorals en Xarxa (TDX)", "internal_name": "ftuicatalunya" }, - { - "name": "University College Dublin: Research Repository UCD", - "internal_name": "ftunivcolldublin" - }, { "name": "Corvinus University of Budapest: Ph. D. Dissertations", "internal_name": "ftcorvinus" @@ -48203,10 +46407,6 @@ "name": "Ball State University: Digital Media Repository", "internal_name": "ftballstate" }, - { - "name": "SETU Waterford Libraries Open Access Repository", - "internal_name": "ftwit" - }, { "name": "New Bulgarian University: Scholar Electronic Repository (SER of NBU)", "internal_name": "ftnewbulguniv" @@ -48415,10 +46615,6 @@ "name": "HighWire Press (Stanford University)", "internal_name": "fthighwire" }, - { - "name": "Informatik an der Universität Stuttgart: Veröffentlichungen", - "internal_name": "ftunivstucsa" - }, { "name": "Papyrus Projekt Gießen (Justus-Liebig Universität, JLU)", "internal_name": "ftubgiessdig" @@ -48427,10 +46623,6 @@ "name": "Digitale Bibliothek Thüringen", "internal_name": "ftdbthueringen" }, - { - "name": "Università degli studi di Torino: AperTo (Archivio Istituzionale ad Accesso Aperto)", - "internal_name": "ftunivtorino" - }, { "name": "Universitat de Vic (UVIC): Tesis Doctorals en Xarxa (TDX)", "internal_name": "ftuvic" @@ -48567,10 +46759,6 @@ "name": "The University of Dublin, Trinity College: TARA (Trinity's Access to Research Archive)", "internal_name": "fttrinitycoll" }, - { - "name": "Bond University: e-publications@bond", - "internal_name": "ftbondunivpubl" - }, { "name": "Universidad Nacional de La Plata (UNLP): SeDiCI (Servicio de Difusión de la Creación Intelectual)", "internal_name": "ftunivlaplata" @@ -48607,10 +46795,6 @@ "name": "DigitalCommons@Fayetteville State University", "internal_name": "ftfayettevsu" }, - { - "name": "XIOS Hogeschool Limburg: DoKS", - "internal_name": "fthslimburg" - }, { "name": "Florida State University: DigiNole Commons", "internal_name": "ftfloridasu" @@ -49219,10 +47403,6 @@ "name": "Computer Science@Virginia Tech: Computer Science Technical Reports (CSTR)", "internal_name": "ftvirginiatech" }, - { - "name": "Drexel University: iDEA - Drexel Libraries E-Repository And Archives", - "internal_name": "ftdrexeluniv" - }, { "name": "PennState: Electronic Theses and Dissertations (eTD)", "internal_name": "ftpennstate" From b77a730557d7ad4ccbb61ebdcbb2b16a256032ce Mon Sep 17 00:00:00 2001 From: chreman Date: Mon, 20 Jul 2026 21:51:44 +0200 Subject: [PATCH 166/205] update gitignore --- .gitignore | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.gitignore b/.gitignore index 023f0cdc6..4e36eed04 100644 --- a/.gitignore +++ b/.gitignore @@ -81,5 +81,9 @@ local_dev/tools/**/*.log /blob-report/ /playwright/.cache/ +# inspection output +e2e/reproducibility/output/* +e2e/reproducibility/logs/* + # mac os .DS_Store From d03906db9aa62d6c57038a4b235fd0c7669071c2 Mon Sep 17 00:00:00 2001 From: chreman Date: Mon, 20 Jul 2026 22:59:52 +0200 Subject: [PATCH 167/205] debug data dumps --- server/preprocessing/other-scripts/cluster.R | 8 +++ .../other-scripts/run_vis_layout.R | 9 ++++ server/workers/base/src/base.py | 38 +++++++++++++- server/workers/orcid/src/orcid_service.py | 51 ++++++++++++++++++- server/workers/orcid/src/worker.py | 4 ++ 5 files changed, 108 insertions(+), 2 deletions(-) diff --git a/server/preprocessing/other-scripts/cluster.R b/server/preprocessing/other-scripts/cluster.R index dbc33ff6d..d562011f2 100644 --- a/server/preprocessing/other-scripts/cluster.R +++ b/server/preprocessing/other-scripts/cluster.R @@ -9,6 +9,9 @@ get_cut_off <- function(css_cluster, attempt=1){ } create_clusters <- function(distance_matrix, max_clusters=-1, method="ward.D") { + # Debug: the distance matrix that clustering runs on — the deterministic input to + # cutree, keyed by paper id (row labels). + dump_data(as.matrix(distance_matrix), "cluster_00_distance_matrix") if (nrow(distance_matrix) < 2) { warning("Not enough papers for clustering, N < 2.") num_clusters <- 1 @@ -81,6 +84,11 @@ create_clusters <- function(distance_matrix, max_clusters=-1, method="ward.D") { vclog$info(paste("vis_id:", .GlobalEnv$VIS_ID, "Number of Clusters:", num_clusters, sep=" ")) vclog$debug(paste("CutOff-Description:", attributes(cut_off)$description)) + # Debug: per-paper cluster membership + the chosen k. Aligns with + # cluster_00_distance_matrix by row label (paper id). + dump_data(data.frame(id = labels, cluster = unname(groups)), "cluster_01_groups") + dump_data(data.frame(num_clusters = num_clusters, n_items = num_items, + k_attempts = attempt), "cluster_02_meta") } clusters = list("labels"=labels, "groups"=groups, "num_clusters"=num_clusters) return(clusters) diff --git a/server/preprocessing/other-scripts/run_vis_layout.R b/server/preprocessing/other-scripts/run_vis_layout.R index 22645a0e0..e45d60b1a 100644 --- a/server/preprocessing/other-scripts/run_vis_layout.R +++ b/server/preprocessing/other-scripts/run_vis_layout.R @@ -51,6 +51,15 @@ input_data <- data$input_data text <- fromJSON(input_data$text) metadata <- fromJSON(input_data$metadata) +# Debug: what dataprocessing received before any processing. `text` (id + content) +# is the clustering input; `metadata` feeds labelling. Lets a map be traced from the +# data client's output through clustering and labelling. +dump_data(text, "runvis_01_text_received") +dump_data(metadata[, intersect(c("id", "title", "paper_abstract", "subject", + "subject_orig", "oa_state", "content_provider", + "link", "doi"), names(metadata)), drop = FALSE], + "runvis_02_metadata_received") + MAX_CLUSTERS = params$MAX_CLUSTERS failed <- list(params=params) diff --git a/server/workers/base/src/base.py b/server/workers/base/src/base.py index 6a2bdc1f6..2f003f595 100644 --- a/server/workers/base/src/base.py +++ b/server/workers/base/src/base.py @@ -21,6 +21,7 @@ mark_latest_doi, prioritize_OA_and_latest, prioritize_doi_and_provider, + get_provider_priority, doi_title_filter, ) from common.enrichment import enrich_anchor_using_duplicates @@ -382,6 +383,9 @@ def filter_duplicates(df, service, params): non_datasets = df.loc[df.index.difference(pure_datasets.index)] # logger.debug(f"[dedup:split] non_datasets={len(non_datasets)} pure_datasets={len(pure_datasets)}") + # Pre-prioritize snapshot: records in raw pre-tie-break order, with resp_pos / + # collection / provider_priority, so anchor decisions can be traced. + _dump_dedup(non_datasets, params, "base_09_non_datasets_pre_prioritize") non_datasets = prioritize_OA_and_latest(non_datasets, duplicate_groups) non_datasets = prioritize_doi_and_provider(non_datasets, duplicate_groups) # _log_dedup_state(non_datasets, "non_datasets_after_prioritize", params) @@ -391,8 +395,12 @@ def filter_duplicates(df, service, params): pure_datasets.loc[pure_datasets_condition_mask, "is_anchor"] = True # _log_dedup_state(pure_datasets, "pure_datasets_after_mark_latest", params) + _dump_dedup(non_datasets, params, "base_10_non_datasets_pre_enrich") + _dump_dedup(pure_datasets, params, "base_11_pure_datasets_pre_enrich") non_datasets = enrich_anchor_using_duplicates(non_datasets, duplicate_groups) pure_datasets = enrich_anchor_using_duplicates(pure_datasets, duplicate_groups) + _dump_dedup(non_datasets, params, "base_12_non_datasets_post_enrich") + _dump_dedup(pure_datasets, params, "base_13_pure_datasets_post_enrich") filtered_non_datasets = non_datasets[non_datasets.is_anchor == True] filtered_datasets = pure_datasets[pure_datasets.is_anchor == True] @@ -514,9 +522,37 @@ def sanitize_year(year_str): return sanitized_year +def _dump_dedup(df: pd.DataFrame, params: Dict[str, str], name: str): + """Debug dump of a dedup/anchor stage to ./output//.csv. + + Captures the anchor-deciding columns (is_anchor/is_duplicate/oa_state/content_provider/ + collection/provider_priority) and the fields that survive into clustering content + (subject_orig/paper_abstract), plus `resp_pos` = the row's original BASE response + position. Keyed on the BASE request vis_id; correlate to the map via paper `id`. + DEBUG-gated, non-fatal. Traceability of the metadata transformations in dedup. + """ + if not logger.isEnabledFor(logging.DEBUG): + return + try: + vis_id = params.get('vis_id') + out = df.copy() + out['resp_pos'] = out.index + if 'collection' in out.columns: + out['provider_priority'] = out['collection'].map(get_provider_priority) + cols = ['resp_pos', 'id', 'doi', 'collection', 'provider_priority', 'content_provider', + 'is_anchor', 'is_duplicate', 'oa_state', 'year', + 'link', 'subject_orig', 'paper_abstract', 'title'] + cols = [c for c in cols if c in out.columns] + folder = f'./output/{vis_id}' + os.makedirs(folder, exist_ok=True) + out.reindex(columns=cols).fillna('missing').to_csv(f'{folder}/{name}.csv', index=False) + except Exception as e: + logger.warning(f"_dump_dedup failed for {name}: {e}") + + def _log_dataframe(df: pd.DataFrame, params: Dict[str, str], name: str, ): vis_id = params.get('vis_id') - + columns_to_print = ['id', 'title', 'doi', 'additional_dois', 'paper_abstract', 'link', 'subject', 'subject_orig', 'oa_state'] available_columns = df.columns.tolist() diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 5640ce189..e6503dc12 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -67,7 +67,10 @@ def execute_search(self, params: Dict[str, str]) -> Union[SuccessResult, ErrorRe if metadata.empty: return self._handle_insufficient_results(params, orcid_id) - + + # (a) ORCID works as retrieved, before any enrichment. + self._dump_stage(metadata, params, "orcid_01_works_retrieved", columns=self._DUMP_COLS) + metadata = self._process_metadata(metadata, author_info, params) # self.logger.debug('metadata processed inside of _process_metadata') @@ -151,6 +154,37 @@ def _log_dataframe(self, df: pd.DataFrame, params: Dict[str, str], name: str, ): file_path = f"{folder}/{name}.csv" transformed.to_csv(file_path, index=False) + def _dump_stage(self, obj, params: Dict[str, str], stage: str, columns=None): + """Debug dump of one pipeline stage to ./output//.{csv,json}. + + Keyed on the MAP vis_id (set in worker.handle_search) so runs sharing an ORCID + stay separate. DEBUG-gated and non-fatal. DataFrames -> CSV (optionally reduced + to `columns`); other objects -> JSON. Traceability of metadata transformations. + """ + if not self.logger.isEnabledFor(logging.DEBUG): + return + try: + vis_id = params.get("vis_id") or params.get("unique_id") or params.get("orcid") or "unknown" + folder = f"./output/{vis_id}" + os.makedirs(folder, exist_ok=True) + if isinstance(obj, pd.DataFrame): + df = obj + if columns is not None: + df = df.reindex(columns=[c for c in columns if c in df.columns]) + df.fillna("").to_csv(f"{folder}/{stage}.csv", index=False) + else: + with open(f"{folder}/{stage}.json", "w") as fh: + json.dump(obj, fh) + except Exception as e: + self.logger.warning(f"_dump_stage failed for {stage}: {e}") + + # Columns worth capturing across the custody chain: retrieval, enrichment, and the + # fields that build the clustering `content` (title, paper_abstract, subject_orig). + _DUMP_COLS = ["id", "doi", "doi_merge", "title", "paper_abstract", "subject", + "subject_orig", "oa_state", "content_provider", "link", "year", + "is_anchor", "is_duplicate", + "keywords_rank_mesh_specific", "keywords_rank_mesh_generic"] + def request_base_metadata(self, dois: List[str], params: Dict[str, str]) -> pd.DataFrame: orcid = params.get('orcid') batch_size = 25 @@ -324,6 +358,11 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra base_metadata = self.request_base_metadata(dois, params) + # (b) BASE metadata as received (post BASE-side dedup/enrichment): the anchors, + # their oa_state/content_provider, and the subject_orig/paper_abstract that will + # feed clustering content. Correlate to base.py's per-request dedup dumps by `id`. + self._dump_stage(base_metadata, params, "orcid_02_base_metadata", columns=self._DUMP_COLS) + if "doi_merge" not in base_metadata.columns: self.logger.error(f"BASE metadata is missing 'doi_merge' column, cannot proceed with enrichment. Params: {params}") raise ValueError("BASE metadata is missing 'doi_merge' column") @@ -480,6 +519,10 @@ def custom_merge(existing_value, new_value): if self.logger.isEnabledFor(logging.DEBUG): self._log_dataframe(enriched_metadata.sort_values(by='title'), params, '_enriched') + # (c) ORCID metadata after the BASE merge — the enriched subject_orig/paper_abstract + # per surviving row, just before it becomes clustering content in _format_response. + self._dump_stage(enriched_metadata, params, "orcid_03_merged", columns=self._DUMP_COLS) + # temporal solution, for some reason if we have some undefined data, dataprocessing is failing enriched_metadata = enriched_metadata.reindex(columns=list(set(original_columns + ['oa_state', 'subject', 'subject_orig', 'paper_abstract', 'link', 'relation', 'keywords_rank_mesh_specific', 'keywords_rank_mesh_generic']))) @@ -627,6 +670,12 @@ def _format_response(self, data: pd.DataFrame, author_info: AuthorInfo, params: ) text.columns = ["id", "content"] + # (d) THE clustering input: id + content (title + paper_abstract + subtitle + + # published_in + authors + subject_orig). What create_corpus tokenizes. Also dump + # the final metadata that travels alongside it for labelling. + self._dump_stage(text, params, "orcid_05_clustering_content") + self._dump_stage(data, params, "orcid_04_metadata_final", columns=self._DUMP_COLS) + self.logger.debug(f"Returning response for ORCID {params.get('orcid')} len {len(data)}") return { diff --git a/server/workers/orcid/src/worker.py b/server/workers/orcid/src/worker.py index 812eb54fc..2b7b6e252 100644 --- a/server/workers/orcid/src/worker.py +++ b/server/workers/orcid/src/worker.py @@ -53,6 +53,10 @@ def handle_search(self, request_id: str, params: Dict[str, str]) -> None: start_time = time.time() self.logger.debug(f"Handle search with request_id: {request_id} and params {params}") + # request_id is the map vis_id (it becomes res["id"] -> dataprocessing VIS_ID). + # Expose it in params so debug dumps key on the same vis_id the R side uses. + params.setdefault("vis_id", request_id) + res = self.data_retriever.execute_search(params) if res.get("status") == "error" or params.get("raw") is True: From c1f943618c9a8542f06d42b7ae25419f0dd9cf91 Mon Sep 17 00:00:00 2001 From: chreman Date: Mon, 27 Jul 2026 16:05:15 +0200 Subject: [PATCH 168/205] add debug functionality for label generation --- server/preprocessing/other-scripts/ranking.R | 8 +- .../preprocessing/other-scripts/summarize.R | 119 +++++++++++++++++- 2 files changed, 122 insertions(+), 5 deletions(-) diff --git a/server/preprocessing/other-scripts/ranking.R b/server/preprocessing/other-scripts/ranking.R index cab48a07d..dc8c0e633 100644 --- a/server/preprocessing/other-scripts/ranking.R +++ b/server/preprocessing/other-scripts/ranking.R @@ -168,9 +168,13 @@ drop_excluded_terms <- function(tfidf_top, exclusions) { # mesh_specific, mesh_generic, heuristic), each a per-cluster list # of lowercased "_"-joined tokens; NULL disables ranked selection. # legacy_fn : the legacy selector; injectable for testing. +# dbg_stage : dump_data stage name for the per-cluster rank-candidate debug dump; +# overridden by callers (e.g. the min1 fallback) so a second selection +# pass does not overwrite the primary pass's dump. select_cluster_label_names <- function(tfidf_top, top_n, stops, mode = "0", rank_sources = NULL, - legacy_fn = get_top_names) { + legacy_fn = get_top_names, + dbg_stage = "summarize_04c_rank_candidates") { spec <- rank_spec(mode) if (identical(mode, "0") || is.null(spec) || is.null(rank_sources)) { if (!identical(mode, "0") && (is.null(spec) || is.null(rank_sources))) { @@ -208,7 +212,7 @@ select_cluster_label_names <- function(tfidf_top, top_n, stops, mode = "0", } if (exists("dump_data")) { dbg <- dbg[!vapply(dbg, is.null, logical(1))] - if (length(dbg)) dump_data(do.call(rbind, dbg), "summarize_04c_rank_candidates") + if (length(dbg)) dump_data(do.call(rbind, dbg), dbg_stage) } out } diff --git a/server/preprocessing/other-scripts/summarize.R b/server/preprocessing/other-scripts/summarize.R index eaba3446a..beddb993c 100644 --- a/server/preprocessing/other-scripts/summarize.R +++ b/server/preprocessing/other-scripts/summarize.R @@ -145,7 +145,7 @@ add_heuristic_keyword_fields <- function(metadata, stops) { # top_n : number of terms kept. # label_exclusions : curated area-label exclusion list (whole-term, case-insensitive) # applied here too so listed terms never survive as a last resort. -title_abstract_fallback_label <- function(matches, metadata, stops, top_n = 3, label_exclusions = character(0)) { +title_abstract_fallback_label <- function(matches, metadata, stops, top_n = 3, label_exclusions = character(0), cluster = NA_integer_) { batch_size <- 1000 total_length <- length(stops) candidates = mapply(paste, metadata$title[matches], metadata$paper_abstract[matches]) @@ -165,12 +165,93 @@ title_abstract_fallback_label <- function(matches, metadata, stops, top_n = 3, l norm <- trimws(tolower(gsub("_", " ", names(top_ngrams)))) top_ngrams <- top_ngrams[!(norm %in% tolower(trimws(label_exclusions)))] } + # Debug: the title+abstract n-gram candidate pool (n-gram + frequency, post-exclusion) + # this last-resort fallback selects from. One file per cluster that reaches this path. + if (!is.na(cluster) && exists("debug_enabled") && debug_enabled() && length(top_ngrams)) { + tryCatch(dump_data(data.frame(cluster = cluster, term = gsub("_", " ", names(top_ngrams)), + freq = as.integer(top_ngrams), stringsAsFactors = FALSE), + paste0("summarize_04g_titleabstract_candidates_c", cluster)), + error = function(e) NULL) + } summary <- filter_out_nested_ngrams(names(top_ngrams), top_n) summary = lapply(summary, FUN = function(x) {paste(unlist(x), collapse="; ")}) summary = gsub("_", " ", summary) paste(summary, collapse=", ") } +# --- Label-generation debug dumps ------------------------------------------------ +# Fine-grained, DEBUG-gated traces of how each cluster's area label is built, for +# backtracking a label to its inputs: the corpus text that feeds tf-idf, the tf-idf +# candidate terms with their weights, the rank-source provenance (Modes 1-3), the +# terms removed by the exclusion list, and the per-cluster label provenance (which +# path produced it, plus the label before/after casing). Each keyed on VIS_ID via +# dump_data; all no-ops unless LOGLEVEL=DEBUG, and never fatal. + +# Per-cluster corpus document text (what tf-idf actually tokenizes). One row/cluster. +dump_corpus_text <- function(corpus, stage) { + if (!debug_enabled()) return(invisible(NULL)) + tryCatch({ + txt <- vapply(seq_along(corpus), + function(k) paste(as.character(content(corpus[[k]])), collapse = " "), + character(1)) + dump_data(data.frame(cluster = seq_along(txt), text = txt, stringsAsFactors = FALSE), stage) + }, error = function(e) vslog$warn(paste("dump_corpus_text failed:", conditionMessage(e)))) +} + +# Per-cluster tf-idf candidate terms, weight-ordered, with their scores. One row per +# (cluster, term): the candidate pool that selection/ranking draws from. +dump_tfidf_candidates <- function(tfidf_top, stage) { + if (!debug_enabled()) return(invisible(NULL)) + tryCatch({ + rows <- lapply(seq_along(tfidf_top), function(k) { + w <- tfidf_top[[k]] + if (is.null(w) || !length(w) || is.null(names(w))) return(NULL) + data.frame(cluster = k, weight_rank = seq_along(w), + term = gsub("_", " ", names(w)), tfidf = as.numeric(w), + stringsAsFactors = FALSE) + }) + rows <- do.call(rbind, rows[!vapply(rows, is.null, logical(1))]) + if (!is.null(rows) && nrow(rows)) dump_data(rows, stage) + }, error = function(e) vslog$warn(paste("dump_tfidf_candidates failed:", conditionMessage(e)))) +} + +# Per-cluster rank-source token-sets (Modes 1-3): which provenance set (cleaned, +# mesh_specific, ...) each candidate token belongs to — i.e. what fixes its rank. +dump_rank_sources <- function(rank_sources, stage) { + if (!debug_enabled() || is.null(rank_sources)) return(invisible(NULL)) + tryCatch({ + rows <- list() + for (src in names(rank_sources)) { + per_cluster <- rank_sources[[src]] + for (k in seq_along(per_cluster)) { + toks <- per_cluster[[k]] + if (length(toks)) rows[[length(rows) + 1L]] <- + data.frame(cluster = k, source = src, term = gsub("_", " ", toks), + stringsAsFactors = FALSE) + } + } + if (length(rows)) dump_data(do.call(rbind, rows), stage) + }, error = function(e) vslog$warn(paste("dump_rank_sources failed:", conditionMessage(e)))) +} + +# Per-cluster terms removed by the exclusion list (tf-idf candidates before vs after +# drop_excluded_terms). Makes each exclusion drop explicit. +dump_excluded_terms <- function(before, after, stage) { + if (!debug_enabled()) return(invisible(NULL)) + tryCatch({ + rows <- lapply(seq_along(before), function(k) { + b <- before[[k]]; a <- after[[k]] + if (is.null(b) || !length(b) || is.null(names(b))) return(NULL) + dropped <- setdiff(names(b), names(a)) + if (!length(dropped)) return(NULL) + data.frame(cluster = k, term = gsub("_", " ", dropped), + tfidf = as.numeric(b[dropped]), stringsAsFactors = FALSE) + }) + rows <- do.call(rbind, rows[!vapply(rows, is.null, logical(1))]) + if (!is.null(rows) && nrow(rows)) dump_data(rows, stage) + }, error = function(e) vslog$warn(paste("dump_excluded_terms failed:", conditionMessage(e)))) +} + # Entry point: assign a short label ("area title") to every cluster. # Builds one pseudo-document per cluster from its papers' subjects + title # n-grams (or a custom field), ranks terms by tf-idf (SMART "ntn"), and keeps the @@ -221,6 +302,11 @@ create_cluster_labels <- function(clusters, metadata, vslog$debug(paste("create_cluster_labels: ranking mode", mode, "for service", if (is.null(service)) "(none)" else service)) + # Tracks which path produced each cluster's label, for summarize_06b_label_provenance: + # "primary" (tf-idf/ranking), "legacy_fill"/"min1_fallback" (empty-label rescue), or + # "title_abstract_fallback" (last resort). Updated where each fallback fires. + label_source <- rep("primary", clusters$num_clusters) + if (identical(mode, "0")) { # ---- Mode 0: legacy no-ranking path (inline title n-grams, no DF filter) ---- if (!(is.null(cc)) && (cc %in% names(metadata))) { @@ -229,6 +315,7 @@ create_cluster_labels <- function(clusters, metadata, nn_corpus <- get_cluster_corpus_legacy(clusters, metadata, stops, taxonomy_separator) } dump_data(nn_corpus, "summarize_04_corpus") + dump_corpus_text(nn_corpus, "summarize_04_corpus_text") nn_tfidf <- TermDocumentMatrix(nn_corpus, control = list( tokenize = SplitTokenizer, weighting = function(x) weightSMART(x, spec="ntn"), @@ -241,6 +328,8 @@ create_cluster_labels <- function(clusters, metadata, empty_tfidf <- which(apply(nn_tfidf, 2, sum) == 0) tfidf_top[c(empty_tfidf)] <- fill_empty_clusters_legacy(nn_tfidf, nn_corpus)[c(empty_tfidf)] # NB: no label-exclusion filter here — Mode 0 is byte-identical to legacy. + dump_tfidf_candidates(tfidf_top, "summarize_04b_tfidf_candidates") # candidates fed to selection (post empty-fill) + if (length(empty_tfidf)) label_source[empty_tfidf] <- "legacy_fill" tfidf_top_names <- get_top_names(tfidf_top, top_n, stops) } else { # ---- Modes 1-3: DF-filtered corpus + rank-aware selection ------------------ @@ -266,6 +355,8 @@ create_cluster_labels <- function(clusters, metadata, nn_corpus <- corpus_out$corpus rank_sources <- corpus_out$rank_sources dump_data(nn_corpus, "summarize_04_corpus") + dump_corpus_text(nn_corpus, "summarize_04_corpus_text") + dump_rank_sources(rank_sources, "summarize_04d_rank_sources") # Local bound c(1, Inf) so low-frequency real keywords survive into the ranking. nn_tfidf <- TermDocumentMatrix(nn_corpus, control = list( tokenize = SplitTokenizer, @@ -274,7 +365,10 @@ create_cluster_labels <- function(clusters, metadata, tolower = TRUE )) tfidf_top <- apply(nn_tfidf, 2, function(x) {x2 <- sort(x, TRUE);x2[x2>0]}) + dump_tfidf_candidates(tfidf_top, "summarize_04b_tfidf_candidates") # raw candidates (pre-exclusion) + tfidf_top_pre_excl <- tfidf_top tfidf_top <- drop_excluded_terms(tfidf_top, label_exclusions) # post-tf-idf, pre-ranking + dump_excluded_terms(tfidf_top_pre_excl, tfidf_top, "summarize_04e_excluded_terms") vslog$debug(paste("create_cluster_labels: tf-idf matrix", nTerms(nn_tfidf), "terms x", nDocs(nn_tfidf), "clusters")) @@ -296,9 +390,12 @@ create_cluster_labels <- function(clusters, metadata, vslog$debug(paste("create_cluster_labels: min1 fallback for", length(empty_label), "clusters with an empty min2 label")) fallback_top <- drop_excluded_terms(fill_empty_clusters(fallback_corpus), label_exclusions) + dump_tfidf_candidates(fallback_top, "summarize_04f_min1_fallback_candidates") fallback_names <- select_cluster_label_names(fallback_top, top_n, stops, mode = mode, - rank_sources = rank_sources) + rank_sources = rank_sources, + dbg_stage = "summarize_04c_min1_rank_candidates") tfidf_top_names[empty_label] <- fallback_names[empty_label] + label_source[empty_label] <- "min1_fallback" } } dump_data(tfidf_top_names, "summarize_05_tfidf_top_names") @@ -311,7 +408,8 @@ create_cluster_labels <- function(clusters, metadata, # from the papers' titles + abstracts (see title_abstract_fallback_label). vslog$debug(paste("create_cluster_labels: title/abstract fallback for cluster", k, "with", length(matches), "papers")) - summary <- title_abstract_fallback_label(matches, metadata, stops, top_n, label_exclusions) + summary <- title_abstract_fallback_label(matches, metadata, stops, top_n, label_exclusions, cluster = k) + label_source[k] <- "title_abstract_fallback" } clusters$cluster_labels[c(matches)] = summary } @@ -321,6 +419,21 @@ create_cluster_labels <- function(clusters, metadata, clusters$cluster_labels <- fix_cluster_labels(clusters$cluster_labels, type_counts) dump_data(data.frame(cluster = clusters$groups, label = clusters$cluster_labels), "summarize_06_cluster_labels") + # Per-cluster label provenance: which path built the label, plus the label as selected + # by tf-idf/ranking (pre-fallback, pre-casing) vs the final label (post-casing). Lets a + # single label be traced back to its source path and its transformation. + dump_data(data.frame( + cluster = seq_len(clusters$num_clusters), + n_papers = vapply(seq_len(clusters$num_clusters), + function(k) sum(clusters$groups == k, na.rm = TRUE), integer(1)), + source = label_source, + label_selected = vapply(seq_len(clusters$num_clusters), + function(k) { s <- tfidf_top_names[[k]] + if (length(s)) as.character(s[[1]]) else "" }, character(1)), + label_final = vapply(seq_len(clusters$num_clusters), + function(k) { i <- which(clusters$groups == k)[1] + if (is.na(i)) "" else clusters$cluster_labels[i] }, character(1)), + stringsAsFactors = FALSE), "summarize_06b_label_provenance") vslog$debug(paste("create_cluster_labels: done,", length(unique(clusters$cluster_labels)), "distinct labels")) return(clusters) From 3f19cc687956d4719811f2ea000972b5596ed555 Mon Sep 17 00:00:00 2001 From: chreman Date: Mon, 27 Jul 2026 16:21:10 +0200 Subject: [PATCH 169/205] add debug functionality for label generation --- .gitignore | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.gitignore b/.gitignore index 4e36eed04..99ee014f8 100644 --- a/.gitignore +++ b/.gitignore @@ -84,6 +84,8 @@ local_dev/tools/**/*.log # inspection output e2e/reproducibility/output/* e2e/reproducibility/logs/* +local_dev/tools/label_analysis +local_dev/tools/keyword_analysis # mac os .DS_Store From de1bc6050702f1970149ef082e79a3fe10371eba Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 21 Aug 2026 22:37:17 +0200 Subject: [PATCH 170/205] First implementation of anchor resource deterministic selection and ordering --- .gitignore | 1 + server/workers/base/tests/unit/conftest.py | 15 + server/workers/base/tests/unit/test_base.py | 46 ++- .../base/tests/unit/test_dedup_invariants.py | 348 ++++++++++++++++++ .../base/tests/unit/test_doi_title_filter.py | 10 +- server/workers/common/common/deduplication.py | 215 ++++++++++- server/workers/common/common/enrichment.py | 34 +- .../workers/common/tests/test_enrichment.py | 2 +- server/workers/orcid/src/orcid_service.py | 16 +- 9 files changed, 658 insertions(+), 29 deletions(-) create mode 100644 server/workers/base/tests/unit/conftest.py create mode 100644 server/workers/base/tests/unit/test_dedup_invariants.py diff --git a/.gitignore b/.gitignore index 99ee014f8..009c5b84d 100644 --- a/.gitignore +++ b/.gitignore @@ -86,6 +86,7 @@ e2e/reproducibility/output/* e2e/reproducibility/logs/* local_dev/tools/label_analysis local_dev/tools/keyword_analysis +local_dev/tools/reproducibility # mac os .DS_Store diff --git a/server/workers/base/tests/unit/conftest.py b/server/workers/base/tests/unit/conftest.py new file mode 100644 index 000000000..16314a3b8 --- /dev/null +++ b/server/workers/base/tests/unit/conftest.py @@ -0,0 +1,15 @@ +"""Make the base worker's sources importable when running pytest from the repo. + +Mirrors the container layout, where `src/` and the shared `common` package are on +the import path. Allows `pytest tests/unit` from `server/workers/base` without +setting PYTHONPATH manually. +""" + +import sys +from pathlib import Path + +_BASE_DIR = Path(__file__).resolve().parents[2] # server/workers/base +for p in (_BASE_DIR / "src", _BASE_DIR.parent / "common"): + p = str(p) + if p not in sys.path: + sys.path.insert(0, p) diff --git a/server/workers/base/tests/unit/test_base.py b/server/workers/base/tests/unit/test_base.py index 95006a06a..24cfd259a 100644 --- a/server/workers/base/tests/unit/test_base.py +++ b/server/workers/base/tests/unit/test_base.py @@ -155,7 +155,7 @@ def dummy_popen(cmd, stdin, stdout, stderr, encoding): # Patch methods used inside execute_search. monkeypatch.setattr(client_base, "sanitize_metadata", lambda df: df) - # filter_duplicates takes (df, service, params) — use correct arity + # filter_duplicates takes (df, service, params): use correct arity monkeypatch.setattr("base.filter_duplicates", lambda df, service, params: df) monkeypatch.setattr("base.parse_annotations_for_all", lambda metadata, field: pd.DataFrame({"annotations": [{}] * len(metadata)})) @@ -168,6 +168,48 @@ def dummy_popen(cmd, stdin, stdout, stderr, encoding): assert "input_data" in res assert "params" in res + +def test_execute_search_emits_deterministic_row_order(client_base, monkeypatch): + """The emitted metadata order must not depend on BASE's response order. + + The relevance ranking decides the head(list_size) cutoff and stays + available in the `relevance` column; the serialized rows are sorted by id + so downstream consumers (persistence, dataprocessing) receive an + order-stable artifact. + """ + class DummyProcess: + def __init__(self, stdout): + self._stdout = stdout + def communicate(self, input=None): + return (self._stdout, "") + + def make_popen(rows): + def dummy_popen(cmd, stdin, stdout, stderr, encoding): + return DummyProcess("header\n" + json.dumps(rows) + "\ntrailer\n") + return dummy_popen + + rows = [ + {"id": i, "title": f"Title {i}", "paper_abstract": "A", + "subject_orig": "S", "published_in": "J", "sanitized_authors": "X"} + for i in ("ccc", "aaa", "bbb") + ] + + monkeypatch.setattr(client_base, "sanitize_metadata", lambda df: df) + monkeypatch.setattr("base.filter_duplicates", lambda df, service, params: df) + monkeypatch.setattr("base.parse_annotations_for_all", lambda metadata, field: + pd.DataFrame({"annotations": [{}] * len(metadata)})) + monkeypatch.setattr(client_base, "enrich_metadata", lambda df: df) + + params = {"q": "dummy query", "service": "base", "list_size": 100} + emissions = [] + for order in (rows, rows[::-1]): + monkeypatch.setattr(subprocess, "Popen", make_popen(order)) + res = client_base.execute_search(params) + ids = [r["id"] for r in json.loads(res["input_data"]["metadata"])] + emissions.append(ids) + + assert emissions[0] == emissions[1] == ["aaa", "bbb", "ccc"] + def test_sanitize_metadata(client_base): # Create a dummy DataFrame with an "authors" column. df = pd.DataFrame({"authors": ["John Doe; Jane Smith"], "year": ["2020"]}) @@ -297,7 +339,7 @@ def test_filter_duplicates_doi_duplicates_resolved(): def test_filter_duplicates_mixed_type_duplicates_no_double_anchor(): """A dataset (typenorm=7) and a non-dataset that are textual duplicates must not - both appear in the output — the split into pure_datasets/non_datasets must not + both appear in the output: the split into pure_datasets/non_datasets must not accidentally give each sub-group an independent anchor.""" df = pd.DataFrame([ _make_record("dataset-A", typenorm="7", duplicates="non-dataset-B", diff --git a/server/workers/base/tests/unit/test_dedup_invariants.py b/server/workers/base/tests/unit/test_dedup_invariants.py new file mode 100644 index 000000000..85a9b259b --- /dev/null +++ b/server/workers/base/tests/unit/test_dedup_invariants.py @@ -0,0 +1,348 @@ +"""Metamorphic invariant tests for filter_duplicates. + +The invariants need no ground truth, they assert relations between an input and a transformed input +(shuffled, re-fed, or with an injected record). Machinery in dedup_invariants.py. + +Two kinds of tests: + * guards: properties that hold on the current code. + * xfail: properties the change is meant to establish. Each names the step + that flips it to pass; remove the marker in that step. + +Run from the package directory: cd server/workers/base && pytest tests/unit +""" + +import pytest + +from dedup_invariants import ( + assert_idempotent, + assert_injection, + assert_order_invariant, + fixture_to_df, + load_catalog, + load_extracted_fixtures, + make_df, + make_record, + run_dedup, +) + + +# --- catalog & fixtures load (guards) ---------------------------------------- + +def test_catalog_parses_and_covers_all_fcs(): + catalog = load_catalog() + assert catalog["schema_version"] == 1 + ids = [c["id"] for c in catalog["cases"]] + assert len(ids) == len(set(ids)), "duplicate case ids in catalog" + covered = {c["fc"] for c in catalog["cases"] if "fc" in c} + assert {"FC1", "FC2", "FC3", "FC4", "FC5"} <= covered + + +def test_extracted_fixtures_load_and_convert(): + for corpus in ("orcid_v2", "base_v1"): + data = load_extracted_fixtures(corpus) + assert data["merge_fixtures"] and data["split_fixtures"] + fx = load_extracted_fixtures("orcid_v2")["merge_fixtures"][0] + df = fixture_to_df(fx, mark_mutual_duplicates=True) + assert len(df) == fx["n_members"] + assert "doi_merge" in df.columns + + +# --- order-invariance (the core property) --------------------------------- + +def test_disjoint_records_are_order_invariant(): + # No duplicate relations at all: dedup must be a no-op in any order. + df = make_df([ + make_record("a", title="Alpha decay measurement", doi="10.1/a"), + make_record("b", title="Beta cell function", doi="10.1/b"), + make_record("c", title="Gamma ray bursts", doi="10.1/c"), + ]) + out = assert_order_invariant(df) + assert out["ids"] == ("a", "b", "c") + + +def test_oa_discriminated_group_is_order_invariant(): + # The ladder discriminates (exactly one OA member): no tie to fall through. + df = make_df([ + make_record("oa", duplicates="closed", oa_state="1", year="2020"), + make_record("closed", duplicates="oa", oa_state="0", year="2021"), + ]) + out = assert_order_invariant(df) + assert out["ids"] == ("oa",) + + +def test_full_ladder_tie_is_order_invariant(): + # Same title group, identical oa_state and year, no DOI (X14): the ladder + # is exhausted and the content tie-break (title, then id) must decide. + df = make_df([ + make_record("first", duplicates="second", subject_orig="kw-a", + paper_abstract="An abstract about the topic A"), + make_record("second", duplicates="first", subject_orig="kw-b", + paper_abstract="An abstract about the topic B"), + ]) + assert_order_invariant(df) + + +def test_dataset_version_tie_is_order_invariant(): + # Two URL variants of the same DOI share an unversioned key (the key is the + # URL path), both parse to doi_version=None, and mark_latest_doi re-picks + # the anchor inside the group (X5): an all-NaN version sort that the + # content tie-break must decide. DOIs must be URL-form here: + # get_unversioned_doi keys on the URL path and returns "" for bare DOIs, + # which would skip the group. + df = make_df([ + make_record("v-plain", typenorm="7", duplicates="v-dx", + doi="https://doi.org/10.6084/m9.figshare.23691672", + subject_orig="kw-a"), + make_record("v-dx", typenorm="7", duplicates="v-plain", + doi="https://dx.doi.org/10.6084/m9.figshare.23691672", + subject_orig="kw-b"), + ]) + assert_order_invariant(df) + + +def test_equal_length_abstract_tie_is_order_invariant(): + # The anchor is discriminated (OA member wins), but the enriched abstract + # must be too: two duplicates carry different abstracts of equal length + # (X13), so the longest-wins rule ties and the text tie-break must decide. + df = make_df([ + make_record("anchor", duplicates="d1,d2", oa_state="1", + paper_abstract=""), + make_record("d1", duplicates="anchor,d2", oa_state="0", + paper_abstract="Equal length abstract text A"), + make_record("d2", duplicates="anchor,d1", oa_state="0", + paper_abstract="Equal length abstract text B"), + ]) + assert_order_invariant(df) + + +# --- idempotence / duplication-invariance (guards) --------------------- + +def test_dedup_is_idempotent(): + df = make_df([ + make_record("oa", duplicates="closed", oa_state="1", year="2020"), + make_record("closed", duplicates="oa", oa_state="0", year="2021"), + make_record("solo", title="An unrelated record", doi="10.1/solo"), + ]) + assert_idempotent(df) + + +def test_exact_copy_is_absorbed(): + df = make_df([ + make_record("a", title="Alpha decay measurement", doi="10.1/a"), + make_record("b", title="Beta cell function", doi="10.1/b"), + ]) + copy_of_a = make_record("a", title="Alpha decay measurement", doi="10.1/a") + assert_injection(df, copy_of_a, expected_delta=0) + + +# --- merge-injection: the DOI-grouping gap ------------------- + +def test_same_doi_unmarked_is_merged(): + # Same DOI, different titles, no duplicates marking: the title pass sees + # nothing; the DOI-key grouping must merge them. + df = make_df([ + make_record("plain", doi="10.1234/test", + title="Detecting moments of stress"), + ]) + journal_copy = make_record( + "journal", doi="10.1234/test", + title="Sensors / Detecting moments of stress") + assert_injection(df, journal_copy, expected_delta=0) + + +def test_doi_only_in_doi_merge_is_merged(): + # the observation gap: the DOI lives in the dcdoi-derived doi_merge + # while the link-derived `doi` is empty: the coalesced key must merge. + df = make_df([ + make_record("linkdoi", doi="10.1234/test", + title="Detecting moments of stress"), + ]) + dcdoi_copy = make_record( + "dcdoi", doi="", doi_merge="https://dx.doi.org/10.1234/TEST", + title="Sensors / Detecting moments of stress") + assert_injection(df, dcdoi_copy, expected_delta=0) + + +def test_case_variant_dois_are_merged(): + # case-only DOI variants share the lowercased key. + df = make_df([ + make_record("upper", doi="https://doi.org/10.1016/B978.12", + title="Chapter on sensing"), + ]) + lower_copy = make_record("lower", doi="https://doi.org/10.1016/b978.12", + title="Chapter on sensing") + assert_injection(df, lower_copy, expected_delta=0) + + +def test_dataset_version_group_merges_to_latest(): + # version variants share the unversioned key on the dataset path, + # where mark_latest_doi decides; the anchor must be the latest version, + # in any input order. (On the non-dataset path the OA/year ladder ranks + # above the version, so this expectation is dataset-specific.) + df = make_df([ + make_record("v1", typenorm="7", + doi="https://doi.org/10.6084/m9.figshare.111.v1", + title="A dataset of measurements"), + make_record("v3", typenorm="7", + doi="https://doi.org/10.6084/m9.figshare.111.v3", + title="A dataset of measurements"), + ]) + out = assert_order_invariant(df) + assert out["ids"] == ("v3",) + + +def test_same_doi_unrelated_titles_are_not_absorbed(): + # two genuinely different papers mis-indexed under one DOI. The + # doi_title_filter guard (now on the DOI-key groups) drops the + # false-positive side; which paper survives is arbitrary by nature, but + # the survivor must be deterministic and must not have absorbed the other + # paper's keywords. + subjects = {"real": "quantum optics", "misindexed": "medieval history"} + df = make_df([ + make_record("real", doi="10.1234/shared", oa_state="1", + title="Quantum entanglement in photonic crystals", + subject_orig=subjects["real"]), + make_record("misindexed", doi="10.1234/shared", + title="Medieval trade routes of the Baltic", + subject_orig=subjects["misindexed"]), + ]) + out = assert_order_invariant(df) + assert len(out["ids"]) == 1 + survivor = out["ids"][0] + assert out["records"][survivor]["subject_orig"] == subjects[survivor] + + +def test_distinct_papers_with_article_number_suffixes_stay_separate(): + # Elsevier-style DOIs of distinct papers in one journal batch differ only + # in the trailing article number; the key must not strip it and collide + # them (which would let the title guard drop a real paper). + df = make_df([ + make_record("paper-a", doi="https://dx.doi.org/10.1016/j.physleta.2015.07.045", + title="Entangled entanglement: A construction procedure"), + make_record("paper-b", oa_state="1", + additional_dois=["https://doi.org/10.1016/j.physleta.2015.07.030"], + title="Weak interaction processes: Which quantum information is revealed?"), + ]) + out = assert_order_invariant(df) + assert out["ids"] == ("paper-a", "paper-b") + + +def test_dcdoi_asserted_same_work_with_retitled_preprint_merges_and_keeps_oa(): + # via dcdoi: a repository copy under the preprint's old title asserts + # the published DOI in additional_dois. It must merge with the publisher + # record: exempt from the doi_title_filter guard despite the dissimilar + # title: and its OA state must survive onto the anchor. + df = make_df([ + make_record("publisher", oa_state="2", + doi="https://dx.doi.org/10.1016/j.physleta.2015.07.045", + title="Entangled entanglement: A construction procedure"), + make_record("repo-copy", oa_state="1", doi="", + additional_dois=["https://doi.org/10.1016/j.physleta.2015.07.045"], + title="Entangled Entanglement: The Geometry of GHZ States"), + ]) + out = assert_order_invariant(df) + assert len(out["ids"]) == 1 + assert str(out["records"][out["ids"][0]]["oa_state"]) == "1" + + +def test_doi_group_straddling_typenorm_split_keeps_one_survivor(): + # a DOI-key group whose members land on different sides of the + # dataset/non-dataset split must not end up with an anchor on each side. + df = make_df([ + make_record("dataset-side", typenorm="7", doi="10.5281/zenodo.42", + title="A shared resource"), + make_record("paper-side", typenorm="1", doi="10.5281/zenodo.42", + title="A shared resource"), + ]) + out = assert_order_invariant(df) + assert len(out["ids"]) == 1 + + +def test_idempotent_after_doi_merge(): + # A DOI-merged survivor set re-fed must not change again. + df = make_df([ + make_record("plain", doi="10.1234/test", oa_state="1", + title="Detecting moments of stress"), + make_record("journal", doi="10.1234/test", + title="Sensors / Detecting moments of stress"), + make_record("solo", doi="10.1/solo", title="An unrelated record"), + ]) + assert_idempotent(df) + + +# --- characterization (scope note, not an invariant) --------------------- + +def test_same_title_different_doi_currently_merges(): + # Two records the title pass marked as duplicates, carrying different DOIs. + # Current behaviour: they collapse to one anchor. The PID-conflict split + # that would keep both is the hybrid mechanism: out of scope for the + # DOI-based solution; this test + # pins the behaviour so a scope change is a conscious decision. + df = make_df([ + make_record("arxiv", duplicates="journal", doi="10.48550/arxiv.1", + title="Designing intent communication"), + make_record("journal", duplicates="arxiv", doi="10.1145/3771882", + title="Designing intent communication"), + ]) + out = run_dedup(df) + assert len(out["ids"]) == 1 + + +# --- real extracted fixtures ------------------------------------------------ + +def test_real_merge_fixtures_collapse_on_doi_key_alone(): + # The purpose: real duplicate groups must collapse via the DOI key even + # when the textual pass did not mark them (mark_mutual_duplicates=False). + # Fixtures whose members only share a key under bare-.N version stripping + # (which the key deliberately does not do) rely on the title pass instead + # and are asserted through that path. + from common.deduplication import add_doi_keys + + for corpus in ("orcid_v2", "base_v1"): + data = load_extracted_fixtures(corpus) + fixtures = [f for f in data["merge_fixtures"] + if f.get("pid_namespace") == "doi"][:40] + for fx in fixtures: + df = fixture_to_df(fx, mark_mutual_duplicates=False) + single_key = add_doi_keys(df.copy())["doi_key"].nunique() == 1 + if not single_key: + df = fixture_to_df(fx, mark_mutual_duplicates=True) + out = run_dedup(df) + assert len(out["ids"]) == fx["expected"]["groups"], ( + f"{corpus} {fx['pid_key']} (single_key={single_key}): expected " + f"{fx['expected']['groups']} group(s), got {out['ids']}" + ) + + +def test_real_split_fixtures_never_merge_across_distinct_keys(): + # Same-title/different-primary-DOI groups, unmarked: survivors must map + # 1:1 onto the distinct coalesced DOI keys (keyless members stay alone). + # Members labelled 'split' by primary DOI can legitimately merge when a + # secondary DOI in additional_dois is shared: e.g. a repository copy + # carrying the journal DOI in its dcdoi: so the expectation is derived + # from the coalesced keys, not from the extraction tool's label. + from common.deduplication import add_doi_keys + + for corpus in ("orcid_v2", "base_v1"): + data = load_extracted_fixtures(corpus) + for fx in data["split_fixtures"][:40]: + df = fixture_to_df(fx, mark_mutual_duplicates=False) + keys = add_doi_keys(df.copy())["doi_key"].tolist() + expected = len({k for k in keys if k}) + sum(1 for k in keys if not k) + out = run_dedup(df) + assert len(out["ids"]) == expected, ( + f"{corpus} {fx['title_key'][:40]!r}: keys={keys}, " + f"expected {expected} survivor(s), got {out['ids']}" + ) + + +# --- on real extracted fixtures ------------------------------------------ + +def test_real_merge_fixtures_are_order_invariant(): + # Real merge groups from the extracted corpora, each run with the mutual + # duplicates marking the title pass would have produced. + for corpus in ("orcid_v2", "base_v1"): + fixtures = load_extracted_fixtures(corpus)["merge_fixtures"][:25] + for fx in fixtures: + df = fixture_to_df(fx, mark_mutual_duplicates=True) + assert_order_invariant(df) diff --git a/server/workers/base/tests/unit/test_doi_title_filter.py b/server/workers/base/tests/unit/test_doi_title_filter.py index 06ec90fa3..c6ed9fefe 100644 --- a/server/workers/base/tests/unit/test_doi_title_filter.py +++ b/server/workers/base/tests/unit/test_doi_title_filter.py @@ -15,7 +15,7 @@ - Near-matches (90–99): trailing period, trailing/leading whitespace, Unicode vs ASCII punctuation (curly vs straight quote) - Journal-prefix prepended (65–89): one version carries "Journal Name / Title", - the other carries only "Title" — caught by substring matching after lowercasing + the other carries only "Title": caught by substring matching after lowercasing - ALL-CAPS vs title-case (23–36): very short titles; case-insensitive comparison resolves these """ @@ -33,7 +33,7 @@ def _case(anchor, candidate, doi=""): # --------------------------------------------------------------------------- -# Ratio ~100 — identical titles, different repository records +# Ratio ~100: identical titles, different repository records # --------------------------------------------------------------------------- EXACT_CASES = [ @@ -71,7 +71,7 @@ def test_exact_match_not_filtered(anchor, candidate): # --------------------------------------------------------------------------- -# Ratio 90–99 — near-identical: trailing period, punctuation, version tag +# Ratio 90–99: near-identical: trailing period, punctuation, version tag # --------------------------------------------------------------------------- NEAR_MATCH_CASES = [ @@ -119,7 +119,7 @@ def test_near_match_not_filtered(anchor, candidate): # --------------------------------------------------------------------------- -# Ratio 65–89 — journal name prepended as title prefix +# Ratio 65–89: journal name prepended as title prefix # One version: "Journal Name / Paper Title" # Other version: "Paper Title" # Substring matching (lowercased) resolves these. @@ -285,7 +285,7 @@ def test_journal_prefix_not_filtered(anchor, candidate): # --------------------------------------------------------------------------- -# Ratio 23–36 — ALL-CAPS vs title-case on short titles +# Ratio 23–36: ALL-CAPS vs title-case on short titles # fuzz.partial_ratio is case-sensitive so these score very low on raw comparison; # lowercasing before comparison resolves them. # --------------------------------------------------------------------------- diff --git a/server/workers/common/common/deduplication.py b/server/workers/common/common/deduplication.py index ae8140c4b..60780c25e 100644 --- a/server/workers/common/common/deduplication.py +++ b/server/workers/common/common/deduplication.py @@ -16,6 +16,12 @@ # false positives on DOIs like 10.1594/pangaea.982329 where the numeric suffix # is a record identifier, not a version number. pattern_doi = re.compile(r"(?:\.?v|\.)([0-9]{1,3})(?:-\d+)?$") +# Version stripping for the DOI merge key: only the explicit v-forms. The bare +# .N alternative must not apply here: article-number suffixes in the same +# style (10.1016/j.physleta.2015.07.045) would collide distinct papers of one +# journal batch onto a single key. Costs the key the Mendeley-style bare-.N +# version merge; those still merge via the title pass + mark_latest_doi. +pattern_doi_version_only = re.compile(r"\.?v([0-9]{1,3})(?:-\d+)?$") _pattern_punctuation = re.compile(r"[^\w\s]") _DOI_TITLE_CUTOFF = 1/15.83*100 # ≈ 6.32 on rapidfuzz's 0–100 scale @@ -56,13 +62,101 @@ def get_publisher_doi(doi): else: return "" -def find_duplicate_groups(df): +def find_duplicate_groups(df): duplicate_groups = df.id.map(lambda x: df[df.duplicates.str.contains(x)].index) tmp = pd.DataFrame(duplicate_groups).astype(str).drop_duplicates().index return duplicate_groups[tmp] -def mark_duplicate_dois(df): - for doi, index in df.groupby("doi").groups.items(): + +# --- DOI merge key ----------------------------------------------------------- +# The deterministic grouping key: records sharing a normalized DOI are one +# duplicate group regardless of title or input order. The key coalesces the +# DOI-bearing fields (doi_merge and additional_dois carry the dcdoi-derived +# DOIs that the link-derived `doi` misses) and mirrors the normalization the +# ORCID worker applies downstream, lifted here so every consumer benefits. + +_DOI_URL_PREFIX = re.compile(r"^https?://(dx\.)?doi\.org/", re.IGNORECASE) + + +def _doi_candidates(value): + """DOI strings contained in a field value (list / ';'-joined str / NaN).""" + if isinstance(value, list): + parts = [] + for element in value: + parts.extend(str(element).split(";")) + elif value is None: + return [] + else: + try: + if pd.isna(value): + return [] + except (TypeError, ValueError): + return [] + parts = str(value).split(";") + return [p.strip() for p in parts if p.strip()] + + +def normalize_doi_key(raw): + """Normalized grouping key for one DOI value: bare, lowercased, unversioned. + + Returns "" for empty values and for values that are not DOIs (the + link-derived `doi` field can hold arbitrary URLs). + """ + if not isinstance(raw, str) or not raw.strip(): + return "" + bare = _DOI_URL_PREFIX.sub("", raw.strip()) + if not bare.lower().startswith("10."): + return "" + return pattern_doi_version_only.sub("", bare.lower()) + + +def compute_doi_key(doi_merge, additional_dois, doi): + """The record's primary DOI key: coalesce doi_merge -> additional_dois -> doi.""" + for value in (doi_merge, additional_dois, doi): + for candidate in _doi_candidates(value): + key = normalize_doi_key(candidate) + if key: + return key + return "" + + +def add_doi_keys(df): + """Adds the doi_key column; missing source columns contribute nothing.""" + def _get(row, col): + return row[col] if col in row.index else None + + df["doi_key"] = df.apply( + lambda row: compute_doi_key(_get(row, "doi_merge"), + _get(row, "additional_dois"), + _get(row, "doi")), + axis=1, + ) + return df + + +def extend_duplicates_with_doi_groups(df): + """Folds DOI-key partners into the `duplicates` marking. + + Records sharing a doi_key become one duplicate group exactly like the + upstream textual marking would have made them, so the whole existing + pipeline (grouping, anchor selection, enrichment) applies unchanged. + Member ids are appended in sorted order: the resulting marking is a + function of record content, not of input row order. + """ + if "doi_key" not in df.columns: + return df + for key, index in df.groupby("doi_key").groups.items(): + if key and len(index) > 1: + member_ids = sorted(df.loc[index, "id"]) + for idx in index: + existing = [p for p in str(df.at[idx, "duplicates"]).split(",") if p] + merged = existing + [m for m in member_ids if m not in existing] + df.at[idx, "duplicates"] = ",".join(merged) + return df + + +def mark_duplicate_dois(df, column="doi"): + for doi, index in df.groupby(column).groups.items(): if doi: if len(index) > 1: df.loc[index, "doi_duplicate"] = True @@ -100,6 +194,98 @@ def add_false_negatives(df): df.loc[df[(~df.is_duplicate) & (df.doi_duplicate)].index, "is_duplicate"] = True return df +def _tie_break_norm(t): + """Normalized title for tie-break comparison (case/punctuation/whitespace).""" + if not isinstance(t, str): + return "" + return re.sub(r"\s+", " ", _normalize_title(t)).strip() + + +def _title_preference_keys(norms): + """Sort keys implementing the title preference among tie-break candidates. + + Rule (decided 2026-08-21): + 1. Across titles with *different* beginnings, prefer the SHORTER one. + Target case: journal-name prefixes: "Frontiers in Earth Science / + Microplastic emission and socioeconomic data…" vs the bare + "Microplastic emission and socioeconomic data…". + 2. Among titles where one is a lexicographic PREFIX of the other, + prefer the LONGER one. Target case: truncated titles: "…A novel + approach combining SO" (cut mid-word) vs the full "…combining SO2 + concentrations from satellite data…"; also missing subtitles. + + Implemented as a total order (a naive pairwise "shorter unless prefix" + preference is intransitive and could cycle): each title is keyed by the + shortest title in the candidate set that is a prefix of it (its "stem"). + Sorting by (stem length asc, stem, length desc, full title) makes rule 1 + decide between stems and rule 2 decide within a stem chain. + + This solution is SUB-OPTIMAL by construction: whatever direction is + chosen, some real cases pick a false positive and keep noisy metadata: + - Rule 1 wrongly prefers truncated or subtitle-less variants whenever + normalization noise (punctuation, encoding, spacing) breaks the + prefix relation, so the pair falls through to "shorter wins". + - Rule 2 wrongly prefers titles with appended junk: venue/year + suffixes ("…. GI_Forum 2018") or repository language tags + ("… ; ENEngelskEnglish…"): over the clean shorter variant. + - For variants with genuinely different wording (translations, + bilingual repository titles, preprint renamed at publication — + roughly half of the observed differing-title pairs), title length + carries no signal at all and the choice is arbitrary. + - Correction/erratum records ("Publisher Correction: X") sharing the + DOI of X are distinct documents; no title heuristic repairs that. + The rule only decides when OA state, provider, version and year all tie, + so the impact is small; it optimizes the common observed patterns, + not correctness in general. + + Empty titles are excluded as stems so a record without a title cannot + chain every other title into "longer wins". + """ + stems = [] + for t in norms: + prefixes = [s for s in norms if s and t.startswith(s)] + stems.append(min(prefixes, key=len) if prefixes else t) + return ( + [len(s) for s in stems], # rule 1: shorter stem first + stems, # deterministic among equal lengths + [-len(t) for t in norms], # rule 2: longer within a stem chain + list(norms), # stable final text key + ) + + +def select_anchor_index(candidates, by=None, ascending=None): + """Index of the deterministic anchor among candidate rows. + + Sorts by the caller's priority columns, then by the content tie-break + keys: the title preference (see _title_preference_keys), then id. This is + a total order over record content, so no tie ever falls through to input + row position (BASE response order is not stable between runs). NaNs sort + last in the caller's columns, matching the head(1) semantics the call + sites previously relied on. + """ + by = list(by) if by else [] + ascending = list(ascending) if ascending is not None else [True] * len(by) + if not by and "title" not in candidates.columns and "id" not in candidates.columns: + return candidates.index[0] + # Sort a positionally re-indexed copy: callers may index the frame by id, + # which would make a sort on the "id" column ambiguous. + positional = candidates.reset_index(drop=True) + if "title" in positional.columns: + norms = [_tie_break_norm(t) for t in positional["title"]] + stem_len, stem, len_desc, norm = _title_preference_keys(norms) + positional["_title_stem_len"] = stem_len + positional["_title_stem"] = stem + positional["_title_len_desc"] = len_desc + positional["_title_norm"] = norm + by += ["_title_stem_len", "_title_stem", "_title_len_desc", "_title_norm"] + ascending += [True, True, True, True] + if "id" in positional.columns and "id" not in by: + by.append("id") + ascending.append(True) + winner_pos = positional.sort_values(by, ascending=ascending).index[0] + return candidates.index[winner_pos] + + def remove_textual_duplicates_from_different_sources(df, duplicate_groups): for _, idx in duplicate_groups.items(): if len(idx) > 1: @@ -111,7 +297,7 @@ def remove_textual_duplicates_from_different_sources(df, duplicate_groups): # keep entry with doi df.loc[tmp[tmp.publisher_doi!=""].index, "is_anchor"] = True else: - df.loc[tmp.sort_values(["doi", "year"], ascending=[False, False]).head(1).index, "is_anchor"] = True + df.loc[[select_anchor_index(tmp, ["doi", "year"], [False, False])], "is_anchor"] = True return df def mark_latest_doi(df, duplicate_groups): @@ -123,7 +309,7 @@ def mark_latest_doi(df, duplicate_groups): if len(tmp2) > 0: df.loc[tmp2.index, "is_anchor"] = False versions = tmp2.id - latest = tmp2.sort_values("doi_version", ascending=False).head(1).id + latest = tmp2.loc[[select_anchor_index(tmp2, ["doi_version"], [False])]].id v = [{"versions": versions.values.tolist(), "latest": latest.values.tolist()}]*len(tmp2) df.loc[versions.index, "versions"] = v df.loc[latest.index, "is_anchor"] = True @@ -136,9 +322,9 @@ def prioritize_OA_and_latest(df, duplicate_groups): tmp = df.loc[idx] df.loc[idx, "is_anchor"] = False if len(tmp[tmp.oa_state=="1"]) > 0: - df.loc[tmp[tmp.oa_state=="1"].sort_values("year", ascending=False).head(1).index, "is_anchor"] = True + df.loc[[select_anchor_index(tmp[tmp.oa_state=="1"], ["year"], [False])], "is_anchor"] = True else: - df.loc[tmp.sort_values("year", ascending=False).head(1).index, "is_anchor"] = True + df.loc[[select_anchor_index(tmp, ["year"], [False])], "is_anchor"] = True return df def mark_duplicates(metadata): @@ -232,7 +418,7 @@ def prioritize_doi_and_provider(df, duplicate_groups): highest_priority_candidates = candidates[candidates["provider_priority"] == max_priority] if len(highest_priority_candidates) > 0: - anchor_idx = highest_priority_candidates.index[0] + anchor_idx = select_anchor_index(highest_priority_candidates) df.loc[anchor_idx, "is_anchor"] = True other_idx = idx.difference([anchor_idx]) @@ -275,7 +461,11 @@ def deduplicate_keywords(keywords, similarity_threshold): if not keywords: return [] - keywords_list = list(keywords) + # Sorted iteration: callers pass sets, whose iteration order is hash-seed + # dependent. The similar-keyword fold below is order-sensitive (which of + # two equal-length variants survives, chains of pairwise-similar terms), + # so a fixed input order is required for a deterministic result. + keywords_list = sorted(keywords) unique_keywords = [] for keyword in keywords_list: @@ -314,7 +504,10 @@ def deduplicate_links(links): normalized_to_link = {} invalid_urls = set() - for link in links: + # Sorted iteration: callers pass sets, and a same-protocol collision on a + # normalized URL keeps the first-seen variant: fix the order so the kept + # variant is deterministic. + for link in sorted(links, key=str): link_str = str(link).strip() if not link_str: continue @@ -347,5 +540,5 @@ def deduplicate_links(links): except Exception: invalid_urls.add(link_str) - result = list(normalized_to_link.values()) + list(invalid_urls) + result = list(normalized_to_link.values()) + sorted(invalid_urls) return result \ No newline at end of file diff --git a/server/workers/common/common/enrichment.py b/server/workers/common/common/enrichment.py index 67e32e712..f7eeecbde 100644 --- a/server/workers/common/common/enrichment.py +++ b/server/workers/common/common/enrichment.py @@ -2,7 +2,11 @@ import re import logging import pandas as pd -from common.deduplication import deduplicate_keywords, deduplicate_links +from common.deduplication import ( + deduplicate_keywords, + deduplicate_links, + select_anchor_index, +) logger = logging.getLogger(__name__) @@ -103,7 +107,10 @@ def enrich_anchor_using_duplicates(df, duplicate_groups): if is_no_anchors: continue - anchor = anchors.iloc[0] + # A group can carry several anchors (e.g. the publisher-DOI branch marks + # every DOI-bearing member); pick the one to enrich by the content + # total order, not by row position. + anchor = anchors.loc[select_anchor_index(anchors)] anchor_idx = anchor.name # _log_anchor_state('BEFORE', df, anchor_idx, group_data=group_data) @@ -225,6 +232,12 @@ def process_paper_abstract_element(value, accumulator): if abstract_length > accumulator['best_length']: accumulator['best_length'] = abstract_length accumulator['best_value'] = value + elif (abstract_length == accumulator['best_length'] + and accumulator['best_value'] is not None + and str(value) < str(accumulator['best_value'])): + # Equal-length tie: break on the text itself so the winner does not + # depend on member iteration order. + accumulator['best_value'] = value def oa_state_priority(value): """ @@ -257,6 +270,12 @@ def process_oa_state_element(value, accumulator): if priority < accumulator['best_priority']: accumulator['best_priority'] = priority accumulator['best_value'] = value + elif (priority == accumulator['best_priority'] + and accumulator['best_value'] is not None + and str(value) < str(accumulator['best_value'])): + # Equal priority can still mean different representations of the same + # state (e.g. "1" vs 1.0 after a numeric cast); keep a deterministic one. + accumulator['best_value'] = value def process_link_element(value, accumulator): """ @@ -277,8 +296,8 @@ def process_link_element(value, accumulator): def process_additional_dois_element(doi_value, additional_dois_value, accumulator): """ - Collects every DOI a group member represents — its primary ``doi`` and any - entries in ``additional_dois`` — into the accumulator, so the anchor can later + Collects every DOI a group member represents: its primary ``doi`` and any + entries in ``additional_dois``: into the accumulator, so the anchor can later advertise all of them. Mirrors process_link_element, but for DOIs. Duplicate group members are dropped after enrichment, so unless their DOIs are @@ -307,7 +326,12 @@ def add_raw(raw): for part in parts: bare = _DOI_PREFIX_RE.sub('', part.strip()) if bare: - accumulator.setdefault(bare.lower(), bare) + # Case variants share a key; keep the lexicographically + # smaller display form so the choice is order-independent. + key = bare.lower() + existing = accumulator.get(key) + if existing is None or bare < existing: + accumulator[key] = bare add_raw(doi_value) add_raw(additional_dois_value) diff --git a/server/workers/common/tests/test_enrichment.py b/server/workers/common/tests/test_enrichment.py index 6b96a7ff4..55ce82bb0 100644 --- a/server/workers/common/tests/test_enrichment.py +++ b/server/workers/common/tests/test_enrichment.py @@ -35,7 +35,7 @@ def _make_df(rows): defaults["is_anchor"] = False full_rows = [{**defaults, **r} for r in rows] df = pd.DataFrame(full_rows) - # Match the indexing the base worker uses — string ids as the index. + # Match the indexing the base worker uses: string ids as the index. df.index = df["id"] return df diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index e6503dc12..6f4750989 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -68,8 +68,9 @@ def execute_search(self, params: Dict[str, str]) -> Union[SuccessResult, ErrorRe if metadata.empty: return self._handle_insufficient_results(params, orcid_id) - # (a) ORCID works as retrieved, before any enrichment. - self._dump_stage(metadata, params, "orcid_01_works_retrieved", columns=self._DUMP_COLS) + # Dump ORCID resources as retrieved, before any enrichment. Full column set + # (columns=None) so a DOI can be traced in any field it may occur in. + self._dump_stage(metadata, params, "orcid_01_works_retrieved", columns=None) metadata = self._process_metadata(metadata, author_info, params) @@ -180,7 +181,10 @@ def _dump_stage(self, obj, params: Dict[str, str], stage: str, columns=None): # Columns worth capturing across the custody chain: retrieval, enrichment, and the # fields that build the clustering `content` (title, paper_abstract, subject_orig). - _DUMP_COLS = ["id", "doi", "doi_merge", "title", "paper_abstract", "subject", + # The full DOI provenance (doi, doi_merge, additional_dois, link) is captured so + # the DOI-based merge can be traced across every field a DOI may live in: + # doi/doi_merge derive from link, additional_dois carries the raw dcdoi values. + _DUMP_COLS = ["id", "doi", "doi_merge", "additional_dois", "title", "paper_abstract", "subject", "subject_orig", "oa_state", "content_provider", "link", "year", "is_anchor", "is_duplicate", "keywords_rank_mesh_specific", "keywords_rank_mesh_generic"] @@ -358,10 +362,12 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra base_metadata = self.request_base_metadata(dois, params) - # (b) BASE metadata as received (post BASE-side dedup/enrichment): the anchors, + # Dump BASE metadata as received (post BASE-side dedup/enrichment): the anchors, # their oa_state/content_provider, and the subject_orig/paper_abstract that will # feed clustering content. Correlate to base.py's per-request dedup dumps by `id`. - self._dump_stage(base_metadata, params, "orcid_02_base_metadata", columns=self._DUMP_COLS) + # Full column set (columns=None) so a DOI can be traced in any field it may + # occur in (relation/identifier/published_in), not just doi/doi_merge/additional_dois. + self._dump_stage(base_metadata, params, "orcid_02_base_metadata", columns=None) if "doi_merge" not in base_metadata.columns: self.logger.error(f"BASE metadata is missing 'doi_merge' column, cannot proceed with enrichment. Params: {params}") From 974638aeb212203ec351ff85d056c7665a7d9421 Mon Sep 17 00:00:00 2001 From: chreman Date: Fri, 21 Aug 2026 22:40:20 +0200 Subject: [PATCH 171/205] debug tooling and tests for deterministic anchor selection and ordering --- server/workers/base/src/base.py | 69 ++++++- .../workers/common/tests/test_anchor_order.py | 184 ++++++++++++++++++ server/workers/common/tests/test_doi_key.py | 129 ++++++++++++ 3 files changed, 376 insertions(+), 6 deletions(-) create mode 100644 server/workers/common/tests/test_anchor_order.py create mode 100644 server/workers/common/tests/test_doi_key.py diff --git a/server/workers/base/src/base.py b/server/workers/base/src/base.py index 2f003f595..540cf2e97 100644 --- a/server/workers/base/src/base.py +++ b/server/workers/base/src/base.py @@ -11,6 +11,9 @@ get_unversioned_doi, get_publisher_doi, find_duplicate_groups, + add_doi_keys, + extend_duplicates_with_doi_groups, + select_anchor_index, mark_duplicate_dois, mark_duplicate_links, identify_relations, @@ -95,12 +98,22 @@ def execute_search(self, params): else: metadata = pd.DataFrame(raw_metadata) metadata = self.sanitize_metadata(metadata) + _dump_full(metadata, params, "base_00_raw_retrieved") metadata = filter_duplicates(metadata, original_service, params) metadata = pd.concat( [metadata, parse_annotations_for_all(metadata, "subject_orig")], axis=1, ) metadata = metadata.head(params.get("list_size")) + # Deterministic emission order: the cutoff above selects by + # BASE's relevance ranking (response order), which is not + # stable between identical requests. Row order is not a + # carrier of information. The rank is in the `relevance` + # column, so the survivors are emitted sorted by id, giving + # every downstream consumer an order-stable artifact + # (order-sensitive steps like the label pipeline otherwise + # inherit the response instability). + metadata = metadata.sort_values("id") metadata.reset_index(inplace=True, drop=True) metadata = self.enrich_metadata(metadata) custom_clustering = params.get("custom_clustering") @@ -282,7 +295,7 @@ def _log_group_similarity(df, indexes, group_type, group_key): """Log titles, DOIs, and pairwise Levenshtein ratios for one duplicate group.""" if not logger.isEnabledFor(logging.DEBUG): return - # Intersect with df.index — group members can be dropped by the + # Intersect with df.index: group members can be dropped by the # false-positive DOI/title filter before this log fires. present = df.index.intersection(list(indexes)) if len(present) == 0: @@ -324,6 +337,11 @@ def filter_duplicates(df, service, params): lambda x: get_unversioned_doi(x) if type(x) is str else None ) df["publisher_doi"] = df.doi.map(lambda x: get_publisher_doi(x)) + # DOI merge key: records sharing a normalized DOI (coalesced from + # doi_merge / additional_dois / doi) join one duplicate group regardless + # of whether the textual pass linked them. + df = add_doi_keys(df) + df = extend_duplicates_with_doi_groups(df) duplicate_groups = find_duplicate_groups(df) # logger.debug(f"[dedup:find_duplicate_groups] duplicate_groups groups: {len(duplicate_groups)}, multi-member groups: {sum(1 for idx in duplicate_groups if len(idx) > 1)}") # for grp_id, idx in duplicate_groups.items(): @@ -332,7 +350,7 @@ def filter_duplicates(df, service, params): # f"[dedup:position_check] group id={grp_id!r} size={len(idx)} " # f"member_original_indexes={sorted(idx.tolist())}" # ) - df = mark_duplicate_dois(df) + df = mark_duplicate_dois(df, column="doi_key") df = mark_duplicate_links(df) # _log_dedup_state(df, "after_mark_doi_link_duplicates", params) df = identify_relations(df) @@ -348,12 +366,18 @@ def filter_duplicates(df, service, params): df.loc[df[~df.is_duplicate].index, "is_anchor"] = True # _log_dedup_state(df, "after_non_duplicate_anchors", params) + # X11 guard, scoped to records sharing the same link-derived `doi`: two + # such records claiming one DOI with unrelated titles are mis-indexed and + # the non-anchor side is dropped. dcdoi-derived doi_key groups are exempt + # on purpose: a repository copy asserting the published DOI is trusted + # even when retitled (preprint renamed at publication), matching the + # downstream ORCID DOI-merge this grouping replaces. false_positive_indexes = [] for doi_val, grp in df[df["doi_duplicate"]].groupby("doi"): - if len(grp) < 2: + if not doi_val or len(grp) < 2: continue anchors = grp[grp["is_anchor"]] - anchor_idx = anchors.index[0] if len(anchors) else grp.index[0] + anchor_idx = select_anchor_index(anchors if len(anchors) else grp) anchor_title = df.at[anchor_idx, "title"] for idx in grp.index: if idx == anchor_idx: @@ -453,6 +477,7 @@ def filter_duplicates(df, service, params): "doi_version", "unversioned_doi", "publisher_doi", + "doi_key", "has_relations", "versions", ]: @@ -529,6 +554,11 @@ def _dump_dedup(df: pd.DataFrame, params: Dict[str, str], name: str): collection/provider_priority) and the fields that survive into clustering content (subject_orig/paper_abstract), plus `resp_pos` = the row's original BASE response position. Keyed on the BASE request vis_id; correlate to the map via paper `id`. + + The full DOI provenance is logged so anchor grouping can be assessed against + every field a DOI may live in: `doi`/`doi_merge` derive from `find_dois(link)`, + while `additional_dois` carries the raw `dcdoi` values. `doi_key` is the + normalized grouping key coalesced from those fields (see compute_doi_key). DEBUG-gated, non-fatal. Traceability of the metadata transformations in dedup. """ if not logger.isEnabledFor(logging.DEBUG): @@ -539,7 +569,8 @@ def _dump_dedup(df: pd.DataFrame, params: Dict[str, str], name: str): out['resp_pos'] = out.index if 'collection' in out.columns: out['provider_priority'] = out['collection'].map(get_provider_priority) - cols = ['resp_pos', 'id', 'doi', 'collection', 'provider_priority', 'content_provider', + cols = ['resp_pos', 'id', 'doi', 'doi_merge', 'additional_dois', + 'doi_key', 'collection', 'provider_priority', 'content_provider', 'is_anchor', 'is_duplicate', 'oa_state', 'year', 'link', 'subject_orig', 'paper_abstract', 'title'] cols = [c for c in cols if c in out.columns] @@ -550,10 +581,36 @@ def _dump_dedup(df: pd.DataFrame, params: Dict[str, str], name: str): logger.warning(f"_dump_dedup failed for {name}: {e}") +def _dump_full(df: pd.DataFrame, params: Dict[str, str], name: str): + """Debug dump of the initial-retrieval records with ALL columns. + + Unlike `_dump_dedup` (a curated column subset), this captures every field + base.R populates so a DOI can be traced in any field it may occur in: not + just `doi`/`doi_merge`/`additional_dois`, but also `relation` (dcrelation), + `identifier` (dcidentifier), `published_in` (dcsource), `coverage`, etc. + Written before deduplication, so it reflects the raw BASE response pool. + `resp_pos` = the row's original BASE response position. Keyed on the BASE + request vis_id. DEBUG-gated, non-fatal. + """ + if not logger.isEnabledFor(logging.DEBUG): + return + try: + vis_id = params.get('vis_id') + out = df.copy() + out['resp_pos'] = out.index + front = [c for c in ['resp_pos', 'id'] if c in out.columns] + cols = front + [c for c in out.columns if c not in front] + folder = f'./output/{vis_id}' + os.makedirs(folder, exist_ok=True) + out.reindex(columns=cols).fillna('missing').to_csv(f'{folder}/{name}.csv', index=False) + except Exception as e: + logger.warning(f"_dump_full failed for {name}: {e}") + + def _log_dataframe(df: pd.DataFrame, params: Dict[str, str], name: str, ): vis_id = params.get('vis_id') - columns_to_print = ['id', 'title', 'doi', 'additional_dois', 'paper_abstract', 'link', 'subject', 'subject_orig', 'oa_state'] + columns_to_print = ['id', 'title', 'doi', 'doi_merge', 'additional_dois', 'paper_abstract', 'link', 'subject', 'subject_orig', 'oa_state'] available_columns = df.columns.tolist() columns_to_print = [col for col in columns_to_print if col in available_columns] diff --git a/server/workers/common/tests/test_anchor_order.py b/server/workers/common/tests/test_anchor_order.py new file mode 100644 index 000000000..75547f180 --- /dev/null +++ b/server/workers/common/tests/test_anchor_order.py @@ -0,0 +1,184 @@ +"""Unit tests for the deterministic anchor total order and the enrichment +tie-breaks. + +Anchor selection and every enrichment fold must be a function of record +content, never of input row order: BASE response order is not stable between +runs. These tests pin each tie-break rung and each fold in isolation; the +end-to-end order-invariance tests live in the base worker's suite. + +Run from the package directory: cd server/workers/common && pytest tests +""" + +import pandas as pd + +from common.deduplication import ( + deduplicate_keywords, + deduplicate_links, + select_anchor_index, +) +from common.enrichment import ( + process_additional_dois_element, + process_oa_state_element, + process_paper_abstract_element, +) + + +def _df(rows): + return pd.DataFrame(rows) + + +# --- select_anchor_index ----------------------------------------------------- + +def test_primary_keys_decide_before_tie_break(): + df = _df([ + {"id": "older", "title": "Same title", "year": "2018"}, + {"id": "newer", "title": "Same title", "year": "2022"}, + ]) + idx = select_anchor_index(df, ["year"], [False]) + assert df.loc[idx, "id"] == "newer" + + +def test_shorter_title_wins_across_different_beginnings(): + # Rule 1: different beginnings -> shorter title preferred (regardless of + # alphabetical order; "Zebra…" is shorter but sorts after "Antelope…"). + df = _df([ + {"id": "z", "title": "Zebra stripes", "year": "2020"}, + {"id": "a", "title": "Antelope horns", "year": "2020"}, + ]) + for order in (df, df.iloc[::-1]): + idx = select_anchor_index(order, ["year"], [False]) + assert order.loc[idx, "id"] == "z" + + +def test_journal_prefix_variant_loses_to_bare_title(): + # Rule 1 target case: the journal-name-prefixed variant is longer with a + # different beginning -> the bare title wins. + df = _df([ + {"id": "prefixed", "year": "2020", + "title": "Frontiers in Earth Science / Microplastic emission and socioeconomic data of families"}, + {"id": "bare", "year": "2020", + "title": "Microplastic emission and socioeconomic data of families"}, + ]) + for order in (df, df.iloc[::-1]): + idx = select_anchor_index(order, ["year"], [False]) + assert order.loc[idx, "id"] == "bare" + + +def test_truncated_title_loses_to_full_title(): + # Rule 2 target case: identical lexicographic beginning -> the longer + # (untruncated / subtitled) variant wins. + df = _df([ + {"id": "truncated", "year": "2020", + "title": "Greenwashing in the US metal industry? A novel approach combining SO"}, + {"id": "full", "year": "2020", + "title": "Greenwashing in the US metal industry? A novel approach combining SO2 " + "concentrations from satellite data, a plant-level firm database"}, + ]) + for order in (df, df.iloc[::-1]): + idx = select_anchor_index(order, ["year"], [False]) + assert order.loc[idx, "id"] == "full" + + +def test_title_comparison_is_normalized(): + # Case-only title variants normalize equal; the id must then decide. + df = _df([ + {"id": "b-rec", "title": "SAME TITLE", "year": "2020"}, + {"id": "a-rec", "title": "Same Title", "year": "2020"}, + ]) + for order in (df, df.iloc[::-1]): + idx = select_anchor_index(order, ["year"], [False]) + assert order.loc[idx, "id"] == "a-rec" + + +def test_nan_sorts_last_in_primary_key(): + # Matches the previous head(1) semantics: a present version beats None. + df = _df([ + {"id": "unversioned", "title": "T", "doi_version": None}, + {"id": "v1", "title": "T", "doi_version": 1.0}, + ]) + for order in (df, df.iloc[::-1]): + idx = select_anchor_index(order, ["doi_version"], [False]) + assert order.loc[idx, "id"] == "v1" + + +def test_no_keys_at_all_still_deterministic(): + # No primary keys, no title/id columns: degrades to first row (callers + # always have id, this is the guard for exotic frames). + df = _df([{"x": 1}, {"x": 2}]) + assert select_anchor_index(df) == df.index[0] + + +# --- deduplicate_keywords: insertion-order invariance ------------------------ + +def test_similar_equal_length_keywords_survivor_is_order_independent(): + # Case variants compare equal after lowering and have equal length, so + # neither replaces the other: the survivor is whichever came first, + # which the sorted iteration makes order-independent. + a, b = "Modelling", "modelling" + kept_ab = deduplicate_keywords([a, b], 85) + kept_ba = deduplicate_keywords([b, a], 85) + assert kept_ab == kept_ba + assert len(kept_ab) == 1 + + +def test_longer_variant_still_wins(): + kept = deduplicate_keywords(["color", "colour"], 85) + assert kept == ["colour"] + + +# --- deduplicate_links: collision survivor is order-independent -------------- + +def test_same_protocol_display_variant_is_order_independent(): + a, b = "https://repo.example.org/a", "HTTPS://repo.example.org/a" + kept_ab = deduplicate_links([a, b]) + kept_ba = deduplicate_links([b, a]) + assert kept_ab == kept_ba + assert len(kept_ab) == 1 + + +def test_https_still_preferred_over_http(): + kept = deduplicate_links(["http://x.org/a", "https://x.org/a"]) + assert kept == ["https://x.org/a"] + + +# --- enrichment accumulator ties --------------------------------------------- + +def test_equal_length_abstract_tie_is_order_independent(): + a, b = "Equal length abstract A", "Equal length abstract B" + results = [] + for pair in ((a, b), (b, a)): + acc = {"best_value": None, "best_length": 0} + for v in pair: + process_paper_abstract_element(v, acc) + results.append(acc["best_value"]) + assert results[0] == results[1] == a + + +def test_longer_abstract_still_wins(): + acc = {"best_value": None, "best_length": 0} + for v in ("short", "a longer abstract"): + process_paper_abstract_element(v, acc) + assert acc["best_value"] == "a longer abstract" + + +def test_oa_state_representation_tie_is_order_independent(): + # "1" and 1.0 share a priority; the kept representation must not depend + # on member iteration order. + results = [] + for pair in (("1", 1.0), (1.0, "1")): + acc = {"best_value": None, "best_priority": float("inf")} + for v in pair: + process_oa_state_element(v, acc) + results.append(acc["best_value"]) + assert str(results[0]) == str(results[1]) == "1" + + +def test_additional_dois_case_variant_is_order_independent(): + variants = ("10.1234/ABC", "10.1234/abc") + results = [] + for pair in (variants, variants[::-1]): + acc = {} + for v in pair: + process_additional_dois_element(v, "", acc) + results.append(acc) + assert results[0] == results[1] == {"10.1234/abc": "10.1234/ABC"} diff --git a/server/workers/common/tests/test_doi_key.py b/server/workers/common/tests/test_doi_key.py new file mode 100644 index 000000000..2da20b4d1 --- /dev/null +++ b/server/workers/common/tests/test_doi_key.py @@ -0,0 +1,129 @@ +"""Unit tests for the DOI merge key and the DOI-group duplicates marking. + +The key is the deterministic grouping backbone: coalesce the DOI-bearing +fields (doi_merge -> additional_dois -> doi), strip the doi.org URL prefix, +lowercase, unversion. + +Run from the package directory: cd server/workers/common && pytest tests +""" + +import pandas as pd + +from common.deduplication import ( + compute_doi_key, + extend_duplicates_with_doi_groups, + normalize_doi_key, +) + + +# --- normalize_doi_key ------------------------------------------------------- + +def test_prefix_strip_lowercase_unversion(): + assert normalize_doi_key("https://dx.doi.org/10.1234/ABC.v2") == "10.1234/abc" + + +def test_bare_and_url_forms_normalize_equal(): + assert (normalize_doi_key("10.1234/AbC") + == normalize_doi_key("https://doi.org/10.1234/abc") + == normalize_doi_key("https://dx.doi.org/10.1234/ABC")) + + +def test_version_suffix_forms_are_stripped(): + # the explicit v-forms collapse to the unversioned key, and the + # unversioned variant itself keys identically. + assert (normalize_doi_key("10.6084/m9.figshare.23691672.v3") + == normalize_doi_key("10.6084/m9.figshare.23691672.v1") + == normalize_doi_key("10.6084/m9.figshare.23691672")) + assert normalize_doi_key("10.3886/e115525v3") == "10.3886/e115525" + + +def test_bare_numeric_suffix_is_not_stripped(): + # Bare .N is NOT treated as a version by the key: article-number suffixes + # in the same style would collide distinct papers of one journal batch. + # (Mendeley-style bare-.N versions consequently do not share a key; they + # still merge via the title pass.) + assert normalize_doi_key("10.1016/j.physleta.2015.07.045") == \ + "10.1016/j.physleta.2015.07.045" + assert normalize_doi_key("10.17632/675v9chxnt.2") == "10.17632/675v9chxnt.2" + assert normalize_doi_key("10.1594/pangaea.982329") == "10.1594/pangaea.982329" + + +def test_non_doi_values_yield_no_key(): + assert normalize_doi_key("https://repo.example.org/paper/42") == "" + assert normalize_doi_key("") == "" + assert normalize_doi_key(None) == "" + + +# --- compute_doi_key (coalesce) ---------------------------------------------- + +def test_coalesce_doi_merge_wins(): + assert compute_doi_key("10.2/b", ["10.3/c"], "10.1/a") == "10.2/b" + + +def test_coalesce_falls_to_additional_dois_when_doi_merge_empty(): + # the dcdoi-derived fields carry the DOI the link missed. + assert compute_doi_key("", ["https://doi.org/10.1234/xy"], "") == "10.1234/xy" + + +def test_coalesce_falls_to_doi_last(): + assert compute_doi_key("", [], "https://doi.org/10.9/z") == "10.9/z" + + +def test_additional_dois_list_contract_first_doi_wins(): + # base.R contract: a one-element list holding a "; "-joined string. + value = ["https://doi.org/10.1/first; https://doi.org/10.2/second"] + assert compute_doi_key("", value, "") == "10.1/first" + + +def test_non_doi_candidates_are_skipped_in_coalesce(): + # A doi_merge holding a non-DOI URL must not shadow a real DOI later in + # the coalesce order. + assert compute_doi_key("https://repo.example.org/x", [], "10.1/a") == "10.1/a" + + +def test_all_empty_yields_no_key(): + assert compute_doi_key("", [], "") == "" + assert compute_doi_key(None, None, None) == "" + + +# --- extend_duplicates_with_doi_groups --------------------------------------- + +def _df(rows): + return pd.DataFrame(rows) + + +def test_doi_partners_are_folded_into_duplicates(): + df = _df([ + {"id": "aaa", "duplicates": "aaa,", "doi_key": "10.1/x"}, + {"id": "bbb", "duplicates": "bbb,", "doi_key": "10.1/x"}, + {"id": "ccc", "duplicates": "ccc,", "doi_key": "10.2/y"}, + ]) + out = extend_duplicates_with_doi_groups(df) + assert "bbb" in out.loc[out.id == "aaa", "duplicates"].iloc[0] + assert "aaa" in out.loc[out.id == "bbb", "duplicates"].iloc[0] + # Singleton key untouched. + assert out.loc[out.id == "ccc", "duplicates"].iloc[0] == "ccc," + + +def test_folded_marking_is_row_order_independent(): + rows = [ + {"id": "aaa", "duplicates": "aaa,", "doi_key": "10.1/x"}, + {"id": "bbb", "duplicates": "bbb,", "doi_key": "10.1/x"}, + {"id": "ccc", "duplicates": "ccc,", "doi_key": "10.1/x"}, + ] + outs = [] + for order in ([0, 1, 2], [2, 0, 1]): + df = _df([rows[i] for i in order]) + out = extend_duplicates_with_doi_groups(df) + outs.append(dict(zip(out.id, out.duplicates))) + assert outs[0] == outs[1] + + +def test_empty_keys_never_group(): + df = _df([ + {"id": "aaa", "duplicates": "aaa,", "doi_key": ""}, + {"id": "bbb", "duplicates": "bbb,", "doi_key": ""}, + ]) + out = extend_duplicates_with_doi_groups(df) + assert out.loc[out.id == "aaa", "duplicates"].iloc[0] == "aaa," + assert out.loc[out.id == "bbb", "duplicates"].iloc[0] == "bbb," From d7a2fb30200b3c868e127510f02a721de03c8928 Mon Sep 17 00:00:00 2001 From: chreman Date: Sat, 22 Aug 2026 00:39:17 +0200 Subject: [PATCH 172/205] update of contentprovider.json cache --- .../common/common/contentproviders.json | 224 ++---------------- 1 file changed, 14 insertions(+), 210 deletions(-) diff --git a/server/workers/common/common/contentproviders.json b/server/workers/common/common/contentproviders.json index 59ebf6ece..d30c2ac0b 100644 --- a/server/workers/common/common/contentproviders.json +++ b/server/workers/common/common/contentproviders.json @@ -1,4 +1,8 @@ [ + { + "name": "Afaq Research Horizons in Social and Human Sciences (ARH)", + "internal_name": "ftid14693" + }, { "name": "Research Parks Publishing", "internal_name": "ftid14944" @@ -2420,7 +2424,7 @@ "internal_name": "ftid14302" }, { - "name": "Educação Tecnológica", + "name": "Educação & Inovação", "internal_name": "ftid14303" }, { @@ -8755,10 +8759,6 @@ "name": "E-Journals Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STIKI Malang", "internal_name": "ftstikimalangojs" }, - { - "name": "Khulna University Studies (KU Studies)", - "internal_name": "ftjkus" - }, { "name": "Journal EKSTENSI (Eksplorasi Teknologi Enterprise & Sistem Informasi)", "internal_name": "ftjekstensi" @@ -9467,10 +9467,6 @@ "name": "LCC International University: Virtual Library of Lithuania (LCC VL)", "internal_name": "ftlccintuniv" }, - { - "name": "Ιδρυματικό Αποθετήριο HELLANICUS", - "internal_name": "fthellanicus" - }, { "name": "Repository STIE Tri Bhakti Business School", "internal_name": "ftstietribhakti" @@ -10095,10 +10091,6 @@ "name": "Revista Inteligência Competitiva - RIC", "internal_name": "ftjrinteligencia" }, - { - "name": "Revistas Científicas de la Universidad Andina del Cusco", - "internal_name": "ftuandinacuscojs" - }, { "name": "Portal de Revistas UNCP (Universidad Nacional del Centro del Perú)", "internal_name": "ftunincentroperu" @@ -15376,7 +15368,7 @@ "internal_name": "ftqabascentre" }, { - "name": "ITS OJS (Institut Teknologi Sepuluh Nopember)", + "name": "ITS Journals (Institut Teknologi Sepuluh Nopember)", "internal_name": "ftitsojs2" }, { @@ -19003,10 +18995,6 @@ "name": "E_Journal IAI Latifah Mubarokiyah", "internal_name": "ftiailatiojs" }, - { - "name": "ColNes Publications: Journals", - "internal_name": "ftcolnespubojs" - }, { "name": "Athena Commons - Digital Repository of Mississippi University for Women", "internal_name": "ftmissunivwom" @@ -19116,7 +19104,7 @@ "internal_name": "ftgrodnosmu" }, { - "name": "Illinois Library Digital Collections", + "name": "Digital Collections at the University of Illinois at Urbana-Champaign (UIUC) Library", "internal_name": "ftunivilldl" }, { @@ -19743,10 +19731,6 @@ "name": "Gardu Jurnal MU Pamekasan (LPM STAI Miftaul Ulum Pamekasan)", "internal_name": "ftstaimupamekesa" }, - { - "name": "Spizaetus - Jurnal Biologi dan Pendidikan Biologi", - "internal_name": "ftjspizaetus" - }, { "name": "UARTPress: Open Journal Systems", "internal_name": "ftuartpressojs" @@ -20191,10 +20175,6 @@ "name": "姫路大学学術機関リポジトリ", "internal_name": "fthimejiuniv" }, - { - "name": "Republica Panama Órgano Judicial: Repositorio Digital", - "internal_name": "ftpanamaorganoju" - }, { "name": "IPMAFA Journals (Institut Pesantren Mathali'ul Falah)", "internal_name": "ftinstpmfafaojs" @@ -26588,7 +26568,7 @@ "internal_name": "ftjmgtr" }, { - "name": "mediarep", + "name": "FID Media Publish (Fachinformationsdienst für die Kommunikations- und Medienwissenschaft)", "internal_name": "ftmediarep" }, { @@ -28059,10 +28039,6 @@ "name": "ChungNam Institute (CNI) OAK Repository (Open Access Korea)", "internal_name": "ftchungnaminst" }, - { - "name": "Journal of Maternal and Child Health (JMCH)", - "internal_name": "ftjmch" - }, { "name": "Stellenbosch University: SUNDigital Collections", "internal_name": "ftustellenboschd" @@ -28507,10 +28483,6 @@ "name": "Mineralis (Centro de Tecnologia Mineral - CETEM)", "internal_name": "ftcetem" }, - { - "name": "Indian Institute of Geomagnetism (IIG): Repository", - "internal_name": "ftindinstgeomagn" - }, { "name": "Saint Louis University Libraries Digital Collections", "internal_name": "ftstlouisunivdc" @@ -29507,10 +29479,6 @@ "name": "Repositorio Institucional de la UPCH (Universidad Peruana Cayetano Heredia)", "internal_name": "ftuperucayetanoh" }, - { - "name": "Repositorio Digital Universidad Andina del Cusco (UAC)", - "internal_name": "ftuniandinacusco" - }, { "name": "Universidad Nacional Amazónica de Madre de Dios: Repositorio Institucional Digital", "internal_name": "ftuninamadredios" @@ -29767,10 +29735,6 @@ "name": "Re-visiones", "internal_name": "ftjrevisiones" }, - { - "name": "GIGA Journal Family (German Institute of Global and Area Studies)", - "internal_name": "ftjgiga" - }, { "name": "International Journal of Innovation in Enterprise System (IJIES)", "internal_name": "ftjijies" @@ -30535,10 +30499,6 @@ "name": "Вопросы статистики", "internal_name": "ftjvoprstat" }, - { - "name": "Romanian Journal of History and International Studies (RJHIS)", - "internal_name": "ftjrjhis" - }, { "name": "Open Journal Systems - Universitas Negeri Surabaya", "internal_name": "ftuninsurabaya2" @@ -31139,10 +31099,6 @@ "name": "Jurnal Online Universitas Galuh", "internal_name": "ftunivgaluhojs" }, - { - "name": "Technologies for Lightweight Structures", - "internal_name": "ftjtls" - }, { "name": "Jurnal Agriment ( J. Agr - Jurusan Manajemen Pertanian, Politeknik Pertanian Negeri Samarinda)", "internal_name": "ftjagriment" @@ -31463,10 +31419,6 @@ "name": "Jurnal-Jurnal yang diterbitkan Universitas Katolik Widya Mandala Surabaya", "internal_name": "ftwmcusurabaya" }, - { - "name": "Statistisk sentralbyrå: Open Research Repository (SNORRe - Brage)", - "internal_name": "ftssbcom" - }, { "name": "Università degli Studi di Messina: IRIS", "internal_name": "ftunimessinairis" @@ -31479,14 +31431,6 @@ "name": "Università Commerciale Luigi Bocconi: CINECA IRIS", "internal_name": "ftuniclbocconiir" }, - { - "name": "Høgskolen i Østfold: HiØ Brage", - "internal_name": "fthsoestfoldcom" - }, - { - "name": "Handelshøyskolen BI: BI Open Archive (Brage)", - "internal_name": "fthhsbicom" - }, { "name": "Texas A&M University Galveston Campus: DSpace Repository", "internal_name": "fttexasamunigalv" @@ -33299,10 +33243,6 @@ "name": "OPUS-HFU - Hochschulschriftenserver der Hochschule Furtwangen", "internal_name": "fthsfurtwangen" }, - { - "name": "mdw Repository (Universität für Musik und darstellende Kunst Wien)", - "internal_name": "ftunivmdwien" - }, { "name": "NYBG/125: Mertz Digital Collections (New York Botanical Garden)", "internal_name": "ftnewyorkbgdc" @@ -33575,14 +33515,6 @@ "name": "MedienPädagogik - Zeitschrift für Theorie und Praxis der Medienbildung", "internal_name": "ftjmedienpaed" }, - { - "name": "Norsk institutt for bioøkonomi: NIBIO Brage", - "internal_name": "ftnibiocom" - }, - { - "name": "NTNU Samfunnsforskning (Norges teknisk-naturvitenskapelige universitet): Samforsk Open (Brage)", - "internal_name": "ftntnutrondhsamf" - }, { "name": "Işık Üniversitesi: DSpace Repository", "internal_name": "ftisikuniv" @@ -34751,10 +34683,6 @@ "name": "MAT Journals", "internal_name": "ftmatjournalsojs" }, - { - "name": "Gratis Open Access Publishers: Journals", - "internal_name": "ftgratisoaojs" - }, { "name": "Kalamatika: Jurnal Pendidikan Matematika", "internal_name": "ftjkalamatika" @@ -35267,10 +35195,6 @@ "name": "TU Graz OPEN Library", "internal_name": "fttunivgraz" }, - { - "name": "FWF-E-Book-Library (Fonds zur Förderung der wissenschaftlichen Forschung)", - "internal_name": "ftfwf" - }, { "name": "Vysoká škola ekonomická v Praze", "internal_name": "ftvseprag" @@ -37907,10 +37831,6 @@ "name": "Ruhuna Journal of Science (University of Ruhuna, Sri Lanka)", "internal_name": "ftjrjs" }, - { - "name": "GESIS (Leibniz-Institut für Sozialwissenschaften)", - "internal_name": "ftgesis" - }, { "name": "浜松医科大学学術機関リ", "internal_name": "fthamamed" @@ -38228,7 +38148,7 @@ "internal_name": "ftasiandbank" }, { - "name": "U.S. National Library of Medicine (NLM): Images from the History of Medicine (IHM)", + "name": "National Library of Medicine (NLM): Images from the History of Medicine", "internal_name": "ftnlmihm" }, { @@ -38487,10 +38407,6 @@ "name": "倉敷芸術科学大学学術情報リポジトリ", "internal_name": "ftkurashikiuniv" }, - { - "name": "Hong Kong Baptist University (HKBU): Heritage", - "internal_name": "fthkbaptistunidy" - }, { "name": "Journal for Foundations and Applications of Physics", "internal_name": "ftjfap" @@ -38547,10 +38463,6 @@ "name": "University of Cape Town: OpenUCT", "internal_name": "ftunivcapetownir" }, - { - "name": "Liburuklik (Biblioteca Digital Vasca)", - "internal_name": "ftliburuklik" - }, { "name": "Universidad de Deusto: Biblioteca Digital Loyola", "internal_name": "ftunivdeustodc" @@ -39223,10 +39135,6 @@ "name": "Socrates", "internal_name": "ftjsocrates" }, - { - "name": "Urbe et Ius", - "internal_name": "fturbeetiusojs" - }, { "name": "Uniwersytet Papieski Jana Pawła II w Krakowie: Platforma czasopism", "internal_name": "ftunipjpkrakow" @@ -39563,10 +39471,6 @@ "name": "Scholink Journals", "internal_name": "ftscholinkojs" }, - { - "name": "Portal de Revistas Académicas de la UNI (Universidad Nacional de Ingeniería, Nicaragua)", - "internal_name": "ftunivningeneria" - }, { "name": "Horizon e-Publishing Group (HePG): E-Journals", "internal_name": "fthorizonepubl" @@ -40031,10 +39935,6 @@ "name": "Universidade Municipal de São Caetano do Sul: Portal Periódicos USCS", "internal_name": "ftunivscsojs" }, - { - "name": "Center for Scientific Publication: PPI (Pusat Publikasi Ilmiah - LPPM Institut Teknologi Sepuluh Nopember)", - "internal_name": "ftitsojs" - }, { "name": "Centro Universitário de Belo Horizonte: Portal de Revistas Eletrônicas do UniBH", "internal_name": "ftcubelohorizone" @@ -40391,10 +40291,6 @@ "name": "Kingston University London: Research Repository", "internal_name": "ftunivkingston" }, - { - "name": "Université de Valenciennes et du Hainaut-Cambrésis (UVHC): THEOREME (THEses, Open access, Recherche, MEmoires)", - "internal_name": "ftunvalenciennes" - }, { "name": "Universidad de Las Palmas de Gran Canaria: Jable", "internal_name": "ftunilaspalmasj" @@ -40803,10 +40699,6 @@ "name": "University of Tulsa, College of Law: TU Law Digital Commons", "internal_name": "ftunitulsacollaw" }, - { - "name": "Institut Teknologi Sepuluh Nopember (ITS): Publikasi Ilmiah Online Mahasiswa ITS (POMITS)", - "internal_name": "ftitssurabayaojs" - }, { "name": "University of Missouri, School of Law: Scholarship Repository", "internal_name": "ftunimissourilaw" @@ -40896,7 +40788,7 @@ "internal_name": "ftcubrasiliaojs" }, { - "name": "Cadernos de Educação, Tecnologia e Sociedade (Instituto Federal de Educação, Ciência e Tecnologia de Goiás)", + "name": "Cadernos de Educação, Tecnologia e Sociedade - CETS (Brazilian Journal of Education, Technology and Society - BRAJETS)", "internal_name": "ftjcets" }, { @@ -41196,7 +41088,7 @@ "internal_name": "ftseattleunivlaw" }, { - "name": "Miskin Hill: Journals", + "name": "Australian Slavonic and East European Studies (ASEES)", "internal_name": "ftmiskinhill" }, { @@ -41423,10 +41315,6 @@ "name": "Deutsches Textarchiv (DTA, Berlin-Brandenburgische Akademie der Wissenschaften)", "internal_name": "ftdta" }, - { - "name": "ISAE (Institut Supérieur de l'Aéronautique et de l'Espace): ArTeMIS", - "internal_name": "ftisae" - }, { "name": "Bibliothèque Royale, Bruxelles: Archives & Musée de la Littérature (AML)", "internal_name": "ftaml" @@ -42431,10 +42319,6 @@ "name": "Universida de San Andrés: Repositorio Digital San Andrés", "internal_name": "ftunivsanandres" }, - { - "name": "International Journal of Scientific and Research Publications (IJSRP)", - "internal_name": "ftjijsrp" - }, { "name": "Cedarville University: DigitalCommons@Cedarville", "internal_name": "ftcedarvilleuniv" @@ -42443,10 +42327,6 @@ "name": "Hong Kong Polytechnic University: PolyU Institutional Repository (PolyU IR)", "internal_name": "ftpolyuhongkong" }, - { - "name": "Université de Lorraine: PETALE (Publications Et Travaux Académiques de LorrainE)", - "internal_name": "ftunivlorraine" - }, { "name": "Pontificia Universidad Católica del Perú: Portal de Revistas PUCP", "internal_name": "ftpunivcperuojs" @@ -42491,10 +42371,6 @@ "name": "Multimedia University, Malaysia: SHDL@MMU Digital Repository", "internal_name": "ftmultimediauniv" }, - { - "name": "University of the Sunshine Coast, Queensland, Australia: COAST Research Database", - "internal_name": "ftunivscoast" - }, { "name": "Maison de l’Orient et de la Méditerranée, Université Lumière Lyon 2: DIGIMOM", "internal_name": "ftdigimom" @@ -42983,10 +42859,6 @@ "name": "Institutional Knowledge (InK) at Singapore Management University", "internal_name": "ftsingaporemuniv" }, - { - "name": "La Trobe University (Melbourne): Research Online", - "internal_name": "ftlatrobeuniv" - }, { "name": "BRAC University, Bangladesh: Institutional Repository", "internal_name": "ftbracuniv" @@ -43011,10 +42883,6 @@ "name": "OAK - The Novartis Repository", "internal_name": "ftnovartisinst" }, - { - "name": "Language Box (University of Southampton)", - "internal_name": "ftlanguagebox" - }, { "name": "神奈川大学　学術機関リポジトリ", "internal_name": "ftkanagawauniv" @@ -43935,10 +43803,6 @@ "name": "University of New Brunswick: Centre for Digital Scholarship Journals", "internal_name": "ftuninewbrunojs" }, - { - "name": "Revistas Eletrônicas da Toledo Presidente Prudente, São Paulo", - "internal_name": "ftunivtoledoojs" - }, { "name": "Cadernos NPGA (Universidade Federal da Bahia)", "internal_name": "ftjccnpga" @@ -44155,10 +44019,6 @@ "name": "Cornell University Law School: Scholarship@Cornell Law", "internal_name": "ftcornellunivlaw" }, - { - "name": "Murdoch University Research Portal", - "internal_name": "ftmurdochuniv" - }, { "name": "Journals@UIC (The University of Illinois at Chicago)", "internal_name": "ftunivillchojs" @@ -44315,10 +44175,6 @@ "name": "Pomorska Biblioteka Cyfrowa (PBC)", "internal_name": "ftpomorskadl" }, - { - "name": "Universidad de Sevilla: Fondos digitalizados", - "internal_name": "ftunivsevilla" - }, { "name": "Tạp chí khoa học Việt Nam Trực tuyến", "internal_name": "ftjvietnamjo" @@ -44456,7 +44312,7 @@ "internal_name": "ftunivcincinatti" }, { - "name": "ARCA - IGC Repository (Access to Research and Communication Annals: Instituto Gulbenkian de Ciência)", + "name": "ARCA - Repositório do GIMM (Gulbenkian Institute for Molecular Medicine)", "internal_name": "ftinstgulbenkian" }, { @@ -44879,10 +44735,6 @@ "name": "中国学園リポジトリ", "internal_name": "ftchugokuguniv" }, - { - "name": "Central Queensland University: aCQUIRe", - "internal_name": "ftcquniv" - }, { "name": "Biblioteka Cyfrowa UMCS (Uniwersytet Marii Curie-Skłodowskiej, Lublin)", "internal_name": "ftmcsuniv" @@ -44975,10 +44827,6 @@ "name": "鹿屋体育大学学術情報リポジトリ", "internal_name": "ftnationalinstsp" }, - { - "name": "Search4Dev (Digital documents by Dutch development organizations)", - "internal_name": "ftdprn" - }, { "name": "Tropicos.org (Missouri Botanical Garden)", "internal_name": "ftmbgarden" @@ -44999,10 +44847,6 @@ "name": "Iowa Research Online - University of Iowa", "internal_name": "ftuniviowa" }, - { - "name": "UNIT - Université Numérique Ingénierie et Technologie", - "internal_name": "ftunit" - }, { "name": "Universidad del Rosario, Bogotá: E-docUR", "internal_name": "ftunivrosario" @@ -45172,7 +45016,7 @@ "internal_name": "ftnaturalis" }, { - "name": "Indiana University - Purdue University Indianapolis (IUPUI): eArchives", + "name": "IUPUI University eArchives (Indian University Purdue University Indianapolis)", "internal_name": "ftiupuiearch" }, { @@ -45299,10 +45143,6 @@ "name": "University of Massachusetts: ScholarWorks@UMass Amherst", "internal_name": "ftunivmassamh" }, - { - "name": "مجلات دانشگاه علوم پزشکی اصفها", - "internal_name": "ftmui" - }, { "name": "高知工科大学学術情報リポジトリ", "internal_name": "ftkochiunivtech" @@ -45459,10 +45299,6 @@ "name": "The University of Melbourne: Digital Repository", "internal_name": "ftumelbourne" }, - { - "name": "DIR - Zasoby polskie (Interdyscyplinarne Centrum Modelowania Matematycznego i Komputeroweg, Uniwersytet Warszawski)", - "internal_name": "fticmwarschau" - }, { "name": "Repositorio Digital de la Universidad Politécnica de Cartagena", "internal_name": "ftunivcartag" @@ -45651,10 +45487,6 @@ "name": "Universidade de Coimbra: Estudo Geral", "internal_name": "ftunivcoimbra" }, - { - "name": "Flinders Academic Commons (FAC - Flinders University)", - "internal_name": "ftflindersuniv" - }, { "name": "University of Hong Kong: HKU Scholars Hub", "internal_name": "ftunivhongkonghu" @@ -45871,10 +45703,6 @@ "name": "Athabasca University: AUSpace", "internal_name": "ftathabasuniv" }, - { - "name": "大阪教育大学リポジトリ", - "internal_name": "ftosakakyuniv" - }, { "name": "Rijksinstituut voor Volksgezondheid en Milieu (RIVM): Webbased Archive of RIVM Publications (WARP)", "internal_name": "ftrivm" @@ -45983,10 +45811,6 @@ "name": "筑波大学つくばリポジトリ", "internal_name": "fttsukubauniv" }, - { - "name": "神戸大学学術成果リポジトリ", - "internal_name": "ftkobeuniv" - }, { "name": "山口大学学術機関リポジトリ", "internal_name": "ftyamaguchiuniv" @@ -46019,10 +45843,6 @@ "name": "学術研究成果リポジトリ", "internal_name": "ftjaist" }, - { - "name": "Université Lumière Lyon 2: Presses Universitaires de Lyon (PUL)", - "internal_name": "ftunivlyon2unipr" - }, { "name": "oURspace - The University of Regina's Institutional Repository", "internal_name": "ftunivregina" @@ -46356,7 +46176,7 @@ "internal_name": "ftunivpaderb" }, { - "name": "University of Illinois at Urbana-Champaign: UIUC Digitized Books", + "name": "Digitized Books from the University of Illinois at Urbana-Champaign (UIUC)", "internal_name": "ftunivillratri" }, { @@ -46631,10 +46451,6 @@ "name": "Universitat de Vic (UVIC): Tesis Doctorals en Xarxa (TDX)", "internal_name": "ftuvic" }, - { - "name": "Universidad de Oviedo: Tesis Doctorals en Xarxa (TDX)", - "internal_name": "ftuoviedo" - }, { "name": "epub.oeaw (Österreichische Akademie der Wissenschaften)", "internal_name": "ftoeakadwiss" @@ -46835,10 +46651,6 @@ "name": "University of Texas at El Paso: Digital Commons@UTEP", "internal_name": "ftutep" }, - { - "name": "Environmental Protection Agency (EPA): Science Inventory", - "internal_name": "ftepa" - }, { "name": "Universitat Ramon Llull, Barcelona: Tesis Doctorals en Xarxa (TDX)", "internal_name": "ftunivramon" @@ -47123,10 +46935,6 @@ "name": "Gallica - bibliothèque numérique de la Bibliothèque nationale de France (BnF)", "internal_name": "ftbnfgallica" }, - { - "name": "Eindhoven University of Technology (TU/e): Research Portal", - "internal_name": "ftuniveindhoven" - }, { "name": "University of Southern Queensland: USQ ePrints", "internal_name": "ftusqland" @@ -47507,10 +47315,6 @@ "name": "University of Maryland: Digital Repository (DRUM)", "internal_name": "ftunivmaryland" }, - { - "name": "BioMed Central", - "internal_name": "ftbiomed" - }, { "name": "Krause & Pachernegg, Verlag für Medizin und Wirtschaft: Medizinische Publikationen", "internal_name": "ftkupat" From dc5886f82a81b9e0b444b06ca24784295f6ca881 Mon Sep 17 00:00:00 2001 From: chreman Date: Sat, 22 Aug 2026 17:51:22 +0200 Subject: [PATCH 173/205] update e2e tests with new expected outcomes --- e2e/keywordsCleaning/BASE.spec.ts | 16 ++++++++++++---- e2e/keywordsEnrichment/ORCID.spec.ts | 2 +- e2e/oaStatusEnrichment/ORCID.spec.ts | 23 ++++++++++++++++------- 3 files changed, 29 insertions(+), 12 deletions(-) diff --git a/e2e/keywordsCleaning/BASE.spec.ts b/e2e/keywordsCleaning/BASE.spec.ts index 6aa3a74c4..2e90d70f8 100644 --- a/e2e/keywordsCleaning/BASE.spec.ts +++ b/e2e/keywordsCleaning/BASE.spec.ts @@ -39,14 +39,22 @@ const searches: BaseSearch[] = [ { query: "medicine", search_params: ARTICLES }, ]; +// Readiness check only (the assertions under test are the leak invariants). The +// heading currently renders "&" as a literal "&" (double HTML escaping in the +// title component), so the expected display text mirrors that until the display +// bug is fixed; quotes are stripped by the UI. +function displayedQuery(query: string): string { + return query.replace(/"/g, "").replace(/&/g, "&"); +} + // Pre-load the BASE searches so subsequent runs are faster. // npx playwright test "e2e/keywordsCleaning/BASE.spec.ts" --grep "Warm-up: pre-load BASE searches" test.describe("Warm-up: pre-load BASE searches", () => { for (const { query, search_params } of searches) { test(`${query}`, async ({ page }) => { - const url = `/search?type=get&vis_type=overview&q=${query}${search_params}`; + const url = `/search?type=get&vis_type=overview&q=${encodeURIComponent(query)}${search_params}`; await prepareVisualisation(page, url); - await expect(page.locator("#search-term-unique")).toContainText(query.replace(/"/g, "")); + await expect(page.locator("#search-term-unique")).toContainText(displayedQuery(query)); }); } }); @@ -54,7 +62,7 @@ test.describe("Warm-up: pre-load BASE searches", () => { test.describe("No classification markers leak into subject or area titles", () => { for (const { query, search_params } of searches) { test(`subject + area titles are clean — ${query}`, async ({ page }) => { - const url = `/search?type=get&vis_type=overview&q=${query}${search_params}`; + const url = `/search?type=get&vis_type=overview&q=${encodeURIComponent(query)}${search_params}`; // Register before navigation so we capture the data load that populates // the visualisation. The backend pipeline can take minutes, so match the @@ -65,7 +73,7 @@ test.describe("No classification markers leak into subject or area titles", () = ); await prepareVisualisation(page, url); - await expect(page.locator("#search-term-unique")).toContainText(query.replace(/"/g, "")); + await expect(page.locator("#search-term-unique")).toContainText(displayedQuery(query)); const json = await (await responsePromise).json(); assertNoClassificationLeaks(extractDocuments(json), query); diff --git a/e2e/keywordsEnrichment/ORCID.spec.ts b/e2e/keywordsEnrichment/ORCID.spec.ts index 891639539..78d5788ac 100644 --- a/e2e/keywordsEnrichment/ORCID.spec.ts +++ b/e2e/keywordsEnrichment/ORCID.spec.ts @@ -185,7 +185,7 @@ const testCases: EnrichmentTestCase[] = [ abstract: 'Zur Zeit gibt es starke Bemühungen, die offensichtlichen Defizite des wissenschaftlichen Kommunikationssystems zu beheben. Open Science hat das Potenzial, die Produktion und Verbreitung von wissenschaftlichem Wissen positiv zu verändern; es existiert aber keine gemeinsam geteilte Vision, die das System wissenschaftlicher Kommunikation beschreibt, welches wir erschaffen wollen. Zwischen April 2015 und Juni 2016 trafen sich in Wien die Mitglieder der Open Access Network Austria (OANA) Arbeitsgruppe "Open Access and Scholarly Communication", um diese Angelegenheit zu diskutieren. Das Hauptergebnis unserer Überlegungen sind zwölf Prinzipien, die die Eckpfeiler eines künftigen wissenschaftlichen Kommunikationssystems dedarstellen. Diese Prinzipien sollen einen kohärenten Bezugsrahmen für die Debatte zur Verbesserung des derzeitigen Systems liefern. Mit diesem Dokument hoffen wir, eine breite Diskussion über eine gemeinsame Vision für die wissenschaftliche Kommunikation im 21. Jahrhundert anzustoßen.', keywords: - "Wissenschaftskommunikation; Open Access; Open Science; Vision; Bibliography. Library science. Information resources; Z", + "Bibliography. Library science. Information resources; Open Access; Open Science; Scholarly Communication; Vision; Wissenschaftskommunikation; Z", }, { suiteName: "regression test DOI case-insensitive merge", diff --git a/e2e/oaStatusEnrichment/ORCID.spec.ts b/e2e/oaStatusEnrichment/ORCID.spec.ts index 8726ab70f..9be5f49f9 100644 --- a/e2e/oaStatusEnrichment/ORCID.spec.ts +++ b/e2e/oaStatusEnrichment/ORCID.spec.ts @@ -14,6 +14,10 @@ interface OaStatusTestCase { // start passing, the framework will report it as an unexpected pass, and // this annotation should be removed. regression?: string; + // Known issue: the assertion cannot pass until an external dependency is + // resolved. Marked with test.fixme() (skipped, not failed) so the suite stays + // green while the issue is tracked; remove when resolved. + knownIssue?: string; } const testCases: OaStatusTestCase[] = [ @@ -116,7 +120,10 @@ const testCases: OaStatusTestCase[] = [ flaky: true, }, - // --- Known regression: case-sensitive DOI matching --- + // --- Known issue: case-sensitive DOI matching --- + // BASE's dcdoi search is case-sensitive and ORCID supplies these DOIs in + // upper case, so the BASE request returns no record and no OA state can be + // enriched. Skipped until DOI casing is resolved on the request path. // These papers should be OA but currently render without the open-access // tag because the duplicate group that carries oa_state="1" is no longer // being matched. Tracked with test.fail() until the DOI-matching @@ -126,42 +133,42 @@ const testCases: OaStatusTestCase[] = [ orcid: "0000-0003-4221-6275", paperTitle: "Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling", expectedOaState: "1", - regression: "case-sensitive DOI matching regression", + knownIssue: "Known issue until DOI casing resolved: BASE dcdoi search is case-sensitive, ORCID supplies uppercase DOIs", }, { suiteName: "regression: case-sensitive DOI matching", orcid: "0000-0003-4221-6275", paperTitle: "Improving the adaptive and continuous learning capabilities of artificial neural networks: Lessons from multi-neuromodulatory dynamics", expectedOaState: "1", - regression: "case-sensitive DOI matching regression", + knownIssue: "Known issue until DOI casing resolved: BASE dcdoi search is case-sensitive, ORCID supplies uppercase DOIs", }, { suiteName: "regression: case-sensitive DOI matching", orcid: "0000-0003-4221-6275", paperTitle: "Neural mechanisms of predictive processing: a collaborative community experiment through the OpenScope program", expectedOaState: "1", - regression: "case-sensitive DOI matching regression", + knownIssue: "Known issue until DOI casing resolved: BASE dcdoi search is case-sensitive, ORCID supplies uppercase DOIs", }, { suiteName: "regression: case-sensitive DOI matching", orcid: "0000-0003-4221-6275", paperTitle: "Refining Humane Endpoints in Mouse Models of Disease by Systematic Review and Machine Learning-Based Endpoint Definition", expectedOaState: "1", - regression: "case-sensitive DOI matching regression", + knownIssue: "Known issue until DOI casing resolved: BASE dcdoi search is case-sensitive, ORCID supplies uppercase DOIs", }, { suiteName: "regression: case-sensitive DOI matching", orcid: "0000-0003-4221-6275", paperTitle: "Sepsis-associated cognitive dysfunction: an investigation using stress-free, automated behavioral tests", expectedOaState: "1", - regression: "case-sensitive DOI matching regression", + knownIssue: "Known issue until DOI casing resolved: BASE dcdoi search is case-sensitive, ORCID supplies uppercase DOIs", }, { suiteName: "regression: case-sensitive DOI matching", orcid: "0000-0003-4221-6275", paperTitle: "The role of gain neuromodulation in layer-5 pyramidal neurons", expectedOaState: "1", - regression: "case-sensitive DOI matching regression", + knownIssue: "Known issue until DOI casing resolved: BASE dcdoi search is case-sensitive, ORCID supplies uppercase DOIs", }, { suiteName: "bugfix SOLR parsing of DOI", @@ -224,10 +231,12 @@ for (const tc of testCases) { const expectedOaState = tc.expectedOaState; const flaky = tc.flaky; const regression = tc.regression; + const knownIssue = tc.knownIssue; test(`OA status (${expectedOaState}) for the '${tc.paperTitle}' document`, async ({ page, }) => { test.fixme(!!flaky, "Flaky: depends on BASE API returning duplicate records consistently"); + test.fixme(!!knownIssue, knownIssue ?? ""); test.fail(!!regression, regression ?? ""); await openPaper(page); From bdb36d7d0c52d2a225b2c07190979504df3b2185 Mon Sep 17 00:00:00 2001 From: chreman Date: Sat, 22 Aug 2026 18:04:41 +0200 Subject: [PATCH 174/205] update of contentprovider.json cache --- server/workers/common/common/contentproviders.json | 4 ---- 1 file changed, 4 deletions(-) diff --git a/server/workers/common/common/contentproviders.json b/server/workers/common/common/contentproviders.json index d30c2ac0b..5da23f74e 100644 --- a/server/workers/common/common/contentproviders.json +++ b/server/workers/common/common/contentproviders.json @@ -46839,10 +46839,6 @@ "name": "Queensland University of Technology: QUT ePrints", "internal_name": "ftqueensland" }, - { - "name": "RERO DOC Digitale Bibliothek", - "internal_name": "ftreroch" - }, { "name": "University of Waterloo, Canada: Institutional Repository", "internal_name": "ftunivwaterloo" From 78539748d402cf2ba785b2fd44cdeebbce062c56 Mon Sep 17 00:00:00 2001 From: chreman Date: Sun, 23 Aug 2026 23:42:55 +0200 Subject: [PATCH 175/205] fix stability bug --- .../base/tests/unit/test_dedup_invariants.py | 32 +++++++++++++++++++ server/workers/common/common/deduplication.py | 10 +++++- 2 files changed, 41 insertions(+), 1 deletion(-) diff --git a/server/workers/base/tests/unit/test_dedup_invariants.py b/server/workers/base/tests/unit/test_dedup_invariants.py index 85a9b259b..4d102335b 100644 --- a/server/workers/base/tests/unit/test_dedup_invariants.py +++ b/server/workers/base/tests/unit/test_dedup_invariants.py @@ -245,6 +245,38 @@ def test_dcdoi_asserted_same_work_with_retitled_preprint_merges_and_keeps_oa(): assert str(out["records"][out["ids"][0]]["oa_state"]) == "1" +def test_overlapping_groups_are_order_invariant(): + # Real-world topology (an ORCID base batch): four copies of one work — + # journal + repository sharing the journal DOI key, an arXiv-collection + # copy also keyed on the journal DOI, and a DataCite copy whose PRIMARY + # key is the arXiv DOI (first in its dcdoi) but which the textual pass + # pairs with the arXiv copy. The textual pair bridges two DOI-key groups, + # so the groups OVERLAP; the anchor re-marking across overlapping groups + # must not depend on group iteration order (= row order before the fix). + df = make_df([ + make_record("journal", oa_state="1", year="2023", + doi="https://doi.org/10.1088/1361-6471/ac9fe6", + title="On the geometric phase for Majorana and Dirac neutrinos", + paper_abstract="A" * 100), + make_record("salerno", oa_state="2", year="2023", doi="", + additional_dois=["https://doi.org/10.1088/1361-6471/ac9fe6"], + title="On the geometric phase for Majorana and Dirac neutrinos", + paper_abstract="B" * 90), + make_record("arxiv-copy", oa_state="1", year="2022", doi="", + additional_dois=["https://doi.org/10.1088/1361-6471/ac9fe6"], + duplicates="datacite", + title="On the geometric phase for Majorana and Dirac neutrinos", + paper_abstract="C" * 95), + make_record("datacite", oa_state="1", year="2022", doi="", + additional_dois=["https://doi.org/10.48550/arxiv.2107.08719; " + "https://doi.org/10.1088/1361-6471/ac9fe6"], + duplicates="arxiv-copy", + title="On the geometric phase for Majorana and Dirac neutrinos", + paper_abstract="D" * 105), + ]) + assert_order_invariant(df) + + def test_doi_group_straddling_typenorm_split_keeps_one_survivor(): # a DOI-key group whose members land on different sides of the # dataset/non-dataset split must not end up with an anchor on each side. diff --git a/server/workers/common/common/deduplication.py b/server/workers/common/common/deduplication.py index 60780c25e..971507c06 100644 --- a/server/workers/common/common/deduplication.py +++ b/server/workers/common/common/deduplication.py @@ -65,7 +65,15 @@ def get_publisher_doi(doi): def find_duplicate_groups(df): duplicate_groups = df.id.map(lambda x: df[df.duplicates.str.contains(x)].index) tmp = pd.DataFrame(duplicate_groups).astype(str).drop_duplicates().index - return duplicate_groups[tmp] + duplicate_groups = duplicate_groups[tmp] + # Deterministic processing order. Groups can OVERLAP (e.g. a textual pair + # bridging two DOI-key groups); the anchor-marking passes reset and re-mark + # anchors per group, so for overlapping groups the last-processed group + # wins. Iterating in row order would make that outcome depend on response + # order — order groups by their member ids instead. + order = sorted(duplicate_groups.index, + key=lambda i: tuple(sorted(df.id.loc[duplicate_groups[i]]))) + return duplicate_groups.loc[order] # --- DOI merge key ----------------------------------------------------------- From 205cfcbf6f715346b264af51a4baf98bbce0051f Mon Sep 17 00:00:00 2001 From: chreman Date: Wed, 26 Aug 2026 00:13:27 +0200 Subject: [PATCH 176/205] update of contentprovider.json cache --- server/workers/common/common/contentproviders.json | 4 ---- 1 file changed, 4 deletions(-) diff --git a/server/workers/common/common/contentproviders.json b/server/workers/common/common/contentproviders.json index 5da23f74e..a2654f4f9 100644 --- a/server/workers/common/common/contentproviders.json +++ b/server/workers/common/common/contentproviders.json @@ -38047,10 +38047,6 @@ "name": "The University of Melbourne: Digitised Collections", "internal_name": "ftumelbournedc" }, - { - "name": "The University of Melbourne: Course Work", - "internal_name": "ftumelbournecw" - }, { "name": "Xavier University Cincinnati: Exhibit", "internal_name": "ftxavieruniv" From b2c7da2a71fd83a4a17cd0561e42841e575bde65 Mon Sep 17 00:00:00 2001 From: chreman Date: Wed, 26 Aug 2026 10:12:27 +0200 Subject: [PATCH 177/205] bugfixes for different keyword cleaning issues in BASE --- server/preprocessing/other-scripts/base.R | 69 +--- server/preprocessing/other-scripts/features.R | 8 +- .../preprocessing/other-scripts/preprocess.R | 32 +- .../other-scripts/subject_cleaning.R | 308 +++++++++++++++++- .../preprocessing/other-scripts/summarize.R | 151 ++++++++- 5 files changed, 463 insertions(+), 105 deletions(-) diff --git a/server/preprocessing/other-scripts/base.R b/server/preprocessing/other-scripts/base.R index 9e560250b..526c5551b 100644 --- a/server/preprocessing/other-scripts/base.R +++ b/server/preprocessing/other-scripts/base.R @@ -226,69 +226,12 @@ etl <- function(res, repo, non_public) { metadata$subject_orig = subject_all - subject_cleaned = gsub("DOAJ:[^;]*(;|$)?", "", subject_all) # remove DOAJ classification - subject_cleaned = gsub("/dk/atira[^;]*(;|$)?", "", subject_cleaned) # remove atira classification - subject_cleaned = gsub("ddc:[0-9]+(;|$)?", "", subject_cleaned) # remove Dewey Decimal Classification - subject_cleaned = gsub("([\\w\\/\\:-])*?\\/ddc\\/([\\/0-9\\.])*", "", subject_cleaned) # remove Dewey Decimal Classification in URI form - subject_cleaned = gsub("[A-Z,0-9\\.]{2,}-[A-Z,0-9\\.]{2,}(;|$)?", "", subject_cleaned) #remove LOC classification (range form, incl. decimal-before-dash e.g. HT165.5-169.9) - subject_cleaned = gsub("[^\\(;]+\\(General\\)(;|$)?", "", subject_cleaned) # remove general subjects - subject_cleaned = gsub("[^\\(;]+\\(all\\)(;|$)?", "", subject_cleaned) # remove general subjects - subject_cleaned = gsub("[^:;]+ ?:: ?[^;]+(;|$)?", "", subject_cleaned) #remove classification with separator :: - subject_cleaned = gsub("[^\\[;]+\\[[A-Z,0-9]+\\](;|$)?", "", subject_cleaned) # remove WHO classification - subject_cleaned = gsub("Info:\\w+-(\\w+\\/)+", "", subject_cleaned) # remove Info:eu-repo/classification/ - subject_cleaned = gsub("([A-Za-z]+:[A-Za-z0-9 \\/\\.-]+);?", "", subject_cleaned, perl=TRUE) # clean up annotations with prefix e.g. theme:annotation - if (!is.null(params$vis_type) && params$vis_type == "timeline") { - subject_cleaned = gsub("FOS ", "", subject_cleaned) # remove FOS classification tag, but keep classifcation name - arxiv_classification_string = "(cs|econ|eess|math|astro-ph|nlin|q-bio|q-fin|stat)\\.[A-Z]{2}|cond-mat\\.[a-z\\-]+|hep-(ex|lat|ph|th)|math-ph|nucl-(ex|th)|physics\\.[a-z\\-]+|(astro-ph|gr-qc|quant-ph|cond-mat)" - subject_cleaned = gsub(arxiv_classification_string, "", subject_cleaned, perl=TRUE) # remove arXiv classification short code, but keep classifcation name - } else { - subject_cleaned = gsub("FOS [A-Za-z ]+", "", subject_cleaned) # remove FOS classifications (Fields of Science and Technology) - arxiv_classification_string = "(([A-Za-z ]+ )?cond-mat\\.[a-z\\-]+)|([\\w ]+ )?(cs|econ|eess|math|astro-ph|nlin|q-bio|q-fin|stat)\\.[A-Z]{2}|cond-mat\\.[a-z\\-]+|hep-(ex|lat|ph|th)|math-ph|nucl-(ex|th)|physics\\.[a-z\\-]+|([\\w ]+ )(astro-ph|gr-qc|quant-ph|cond-mat)" - subject_cleaned = gsub(arxiv_classification_string, "", subject_cleaned, perl=TRUE) # remove arXiv classification, except on streamgraphs - } - subject_cleaned = gsub("([A-Za-z]+:[A-Za-z0-9 \\/\\.]+);?", "", subject_cleaned, perl=TRUE) # clean up annotations with prefix e.g. theme:annotation - subject_cleaned = gsub("(wikidata)?\\.org/entity/[qQ]([\\d]+)?", "", subject_cleaned) # remove wikidata classification - subject_cleaned = gsub("", "", subject_cleaned) # remove - subject_cleaned = gsub("\\[No keyword\\]", "", subject_cleaned) - - if (!is.null(params$vis_type) && params$vis_type == "timeline") { - # These classifications have not been cleaned for the streamgraph as the - # impact of cleaning them has not been evaluated - subject_cleaned = remove_keywords_with_text_in_square_brackets(subject_cleaned) - } else { - # de-invert comma-inverted MeSH descriptors (marker preserved). - # Runs before the marker-stripping steps so [MeSH]/(mesh) are still present. - subject_cleaned = deinvert_marked_mesh_keywords(subject_cleaned) - # drop whole keywords that are additional classifications. Runs before the - # bracket strip so leading-bracket classifications (e.g. HAL [SHS.ECO]...) - # are still intact. - subject_cleaned = clean_classification_keywords(subject_cleaned) - # strip the "(mesh)" marker ("[MeSH]" is handled just below). - subject_cleaned = remove_mesh_round_bracket_marker(subject_cleaned) - subject_cleaned = remove_text_in_square_brackets_from_keywords(subject_cleaned) - # strip MeSH subheading qualifiers, keeping the descriptor - # ("Autistic Disorder/genetics" -> "Autistic Disorder"). Runs AFTER marker - # removal so a trailing "[MeSH]"/"(mesh)" does not sit between the qualifier - # and the heading boundary and block the strip. - subject_cleaned = strip_mesh_qualifier(subject_cleaned) - } - - subject_cleaned = gsub("\\[[^\\[]+\\][^\\;]+(;|$)?", "", subject_cleaned) # remove classification - subject_cleaned = gsub("[0-9]{2,} [A-Z]+[^;]*(;|$)?", "", subject_cleaned) #remove classification - subject_cleaned = gsub(" -- ", "; ", subject_cleaned) #replace inconsistent keyword separation - subject_cleaned = gsub("[-]{2,}", "; ", subject_cleaned) #replace inconsistent keyword separation - subject_cleaned = gsub("[A-Z]\\.\\d\\.\\d+", "", subject_cleaned) #replace inconsistent keyword separation - subject_cleaned = gsub(" \\( ", "; ", subject_cleaned) #replace inconsistent keyword separation - subject_cleaned = gsub("(\\w* \\w*(\\.)( \\w* \\w*)?)", "; ", subject_cleaned) # remove overly broad keywords separated by . - subject_cleaned = gsub("\\. ", "; ", subject_cleaned) # replace inconsistent keyword separation - subject_cleaned = gsub(" ?\\d[:?-?]?(\\d+.)+", "", subject_cleaned) # replace residuals like 5:621.313.323 or '5-76.95' - subject_cleaned = gsub(": ", "", subject_cleaned) # clean up keyword separation - subject_cleaned = gsub("^; $", "", subject_cleaned) # clean up keyword separation - subject_cleaned = gsub(";+", ";", subject_cleaned) # clean up keyword separation - subject_cleaned = gsub(",+", ",", subject_cleaned) # clean up keyword separation - subject_cleaned = gsub(",", ", ", subject_cleaned) # clean up keyword separation - subject_cleaned = gsub("\\s+", " ", subject_cleaned) # clean up keyword separation - subject_cleaned = stringi::stri_trim(subject_cleaned) # clean up keyword separation + # The cleaning chain lives in subject_cleaning.R (clean_subject_string) so it + # can be tested in isolation. DOAJ records additionally get the LCC + # caption/code block removed; the collection is read from the raw response + # because metadata$collection is only assigned further down. + doaj_records = check_metadata(res$dccollection) %in% "ftdoajarticles" + subject_cleaned = clean_subject_string(subject_all, params$vis_type, doaj_records) metadata$subject = subject_cleaned # Additive MeSH rank-provenance columns for ranking Modes 2/3 (derived from the # raw [MeSH]-marked subject_orig; subject/subject_orig untouched). Shared module. diff --git a/server/preprocessing/other-scripts/features.R b/server/preprocessing/other-scripts/features.R index 87e6b6fed..7aae273eb 100644 --- a/server/preprocessing/other-scripts/features.R +++ b/server/preprocessing/other-scripts/features.R @@ -7,7 +7,13 @@ TypeCountTokenizer <- function(x) { create_corpus <- function(metadata, text, stops) { - docs <- data.frame(doc_id = text$id, text = text$content) + # Corpus-side text hygiene (helpers in summarize.R): decode HTML entities + # before removePunctuation can reduce them to bare digits, and strip + # URL/HTML/signature noise that would otherwise become corpus terms. Runs + # before the unlowered snapshot, so the casing vocabulary (get_type_counts) + # is cleaned as well. + content <- sanitize_corpus_noise(decode_html_entities(text$content)) + docs <- data.frame(doc_id = text$id, text = content) corpus <- VCorpus(DataframeSource(docs)) # Replace non-convertible bytes in with strings showing their hex codes, diff --git a/server/preprocessing/other-scripts/preprocess.R b/server/preprocessing/other-scripts/preprocess.R index d2c9a75c0..1bafd4fd6 100644 --- a/server/preprocessing/other-scripts/preprocess.R +++ b/server/preprocessing/other-scripts/preprocess.R @@ -125,16 +125,14 @@ replace_keywords_if_empty <- function(metadata, stops) { } metadata$subject_is_heuristic[missing_subjects] <- TRUE vplog$info(paste("vis_id:", .GlobalEnv$VIS_ID, "Documents without subjects:", length(missing_subjects))) - candidates = mapply(paste, metadata$title) - batch_size <- 1000 - total_length <- length(stops) - for (i in seq(1, total_length, batch_size)) { - candidates = mclapply(candidates, function(x)paste(removeWords(x, stops[i:min(i+batch_size -1, total_length)]), collapse="")) - } - candidates = lapply(candidates, function(x) {gsub("[^[:alpha:]]", " ", x)}) - candidates = lapply(candidates, function(x) {gsub(" +", " ", x)}) - candidates_bigrams = lapply(lapply(candidates, expand_ngrams, n=2), paste, collapse=" ") - candidates = mapply(paste, candidates, candidates_bigrams) + # Unigram + bigram candidates per title, built on the stopword-retaining + # token stream (ngram_candidates, summarize.R): digit/hyphen tokens stay + # whole ("covid-19", "21st"), interior stopwords stay in place so no fused + # bigram is formed, and stopword matching is case-insensitive. Lone + # stopword/numeric unigrams are dropped inside the helper. + candidates = lapply(metadata$title, ngram_candidates, stops = stops, + ngram_lengths = 2, include_unigrams = TRUE) + candidates = vapply(candidates, paste, character(1), collapse = " ") nn_corpus = Corpus(VectorSource(candidates)) nn_tfidf = TermDocumentMatrix(nn_corpus) @@ -154,14 +152,12 @@ replace_keywords_if_empty <- function(metadata, stops) { } else { candidates = mapply(paste, metadata$title[i,], metadata$paper_abstract[i,]) } - for (i in seq(1, total_length, batch_size)) { - candidates = mclapply(candidates, function(x)paste(removeWords(x, stops[i:min(i+batch_size -1, total_length)]), collapse="")) - } - candidates = lapply(candidates, function(x) {gsub("[^[:alpha:]]", " ", x)}) - candidates = lapply(candidates, function(x) {gsub(" +", " ", x)}) - candidates_bigrams = lapply(lapply(candidates, expand_ngrams, n=2), paste, collapse=" ") - candidates = mapply(paste, candidates, candidates_bigrams) - nn_count = sort(table(strsplit(candidates, " ")), decreasing = T) + # same unigram + bigram method as the title pass above + candidates = lapply(candidates, ngram_candidates, stops = stops, + ngram_lengths = 2, include_unigrams = TRUE) + candidates = unlist(candidates) + if (!length(candidates)) candidates <- "" + nn_count = sort(table(candidates), decreasing = T) replacement_keywords <- filter_out_nested_ngrams(names(nn_count), 3) replacement_keywords = lapply(replacement_keywords, FUN = function(x) {paste(unlist(x), collapse="; ")}) replacement_keywords = gsub("_", " ", replacement_keywords) diff --git a/server/preprocessing/other-scripts/subject_cleaning.R b/server/preprocessing/other-scripts/subject_cleaning.R index 17cc7dac3..c51996131 100644 --- a/server/preprocessing/other-scripts/subject_cleaning.R +++ b/server/preprocessing/other-scripts/subject_cleaning.R @@ -145,11 +145,10 @@ strip_mesh_qualifier <- function(x) { # a real descriptor, but runs of qualifier-less headings (MeSH check tags such # as "Animals", "Humans") stay merged because there is no qualifier to anchor on. # - # KNOWN LIMITATION: in the live base.R order the colon form ("Hypothermia: - # chemically induced") is usually already removed whole by the generic - # "prefix:annotation" gsub before this runs, taking the descriptor with it. So - # in practice this mainly affects the slash and dash forms; the colon form is - # the same legacy-ordering interference that trips up the classification drops. + # The colon form ("Hypothermia: chemically induced") reaches this function + # because the generic "prefix:annotation" strips in the cleaning chain only + # match a lowercase scheme with no space after the colon; a + # "Descriptor: qualifier" construct (capitalised, spaced) passes them. qual <- MESH_QUALIFIER_ALTERNATION # a single "<*?>qualifier<*?>" unit (separator is /, : or a spaced dash). unit <- paste0("(?:\\s*[/:]\\s*|\\s+-\\s+)\\*?\\s*(?i:", qual, ")\\s*\\*?") @@ -169,10 +168,13 @@ strip_mesh_qualifier <- function(x) { out <- vapply(kws, function(kw) { s <- gsub(stack, "; ", kw, perl = TRUE) s <- gsub(single, "; ", s, perl = TRUE) - if (identical(s, kw)) return(kw) # no qualifier: not a MeSH pair/blob - # a "*" major-topic marker also starts a new heading; trim leftover "*". + # The "*" major-topic marker is handled independently of the qualifier + # match: a standalone "*Descriptor" carries no qualifier, so it must not + # depend on the gsubs above having fired. A " *Word" mid-string also + # starts a new heading; a leading "*" is plain marker noise. s <- gsub("\\s+\\*(?=[A-Za-z(])", "; ", s, perl = TRUE) s <- sub("^\\*", "", s) + if (identical(s, kw)) return(kw) # no qualifier, no marker: not MeSH-shaped parts <- trimws(gsub("*", "", trimws(strsplit(s, ";", fixed = TRUE)[[1]]), fixed = TRUE)) paste(parts[nzchar(parts)], collapse = "; ") }, character(1), USE.NAMES = FALSE) @@ -225,6 +227,17 @@ drop_for <- function(keywords) { keywords, ignore.case = TRUE)] } +drop_lettered_code_dashed <- function(keywords) { + # Letter-prefixed classification codes in "CODE - Caption" form, e.g. + # "F331 - Atmospheric physics", "F800 - Physical geographical sciences" + # (ANZSRC-FOR-style serialisation with a letter prefix). Neither the generic + # classification strips nor the pure-digit rule match this shape, and the + # later residual-digit cleanup then fragments it to "F- Caption". The spaced + # dash and caption are required, so a real keyword that merely looks + # code-like ("B12", "T2 MRI sequences", "3D printing") is never matched. + keywords[!grepl("^[A-Z]{1,3}[0-9]{2,4} - .+$", keywords)] +} + drop_hrcs <- function(keywords) { # UK Health Research Classification System, e.g. "... (hrcs-rac)", "... (hrcs-hc)". keywords[!grepl("\\(hrcs-[a-z]+\\)\\s*$", keywords, ignore.case = TRUE)] @@ -566,6 +579,7 @@ clean_classification_keywords <- function(x) { keywords <- drop_rcdc(keywords) keywords <- drop_not_elsewhere_classified(keywords) keywords <- drop_for(keywords) + keywords <- drop_lettered_code_dashed(keywords) keywords <- drop_hrcs(keywords) keywords <- drop_science_metrix(keywords) keywords <- drop_sdg(keywords) @@ -583,3 +597,283 @@ clean_classification_keywords <- function(x) { } vapply(x, one, character(1), USE.NAMES = FALSE) } + +# --- DOAJ LCC caption/code block removal -------------------------------------- +# DOAJ appends journal-level Library of Congress classifications to dcsubject as +# separate keywords: real keywords first, then "caption; code" pairs ordered +# general -> specific (e.g. "Science; Q; Physics; QC1-999; Geophysics. Cosmic +# physics; QC801-809"); records without author keywords carry only the block. +# The codes are removed by other rules, but the caption keywords survive (or get +# fragmented by the ". "-split steps) and then dominate tf-idf in +# journal-homogeneous clusters. This filter drops the captions while the codes +# are still present to pair against, so it must run before the code-removal and +# "."-split steps of the cleaning chain. +# +# Caption vocabulary: normalized caption/fragment -> LCC class of the code it is +# paired with in DOAJ data. Includes range-level captions ("Renewable energy +# sources" TJ807-830) that the LCC_* heading lists above do not cover, and the +# fragments of comma-split captions ("Economic growth, development, planning" +# arrives as three keywords). Curated from a corpus sweep of DOAJ records; a +# keyword is only dropped when BOTH its text matches this vocabulary AND a code +# of the matching class is present in the same subject line, so caption +# lookalikes used as real keywords ("Technology", "Ecology") survive on records +# without the block, and real keywords that merely sit next to a code (a record +# carrying "solar eclipse; RE1-994" without a caption) are never dropped. +LCC_DOAJ_CAPTION_CLASSES <- c( + "agriculture" = "S", + "agriculture (general)" = "S", + "anthropology" = "GN", + "applied mathematics. quantitative methods" = "T", + "architecture" = "NA", + "arts in general" = "NX", + "astronomy" = "QB", + "astrophysics" = "QB", + "bibliography. library science. information resources" = "Z", + "biology (general)" = "QH", + "business" = "HF", + "business communication. including business report writing" = "HF", + "business correspondence" = "HF", + "chemical engineering" = "TP", + "chemical industries" = "HD", + "chemical technology" = "TP", + "chemistry" = "QD", + "cities. urban geography" = "GF", + "city planning" = "HT", + "colonies and colonization. emigration and immigration. international migration" = "JV", + "commerce" = "HF", + "communication. mass media" = "P", + "communities. classes. races" = "HT", + "computer applications to medicine. medical informatics" = "R", + "computer software" = "QA", + "crisis management. emergency management. inflation" = "HD", + "demography. population. vital events" = "HB", + "development" = "HD", + "diseases of the circulatory (cardiovascular) system" = "RC", + "diseases of the genitourinary system. urology" = "RC", + "ecology" = "QH", + "economic growth" = "HD", + "economic theory. demography" = "HB", + "economics as a science" = "HB", + "education" = "L", + "education (general)" = "L", + "electric apparatus and materials. electric circuits. electric networks" = "TK", + "electrical engineering. electronics. nuclear engineering" = "TK", + "electronic computers. computer science" = "QA", + "electronics" = "TK", + "engineering (general). civil engineering (general)" = "TA", + "engineering economy" = "TA", + "environmental effects of industries and plants" = "TD", + "environmental law" = "K", + "environmental pollution" = "TD", + "environmental sciences" = "GE", + "environmental technology. sanitary engineering" = "TD", + "etc" = "PN", + "ethics" = "BJ", + "forestry" = "SD", + "general works" = "A", + "general. including nature conservation" = "QH", + "genetics" = "QH", + "geodesy" = "QB", + "geographical distribution" = "QH", + "geography (general)" = "G", + "geography. anthropology. recreation" = "G", + "geology" = "QE", + "geophysics. cosmic physics" = "QC", + "history (general)" = "D", + "history of africa" = "DT", + "history of scholarship and learning. the humanities" = "AZ", + "human ecology. anthropogeography" = "GF", + "human settlements. communities" = "HT", + "industries. land use. labor" = "HD", + "infectious and parasitic diseases" = "RC", + "information technology" = "T", + "islam" = "BP", + "islamic law" = "KBP", + "journalism. the periodical press" = "PN", + "language and literature" = "P", + "law" = "K", + "management information systems" = "T", + "management. industrial management" = "HD", + "mathematical geography. cartography" = "GA", + "mathematics" = "QA", + "mechanical engineering and machinery" = "TJ", + "medical physics. medical radiology. nuclear medicine" = "R", + "medicine" = "R", + "medicine (general)" = "R", + "meteorology. climatology" = "QC", + "microbiology" = "QR", + "military science" = "U", + "naval architecture. shipbuilding. marine engineering" = "VM", + "neurology. diseases of the nervous system" = "RC", + "neurosciences. biological psychiatry. neuropsychiatry" = "RC", + "nuclear and particle physics. atomic energy. radioactivity" = "QC", + "nursing" = "RT", + "oceanography" = "GC", + "ophthalmology" = "RE", + "orthopedic surgery" = "RD", + "pediatrics" = "RJ", + "philosophy. psychology. religion" = "B", + "physical geography" = "GB", + "physics" = "QC", + "physiology" = "QP", + "planning" = "HD", + "political science" = "J", + "polymers and polymer manufacture" = "TP", + "practical theology" = "BV", + "psychiatry" = "RC", + "psychology" = "BF", + "public aspects of medicine" = "RA", + "regional economics. space in economics" = "HT", + "renewable energy sources" = "TJ", + "risk in industry. risk management" = "HD", + "science" = "Q", + "science (general)" = "Q", + "shipment of goods. delivery of goods" = "HF", + "social sciences" = "H", + "social sciences (general)" = "H", + "sociology (general)" = "HM", + "special aspects of education" = "LC", + "sports" = "GV", + "sports medicine" = "RC", + "surgery" = "RD", + "technology" = "T", + "telecommunication" = "TK", + "the family. marriage. woman" = "HQ", + "theory and practice of education" = "LB", + "transportation and communications" = "HE", + "urban groups. the city. urban sociology" = "HT", + "urbanization. city and country" = "HT" +) + +LCC_RANGE_CODE_PATTERN <- "^[A-Z]{1,3}[0-9]+(\\.[0-9]+)?-[0-9]+(\\.[0-9]+)?$" + +drop_doaj_lcc_pairs <- function(x) { + # Drop LCC caption keywords whose class code is present in the same subject + # line (see the vocabulary comment above). The codes themselves (range form + # and bare top-level letter) are left in place for the existing removal rules. + # Class markers are range codes, single-number codes with a 2-3 letter prefix + # ("HT388"), and bare SINGLE top-level letters. A bare 2-3-letter token is a + # real acronym (OCT, GPS, UK) far more often than an LCC subclass code, and a + # single-letter prefix with digits ("T2", "B12") is a real keyword, so + # neither counts as a marker. + toplevel_letters <- names(LCC_TOPLEVEL_CAPTIONS) + single_code <- "^[A-Z]{2,3}[0-9]{1,4}(\\.[0-9]+)?$" + one <- function(subject) { + if (is.na(subject) || subject == "") return(subject) + kws <- split_keywords(subject) + if (length(kws) == 0) return(subject) + is_code <- grepl(LCC_RANGE_CODE_PATTERN, kws) | grepl(single_code, kws) + range_prefix <- ifelse(is_code, sub("^([A-Z]{1,3}).*$", "\\1", kws), NA_character_) + classes <- unique(c(range_prefix[!is.na(range_prefix)], + kws[kws %in% toplevel_letters])) + if (length(classes) == 0) return(subject) + norm <- tolower(gsub("\\s+", " ", trimws(kws))) + caption_class <- LCC_DOAJ_CAPTION_CLASSES[norm] + # a caption's class and a present marker pair up when one is a prefix of + # the other (bare "G" marks the "GE..." subclass captions and vice versa). + compatible <- vapply(caption_class, function(cc) { + if (is.na(cc)) return(FALSE) + any(startsWith(classes, cc) | startsWith(cc, classes)) + }, logical(1), USE.NAMES = FALSE) + join_keywords(kws[!compatible]) + } + vapply(x, one, character(1), USE.NAMES = FALSE) +} + +# --- Full subject-cleaning chain ---------------------------------------------- + +clean_subject_string <- function(subject_all, vis_type = NULL, doaj = FALSE) { + # The BASE subject/keyword cleaning chain, extracted from the etl() inline + # gsub sequence so it is testable in isolation. Vectorized over records; + # `doaj` is a logical (recycled or per-record) marking records from the DOAJ + # collection, which get the LCC caption/code block removal first — that + # filter needs the codes still present and the captions unfragmented, so it + # must precede the code-removal and "."-split steps below. + subject_cleaned = ifelse(rep_len(doaj, length(subject_all)), + drop_doaj_lcc_pairs(subject_all), subject_all) + subject_cleaned = gsub("DOAJ:[^;]*(;|$)?", "", subject_cleaned) # remove DOAJ classification + subject_cleaned = gsub("/dk/atira[^;]*(;|$)?", "", subject_cleaned) # remove atira classification + subject_cleaned = gsub("ddc:[0-9]+(;|$)?", "", subject_cleaned) # remove Dewey Decimal Classification + subject_cleaned = gsub("([\\w\\/\\:-])*?\\/ddc\\/([\\/0-9\\.])*", "", subject_cleaned) # remove Dewey Decimal Classification in URI form + subject_cleaned = gsub("[A-Z,0-9\\.]{2,}-[A-Z,0-9\\.]{2,}(;|$)?", "", subject_cleaned) #remove LOC classification (range form, incl. decimal-before-dash e.g. HT165.5-169.9) + subject_cleaned = gsub("[^\\(;]+\\(General\\)(;|$)?", "", subject_cleaned) # remove general subjects + subject_cleaned = gsub("[^\\(;]+\\(all\\)(;|$)?", "", subject_cleaned) # remove general subjects + subject_cleaned = gsub("[^:;]+ ?:: ?[^;]+(;|$)?", "", subject_cleaned) #remove classification with separator :: + subject_cleaned = gsub("[^\\[;]+\\[[A-Z,0-9]+\\](;|$)?", "", subject_cleaned) # remove WHO classification + subject_cleaned = gsub("Info:\\w+-(\\w+\\/)+", "", subject_cleaned) # remove Info:eu-repo/classification/ + # Annotation prefixes ("theme:annotation") are a lowercase scheme token with + # no space after the colon; requiring both keeps "Descriptor: qualifier" and + # "Title: Subtitle" constructs (capitalised, spaced) intact, and the + # lookbehind stops the scheme from matching a lowercase tail of a longer + # word ("Lipopolysaccharides:" must not match as "ipopolysaccharides:"). + subject_cleaned = gsub("(?", "", subject_cleaned) # remove + subject_cleaned = gsub("\\[No keyword\\]", "", subject_cleaned) + + if (!is.null(vis_type) && vis_type == "timeline") { + # These classifications have not been cleaned for the streamgraph as the + # impact of cleaning them has not been evaluated + subject_cleaned = remove_keywords_with_text_in_square_brackets(subject_cleaned) + } else { + # de-invert comma-inverted MeSH descriptors (marker preserved). + # Runs before the marker-stripping steps so [MeSH]/(mesh) are still present. + subject_cleaned = deinvert_marked_mesh_keywords(subject_cleaned) + # drop whole keywords that are additional classifications. Runs before the + # bracket strip so leading-bracket classifications (e.g. HAL [SHS.ECO]...) + # are still intact. + subject_cleaned = clean_classification_keywords(subject_cleaned) + # strip the "(mesh)" marker ("[MeSH]" is handled just below). + subject_cleaned = remove_mesh_round_bracket_marker(subject_cleaned) + subject_cleaned = remove_text_in_square_brackets_from_keywords(subject_cleaned) + # strip MeSH subheading qualifiers, keeping the descriptor + # ("Autistic Disorder/genetics" -> "Autistic Disorder"). Runs AFTER marker + # removal so a trailing "[MeSH]"/"(mesh)" does not sit between the qualifier + # and the heading boundary and block the strip. + subject_cleaned = strip_mesh_qualifier(subject_cleaned) + } + + subject_cleaned = gsub("\\[[^\\[]+\\][^\\;]+(;|$)?", "", subject_cleaned) # remove classification + subject_cleaned = gsub("[0-9]{2,} [A-Z]+[^;]*(;|$)?", "", subject_cleaned) #remove classification + subject_cleaned = gsub(" -- ", "; ", subject_cleaned) #replace inconsistent keyword separation + subject_cleaned = gsub("[-]{2,}", "; ", subject_cleaned) #replace inconsistent keyword separation + subject_cleaned = gsub("[A-Z]\\.\\d\\.\\d+", "", subject_cleaned) #replace inconsistent keyword separation + subject_cleaned = gsub(" \\( ", "; ", subject_cleaned) #replace inconsistent keyword separation + subject_cleaned = gsub("(\\w* \\w*(\\.)( \\w* \\w*)?)", "; ", subject_cleaned) # remove overly broad keywords separated by . + subject_cleaned = gsub("\\. ", "; ", subject_cleaned) # replace inconsistent keyword separation + subject_cleaned = gsub(" ?\\d[:?-?]?(\\d+.)+", "", subject_cleaned) # replace residuals like 5:621.313.323 or '5-76.95' + # replace with a space, not the empty string: a "Title: Subtitle" keyword the + # annotation strip now preserves must not have its words fused together. + subject_cleaned = gsub(": ", " ", subject_cleaned) # clean up keyword separation + subject_cleaned = gsub("^; $", "", subject_cleaned) # clean up keyword separation + subject_cleaned = gsub(";+", ";", subject_cleaned) # clean up keyword separation + subject_cleaned = gsub(",+", ",", subject_cleaned) # clean up keyword separation + # NOTE: a comma is NOT normalised to comma-space here: a comma without a + # following space is an intra-tag join ("spaCy,Geography" is one keyword), + # and inserting the space would turn it into a separator downstream. + subject_cleaned = gsub("\\s+", " ", subject_cleaned) # clean up keyword separation + # stri_trim in the pipeline image; trimws is the dependency-free equivalent + # for plain-R test environments (identical on ASCII whitespace). + if (requireNamespace("stringi", quietly = TRUE)) { + subject_cleaned = stringi::stri_trim(subject_cleaned) + } else { + subject_cleaned = trimws(subject_cleaned) + } + subject_cleaned +} diff --git a/server/preprocessing/other-scripts/summarize.R b/server/preprocessing/other-scripts/summarize.R index beddb993c..961368465 100644 --- a/server/preprocessing/other-scripts/summarize.R +++ b/server/preprocessing/other-scripts/summarize.R @@ -50,6 +50,13 @@ normalize_corpus_tokens <- function(s) { s } +# Remove a leading MeSH major-topic "*" from each "; "-separated keyword. Only +# a keyword-initial asterisk is a marker; an interior one ("2*2 design") is +# real content and stays. +strip_major_topic_markers <- function(x) { + gsub("(^|;\\s*)\\*+", "\\1", x) +} + # Generate all contiguous n-grams of length n from each input string. The words # within an n-gram are joined with "_" so a whitespace tokenizer keeps the n-gram @@ -120,6 +127,108 @@ paper_title_ngrams <- function(title, stops) { unique(grams[keep]) } +# Decode HTML character entities so they cannot fragment into bare digits or +# stray tokens downstream (removePunctuation turns "–" into "8211" fused +# into the surrounding word). Handles numeric decimal and hex forms and the +# common named entities; "&" is decoded last so a double-encoded entity is +# only unwrapped one level. Decoded en-dash/hyphen codepoints are normalised to +# "-" so they do not multiply spelling variants of hyphenated terms. +decode_html_entities <- function(x) { + decode_one <- function(s) { + if (is.na(s) || !grepl("&", s, fixed = TRUE)) return(s) + m <- gregexpr("&#[0-9]{1,7};", s) + regmatches(s, m) <- lapply(regmatches(s, m), function(v) { + if (!length(v)) return(v) + vapply(v, function(e) intToUtf8(as.integer(sub("&#([0-9]+);", "\\1", e))), + character(1), USE.NAMES = FALSE) + }) + m <- gregexpr("&#[xX][0-9a-fA-F]{1,6};", s) + regmatches(s, m) <- lapply(regmatches(s, m), function(v) { + if (!length(v)) return(v) + vapply(v, function(e) intToUtf8(strtoi(sub("&#[xX]([0-9a-fA-F]+);", "\\1", e), 16L)), + character(1), USE.NAMES = FALSE) + }) + s <- gsub(" ", " ", s, fixed = TRUE) + s <- gsub("<", "<", s, fixed = TRUE) + s <- gsub(">", ">", s, fixed = TRUE) + s <- gsub(""", "\"", s, fixed = TRUE) + s <- gsub("'", "'", s, fixed = TRUE) + s <- gsub("&", "&", s, fixed = TRUE) + s + } + x <- vapply(x, decode_one, character(1), USE.NAMES = FALSE) + # No-break/narrow spaces (decoded " "/" " or already present in the + # source) become plain spaces: they render invisibly but count as non-space + # in regex classes, which would let a "no-space run" span whole sentences. + x <- gsub("[\u00a0\u202f]", " ", x) + gsub("[\u2013\u2010]", "-", x) +} + +# Strip text noise that would otherwise surface as corpus terms or label +# candidates: URLs (including signed URLs with their query strings), HTML tags +# and stray closing-tag fragments ("]*>", " ", x, perl = TRUE) + x <- gsub("<\\s*/\\s*[A-Za-z]*>?", " ", x, perl = TRUE) + x <- gsub("\\S{80,}", " ", x, perl = TRUE) + x +} + +# Shared n-gram candidate builder for the heuristic-keyword synthesizer +# (replace_keywords_if_empty) and the last-resort fallback label +# (title_abstract_fallback_label). Same method as paper_title_ngrams: keep +# digits and intra-word hyphens, form n-grams on the stopword-RETAINING token +# stream, then drop n-grams that start or end with a stopword — so interior +# stopwords survive ("biomedical big data" stays a trigram; the fused +# "biomedical data" bigram is never formed) and digit-bearing tokens stay whole +# ("covid-19", "21st"). Stopword matching is case-insensitive so the outcome +# does not depend on source casing. paper_title_ngrams stays its own +# implementation: its per-word stopword semantics are pinned by the Mode 1-3 +# heuristic rank sources. +# text : one string (title, or title + abstract). +# ngram_lengths : which n-gram sizes to form. +# include_unigrams: also return single tokens that are neither stopwords nor +# purely numeric (a lone number/year is noise; digits stay +# inside tokens). +# Returns a character vector of "_"-joined n-grams / bare tokens, in formation +# order (not de-duplicated - callers count frequencies). +ngram_candidates <- function(text, stops, ngram_lengths = c(2, 3), + include_unigrams = FALSE) { + text <- if (is.na(text)) "" else text + # entity/URL/HTML hygiene before tokenization: an undecoded "–" would + # fragment into a bare-digit token, and abstract URLs would become candidate + # words. + text <- sanitize_corpus_noise(decode_html_entities(text)) + clean <- gsub("[^[:alnum:]-]", " ", text) + clean <- trimws(gsub("\\s+", " ", clean)) + if (!nzchar(clean)) return(character(0)) + stops_lower <- tolower(stops) + grams <- unlist(lapply(ngram_lengths, function(n) expand_ngrams(clean, n))) + grams <- unlist(strsplit(paste(grams, collapse = " "), " ")) + grams <- grams[nzchar(grams)] + keep <- vapply(grams, function(g) { + toks <- strsplit(g, "_", fixed = TRUE)[[1]] + length(toks) >= 2 && + !(tolower(toks[1]) %in% stops_lower) && + !(tolower(toks[length(toks)]) %in% stops_lower) && + toks[1] != toks[length(toks)] + }, logical(1), USE.NAMES = FALSE) + out <- grams[keep] + if (include_unigrams) { + words <- strsplit(clean, " ", fixed = TRUE)[[1]] + words <- words[nzchar(words) & !(tolower(words) %in% stops_lower) & + !grepl("^[0-9]+$", words)] + out <- c(words, out) + } + out +} + # Add the two heuristic-keyword metadata columns (HEUR_MIN1 / HEUR_MIN2) to the # metadata data frame. Generates each paper's title n-grams, computes each n-gram's # MAP-WIDE document frequency (number of distinct resources it appears in), and stores @@ -146,21 +255,15 @@ add_heuristic_keyword_fields <- function(metadata, stops) { # label_exclusions : curated area-label exclusion list (whole-term, case-insensitive) # applied here too so listed terms never survive as a last resort. title_abstract_fallback_label <- function(matches, metadata, stops, top_n = 3, label_exclusions = character(0), cluster = NA_integer_) { - batch_size <- 1000 - total_length <- length(stops) candidates = mapply(paste, metadata$title[matches], metadata$paper_abstract[matches]) candidates = lapply(candidates, tolower) - for (i in seq(1, total_length, batch_size)) { - candidates = lapply(candidates, function(x) {paste(removeWords(x, stops[i:min(i+batch_size -1, total_length)]), collapse="")}) - } - candidates = lapply(candidates, function(x) {gsub("[^[:alpha:]]", " ", x)}) - candidates = lapply(candidates, function(x) {gsub(" +", " ", x)}) - candidates_bigrams = lapply(lapply(candidates, expand_ngrams, n=2), paste, collapse=" ") - candidates_trigrams = lapply(lapply(candidates, expand_ngrams, n=3), paste, collapse=" ") - candidates = unname(mapply(paste, candidates_bigrams, candidates_trigrams)) - candidates = unlist(lapply(candidates, str_split, " "), recursive = F) - candidates = unlist(lapply(candidates, function(x) {another_prune_ngrams(x, stops)})) - top_ngrams = sort(table(strsplit(paste(candidates, collapse=" "), " ")), decreasing = T) + # n-gram formation on the stopword-retaining stream (see ngram_candidates): + # keeps digit/hyphen tokens whole and interior stopwords in place; boundary + # stopword n-grams are pruned inside the helper. + candidates = unlist(lapply(candidates, ngram_candidates, stops = stops, + ngram_lengths = c(2, 3))) + if (!length(candidates)) return("") + top_ngrams = sort(table(candidates), decreasing = T) if (length(label_exclusions)) { # whole-term exclusion (see drop_excluded_terms) norm <- trimws(tolower(gsub("_", " ", names(top_ngrams)))) top_ngrams <- top_ngrams[!(norm %in% tolower(trimws(label_exclusions)))] @@ -287,6 +390,15 @@ create_cluster_labels <- function(clusters, metadata, weightingspec = weightingspec, top_n = top_n, stops = stops, taxonomy_separator = taxonomy_separator, params = params, service = service), "summarize_00_label_inputs") + # Leading "*" (MeSH major-topic marker) can still be attached to subject + # keywords at this point: source-side cleaning strips it, but merging the + # subjects of duplicate records can re-introduce a marked spelling. Strip it + # here, where the subject tokens for the corpus and every rank source + # originate, so the marked and unmarked spelling of a keyword cannot compete + # as two distinct candidates. All modes and services. + if ("subject" %in% names(metadata)) { + metadata$subject <- strip_major_topic_markers(metadata$subject) + } # Resolve the ranking mode BEFORE building the corpus. Mode 0 is gated to the # verbatim legacy path (get_cluster_corpus_legacy + zero-sum fill_empty_clusters_ # legacy), so it stays BYTE-IDENTICAL to the pre-ranking pipeline; Modes 1-3 take @@ -464,10 +576,17 @@ fix_keyword_casing <- function(keyword, type_counts) { } # Return the canonical (original-corpus) casing of a token: looks it up in -# type_counts case-insensitively, ignoring hyphens. Falls back to the input -# token if there is no match. +# type_counts case-insensitively. The lookup is exact on everything but case — +# in particular hyphen-preserving: the vocabulary regularly holds a +# de-hyphenated twin of a hyphenated token (source spelling variants), and a +# hyphen-insensitive lookup would respell the token instead of re-casing it. +# Falls back to the input token if there is no match. Edge hyphens are +# separator debris, not part of the token, and are trimmed before the lookup +# (a token that is only hyphens is returned unchanged). match_keyword_case <- function(x, type_counts) { - y <- names(type_counts[which(tolower(names(type_counts)) == gsub("-", "", tolower(x)))][1]) + stripped <- gsub("^-+|-+$", "", x) + if (nzchar(stripped)) x <- stripped + y <- names(type_counts[which(tolower(names(type_counts)) == tolower(x))][1]) if (!is.na(y)) return(y) else return(x) } From 9e4a94810bafea5a41c8639b5e355fd3cdd5cae6 Mon Sep 17 00:00:00 2001 From: chreman Date: Wed, 26 Aug 2026 10:25:04 +0200 Subject: [PATCH 178/205] test updates for keyword cleaning improvements --- .../openaire_fight_ncov.expected.mode1.rds | Bin 420 -> 424 bytes .../openaire_fight_ncov.expected.mode2.rds | Bin 420 -> 424 bytes .../openaire_fight_ncov.expected.mode3.rds | Bin 420 -> 424 bytes .../replay/openaire_fight_ncov.expected.rds | Bin 617 -> 628 bytes .../replay/orcid_22336926.expected.mode1.rds | Bin 723 -> 725 bytes .../replay/orcid_22336926.expected.mode2.rds | Bin 730 -> 733 bytes .../replay/orcid_22336926.expected.mode3.rds | Bin 725 -> 729 bytes .../test/replay/orcid_24414043.expected.rds | Bin 694 -> 688 bytes .../replay/orcid_28976075.expected.mode1.rds | Bin 638 -> 639 bytes .../replay/orcid_28976075.expected.mode2.rds | Bin 642 -> 643 bytes .../replay/orcid_28976075.expected.mode3.rds | Bin 642 -> 643 bytes .../replay/orcid_42216275.expected.mode3.rds | Bin 719 -> 718 bytes .../test/replay/orcid_42216275.expected.rds | Bin 655 -> 648 bytes .../replay/orcid_45050517.expected.mode1.rds | Bin 562 -> 562 bytes .../replay/orcid_45050517.expected.mode2.rds | Bin 562 -> 562 bytes .../replay/orcid_45050517.expected.mode3.rds | Bin 566 -> 566 bytes .../test/replay/orcid_45050517.expected.rds | Bin 519 -> 520 bytes .../replay/orcid_49712944.expected.mode1.rds | Bin 577 -> 579 bytes .../replay/orcid_49712944.expected.mode2.rds | Bin 576 -> 579 bytes .../replay/orcid_49712944.expected.mode3.rds | Bin 576 -> 579 bytes .../replay/orcid_5116955x.expected.mode1.rds | Bin 618 -> 617 bytes .../replay/orcid_5116955x.expected.mode2.rds | Bin 617 -> 615 bytes .../replay/orcid_5116955x.expected.mode3.rds | Bin 686 -> 687 bytes .../test/replay/orcid_5116955x.expected.rds | Bin 622 -> 609 bytes .../orcid_5116955x_r2.expected.mode1.rds | Bin 580 -> 581 bytes .../orcid_5116955x_r2.expected.mode2.rds | Bin 584 -> 586 bytes .../orcid_5116955x_r2.expected.mode3.rds | Bin 654 -> 654 bytes .../replay/orcid_5116955x_r2.expected.rds | Bin 657 -> 645 bytes .../replay/orcid_60114382.expected.mode1.rds | Bin 630 -> 630 bytes .../replay/orcid_60114382.expected.mode2.rds | Bin 630 -> 630 bytes .../replay/orcid_60114382.expected.mode3.rds | Bin 630 -> 630 bytes .../test/replay/orcid_60114382.expected.rds | Bin 721 -> 721 bytes .../replay/orcid_90626039.expected.mode1.rds | Bin 654 -> 655 bytes .../replay/orcid_90626039.expected.mode2.rds | Bin 644 -> 644 bytes .../replay/orcid_90626039.expected.mode3.rds | Bin 654 -> 654 bytes .../test/replay/orcid_90626039.expected.rds | Bin 697 -> 698 bytes .../replay/orcid_92873770.expected.mode1.rds | Bin 538 -> 539 bytes .../replay/orcid_92873770.expected.mode2.rds | Bin 538 -> 539 bytes .../replay/orcid_92873770.expected.mode3.rds | Bin 538 -> 539 bytes .../replay/orcid_96127791.expected.mode1.rds | Bin 736 -> 750 bytes .../replay/orcid_96127791.expected.mode2.rds | Bin 738 -> 746 bytes .../replay/orcid_96127791.expected.mode3.rds | Bin 727 -> 765 bytes .../test/replay/orcid_96127791.expected.rds | Bin 784 -> 797 bytes .../orcid_96127791_r2.expected.mode1.rds | Bin 785 -> 784 bytes .../orcid_96127791_r2.expected.mode2.rds | Bin 767 -> 766 bytes .../orcid_96127791_r2.expected.mode3.rds | Bin 754 -> 754 bytes .../replay/orcid_96127791_r2.expected.rds | Bin 779 -> 787 bytes .../replay/orcid_98436798.expected.mode1.rds | Bin 686 -> 687 bytes .../replay/orcid_98436798.expected.mode2.rds | Bin 686 -> 687 bytes .../replay/orcid_98436798.expected.mode3.rds | Bin 686 -> 687 bytes .../test/replay/orcid_98436798.expected.rds | Bin 741 -> 743 bytes .../pubmed_cancer_datasets.expected.mode1.rds | Bin 567 -> 568 bytes .../pubmed_cancer_datasets.expected.mode2.rds | Bin 567 -> 568 bytes .../pubmed_cancer_datasets.expected.mode3.rds | Bin 567 -> 568 bytes .../pubmed_cancer_datasets.expected.rds | Bin 615 -> 615 bytes .../replay/pubmed_disease.expected.mode1.rds | Bin 626 -> 626 bytes .../replay/pubmed_disease.expected.mode2.rds | Bin 626 -> 626 bytes .../replay/pubmed_disease.expected.mode3.rds | Bin 626 -> 626 bytes .../test/replay/pubmed_disease.expected.rds | Bin 625 -> 625 bytes .../pubmed_disease_all.expected.mode1.rds | Bin 708 -> 712 bytes .../pubmed_disease_all.expected.mode2.rds | Bin 708 -> 712 bytes .../pubmed_disease_all.expected.mode3.rds | Bin 708 -> 712 bytes .../replay/pubmed_disease_all.expected.rds | Bin 682 -> 682 bytes .../pubmed_infection.expected.mode1.rds | Bin 618 -> 622 bytes .../pubmed_infection.expected.mode2.rds | Bin 618 -> 622 bytes .../pubmed_infection.expected.mode3.rds | Bin 618 -> 622 bytes .../test/replay/pubmed_infection.expected.rds | Bin 621 -> 623 bytes .../pubmed_infection_all.expected.mode1.rds | Bin 629 -> 633 bytes .../pubmed_infection_all.expected.mode2.rds | Bin 629 -> 633 bytes .../pubmed_infection_all.expected.mode3.rds | Bin 629 -> 633 bytes .../replay/pubmed_infection_all.expected.rds | Bin 619 -> 623 bytes ...bmed_infection_datasets.expected.mode1.rds | Bin 635 -> 634 bytes ...bmed_infection_datasets.expected.mode2.rds | Bin 635 -> 634 bytes ...bmed_infection_datasets.expected.mode3.rds | Bin 635 -> 634 bytes .../pubmed_infection_datasets.expected.rds | Bin 623 -> 624 bytes .../pubmed_species_all.expected.mode1.rds | Bin 645 -> 645 bytes .../pubmed_species_all.expected.mode2.rds | Bin 645 -> 645 bytes .../pubmed_species_all.expected.mode3.rds | Bin 645 -> 645 bytes .../pubmed_vienna_all.expected.mode1.rds | Bin 732 -> 733 bytes .../pubmed_vienna_all.expected.mode2.rds | Bin 732 -> 733 bytes .../pubmed_vienna_all.expected.mode3.rds | Bin 732 -> 733 bytes .../replay/pubmed_vienna_all.expected.rds | Bin 711 -> 711 bytes .../other-scripts/test/run_tests.R | 4 + .../other-scripts/test/test_corpus_hygiene.R | 92 +++++++++ .../other-scripts/test/test_label_casing.R | 78 ++++++++ .../test/test_ngram_candidates.R | 88 +++++++++ .../other-scripts/test/test_subject_chain.R | 177 ++++++++++++++++++ .../test/test_subject_cleaning.R | 10 + 88 files changed, 449 insertions(+) create mode 100644 server/preprocessing/other-scripts/test/test_corpus_hygiene.R create mode 100644 server/preprocessing/other-scripts/test/test_label_casing.R create mode 100644 server/preprocessing/other-scripts/test/test_ngram_candidates.R create mode 100644 server/preprocessing/other-scripts/test/test_subject_chain.R diff --git a/server/preprocessing/other-scripts/test/replay/openaire_fight_ncov.expected.mode1.rds 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a/server/preprocessing/other-scripts/test/run_tests.R +++ b/server/preprocessing/other-scripts/test/run_tests.R @@ -23,6 +23,7 @@ if (use_testthat) { # Pure-base-R test files (run anywhere, no tm/logging needed). base_files <- c( "test/test_subject_cleaning.R", + "test/test_subject_chain.R", "test/test_ranking_config.R", "test/test_ranking_wedge.R", "test/test_ranking_select.R", @@ -32,6 +33,9 @@ base_files <- c( # Replay tests need the tm stack — included only when tm is available (i.e. inside # the pipeline image, run via test/run_tests.sh), skipped on a bare host. replay_files <- c( + "test/test_label_casing.R", + "test/test_ngram_candidates.R", + "test/test_corpus_hygiene.R", "test/test_replay_harness.R", "test/test_replay_modes.R", "test/test_replace_keywords_routing.R", diff --git a/server/preprocessing/other-scripts/test/test_corpus_hygiene.R b/server/preprocessing/other-scripts/test/test_corpus_hygiene.R new file mode 100644 index 000000000..4cbb0f02e --- /dev/null +++ b/server/preprocessing/other-scripts/test/test_corpus_hygiene.R @@ -0,0 +1,92 @@ +# Unit tests for the corpus-side text hygiene: HTML entity decoding +# (decode_html_entities) and URL/HTML/noise stripping (sanitize_corpus_noise) +# in summarize.R, plus their integration into ngram_candidates. +# +# Runs inside the pipeline image (summarize.R needs tm/stringr) — via +# test/run_tests.sh. + +if (!exists("replay_labels")) source("test/replay_harness.R") +if (!requireNamespace("testthat", quietly = TRUE)) { + if (!exists("test_that")) source("test/testthat_shim.R") +} else { + library(testthat) +} + +# --- decode_html_entities ----------------------------------------------------- + +test_that("numeric entities decode and dashes normalise to '-'", { + expect_equal(decode_html_entities("Rainfall–Runoff"), "Rainfall-Runoff") +}) + +test_that("hex entities decode", { + expect_equal(decode_html_entities("a–b"), "a-b") +}) + +test_that("non-breaking space becomes a plain space", { + expect_equal(decode_html_entities("Ma na"), "Ma na") + expect_equal(decode_html_entities("nonconformity "), "nonconformity ") +}) + +test_that("named entities decode; double-encoded unwraps one level only", { + expect_equal(decode_html_entities("<q>"), "") + expect_equal(decode_html_entities("A & B"), "A & B") + expect_equal(decode_html_entities("&lt;"), "<") +}) + +test_that("non-entity ampersands and hashes are untouched", { + expect_equal(decode_html_entities("AT&T research"), "AT&T research") + expect_equal(decode_html_entities("C# programming"), "C# programming") +}) + +test_that("non-ASCII text passes through unchanged", { + s <- c("Künstliche Intelligenz", "Штучний інтелект", "Außerschulische Tätigkeit") + expect_equal(decode_html_entities(s), s) +}) + +test_that("bare unicode dashes normalise to '-'", { + expect_equal(decode_html_entities("rainfall–runoff"), "rainfall-runoff") + expect_equal(decode_html_entities("rainfall‐runoff"), "rainfall-runoff") +}) + +# --- sanitize_corpus_noise ---------------------------------------------------- + +test_that("URLs and signed-URL fragments are removed, words around them kept", { + out <- sanitize_corpus_noise("see https://covid19-phenomics.org/OurRiskCoV.html for context") + expect_false(grepl("http|phenomics", out)) + expect_true(grepl("see", out) && grepl("for context", out)) + out2 <- sanitize_corpus_noise("data public/journal/x/7/2/file.zip?sig=1&key-pair-id=APKAI here") + expect_false(grepl("key-pair-id", out2)) + expect_true(grepl("here", out2)) +}) + +test_that("HTML tags and stray closing fragments are removed", { + expect_false(grepl("", sanitize_corpus_noise("the unit hydrograph method"), fixed = TRUE)) + expect_true(grepl("unit hydrograph", sanitize_corpus_noise("the unit hydrograph method"))) + expect_equal(trimws(sanitize_corpus_noise("uganda Date: Thu, 27 Aug 2026 11:20:27 +0200 Subject: [PATCH 179/205] expand unit tests for keyword cleaning --- .../test/replay/orcid_96127791.expected.rds | Bin 797 -> 799 bytes .../other-scripts/test/test_label_casing.R | 7 ++++ .../other-scripts/test/test_subject_chain.R | 32 ++++++++++++++++-- .../test/test_subject_cleaning.R | 27 ++++++++++++++- 4 files changed, 63 insertions(+), 3 deletions(-) diff --git a/server/preprocessing/other-scripts/test/replay/orcid_96127791.expected.rds b/server/preprocessing/other-scripts/test/replay/orcid_96127791.expected.rds index 9a19521378ecb9c8720b72b42703034d84e52868..559e722b67ba600457976c7457e756e49e0ab770 100644 GIT binary patch literal 799 zcmV+)1K|80iwFP!0000019eo}j@vd6RX4lo{j%r}@U?&iv_a9PeJJ4CF0$!u>ueT% zD`;{o4MLGBIr2h3-A@R5C{r6}i#`yxhMY5JX3or?A5>NKsCx7m=K_DLi@$2^R_E3G_>H(B6Cz97rG;9Bt_xmSEn0QKl7s2@+pD!AM@C}sY*b6y zNvk565N#@me_V%ta9TL{dZ!avWkMUGs3e0O7wS54r{k!Mj&uUHP@5#UkxayLBDkvE z5H0xv{KGLm{49rqP6kop{1l9rUf2ZP!0ZLz>o_!^?pctOZwkuyt2b;oyKM;sQV$+{ zpf~1Uv``&_YAK28HT(Jb|7zhGX_Ru7QOrfyYXc$7$WMr=9c|swvL2qjwQMkA4`Hk7{0!dj6qF0EvyiIl zADq*RPc5OWVD=Y^mlwm^*XMa@72C&?y{huYe0^fOEcUZ$#lk1Y!+DJJF3x*687r^g d4{<&!exKy<1@H0%zbg2z{{^02w@tML008lbhFbst literal 797 zcmV+&1LFK2iwFP!0000019eo}ZW}ic-8y#K^ltPA_*y^2Pa4>GzI0!;*O2WZfgR8)PslH z(Vh9F7OFWU5=Fi9)Tezxpm`Lxb;fD0ToCf!H53e~l`t;3XlpT7?G5+L^k( zUykDb-P)zMs*hxyIRsuh-Gq#Tw%DBLYXK#cK9d<6J^4Og@Xp!#+n>}<8{!_t9s@E<|&;M5o&q$+`lZ;|6!d4pyVM2a-c(Dc7A%f6lt7E4Pqmu-V zfcO=K@2DO*>XaD$ma$Kn@2wdZaJ_R14R%qafq#<%Vsy6mfTY<54cSL(Mbwilk+m)RrSS#lZ*?u?2E@ zG=vF6NI`FJ5#fM&MIC5>U!uPUXmnWhUR}lZ*=Vn-yf@z++0KjoWL&iH$? Date: Thu, 27 Aug 2026 11:20:51 +0200 Subject: [PATCH 180/205] asterisk cleanup bugfix --- .../other-scripts/subject_cleaning.R | 44 +++++++++++++++---- .../preprocessing/other-scripts/summarize.R | 10 +++-- 2 files changed, 41 insertions(+), 13 deletions(-) diff --git a/server/preprocessing/other-scripts/subject_cleaning.R b/server/preprocessing/other-scripts/subject_cleaning.R index c51996131..695cc61bb 100644 --- a/server/preprocessing/other-scripts/subject_cleaning.R +++ b/server/preprocessing/other-scripts/subject_cleaning.R @@ -169,11 +169,13 @@ strip_mesh_qualifier <- function(x) { s <- gsub(stack, "; ", kw, perl = TRUE) s <- gsub(single, "; ", s, perl = TRUE) # The "*" major-topic marker is handled independently of the qualifier - # match: a standalone "*Descriptor" carries no qualifier, so it must not - # depend on the gsubs above having fired. A " *Word" mid-string also - # starts a new heading; a leading "*" is plain marker noise. + # match: a standalone "*Descriptor" or "Descriptor*" carries no + # qualifier, so it must not depend on the gsubs above having fired. A + # " *Word" mid-string also starts a new heading; leading and trailing + # "*" are plain marker noise (some sources put the marker at the end). s <- gsub("\\s+\\*(?=[A-Za-z(])", "; ", s, perl = TRUE) s <- sub("^\\*", "", s) + s <- sub("\\*+\\s*$", "", s) if (identical(s, kw)) return(kw) # no qualifier, no marker: not MeSH-shaped parts <- trimws(gsub("*", "", trimws(strsplit(s, ";", fixed = TRUE)[[1]]), fixed = TRUE)) paste(parts[nzchar(parts)], collapse = "; ") @@ -276,6 +278,13 @@ drop_url <- function(keywords) { keywords[!grepl("^https?://", keywords, ignore.case = TRUE)] } +drop_standalone_number <- function(keywords) { + # A keyword that is only a number (or dot/comma/dash-joined digit groups, + # e.g. "004", "2020", "5-76.95") carries no topical meaning. Digits inside a + # word ("COVID-19", "H5N1") are untouched. + keywords[!grepl("^[0-9]+([.,-][0-9]+)*$", keywords)] +} + drop_numeric_path <- function(keywords) { # Numeric ontology path codes (digits and slashes only), e.g. "/692/308/174". keywords[!grepl("^[0-9/]*/[0-9/]*$", keywords)] @@ -586,6 +595,7 @@ clean_classification_keywords <- function(x) { keywords <- drop_acm_ccs(keywords) keywords <- drop_hal_shs(keywords) keywords <- drop_url(keywords) + keywords <- drop_standalone_number(keywords) keywords <- drop_numeric_path(keywords) keywords <- drop_grant_id(keywords) keywords <- drop_letter_domain(keywords) @@ -795,7 +805,12 @@ clean_subject_string <- function(subject_all, vis_type = NULL, doaj = FALSE) { subject_cleaned = gsub("/dk/atira[^;]*(;|$)?", "", subject_cleaned) # remove atira classification subject_cleaned = gsub("ddc:[0-9]+(;|$)?", "", subject_cleaned) # remove Dewey Decimal Classification subject_cleaned = gsub("([\\w\\/\\:-])*?\\/ddc\\/([\\/0-9\\.])*", "", subject_cleaned) # remove Dewey Decimal Classification in URI form - subject_cleaned = gsub("[A-Z,0-9\\.]{2,}-[A-Z,0-9\\.]{2,}(;|$)?", "", subject_cleaned) #remove LOC classification (range form, incl. decimal-before-dash e.g. HT165.5-169.9) + # LOC classification range form (HT165.5-169.9, GE1-350, DH1-925): at most a + # 3-letter class prefix, then digit groups either side of the dash; the right + # side of a range is always digits only. Both bounds matter: a looser + # "uppercase-or-digit run" on both sides also matches ordinary keywords of + # the same shape ("COVID-19", "CD4-CD8") and deletes them. + subject_cleaned = gsub("[A-Z]{0,3}[0-9]+(\\.[0-9]+)?-[0-9]+(\\.[0-9]+)?(;|$)?", "", subject_cleaned) subject_cleaned = gsub("[^\\(;]+\\(General\\)(;|$)?", "", subject_cleaned) # remove general subjects subject_cleaned = gsub("[^\\(;]+\\(all\\)(;|$)?", "", subject_cleaned) # remove general subjects subject_cleaned = gsub("[^:;]+ ?:: ?[^;]+(;|$)?", "", subject_cleaned) #remove classification with separator :: @@ -850,17 +865,25 @@ clean_subject_string <- function(subject_all, vis_type = NULL, doaj = FALSE) { } subject_cleaned = gsub("\\[[^\\[]+\\][^\\;]+(;|$)?", "", subject_cleaned) # remove classification - subject_cleaned = gsub("[0-9]{2,} [A-Z]+[^;]*(;|$)?", "", subject_cleaned) #remove classification + # digit-code classifications ("32 Biomedical and clinical sciences"): the + # digits must start a keyword, otherwise the rule eats "19 Vaccines" out of + # "COVID-19 Vaccines" and leaves a bare "COVID-". + subject_cleaned = gsub("(? Date: Wed, 2 Sep 2026 13:06:47 +0200 Subject: [PATCH 181/205] update of contentprovider.json cache --- server/workers/common/common/contentproviders.json | 4 ---- 1 file changed, 4 deletions(-) diff --git a/server/workers/common/common/contentproviders.json b/server/workers/common/common/contentproviders.json index a2654f4f9..412648a2f 100644 --- a/server/workers/common/common/contentproviders.json +++ b/server/workers/common/common/contentproviders.json @@ -23595,10 +23595,6 @@ "name": "CLEARvoz Journal (Center for Leadership, Equity and Research)", "internal_name": "ftjcvj" }, - { - "name": "York University Digital Library", - "internal_name": "ftyorkunivdc" - }, { "name": "Portal de Periódicos Eletrônicos da Universidade Estadual de Feira de Santana (UEFS)", "internal_name": "ftuniefeirasanta" From e511a0e64dd1fa7cacad6ccf2f31d852b2e462e0 Mon Sep 17 00:00:00 2001 From: chreman Date: Wed, 2 Sep 2026 16:03:40 +0200 Subject: [PATCH 182/205] punctuation processing in heuristic keyword generation functions updated; including tests --- .../preprocessing/other-scripts/summarize.R | 179 ++++-- .../other-scripts/test/run_tests.R | 1 + .../test/test_punctuation_segments.R | 531 ++++++++++++++++++ .../other-scripts/test/test_ranking_select.R | 11 + .../resources/label_exclusions.txt | 1 + 5 files changed, 672 insertions(+), 51 deletions(-) create mode 100644 server/preprocessing/other-scripts/test/test_punctuation_segments.R diff --git a/server/preprocessing/other-scripts/summarize.R b/server/preprocessing/other-scripts/summarize.R index 590e7bc92..b2565b425 100644 --- a/server/preprocessing/other-scripts/summarize.R +++ b/server/preprocessing/other-scripts/summarize.R @@ -73,10 +73,13 @@ expand_ngrams <- function(text, n) { # are shorter than 2 tokens; stopwords are checked in batches for speed. Returns, # per input, the surviving n-grams joined with ";". prune_ngrams <- function(ngrams, stops){ - ngrams = mapply(strsplit, ngrams, split=" |;") + # lapply/SIMPLIFY = FALSE: with mapply's default simplification, equal + # n-gram counts across all inputs collapse the result into a matrix and + # every n-gram is silently lost + ngrams = lapply(ngrams, function(x) strsplit(x[[1]], split = " |;")[[1]]) tokenized_ngrams = mapply(function(x) { strsplit(x, split="_") - }, ngrams) + }, ngrams, SIMPLIFY = FALSE) # filter out empty tokens tokenized_ngrams = lapply(tokenized_ngrams, function(ngrams){ngrams[lapply(ngrams, length)>0]}) # remove ngrams starting with a stopword @@ -107,15 +110,15 @@ prune_ngrams <- function(ngrams, stops){ } # Heuristically generated keywords for a single paper: the bi- and tri-grams of -# its title, pruned of degenerate forms (n-grams that start or end with a -# stopword, or whose first and last token are identical). Words within an n-gram -# are "_"-joined so the tokenizer keeps them intact. Returns a character vector of -# unique "_"-joined n-grams (possibly empty). +# its title's punctuation-delimited segments (punctuation_segments), pruned of +# degenerate forms (n-grams that start or end with a stopword, or whose first +# and last token are identical). Words within an n-gram are "_"-joined so the +# tokenizer keeps them intact. Returns a character vector of unique "_"-joined +# n-grams (possibly empty). paper_title_ngrams <- function(title, stops) { - clean <- gsub("[^[:alnum:]-]", " ", if (is.na(title)) "" else title) - clean <- trimws(gsub("\\s+", " ", clean)) - if (!nzchar(clean)) return(character(0)) - grams <- unlist(c(expand_ngrams(clean, 2), expand_ngrams(clean, 3))) + segments <- punctuation_segments(title) + if (!length(segments)) return(character(0)) + grams <- unlist(c(expand_ngrams(segments, 2), expand_ngrams(segments, 3))) grams <- unlist(strsplit(paste(grams, collapse = " "), " ")) grams <- grams[nzchar(grams)] if (!length(grams)) return(character(0)) @@ -182,17 +185,79 @@ sanitize_corpus_noise <- function(x) { x } +# Tight-colon tokens that keep their colon instead of splitting (matched as the +# whole word:word token). Extend as legitimate ratio-style terms are found. +COLON_KEEP_TOKENS <- c("80:20", "50:50") + +# Split a text into punctuation-delimited segments for n-gram formation, so +# that no n-gram crosses a clause/subtitle boundary and no intra-word +# punctuation compound is broken. A punctuation mark splits when whitespace +# (or a string edge, or another boundary) adjoins it; a mark tight between two +# word characters stays inside its token. Deviations: colon, em dash, pipe and +# underscore always split (underscore because it is the n-gram joiner +# character); colon keep-list tokens and multi-period abbreviation chains +# ("U.S.", "e.g." — trailing period included) stay whole; a run of >= 2 +# consecutive punctuation marks always splits as a unit. Placeholders \x01 +# (chain periods), \x03 (kept colons) and \x02 (boundaries) cannot occur in +# decoded titles. Returns a character vector of trimmed, whitespace-collapsed, +# non-empty segments; case is preserved. NA/empty input -> character(0). +punctuation_segments <- function(text) { + text <- if (is.null(text) || is.na(text)) "" else text + text <- sanitize_corpus_noise(decode_html_entities(text)) + # C0 control characters are removed before anything else: \x01-\x03 are this + # function's own placeholders, so a source string containing them would be + # restored as a period/colon or silently split the text + text <- gsub("[\x01-\x08\x0b\x0c\x0e-\x1f]", " ", text) + # soft hyphen and zero-width characters render as nothing but count as + # non-space, so they would be kept inside a token and stop it matching the + # same word spelled without them + text <- gsub("[\u00ad\u200b\u200c\u200d\ufeff]", "", text) + # normalize spelling variants to one token identity (decode already maps + # en dash U+2013 and hyphen U+2010 to "-"); a tight "--" is a TeX en dash + # inside a compound, a spaced "--" is left as a run to split + text <- gsub("\u2011", "-", text) + text <- gsub("[\u2019\u2018\u02bc]", "'", text) + text <- gsub("\uff1a", ":", text) + text <- gsub("(?<=[^\\s-])--(?=[^\\s-])", "-", text, perl = TRUE) + # protect abbreviation chains (>= 2 single-letter.period components) before + # any boundary rule, so their periods - the trailing one included - survive + m <- gregexpr("(? - # drop_excluded_terms is a no-op). See get_label_exclusions. - label_exclusions <- if (identical(mode, "0")) character(0) else get_label_exclusions() + # terms can never become a label. Applied in EVERY mode, Mode 0 included: a + # generic term is unwanted as a label regardless of which selection path + # produced it. See get_label_exclusions. + label_exclusions <- get_label_exclusions() vslog$debug(paste("create_cluster_labels: ranking mode", mode, "for service", if (is.null(service)) "(none)" else service)) @@ -441,8 +505,10 @@ create_cluster_labels <- function(clusters, metadata, # SAME corpus at bound c(1, Inf). empty_tfidf <- which(apply(nn_tfidf, 2, sum) == 0) tfidf_top[c(empty_tfidf)] <- fill_empty_clusters_legacy(nn_tfidf, nn_corpus)[c(empty_tfidf)] - # NB: no label-exclusion filter here — Mode 0 is byte-identical to legacy. - dump_tfidf_candidates(tfidf_top, "summarize_04b_tfidf_candidates") # candidates fed to selection (post empty-fill) + dump_tfidf_candidates(tfidf_top, "summarize_04b_tfidf_candidates") # raw candidates (post empty-fill, pre-exclusion) + tfidf_top_pre_excl <- tfidf_top + tfidf_top <- drop_excluded_terms(tfidf_top, label_exclusions) # post-tf-idf, pre-selection (all modes) + dump_excluded_terms(tfidf_top_pre_excl, tfidf_top, "summarize_04e_excluded_terms") if (length(empty_tfidf)) label_source[empty_tfidf] <- "legacy_fill" tfidf_top_names <- get_top_names(tfidf_top, top_n, stops) } else { @@ -622,10 +688,11 @@ get_custom_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separa } # --- Mode 0 (legacy) corpus + fallback ------------------------------------- -# Verbatim from feat/keyword-label-improvements: Mode 0 is gated to this path so -# it stays BYTE-IDENTICAL to the pre-ranking pipeline. It generates the -# title n-grams INLINE (get_title_ngrams, unchanged) rather than reading the -# DF-filtered min1/min2 columns, and has no rank_sources. +# Mode 0 keeps the legacy corpus/selection STRUCTURE: title n-grams are +# generated INLINE (get_title_ngrams) rather than read from the DF-filtered +# min1/min2 columns, and there are no rank_sources. The title stream itself is +# punctuation-aware (punctuation_segments) like every other mode, so Mode-0 +# labels are no longer byte-identical to the pre-ranking pipeline. get_cluster_corpus_legacy <- function(clusters, metadata, stops, taxonomy_separator, add_title_ngrams = T, custom_clustering=NULL) { subjectlist = list() @@ -633,9 +700,12 @@ get_cluster_corpus_legacy <- function(clusters, metadata, stops, taxonomy_separa matches = which(unname(clusters$groups == k) == TRUE) titles = metadata$title[matches] subjects = metadata$subject[matches] - titles = lapply(titles, function(x) {gsub("[^[:alnum:]-]", " ", x)}) - titles = lapply(titles, gsub, pattern="\\s+", replacement=" ") - title_ngrams <- get_title_ngrams(titles, stops, c(2, 3)) + # segment each title at punctuation boundaries so the inline n-grams cannot + # span a boundary and tight compounds stay whole; the unigram word stream + # is built from the same segment tokens + title_segments = lapply(titles, punctuation_segments) + titles = lapply(title_segments, paste, collapse = " ") + title_ngrams <- get_title_ngrams(title_segments, stops, c(2, 3)) batch_size <- 1000 total_length <- length(stops) for (i in seq(1, total_length, batch_size)) { @@ -875,12 +945,19 @@ fill_empty_clusters <- function(fallback_corpus){ # Extract pruned bi- and tri-grams from a set of titles (see prune_ngrams) and -# return them concatenated. Note: ngram_lengths is currently unused — lengths 2 -# and 3 are hardcoded. +# return them concatenated. Each title arrives as its punctuation segments (one +# character vector per title, from punctuation_segments); n-grams are formed +# within a segment, so they cannot cross a punctuation boundary. The per-title +# list structure is preserved because callers align the result with per-paper +# subjects. Note: ngram_lengths is currently unused — lengths 2 and 3 are +# hardcoded. get_title_ngrams <- function(titles, stops, ngram_lengths) { # for ngrams: we have to collapse with "_" or else tokenizers will split ngrams again at that point and we'll be left with unigrams - titles_bigrams = prune_ngrams(expand_ngrams(titles, 2), stops) - titles_trigrams = prune_ngrams(expand_ngrams(titles, 3), stops) + per_title <- function(n) lapply(titles, function(segments) { + paste(unlist(expand_ngrams(segments, n)), collapse = " ") + }) + titles_bigrams = prune_ngrams(per_title(2), stops) + titles_trigrams = prune_ngrams(per_title(3), stops) return(c(titles_bigrams, titles_trigrams)) } diff --git a/server/preprocessing/other-scripts/test/run_tests.R b/server/preprocessing/other-scripts/test/run_tests.R index 91b077337..17b470a87 100644 --- a/server/preprocessing/other-scripts/test/run_tests.R +++ b/server/preprocessing/other-scripts/test/run_tests.R @@ -35,6 +35,7 @@ base_files <- c( replay_files <- c( "test/test_label_casing.R", "test/test_ngram_candidates.R", + "test/test_punctuation_segments.R", "test/test_corpus_hygiene.R", "test/test_replay_harness.R", "test/test_replay_modes.R", diff --git a/server/preprocessing/other-scripts/test/test_punctuation_segments.R b/server/preprocessing/other-scripts/test/test_punctuation_segments.R new file mode 100644 index 000000000..67ab63a7f --- /dev/null +++ b/server/preprocessing/other-scripts/test/test_punctuation_segments.R @@ -0,0 +1,531 @@ +# Unit tests for the punctuation-aware segmentation helper +# (punctuation_segments, summarize.R) and its use in the n-gram builders +# (ngram_candidates, paper_title_ngrams). The helper splits at punctuation +# with adjoining whitespace (string edges and punctuation runs included) and +# keeps tight marks inside their tokens; colon, em dash, pipe and underscore +# always split; colon keep-list tokens and multi-period abbreviation chains +# stay whole. +# +# Runs inside the pipeline image (summarize.R needs stringr/logging) — via +# test/run_tests.sh. + +if (!exists("replay_labels")) source("test/replay_harness.R") +if (!requireNamespace("testthat", quietly = TRUE)) { + if (!exists("test_that")) source("test/testthat_shim.R") +} else { + library(testthat) +} + +STOPS <- c("and", "in", "the", "of", "to", "on", "for", "a", "or", "than", + "just", "more", "an", "from", "vs") + +seg <- punctuation_segments + +# --- token-identity normalization ----------------------------------------- + +test_that("hyphen variants normalize to ASCII hyphen and stay tight", { + expect_equal(seg("Event‐Driven Architecture"), "Event-Driven Architecture") + expect_equal(seg("Event‑Driven Architecture"), "Event-Driven Architecture") + expect_equal(seg("human–primate interactions"), "human-primate interactions") +}) + +test_that("curly apostrophes normalize to ASCII apostrophe", { + expect_equal(seg("the author’s view"), "the author's view") +}) + +test_that("tight double hyphen folds to one hyphen; spaced double hyphen splits", { + expect_equal(seg("Rainfall--Runoff modeling"), "Rainfall-Runoff modeling") + expect_equal(seg("FiLLM -- A framework"), c("FiLLM", "A framework")) +}) + +test_that("em dash is not folded to hyphen — it splits", { + expect_equal(seg("Game Theory—More Than Just Games"), + c("Game Theory", "More Than Just Games")) +}) + +# --- Unicode letters are never punctuation -------------------------------- + +test_that("accented and non-Latin letters survive intact", { + expect_equal(seg("Modelos energéticos"), "Modelos energéticos") + expect_equal(seg("análise geográfica"), "análise geográfica") + expect_equal(seg("Ҡ-mesons decay"), "Ҡ-mesons decay") +}) + +# --- spacing classification ----------------------------------------------- + +test_that("tight compounds keep themselves", { + expect_equal(seg("Developing location-based services"), + "Developing location-based services") + expect_equal(seg("children's understanding"), "children's understanding") + expect_equal(seg("physician assistant/associate education"), + "physician assistant/associate education") + expect_equal(seg("Virtual R&D Teams"), "Virtual R&D Teams") + expect_equal(seg("TRIPOD+AI statement"), "TRIPOD+AI statement") + expect_equal(seg("Education 4.0 Readiness"), "Education 4.0 Readiness") + expect_equal(seg("study of 350,067 individuals"), "study of 350,067 individuals") +}) + +test_that("spaced marks split", { + expect_equal(seg("Towards 4D Cartography - Four-dimensional views"), + c("Towards 4D Cartography", "Four-dimensional views")) + expect_equal(seg("Climate Policy / Special issue"), + c("Climate Policy", "Special issue")) + expect_equal(seg("Crowdsourcing, citizen sensing"), + c("Crowdsourcing", "citizen sensing")) +}) + +test_that("string edges count as whitespace", { + expect_equal(seg("'Quoted title'"), "Quoted title") + expect_equal(seg("current methods."), "current methods") + expect_equal(seg("[tag] Some Title"), c("tag", "Some Title")) +}) + +test_that("punctuation runs split as a unit, whatever their spacing", { + expect_equal(seg("surgery publications]."), "surgery publications") + expect_equal(seg("professors?:Professorial leadership"), + c("professors", "Professorial leadership")) + # accepted miss: a compound hyphen inside a run collapses to a boundary + expect_equal(seg("Digital (LLM)-Powered assistant"), + c("Digital", "LLM", "Powered assistant")) +}) + +test_that("tight single wordplay parens keep (odd tokens accepted)", { + expect_equal(seg("Organisational (in)justice"), c("Organisational", "in)justice")) + expect_equal(seg("micro(nano) plastic pollution"), + c("micro(nano", "plastic pollution")) +}) + +# --- per-character deviations --------------------------------------------- + +test_that("fullwidth colon normalizes and splits like an ASCII colon", { + expect_equal(seg("oxidation processes：a review"), + c("oxidation processes", "a review")) +}) + +test_that("colon always splits, tight or spaced", { + expect_equal(seg("academic leaders:professorial leadership"), + c("academic leaders", "professorial leadership")) + expect_equal(seg("Wind Energy in Germany: Potential Areas"), + c("Wind Energy in Germany", "Potential Areas")) + expect_equal(seg("ratio 70:30 something"), c("ratio 70", "30 something")) +}) + +test_that("colon keep-list tokens stay whole", { + expect_equal(seg("a 80:20 split"), "a 80:20 split") + expect_equal(seg("mixed 50:50."), "mixed 50:50") +}) + +test_that("colon splits around an intact tight-symbol name", { + expect_equal(seg("PROBAST+AI:an updated guideline"), + c("PROBAST+AI", "an updated guideline")) +}) + +test_that("multi-period abbreviation chains keep their trailing period", { + expect_equal(seg("U.S. policy"), "U.S. policy") + expect_equal(seg("see e.g. something"), "see e.g. something") + expect_equal(seg("regulation in the U.S., and the U.K. from 2020"), + c("regulation in the U.S.", "and the U.K. from 2020")) + expect_equal(seg("the U.S.E. framework"), "the U.S.E. framework") +}) + +test_that("period plus whitespace splits after a full word", { + expect_equal(seg("Man vs. Machine"), c("Man vs", "Machine")) +}) + +test_that("tight periods keep dotted identifiers", { + expect_equal(seg("B.1.351 variant"), "B.1.351 variant") + expect_equal(seg("core.ac.uk repository"), "core.ac.uk repository") + expect_equal(seg("Fisheries Sector.docx"), "Fisheries Sector.docx") +}) + +test_that("em dash splits even tight; en dash does not trip the exception", { + expect_equal(seg("Urban Emotions—Geo-Semantic Emotion Extraction"), + c("Urban Emotions", "Geo-Semantic Emotion Extraction")) + expect_equal(seg("rainfall–runoff modeling"), "rainfall-runoff modeling") +}) + +test_that("pipe and underscore always split", { + out <- seg("Twitter. GI_Forum|GI_Forum 2018, Volume 1 |") + expect_false(any(grepl("[|_]", out))) + expect_true("Twitter" %in% out) +}) + +# --- invariants ---------------------------------------------------------- + +test_that("I2 no-span: every n-gram lies within one segment", { + titles <- c( + "The beauty or the beast? Attacking rate limits of the xen hypervisor", + "Wind Energy in Germany: Potential Areas", + "Urban Emotions—Geo-Semantic Emotion Extraction", + "Strength in numbers:How citizen science helps", + "Tough Decisions? Supporting System Classification According to the AI Act" + ) + for (t in titles) { + segments <- seg(t) + for (g in ngram_candidates(t, STOPS)) { + phrase <- gsub("_", " ", g, fixed = TRUE) + expect_true(any(grepl(phrase, segments, fixed = TRUE))) + } + } +}) + +test_that("motivating span failures are gone", { + out <- ngram_candidates( + "The beauty or the beast? Attacking rate limits of the xen hypervisor", STOPS) + expect_false(any(grepl("beast_", out, fixed = TRUE))) + out <- ngram_candidates("Wind Energy in Germany: Potential Areas", STOPS) + expect_false(any(grepl("Germany_Potential", out, fixed = TRUE))) + expect_true("Wind_Energy" %in% out) + expect_true("Potential_Areas" %in% out) + out <- ngram_candidates( + "Tough Decisions? Supporting System Classification According to the AI Act", STOPS) + expect_false(any(grepl("Decisions_Supporting", out, fixed = TRUE))) + expect_true("Tough_Decisions" %in% out) + expect_true("Supporting_System" %in% out) +}) + +test_that("I1 boundary equivalence: any boundary char at the same position yields the same keywords", { + stem <- "Calls of Care%s Materializing Posthuman Personhood with Conversational Agents in Dementia Care" + variants <- lapply(c(":", "?", ",", ";", " -", "."), + function(p) ngram_candidates(sprintf(stem, p), STOPS)) + for (v in variants[-1]) expect_identical(v, variants[[1]]) + segments <- seg(sprintf(stem, ":")) + expect_equal(segments[1], "Calls of Care") +}) + +test_that("I1 scoping: tight positions are not equivalent across chars", { + # at a tight position a comma keeps (fused token) while a colon still + # splits (always-split deviation) — intentionally different + expect_equal(seg("word,word here"), "word,word here") + expect_equal(seg("word:word here"), c("word", "word here")) +}) + +test_that("I3 boundary insertion removes exactly the spanning n-grams", { + base <- ngram_candidates("alpha beta gamma delta", STOPS) + split <- ngram_candidates("alpha beta: gamma delta", STOPS) + expect_true(all(split %in% base)) + expect_identical(sort(setdiff(base, split)), + sort(c("beta_gamma", "alpha_beta_gamma", "beta_gamma_delta"))) + expect_identical(sort(split), sort(c("alpha_beta", "gamma_delta"))) +}) + +test_that("I3 on a real title: only the n-grams spanning the inserted boundary go", { + base <- ngram_candidates( + "Unpacking Forms of Relatedness around Older People and Telecare", STOPS) + split <- ngram_candidates( + "Unpacking: Forms of Relatedness around Older People and Telecare", STOPS) + # the only surviving candidate that crossed the insertion point is the + # bigram (the spanning trigram ends in a stopword and never formed) + expect_identical(setdiff(base, split), "Unpacking_Forms") + expect_identical(split, setdiff(base, "Unpacking_Forms")) + expect_true("Forms_of_Relatedness" %in% split) +}) + +test_that("I4 compound atomicity: Unicode variant forms yield identical output", { + expect_identical(ngram_candidates("Event‐Driven services", STOPS), + ngram_candidates("Event-Driven services", STOPS)) + expect_identical(ngram_candidates("the author’s view of things", STOPS), + ngram_candidates("the author's view of things", STOPS)) +}) + +# every n-gram token that carries part of the compound must carry all of it +expect_atomic <- function(out, compound, parts) { + toks <- unlist(strsplit(out, "_", fixed = TRUE)) + for (p in parts) expect_false(p %in% toks) + expect_true(compound %in% toks) +} + +test_that("I4 compounds stay one token inside every n-gram", { + DE <- c(STOPS, "der", "und", "im", "von") + out <- ngram_candidates("Der AMS-Algorithmus bewertet Arbeitsmarktchancen automatisch", DE) + expect_atomic(out, "AMS-Algorithmus", c("AMS", "Algorithmus")) + expect_true("AMS-Algorithmus_bewertet" %in% out) + out <- ngram_candidates("children's rights across Europe today", STOPS) + expect_atomic(out, "children's", c("children", "s")) + expect_true("children's_rights" %in% out) +}) + +test_that("I4 a compound next to a punctuation run stays atomic", { + DE <- c(STOPS, "der", "und", "im", "von") + t <- paste("Der AMS-Algorithmus. ; Transparenz, Verantwortung und Diskriminierung", + "im Kontext von digitalem staatlichem Handeln") + # the ". ;" run and the comma are boundaries; the compound is not touched + expect_equal(punctuation_segments(t)[1:2], c("Der AMS-Algorithmus", "Transparenz")) + # the compound survives the segmentation as a whole unigram candidate + # (its only bigram, "Der_AMS-Algorithmus", starts with a stopword and prunes) + uni <- ngram_candidates(t, DE, include_unigrams = TRUE) + expect_atomic(uni, "AMS-Algorithmus", c("AMS", "Algorithmus")) + # U+2010 spelling of the same title is indistinguishable + expect_identical(ngram_candidates(sub("AMS-", "AMS‐", t, fixed = TRUE), DE, + include_unigrams = TRUE), uni) +}) + +test_that("I5 letter preservation: accenting a word only respells its token", { + plain <- ngram_candidates("Modelos energeticos para la transicion", STOPS) + accented <- ngram_candidates("Modelos energéticos para la transición", STOPS) + respelled <- gsub("transicion", "transición", + gsub("energeticos", "energéticos", plain)) + expect_identical(accented, respelled) + expect_equal(length(accented), length(plain)) +}) + +test_that("I5 non-Latin letters neither drop the token nor split its n-grams", { + latin <- ngram_candidates("K-mesons decay rates measured", STOPS) + cyrillic <- ngram_candidates("Ҡ-mesons decay rates measured", STOPS) + expect_identical(cyrillic, gsub("K-mesons", "Ҡ-mesons", latin, fixed = TRUE)) + expect_true("Ҡ-mesons_decay" %in% cyrillic) + expect_true("energético" %in% + ngram_candidates("un modelo energético nuevo", STOPS, + include_unigrams = TRUE)) +}) + +test_that("I6 determinism: the same input always gives the same output", { + t <- "Towards 4D Cartography - Four-dimensional views" + expect_identical(ngram_candidates(t, STOPS), ngram_candidates(t, STOPS)) + expect_identical(seg(t), seg(t)) + expect_identical(paper_title_ngrams(t, STOPS), paper_title_ngrams(t, STOPS)) +}) + +test_that("I6 whitespace around a boundary does not change the outcome", { + expect_identical(seg("A word - another word"), seg("A word - another word")) + spacings <- c("Care: Materializing agents", "Care : Materializing agents", + "Care :Materializing agents", "Care:Materializing agents") + outs <- lapply(spacings, ngram_candidates, stops = STOPS) + for (o in outs[-1]) expect_identical(o, outs[[1]]) +}) + +test_that("I6 run length does not change the outcome", { + runs <- c("the beast? Attacking rate limits", "the beast?! Attacking rate limits", + "the beast?!... Attacking rate limits", "the beast]. Attacking rate limits") + outs <- lapply(runs, ngram_candidates, stops = STOPS) + for (o in outs[-1]) expect_identical(o, outs[[1]]) +}) + +test_that("I6 idempotence: segmenting a segment returns it unchanged", { + for (t in c("Wind Energy in Germany: Potential Areas", + "Der AMS-Algorithmus. ; Transparenz, Verantwortung", + "U.S. policy and 4.0 readiness")) { + segments <- seg(t) + expect_identical(unlist(lapply(segments, seg)), segments) + } +}) + +test_that("accepted misses behave as documented", { + # tight comma and tight run-on period fuse (singleton formatting errors) + expect_equal(seg("Necessary,feasible steps"), "Necessary,feasible steps") + expect_equal(seg("school administrators.Under Kalasin"), + "school administrators.Under Kalasin") +}) + +# --- site routing --------------------------------------------------------- + +test_that("ngram_candidates segments its input (synthesizer/fallback path)", { + out <- ngram_candidates("Publisher Correction: Reporting guideline", STOPS) + expect_true("Reporting_guideline" %in% out) + expect_false("Correction_Reporting" %in% out) +}) + +test_that("unigram numeric prune covers separator-bearing numbers", { + out <- ngram_candidates("Education 4.0 Readiness study", STOPS, + include_unigrams = TRUE) + expect_false("4.0" %in% out) + expect_true("Education" %in% out) + out <- ngram_candidates("Trends (2013–2023) analysis", STOPS, + include_unigrams = TRUE) + expect_false("2013-2023" %in% out) + out <- ngram_candidates("cohort of 350,067 individuals", STOPS, + include_unigrams = TRUE) + expect_false("350,067" %in% out) + expect_true("covid-19" %in% ngram_candidates("covid-19 spread", STOPS, + include_unigrams = TRUE)) +}) + +test_that("paper_title_ngrams segments its input (label-candidate path)", { + out <- paper_title_ngrams("Urban Emotions—Geo-Semantic Emotion Extraction", STOPS) + expect_true("Emotion_Extraction" %in% out) + expect_false(any(grepl("Emotions_Geo", out, fixed = TRUE))) +}) + +test_that("gold set: hand-written expected keyword sets", { + expect_identical(sort(ngram_candidates("Developing location-based services", STOPS)), + sort(c("Developing_location-based", + "location-based_services", + "Developing_location-based_services"))) + expect_identical(sort(paper_title_ngrams("Standardised geo-sensor webs", STOPS)), + sort(c("Standardised_geo-sensor", "geo-sensor_webs", + "Standardised_geo-sensor_webs"))) +}) + +# --- no-op regression ---------------------------------------------------- +# +# The pre-change tokenizers, reimplemented verbatim: punctuation was replaced by +# spaces ("[^[:alnum:]-]") and n-grams were formed over the whole string. For a +# title with no boundary punctuation and no stripped characters the two +# implementations must agree exactly - the change is a no-op there. + +legacy_keep <- function(grams, stops_lower) { + vapply(grams, function(g) { + toks <- strsplit(g, "_", fixed = TRUE)[[1]] + length(toks) >= 2 && + !(tolower(toks[1]) %in% stops_lower) && + !(tolower(toks[length(toks)]) %in% stops_lower) && + toks[1] != toks[length(toks)] + }, logical(1), USE.NAMES = FALSE) +} + +legacy_ngram_candidates <- function(text, stops, ngram_lengths = c(2, 3), + include_unigrams = FALSE) { + text <- if (is.na(text)) "" else text + text <- sanitize_corpus_noise(decode_html_entities(text)) + clean <- trimws(gsub("\\s+", " ", gsub("[^[:alnum:]-]", " ", text))) + if (!nzchar(clean)) return(character(0)) + stops_lower <- tolower(stops) + grams <- unlist(lapply(ngram_lengths, function(n) expand_ngrams(clean, n))) + grams <- unlist(strsplit(paste(grams, collapse = " "), " ")) + grams <- grams[nzchar(grams)] + out <- grams[legacy_keep(grams, stops_lower)] + if (include_unigrams) { + words <- strsplit(clean, " ", fixed = TRUE)[[1]] + words <- words[nzchar(words) & !(tolower(words) %in% stops_lower) & + !grepl("^[0-9]+$", words)] + out <- c(words, out) + } + out +} + +legacy_paper_title_ngrams <- function(title, stops) { + clean <- trimws(gsub("\\s+", " ", + gsub("[^[:alnum:]-]", " ", if (is.na(title)) "" else title))) + if (!nzchar(clean)) return(character(0)) + grams <- unlist(c(expand_ngrams(clean, 2), expand_ngrams(clean, 3))) + grams <- unlist(strsplit(paste(grams, collapse = " "), " ")) + grams <- grams[nzchar(grams)] + if (!length(grams)) return(character(0)) + unique(grams[legacy_keep(grams, stops)]) +} + +# Real punctuation-free titles from the four corpora (BASE, ORCID, PubMed, +# OpenAIRE) +CLEAN_TITLES <- c( + "Zur Entwicklung der Altersarmut in Deutschland", + "Leading Online Education from Participation to Success", + "Information Geometry and Evolutionary Game Theory", + "Reducible and nonsensical uses of game theory", + "Experiences of autistic children with technologies", + "Workshop on Computational User Models for Work", + "A Computational Method for Indoor Landmark Extraction", + "Evolution of reciprocity with limited payoff memory", + "WHO Housing and Health Guidelines", + "List Public communication to specialized and general audiences", + "Opening Up The Research Lifecycle", + "Report on Global Data Retrieval", + "Comparing SSH vocabularies and their applications in different systems" +) + +test_that("mode-0 inline title n-grams (get_title_ngrams) respect segmentation", { + segs <- lapply(list( + "Urban Emotions: Benefits and Risks for Urban Planning", + "Digitale Transformation der Lehre an Hochschulen – ein Werkstattbericht" + ), punctuation_segments) + out <- unlist(get_title_ngrams(segs, STOPS, c(2, 3))) + grams <- unlist(strsplit(out, "[ ;]")) + # no n-gram crosses the colon / spaced en dash + expect_false(any(grepl("Emotions_Benefits", grams, fixed = TRUE))) + expect_false(any(grepl("Hochschulen_ein", grams, fixed = TRUE))) + # within-segment n-grams survive + expect_true("Urban_Emotions" %in% grams) + expect_true("Digitale_Transformation" %in% grams) +}) + +test_that("no-op: clean titles are byte-identical to the pre-change output", { + expect_true(length(CLEAN_TITLES) >= 10) + for (t in CLEAN_TITLES) { + expect_identical(ngram_candidates(t, STOPS), + legacy_ngram_candidates(t, STOPS)) + expect_identical(ngram_candidates(t, STOPS, include_unigrams = TRUE), + legacy_ngram_candidates(t, STOPS, include_unigrams = TRUE)) + expect_identical(paper_title_ngrams(t, STOPS), + legacy_paper_title_ngrams(t, STOPS)) + } +}) + +# --- adversarial / robustness ------------------------------------------------- + +test_that("control characters in the source cannot forge placeholders", { + # \x01/\x03 are the chain-period and kept-colon placeholders, \x02 the + # boundary marker: a source string carrying them must not gain a period or a + # colon, and must not be split by them - they are inert whitespace + expect_equal(seg("alpha\x01beta gamma"), "alpha beta gamma") + expect_equal(seg("alpha\x03beta gamma"), "alpha beta gamma") + expect_equal(seg("alpha\x02beta gamma"), "alpha beta gamma") + expect_false(any(grepl("[.:]", seg("alpha\x01beta\x03gamma")))) + # tab/newline/CR keep their whitespace meaning + expect_equal(seg("first line\nsecond\tline"), "first line second line") +}) + +test_that("invisible characters do not fork a token's identity", { + expect_identical(ngram_candidates("co­operation between states", STOPS), + ngram_candidates("cooperation between states", STOPS)) + expect_identical(ngram_candidates("data​science methods today", STOPS), + ngram_candidates("datascience methods today", STOPS)) + expect_equal(seg("Leading edge research"), "Leading edge research") +}) + +test_that("degenerate inputs return an empty result, never an error", { + for (x in list("", " ", "...!?", "-", NA, NA_character_, NULL)) { + expect_identical(seg(x), character(0)) + } + expect_identical(ngram_candidates(NA, STOPS), character(0)) + expect_identical(paper_title_ngrams(NA, STOPS), character(0)) +}) + +test_that("keep-list matching is whole-token and survives surrounding punctuation", { + expect_equal(seg("a (80:20) split of data"), c("a", "80:20", "split of data")) + expect_equal(seg("80:20"), "80:20") + # a near-miss must NOT be protected by the keep-list entry it resembles + expect_equal(seg("ratio 800:20 here"), c("ratio 800", "20 here")) + expect_equal(seg("x80:20y here"), c("x80", "20y here")) +}) + +test_that("abbreviation-chain protection does not overreach", { + expect_equal(seg("U.S.-based policy research"), "U.S.-based policy research") + expect_equal(seg("in the U.S., and beyond"), c("in the U.S.", "and beyond")) + # a chain glued to a preceding word is not an abbreviation + expect_equal(seg("aU.S. policy"), c("aU.S", "policy")) + # three hyphens are a run, not a compound + expect_equal(seg("word---word here"), c("word", "word here")) +}) + +test_that("a quoted inner word is isolated (documented P22 behaviour)", { + # both quotes are spaced on their outer side, so the quoted word becomes its + # own segment and the phrase around it does not form n-grams. Pinned so any + # future "transparent quote pair" rule is a deliberate change, not a drift. + expect_equal(seg("Wirtschaftspolitik \"schlägt\" Sozialpolitik"), + c("Wirtschaftspolitik", "schlägt", "Sozialpolitik")) + expect_equal(seg("the 'best' method for testing"), + c("the", "best", "method for testing")) + # the apostrophe inside a word is unaffected by this + expect_equal(seg("the author's best method"), "the author's best method") +}) + +test_that("non-Latin scripts pass through untouched", { + expect_equal(seg("دراسة حول التعليم الرقمي"), "دراسة حول التعليم الرقمي") + expect_equal(seg("机器学习 在 教育 中的 应用"), "机器学习 在 教育 中的 应用") +}) + +test_that("pathological punctuation runs terminate", { + expect_identical(seg(strrep("?!.", 400)), character(0)) + expect_equal(seg(paste0("start ", strrep("-", 20), " end")), c("start", "end")) + # beyond 80 non-space chars the existing corpus-noise guard removes the run + # before segmentation sees it, so it degrades to whitespace, not a boundary + expect_equal(seg(paste0("start ", strrep("-", 300), " end")), "start end") +}) + +test_that("control: the legacy reimplementation does differ on punctuation", { + # guards that the no-op assertions above are not comparing two identical + # code paths - on a punctuated title the implementations must diverge + t <- "Wind Energy in Germany: Potential Areas" + expect_false(identical(ngram_candidates(t, STOPS), + legacy_ngram_candidates(t, STOPS))) + expect_true("Germany_Potential" %in% legacy_ngram_candidates(t, STOPS)) +}) diff --git a/server/preprocessing/other-scripts/test/test_ranking_select.R b/server/preprocessing/other-scripts/test/test_ranking_select.R index 19f92bdb8..fcbcccb3f 100644 --- a/server/preprocessing/other-scripts/test/test_ranking_select.R +++ b/server/preprocessing/other-scripts/test/test_ranking_select.R @@ -133,6 +133,17 @@ test_that("drop_excluded_terms is a no-op with empty exclusions or empty cluster expect_equal(length(drop_excluded_terms(tt, c("a"))[[2]]), 0) }) +test_that("the shipped exclusion list carries the curated generic terms", { + if (!exists("get_label_exclusions")) source("utils.R") + ex <- get_label_exclusions() + expect_true(all(c("humans", "animals", "science", "medicine", "article") %in% ex)) + # generic document-type words are dropped, specific n-grams containing them + # are not (whole-term matching) + tt <- list(c(Article = 9, `Article processing charges` = 4, Neoplasms = 2)) + out <- drop_excluded_terms(tt, ex) + expect_equal(sort(names(out[[1]])), sort(c("Article processing charges", "Neoplasms"))) +}) + # --- Mode-3 cross-rank de-nesting (rank 1 keywords <-> rank 2 specific MeSH) -- spec3 <- rank_spec("3") # Faithful string-nesting de-nester mirroring filter_out_nested_ngrams: substring diff --git a/server/preprocessing/resources/label_exclusions.txt b/server/preprocessing/resources/label_exclusions.txt index 1bc3ae42c..fe542869d 100644 --- a/server/preprocessing/resources/label_exclusions.txt +++ b/server/preprocessing/resources/label_exclusions.txt @@ -2,3 +2,4 @@ humans animals science medicine +article From 183f7b8a0076b5af36051e95213605e86b8ea0c8 Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 3 Sep 2026 12:23:13 +0200 Subject: [PATCH 183/205] test case updates --- e2e/basicTests/OpenAIRE.spec.ts | 2 +- e2e/keywordsEnrichment/BASE.spec.ts | 1 + .../replay/base_cancer_fallback.expected.rds | Bin 630 -> 606 bytes .../replay/base_cancer_research.expected.rds | Bin 601 -> 579 bytes .../replay/base_digital_education.expected.rds | Bin 632 -> 617 bytes .../replay/openaire_fight_ncov.expected.rds | Bin 628 -> 606 bytes .../replay/orcid_0204881x.expected.mode1.rds | Bin 540 -> 508 bytes .../replay/orcid_0204881x.expected.mode2.rds | Bin 540 -> 508 bytes .../replay/orcid_0204881x.expected.mode3.rds | Bin 540 -> 508 bytes .../test/replay/orcid_0204881x.expected.rds | Bin 531 -> 497 bytes .../replay/orcid_02979614.expected.mode1.rds | Bin 334 -> 313 bytes .../replay/orcid_02979614.expected.mode2.rds | Bin 334 -> 313 bytes .../replay/orcid_02979614.expected.mode3.rds | Bin 334 -> 313 bytes .../test/replay/orcid_02979614.expected.rds | Bin 338 -> 306 bytes .../replay/orcid_22336926.expected.mode1.rds | Bin 725 -> 697 bytes .../replay/orcid_22336926.expected.mode2.rds | Bin 733 -> 706 bytes .../replay/orcid_22336926.expected.mode3.rds | Bin 729 -> 703 bytes .../test/replay/orcid_22336926.expected.rds | Bin 728 -> 686 bytes .../replay/orcid_24414043.expected.mode1.rds | Bin 646 -> 606 bytes .../replay/orcid_24414043.expected.mode2.rds | Bin 646 -> 606 bytes .../replay/orcid_24414043.expected.mode3.rds | Bin 646 -> 606 bytes .../test/replay/orcid_24414043.expected.rds | Bin 688 -> 637 bytes .../replay/orcid_28976075.expected.mode1.rds | Bin 639 -> 621 bytes .../replay/orcid_28976075.expected.mode2.rds | Bin 643 -> 626 bytes .../replay/orcid_28976075.expected.mode3.rds | Bin 643 -> 626 bytes .../test/replay/orcid_28976075.expected.rds | Bin 627 -> 633 bytes .../test/replay/orcid_39246636.expected.rds | Bin 330 -> 297 bytes .../replay/orcid_42216275.expected.mode1.rds | Bin 657 -> 671 bytes .../replay/orcid_42216275.expected.mode2.rds | Bin 658 -> 676 bytes .../replay/orcid_42216275.expected.mode3.rds | Bin 718 -> 690 bytes .../test/replay/orcid_42216275.expected.rds | Bin 648 -> 675 bytes .../test/replay/orcid_45050517.expected.rds | Bin 520 -> 544 bytes .../replay/orcid_49712944.expected.mode1.rds | Bin 579 -> 583 bytes .../replay/orcid_49712944.expected.mode2.rds | Bin 579 -> 582 bytes .../replay/orcid_49712944.expected.mode3.rds | Bin 579 -> 582 bytes .../test/replay/orcid_49712944.expected.rds | Bin 622 -> 574 bytes .../replay/orcid_5116955x.expected.mode1.rds | Bin 617 -> 597 bytes .../replay/orcid_5116955x.expected.mode2.rds | Bin 615 -> 595 bytes .../replay/orcid_5116955x.expected.mode3.rds | Bin 687 -> 666 bytes .../test/replay/orcid_5116955x.expected.rds | Bin 609 -> 600 bytes .../replay/orcid_5116955x_r2.expected.mode1.rds | Bin 581 -> 554 bytes .../replay/orcid_5116955x_r2.expected.mode2.rds | Bin 586 -> 558 bytes .../replay/orcid_5116955x_r2.expected.mode3.rds | Bin 654 -> 640 bytes .../test/replay/orcid_5116955x_r2.expected.rds | Bin 645 -> 590 bytes .../test/replay/orcid_58498137.expected.rds | Bin 297 -> 276 bytes .../replay/orcid_60114382.expected.mode1.rds | Bin 630 -> 576 bytes .../replay/orcid_60114382.expected.mode2.rds | Bin 630 -> 576 bytes .../replay/orcid_60114382.expected.mode3.rds | Bin 630 -> 576 bytes .../test/replay/orcid_60114382.expected.rds | Bin 721 -> 667 bytes .../test/replay/orcid_89117832.expected.rds | Bin 274 -> 246 bytes .../test/replay/orcid_90626039.expected.rds | Bin 698 -> 682 bytes .../replay/orcid_92873770.expected.mode1.rds | Bin 539 -> 510 bytes .../replay/orcid_92873770.expected.mode2.rds | Bin 539 -> 510 bytes .../replay/orcid_92873770.expected.mode3.rds | Bin 539 -> 510 bytes .../test/replay/orcid_92873770.expected.rds | Bin 551 -> 518 bytes .../replay/orcid_96127791.expected.mode1.rds | Bin 750 -> 720 bytes .../replay/orcid_96127791.expected.mode2.rds | Bin 746 -> 722 bytes .../replay/orcid_96127791.expected.mode3.rds | Bin 765 -> 744 bytes .../test/replay/orcid_96127791.expected.rds | Bin 799 -> 800 bytes .../replay/orcid_96127791_r2.expected.mode1.rds | Bin 784 -> 760 bytes .../replay/orcid_96127791_r2.expected.mode2.rds | Bin 766 -> 739 bytes .../replay/orcid_96127791_r2.expected.mode3.rds | Bin 754 -> 727 bytes .../test/replay/orcid_96127791_r2.expected.rds | Bin 787 -> 805 bytes .../test/replay/orcid_98436798.expected.rds | Bin 743 -> 732 bytes .../replay/pubmed_cancer_datasets.expected.rds | Bin 615 -> 596 bytes .../pubmed_climate_change.expected.mode1.rds | Bin 566 -> 547 bytes .../pubmed_climate_change.expected.mode2.rds | Bin 566 -> 547 bytes .../pubmed_climate_change.expected.mode3.rds | Bin 566 -> 547 bytes .../pubmed_climate_change_all.expected.rds | Bin 572 -> 552 bytes .../replay/pubmed_disease.expected.mode1.rds | Bin 626 -> 602 bytes .../replay/pubmed_disease.expected.mode2.rds | Bin 626 -> 602 bytes .../replay/pubmed_disease.expected.mode3.rds | Bin 626 -> 602 bytes .../test/replay/pubmed_disease.expected.rds | Bin 625 -> 577 bytes .../test/replay/pubmed_disease_all.expected.rds | Bin 682 -> 668 bytes .../replay/pubmed_infection.expected.mode1.rds | Bin 622 -> 595 bytes .../replay/pubmed_infection.expected.mode2.rds | Bin 622 -> 595 bytes .../replay/pubmed_infection.expected.mode3.rds | Bin 622 -> 595 bytes .../test/replay/pubmed_infection.expected.rds | Bin 623 -> 617 bytes .../pubmed_infection_all.expected.mode1.rds | Bin 633 -> 607 bytes .../pubmed_infection_all.expected.mode2.rds | Bin 633 -> 607 bytes .../pubmed_infection_all.expected.mode3.rds | Bin 633 -> 607 bytes .../replay/pubmed_infection_all.expected.rds | Bin 623 -> 610 bytes .../pubmed_infection_datasets.expected.rds | Bin 624 -> 592 bytes .../replay/pubmed_space_travel.expected.rds | Bin 328 -> 295 bytes .../replay/pubmed_space_travel_all.expected.rds | Bin 611 -> 587 bytes .../test/replay/pubmed_species.expected.rds | Bin 629 -> 590 bytes .../test/replay/pubmed_species_all.expected.rds | Bin 669 -> 668 bytes .../test/replay/pubmed_vienna.expected.rds | Bin 614 -> 589 bytes .../replay/pubmed_vienna_all.expected.mode1.rds | Bin 733 -> 711 bytes .../replay/pubmed_vienna_all.expected.mode2.rds | Bin 733 -> 711 bytes .../replay/pubmed_vienna_all.expected.mode3.rds | Bin 733 -> 711 bytes .../test/replay/pubmed_vienna_all.expected.rds | Bin 711 -> 685 bytes .../test/replay/pubmed_work.expected.rds | Bin 640 -> 599 bytes .../replay/pubmed_work_all.expected.mode1.rds | Bin 635 -> 609 bytes .../replay/pubmed_work_all.expected.mode2.rds | Bin 635 -> 609 bytes .../replay/pubmed_work_all.expected.mode3.rds | Bin 635 -> 609 bytes .../test/replay/pubmed_work_all.expected.rds | Bin 665 -> 632 bytes .../test/test_punctuation_segments.R | 4 ++-- 98 files changed, 4 insertions(+), 3 deletions(-) diff --git a/e2e/basicTests/OpenAIRE.spec.ts b/e2e/basicTests/OpenAIRE.spec.ts index 60eac7f59..3870517e5 100644 --- a/e2e/basicTests/OpenAIRE.spec.ts +++ b/e2e/basicTests/OpenAIRE.spec.ts @@ -17,7 +17,7 @@ test.describe("Basic tests for OpenAIRE integration (Knowledge Map visualisation ); await page.getByTestId("context").getByText("More information").click(); await expect(page.locator("#info-body")).toContainText( - "This knowledge map presents you with a topical overview of research conducted in the following project:Project DetailsTitleOPENing UP new methods, indicators and tools for peer review, impact measurement and dissemination of research resultsAcronymOpenUPFunderECFunding programCSAH2020CallH2020-GARRI-2015-1Contract (GA) number710722Start Date2016-06-01End Date2018-11-30Open Access mandatetrueOrganizationsUGOE, UvA, PUBLIC POLICY AND MANAGEMENT INSTITUTE, FRONTIERS MEDIA SA, UoA, DZHW, AIT, Know Center, CNROpenAire LinkLinkWe use text similarity to create a knowledge map. The algorithm groups those resources together that have many words in common. Area titles are created from subject keywords of resources that have been assigned to the same area. We select those keywords and phrases that appear frequently in one area, and seldom in other areas.Knowledge maps provide an instant overview of a topic by showing the main areas at a glance, and resources related to them. This makes it possible to easily identify useful, pertinent information.Please read our FAQs to find out more about knowledge maps.Data sourceThe data is taken from OpenAIRE. OpenAIRE is a key infrastructure that enables the European transition to open science. It provides access to 3 million research projects and more than 70 million research outputs from more than 100,000 data sources.Open source softwareThe visualization is created with the award winning open source software Head Start provided by Open Knowledge Maps. Open Knowledge Maps is a non-profit organisation run by a group of dedicated team members and volunteers. In order to improve our free and open service, we need your support. Please send us your feedback to info@openknowledgemaps.org.If you want to support us financially, you can:Make a donationBecome an organisational memberBecome a funder of our roadmapSign-up for our newsletter to receive occasional updates.", + "This knowledge map presents you with a topical overview of research conducted in the following project:Project DetailsTitleOPENing UP new methods, indicators and tools for peer review, impact measurement and dissemination of research resultsAcronymOpenUPFunderECFunding programCSAH2020CallH2020-GARRI-2015-1Contract (GA) number710722Start Date2016-06-01End Date2018-11-30Open Access mandatetrueOrganizationsKnow Center, UvA, UOA (ΕΚΠΑ), DZHW, CNR, AIT, PPMI, UGOE, FRONTIERS MEDIA SAOpenAire LinkLinkWe use text similarity to create a knowledge map. The algorithm groups those resources together that have many words in common. Area titles are created from subject keywords of resources that have been assigned to the same area. We select those keywords and phrases that appear frequently in one area, and seldom in other areas.Knowledge maps provide an instant overview of a topic by showing the main areas at a glance, and resources related to them. This makes it possible to easily identify useful, pertinent information.Please read our FAQs to find out more about knowledge maps.Data sourceThe data is taken from OpenAIRE. OpenAIRE is a key infrastructure that enables the European transition to open science. It provides access to 3 million research projects and more than 70 million research outputs from more than 100,000 data sources.Open source softwareThe visualization is created with the award winning open source software Head Start provided by Open Knowledge Maps. Open Knowledge Maps is a non-profit organisation run by a group of dedicated team members and volunteers. In order to improve our free and open service, we need your support. Please send us your feedback to info@openknowledgemaps.org.If you want to support us financially, you can:Make a donationBecome an organisational memberBecome a funder of our roadmapSign-up for our newsletter to receive occasional updates.", ); }); }); diff --git a/e2e/keywordsEnrichment/BASE.spec.ts b/e2e/keywordsEnrichment/BASE.spec.ts index 714b16888..0d0a53c2f 100644 --- a/e2e/keywordsEnrichment/BASE.spec.ts +++ b/e2e/keywordsEnrichment/BASE.spec.ts @@ -110,6 +110,7 @@ const testCases: EnrichmentTestCase[] = [ query: '"game theory"', search_params: "&service=base&sorting=most-relevant&document_types%5B%5D=121&lang_id%5B%5D=all-lang&min_descsize=300", paperTitle: "Medical ethics, logic traps, and game theory: an illustrative tale of brain death", + flaky: true, keywords: "Adult [MeSH]; Analytical Approach; Attitude to Death [MeSH]; Brain Death [MeSH]; Death and Euthanasia; Decision Making [MeSH]; Female [MeSH]; Game Theory [MeSH]; Humans [MeSH]; Life Support Care [MeSH]; Logic 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zt>v(+f7P3s;-Z8jKZr6`gPF)qy+lmS2H|j>Q6Daen>K>=Axtfjj$=%DY7CKGQ^GmL zK^#O6)b-Xc(wLV6+jA6ROM!9hs`h-4iX8Y_Pfr-r-YkT)!x?RN#?FoiZEPXLq0-}C zc%#(c6&`XN?@R(PPZlL`K}vWDt3e%2DANsf+%-KH8yr?#10WG1#)djj(sBAi|Dy5kux Date: Fri, 4 Sep 2026 14:07:45 +0200 Subject: [PATCH 184/205] ngram generation rework: foundational settings and test setup --- .gitignore | 1 + local_dev/dev.env.example | 14 ++ server/preprocessing/other-scripts/ranking.R | 82 +++++++ .../preprocessing/other-scripts/summarize.R | 80 +++++-- .../replay/itu_0204881x.expected.mode1.rds | Bin 0 -> 537 bytes .../replay/itu_0204881x.expected.mode2.rds | Bin 0 -> 537 bytes .../replay/itu_0204881x.expected.mode3.rds | Bin 0 -> 537 bytes .../test/replay/itu_0204881x.expected.rds | Bin 0 -> 561 bytes .../test/replay/itu_0204881x.inputs.rds | Bin 0 -> 87635 bytes .../replay/itu_02979614.expected.mode1.rds | Bin 0 -> 341 bytes .../replay/itu_02979614.expected.mode2.rds | Bin 0 -> 341 bytes .../replay/itu_02979614.expected.mode3.rds | Bin 0 -> 341 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server/preprocessing/other-scripts/test/replay/itu_96127791.expected.rds create mode 100644 server/preprocessing/other-scripts/test/replay/itu_96127791.inputs.rds create mode 100644 server/preprocessing/other-scripts/test/replay/itu_98436798.expected.mode1.rds create mode 100644 server/preprocessing/other-scripts/test/replay/itu_98436798.expected.mode2.rds create mode 100644 server/preprocessing/other-scripts/test/replay/itu_98436798.expected.mode3.rds create mode 100644 server/preprocessing/other-scripts/test/replay/itu_98436798.expected.rds create mode 100644 server/preprocessing/other-scripts/test/replay/itu_98436798.inputs.rds create mode 100644 server/preprocessing/other-scripts/test/test_ngram_generator.R diff --git a/.gitignore b/.gitignore index 009c5b84d..24371ab23 100644 --- a/.gitignore +++ b/.gitignore @@ -87,6 +87,7 @@ e2e/reproducibility/logs/* local_dev/tools/label_analysis local_dev/tools/keyword_analysis local_dev/tools/reproducibility +local_dev/tools/dedup_review/* # mac os .DS_Store diff --git a/local_dev/dev.env.example b/local_dev/dev.env.example index 485e39e27..a8bf15db2 100644 --- a/local_dev/dev.env.example +++ b/local_dev/dev.env.example @@ -25,6 +25,20 @@ RANKING_MODE_PUBMED= RANKING_MODE_ORCID= RANKING_MODE_OPENAIRE= +# N-gram setting for area-label candidate generation (dataprocessing worker +# only; docs/ngram-generation-simplify.md). Values: 0|1|2|3|4|5 — 0 replicates +# current behaviour (generator-routed baseline). Resolution mirrors +# RANKING_MODE: per-integration override NGRAM_SETTING_ beats the +# global; empty falls back. Ranking mode 0 always forces setting 0. +NGRAM_SETTING=0 +NGRAM_SETTING_BASE= +NGRAM_SETTING_PUBMED= +NGRAM_SETTING_ORCID= +NGRAM_SETTING_OPENAIRE= +# Abstract inclusion for no-keyword papers (settings >= 1 only): true|false, +# same per-integration resolution (INCLUDE_ABSTRACTS_). +INCLUDE_ABSTRACTS=false + BEHIND_PROXY=True DEFAULT_DATABASE=dev FLASK_ENV=development diff --git a/server/preprocessing/other-scripts/ranking.R b/server/preprocessing/other-scripts/ranking.R index dc8c0e633..129c3c35d 100644 --- a/server/preprocessing/other-scripts/ranking.R +++ b/server/preprocessing/other-scripts/ranking.R @@ -46,6 +46,88 @@ ranking_mode <- function(service = NULL) { "0" } +NGRAM_SETTINGS <- c("0", "1", "2", "3", "4", "5") + +# Resolve the n-gram setting for a data integration +# (docs/ngram-generation-simplify.md §7.2). Same resolution shape as +# ranking_mode(): +# 1. per-integration override NGRAM_SETTING_ (e.g. NGRAM_SETTING_BASE) +# 2. global NGRAM_SETTING +# 3. default "0" (current sites unchanged) +# A value is only honoured if it is one of NGRAM_SETTINGS; an invalid value at +# one level falls through to the next, warning only when something was +# configured but nothing valid resolved. +ngram_setting <- function(service = NULL) { + pick <- function(v) if (nzchar(v) && v %in% NGRAM_SETTINGS) v else NA_character_ + + raw_svc <- if (!is.null(service) && nzchar(service)) { + Sys.getenv(paste0("NGRAM_SETTING_", toupper(service))) + } else { + "" + } + raw_glob <- Sys.getenv("NGRAM_SETTING") + + setting <- pick(raw_svc) + if (is.na(setting)) setting <- pick(raw_glob) + if (!is.na(setting)) return(setting) + + if (nzchar(raw_svc) || nzchar(raw_glob)) { + msg <- sprintf( + "ngram_setting: no valid n-gram setting for service '%s' (per-integration='%s', global='%s'); using setting 0", + if (is.null(service)) "" else service, raw_svc, raw_glob) + if (exists("logwarn")) logwarn(msg) else warning(msg) + } + "0" +} + +# Resolve the abstract-inclusion flag for a data integration +# (docs/ngram-generation-simplify.md §7.2/§7.3): with the flag on, no-keyword +# (subject_is_heuristic) papers feed title+abstract to the n-gram generator. +# Resolution mirrors ngram_setting(); valid values are "true"/"false" +# (case-insensitive), anything else falls through. Default FALSE. +include_abstracts <- function(service = NULL) { + pick <- function(v) { + v <- tolower(v) + if (v %in% c("true", "false")) v else NA_character_ + } + + raw_svc <- if (!is.null(service) && nzchar(service)) { + Sys.getenv(paste0("INCLUDE_ABSTRACTS_", toupper(service))) + } else { + "" + } + raw_glob <- Sys.getenv("INCLUDE_ABSTRACTS") + + flag <- pick(raw_svc) + if (is.na(flag)) flag <- pick(raw_glob) + if (!is.na(flag)) return(identical(flag, "true")) + + if (nzchar(raw_svc) || nzchar(raw_glob)) { + msg <- sprintf( + "include_abstracts: no valid value for service '%s' (per-integration='%s', global='%s'); abstracts stay off", + if (is.null(service)) "" else service, raw_svc, raw_glob) + if (exists("logwarn")) logwarn(msg) else warning(msg) + } + FALSE +} + +# The setting -> title-site ngram_lengths mapping +# (docs/ngram-generation-simplify.md §7.3). Setting 0 is the generator-routed +# replication of the architectural no-op baseline: the title sites form +# bi+tri-grams (the corpus-level "1,2,2,3" of Setting 0 is emergent — the +# unigrams and second bigram route come from the untouched G1 subject +# synthesis, which only Settings >= 1 bypass). +ngram_setting_lengths <- function(setting) { + switch(as.character(setting), + "0" = c(2, 3), + "1" = c(1, 2, 3), + "2" = c(1, 2, 3, 4), + "3" = c(2, 3, 4), + "4" = c(1, 2, 3, 4, 5), + "5" = c(2, 3, 4, 5), + NULL) +} + # Ordered rank spec for a mode. Each entry is list(rank, sources, policy): # rank : the rank number (1 = highest priority). # sources : names of the rank_sources token-sets that feed this rank (pooled). diff --git a/server/preprocessing/other-scripts/summarize.R b/server/preprocessing/other-scripts/summarize.R index b2565b425..0f394d4cb 100644 --- a/server/preprocessing/other-scripts/summarize.R +++ b/server/preprocessing/other-scripts/summarize.R @@ -196,17 +196,20 @@ COLON_KEEP_TOKENS <- c("80:20", "50:50") # word characters stays inside its token. Deviations: colon, em dash, pipe and # underscore always split (underscore because it is the n-gram joiner # character); colon keep-list tokens and multi-period abbreviation chains -# ("U.S.", "e.g." — trailing period included) stay whole; a run of >= 2 -# consecutive punctuation marks always splits as a unit. Placeholders \x01 -# (chain periods), \x03 (kept colons) and \x02 (boundaries) cannot occur in -# decoded titles. Returns a character vector of trimmed, whitespace-collapsed, +# ("U.S.", "e.g." — trailing period included) stay whole; a balanced (word) +# pair fused to a word character on at least one side keeps its parens as +# token content ("(in)justice", "micro(nano)") while its spaced outer side +# still bounds the segment; a run of >= 2 consecutive punctuation marks always +# splits as a unit. Placeholders \x01 (chain periods), \x03 (kept colons), +# \x04/\x05 (kept parens) and \x02 (boundaries) cannot occur in decoded +# titles. Returns a character vector of trimmed, whitespace-collapsed, # non-empty segments; case is preserved. NA/empty input -> character(0). punctuation_segments <- function(text) { text <- if (is.null(text) || is.na(text)) "" else text text <- sanitize_corpus_noise(decode_html_entities(text)) - # C0 control characters are removed before anything else: \x01-\x03 are this + # C0 control characters are removed before anything else: \x01-\x05 are this # function's own placeholders, so a source string containing them would be - # restored as a period/colon or silently split the text + # restored as a period/colon/paren or silently split the text text <- gsub("[\x01-\x08\x0b\x0c\x0e-\x1f]", " ", text) # soft hyphen and zero-width characters render as nothing but count as # non-space, so they would be kept inside a token and stop it matching the @@ -231,9 +234,20 @@ punctuation_segments <- function(text) { pat <- paste0("(?= 1) # HEUR_MIN2 = the paper's n-grams with map-wide document frequency >= 2 -# Computed once, early in labelling. -add_heuristic_keyword_fields <- function(metadata, stops) { - per_paper <- lapply(metadata$title, paper_title_ngrams, stops = stops) +# Computed once, early in labelling. ngram_lengths comes from the resolved +# n-gram setting (ngram_setting_lengths); the Setting-0 default c(2, 3) +# reproduces paper_title_ngrams exactly (pinned by the drop-in equivalence +# test in test_ngram_generator.R). +add_heuristic_keyword_fields <- function(metadata, stops, ngram_lengths = c(2, 3)) { + per_paper <- lapply(metadata$title, function(t) + unique(ngram_candidates(t, stops, ngram_lengths = ngram_lengths))) df <- table(unlist(lapply(per_paper, unique))) # map-wide document frequency min2_set <- names(df)[df >= 2] metadata[[HEUR_MIN1]] <- vapply(per_paper, @@ -471,6 +497,25 @@ create_cluster_labels <- function(clusters, metadata, # title segmentation applies in every mode. mode <- ranking_mode(service) cc <- params$custom_clustering + # N-gram setting axis (docs/ngram-generation-simplify.md §7.2/§7.3), resolved + # per service like the ranking mode. Mode 0 always runs Setting 0 (it keeps + # the legacy corpus/selection structure), and the setting is a no-op on the + # custom-clustering path (no title n-grams there). Setting 0 leaves every + # call site on its current behaviour. + nset <- ngram_setting(service) + if (identical(mode, "0") && !identical(nset, "0")) { + vslog$warn(paste("create_cluster_labels: ranking mode 0 forces ngram setting 0", + "(requested:", nset, ")")) + nset <- "0" + } + if (!(is.null(cc)) && (cc %in% names(metadata)) && !identical(nset, "0")) { + vslog$info(paste("create_cluster_labels: ngram setting", nset, + "is a no-op on the custom-clustering path")) + } + nset_lengths <- ngram_setting_lengths(nset) + nset_abstracts <- !identical(nset, "0") && include_abstracts(service) + vslog$debug(paste("create_cluster_labels: ngram setting", nset, + "abstracts", nset_abstracts)) # Curated area-label exclusion list, applied post-tf-idf / pre-ranking at every # candidate-producing tier (initial, fallback, title/abstract) so listed generic # terms can never become a label. 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    Number of total citations: {params.total_citations ? params.total_citations : localization.notAvailable}

    - +

    ALTMETRICS

    From 868ee98c92909fa6262812319e6a3dfd83cd750c Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 15 Sep 2026 11:06:03 +0200 Subject: [PATCH 192/205] wip for deduplication bugfix --- server/workers/base/src/base.py | 11 +++ server/workers/common/common/deduplication.py | 88 +++++++++++++++++++ 2 files changed, 99 insertions(+) diff --git a/server/workers/base/src/base.py b/server/workers/base/src/base.py index 540cf2e97..00a24b19f 100644 --- a/server/workers/base/src/base.py +++ b/server/workers/base/src/base.py @@ -26,6 +26,7 @@ prioritize_doi_and_provider, get_provider_priority, doi_title_filter, + split_correction_groups, ) from common.enrichment import enrich_anchor_using_duplicates import re @@ -393,6 +394,16 @@ def filter_duplicates(df, service, params): df.drop(index=false_positive_indexes, inplace=True) logger.info(f"[dedup:doi_title_filter] dropped {len(false_positive_indexes)} false-positive records") + # Second-pass guard over ALL assembled groups (textual + dcdoi-key): + # article/correction-notice conflations asserted by source dcdoi fields + # are severed into two works, so prioritization and enrichment below + # operate on the split groups and the correction cannot inherit the + # article's abstract or DOIs. See split_correction_groups. + df, n_correction_splits = split_correction_groups(df) + if n_correction_splits: + logger.info(f"[dedup:correction_split] severed {n_correction_splits} article/correction groups") + duplicate_groups = find_duplicate_groups(df) + # if logger.isEnabledFor(logging.DEBUG): # for idx_group in duplicate_groups: # if len(idx_group) > 1: diff --git a/server/workers/common/common/deduplication.py b/server/workers/common/common/deduplication.py index 971507c06..e735c8052 100644 --- a/server/workers/common/common/deduplication.py +++ b/server/workers/common/common/deduplication.py @@ -294,6 +294,94 @@ def select_anchor_index(candidates, by=None, ascending=None): return candidates.index[winner_pos] +# --- correction-notice split guard ------------------------------------------ +# A correction/erratum notice and its article are related-but-distinct works, +# but source metadata routinely conflates them: repositories list the +# correction's DOI in the article's dcdoi field (or vice versa), and the two +# titles differ only by a short prefix, so both the DOI-key pass and the +# textual pass merge them into one duplicate group, and the correction +# anchor then inherits the article's abstract and DOIs. doi_title_filter +# cannot split such a pair and must not be loosened (it would tear apart +# trusted retitled-preprint merges), so the guard uses a dedicated criterion: +# exactly one of the two titles carries a correction-family prefix and the +# remainders are the same title. Curated, English-language prefix list; +# longer alternatives must precede their own prefixes. +correction_prefix_pattern = re.compile( + r"(publisher correction|author correction|correction to" + r"|corrigendum to|corrigendum|erratum zu|erratum to|erratum" + r"|retraction note to|retraction note|retraction of" + r"|expression of concern on|expression of concern" + r"|addendum to|addendum)\s+" +) +# Note: bare "retracted" is deliberately NOT in the family: "[Retracted] X" is +# the retracted article ITSELF with a marker added to its title (same work, +# must keep merging with plain-titled copies), unlike a retraction notice +# ("Retraction of: X"), which is a separate work. + + +def _correction_prefix_match(title): + return correction_prefix_pattern.match(_normalize_title(title)) + + +def is_correction_variant(title_a, title_b): + """True if one title is a correction-family variant of the other. + + Exactly one of the two titles must carry a correction-family prefix, and + stripping it must leave the other title (case- and punctuation-folded). + Two plain or two prefixed titles never match, so corrections of one + article still deduplicate normally, and a title that merely happens to + start with a correction word does not match its own copies. + """ + ma = _correction_prefix_match(title_a) + mb = _correction_prefix_match(title_b) + if bool(ma) == bool(mb): + return False + if ma: + stem, plain = _normalize_title(title_a)[ma.end():], _normalize_title(title_b) + else: + stem, plain = _normalize_title(title_b)[mb.end():], _normalize_title(title_a) + return bool(stem) and stem == plain + + +def split_correction_groups(df): + """Second-pass guard over the assembled duplicate groups. + + A group containing both an article and its correction-notice variant (see + is_correction_variant) is severed into its article side and its correction + side. Both works are real, so the group is split, not dropped: cross-side + ids are removed from the `duplicates` marking and each side keeps (or + gets) its own anchor. Callers must recompute duplicate_groups afterwards + so prioritization and enrichment operate on the split groups. + + Returns (df, number_of_groups_split). + """ + n_split = 0 + for _, idx in find_duplicate_groups(df).items(): + idx = df.index.intersection(idx) + if len(idx) < 2: + continue + prefixed = [i for i in idx if _correction_prefix_match(df.at[i, "title"])] + plain = [i for i in idx if not _correction_prefix_match(df.at[i, "title"])] + if not prefixed or not plain: + continue + if not any(is_correction_variant(df.at[p, "title"], df.at[q, "title"]) + for p in prefixed for q in plain): + continue + for side, other in ((prefixed, plain), (plain, prefixed)): + other_ids = set(df.loc[other, "id"]) + for i in side: + members = [m for m in str(df.at[i, "duplicates"]).split(",") + if m and m not in other_ids] + df.at[i, "duplicates"] = ",".join(members) + side_frame = df.loc[side] + if not side_frame.is_anchor.any(): + anchor_idx = select_anchor_index(side_frame) + df.at[anchor_idx, "is_anchor"] = True + df.at[anchor_idx, "is_duplicate"] = False + n_split += 1 + return df, n_split + + def remove_textual_duplicates_from_different_sources(df, duplicate_groups): for _, idx in duplicate_groups.items(): if len(idx) > 1: From 67d5fe990e495c3a74af649a152dad048a65f0d2 Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 15 Sep 2026 11:06:13 +0200 Subject: [PATCH 193/205] wip for deduplication bugfix --- server/workers/common/common/deduplication.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/server/workers/common/common/deduplication.py b/server/workers/common/common/deduplication.py index e735c8052..0bfdf0b3a 100644 --- a/server/workers/common/common/deduplication.py +++ b/server/workers/common/common/deduplication.py @@ -304,7 +304,7 @@ def select_anchor_index(candidates, by=None, ascending=None): # cannot split such a pair and must not be loosened (it would tear apart # trusted retitled-preprint merges), so the guard uses a dedicated criterion: # exactly one of the two titles carries a correction-family prefix and the -# remainders are the same title. Curated, English-language prefix list; +# remainders are the same title. Curated, mainly English-language prefix list; # longer alternatives must precede their own prefixes. correction_prefix_pattern = re.compile( r"(publisher correction|author correction|correction to" From 90b849bf921ac0762337d68e5173d2fa0f1ced58 Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 15 Sep 2026 11:23:42 +0200 Subject: [PATCH 194/205] make pandas sorting deterministic --- server/workers/common/common/enrichment.py | 39 ++++++++++++++++++++++ server/workers/orcid/src/orcid_service.py | 21 ++++++------ 2 files changed, 50 insertions(+), 10 deletions(-) diff --git a/server/workers/common/common/enrichment.py b/server/workers/common/common/enrichment.py index f7eeecbde..e018103ef 100644 --- a/server/workers/common/common/enrichment.py +++ b/server/workers/common/common/enrichment.py @@ -443,3 +443,42 @@ def get_anchor_field_value(df, anchor_idx, column_name): if pd.isna(value) or value == '': return None return value + + +def select_rows_per_doi(base_metadata): + """Deterministic per-DOI row selection for the ORCID enrichment merge. + + Input rows are BASE records exploded over their dcdoi values, one row per + (record, DOI) pair, carrying `_is_direct_fetch` (the record was fetched + for this DOI) and `_dcdoi_pos` (the DOI's position in the record's dcdoi + list). Exactly one row survives per lowercased `doi_merge`, ranked by: + + 1. direct fetches before rows that acquired the DOI through dcdoi + explosion; + 2. front-of-list dcdoi assertions before deep-list mentions — a DOI + deep in a long dcdoi list is a bibliography entry, not an identity + claim (buckets: position 0, positions 1-4, position >= 5); + 3. longer abstracts first — an enrichment candidate without an + abstract must not shadow one that has it; + 4. id, as the final tie-break. + + The sort is stable and every key is row content, so the winner is a pure + function of the records, never of frame size or layout. The previous + implementation sorted by the direct flag alone with pandas' default + unstable quicksort: tied rows won by an arbitrary, layout-dependent + permutation, and unrelated upstream changes flipped which record + enriched a work. + """ + pos = pd.to_numeric(base_metadata['_dcdoi_pos'], errors='coerce').fillna(0) + ranked = base_metadata.assign( + _direct_sort=(~base_metadata['_is_direct_fetch']).astype(int), + _assert_rank=(pos > 0).astype(int) + (pos >= 5).astype(int), + _abs_len_neg=-base_metadata['paper_abstract'].fillna('').astype(str).str.len(), + _doi_key=base_metadata['doi_merge'].str.lower(), + ) + ranked = ranked.sort_values( + by=['_direct_sort', '_assert_rank', '_abs_len_neg', 'id'], + kind='stable', + ).drop_duplicates(subset='_doi_key', keep='first') + return ranked.drop(columns=['_direct_sort', '_assert_rank', '_abs_len_neg', + '_doi_key', '_is_direct_fetch', '_dcdoi_pos']) diff --git a/server/workers/orcid/src/orcid_service.py b/server/workers/orcid/src/orcid_service.py index 6f4750989..663ffcad7 100644 --- a/server/workers/orcid/src/orcid_service.py +++ b/server/workers/orcid/src/orcid_service.py @@ -5,7 +5,7 @@ import os import uuid from common.decorators import error_logging_aspect -from common.enrichment import oa_state_priority +from common.enrichment import oa_state_priority, select_rows_per_doi import numpy as np from pyorcid import Orcid, errors as pyorcid_errors from pyorcid.orcid_authentication import OrcidAuthentication @@ -395,7 +395,13 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra # Save the normalized original doi before explode so we can rank direct fetches # above rows whose doi was reassigned from additional_dois after explosion. base_metadata['_fetch_doi'] = base_metadata['doi_merge'].apply(remove_doi_prefix) + base_metadata['_orig_row'] = np.arange(len(base_metadata)) base_metadata = base_metadata.explode('additional_dois', ignore_index=True) + # Position of each exploded DOI within its source record's dcdoi list: + # front positions are identity assertions, deep positions are closer to + # bibliography entries. Consumed by select_rows_per_doi. + base_metadata['_dcdoi_pos'] = base_metadata.groupby('_orig_row').cumcount() + base_metadata.drop(columns='_orig_row', inplace=True) # replace doi_merge with additional_dois if additional_dois is not empty, otherwise keep doi_merge base_metadata.loc[base_metadata['additional_dois'].notna() & (base_metadata['additional_dois'] != ''), 'doi_merge'] = base_metadata.loc[base_metadata['additional_dois'].notna() & (base_metadata['additional_dois'] != ''), 'additional_dois'] base_metadata.loc[:, 'doi_merge'] = base_metadata['doi_merge'].apply(remove_doi_prefix) @@ -444,15 +450,10 @@ def enrich_metadata_with_base(self, params: Dict[str, str], metadata: pd.DataFra .set_index('_doi_key')['oa_state'] ) base_metadata['oa_state'] = doi_key.map(best_oa_state) - # Sort by: direct fetch before exploded-from-additional_dois rows, - # This ensures the record actually fetched for a DOI wins over a record - # that acquired that DOI via additional_dois expansion. - base_metadata = base_metadata.assign( - _direct_sort=(~base_metadata['_is_direct_fetch']).astype(int), - _doi_key=base_metadata['doi_merge'].str.lower(), - ).sort_values(by=['_direct_sort']).drop_duplicates( - subset='_doi_key', keep='first' - ).drop(columns=['_direct_sort', '_is_direct_fetch', '_doi_key']) + # Deterministic per-DOI selection: direct fetch first, then + # front-of-dcdoi assertions, then abstract-bearing rows, stable + # throughout — see common.enrichment.select_rows_per_doi. + base_metadata = select_rows_per_doi(base_metadata) # if self.logger.isEnabledFor(logging.DEBUG): # self._log_dataframe(base_metadata.sort_values(by='title'), params, 'base_metadata_after_doi_dedup') From 0ba5adbdf10c50a8af3b2c295d3553c6a3f93bcc Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 15 Sep 2026 11:36:22 +0200 Subject: [PATCH 195/205] update of contentprovider.json cache --- server/workers/common/common/contentproviders.json | 8 -------- 1 file changed, 8 deletions(-) diff --git a/server/workers/common/common/contentproviders.json b/server/workers/common/common/contentproviders.json index b6eef62c8..ee15999d7 100644 --- a/server/workers/common/common/contentproviders.json +++ b/server/workers/common/common/contentproviders.json @@ -7983,10 +7983,6 @@ "name": "Universitas Sains Cut Nyak Dhien (USCND): OJS", "internal_name": "ftunivscndojs" }, - { - "name": "Sekolah Tinggi Pariwisata Mataram: OJS", - "internal_name": "ftstpmataramojs2" - }, { "name": "Portal de Revistas ESDEG (Escuela Superior de Guerra \"General Rafael Reyes Prieto\")", "internal_name": "ftescsdguerra" @@ -10655,10 +10651,6 @@ "name": "International Journal Of Humanities Education and Social Sciences", "internal_name": "ftjijhess" }, - { - "name": "Hawalah - Kajian Ilmu Ekonomi Syariah", - "internal_name": "ftjhawalah" - }, { "name": "The University of Toledo Open Journals", "internal_name": "ftunivtoledous" From c2b66ed6ba1e33038dc93159de356fc971a9c387 Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 15 Sep 2026 12:42:09 +0200 Subject: [PATCH 196/205] test cases for JEL, AMS, PACS classification cleanup --- .../other-scripts/test/test_subject_chain.R | 60 +++++++++++++++++++ .../test/test_subject_cleaning.R | 42 +++++++++++++ 2 files changed, 102 insertions(+) diff --git a/server/preprocessing/other-scripts/test/test_subject_chain.R b/server/preprocessing/other-scripts/test/test_subject_chain.R index 94f7fc1be..73cad97fb 100644 --- a/server/preprocessing/other-scripts/test/test_subject_chain.R +++ b/server/preprocessing/other-scripts/test/test_subject_chain.R @@ -203,3 +203,63 @@ test_that("the doaj flag is applied per record", { doaj = c(TRUE, FALSE)) expect_equal(out, c("hydrology", "Environmental sciences; hydrology")) }) + +# --- JEL / AMS MSC / PACS classifications (corpus cases) ---------------------- + +test_that("chain removes JEL code clusters, keeps topic keywords", { + expect_equal(chain("ddc:330; C72; C73; D03; D64; evolutionary game theory; cooperation"), + "evolutionary game theory; cooperation") +}) + +test_that("chain keeps JEL false positives", { + expect_equal(chain("R1; Supplementary Data; artificial intelligence"), + "R1; Supplementary Data; artificial intelligence") +}) + +test_that("chain removes AMS MSC codes; dd-dd is covered by the LCC range rule", { + expect_equal(chain("Geometric phase; 81V25; Majorana fermion"), + "Geometric phase; Majorana fermion") + expect_equal(chain("81-06; 81Vxx; Mathematical physics"), "Mathematical physics") +}) + +test_that("chain removes PACS codes whole instead of mangling them", { + expect_equal(chain("Quantum physics; Statistical mechanics; 05.30.Rt"), + "Quantum physics; Statistical mechanics") + expect_equal(chain("quantum entanglement; 03.65.Ud; 03.67.-a"), + "quantum entanglement") +}) + +test_that("classification drops stay off the timeline branch", { + expect_equal(chain("81V25; Geometric phase", vis_type = "timeline"), + "81V25; Geometric phase") +}) + +# --- arXiv name+code keywords (corpus cases) ---------------------------------- + +test_that("hyphenated class names are removed whole, not left as partials", { + expect_equal(chain("Adaptation and Self-Organizing Systems nlin.AO"), "") + expect_equal(chain("Human-Computer Interaction cs.HC; real keyword"), "real keyword") +}) + +test_that("physics/hep/nucl/math-ph class names are removed like cs/stat names", { + expect_equal(chain("Applied Physics physics.app-ph"), "") + expect_equal(chain("Medical Physics physics.med-ph"), "") + expect_equal(chain("High Energy Physics - Experiment hep-ex"), "") + expect_equal(chain("Mathematical Physics math-ph"), "") + expect_equal(chain("Nuclear Experiment nucl-ex"), "") + expect_equal(chain("Data Analysis, Statistics and Probability physics.data-an"), "") +}) + +test_that("the arXiv name prefix never eats across a keyword boundary", { + expect_equal(chain("machine learning; Computation and Language cs.CL; corpora"), + "machine learning; corpora") +}) + +# --- TeX-style quote pairs at keyword boundaries ------------------------------ + +test_that("leading `` and trailing '' are stripped, single apostrophes stay", { + expect_equal(chain("``Commodification''; ``Valuation languages''"), + "Commodification; Valuation languages") + expect_equal(chain("climate policy; teachers'"), "climate policy; teachers'") + expect_equal(chain("women's rights"), "women's rights") +}) diff --git a/server/preprocessing/other-scripts/test/test_subject_cleaning.R b/server/preprocessing/other-scripts/test/test_subject_cleaning.R index 369c0ffaf..74c8fdbe9 100644 --- a/server/preprocessing/other-scripts/test/test_subject_cleaning.R +++ b/server/preprocessing/other-scripts/test/test_subject_cleaning.R @@ -301,3 +301,45 @@ test_that("a purely numeric keyword is dropped, digits inside words are kept", { expect_equal(clean_classification_keywords("COVID-19"), "COVID-19") expect_equal(clean_classification_keywords("H5N1"), "H5N1") }) + +# --- JEL / AMS MSC / PACS classification filters ------------------------------ + +test_that("drop_jel removes isolated official codes but keeps everything else", { + expect_equal(drop_jel(c("C72", "C73", "D03", "D64", "Game theory")), "Game theory") + # false-positive list: valid code shapes that are known real-world terms + expect_equal(drop_jel(c("R1", "B12", "D3", "C4", "L2")), + c("R1", "B12", "D3", "C4", "L2")) + # not on the official list (S/T/U/V/W/X are not JEL letters) + expect_equal(drop_jel(c("X99", "vitamin B12 deficiency")), + c("X99", "vitamin B12 deficiency")) +}) + +test_that("drop_jel removes code+caption keywords in all separator forms", { + expect_equal(drop_jel("C71 Cooperative Games"), character(0)) + expect_equal(drop_jel("C71 - Cooperative Games"), character(0)) + expect_equal(drop_jel("C71: Cooperative Games"), character(0)) + # leading caption fragment (captions contain semicolons; the first fragment + # stays attached to the code when a provider serializes code+caption) + expect_equal(drop_jel("J26 Retirement"), character(0)) + # trailing translation tail after " / " + expect_equal(drop_jel("C71 Cooperative Games / kooperative Spiele"), character(0)) + # code followed by text that is NOT the official caption stays + expect_equal(drop_jel("C71 Something Else"), "C71 Something Else") +}) + +test_that("drop_jel never removes caption-only keywords", { + expect_equal(drop_jel(c("Social Security", "Cooperative Games")), + c("Social Security", "Cooperative Games")) +}) + +test_that("drop_ams_msc removes MSC code forms, leaves dd-dd to the LCC rule", { + expect_equal(drop_ams_msc(c("81V25", "86A05", "81Vxx", "81-XX", "Majorana fermion")), + "Majorana fermion") + expect_equal(drop_ams_msc("81-06"), "81-06") +}) + +test_that("drop_pacs removes PACS code forms including hyphen/plus suffixes", { + expect_equal(drop_pacs(c("05.30.Rt", "03.65.Ud", "89.75.Da", + "03.67.-a", "42.50.+x", "keyword")), "keyword") + expect_equal(drop_pacs(c("1.2.3", "10.1234")), c("1.2.3", "10.1234")) +}) From 051fc42ad273052e31dd9a70ab17ef8510bba23e Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 15 Sep 2026 12:44:18 +0200 Subject: [PATCH 197/205] additional keyword cleanup for JEL, AMS, PACS classifications; arxiv classification parsing improvements; TeX quotation cleanup --- .../preprocessing/other-scripts/jel_codes.R | 1023 +++++++++++++++++ .../other-scripts/subject_cleaning.R | 96 +- 2 files changed, 1117 insertions(+), 2 deletions(-) create mode 100644 server/preprocessing/other-scripts/jel_codes.R diff --git a/server/preprocessing/other-scripts/jel_codes.R b/server/preprocessing/other-scripts/jel_codes.R new file mode 100644 index 000000000..7f63ed2fd --- /dev/null +++ b/server/preprocessing/other-scripts/jel_codes.R @@ -0,0 +1,1023 @@ +# jel_codes.R +# +# JEL classification lookup for drop_jel (subject_cleaning.R). Generated from +# the official AEA lists: +# https://www.aeaweb.org/econlit/classifications.xml (3-char codes) +# https://www.aeaweb.org/econlit/classificationTree.xml (2-char levels) +# via local_dev/tools/label_analysis/jel_codes_2026-09-14.tsv. Bare top-level +# letters are excluded by design (a single letter can never be safely +# attributed to a classification). Codes marked "[retired code, observed in +# corpus]" are absent from the current official scheme but occur in provider +# metadata and must stay on the list. +# +# JEL_FALSE_POSITIVES: keywords that match a valid JEL code but are known +# real-world terms and must never be removed. Corpus-evidenced: R1 (an LCC +# Medicine code emitted next to its caption "R Medicine (General)"). +# Proactive homonyms: B12/D3 (vitamins), C4 (photosynthesis), L2 (second +# language). + +JEL_FALSE_POSITIVES <- c("R1", "B12", "D3", "C4", "L2") + +JEL_CAPTIONS <- c( + "A00" = "General Economics and Teaching", + "A1" = "General Economics", + "A10" = "General Economics: General", + "A11" = "Role of Economics; Role of Economists; Market for Economists", + "A12" = "Relation of Economics to Other Disciplines", + "A13" = "Relation of Economics to Social Values", + "A14" = "Sociology of Economics", + "A19" = "General Economics: Other", + "A2" = "Economic Education and Teaching of Economics", + "A20" = "Economic Education and Teaching of Economics: General", + "A21" = "Economic Education and Teaching of Economics: Pre-college", + "A22" = "Economic Education and Teaching of Economics: Undergraduate", + "A23" = "Economic Education and Teaching of Economics: Graduate", + "A29" = "Economic Education and Teaching of Economics: Other", + "A3" = "Collective Works", + "A30" = "Collective Works: General", + "A31" = "Collected Writings of Individuals", + "A32" = "Collective Volumes", + "A33" = "Handbooks", + "A39" = "Collective Works: Other", + "B0" = "General", + "B00" = "History of Economic Thought, Methodology, and Heterodox Approaches", + "B1" = "History of Economic Thought through 1925", + "B10" = "History of Economic Thought through 1925: General", + "B11" = "History of Economic Thought: Preclassical (Ancient, Medieval, Mercantilist, Physiocratic)", + "B12" = "History of Economic Thought: Classical (includes Adam Smith)", + "B13" = "History of Economic Thought: Neoclassical through 1925 (Austrian, Marshallian, Walrasian, Wicksellian)", + "B14" = "History of Economic Thought through 1925: Socialist; Marxist", + "B15" = "History of Economic Thought through 1925: Historical; Institutional; Evolutionary", + "B16" = "History of Economic Thought through 1925: Quantitative and Mathematical", + "B17" = "History of Economic Thought through 1925: International Trade and Finance", + "B19" = "History of Economic Thought through 1925: Other", + "B2" = "History of Economic Thought since 1925", + "B20" = "History of Economic Thought since 1925: General", + "B21" = "History of Economic Thought: Microeconomics", + "B22" = "History of Economic Thought: Macroeconomics", + "B23" = "History of Economic Thought: Quantitative and Mathematical", + "B24" = "History of Economic Thought since 1925: Socialist; Marxist; Sraffian", + "B25" = "History of Economic Thought since 1925: Historical; Institutional; Evolutionary; Austrian; Stockholm School", + "B26" = "History of Economic Thought since 1925: Financial Economics", + "B27" = "History of Economic Thought since 1925: International Trade and Finance", + "B29" = "History of Economic Thought since 1925: Other", + "B3" = "History of Economic Thought: Individuals", + "B30" = "History of Economic Thought: Individuals: General", + "B31" = "History of Economic Thought: Individuals", + "B32" = "Obituaries", + "B4" = "Economic Methodology", + "B40" = "Economic Methodology: General", + "B41" = "Economic Methodology", + "B49" = "Economic Methodology: Other", + "B5" = "Current Heterodox Approaches", + "B50" = "Current Heterodox Approaches: General", + "B51" = "Current Heterodox Approaches: Socialist; Marxian; Sraffian", + "B52" = "Current Heterodox Approaches: Historical; Institutional; Evolutionary; Modern Monetary Theory", + "B53" = "Current Heterodox Approaches: Austrian", + "B54" = "Feminist Economics", + "B55" = "Social Economics", + "B59" = "Current Heterodox Approaches: Other", + "C0" = "General", + "C00" = "Mathematical and Quantitative Methods: General", + "C01" = "Econometrics", + "C02" = "Mathematical Methods", + "C1" = "Econometric and Statistical Methods and Methodology: General", + "C10" = "Econometric and Statistical Methods and Methodology: General", + "C11" = "Bayesian Analysis: General", + "C12" = "Hypothesis Testing: General", + "C13" = "Estimation: General", + "C14" = "Semiparametric and Nonparametric Methods: General", + "C15" = "Statistical Simulation Methods: General", + "C18" = "Methodological Issues: General", + "C19" = "Econometric and Statistical Methods: Other", + "C2" = "Single Equation Models • Single Variables", + "C20" = "Single Equation Models; Single Variables: General", + "C21" = "Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions", + "C22" = "Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes", + "C23" = "Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models", + "C24" = "Single Equation Models; Single Variables: Truncated and Censored Models; Switching Regression Models; Threshold Regression Models", + "C25" = "Single Equation Models; Single Variables: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities", + "C26" = "Single Equation Models: Single Variables: Instrumental Variables (IV) Estimation", + "C29" = "Single Equation Models; Single Variables: Other", + "C3" = "Multiple or Simultaneous Equation Models • Multiple Variables", + "C30" = "Multiple or Simultaneous Equation Models; Multiple Variables: General", + "C31" = "Multiple or Simultaneous Equation Models: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models", + "C32" = "Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models", + "C33" = "Multiple or Simultaneous Equation Models: Panel Data Models; Spatio-temporal Models", + "C34" = "Multiple or Simultaneous Equation Models: Truncated and Censored Models; Switching Regression Models", + "C35" = "Multiple or Simultaneous Equation Models: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions", + "C36" = "Multiple or Simultaneous Equation Models: Instrumental Variables (IV) Estimation", + "C38" = "Multiple or Simultaneous Equation Models: Classification Methods; Cluster Analysis; Principal Components; Factor Models", + "C39" = "Multiple or Simultaneous Equation Models; Multiple Variables: Other", + "C4" = "Econometric and Statistical Methods: Special Topics", + "C40" = "Econometric and Statistical Methods: Special Topics: General", + "C41" = "Duration Analysis; Optimal Timing Strategies", + "C42" = "Classification Discontinued 2008. See C83.", + "C43" = "Index Numbers and Aggregation; Leading indicators", + "C44" = "Operations Research; Statistical Decision Theory", + "C45" = "Neural Networks and Related Topics", + "C46" = "Specific Distributions; Specific Statistics", + "C49" = "Econometric and Statistical Methods: Special Topics: Other", + "C5" = "Econometric Modeling", + "C50" = "Econometric Modeling: General", + "C51" = "Model Construction and Estimation", + "C52" = "Model Evaluation, Validation, and Selection", + "C53" = "Forecasting Models; Simulation Methods", + "C54" = "Quantitative Policy Modeling", + "C55" = "Large Data Sets: Modeling and Analysis", + "C57" = "Econometrics of Games and Auctions", + "C58" = "Financial Econometrics", + "C59" = "Econometric Modeling: Other", + "C6" = "Mathematical Methods • Programming Models • Mathematical and Simulation Modeling", + "C60" = "Mathematical Methods; Programming Models; Mathematical and Simulation Modeling: General", + "C61" = "Optimization Techniques; Programming Models; Dynamic Analysis", + "C62" = "Existence and Stability Conditions of Equilibrium", + "C63" = "Computational Techniques; Simulation Modeling", + "C65" = "Miscellaneous Mathematical Tools", + "C67" = "Input-Output Models", + "C68" = "Computable General Equilibrium Models", + "C69" = "Mathematical Methods; Programming Models; Mathematical and Simulation Modeling: Other", + "C7" = "Game Theory and Bargaining Theory", + "C70" = "Game Theory and Bargaining Theory: General", + "C71" = "Cooperative Games", + "C72" = "Noncooperative Games", + "C73" = "Stochastic and Dynamic Games; Evolutionary Games; Repeated Games", + "C78" = "Bargaining Theory; Matching Theory", + "C79" = "Game Theory and Bargaining Theory: Other", + "C8" = "Data Collection and Data Estimation Methodology • Computer Programs", + "C80" = "Data Collection and Data Estimation Methodology; Computer Programs: General", + "C81" = "Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access", + "C82" = "Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access", + "C83" = "Survey Methods; Sampling Methods", + "C87" = "Econometric Software", + "C88" = "Data Collection and Data Estimation Methodology; Computer Programs: Other Computer Software", + "C89" = "Data Collection and Data Estimation Methodology; Computer Programs: Other", + "C9" = "Design of Experiments", + "C90" = "Design of Experiments: General", + "C91" = "Design of Experiments: Laboratory, Individual", + "C92" = "Design of Experiments: Laboratory, Group Behavior", + "C93" = "Field Experiments", + "C99" = "Design of Experiments: Other", + "D0" = "General", + "D00" = "Microeconomics: General", + "D01" = "Microeconomic Behavior: Underlying Principles", + "D02" = "Institutions: Design, Formation, Operations, and Impact", + "D03" = "Behavioral Microeconomics: Underlying Principles [retired code, observed in corpus]", + "D04" = "Microeconomic Policy: Formulation, Implementation, and Evaluation", + "D1" = "Household Behavior and Family Economics", + "D10" = "Household Behavior: General", + "D11" = "Consumer Economics: Theory", + "D12" = "Consumer Economics: Empirical Analysis", + "D13" = "Household Production and Intrahousehold Allocation", + "D14" = "Household Saving; Personal Finance", + "D15" = "Intertemporal Household Choice; Life Cycle Models and Saving", + "D16" = "Collaborative Consumption", + "D18" = "Consumer Protection", + "D19" = "Household Behavior and Family Economics: Other", + "D2" = "Production and Organizations", + "D20" = "Production and Organizations: General", + "D21" = "Firm Behavior: Theory", + "D22" = "Firm Behavior: Empirical Analysis", + "D23" = "Organizational Behavior; Transaction Costs; Property Rights", + "D24" = "Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity", + "D25" = "Intertemporal Firm Choice: Investment, Capacity, and Financing", + "D26" = "Crowd-Based Firms", + "D29" = "Production and Organizations: Other", + "D3" = "Distribution", + "D30" = "Distribution: General", + "D31" = "Personal Income, Wealth, and Their Distributions", + "D33" = "Factor Income Distribution", + "D39" = "Distribution: Other", + "D4" = "Market Structure, Pricing, and Design", + "D40" = "Market Structure, Pricing, and Design: General", + "D41" = "Market Structure, Pricing, and Design: Perfect Competition", + "D42" = "Market Structure, Pricing, and Design: Monopoly", + "D43" = "Market Structure, Pricing, and Design: Oligopoly and Other Forms of Market Imperfection", + "D44" = "Auctions", + "D45" = "Rationing; Licensing", + "D46" = "Value Theory", + "D47" = "Market Design", + "D49" = "Market Structure and Pricing: Other", + "D5" = "General Equilibrium and Disequilibrium", + "D50" = "General Equilibrium and Disequilibrium: General", + "D51" = "Exchange and Production Economies", + "D52" = "Incomplete Markets", + "D53" = "General Equilibrium and Disequilibrium: Financial Markets", + "D57" = "General Equilibrium and Disequilibrium: Input-Output Tables and Analysis", + "D58" = "Computable and Other Applied General Equilibrium Models", + "D59" = "General Equilibrium and Disequilibrium: Other", + "D6" = "Welfare Economics", + "D60" = "Welfare Economics: General", + "D61" = "Allocative Efficiency; Cost-Benefit Analysis", + "D62" = "Externalities", + "D63" = "Equity, Justice, Inequality, and Other Normative Criteria and Measurement", + "D64" = "Altruism; Philanthropy; Intergenerational Transfers", + "D69" = "Welfare Economics: Other", + "D7" = "Analysis of Collective Decision-Making", + "D70" = "Analysis of Collective Decision-Making: General", + "D71" = "Social Choice; Clubs; Committees; Associations", + "D72" = "Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior", + "D73" = "Bureaucracy; Administrative Processes in Public Organizations; Corruption", + "D74" = "Conflict; Conflict Resolution; Alliances; Revolutions", + "D78" = "Positive Analysis of Policy Formulation and Implementation", + "D79" = "Analysis of Collective Decision-Making: Other", + "D8" = "Information, Knowledge, and Uncertainty", + "D80" = "Information, Knowledge, and Uncertainty: General", + "D81" = "Criteria for Decision-Making under Risk and Uncertainty", + "D82" = "Asymmetric and Private Information; Mechanism Design", + "D83" = "Search; Learning; Information and Knowledge; Communication; Belief; Unawareness", + "D84" = "Expectations; Speculations", + "D85" = "Network Formation and Analysis: Theory", + "D86" = "Economics of Contract: Theory", + "D87" = "Neuroeconomics", + "D89" = "Information and Uncertainty: Other", + "D9" = "Micro-Based Behavioral Economics", + "D90" = "Micro-Based Behavioral Economics: General", + "D91" = "Micro-Based Behavioral Economics: Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making", + "E0" = "General", + "E00" = "Macroeconomics and Monetary Economics: General", + "E01" = "Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts", + "E02" = "Institutions and the Macroeconomy", + "E1" = "General Aggregative Models", + "E10" = "General Aggregative Models: General", + "E11" = "General Aggregative Models: Marxian; Sraffian; Kaleckian", + "E12" = "General Aggregative Models: Keynes; Keynesian; Post-Keynesian; Modern Monetary Theory", + "E13" = "General Aggregative Models: Neoclassical", + "E14" = "Austrian; Evolutionary; Institutional", + "E16" = "General Aggregative Models: Social Accounting Matrix", + "E17" = "General Aggregative Models: Forecasting and Simulation: Models and Applications", + "E19" = "General Aggregative Models: Other", + "E2" = "Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy", + "E20" = "Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy: General (includes Measurement and Data)", + "E21" = "Macroeconomics: Consumption; Saving; Wealth", + "E22" = "Investment; Capital; Intangible Capital; Capacity", + "E23" = "Macroeconomics: Production", + "E24" = "Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity", + "E25" = "Aggregate Factor Income Distribution", + "E26" = "Informal Economy; Underground Economy", + "E27" = "Macroeconomics: Consumption, Saving, Production, Employment, and Investment: Forecasting and Simulation: Models and Applications", + "E29" = "Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy: Other", + "E3" = "Prices, Business Fluctuations, and Cycles", + "E30" = "Prices, Business Fluctuations, and Cycles: General (includes Measurement and Data)", + "E31" = "Price Level; Inflation; Deflation", + "E32" = "Business Fluctuations; Cycles", + "E37" = "Prices, Business Fluctuations, and Cycles: Forecasting and Simulation: Models and Applications", + "E39" = "Prices, Business Fluctuations, and Cycles: Other", + "E4" = "Money and Interest Rates", + "E40" = "Money and Interest Rates: General", + "E41" = "Demand for Money", + "E42" = "Monetary Systems; Standards; Regimes; Government and the Monetary System; Payment Systems", + "E43" = "Interest Rates: Determination, Term Structure, and Effects", + "E44" = "Financial Markets and the Macroeconomy", + "E47" = "Money and Interest Rates: Forecasting and Simulation: Models and Applications", + "E49" = "Money and Interest Rates: Other", + "E5" = "Monetary Policy, Central Banking, and the Supply of Money and Credit", + "E50" = "Monetary Policy, Central Banking, and the Supply of Money and Credit: General", + "E51" = "Money Supply; Credit; Money Multipliers", + "E52" = "Monetary Policy", + "E58" = "Central Banks and Their Policies", + "E59" = "Monetary Policy, Central Banking, and the Supply of Money and Credit: Other", + "E6" = "Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook", + "E60" = "Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook: General", + "E61" = "Policy Objectives; Policy Designs and Consistency; Policy Coordination", + "E62" = "Fiscal Policy; Modern Monetary Theory", + "E63" = "Comparative or Joint Analysis of Fiscal and Monetary Policy; Stabilization; Treasury Policy", + "E64" = "Incomes Policy; Price Policy", + "E65" = "Studies of Particular Policy Episodes", + "E66" = "General Outlook and Conditions", + "E69" = "Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook: Other", + "E7" = "Macro-Based Behavioral Economics", + "E70" = "Macro-Based Behavioral Economics: General", + "E71" = "Macro-Based Behavioral Economics: Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on the Macro Economy", + "F0" = "General", + "F00" = "International Economics: General", + "F01" = "Global Outlook", + "F02" = "International Economic Order and Integration", + "F1" = "Trade", + "F10" = "Trade: General", + "F11" = "Neoclassical Models of Trade", + "F12" = "Models of Trade with Imperfect Competition and Scale Economies; Fragmentation", + "F13" = "Trade Policy; International Trade Organizations", + "F14" = "Empirical Studies of Trade", + "F15" = "Economic Integration", + "F16" = "Trade and Labor Market Interactions", + "F17" = "Trade: Forecasting and Simulation", + "F18" = "Trade and Environment", + "F19" = "Trade: Other", + "F2" = "International Factor Movements and International Business", + "F20" = "International Factor Movements and International Business: General", + "F21" = "International Investment; Long-term Capital Movements", + "F22" = "International Migration", + "F23" = "Multinational Firms; International Business", + "F24" = "Remittances", + "F29" = "International Factor Movements: Other", + "F3" = "International Finance", + "F30" = "International Finance: General", + "F31" = "Foreign Exchange", + "F32" = "Current Account Adjustment; Short-term Capital Movements", + "F33" = "International Monetary Arrangements and Institutions", + "F34" = "International Lending and Debt Problems", + "F35" = "Foreign Aid", + "F36" = "Financial Aspects of Economic Integration", + "F37" = "International Finance Forecasting and Simulation: Models and Applications", + "F38" = "International Financial Policy: Financial Transactions Tax; Capital Controls", + "F39" = "International Finance: Other", + "F4" = "Macroeconomic Aspects of International Trade and Finance", + "F40" = "Macroeconomic Aspects of International Trade and Finance: General", + "F41" = "Open Economy Macroeconomics", + "F42" = "International Policy Coordination and Transmission", + "F43" = "Economic Growth of Open Economies", + "F44" = "International Business Cycles", + "F45" = "Macroeconomic Issues of Monetary Unions", + "F47" = "Macroeconomic Aspects of International Trade and Finance: Forecasting and Simulation: Models and Applications", + "F49" = "Macroeconomic Aspects of International Trade and Finance: Other", + "F5" = "International Relations, National Security, and International Political Economy", + "F50" = "International Relations, National Security, and International Political Economy: General", + "F51" = "International Conflicts; Negotiations; Sanctions", + "F52" = "National Security; Economic Nationalism", + "F53" = "International Agreements and Observance; International Organizations", + "F54" = "Colonialism; Imperialism; Postcolonialism", + "F55" = "International Institutional Arrangements", + "F59" = "International Relations and International Political Economy: Other", + "F6" = "Economic Impacts of Globalization", + "F60" = "Economic Impacts of Globalization: General", + "F61" = "Economic Impacts of Globalization: Microeconomic Impacts", + "F62" = "Economic Impacts of Globalization: Macroeconomic Impacts", + "F63" = "Economic Impacts of Globalization: Economic Development", + "F64" = "Economic Impacts of Globalization: Environment", + "F65" = "Economic Impacts of Globalization: Finance", + "F66" = "Economic Impacts of Globalization: Labor", + "F68" = "Economic Impacts of Globalization: Policy", + "F69" = "Economic Impacts of Globalization: Other", + "G0" = "General", + "G00" = "Financial Economics: General", + "G01" = "Financial Crises", + "G1" = "General Financial Markets", + "G10" = "Asset Markets and Pricing", + "G11" = "Portfolio Choice; Investment Decisions", + "G12" = "Equities; Fixed Income Securities", + "G13" = "Contingent Pricing; Futures Pricing; option pricing", + "G14" = "Information and Market Efficiency; Event Studies; Insider Trading", + "G15" = "International Financial Markets", + "G17" = "Financial Forecasting and Simulation", + "G18" = "General Financial Markets: Government Policy and Regulation", + "G19" = "General Financial Markets: Other", + "G2" = "Financial Institutions and Services", + "G20" = "Financial Institutions and Services: General", + "G21" = "Banks; Depository Institutions; Micro Finance Institutions; Mortgages", + "G22" = "Insurance; Insurance Companies; Actuarial Studies", + "G23" = "Pension Funds; Non-bank Financial Institutions; Financial Instruments; Institutional Investors", + "G24" = "Investment Banking; Venture Capital; Brokerage; Ratings and Ratings Agencies", + "G28" = "Financial Institutions and Services: Government Policy and Regulation", + "G29" = "Financial Institutions and Services: Other", + "G3" = "Corporate Finance and Governance", + "G30" = "Corporate Finance and Governance: General", + "G31" = "Capital Budgeting; Fixed Investment and Inventory Studies; Capacity", + "G32" = "Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill", + "G33" = "Bankruptcy; Liquidation", + "G34" = "Mergers; Acquisitions; Restructuring; Voting; Proxy Contests; Corporate Governance", + "G35" = "Payout Policy", + "G38" = "Corporate Finance and Governance: Government Policy and Regulation", + "G39" = "Corporate Finance and Governance: Other", + "G4" = "Behavioral Finance", + "G40" = "Behavioral Finance: General", + "G41" = "Behavioral Finance: Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets", + "G5" = "Household Finance", + "G50" = "Household Finance: General", + "G51" = "Household Saving, Borrowing, Debt, and Wealth", + "G52" = "Household Finance: Insurance", + "G53" = "Household Finance: Financial Literacy", + "G59" = "Household Finance: Other", + "H0" = "General", + "H00" = "Public Economics: General", + "H1" = "Structure and Scope of Government", + "H10" = "Structure and Scope of Government: General", + "H11" = "Structure, Scope, and Performance of Government", + "H12" = "Crisis Management", + "H13" = "Economics of Eminent Domain; Expropriation; Nationalization", + "H19" = "Structure and Scope of Government: Other", + "H2" = "Taxation, Subsidies, and Revenue", + "H20" = "Taxation, Subsidies, and Revenue: General", + "H21" = "Taxation and Subsidies: Efficiency; Optimal Taxation", + "H22" = "Taxation and Subsidies: Incidence", + "H23" = "Taxation and Subsidies: Externalities; Redistributive Effects; Environmental Taxes and Subsidies", + "H24" = "Personal Income and Other Nonbusiness Taxes and Subsidies; includes inheritance and gift taxes", + "H25" = "Business Taxes and Subsidies including sales and value-added (VAT)", + "H26" = "Tax Evasion and Avoidance", + "H27" = "Taxation, Subsidies, and Revenues: Other Sources of Revenue", + "H29" = "Taxation and Subsidies: Other", + "H3" = "Fiscal Policies and Behavior of Economic Agents", + "H30" = "Fiscal Policies and Behavior of Economic Agents: General", + "H31" = "Fiscal Policies and Behavior of Economic Agents: Household", + "H32" = "Fiscal Policies and Behavior of Economic Agents: Firm", + "H39" = "Fiscal Policies and Behavior of Economic Agents: Other", + "H4" = "Publicly Provided Goods", + "H40" = "Publicly Provided Goods: General", + "H41" = "Public Goods", + "H42" = "Publicly Provided Private Goods", + "H43" = "Project Evaluation; Social Discount Rate", + "H44" = "Publicly Provided Goods: Mixed Markets", + "H49" = "Publicly Provided Goods: Other", + "H5" = "National Government Expenditures and Related Policies", + "H50" = "National Government Expenditures and Related Policies: General", + "H51" = "National Government Expenditures and Health", + "H52" = "National Government Expenditures and Education", + "H53" = "National Government Expenditures and Welfare Programs", + "H54" = "National Government Expenditures and Related Policies: Infrastructures; Other Public Investment and Capital Stock", + "H55" = "Social Security and Public Pensions", + "H56" = "National Security and War", + "H57" = "National Government Expenditures and Related Policies: Procurement", + "H59" = "National Government Expenditures and Related Policies: Other", + "H6" = "National Budget, Deficit, and Debt", + "H60" = "National Budget, Deficit, and Debt: General", + "H61" = "National Budget; Budget Systems", + "H62" = "National Deficit; Surplus", + "H63" = "National Debt; Debt Management; Sovereign Debt", + "H68" = "Forecasts of Budgets, Deficits, and Debt", + "H69" = "National Budget, Deficit, and Debt: Other", + "H7" = "State and Local Government • Intergovernmental Relations", + "H70" = "State and Local Government; Intergovernmental Relations: General", + "H71" = "State and Local Taxation, Subsidies, and Revenue", + "H72" = "State and Local Budget and Expenditures", + "H73" = "State and Local Government; Intergovernmental Relations: Interjurisdictional Differentials and Their Effects", + "H74" = "State and Local Borrowing", + "H75" = "State and Local Government: Health; Education; Welfare; Public Pensions", + "H76" = "State and Local Government: Other Expenditure Categories", + "H77" = "Intergovernmental Relations; Federalism; Secession", + "H79" = "State and Local Government; Intergovernmental Relations: Other", + "H8" = "Miscellaneous Issues", + "H80" = "Public Economics: Miscellaneous Issues: General", + "H81" = "Governmental Loans; Loan Guarantees; Credits; Grants; Bailouts", + "H82" = "Governmental Property", + "H83" = "Public Administration; Public Sector Accounting and Audits", + "H84" = "Disaster Aid", + "H87" = "International Fiscal Issues; International Public Goods", + "H89" = "Public Economics: Miscellaneous Issues: Other", + "I0" = "General", + "I00" = "Health, Education, and Welfare: General", + "I1" = "Health", + "I10" = "Health: General", + "I11" = "Analysis of Health Care Markets", + "I12" = "Health Behavior", + "I13" = "Health Insurance, Public and Private", + "I14" = "Health and Inequality", + "I15" = "Health and Economic Development", + "I18" = "Health: Government Policy; Regulation; Public Health", + "I19" = "Health: Other", + "I2" = "Education and Research Institutions", + "I20" = "Education and Research Institutions: General", + "I21" = "Analysis of Education", + "I22" = "Educational Finance; Financial Aid", + "I23" = "Higher Education; Research Institutions", + "I24" = "Education and Inequality", + "I25" = "Education and Economic Development", + "I26" = "Returns to Education", + "I28" = "Education: Government Policy", + "I29" = "Education: Other", + "I3" = "Welfare, Well-Being, and Poverty", + "I30" = "Welfare, Well-Being, and Poverty: General", + "I31" = "General Welfare; Well-Being", + "I32" = "Measurement and Analysis of Poverty", + "I38" = "Welfare, Well-Being, and Poverty: Government Programs; Provision and Effects of Welfare Programs", + "I39" = "Welfare, Well-Being, and Poverty: Other", + "J0" = "General", + "J00" = "Labor and Demographic Economics: General", + "J01" = "Labor Economics: General", + "J08" = "Labor Economics Policies", + "J1" = "Demographic Economics", + "J10" = "Demographic Economics: General", + "J11" = "Demographic Trends, Macroeconomic Effects, and Forecasts", + "J12" = "Marriage; Marital Dissolution; Family Structure; Domestic Abuse", + "J13" = "Fertility; Family Planning; Child Care; Children; Youth", + "J14" = "Economics of the Elderly; Economics of Disability; Non-labor Market Discrimination", + "J15" = "Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination", + "J16" = "Economics of Gender; Non-labor Discrimination", + "J17" = "Value of Life; Forgone Income", + "J18" = "Demographic Economics: Public Policy", + "J19" = "Demographic Economics: Other", + "J2" = "Demand and Supply of Labor", + "J20" = "Demand and Supply of Labor: General", + "J21" = "Labor Force and Employment, Size, and Structure", + "J22" = "Time Allocation and Labor Supply", + "J23" = "Labor Demand", + "J24" = "Human Capital; Skills; Occupational Choice; Labor Productivity", + "J26" = "Retirement; Retirement Policies", + "J28" = "Safety; Job Satisfaction; Related Public Policy", + "J29" = "Time Allocation, Work Behavior, and Employment Determination: Other", + "J3" = "Wages, Compensation, and Labor Costs", + "J30" = "Wages, Compensation, and Labor Costs: General", + "J31" = "Wage Level and Structure; Wage Differentials", + "J32" = "Nonwage Labor Costs and Benefits; Retirement Plans; Private Pensions", + "J33" = "Compensation Packages; Payment Methods", + "J38" = "Wages, Compensation, and Labor Costs: Public Policy", + "J39" = "Wages, Compensation, and Labor Costs: Other", + "J4" = "Particular Labor Markets", + "J40" = "Particular Labor Markets: General", + "J41" = "Labor Contracts", + "J42" = "Monopsony; Segmented Labor Markets", + "J43" = "Agricultural Labor Markets", + "J44" = "Professional Labor Markets; Occupational Licensing", + "J45" = "Public Sector Labor Markets", + "J46" = "Informal Labor Markets", + "J47" = "Coercive Labor Markets", + "J48" = "Particular Labor Markets: Public Policy", + "J49" = "Particular Labor Markets: Other", + "J5" = "Labor–Management Relations, Trade Unions, and Collective Bargaining", + "J50" = "Labor-Management Relations, Trade Unions, and Collective Bargaining: General", + "J51" = "Trade Unions: Objectives, Structure, and Effects", + "J52" = "Dispute Resolution: Strikes, Arbitration, and Mediation; Collective Bargaining", + "J53" = "Labor-Management Relations; Industrial Jurisprudence", + "J54" = "Producer Cooperatives; Labor Managed Firms; Employee Ownership", + "J58" = "Labor-Management Relations, Trade Unions, and Collective Bargaining: Public Policy", + "J59" = "Labor-Management Relations, Trade Unions, and Collective Bargaining: Other", + "J6" = "Mobility, Unemployment, Vacancies, and Immigrant Workers", + "J60" = "Mobility, Unemployment, Vacancies, and Immigrant Workers: General", + "J61" = "Geographic Labor Mobility; Immigrant Workers", + "J62" = "Job, Occupational, and Intergenerational Mobility; Promotion", + "J63" = "Labor Turnover; Vacancies; Layoffs", + "J64" = "Unemployment: Models, Duration, Incidence, and Job Search", + "J65" = "Unemployment Insurance; Severance Pay; Plant Closings", + "J68" = "Mobility, Unemployment, and Vacancies: Public Policy", + "J69" = "Mobility, Unemployment, and Vacancies: Other", + "J7" = "Labor Discrimination", + "J70" = "Labor Discrimination: General", + "J71" = "Labor Discrimination", + "J78" = "Labor Discrimination: Public Policy", + "J79" = "Labor Discrimination: Other", + "J8" = "Labor Standards: National and International", + "J80" = "Labor Standards: General", + "J81" = "Labor Standards: Working Conditions", + "J82" = "Labor Standards: Labor Force Composition", + "J83" = "Labor Standards: Workers' Rights", + "J88" = "Labor Standards: Public Policy", + "J89" = "Labor Standards: Other", + "K0" = "General", + "K00" = "Law and Economics: General", + "K1" = "Basic Areas of Law", + "K10" = "Basic Areas of Law: General (Constitutional Law)", + "K11" = "Property Law", + "K12" = "Contract Law", + "K13" = "Tort Law and Product Liability; Forensic Economics", + "K14" = "Criminal Law", + "K15" = "Civil Law; Common Law", + "K16" = "Election Law", + "K19" = "Basic Areas of Law: Other", + "K2" = "Regulation and Business Law", + "K20" = "Regulation and Business Law: General", + "K21" = "Antitrust Law", + "K22" = "Business and Securities Law", + "K23" = "Regulated Industries and Administrative Law", + "K24" = "Cyber Law", + "K25" = "Real Estate Law", + "K29" = "Regulation and Business Law: Other", + "K3" = "Other Substantive Areas of Law", + "K30" = "Other Substantive Areas of Law: General", + "K31" = "Labor Law", + "K32" = "Energy, Environmental, Health, and Safety Law", + "K33" = "International Law", + "K34" = "Tax Law", + "K35" = "Personal Bankruptcy Law", + "K36" = "Family and Personal Law", + "K37" = "Immigration Law", + "K38" = "Human Rights Law; Gender Law; Animal Rights Law", + "K39" = "Other Substantive Areas of Law: Other", + "K4" = "Legal Procedure, the Legal System, and Illegal Behavior", + "K40" = "Legal Procedure, the Legal System, and Illegal Behavior: General", + "K41" = "Litigation Process", + "K42" = "Illegal Behavior and the Enforcement of Law", + "K49" = "Legal Procedure, the Legal System, and Illegal Behavior: Other", + "L0" = "General", + "L00" = "Industrial Organization: General", + "L1" = "Market Structure, Firm Strategy, and Market Performance", + "L10" = "Market Structure, Firm Strategy, and Market Performance: General", + "L11" = "Production, Pricing, and Market Structure; Size Distribution of Firms", + "L12" = "Monopoly; Monopolization Strategies", + "L13" = "Oligopoly and Other Imperfect Markets", + "L14" = "Transactional Relationships; Contracts and Reputation; Networks", + "L15" = "Information and Product Quality; Standardization and Compatibility", + "L16" = "Industrial Organization and Macroeconomics: Industrial Structure and Structural Change; Industrial Price Indices", + "L17" = "Open Source Products and Markets", + "L19" = "Market Structure, Firm Strategy, and Market Performance: Other", + "L2" = "Firm Objectives, Organization, and Behavior", + "L20" = "Firm Objectives, Organization, and Behavior: General", + "L21" = "Business Objectives of the Firm", + "L22" = "Firm Organization and Market Structure", + "L23" = "Organization of Production", + "L24" = "Contracting Out; Joint Ventures; Technology Licensing", + "L25" = "Firm Performance: Size, Diversification, and Scope", + "L26" = "Entrepreneurship", + "L29" = "Firm Objectives, Organization, and Behavior: Other", + "L3" = "Nonprofit Organizations and Public Enterprise", + "L30" = "Nonprofit Organizations and Public Enterprise: General", + "L31" = "Nonprofit Institutions; NGOs; Social Entrepreneurship", + "L32" = "Public Enterprises; Public-Private Enterprises", + "L33" = "Comparison of Public and Private Enterprises and Nonprofit Institutions; Privatization; Contracting Out", + "L38" = "Public Policy", + "L39" = "Nonprofit Organizations and Public Enterprise: Other", + "L4" = "Antitrust Issues and Policies", + "L40" = "Antitrust Issues and Policies: General", + "L41" = "Monopolization; Horizontal Anticompetitive Practices", + "L42" = "Vertical Restraints; Resale Price Maintenance; Quantity Discounts", + "L43" = "Legal Monopolies and Regulation or Deregulation", + "L44" = "Antitrust Policy and Public Enterprises, Nonprofit Institutions, and Professional Organizations", + "L49" = "Antitrust Policy: Other", + "L5" = "Regulation and Industrial Policy", + "L50" = "Regulation and Industrial Policy: General", + "L51" = "Economics of Regulation", + "L52" = "Industrial Policy; Sectoral Planning Methods", + "L53" = "Enterprise Policy", + "L59" = "Regulation and Industrial Policy: Other", + "L6" = "Industry Studies: Manufacturing", + "L60" = "Industry Studies: Manufacturing: General", + "L61" = "Metals and Metal Products; Cement; Glass; Ceramics", + "L62" = "Automobiles; Other Transportation Equipment; Related Parts and Equipment", + "L63" = "Microelectronics; Computers; Communications Equipment", + "L64" = "Other Machinery; Business Equipment; Armaments", + "L65" = "Chemicals; Rubber; Drugs; Biotechnology; Plastics", + "L66" = "Food; Beverages; Cosmetics; Tobacco; Wine and Spirits", + "L67" = "Other Consumer Nondurables: Clothing, Textiles, Shoes, and Leather Goods; Household Goods; Sports Equipment", + "L68" = "Appliances; Furniture; Other Consumer Durables", + "L69" = "Industry Studies: Manufacturing: Other", + "L7" = "Industry Studies: Primary Products and Construction", + "L70" = "Industry Studies: Primary Products and Construction: General", + "L71" = "Mining, Extraction, and Refining: Hydrocarbon Fuels", + "L72" = "Mining, Extraction, and Refining: Other Nonrenewable Resources", + "L73" = "Forest Products", + "L74" = "Construction", + "L78" = "Industry Studies: Primary Products and Construction: Government Policy", + "L79" = "Industry Studies: Primary Products and Construction: Other", + "L8" = "Industry Studies: Services", + "L80" = "Industry Studies: Services: General", + "L81" = "Retail and Wholesale Trade; e-Commerce", + "L82" = "Entertainment; Media", + "L83" = "Sports; Gambling; Restaurants; Recreation; Tourism", + "L84" = "Personal, Professional, and Business Services", + "L85" = "Real Estate Services", + "L86" = "Information and Internet Services; Computer Software", + "L87" = "Postal and Delivery Services", + "L88" = "Industry Studies: Services: Government Policy", + "L89" = "Industry Studies: Services: Other", + "L9" = "Industry Studies: Transportation and Utilities", + "L90" = "Industry Studies: Transportation and Utilities: General", + "L91" = "Transportation: General", + "L92" = "Railroads and Other Surface Transportation", + "L93" = "Air Transportation", + "L94" = "Electric Utilities", + "L95" = "Gas Utilities; Pipelines; Water Utilities", + "L96" = "Telecommunications", + "L97" = "Utilities: General", + "L98" = "Industry Studies: Utilities and Transportation: Government Policy", + "L99" = "Industry Studies: Utilities and Transportation: Other", + "M0" = "General", + "M00" = "Business Administration and Business Economics; Marketing; Accounting; Personnel Economics: General", + "M1" = "Business Administration", + "M10" = "Business Administration: General", + "M11" = "Production Management", + "M12" = "Personnel Management; Executives; Executive Compensation", + "M13" = "New Firms; Startups", + "M14" = "Corporate Culture; Diversity; Social Responsibility", + "M15" = "IT Management", + "M16" = "International Business Administration", + "M19" = "Business Administration: Other", + "M2" = "Business Economics", + "M20" = "Business Economics: General", + "M21" = "Business Economics", + "M29" = "Business Economics: Other", + "M3" = "Marketing and Advertising", + "M30" = "Marketing and Advertising: General", + "M31" = "Marketing", + "M37" = "Advertising", + "M38" = "Marketing and Advertising: Government Policy and Regulation", + "M39" = "Marketing and Advertising: Other", + "M4" = "Accounting and Auditing", + "M40" = "Accounting and Auditing: General", + "M41" = "Accounting", + "M42" = "Auditing", + "M48" = "Accounting and Auditing: Government Policy and Regulation", + "M49" = "Accounting: Other", + "M5" = "Personnel Economics", + "M50" = "Personnel Economics: General", + "M51" = "Personnel Economics: Firm Employment Decisions; Promotions", + "M52" = "Personnel Economics: Compensation and Compensation Methods and Their Effects", + "M53" = "Personnel Economics: Training", + "M54" = "Personnel Economics: Labor Management", + "M55" = "Personnel Economics: Labor Contracting Devices", + "M59" = "Personnel Economics: Other", + "N0" = "General", + "N00" = "Economic History: General", + "N01" = "Development of the Discipline: Historiographical; Sources and Methods", + "N1" = "Macroeconomics and Monetary Economics • Industrial Structure • Growth • Fluctuations", + "N10" = "Economic History: Macroeconomics and Monetary Economics; Industrial Structure; Growth; Fluctuations: General, International, or Comparative", + "N11" = "Economic History: Macroeconomics and Monetary Economics; Industrial Structure; Growth; Fluctuations: U.S.; Canada: Pre-1913", + "N12" = "Economic History: Macroeconomics and Monetary Economics; Industrial Structure; Growth; Fluctuations: U.S.; Canada: 1913-", + "N13" = "Economic History: Macroeconomics and Monetary Economics; Industrial Structure; Growth; Fluctuations: Europe: Pre-1913", + "N14" = "Economic History: Macroeconomics and Monetary Economics; Industrial Structure; Growth; Fluctuations: Europe: 1913-", + "N15" = "Economic History: Macroeconomics and Monetary Economics; Industrial Structure; Growth; Fluctuations: Asia including Middle East", + "N16" = "Economic History: Macroeconomics and Monetary Economics; Industrial Structure; Growth; Fluctuations: Latin America; Caribbean", + "N17" = "Economic History: Macroeconomics and Monetary Economics; Industrial Structure; Growth; Fluctuations: Africa; Oceania", + "N2" = "Financial Markets and Institutions", + "N20" = "Economic History: Financial Markets and Institutions: General, International, or Comparative", + "N21" = "Economic History: Financial Markets and Institutions: U.S.; Canada: Pre-1913", + "N22" = "Economic History: Financial Markets and Institutions: U.S.; Canada: 1913-", + "N23" = "Economic History: Financial Markets and Institutions: Europe: Pre-1913", + "N24" = "Economic History: Financial Markets and Institutions: Europe: 1913-", + "N25" = "Economic History: Financial Markets and Institutions: Asia including Middle East", + "N26" = "Economic History: Financial Markets and Institutions: Latin America; Caribbean", + "N27" = "Economic History: Financial Markets and Institutions: Africa; Oceania", + "N3" = "Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy", + "N30" = "Economic History: Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy: General, International, or Comparative", + "N31" = "Economic History: Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy: U.S.; Canada: Pre-1913", + "N32" = "Economic History: Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy: U.S.; Canada: 1913-", + "N33" = "Economic History: Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy: Europe: Pre-1913", + "N34" = "Economic History: Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy: Europe: 1913-", + "N35" = "Economic History: Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy: Asia including Middle East", + "N36" = "Economic History: Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy: Latin America; Caribbean", + "N37" = "Economic History: Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy: Africa; Oceania", + "N4" = "Government, War, Law, International Relations, and Regulation", + "N40" = "Economic History: Government, War, Law, International Relations, and Regulation: General, International, or Comparative", + "N41" = "Economic History: Government, War, Law, International Relations, and Regulation: U.S.; Canada: Pre-1913", + "N42" = "Economic History: Government, War, Law, International Relations, and Regulation: U.S.; Canada: 1913-", + "N43" = "Economic History: Government, War, Law, International Relations, and Regulation: Europe: Pre-1913", + "N44" = "Economic History: Government, War, Law, International Relations, and Regulation: Europe: 1913-", + "N45" = "Economic History: Government, War, Law, International Relations, and Regulation: Asia including Middle East", + "N46" = "Economic History: Government, War, Law, International Relations, and Regulation: Latin America; Caribbean", + "N47" = "Economic History: Government, War, Law, International Relations, and Regulation: Africa; Oceania", + "N5" = "Agriculture, Natural Resources, Environment, and Extractive Industries", + "N50" = "Economic History: Agriculture, Natural Resources, Environment, and Extractive Industries: General, International, or Comparative", + "N51" = "Economic History: Agriculture, Natural Resources, Environment, and Extractive Industries: U.S.; Canada: Pre-1913", + "N52" = "Economic History: Agriculture, Natural Resources, Environment, and Extractive Industries: U.S.; Canada: 1913-", + "N53" = "Economic History: Agriculture, Natural Resources, Environment, and Extractive Industries: Europe: Pre-1913", + "N54" = "Economic History: Agriculture, Natural Resources, Environment, and Extractive Industries: Europe: 1913-", + "N55" = "Economic History: Agriculture, Natural Resources, Environment, and Extractive Industries: Asia including Middle East", + "N56" = "Economic History: Agriculture, Natural Resources, Environment, and Extractive Industries: Latin America; Caribbean", + "N57" = "Economic History: Agriculture, Natural Resources, Environment, and Extractive Industries: Africa; Oceania", + "N6" = "Manufacturing and Construction", + "N60" = "Economic History: Manufacturing and Construction: General, International, or Comparative", + "N61" = "Economic History: Manufacturing and Construction: U.S.; Canada: Pre-1913", + "N62" = "Economic History: Manufacturing and Construction: U.S.; Canada: 1913-", + "N63" = "Economic History: Manufacturing and Construction: Europe: Pre-1913", + "N64" = "Economic History: Manufacturing and Construction: Europe: 1913-", + "N65" = "Economic History: Manufacturing and Construction: Asia including Middle East", + "N66" = "Economic History: Manufacturing and Construction: Latin America; Caribbean", + "N67" = "Economic History: Manufacturing and Construction: Africa; Oceania", + "N7" = "Transport, Trade, Energy, Technology, and Other Services", + "N70" = "Economic History: Transport, International and Domestic Trade, Energy, Technology, and Other Services: General, International, or Comparative", + "N71" = "Economic History: Transport, Trade, Energy, Technology, and Other Services: U.S.; Canada: Pre-1913", + "N72" = "Economic History: Transport, Trade, Energy, Technology, and Other Services: U.S.; Canada: 1913-", + "N73" = "Economic History: Transport, Trade, Energy, Technology, and Other Services: Europe: Pre-1913", + "N74" = "Economic History: Transport, Trade, Energy, Technology, and Other Services: Europe: 1913-", + "N75" = "Economic History: Transport, Trade, Energy, Technology, and Other Services: Asia including Middle East", + "N76" = "Economic History: Transport, Trade, Energy, Technology, and Other Services: Latin America; Caribbean", + "N77" = "Economic History: Transport, Trade, Energy, Technology, and Other Services: Africa; Oceania", + "N8" = "Micro-Business History", + "N80" = "Micro-Business History: General, International, or Comparative", + "N81" = "Micro-Business History: U.S.; Canada: Pre-1913", + "N82" = "Micro-Business History: U.S.; Canada: 1913-", + "N83" = "Micro-Business History: Europe: Pre-1913", + "N84" = "Micro-Business History: Europe: 1913-", + "N85" = "Micro-Business History: Asia including Middle East", + "N86" = "Micro-Business History: Latin America; Caribbean", + "N87" = "Micro-Business History: Africa; Oceania", + "N9" = "Regional and Urban History", + "N90" = "Regional and Urban History: General", + "N91" = "Regional and Urban History: U.S.; Canada: Pre-1913", + "N92" = "Regional and Urban History: U.S.; Canada: 1913-", + "N93" = "Regional and Urban History: Europe: Pre-1913", + "N94" = "Regional and Urban History: Europe: 1913-", + "N95" = "Regional and Urban History: Asia including Middle East", + "N96" = "Regional and Urban History: Latin America; Caribbean", + "N97" = "Regional and Urban History: Africa; Oceania", + "O00" = "Economic Development, Innovation, Technological Change, and Growth", + "O1" = "Economic Development", + "O10" = "Economic Development: General", + "O11" = "Macroeconomic Analyses of Economic Development", + "O12" = "Microeconomic Analyses of Economic Development", + "O13" = "Economic Development: Agriculture; Natural Resources; Energy; Environment; Other Primary Products", + "O14" = "Industrialization; Manufacturing and Service Industries; Choice of Technology", + "O15" = "Economic Development: Human Resources; Human Development; Income Distribution; Migration", + "O16" = "Economic Development: Financial Markets; Saving and Capital Investment; Corporate Finance and Governance", + "O17" = "Formal and Informal Sectors; Shadow Economy; Institutional Arrangements", + "O18" = "Economic Development: Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure", + "O19" = "International Linkages to Development; Role of International Organizations", + "O2" = "Development Planning and Policy", + "O20" = "Development Planning and Policy: General", + "O21" = "Planning Models; Planning Policy", + "O22" = "Project Analysis", + "O23" = "Fiscal and Monetary Policy in Development", + "O24" = "Development Planning and Policy: Trade Policy; Factor Movement; Foreign Exchange Policy", + "O25" = "Industrial Policy", + "O29" = "Development Planning and Policy: Other", + "O3" = "Innovation • Research and Development • Technological Change • Intellectual Property Rights", + "O30" = "Innovation; Research and Development; Technological Change; Intellectual Property Rights: General", + "O31" = "Innovation and Invention: Processes and Incentives", + "O32" = "Management of Technological Innovation and R&D", + "O33" = "Technological Change: Choices and Consequences; Diffusion Processes", + "O34" = "Intellectual Property and Intellectual Capital", + "O35" = "Social Innovation", + "O36" = "Open Innovation", + "O38" = "Technological Change: Government Policy", + "O39" = "Technological Change: Other", + "O4" = "Economic Growth and Aggregate Productivity", + "O40" = "Economic Growth and Aggregate Productivity: General", + "O41" = "One, Two, and Multisector Growth Models", + "O42" = "Monetary Growth Models", + "O43" = "Institutions and Growth", + "O44" = "Environment and Growth", + "O47" = "Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence", + "O49" = "Economic Growth and Aggregate Productivity: Other", + "O5" = "Economywide Country Studies", + "O50" = "Economywide Country Studies: General", + "O51" = "Economywide Country Studies: U.S.; Canada", + "O52" = "Economywide Country Studies: Europe", + "O53" = "Economywide Country Studies: Asia including Middle East", + "O54" = "Economywide Country Studies: Latin America; Caribbean", + "O55" = "Economywide Country Studies: Africa", + "O56" = "Economywide Country Studies: Oceania", + "O57" = "Comparative Studies of Countries", + "P0" = "General", + "P00" = "Political Economy and Comparative Economic Systems: General", + "P1" = "Capitalist Economies", + "P10" = "Capitalist Economies: General", + "P11" = "Capitalist Economies: Planning, Coordination, and Reform", + "P12" = "Capitalist Enterprises", + "P13" = "Cooperative Enterprises", + "P14" = "Capitalist Economies: Property Rights", + "P16" = "Capitalist Institutions; Welfare State", + "P17" = "Capitalist Economies: Performance and Prospects", + "P18" = "Energy; Environment", + "P19" = "Capitalist Economies: Other", + "P2" = "Socialist and Transition Economies", + "P20" = "Socialist and Transition Economies: General", + "P21" = "Socialist and Transitional Economies: Planning, Coordination, and Reform", + "P22" = "Socialist and Transitional Economies: Prices", + "P23" = "Socialist and Transitional Economies: Factor and Product Markets; Industry Studies; Population", + "P24" = "Socialist and Transitional Economies: National Income, Product, and Expenditure; Money; Inflation", + "P25" = "Socialist and Transitional Economies: Urban, Rural, and Regional Economics", + "P26" = "Socialist and Transitional Economies: Property Rights", + "P27" = "Socialist and Transitional Economies: Performance and Prospects", + "P28" = "Socialist and Transitional Economies: Natural Resources; Energy; Environment", + "P29" = "Socialist and Transitional Economies: Other", + "P3" = "Socialist Institutions and Their Transitions", + "P30" = "Socialist Institutions and Their Transitions: General", + "P31" = "Socialist Enterprises and Their Transitions", + "P32" = "Collectives; Communes; Agriculture", + "P33" = "Socialist Institutions and Their Transitions: International Trade, Finance, Investment, Relations, and Aid", + "P34" = "Socialist Institutions and Their Transitions: Financial Economics", + "P35" = "Socialist Institutions and Their Transitions: Public Economics", + "P36" = "Socialist Institutions and Their Transitions: Consumer Economics; Health; Education and Training: Welfare, Income, Wealth, and Poverty", + "P37" = "Socialist Systems and Transitional Economies: Legal Institutions; Illegal Behavior", + "P39" = "Socialist Institutions and Their Transitions: Other", + "P4" = "Other Economic Systems", + "P40" = "Other Economic Systems: General", + "P41" = "Other Economic Systems: Planning, Coordination, and Reform", + "P42" = "Other Economic Systems: Productive Enterprises; Factor and Product Markets; Prices; Population", + "P43" = "Other Economic Systems: Public Economics; Financial Economics", + "P44" = "Other Economic Systems: National Income, Product, and Expenditure; Money; Inflation", + "P45" = "Other Economic Systems: International Trade, Finance, Investment and Aid", + "P46" = "Other Economic Systems: Consumer Economics; Health; Education and Training; Welfare, Income, Wealth, and Poverty", + "P47" = "Other Economic Systems: Performance and Prospects", + "P48" = "Other Economic Systems: Legal Institutions; Property Rights; Natural Resources; Energy; Environment; Regional Studies", + "P49" = "Other Economic Systems: Other", + "P5" = "Comparative Economic Systems", + "P50" = "Comparative Economic Systems: General", + "P51" = "Comparative Analysis of Economic Systems", + "P52" = "Comparative Studies of Particular Economies", + "P59" = "Comparative Economic Systems: Other", + "Q0" = "General", + "Q00" = "Agricultural and Natural Resource Economics; Environmental and Ecological Economics: General", + "Q01" = "Sustainable Development", + "Q02" = "Commodity Markets", + "Q1" = "Agriculture", + "Q10" = "Agriculture: General", + "Q11" = "Agriculture: Aggregate Supply and Demand Analysis; Prices", + "Q12" = "Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets", + "Q13" = "Agricultural Markets and Marketing; Cooperatives; Agribusiness", + "Q14" = "Agricultural Finance", + "Q15" = "Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment", + "Q16" = "Agricultural R&D; Agricultural Technology; Biofuels; Agricultural Extension Services", + "Q17" = "Agriculture in International Trade", + "Q18" = "Agricultural Policy; Food Policy; Animal Welfare Policy", + "Q19" = "Agriculture: Other", + "Q2" = "Renewable Resources and Conservation", + "Q20" = "Renewable Resources and Conservation: General", + "Q21" = "Renewable Resources and Conservation: Demand and Supply; Prices", + "Q22" = "Renewable Resources and Conservation: Fishery; Aquaculture", + "Q23" = "Renewable Resources and Conservation: Forestry", + "Q24" = "Renewable Resources and Conservation: Land", + "Q25" = "Renewable Resources and Conservation: Water", + "Q26" = "Recreational Aspects of Natural Resources", + "Q27" = "Renewable Resources and Conservation: Issues in International Trade", + "Q28" = "Renewable Resources and Conservation: Government Policy", + "Q29" = "Renewable Resources and Conservation: Other", + "Q3" = "Nonrenewable Resources and Conservation", + "Q30" = "Nonrenewable Resources and Conservation: General", + "Q31" = "Nonrenewable Resources and Conservation: Demand and Supply; Prices", + "Q32" = "Exhaustible Resources and Economic Development", + "Q33" = "Resource Booms", + "Q34" = "Natural Resources and Domestic and International Conflicts", + "Q35" = "Hydrocarbon Resources", + "Q37" = "Nonrenewable Resources and Conservation: Issues in International Trade", + "Q38" = "Nonrenewable Resources and Conservation: Government Policy", + "Q39" = "Nonrenewable Resources and Conservation: Other", + "Q4" = "Energy", + "Q40" = "Energy: General", + "Q41" = "Energy: Demand and Supply; Prices", + "Q42" = "Alternative Energy Sources", + "Q43" = "Energy and the Macroeconomy", + "Q47" = "Energy Forecasting", + "Q48" = "Energy: Government Policy", + "Q49" = "Energy: Other", + "Q5" = "Environmental Economics", + "Q50" = "Environmental Economics: General", + "Q51" = "Valuation of Environmental Effects", + "Q52" = "Pollution Control Adoption and Costs; Distributional Effects; Employment Effects", + "Q53" = "Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling", + "Q54" = "Climate; Natural Disasters and Their Management; Global Warming", + "Q55" = "Environmental Economics: Technological Innovation", + "Q56" = "Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth", + "Q57" = "Ecological Economics: Ecosystem Services; Biodiversity Conservation; Bioeconomics; Industrial Ecology", + "Q58" = "Environmental Economics: Government Policy", + "Q59" = "Environmental Economics: Other", + "R0" = "General", + "R00" = "Urban, Rural, Regional, Real Estate, and Transportation Economics: General", + "R1" = "General Regional Economics", + "R10" = "General Regional Economics (includes Regional Data)", + "R11" = "Regional Economic Activity: Growth, Development, Environmental Issues, and Changes", + "R12" = "Size and Spatial Distributions of Regional Economic Activity", + "R13" = "General Equilibrium and Welfare Economic Analysis of Regional Economies", + "R14" = "Land Use Patterns", + "R15" = "General Regional Economics: Econometric and Input-Output Models; Other Models", + "R19" = "General Regional Economics: Other", + "R2" = "Household Analysis", + "R20" = "Urban, Rural, Regional, Real Estate, and Transportation Economics: Household Analysis: General", + "R21" = "Urban, Rural, Regional, Real Estate, and Transportation Economics: Housing Demand", + "R22" = "Urban, Rural, Regional, Real Estate, and Transportation Economics: Other Demand", + "R23" = "Urban, Rural, Regional, Real Estate, and Transportation Economics: Regional Migration; Regional Labor Markets; Population; Neighborhood Characteristics", + "R28" = "Urban, Rural, Regional, Real Estate, and Transportation Economics: Government Policy", + "R29" = "Urban, Rural, Regional, Real Estate, and Transportation Economics: Household Analysis: Other", + "R3" = "Real Estate Markets, Spatial Production Analysis, and Firm Location", + "R30" = "Real Estate Markets, Spatial Production Analysis, and Firm Location: General", + "R31" = "Housing Supply and Markets", + "R32" = "Other Spatial Production and Pricing Analysis", + "R33" = "Nonagricultural and Nonresidential Real Estate Markets", + "R38" = "Production Analysis and Firm Location: Government Policy", + "R39" = "Real Estate Markets, Spatial Production Analysis, and Firm Location: Other", + "R4" = "Transportation Economics", + "R40" = "Transportation Economics: General", + "R41" = "Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise", + "R42" = "Transportation Economics: Government and Private Investment Analysis; Road Maintenance, Transportation Planning", + "R48" = "Transportation Economics: Government Pricing and Policy", + "R49" = "Transportation Economics: Other", + "R5" = "Regional Government Analysis", + "R50" = "Regional Government Analysis: General", + "R51" = "Finance in Urban and Rural Economies", + "R52" = "Regional Government Analysis: Land Use and Other Regulations", + "R53" = "Public Facility Location Analysis; Public Investment and Capital Stock", + "R58" = "Regional Development Planning and Policy", + "R59" = "Regional Government Analysis: Other", + "Y1" = "Data: Tables and Charts", + "Y10" = "Data: Tables and Charts", + "Y2" = "Introductory Material", + "Y20" = "Introductory Material", + "Y3" = "Book Reviews (unclassified)", + "Y30" = "Book Reviews (unclassified)", + "Y4" = "Dissertations (unclassified)", + "Y40" = "Dissertations (unclassified)", + "Y5" = "Further Reading (unclassified)", + "Y50" = "Further Reading (unclassified)", + "Y6" = "Excerpts", + "Y60" = "Excerpt", + "Y7" = "No Author General Discussions", + "Y70" = "No Author General Discussions", + "Y8" = "Related Disciplines", + "Y80" = "Related Disciplines", + "Y9" = "Other", + "Y90" = "Miscellaneous Categories: Other", + "Y91" = "Pictures and Maps", + "Y92" = "Novels, Self-Help Books, etc.", + "Z0" = "General", + "Z00" = "Other Special Topics: General", + "Z1" = "Cultural Economics • Economic Sociology • Economic Anthropology", + "Z10" = "Cultural Economics; Economic Sociology; Economic Anthropology: General", + "Z11" = "Cultural Economics: Economics of the Arts and Literature", + "Z12" = "Cultural Economics: Religion", + "Z13" = "Economic Sociology; Economic Anthropology; Language; Social and Economic Stratification", + "Z18" = "Cultural Economics: Public Policy", + "Z19" = "Cultural Economics: Other", + "Z2" = "Sports Economics", + "Z20" = "Sports Economics: General", + "Z21" = "Sports Economics: Industry Studies", + "Z22" = "Sports Economics: Labor Issues", + "Z23" = "Sports Economics: Finance", + "Z28" = "Sports Economics: Policy", + "Z29" = "Sports Economics: Other", + "Z3" = "Tourism Economics", + "Z30" = "Tourism Economics: General", + "Z31" = "Tourism: Industry Studies", + "Z32" = "Tourism and Development", + "Z33" = "Tourism: Marketing and Finance", + "Z38" = "Tourism: Policy", + "Z39" = "Tourism: Other" +) + +JEL_CODES <- names(JEL_CAPTIONS) diff --git a/server/preprocessing/other-scripts/subject_cleaning.R b/server/preprocessing/other-scripts/subject_cleaning.R index 695cc61bb..458940f2a 100644 --- a/server/preprocessing/other-scripts/subject_cleaning.R +++ b/server/preprocessing/other-scripts/subject_cleaning.R @@ -9,6 +9,9 @@ # classifications (split the subject, filter the keyword vector, rejoin). +# JEL code/caption lookup for drop_jel (generated file, see its header). +if (!exists("JEL_CAPTIONS")) source("jel_codes.R") + remove_keywords_with_text_in_square_brackets <- function(x) { # This function removes whole keywords that contain text in square brackets. # Example: 'Climate [MeSH]' | 'Some keywords [Chemical]'. @@ -580,6 +583,67 @@ drop_lcc_subclass_bare <- function(keywords) { # keywords[!grepl("\\([Gg]eneral\\)\\s*$", keywords)] # } +# JEL classification (econstor and other economics repositories). LoC-style +# approach: exact match against the official code list (jel_codes.R; 2- and +# 3-char codes with captions), an explicit false-positive list of keywords +# that must never be removed, and two removal forms: +# * an isolated code keyword ("C72"), +# * a code+caption keyword ("C71 Cooperative Games", "C71 - Cooperative +# Games"), where the text matches the official caption or its leading +# ";"-fragment (captions contain semicolons; a provider serializing +# code+caption into a ";"-separated field keeps the leading fragment +# attached to the code), tolerating a trailing " / translation" tail. +# A bare letter+digit pattern without the list would collide with genuine +# keywords (vitamin B12, C4 plants, L2); caption-only keywords are never +# removed (an author keyword may equal a caption). +normalize_jel_caption <- function(x) { + x <- tolower(x) + x <- gsub("[^a-z0-9]+", " ", x) + trimws(x) +} + +JEL_CAPTIONS_NORM <- vapply(JEL_CAPTIONS, normalize_jel_caption, + character(1)) +JEL_CAPTION_FIRST_NORM <- vapply( + JEL_CAPTIONS, + function(cap) normalize_jel_caption(strsplit(cap, ";", fixed = TRUE)[[1]][1]), + character(1)) + +drop_jel <- function(keywords) { + if (!length(keywords)) return(keywords) + kw <- trimws(keywords) + is_isolated <- kw %in% JEL_CODES & !(kw %in% JEL_FALSE_POSITIVES) + is_code_caption <- vapply(kw, function(k) { + m <- regmatches(k, regexec("^([A-Z][0-9]{1,2})[ :-]+(.+)$", k))[[1]] + if (length(m) < 3) return(FALSE) + code <- m[2] + if (!(code %in% JEL_CODES)) return(FALSE) + text <- sub(" */.*$", "", m[3]) # drop a translation tail after " / " + text <- normalize_jel_caption(text) + nzchar(text) && (identical(text, JEL_CAPTIONS_NORM[[code]]) || + identical(text, JEL_CAPTION_FIRST_NORM[[code]])) + }, logical(1), USE.NAMES = FALSE) + keywords[!(is_isolated | is_code_caption)] +} + +# AMS Mathematics Subject Classification (MSC 2020, +# https://mathscinet.ams.org/msc/): "ddWdd" (81V25), the wildcard forms +# "ddWxx" (81Vxx) and "dd-WW" (81-XX). The "dd-dd" form (81-06) is already +# removed by the LCC range rule in the legacy chain and is pinned by a chain +# test, not re-implemented here. +drop_ams_msc <- function(keywords) { + keywords[!grepl("^[0-9]{2}([A-Z]([0-9]{2}|xx)|-[A-Z]{2})$", keywords)] +} + +# Physics and Astronomy Classification Scheme (legacy PACS): "dd.dd.Ww" +# (05.30.Rt) including the hyphen/plus suffix forms "dd.dd.-w" (03.67.-a) and +# "dd.dd.+w" (42.50.+x). Whole-keyword removal here, before the residual +# digit rules of the legacy chain mangle the code into a partial token +# ("05.30.Rt" -> "30.Rt"). The successor scheme PhySH is out of scope. +drop_pacs <- function(keywords) { + keywords[!grepl("^[0-9]{2}\\.[0-9]{2}\\.[+-]?[A-Za-z]{1,2}$", keywords)] +} + clean_classification_keywords <- function(x) { one <- function(subject) { if (is.na(subject) || subject == "") return(subject) @@ -602,6 +666,9 @@ clean_classification_keywords <- function(x) { keywords <- drop_lcc_toplevel(keywords) keywords <- drop_lcc_subclass(keywords) keywords <- drop_lcc_subclass_bare(keywords) + keywords <- drop_jel(keywords) + keywords <- drop_ams_msc(keywords) + keywords <- drop_pacs(keywords) # keywords <- drop_domain_general(keywords) # handled by legacy chain for now join_keywords(keywords) } @@ -834,8 +901,25 @@ clean_subject_string <- function(subject_all, vis_type = NULL, doaj = FALSE) { subject_cleaned = gsub(arxiv_classification_string, "", subject_cleaned, perl=TRUE) # remove arXiv classification short code, but keep classifcation name } else { subject_cleaned = gsub("FOS [A-Za-z ]+", "", subject_cleaned) # remove FOS classifications (Fields of Science and Technology) - arxiv_classification_string = "(([A-Za-z ]+ )?cond-mat\\.[a-z\\-]+)|([\\w ]+ )?(cs|econ|eess|math|astro-ph|nlin|q-bio|q-fin|stat)\\.[A-Z]{2}|cond-mat\\.[a-z\\-]+|hep-(ex|lat|ph|th)|math-ph|nucl-(ex|th)|physics\\.[a-z\\-]+|([\\w ]+ )(astro-ph|gr-qc|quant-ph|cond-mat)" - subject_cleaned = gsub(arxiv_classification_string, "", subject_cleaned, perl=TRUE) # remove arXiv classification, except on streamgraphs + # arXiv "name + short code" keywords ("Adaptation and Self-Organizing + # Systems nlin.AO") are removed whole. The optional name prefix must + # include hyphen and comma (class names like "Self-Organizing Systems", + # "Data Analysis, Statistics and Probability") — a prefix class without + # them starts matching after the hyphen and leaves a partial keyword + # ("Adaptation and Self-"). The prefix is anchored to the keyword start + # (begin of string or after ";") so it can never eat across a keyword + # boundary, and applies to EVERY class family: physics.*, hep-*, nucl-* + # and math-ph codes previously had no name branch, so "Medical Physics", + # "High Energy Physics - Theory" etc. survived as plausible-looking + # keywords while cs.*/stat.* names were removed. + arxiv_classification_string = paste0( + "(^|;)[\\w ,-]*(", + "(cs|econ|eess|math|astro-ph|nlin|q-bio|q-fin|stat)\\.[A-Z]{2}", + "|cond-mat\\.[a-z-]+|physics\\.[a-z-]+", + "|hep-(ex|lat|ph|th)|math-ph|nucl-(ex|th)", + "|astro-ph|gr-qc|quant-ph|cond-mat", + ")") + subject_cleaned = gsub(arxiv_classification_string, "\\1", subject_cleaned, perl=TRUE) # remove arXiv classification, except on streamgraphs } subject_cleaned = gsub("(? Date: Tue, 15 Sep 2026 12:44:28 +0200 Subject: [PATCH 198/205] update of contentprovider.json cache --- server/workers/common/common/contentproviders.json | 8 -------- 1 file changed, 8 deletions(-) diff --git a/server/workers/common/common/contentproviders.json b/server/workers/common/common/contentproviders.json index b6eef62c8..ee15999d7 100644 --- a/server/workers/common/common/contentproviders.json +++ b/server/workers/common/common/contentproviders.json @@ -7983,10 +7983,6 @@ "name": "Universitas Sains Cut Nyak Dhien (USCND): OJS", "internal_name": "ftunivscndojs" }, - { - "name": "Sekolah Tinggi Pariwisata Mataram: OJS", - "internal_name": "ftstpmataramojs2" - }, { "name": "Portal de Revistas ESDEG (Escuela Superior de Guerra \"General Rafael Reyes Prieto\")", "internal_name": "ftescsdguerra" @@ -10655,10 +10651,6 @@ "name": "International Journal Of Humanities Education and Social Sciences", "internal_name": "ftjijhess" }, - { - "name": "Hawalah - Kajian Ilmu Ekonomi Syariah", - "internal_name": "ftjhawalah" - }, { "name": "The University of Toledo Open Journals", "internal_name": "ftunivtoledous" From f452b445b661a6895356170f97ad08c9e812f539 Mon Sep 17 00:00:00 2001 From: chreman Date: Wed, 16 Sep 2026 11:10:16 +0200 Subject: [PATCH 199/205] case restoration bugfix work-in-progress --- .../other-scripts/label_casing.R | 103 ++++++++++++++++-- .../preprocessing/other-scripts/summarize.R | 7 ++ .../other-scripts/test/test_label_casing.R | 68 ++++++++++++ 3 files changed, 169 insertions(+), 9 deletions(-) diff --git a/server/preprocessing/other-scripts/label_casing.R b/server/preprocessing/other-scripts/label_casing.R index 54a0ef760..96aa03c24 100644 --- a/server/preprocessing/other-scripts/label_casing.R +++ b/server/preprocessing/other-scripts/label_casing.R @@ -29,17 +29,102 @@ fix_keyword_casing <- function(keyword, type_counts) { } +# Attestation floor for an ALL-CAPS variant: one seen fewer times than this +# yields to the best mixed/Titlecase variant, so a single shouting title does +# not set a word's casing for the whole map. At 2 only a one-occurrence variant +# yields; a variant attested twice or more sets the casing even against a +# mixed-case twin. +CAPS_ATTESTATION_FLOOR <- 2 + + # Return the canonical (original-corpus) casing of a token: looks it up in -# type_counts case-insensitively. The lookup is exact on everything but case — -# in particular hyphen-preserving: the vocabulary regularly holds a -# de-hyphenated twin of a hyphenated token (source spelling variants), and a -# hyphen-insensitive lookup would respell the token instead of re-casing it. -# Falls back to the input token if there is no match. Edge hyphens are -# separator debris, not part of the token, and are trimmed before the lookup -# (a token that is only hyphens is returned unchanged). +# type_counts case-insensitively and picks among the variants by weight of +# evidence. The lookup is exact on everything but case — in particular +# hyphen-preserving: the vocabulary regularly holds a de-hyphenated twin of a +# hyphenated token (source spelling variants), and a hyphen-insensitive lookup +# would respell the token instead of re-casing it. Falls back to the input token +# if there is no match. Edge hyphens are separator debris, not part of the +# token, and are trimmed before the lookup (a token that is only hyphens is +# returned unchanged). +# +# The pick is a guarded majority: the most frequent variant wins, except that +# a non-lowercase variant must reach twice the lowercase count to displace +# lowercase (so an occasional capitalised sentence start does not promote a +# common noun), and an ALL-CAPS variant below CAPS_ATTESTATION_FLOOR yields. +# Ties break on count, then on the string in C order, so the result does not +# depend on the collation locale of the machine running the pipeline. match_keyword_case <- function(x, type_counts) { stripped <- gsub("^-+|-+$", "", x) if (nzchar(stripped)) x <- stripped - y <- names(type_counts[which(tolower(names(type_counts)) == tolower(x))][1]) - if (!is.na(y)) return(y) else return(x) + idx <- which(tolower(names(type_counts)) == tolower(x)) + if (!length(idx)) return(x) + + variants <- names(type_counts)[idx] + counts <- as.numeric(type_counts[idx]) + lower <- tolower(x) + lower_n <- sum(counts[variants == lower]) + + keep <- variants != lower + if (!any(keep)) return(lower) + variants <- variants[keep] + counts <- counts[keep] + is_caps <- variants == toupper(variants) & grepl("[[:alpha:]]", variants) + + o <- order(-counts, variants, method = "radix") + variants <- variants[o]; counts <- counts[o]; is_caps <- is_caps[o] + + pick <- 1 + if (is_caps[1] && counts[1] < CAPS_ATTESTATION_FLOOR && any(!is_caps)) { + pick <- which(!is_caps)[1] + } + if (lower_n > 0 && counts[pick] < 2 * lower_n) return(lower) + variants[pick] +} + + +# Classify a spelling: lowercase / allcaps / titlecase / mixed. Used to report +# the shape of each casing decision (see casing_decisions). +casing_shape <- function(s) { + if (!nzchar(s)) return("empty") + if (identical(s, tolower(s))) return("lowercase") + if (identical(s, toupper(s)) && grepl("[[:alpha:]]", s)) return("allcaps") + titled <- paste0(toupper(substr(s, 1, 1)), tolower(substring(s, 2))) + if (identical(s, titled)) return("titlecase") + "mixed" +} + + +# Per-token record of the casing decisions behind a set of labels: the variants +# the vocabulary offered with their counts, the variant picked, and its shape. +# Counting rows by `shape` gives a map's promotion rate away from lowercase, +# which is the quantity under qualitative review for the guarded-majority pick. +# Takes the labels BEFORE casing restoration. Debug-only (see dump_data). +casing_decisions <- function(clusterlabels, type_counts) { + empty <- data.frame(token = character(0), chosen = character(0), + shape = character(0), lower_count = numeric(0), + chosen_count = numeric(0), n_variants = integer(0), + variants = character(0), stringsAsFactors = FALSE) + tokens <- unlist(strsplit(unlist(clusterlabels), "[ ,]+")) + tokens <- unique(tokens[nzchar(tokens)]) + if (!length(tokens)) return(empty) + + vocab_lower <- tolower(names(type_counts)) + rows <- lapply(tokens, function(t) { + stripped <- gsub("^-+|-+$", "", t) + key <- tolower(if (nzchar(stripped)) stripped else t) + idx <- which(vocab_lower == key) + chosen <- match_keyword_case(t, type_counts) + v <- names(type_counts)[idx] + n <- as.numeric(type_counts[idx]) + o <- order(-n, v, method = "radix") + data.frame(token = key, + chosen = chosen, + shape = casing_shape(chosen), + lower_count = sum(n[v == key]), + chosen_count = sum(n[v == chosen]), + n_variants = length(idx), + variants = paste(sprintf("%s(%g)", v[o], n[o]), collapse = " "), + stringsAsFactors = FALSE) + }) + do.call(rbind, rows) } diff --git a/server/preprocessing/other-scripts/summarize.R b/server/preprocessing/other-scripts/summarize.R index 21be97768..6e4f77847 100644 --- a/server/preprocessing/other-scripts/summarize.R +++ b/server/preprocessing/other-scripts/summarize.R @@ -274,7 +274,14 @@ create_cluster_labels <- function(clusters, metadata, if (!(is.null(cc)) && (cc %in% names(metadata$annotations))) { clusters$cluster_labels = metadata$annotations[[cc]] } + pre_casing_labels <- clusters$cluster_labels clusters$cluster_labels <- fix_cluster_labels(clusters$cluster_labels, type_counts) + # Which spelling each label word was restored to, and out of which variants. + # Computed only under DEBUG: it walks the vocabulary once per distinct token. + if (debug_enabled()) { + dump_data(casing_decisions(pre_casing_labels, type_counts), + "summarize_06c_casing_decisions") + } dump_data(data.frame(cluster = clusters$groups, label = clusters$cluster_labels), "summarize_06_cluster_labels") # Per-cluster label provenance: which path built the label, plus the label as selected diff --git a/server/preprocessing/other-scripts/test/test_label_casing.R b/server/preprocessing/other-scripts/test/test_label_casing.R index d9c63aba1..c4678e845 100644 --- a/server/preprocessing/other-scripts/test/test_label_casing.R +++ b/server/preprocessing/other-scripts/test/test_label_casing.R @@ -83,3 +83,71 @@ test_that("plain subjects are untouched", { s <- "Artificial Intelligence; Decision Support Systems" expect_equal(strip_major_topic_markers(s), s) }) + +# --- match_keyword_case: guarded-majority variant pick ------------------------ +# +# The pick used to take the FIRST variant in locale collation order and ignore +# the counts entirely, so under en_US.UTF-8 the lowercase form won whatever the +# evidence said (HIV 806 lost to hiv 3). The rule below uses the counts, with +# two guards, and breaks ties on count-then-string so the result does not depend +# on the collation locale. + +# A vocabulary with explicit counts: vocab_n(HIV = 806, hiv = 3). +vocab_n <- function(...) c(...) + +test_that("the most frequent variant wins over a rare lowercase twin", { + expect_equal(match_keyword_case("hiv", vocab_n(HIV = 806, hiv = 3)), "HIV") + expect_equal(match_keyword_case("covid-19", vocab_n(`COVID-19` = 324, `covid-19` = 8)), + "COVID-19") + expect_equal(match_keyword_case("lstm", vocab_n(LSTM = 106, Lstm = 1)), "LSTM") +}) + +test_that("a one-occurrence misspelling loses to the attested variant", { + expect_equal(match_keyword_case("sars-cov-2", + vocab_n(`SARS-CoV-2` = 85, `SARs-CoV-2` = 1)), + "SARS-CoV-2") +}) + +test_that("a non-lowercase variant needs twice the lowercase count to displace it", { + # 140 Titlecase against 196 lowercase: not enough, the word stays lowercase. + expect_equal(match_keyword_case("health", vocab_n(health = 196, Health = 140)), "health") + # Exactly 2x displaces; one short of it does not. + expect_equal(match_keyword_case("word", vocab_n(word = 10, Word = 20)), "Word") + expect_equal(match_keyword_case("word", vocab_n(word = 10, Word = 19)), "word") +}) + +test_that("a single-occurrence ALL-CAPS variant yields to the best mixed variant", { + # One shouting title must not set the casing for the whole map. + expect_equal(match_keyword_case("token", vocab_n(TOKEN = 1, Token = 1)), "Token") +}) + +test_that("an ALL-CAPS variant seen more than once wins", { + # DECIDED 2026-09-16: the guard fires only at count == 1. A variant attested + # twice or more sets the casing even against a mixed-case twin, so a single + # ALL-CAPS title contributing a repeated token can still shout. Accepted + # trade-off: the alternative (a <5 attestation floor) was measured to change + # 10 tokens across the replay corpus and was not chosen. + expect_equal(match_keyword_case("dimensionality", + vocab_n(DIMENSIONALITY = 3, Dimensionality = 1)), + "DIMENSIONALITY") + expect_equal(match_keyword_case("unlabelled", + vocab_n(UNLABELLED = 2, Unlabelled = 1)), + "UNLABELLED") +}) + +test_that("the pick does not depend on the order of the vocabulary", { + expect_equal(match_keyword_case("hiv", vocab_n(hiv = 3, HIV = 806)), + match_keyword_case("hiv", vocab_n(HIV = 806, hiv = 3))) + # A count tie is broken on the string, not on locale collation. + expect_equal(match_keyword_case("alpha", vocab_n(Alpha = 5, ALPHA = 5)), + match_keyword_case("alpha", vocab_n(ALPHA = 5, Alpha = 5))) +}) + +test_that("a word with only a lowercase form stays lowercase", { + expect_equal(match_keyword_case("models", vocab_n(models = 12)), "models") +}) + +test_that("a word with a single non-lowercase form takes it", { + expect_equal(match_keyword_case("frauenberger", vocab_n(Frauenberger = 135)), + "Frauenberger") +}) From 664687e243fd0ebf68e8b7b9e6223073c24ebaaf Mon Sep 17 00:00:00 2001 From: chreman Date: Wed, 16 Sep 2026 17:59:27 +0200 Subject: [PATCH 200/205] adjustment of case restoration to ignore ALLCAPS titles --- server/preprocessing/other-scripts/features.R | 32 ++++++++ .../other-scripts/test/test_label_casing.R | 75 +++++++++++++++++++ .../preprocessing/other-scripts/vis_layout.R | 4 +- 3 files changed, 110 insertions(+), 1 deletion(-) diff --git a/server/preprocessing/other-scripts/features.R b/server/preprocessing/other-scripts/features.R index 7aae273eb..865a1e7d8 100644 --- a/server/preprocessing/other-scripts/features.R +++ b/server/preprocessing/other-scripts/features.R @@ -57,6 +57,38 @@ get_type_counts <- function(corpus) { return(type_counts) } + +# TRUE for a string written entirely in capitals: it has letters and none of +# them is lowercase. Digits and punctuation do not count either way. +is_allcaps <- function(s) { + s <- as.character(s) + !is.na(s) & nzchar(s) & grepl("[[:alpha:]]", s) & !grepl("[[:lower:]]", s) +} + + +# Lowercase the title span of every document whose title is ALL-CAPS, in the +# unlowered corpus that feeds the casing vocabulary (get_type_counts). A +# shouting title otherwise attests a capitalised spelling of every one of its +# words, and the casing restoration would carry that into the area labels. +# Documents are matched to metadata rows by id. The title is put through the +# same hygiene the document content received, so it matches verbatim; a title +# that does not match (altered by the noise sanitiser) is left as it is. +# Returns the modified corpus; the caller's other corpus copies are untouched. +lower_allcaps_titles <- function(corpus, metadata) { + caps <- which(is_allcaps(metadata$title)) + if (!length(caps)) return(corpus) + ids <- vapply(seq_along(corpus), function(k) as.character(meta(corpus[[k]], "id")), "") + for (i in caps) { + j <- match(as.character(metadata$id[i]), ids) + if (is.na(j)) next + title <- sanitize_corpus_noise(decode_html_entities(metadata$title[i])) + doc <- corpus[[j]] + content(doc) <- sub(title, tolower(title), content(doc), fixed = TRUE) + corpus[[j]] <- doc + } + corpus +} + concatenate_features <- function(...) { # expects a list of feature matrices which can be extended horizontally return(cbind(...)) diff --git a/server/preprocessing/other-scripts/test/test_label_casing.R b/server/preprocessing/other-scripts/test/test_label_casing.R index c4678e845..8c751604a 100644 --- a/server/preprocessing/other-scripts/test/test_label_casing.R +++ b/server/preprocessing/other-scripts/test/test_label_casing.R @@ -151,3 +151,78 @@ test_that("a word with a single non-lowercase form takes it", { expect_equal(match_keyword_case("frauenberger", vocab_n(Frauenberger = 135)), "Frauenberger") }) + +# --- lower_allcaps_titles: ALL-CAPS titles do not attest capitalised spellings -- +# +# The casing vocabulary is built from the unlowered corpus, where each document +# starts with the paper's title. A title written entirely in capitals is +# lowered there first, so a single shouting title cannot set the casing of its +# words for the whole map. Only the casing vocabulary sees this; the clustering +# and tf-idf corpora are lowercased anyway. + +if (!exists("getLogger")) suppressMessages(library(logging)) +if (!exists("lower_allcaps_titles")) source("features.R") + +allcaps_fixture <- function() { + metadata <- data.frame( + id = c("p1", "p2", "p3"), + title = c("DIMENSIONALITY REDUCTION FOR FEW-SHOT LEARNING", + "Dimensionality reduction in practice", + "COVID-19 outcomes in adults"), + paper_abstract = c("We study gradient space methods.", + "Gradient methods are common.", + "COVID-19 is compared with COVID-19 variants."), + stringsAsFactors = FALSE) + text <- data.frame(id = metadata$id, + content = paste(metadata$title, metadata$paper_abstract), + stringsAsFactors = FALSE) + list(metadata = metadata, corpus = create_corpus(metadata, text, c("the"))) +} + +test_that("is_allcaps flags capital-only titles and nothing else", { + expect_equal(is_allcaps(c("DIMENSIONALITY REDUCTION", "COVID-19 IN 2020", "Covid-19 outcomes", + "COVID-19 outcomes", "2020", "", NA)), + c(TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE)) +}) + +test_that("an ALL-CAPS title no longer attests capitalised variants", { + fx <- allcaps_fixture() + before <- get_type_counts(fx$corpus$unlowered) + after <- get_type_counts(lower_allcaps_titles(fx$corpus$unlowered, fx$metadata)) + expect_equal(unname(before["DIMENSIONALITY"]), 1) + expect_true(is.na(after["DIMENSIONALITY"])) + expect_equal(unname(after["dimensionality"]), 1) + expect_true(is.na(after["FEW-SHOT"])) + expect_equal(unname(after["few-shot"]), 1) +}) + +test_that("mixed-case titles and abstracts are left as they are", { + fx <- allcaps_fixture() + after <- get_type_counts(lower_allcaps_titles(fx$corpus$unlowered, fx$metadata)) + # p2's Titlecase title word and p3's acronym are untouched... + expect_equal(unname(after["Dimensionality"]), 1) + expect_equal(unname(after["COVID-19"]), 3) + # ...and so is the abstract of the ALL-CAPS paper. + expect_equal(unname(after["gradient"]), 1) + expect_equal(unname(after["Gradient"]), 1) +}) + +test_that("the lowered title feeds the pick: the shouting title no longer wins", { + fx <- allcaps_fixture() + tc <- get_type_counts(lower_allcaps_titles(fx$corpus$unlowered, fx$metadata)) + expect_equal(match_keyword_case("dimensionality", tc), "dimensionality") +}) + +test_that("the input corpus and the metadata are not modified", { + fx <- allcaps_fixture() + invisible(lower_allcaps_titles(fx$corpus$unlowered, fx$metadata)) + expect_true(startsWith(content(fx$corpus$unlowered[[1]]), "DIMENSIONALITY")) + expect_equal(fx$metadata$title[1], "DIMENSIONALITY REDUCTION FOR FEW-SHOT LEARNING") +}) + +test_that("a map without ALL-CAPS titles is returned unchanged", { + fx <- allcaps_fixture() + md <- fx$metadata; md$title[1] <- "Dimensionality reduction for few-shot learning" + expect_identical(get_type_counts(lower_allcaps_titles(fx$corpus$unlowered, md)), + get_type_counts(fx$corpus$unlowered)) +}) diff --git a/server/preprocessing/other-scripts/vis_layout.R b/server/preprocessing/other-scripts/vis_layout.R index 88cb041ef..ef13e504c 100644 --- a/server/preprocessing/other-scripts/vis_layout.R +++ b/server/preprocessing/other-scripts/vis_layout.R @@ -52,7 +52,9 @@ vis_layout <- function(text, metadata, service, vlog$debug("get cluster summaries") metadata = replace_keywords_if_empty(metadata, stops) - type_counts <- get_type_counts(corpus$unlowered) + # Casing vocabulary for the area labels. ALL-CAPS titles are lowered first + # so they do not attest capitalised spellings (see lower_allcaps_titles). + type_counts <- get_type_counts(lower_allcaps_titles(corpus$unlowered, metadata)) named_clusters <- create_cluster_labels(clusters, metadata, type_counts, weightingspec="ntn", top_n=3, From e0e25896c3d09d4454ac5548bd9668ed98502468 Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 17 Sep 2026 09:33:35 +0200 Subject: [PATCH 201/205] adjustment of case restoration to ignore ALLCAPS keywords of length 2+ --- server/preprocessing/other-scripts/features.R | 64 +++++++++++++----- .../other-scripts/test/test_label_casing.R | 67 ++++++++++++++++--- .../preprocessing/other-scripts/vis_layout.R | 7 +- 3 files changed, 110 insertions(+), 28 deletions(-) diff --git a/server/preprocessing/other-scripts/features.R b/server/preprocessing/other-scripts/features.R index 865a1e7d8..1c2a745b3 100644 --- a/server/preprocessing/other-scripts/features.R +++ b/server/preprocessing/other-scripts/features.R @@ -66,24 +66,58 @@ is_allcaps <- function(s) { } -# Lowercase the title span of every document whose title is ALL-CAPS, in the -# unlowered corpus that feeds the casing vocabulary (get_type_counts). A -# shouting title otherwise attests a capitalised spelling of every one of its -# words, and the casing restoration would carry that into the area labels. -# Documents are matched to metadata rows by id. The title is put through the -# same hygiene the document content received, so it matches verbatim; a title -# that does not match (altered by the noise sanitiser) is left as it is. -# Returns the modified corpus; the caller's other corpus copies are untouched. -lower_allcaps_titles <- function(corpus, metadata) { - caps <- which(is_allcaps(metadata$title)) - if (!length(caps)) return(corpus) - ids <- vapply(seq_along(corpus), function(k) as.character(meta(corpus[[k]], "id")), "") - for (i in caps) { +# TRUE for a keyword written in capitals that is a phrase of at least two +# alphabetic words ("REDES COMPLEXAS", "PROSOCIAL BEHAVIOR"). A single capital +# word is kept as it may be an acronym (HIV, LSTM), and so is an acronym with a +# number attached ("EORTC 1709"): the number is not a word. +is_allcaps_phrase <- function(s) { + is_allcaps(s) & vapply(strsplit(as.character(s), "[[:space:]]+"), function(w) { + sum(grepl("[[:alpha:]]", w)) >= 2 + }, logical(1)) +} + + +# The spans of a paper's metadata that are written in capitals and should not +# attest capitalised spellings: an ALL-CAPS title, and every ALL-CAPS +# multi-word keyword (see is_allcaps_phrase). Keywords are taken from +# subject_orig when present, else subject; both are ";"-separated. +allcaps_spans <- function(metadata, i) { + spans <- character(0) + if (is_allcaps(metadata$title[i])) spans <- metadata$title[i] + col <- if ("subject_orig" %in% names(metadata)) "subject_orig" else "subject" + if (col %in% names(metadata)) { + kws <- trimws(unlist(strsplit(as.character(metadata[[col]][i]), ";", fixed = TRUE))) + spans <- c(spans, kws[is_allcaps_phrase(kws)]) + } + unique(spans[!is.na(spans) & nzchar(spans)]) +} + + +# Lowercase the ALL-CAPS spans (title, multi-word keywords) of every document +# in the unlowered corpus that feeds the casing vocabulary (get_type_counts). +# A shouting title or keyword otherwise attests a capitalised spelling of every +# one of its words, and the casing restoration would carry that into the area +# labels. Documents are matched to metadata rows by id. Each span is put +# through the same hygiene the document content received, so it matches +# verbatim; a span that does not match (altered by the noise sanitiser) is +# left as it is. Returns the modified corpus; the caller's other corpus copies +# are untouched. +lower_allcaps_spans <- function(corpus, metadata) { + ids <- NULL + for (i in seq_len(nrow(metadata))) { + spans <- allcaps_spans(metadata, i) + if (!length(spans)) next + if (is.null(ids)) { + ids <- vapply(seq_along(corpus), function(k) as.character(meta(corpus[[k]], "id")), "") + } j <- match(as.character(metadata$id[i]), ids) if (is.na(j)) next - title <- sanitize_corpus_noise(decode_html_entities(metadata$title[i])) doc <- corpus[[j]] - content(doc) <- sub(title, tolower(title), content(doc), fixed = TRUE) + text <- content(doc) + for (span in sanitize_corpus_noise(decode_html_entities(spans))) { + text <- sub(span, tolower(span), text, fixed = TRUE) + } + content(doc) <- text corpus[[j]] <- doc } corpus diff --git a/server/preprocessing/other-scripts/test/test_label_casing.R b/server/preprocessing/other-scripts/test/test_label_casing.R index 8c751604a..3a60d0995 100644 --- a/server/preprocessing/other-scripts/test/test_label_casing.R +++ b/server/preprocessing/other-scripts/test/test_label_casing.R @@ -152,7 +152,7 @@ test_that("a word with a single non-lowercase form takes it", { "Frauenberger") }) -# --- lower_allcaps_titles: ALL-CAPS titles do not attest capitalised spellings -- +# --- lower_allcaps_spans: ALL-CAPS titles and keywords do not attest capitalised spellings -- # # The casing vocabulary is built from the unlowered corpus, where each document # starts with the paper's title. A title written entirely in capitals is @@ -161,7 +161,7 @@ test_that("a word with a single non-lowercase form takes it", { # and tf-idf corpora are lowercased anyway. if (!exists("getLogger")) suppressMessages(library(logging)) -if (!exists("lower_allcaps_titles")) source("features.R") +if (!exists("lower_allcaps_spans")) source("features.R") allcaps_fixture <- function() { metadata <- data.frame( @@ -172,9 +172,12 @@ allcaps_fixture <- function() { paper_abstract = c("We study gradient space methods.", "Gradient methods are common.", "COVID-19 is compared with COVID-19 variants."), + subject_orig = c("REDES COMPLEXAS; HIV; EORTC 1709", + "Prosocial behavior; machine learning", + "SNOMED CT"), stringsAsFactors = FALSE) text <- data.frame(id = metadata$id, - content = paste(metadata$title, metadata$paper_abstract), + content = paste(metadata$title, metadata$paper_abstract, metadata$subject_orig), stringsAsFactors = FALSE) list(metadata = metadata, corpus = create_corpus(metadata, text, c("the"))) } @@ -188,7 +191,7 @@ test_that("is_allcaps flags capital-only titles and nothing else", { test_that("an ALL-CAPS title no longer attests capitalised variants", { fx <- allcaps_fixture() before <- get_type_counts(fx$corpus$unlowered) - after <- get_type_counts(lower_allcaps_titles(fx$corpus$unlowered, fx$metadata)) + after <- get_type_counts(lower_allcaps_spans(fx$corpus$unlowered, fx$metadata)) expect_equal(unname(before["DIMENSIONALITY"]), 1) expect_true(is.na(after["DIMENSIONALITY"])) expect_equal(unname(after["dimensionality"]), 1) @@ -198,7 +201,7 @@ test_that("an ALL-CAPS title no longer attests capitalised variants", { test_that("mixed-case titles and abstracts are left as they are", { fx <- allcaps_fixture() - after <- get_type_counts(lower_allcaps_titles(fx$corpus$unlowered, fx$metadata)) + after <- get_type_counts(lower_allcaps_spans(fx$corpus$unlowered, fx$metadata)) # p2's Titlecase title word and p3's acronym are untouched... expect_equal(unname(after["Dimensionality"]), 1) expect_equal(unname(after["COVID-19"]), 3) @@ -209,20 +212,64 @@ test_that("mixed-case titles and abstracts are left as they are", { test_that("the lowered title feeds the pick: the shouting title no longer wins", { fx <- allcaps_fixture() - tc <- get_type_counts(lower_allcaps_titles(fx$corpus$unlowered, fx$metadata)) + tc <- get_type_counts(lower_allcaps_spans(fx$corpus$unlowered, fx$metadata)) expect_equal(match_keyword_case("dimensionality", tc), "dimensionality") }) test_that("the input corpus and the metadata are not modified", { fx <- allcaps_fixture() - invisible(lower_allcaps_titles(fx$corpus$unlowered, fx$metadata)) + invisible(lower_allcaps_spans(fx$corpus$unlowered, fx$metadata)) expect_true(startsWith(content(fx$corpus$unlowered[[1]]), "DIMENSIONALITY")) expect_equal(fx$metadata$title[1], "DIMENSIONALITY REDUCTION FOR FEW-SHOT LEARNING") }) -test_that("a map without ALL-CAPS titles is returned unchanged", { +test_that("a map without ALL-CAPS titles or keywords is returned unchanged", { fx <- allcaps_fixture() - md <- fx$metadata; md$title[1] <- "Dimensionality reduction for few-shot learning" - expect_identical(get_type_counts(lower_allcaps_titles(fx$corpus$unlowered, md)), + md <- fx$metadata + md$title[1] <- "Dimensionality reduction for few-shot learning" + md$subject_orig <- c("Redes complexas; HIV; EORTC 1709", "Prosocial behavior; machine learning", "Snomed CT") + expect_identical(get_type_counts(lower_allcaps_spans(fx$corpus$unlowered, md)), get_type_counts(fx$corpus$unlowered)) }) + +test_that("is_allcaps_phrase needs capitals and at least two alphabetic words", { + expect_equal(is_allcaps_phrase(c("REDES COMPLEXAS", "SNOMED CT", "HIV", "EORTC 1709", + "Machine LEARNING", "COVID-19", "", NA)), + c(TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE)) +}) + +test_that("a multi-word ALL-CAPS keyword no longer attests capitalised variants", { + fx <- allcaps_fixture() + before <- get_type_counts(fx$corpus$unlowered) + after <- get_type_counts(lower_allcaps_spans(fx$corpus$unlowered, fx$metadata)) + expect_equal(unname(before["REDES"]), 1) + expect_true(is.na(after["REDES"])) + expect_equal(unname(after["redes"]), 1) + expect_equal(unname(after["complexas"]), 1) + # a phrase of two acronyms is still a phrase and is lowered too + expect_true(is.na(after["SNOMED"])) + expect_equal(unname(after["snomed"]), 1) +}) + +test_that("single-word acronym keywords and acronym-plus-number keywords are kept", { + fx <- allcaps_fixture() + after <- get_type_counts(lower_allcaps_spans(fx$corpus$unlowered, fx$metadata)) + expect_equal(unname(after["HIV"]), 1) + expect_equal(unname(after["EORTC"]), 1) + expect_true(is.na(after["eortc"])) +}) + +test_that("mixed-case keywords are left as they are", { + fx <- allcaps_fixture() + after <- get_type_counts(lower_allcaps_spans(fx$corpus$unlowered, fx$metadata)) + expect_equal(unname(after["Prosocial"]), 1) + expect_equal(unname(after["machine"]), 1) +}) + +test_that("keywords come from subject when subject_orig is absent", { + fx <- allcaps_fixture() + md <- fx$metadata; md$subject <- md$subject_orig; md$subject_orig <- NULL + after <- get_type_counts(lower_allcaps_spans(fx$corpus$unlowered, md)) + expect_true(is.na(after["REDES"])) + expect_equal(unname(after["redes"]), 1) +}) diff --git a/server/preprocessing/other-scripts/vis_layout.R b/server/preprocessing/other-scripts/vis_layout.R index ef13e504c..ab32407a9 100644 --- a/server/preprocessing/other-scripts/vis_layout.R +++ b/server/preprocessing/other-scripts/vis_layout.R @@ -52,9 +52,10 @@ vis_layout <- function(text, metadata, service, vlog$debug("get cluster summaries") metadata = replace_keywords_if_empty(metadata, stops) - # Casing vocabulary for the area labels. ALL-CAPS titles are lowered first - # so they do not attest capitalised spellings (see lower_allcaps_titles). - type_counts <- get_type_counts(lower_allcaps_titles(corpus$unlowered, metadata)) + # Casing vocabulary for the area labels. ALL-CAPS titles and multi-word + # keywords are lowered first so they do not attest capitalised spellings + # (see lower_allcaps_spans). + type_counts <- get_type_counts(lower_allcaps_spans(corpus$unlowered, metadata)) named_clusters <- create_cluster_labels(clusters, metadata, type_counts, weightingspec="ntn", top_n=3, From 658477316559bbc5efd33bb8645d03efa60d20b0 Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 17 Sep 2026 16:39:00 +0200 Subject: [PATCH 202/205] adjustment of case restoration by improving recognition of intra-word punctuation --- .../other-scripts/label_casing.R | 63 +++++++++++++++---- .../other-scripts/test/test_label_casing.R | 52 ++++++++++++++- 2 files changed, 100 insertions(+), 15 deletions(-) diff --git a/server/preprocessing/other-scripts/label_casing.R b/server/preprocessing/other-scripts/label_casing.R index 96aa03c24..15514bd2c 100644 --- a/server/preprocessing/other-scripts/label_casing.R +++ b/server/preprocessing/other-scripts/label_casing.R @@ -39,25 +39,50 @@ CAPS_ATTESTATION_FLOOR <- 2 # Return the canonical (original-corpus) casing of a token: looks it up in # type_counts case-insensitively and picks among the variants by weight of -# evidence. The lookup is exact on everything but case — in particular -# hyphen-preserving: the vocabulary regularly holds a de-hyphenated twin of a -# hyphenated token (source spelling variants), and a hyphen-insensitive lookup -# would respell the token instead of re-casing it. Falls back to the input token -# if there is no match. Edge hyphens are separator debris, not part of the -# token, and are trimmed before the lookup (a token that is only hyphens is +# evidence (see lookup_case). Edge hyphens are separator debris, not part of +# the token, and are trimmed before the lookup (a token that is only hyphens is # returned unchanged). # +# The lookup is on the whole token first, so a hyphenated token is restored as +# one unit ("SARS-CoV-2"). The vocabulary regularly holds a de-hyphenated twin +# of a hyphenated token (source spelling variants), and a hyphen-insensitive +# lookup would respell the token instead of re-casing it, so the whole-token +# lookup is exact on everything but case. A token the vocabulary does not hold +# as a whole is restored piecewise: the vocabulary tokeniser splits on every +# character but letters, digits and hyphens, so a token such as "hiv/aids", +# "(sdgs)", "alzheimer's" or "rj.45" never matches whole; each of its +# alphanumeric runs is looked up on its own and the separators are kept +# ("HIV/AIDS", "(SDGs)", "Alzheimer's", "RJ.45"). A run without a match keeps +# its spelling, so an unknown token comes back unchanged. +match_keyword_case <- function(x, type_counts) { + stripped <- gsub("^-+|-+$", "", x) + if (nzchar(stripped)) x <- stripped + hit <- lookup_case(x, type_counts) + if (!is.null(hit)) return(hit) + if (!grepl("[^[:alnum:]]", x)) return(x) + m <- gregexpr("[[:alnum:]]+", x) + runs <- regmatches(x, m)[[1]] + if (!length(runs)) return(x) + regmatches(x, m) <- list(vapply(runs, function(r) { + h <- lookup_case(r, type_counts) + if (is.null(h)) r else h + }, character(1), USE.NAMES = FALSE)) + x +} + + +# The casing pick for one vocabulary key: NULL when the vocabulary holds no +# variant of `x` (compared case-insensitively), else the chosen variant. +# # The pick is a guarded majority: the most frequent variant wins, except that # a non-lowercase variant must reach twice the lowercase count to displace # lowercase (so an occasional capitalised sentence start does not promote a # common noun), and an ALL-CAPS variant below CAPS_ATTESTATION_FLOOR yields. # Ties break on count, then on the string in C order, so the result does not # depend on the collation locale of the machine running the pipeline. -match_keyword_case <- function(x, type_counts) { - stripped <- gsub("^-+|-+$", "", x) - if (nzchar(stripped)) x <- stripped +lookup_case <- function(x, type_counts) { idx <- which(tolower(names(type_counts)) == tolower(x)) - if (!length(idx)) return(x) + if (!length(idx)) return(NULL) variants <- names(type_counts)[idx] counts <- as.numeric(type_counts[idx]) @@ -82,6 +107,18 @@ match_keyword_case <- function(x, type_counts) { } +# The vocabulary keys match_keyword_case consults for a label token: the token +# itself when the vocabulary holds it, else its alphanumeric runs (the +# piecewise fallback). Edge hyphens are trimmed as in match_keyword_case. +casing_units <- function(x, type_counts) { + stripped <- gsub("^-+|-+$", "", x) + if (nzchar(stripped)) x <- stripped + if (tolower(x) %in% tolower(names(type_counts)) || !grepl("[^[:alnum:]]", x)) return(x) + runs <- regmatches(x, gregexpr("[[:alnum:]]+", x))[[1]] + if (length(runs)) runs else x +} + + # Classify a spelling: lowercase / allcaps / titlecase / mixed. Used to report # the shape of each casing decision (see casing_decisions). casing_shape <- function(s) { @@ -96,6 +133,8 @@ casing_shape <- function(s) { # Per-token record of the casing decisions behind a set of labels: the variants # the vocabulary offered with their counts, the variant picked, and its shape. +# A token restored piecewise (see match_keyword_case) is recorded per +# alphanumeric run. # Counting rows by `shape` gives a map's promotion rate away from lowercase, # which is the quantity under qualitative review for the guarded-majority pick. # Takes the labels BEFORE casing restoration. Debug-only (see dump_data). @@ -107,11 +146,11 @@ casing_decisions <- function(clusterlabels, type_counts) { tokens <- unlist(strsplit(unlist(clusterlabels), "[ ,]+")) tokens <- unique(tokens[nzchar(tokens)]) if (!length(tokens)) return(empty) + tokens <- unique(unlist(lapply(tokens, casing_units, type_counts = type_counts))) vocab_lower <- tolower(names(type_counts)) rows <- lapply(tokens, function(t) { - stripped <- gsub("^-+|-+$", "", t) - key <- tolower(if (nzchar(stripped)) stripped else t) + key <- tolower(t) idx <- which(vocab_lower == key) chosen <- match_keyword_case(t, type_counts) v <- names(type_counts)[idx] diff --git a/server/preprocessing/other-scripts/test/test_label_casing.R b/server/preprocessing/other-scripts/test/test_label_casing.R index 3a60d0995..aee33de4d 100644 --- a/server/preprocessing/other-scripts/test/test_label_casing.R +++ b/server/preprocessing/other-scripts/test/test_label_casing.R @@ -16,6 +16,8 @@ vocab <- function(...) { v <- c(...) setNames(rep(1, length(v)), v) } +# A vocabulary with explicit counts: vocab_n(HIV = 806, hiv = 3). +vocab_n <- function(...) c(...) # --- match_keyword_case: casing only, never respelling ------------------------ @@ -61,6 +63,53 @@ test_that("a label term keeps interior hyphens through casing restoration", { "Rainfall-runoff models") }) +# --- match_keyword_case: piecewise fallback for tokens with punctuation ------- + +test_that("a token the vocabulary does not hold whole is restored per alphanumeric run", { + v <- vocab("HIV", "AIDS", "SDGs", "Alzheimer", "T2", "MRI", "CD4", "CD8") + expect_equal(match_keyword_case("hiv/aids", v), "HIV/AIDS") + expect_equal(match_keyword_case("(sdgs)", v), "(SDGs)") + expect_equal(match_keyword_case("alzheimer's", v), "Alzheimer's") + expect_equal(match_keyword_case("t2/mri", v), "T2/MRI") + expect_equal(match_keyword_case("cd4/cd8", v), "CD4/CD8") +}) + +test_that("a whole-token match takes precedence over the piecewise fallback", { + expect_equal(match_keyword_case("sars-cov-2", vocab("SARS-CoV-2", "SARS", "COV")), + "SARS-CoV-2") + expect_equal(match_keyword_case("e-learning", vocab("e-learning", "E", "Learning")), + "e-learning") +}) + +test_that("runs without a match keep their spelling, and the guard applies per run", { + expect_equal(match_keyword_case("hiv/hcv", vocab("HIV")), "HIV/hcv") + expect_equal(match_keyword_case("rj456", vocab("RJ")), "rj456") + expect_equal(match_keyword_case("hiv/aids", vocab_n(HIV = 5, AIDS = 1, aids = 1)), + "HIV/aids") + expect_equal(match_keyword_case("pa*erns", vocab("other")), "pa*erns") +}) + +test_that("the piecewise fallback covers the punctuation review vector", { + # Spellings the corpus offers; AIDS is attested often enough to displace the + # lowercase twin under the guarded pick (the review vector lists all three). + v <- vocab_n(HIV = 3, aiDs = 1, aids = 1, AIDS = 3, Prevention = 1, LSTM = 1, + MC = 1, Conserving = 1, RJ = 1, RJ45 = 1, J = 1, PET = 1) + starting <- c("hiv", "hiv/aids", "aids", "normal keyword", "hivemind", "maidsen", + "hiv infections", "hiv prevention", "lstm-based rainfall-runoff", + "mc-lstm mass-conserving", "rj45", "rj.45", "rj456", "j-pet detector") + expected <- c("HIV", "HIV/AIDS", "AIDS", "Normal keyword", "Hivemind", "Maidsen", + "HIV infections", "HIV Prevention", "LSTM-based rainfall-runoff", + "MC-LSTM mass-Conserving", "RJ45", "RJ.45", "Rj456", "J-PET detector") + expect_equal(vapply(starting, fix_keyword_casing, "", type_counts = v, + USE.NAMES = FALSE), expected) +}) + +test_that("casing_decisions records a piecewise token per run", { + d <- casing_decisions(list("hiv/aids ratio"), vocab("HIV", "AIDS", "ratio")) + expect_equal(d$token, c("hiv", "aids", "ratio")) + expect_equal(d$chosen, c("HIV", "AIDS", "ratio")) +}) + # --- strip_major_topic_markers ------------------------------------------------ test_that("a leading major-topic '*' is stripped per keyword", { @@ -92,9 +141,6 @@ test_that("plain subjects are untouched", { # two guards, and breaks ties on count-then-string so the result does not depend # on the collation locale. -# A vocabulary with explicit counts: vocab_n(HIV = 806, hiv = 3). -vocab_n <- function(...) c(...) - test_that("the most frequent variant wins over a rare lowercase twin", { expect_equal(match_keyword_case("hiv", vocab_n(HIV = 806, hiv = 3)), "HIV") expect_equal(match_keyword_case("covid-19", vocab_n(`COVID-19` = 324, `covid-19` = 8)), From 00c58dda9e4e6bdffc41f65c12fd3e0074f3a7be Mon Sep 17 00:00:00 2001 From: chreman Date: Thu, 17 Sep 2026 18:26:10 +0200 Subject: [PATCH 203/205] re-baseline of test files for current state --- .../base_cancer_fallback.expected.mode1.rds | Bin 553 -> 532 bytes .../base_cancer_fallback.expected.mode2.rds | Bin 576 -> 556 bytes .../base_cancer_fallback.expected.mode3.rds | Bin 560 -> 544 bytes .../replay/base_cancer_fallback.expected.rds | Bin 606 -> 607 bytes .../base_cancer_research.expected.mode1.rds | Bin 569 -> 548 bytes 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.../openaire_fight_ncov.expected.mode2.rds | Bin 424 -> 398 bytes .../openaire_fight_ncov.expected.mode3.rds | Bin 424 -> 398 bytes .../replay/openaire_fight_ncov.expected.rds | Bin 606 -> 605 bytes .../replay/orcid_0204881x.expected.mode1.rds | Bin 508 -> 509 bytes .../replay/orcid_0204881x.expected.mode2.rds | Bin 508 -> 509 bytes .../replay/orcid_0204881x.expected.mode3.rds | Bin 508 -> 509 bytes .../test/replay/orcid_0204881x.expected.rds | Bin 497 -> 495 bytes .../replay/orcid_02979614.expected.mode1.rds | Bin 313 -> 312 bytes .../replay/orcid_02979614.expected.mode2.rds | Bin 313 -> 312 bytes .../replay/orcid_02979614.expected.mode3.rds | Bin 313 -> 312 bytes .../test/replay/orcid_02979614.expected.rds | Bin 306 -> 305 bytes .../replay/orcid_22336926.expected.mode1.rds | Bin 697 -> 696 bytes .../replay/orcid_22336926.expected.mode2.rds | Bin 706 -> 706 bytes .../replay/orcid_22336926.expected.mode3.rds | Bin 703 -> 702 bytes .../test/replay/orcid_22336926.expected.rds | Bin 686 -> 687 bytes .../replay/orcid_24414043.expected.mode1.rds | Bin 606 -> 607 bytes .../replay/orcid_24414043.expected.mode2.rds | Bin 606 -> 607 bytes .../replay/orcid_24414043.expected.mode3.rds | Bin 606 -> 607 bytes .../test/replay/orcid_24414043.expected.rds | Bin 637 -> 636 bytes .../replay/orcid_28976075.expected.mode1.rds | Bin 621 -> 620 bytes .../replay/orcid_28976075.expected.mode2.rds | Bin 626 -> 625 bytes .../replay/orcid_28976075.expected.mode3.rds | Bin 626 -> 625 bytes .../test/replay/orcid_28976075.expected.rds | Bin 633 -> 632 bytes .../replay/orcid_39246636.expected.mode1.rds | Bin 338 -> 305 bytes .../replay/orcid_39246636.expected.mode2.rds | Bin 346 -> 314 bytes .../replay/orcid_39246636.expected.mode3.rds | Bin 344 -> 312 bytes .../test/replay/orcid_39246636.expected.rds | Bin 297 -> 297 bytes .../replay/orcid_42216275.expected.mode1.rds | Bin 671 -> 672 bytes .../replay/orcid_42216275.expected.mode2.rds | Bin 676 -> 678 bytes .../test/replay/orcid_42216275.expected.rds | Bin 675 -> 675 bytes .../replay/orcid_45050517.expected.mode1.rds | Bin 562 -> 542 bytes .../replay/orcid_45050517.expected.mode2.rds | Bin 562 -> 542 bytes .../replay/orcid_45050517.expected.mode3.rds | Bin 566 -> 546 bytes .../test/replay/orcid_45050517.expected.rds | Bin 544 -> 546 bytes .../replay/orcid_49712944.expected.mode1.rds | Bin 583 -> 592 bytes .../replay/orcid_49712944.expected.mode2.rds | Bin 582 -> 591 bytes .../replay/orcid_49712944.expected.mode3.rds | Bin 582 -> 591 bytes .../test/replay/orcid_49712944.expected.rds | Bin 574 -> 576 bytes .../replay/orcid_5116955x.expected.mode1.rds | Bin 597 -> 595 bytes .../replay/orcid_5116955x.expected.mode2.rds | Bin 595 -> 595 bytes .../replay/orcid_5116955x.expected.mode3.rds | Bin 666 -> 665 bytes .../test/replay/orcid_5116955x.expected.rds | Bin 600 -> 599 bytes .../replay/orcid_5116955x_r2.expected.mode3.rds | Bin 640 -> 640 bytes .../test/replay/orcid_5116955x_r2.expected.rds | Bin 590 -> 591 bytes .../replay/orcid_60114382.expected.mode1.rds | Bin 576 -> 577 bytes .../replay/orcid_60114382.expected.mode2.rds | Bin 576 -> 577 bytes .../replay/orcid_60114382.expected.mode3.rds | Bin 576 -> 577 bytes .../test/replay/orcid_60114382.expected.rds | Bin 667 -> 667 bytes .../replay/orcid_89117832.expected.mode1.rds | Bin 282 -> 249 bytes .../replay/orcid_89117832.expected.mode2.rds | Bin 282 -> 249 bytes .../replay/orcid_89117832.expected.mode3.rds | Bin 274 -> 242 bytes .../test/replay/orcid_89117832.expected.rds | Bin 246 -> 245 bytes .../replay/orcid_90626039.expected.mode1.rds | Bin 655 -> 637 bytes .../replay/orcid_90626039.expected.mode2.rds | Bin 644 -> 627 bytes .../replay/orcid_90626039.expected.mode3.rds | Bin 654 -> 636 bytes .../test/replay/orcid_90626039.expected.rds | Bin 682 -> 688 bytes .../replay/orcid_92873770.expected.mode1.rds | Bin 510 -> 515 bytes .../replay/orcid_92873770.expected.mode2.rds | Bin 510 -> 515 bytes .../replay/orcid_92873770.expected.mode3.rds | Bin 510 -> 515 bytes .../test/replay/orcid_92873770.expected.rds | Bin 518 -> 518 bytes .../replay/orcid_96127791.expected.mode1.rds | Bin 720 -> 726 bytes .../replay/orcid_96127791.expected.mode2.rds | Bin 722 -> 728 bytes .../replay/orcid_96127791.expected.mode3.rds | Bin 744 -> 749 bytes .../test/replay/orcid_96127791.expected.rds | Bin 800 -> 808 bytes .../replay/orcid_96127791_r2.expected.mode1.rds | Bin 760 -> 766 bytes .../replay/orcid_96127791_r2.expected.mode2.rds | Bin 739 -> 746 bytes .../replay/orcid_96127791_r2.expected.mode3.rds | Bin 727 -> 730 bytes .../test/replay/orcid_96127791_r2.expected.rds | Bin 805 -> 811 bytes .../replay/orcid_98436798.expected.mode1.rds | Bin 687 -> 670 bytes .../replay/orcid_98436798.expected.mode2.rds | Bin 687 -> 670 bytes .../replay/orcid_98436798.expected.mode3.rds | Bin 687 -> 670 bytes .../test/replay/orcid_98436798.expected.rds | Bin 732 -> 745 bytes .../replay/pubmed_cancer_datasets.expected.rds | Bin 596 -> 597 bytes .../replay/pubmed_climate_change.expected.rds | Bin 590 -> 572 bytes .../pubmed_disease_all.expected.mode1.rds | Bin 712 -> 689 bytes .../pubmed_disease_all.expected.mode2.rds | Bin 712 -> 689 bytes .../pubmed_disease_all.expected.mode3.rds | Bin 712 -> 689 bytes .../test/replay/pubmed_disease_all.expected.rds | Bin 668 -> 670 bytes .../test/replay/pubmed_infection.expected.rds | Bin 617 -> 617 bytes .../pubmed_infection_all.expected.mode1.rds | Bin 607 -> 607 bytes .../pubmed_infection_all.expected.mode2.rds | Bin 607 -> 607 bytes .../pubmed_infection_all.expected.mode3.rds | Bin 607 -> 607 bytes .../replay/pubmed_infection_all.expected.rds | Bin 610 -> 610 bytes ...pubmed_infection_datasets.expected.mode1.rds | Bin 634 -> 610 bytes ...pubmed_infection_datasets.expected.mode2.rds | Bin 634 -> 610 bytes ...pubmed_infection_datasets.expected.mode3.rds | Bin 634 -> 610 bytes .../pubmed_infection_datasets.expected.rds | Bin 592 -> 593 bytes .../pubmed_space_travel.expected.mode1.rds | Bin 324 -> 291 bytes .../pubmed_space_travel.expected.mode2.rds | Bin 324 -> 291 bytes .../pubmed_space_travel.expected.mode3.rds | Bin 324 -> 291 bytes .../replay/pubmed_space_travel.expected.rds | Bin 295 -> 296 bytes .../pubmed_space_travel_all.expected.mode1.rds | Bin 646 -> 627 bytes .../pubmed_space_travel_all.expected.mode2.rds | Bin 646 -> 627 bytes .../pubmed_space_travel_all.expected.mode3.rds | Bin 646 -> 627 bytes .../replay/pubmed_space_travel_all.expected.rds | Bin 587 -> 587 bytes .../replay/pubmed_vienna.expected.mode1.rds | Bin 591 -> 571 bytes .../replay/pubmed_vienna.expected.mode2.rds | Bin 591 -> 571 bytes .../replay/pubmed_vienna.expected.mode3.rds | Bin 591 -> 571 bytes .../test/replay/pubmed_vienna.expected.rds | Bin 589 -> 590 bytes .../test/replay/pubmed_vienna_all.expected.rds | Bin 685 -> 686 bytes .../test/replay/pubmed_work.expected.mode1.rds | Bin 622 -> 599 bytes .../test/replay/pubmed_work.expected.mode2.rds | Bin 622 -> 599 bytes .../test/replay/pubmed_work.expected.mode3.rds | Bin 622 -> 599 bytes 178 files changed, 0 insertions(+), 0 deletions(-) diff --git a/server/preprocessing/other-scripts/test/replay/base_cancer_fallback.expected.mode1.rds b/server/preprocessing/other-scripts/test/replay/base_cancer_fallback.expected.mode1.rds index 07dba56e140c5c56d11c014d0f0ae92f1ca836e7..b8414cf2f53dd87f36a6fbd0a47ca8ec44f88557 100644 GIT binary patch literal 532 zcmV+v0_*)BiwFP!00000165O9Z__Xo^|}=aUf3%_LRU!elnC0bY)F&P+O1H~#zQxW zSJ%BwEF3%XN45U>iJQa@?L(4d>)d!f!1oq#|j2KgWtI?4q+MJXP$;y^)v_x#(2%UZwl{KSB`FilAs_a|}~D zjzYt#Er{iBVs;O^8X*T2bO8s3?)$_YB_c||br3Q4Yr)r8kvZUc4GZnO^uf1YyP~o? z>a}z=w`~lu>&F>VvQI#4E2+`qX4$Yv7 zcod~>lk7!oxe!wKqd!cI#)B|%BZWjQsN|OJi78Mg^87H}z5CF|B7(J4jIg16nxko5 zfk^^i61YgiwFP!0000016@;1Z__{!UDBj{lU@-L8X>{S77`MI0}^V|1_Ej?p%OQ= zSx=l5>sf0*BJ!vC2^C{|?Is*Xw#W0{ym_$gKM<4k-lnr8l#)y 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b/server/workers/common/common/deduplication.py index 0bfdf0b3a..ad011c51d 100644 --- a/server/workers/common/common/deduplication.py +++ b/server/workers/common/common/deduplication.py @@ -27,6 +27,7 @@ def _normalize_title(title: str) -> str: + """Lowercased title with punctuation removed; whitespace is kept as is.""" return _pattern_punctuation.sub("", title.lower()) @@ -45,17 +46,33 @@ def doi_title_filter(anchor_title: str, candidate_title: str) -> bool: def find_version_in_doi(doi): + """Version number carried by a DOI's trailing suffix, or None. + + Recognizes the suffix forms listed at pattern_doi (".v3", "v3", ".3", + "v3-104960"); the file-level part after the hyphen is ignored. + """ m = pattern_doi.findall(doi) if m: return int(m[0]) else: return None - + def get_unversioned_doi(doi): + """Bare DOI with the version suffix stripped, used to group versions. + + Expects the URL form ("https://doi.org/10.x/suffix"): the scheme and host + are dropped by position, and at most three path segments are kept. A value + that is not in URL form yields an empty or truncated string. + """ doi = "/".join(doi.split("/")[3:6]) return pattern_doi.sub("", doi) def get_publisher_doi(doi): + """Registrant code of a doi.org URL (the digits after "10."), else "". + + A non-empty result marks a record whose `doi` field holds a real DOI + and not an arbitrary link. + """ pdoi = re.findall(r"org/10\.(\d+)", doi) if len(pdoi) > 0: return pdoi[0] @@ -63,6 +80,17 @@ def get_publisher_doi(doi): return "" def find_duplicate_groups(df): + """Duplicate groups derived from the `duplicates` marking. + + For each record, the group is the index of all rows whose comma-joined + `duplicates` string contains the record's id. Callers include each + record's own id in its marking, so a record is a member of its own group + and an unduplicated record forms a group of one. Identical groups are + collapsed to a single entry. + + Returns a Series of pandas Index objects (row labels of df), ordered by + the sorted member ids of each group. + """ duplicate_groups = df.id.map(lambda x: df[df.duplicates.str.contains(x)].index) tmp = pd.DataFrame(duplicate_groups).astype(str).drop_duplicates().index duplicate_groups = duplicate_groups[tmp] @@ -164,6 +192,11 @@ def extend_duplicates_with_doi_groups(df): def mark_duplicate_dois(df, column="doi"): + """Sets doi_duplicate=True on records sharing a non-empty value in `column`. + + `column` selects the DOI representation to compare: the link-derived + `doi`, or the normalized `doi_key`. + """ for doi, index in df.groupby(column).groups.items(): if doi: if len(index) > 1: @@ -171,6 +204,7 @@ def mark_duplicate_dois(df, column="doi"): return df def mark_duplicate_links(df): + """Sets link_duplicate=True on records sharing a non-empty `link`.""" for link, index in df.groupby("link").groups.items(): if link: if len(index) > 1: @@ -179,6 +213,14 @@ def mark_duplicate_links(df): def identify_relations(df): + """Links records that reference the same unversioned DOI. + + For each unversioned DOI, the records whose `identifier` field contains it + (plain substring match) are related: versions of one dataset, or records + citing it as an identifier. When more than one record matches, each gets + the full list of related ids in `relations` and has_relations=True. + Relations are informational and do not affect the duplicate marking. + """ for udoi in df.unversioned_doi.unique(): if udoi: tmp = df[df.identifier.str.contains(udoi, regex=False)] @@ -190,14 +232,29 @@ def identify_relations(df): return df def remove_false_positives_doi(df): + """Clears is_duplicate on records whose DOI is unique in the result set. + + A record flagged as a textual duplicate that carries a DOI no other + record shares is treated as a distinct work with a similar title. + Requires mark_duplicate_dois to have run. + """ df.loc[df[(df.doi != "") & (df.is_duplicate) & (~df.doi_duplicate)].index, "is_duplicate"] = False return df def remove_false_positives_link(df): + """Clears is_duplicate on records whose link is unique in the result set. + + Same reasoning as remove_false_positives_doi, applied to `link`. + Requires mark_duplicate_links to have run. + """ df.loc[df[(df.link != "") & (df.is_duplicate) & (~df.link_duplicate)].index, "is_duplicate"] = False return df def add_false_negatives(df): + """Sets is_duplicate on records sharing a link or DOI with another record. + + Covers duplicates the textual pass missed because their titles differ. + """ df.loc[df[(~df.is_duplicate) & (df.link_duplicate)].index, "is_duplicate"] = True df.loc[df[(~df.is_duplicate) & (df.doi_duplicate)].index, "is_duplicate"] = True return df @@ -320,6 +377,8 @@ def select_anchor_index(candidates, by=None, ascending=None): def _correction_prefix_match(title): + """Match object for a correction-family prefix at the start of the + normalized title, or None. Offsets refer to the normalized title.""" return correction_prefix_pattern.match(_normalize_title(title)) @@ -383,6 +442,15 @@ def split_correction_groups(df): def remove_textual_duplicates_from_different_sources(df, duplicate_groups): + """First anchor pass over the duplicate groups. + + Every member of a multi-member group is marked is_duplicate and loses its + anchor flag; then anchors are set: if any member has a publisher DOI, + all members with one become anchors (so a group can hold several anchors + at this stage; later passes narrow them down). Otherwise a single anchor + is chosen, preferring a non-empty `doi`, then the latest year, then the + content tie-break of select_anchor_index. + """ for _, idx in duplicate_groups.items(): if len(idx) > 1: tmp = df.loc[idx] @@ -397,6 +465,14 @@ def remove_textual_duplicates_from_different_sources(df, duplicate_groups): return df def mark_latest_doi(df, duplicate_groups): + """Anchors the latest version among records sharing an unversioned DOI. + + Within each group, the records of one unversioned DOI lose their anchor + flags, the one with the highest doi_version becomes the anchor, and all of + them get a `versions` entry ({"versions": [ids], "latest": [id]}). Group + members without an unversioned DOI are left untouched. Indices missing + from df (callers pass subsets of the grouped frame) are ignored. + """ for _, idx in duplicate_groups.items(): idx = df.index.intersection(idx) tmp = df.loc[idx] @@ -412,6 +488,12 @@ def mark_latest_doi(df, duplicate_groups): return df def prioritize_OA_and_latest(df, duplicate_groups): + """Re-anchors each multi-member group on its most recent open access record. + + Existing anchors in the group are cleared. The anchor is the latest-year + member with oa_state "1", or the latest-year member overall when the + group has no open access record; ties go to select_anchor_index. + """ for _, idx in duplicate_groups.items(): idx = df.index.intersection(idx) if len(idx) > 1: @@ -424,11 +506,35 @@ def prioritize_OA_and_latest(df, duplicate_groups): return df def mark_duplicates(metadata): + """Adds the is_duplicate column from deduplicate_titles' candidate list. + + deduplicate_titles currently returns an empty candidate list, so every + record is marked False; the pairwise result is in `identified_duplicates`. + Modifies metadata in place. + """ dt = deduplicate_titles(metadata, 0) duplicate_candidates = dt["duplicate_candidates"] metadata["is_duplicate"] = metadata["id"].map(lambda x: x in duplicate_candidates) def deduplicate_titles(metadata, list_size=-1): + """Textual duplicate detection by pairwise title edit distance. + + Two records are duplicates when the Levenshtein distance of their + lowercased titles, divided by the longer title's length, is below 0.03. + Titles without a space or shorter than 15 characters get the authors + appended before comparison, so short generic titles ("Editorial") only + match when the authors match too. The looser 1/15.83 threshold is computed + but not used for the result. `list_size` has no effect on the result. + + Returns a dict with + - "identified_duplicates": DataFrame(id, duplicates), where `duplicates` + is the comma-joined ids of the record's duplicates (own id excluded, + "" when there are none); + - "duplicate_candidates": always an empty list. + + Side effect: oa_state "2" is replaced with 0 in the caller's frame; the + title changes apply to a sorted copy only. + """ duplicate_candidates = [] metadata['oa_state'] = metadata['oa_state'].replace("2", 0) @@ -478,6 +584,11 @@ def deduplicate_titles(metadata, list_size=-1): return {"duplicate_candidates": duplicate_candidates, "identified_duplicates": identified_duplicates_df} def compute_lv_matrix(titles, n): + """Symmetric n x n matrix of Levenshtein distances between the titles. + + Computes each pair once (upper triangle), so cost grows quadratically + with the number of titles. The diagonal is zero. + """ distance_matrix = np.zeros((n, n)) for i in range(n): for j in range(i + 1, n): # Only compute upper triangle @@ -487,6 +598,14 @@ def compute_lv_matrix(titles, n): return distance_matrix def prioritize_doi_and_provider(df, duplicate_groups): + """Re-anchors each multi-member group on its best DOI-bearing record. + + Candidates are the members with both a `doi` and a `collection`. The one + with the highest provider priority (see get_provider_priority) becomes the + group's only anchor; ties go to select_anchor_index. A group without + candidates keeps the anchor set by the earlier passes, so this overrides + prioritize_OA_and_latest only where a DOI-bearing record exists. + """ for _, idx in duplicate_groups.items(): idx = df.index.intersection(idx) @@ -523,6 +642,12 @@ def prioritize_doi_and_provider(df, duplicate_groups): return df def get_provider_priority(provider): + """Anchor priority of a BASE collection code; higher wins. + + 2 for Crossref (collection contains "cr"), 1 for DataCite ("ftdatacite"), + 0 for any other provider, -1 when the collection is missing. Matching is + by case-insensitive substring, with DataCite checked first. + """ is_provider_not_available = pd.isna(provider) or provider == "" if is_provider_not_available: return -1 diff --git a/server/workers/common/common/enrichment.py b/server/workers/common/common/enrichment.py index e018103ef..b5925fa53 100644 --- a/server/workers/common/common/enrichment.py +++ b/server/workers/common/common/enrichment.py @@ -314,6 +314,8 @@ def process_additional_dois_element(doi_value, additional_dois_value, accumulato accumulator: Dict collecting {lowercased_bare_doi: bare_doi}. """ def add_raw(raw): + """Splits one field value (list / ';'-joined str / NaN / empty) into + DOIs and adds each, stripped of its doi.org prefix, to the accumulator.""" if isinstance(raw, list): parts = [] for element in raw: From d9c0d356d913697efbc1987a867a45ecd5d52a94 Mon Sep 17 00:00:00 2001 From: chreman Date: Tue, 22 Sep 2026 10:46:20 +0200 Subject: [PATCH 205/205] correction in env file docstring --- local_dev/dev.env.example | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/local_dev/dev.env.example b/local_dev/dev.env.example index a8bf15db2..d6b3cd3d2 100644 --- a/local_dev/dev.env.example +++ b/local_dev/dev.env.example @@ -29,7 +29,7 @@ RANKING_MODE_OPENAIRE= # only; docs/ngram-generation-simplify.md). Values: 0|1|2|3|4|5 — 0 replicates # current behaviour (generator-routed baseline). Resolution mirrors # RANKING_MODE: per-integration override NGRAM_SETTING_ beats the -# global; empty falls back. Ranking mode 0 always forces setting 0. +# global; empty falls back. NGRAM_SETTING=0 NGRAM_SETTING_BASE= NGRAM_SETTING_PUBMED=

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expect_equal(ngram_setting_lengths("0"), c(2, 3)) + expect_equal(ngram_setting_lengths("1"), c(1, 2, 3)) + expect_equal(ngram_setting_lengths("2"), c(1, 2, 3, 4)) + expect_equal(ngram_setting_lengths("3"), c(2, 3, 4)) + expect_equal(ngram_setting_lengths("4"), c(1, 2, 3, 4, 5)) + expect_equal(ngram_setting_lengths("5"), c(2, 3, 4, 5)) +}) + +# --- Part C: integration tests to add WITH the implementation ---------------- +# (sketched here, not executable yet — they need create_cluster_labels wired): +# * C4 constraint: ranking mode 0 + NGRAM_SETTING=3 -> setting coerced to 0 +# (replay an itu_ fixture in mode 0 with the env var set; labels must equal +# the recorded Setting-0 baseline). +# * G1 bypass (§7.4 edit 0): replay an itu_ fixture in mode 1 with +# NGRAM_SETTING=3; assert no token of a flagged paper's synthesised subject +# reaches the corpus dump (summarize_04_corpus_text). +# * 6.6b bounded-change assertions per setting (S1 dedup, S2/S4 4-/5-grams, +# S3/S5 no unigram labels, abstract flag on/off) over the itu_ fixtures. From db44d7d9a57139b825f843c9d3e4438fc0753925 Mon Sep 17 00:00:00 2001 From: chreman Date: Sat, 5 Sep 2026 12:51:33 +0200 Subject: [PATCH 185/205] implementation of ngram construction; settings; incl. test updates --- .../preprocessing/other-scripts/summarize.R | 49 +++++-- .../other-scripts/test/test_ngram_generator.R | 124 +++++++++++++++--- 2 files changed, 145 insertions(+), 28 deletions(-) diff --git a/server/preprocessing/other-scripts/summarize.R b/server/preprocessing/other-scripts/summarize.R index 0f394d4cb..04af82402 100644 --- a/server/preprocessing/other-scripts/summarize.R +++ b/server/preprocessing/other-scripts/summarize.R @@ -315,8 +315,20 @@ ngram_candidates <- function(text, stops, ngram_lengths = c(2, 3), out } +# bypass for the generator settings (>= 1): papers whose `subject` was +# synthesised from the title (subject_is_heuristic) get it blanked, so the +# synthesis reaches neither the corpus nor any rank source — those papers +# contribute exclusively through the generator columns. Setting 0 must NOT +# apply this: the synthesis is part of the replicated baseline. +bypass_heuristic_subjects <- function(metadata) { + if (!is.null(metadata$subject_is_heuristic)) { + metadata$subject[as.logical(metadata$subject_is_heuristic)] <- "" + } + metadata +} + # Add the two heuristic-keyword metadata columns (HEUR_MIN1 / HEUR_MIN2) to the -# metadata data frame. Generates each paper's title n-grams through the shared +# metadata data frame. Generates each paper's n-grams through the shared # generator (ngram_candidates, de-duplicated per paper), computes each n-gram's # MAP-WIDE document frequency (number of distinct resources it appears in), and stores # per paper, as "; "-joined "_"-n-gram strings: @@ -325,9 +337,19 @@ ngram_candidates <- function(text, stops, ngram_lengths = c(2, 3), # Computed once, early in labelling. ngram_lengths comes from the resolved # n-gram setting (ngram_setting_lengths); the Setting-0 default c(2, 3) # reproduces paper_title_ngrams exactly (pinned by the drop-in equivalence -# test in test_ngram_generator.R). -add_heuristic_keyword_fields <- function(metadata, stops, ngram_lengths = c(2, 3)) { - per_paper <- lapply(metadata$title, function(t) +# test in test_ngram_generator.R). The generator input is the title — or +# title + abstract for no-keyword (subject_is_heuristic) papers when the +# abstract flag is on: the input is assembled per paper by the data, not a +# code branch. +add_heuristic_keyword_fields <- function(metadata, stops, ngram_lengths = c(2, 3), + include_abstracts = FALSE) { + input_text <- metadata$title + if (include_abstracts && !is.null(metadata$subject_is_heuristic) && + !is.null(metadata$paper_abstract)) { + fl <- which(as.logical(metadata$subject_is_heuristic)) + input_text[fl] <- paste(metadata$title[fl], metadata$paper_abstract[fl]) + } + per_paper <- lapply(input_text, function(t) unique(ngram_candidates(t, stops, ngram_lengths = ngram_lengths))) df <- table(unlist(lapply(per_paper, unique))) # map-wide document frequency min2_set <- names(df)[df >= 2] @@ -345,14 +367,16 @@ add_heuristic_keyword_fields <- function(metadata, stops, ngram_lengths = c(2, 3 # top_n : number of terms kept. # label_exclusions : curated area-label exclusion list (whole-term, case-insensitive) # applied here too so listed terms never survive as a last resort. -title_abstract_fallback_label <- function(matches, metadata, stops, top_n = 3, label_exclusions = character(0), cluster = NA_integer_) { +title_abstract_fallback_label <- function(matches, metadata, stops, top_n = 3, label_exclusions = character(0), cluster = NA_integer_, + ngram_lengths = c(2, 3)) { candidates = mapply(paste, metadata$title[matches], metadata$paper_abstract[matches]) candidates = lapply(candidates, tolower) # n-gram formation on the stopword-retaining stream (see ngram_candidates): # keeps digit/hyphen tokens whole and interior stopwords in place; boundary - # stopword n-grams are pruned inside the helper. + # stopword n-grams are pruned inside the helper. ngram_lengths follows the + # resolved n-gram setting (Setting 0 = c(2, 3), the historical fallback). candidates = unlist(lapply(candidates, ngram_candidates, stops = stops, - ngram_lengths = c(2, 3))) + ngram_lengths = ngram_lengths)) if (!length(candidates)) return("") top_ngrams = sort(table(candidates), decreasing = T) if (length(label_exclusions)) { # whole-term exclusion (see drop_excluded_terms) @@ -563,8 +587,14 @@ create_cluster_labels <- function(clusters, metadata, # - the two heuristic columns (min1/min2), pre-binned by MAP-WIDE document # frequency (add_heuristic_keyword_fields). subject_cleaned (metadata$subject) # is left untouched. + # G1 bypass (settings >= 1, non-custom path only): must run BEFORE the + # heuristic columns and keywords_rank_cleaned are derived from `subject` + if (!identical(nset, "0") && (is.null(cc) || !(cc %in% names(metadata)))) { + metadata <- bypass_heuristic_subjects(metadata) + } metadata <- add_heuristic_keyword_fields(metadata, stops, - ngram_lengths = nset_lengths) + ngram_lengths = nset_lengths, + include_abstracts = nset_abstracts) metadata$keywords_rank_cleaned <- metadata$subject if (!(is.null(cc)) && (cc %in% names(metadata))) { corpus_out <- get_custom_cluster_corpus(clusters, metadata, stops, taxonomy_separator, custom_clustering=cc) @@ -634,7 +664,8 @@ create_cluster_labels <- function(clusters, metadata, # from the papers' titles + abstracts (see title_abstract_fallback_label). vslog$debug(paste("create_cluster_labels: title/abstract fallback for cluster", k, "with", length(matches), "papers")) - summary <- title_abstract_fallback_label(matches, metadata, stops, top_n, label_exclusions, cluster = k) + summary <- title_abstract_fallback_label(matches, metadata, stops, top_n, label_exclusions, cluster = k, + ngram_lengths = nset_lengths) label_source[k] <- "title_abstract_fallback" } clusters$cluster_labels[c(matches)] = summary diff --git a/server/preprocessing/other-scripts/test/test_ngram_generator.R b/server/preprocessing/other-scripts/test/test_ngram_generator.R index 350abdcce..f585d4904 100644 --- a/server/preprocessing/other-scripts/test/test_ngram_generator.R +++ b/server/preprocessing/other-scripts/test/test_ngram_generator.R @@ -41,7 +41,7 @@ edge_tokens <- function(g) { # --- Part A: generator invariants (green — guard current behaviour) ---------- -test_that("G-I2 length-membership: every n-gram's token count is a requested length", { +test_that("length-membership: every n-gram's token count is a requested length", { for (L in list(c(2, 3), c(2, 3, 4), c(2, 3, 4, 5))) { for (t in GOLD_TITLES) { out <- ngram_candidates(t, STOPS, ngram_lengths = L) @@ -115,13 +115,13 @@ test_that("drop-in: unique(ngram_candidates(c(2,3))) reproduces paper_title_ngra # --- Part B: spec deltas (until implemented — TDD targets) ------------------ -test_that("B1 lengths accept 1 directly: c(1,2,3) yields unigrams", { +test_that("lengths accept 1 directly: c(1,2,3) yields unigrams", { out <- ngram_candidates("biomedical data opportunities", STOPS, ngram_lengths = c(1, 2, 3)) expect_true(all(c("biomedical", "data", "opportunities") %in% out)) }) -test_that("B1 alias: c(1, n...) is identical to include_unigrams = TRUE", { +test_that("alias: c(1, n...) is identical to include_unigrams = TRUE", { for (t in GOLD_TITLES) { expect_identical(ngram_candidates(t, STOPS, ngram_lengths = c(1, 2, 3)), ngram_candidates(t, STOPS, ngram_lengths = c(2, 3), @@ -129,7 +129,7 @@ test_that("B1 alias: c(1, n...) is identical to include_unigrams = TRUE", { } }) -test_that("B1 unigram-only call works and applies the unigram rules", { +test_that("unigram-only call works and applies the unigram rules", { out <- ngram_candidates("published in 2020 review", STOPS, ngram_lengths = 1) expect_true(setequal(out, c("published", "review"))) }) @@ -141,14 +141,14 @@ clear_ngram_env <- function() { if (length(vars)) Sys.unsetenv(vars) } -test_that("B2 ngram_setting: unset env defaults to setting 0", { +test_that("ngram_setting: unset env defaults to setting 0", { expect_true(exists("ngram_setting")) clear_ngram_env() expect_equal(ngram_setting(), "0") expect_equal(ngram_setting("orcid"), "0") }) -test_that("B2 ngram_setting: global NGRAM_SETTING is honoured, invalid falls through", { +test_that("ngram_setting: global NGRAM_SETTING is honoured, invalid falls through", { clear_ngram_env() Sys.setenv(NGRAM_SETTING = "3") expect_equal(ngram_setting(), "3") @@ -158,7 +158,7 @@ test_that("B2 ngram_setting: global NGRAM_SETTING is honoured, invalid falls thr clear_ngram_env() }) -test_that("B2 ngram_setting: per-integration override beats global; invalid override falls to global", { +test_that("ngram_setting: per-integration override beats global; invalid override falls to global", { clear_ngram_env() Sys.setenv(NGRAM_SETTING = "1", NGRAM_SETTING_ORCID = "5") expect_equal(ngram_setting("orcid"), "5") @@ -168,7 +168,7 @@ test_that("B2 ngram_setting: per-integration override beats global; invalid over clear_ngram_env() }) -test_that("B2 include_abstracts: default FALSE, env-enabled, per-integration override", { +test_that("include_abstracts: default FALSE, env-enabled, per-integration override", { expect_true(exists("include_abstracts")) clear_ngram_env() expect_false(include_abstracts()) @@ -180,7 +180,7 @@ test_that("B2 include_abstracts: default FALSE, env-enabled, per-integration ove clear_ngram_env() }) -test_that("B2 setting -> lengths mapping (C3, §7.3)", { +test_that("setting -> lengths mapping (C3, §7.3)", { expect_true(exists("ngram_setting_lengths")) # Setting 0: generator-routed baseline replication — title sites form bi+tri; # the corpus-level "1,2,2,3" is emergent (G1 subject route stays active) @@ -192,13 +192,99 @@ test_that("B2 setting -> lengths mapping (C3, §7.3)", { expect_equal(ngram_setting_lengths("5"), c(2, 3, 4, 5)) }) -# --- Part C: integration tests to add WITH the implementation ---------------- -# (sketched here, not executable yet — they need create_cluster_labels wired): -# * C4 constraint: ranking mode 0 + NGRAM_SETTING=3 -> setting coerced to 0 -# (replay an itu_ fixture in mode 0 with the env var set; labels must equal -# the recorded Setting-0 baseline). -# * G1 bypass (§7.4 edit 0): replay an itu_ fixture in mode 1 with -# NGRAM_SETTING=3; assert no token of a flagged paper's synthesised subject -# reaches the corpus dump (summarize_04_corpus_text). -# * 6.6b bounded-change assertions per setting (S1 dedup, S2/S4 4-/5-grams, -# S3/S5 no unigram labels, abstract flag on/off) over the itu_ fixtures. +# --- Systematic sweep: the invariants over EVERY setting's length vector ----- + +test_that("sweep: length-membership holds for every setting's lengths", { + for (s in as.character(1:5)) { + L <- ngram_setting_lengths(s) + for (t in GOLD_TITLES) { + out <- ngram_candidates(t, STOPS, ngram_lengths = L) + if (length(out)) + expect_true(all(vapply(out, token_count, integer(1)) %in% L)) + } + } +}) + +test_that("sweep: filter-correctness holds for every setting's lengths", { + for (s in as.character(1:5)) { + L <- ngram_setting_lengths(s) + for (t in GOLD_TITLES) { + out <- ngram_candidates(t, STOPS, ngram_lengths = L) + for (g in out) { + toks <- strsplit(g, "_", fixed = TRUE)[[1]] + expect_false(tolower(toks[1]) %in% STOPS) + expect_false(tolower(toks[length(toks)]) %in% STOPS) + if (length(toks) >= 2) expect_false(toks[1] == toks[length(toks)]) + if (length(toks) == 1) # unigram rules + expect_false(grepl("^[0-9]+([.,:-][0-9]+)*$", g)) + } + } + } +}) + +test_that("sweep: determinism and order-stability hold for every setting's lengths", { + for (s in as.character(1:5)) { + L <- ngram_setting_lengths(s) + for (t in GOLD_TITLES) + expect_identical(ngram_candidates(t, STOPS, ngram_lengths = L), + ngram_candidates(t, STOPS, ngram_lengths = L)) + } +}) + +# --- Part C: integration (the settings pipeline, replay-based) --------------- + +test_that("bypass helper blanks only flagged subjects", { + md <- data.frame(subject = c("real keywords", "synthesised stuff"), + subject_is_heuristic = c(FALSE, TRUE), stringsAsFactors = FALSE) + expect_equal(bypass_heuristic_subjects(md)$subject, c("real keywords", "")) + md2 <- data.frame(subject = "keep", stringsAsFactors = FALSE) # no flag column + expect_equal(bypass_heuristic_subjects(md2)$subject, "keep") +}) + +test_that("Setting 1 heuristic columns contain unigrams (6.6b S1)", { + md <- data.frame(title = c("solar photovoltaic efficiency", "solar energy analysis"), + stringsAsFactors = FALSE) + out <- add_heuristic_keyword_fields(md, STOPS, ngram_lengths = c(1, 2, 3)) + expect_true("solar" %in% strsplit(out[[HEUR_MIN1]][1], "; ", fixed = TRUE)[[1]]) + expect_true("solar" %in% strsplit(out[[HEUR_MIN2]][1], "; ", fixed = TRUE)[[1]]) # DF 2 +}) + +test_that("abstract flag feeds title+abstract for flagged papers only", { + md <- data.frame(title = c("short title", "flagged title"), + paper_abstract = c("alpha ignored", "quantum entanglement experiments"), + subject_is_heuristic = c(FALSE, TRUE), stringsAsFactors = FALSE) + out <- add_heuristic_keyword_fields(md, STOPS, ngram_lengths = c(1, 2, 3), + include_abstracts = TRUE) + expect_false("alpha" %in% strsplit(out[[HEUR_MIN1]][1], "; ", fixed = TRUE)[[1]]) + expect_true("quantum" %in% strsplit(out[[HEUR_MIN1]][2], "; ", fixed = TRUE)[[1]]) + off <- add_heuristic_keyword_fields(md, STOPS, ngram_lengths = c(1, 2, 3)) + expect_false("quantum" %in% strsplit(off[[HEUR_MIN1]][2], "; ", fixed = TRUE)[[1]]) +}) + +ITU_FX <- "test/replay/itu_0204881x.inputs.rds" + +test_that("sweep: EVERY setting is coerced to the mode-0 baseline under ranking mode 0", { + if (!file.exists(ITU_FX)) { cat(" (itu fixture missing - skipped)\n"); expect_true(TRUE) } else { + on.exit(clear_ngram_env(), add = TRUE) + base <- read_expected("itu_0204881x", "0") + for (s in as.character(1:5)) { + clear_ngram_env() + Sys.setenv(NGRAM_SETTING = s) + expect_equal(replay_labels(ITU_FX, mode = "0"), base) + } + } +}) + +test_that("Setting 1 runs end-to-end: every cluster labelled; a bounded behaviour change", { + if (!file.exists(ITU_FX)) { cat(" (itu fixture missing - skipped)\n"); expect_true(TRUE) } else { + clear_ngram_env() + Sys.setenv(NGRAM_SETTING = "1") + on.exit(clear_ngram_env(), add = TRUE) + labels <- replay_labels(ITU_FX, mode = "1") + base <- read_expected("itu_0204881x", "1") + expect_equal(length(labels), length(base)) + expect_true(all(nzchar(as.character(labels)))) + # S1 (bypass + deduped 1,2,3) is a deliberate behaviour change vs baseline + expect_false(identical(as.character(labels), as.character(base))) + } +}) From b95c632898a6e211fcd00095db530d69ff759480 Mon Sep 17 00:00:00 2001 From: chreman Date: Sat, 5 Sep 2026 12:51:42 +0200 Subject: [PATCH 186/205] update of contentprovider.json cache --- server/workers/common/common/contentproviders.json | 4 ---- 1 file changed, 4 deletions(-) diff --git a/server/workers/common/common/contentproviders.json b/server/workers/common/common/contentproviders.json index 412648a2f..b6eef62c8 100644 --- a/server/workers/common/common/contentproviders.json +++ b/server/workers/common/common/contentproviders.json @@ -11715,10 +11715,6 @@ "name": "Repository UNIKAMA (Universitas PGRI Kanjuruhan Malang)", "internal_name": "ftunivkmalang" }, - { - "name": "Repository Universitas Gadjah Mada (UGM)", - "internal_name": "ftunivgadjamada" - }, { "name": "University of Iowa Libraries Publishing Journals", "internal_name": "ftuniiowajaneway" From 2e5cf99f763c38b94b2288174fa2fa5b016fe76e Mon Sep 17 00:00:00 2001 From: chreman Date: Mon, 7 Sep 2026 13:27:29 +0200 Subject: [PATCH 187/205] re-instate overpruning bug in a controlled way for full legacy equivalence of new ngram generation --- .../preprocessing/other-scripts/summarize.R | 74 ++++++++++++++----- .../other-scripts/test/test_ngram_generator.R | 21 ++++-- 2 files changed, 72 insertions(+), 23 deletions(-) diff --git a/server/preprocessing/other-scripts/summarize.R b/server/preprocessing/other-scripts/summarize.R index 04af82402..e071eb29c 100644 --- a/server/preprocessing/other-scripts/summarize.R +++ b/server/preprocessing/other-scripts/summarize.R @@ -528,9 +528,12 @@ create_cluster_labels <- function(clusters, metadata, # call site on its current behaviour. nset <- ngram_setting(service) if (identical(mode, "0") && !identical(nset, "0")) { - vslog$warn(paste("create_cluster_labels: ranking mode 0 forces ngram setting 0", - "(requested:", nset, ")")) - nset <- "0" + # Mode 0 keeps its legacy corpus/selection structure at every setting; a + # non-0 setting switches ONLY the title n-gram generation step of the + # legacy corpus builder to the shared generator (synthesis stays part + # of the mode-0 subject stream — no bypass on this path). + vslog$info(paste("create_cluster_labels: mode 0 with ngram setting", nset, + "- setting switches generation only, structure stays legacy")) } if (!(is.null(cc)) && (cc %in% names(metadata)) && !identical(nset, "0")) { vslog$info(paste("create_cluster_labels: ngram setting", nset, @@ -559,7 +562,9 @@ create_cluster_labels <- function(clusters, metadata, if (!(is.null(cc)) && (cc %in% names(metadata))) { nn_corpus <- get_custom_cluster_corpus(clusters, metadata, stops, taxonomy_separator, custom_clustering=cc)$corpus } else { - nn_corpus <- get_cluster_corpus_legacy(clusters, metadata, stops, taxonomy_separator) + nn_corpus <- get_cluster_corpus_legacy(clusters, metadata, stops, taxonomy_separator, + ngram_lengths = nset_lengths, + legacy_quirks = identical(nset, "0")) } dump_data(nn_corpus, "summarize_04_corpus") dump_corpus_text(nn_corpus, "summarize_04_corpus_text") @@ -764,14 +769,40 @@ get_custom_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separa return(list(corpus = nn_corpus, rank_sources = NULL)) } +# Controlled emulation of the legacy prune_ngrams quirks, applied on top of +# the shared generator's per-title output (Setting 0 only — the flag keeps the +# generator as the single code path while reproducing the reference +# behaviour): +# 1. an n-gram is also dropped when an EDGE TOKEN occurs as a substring of +# any stopword (the legacy stri_detect_fixed haystack/needle inversion — +# over-pruning vs the generator's exact matching); +# 2. a title whose surviving n-grams of one length number <= 1 loses them, +# and its slot is removed from the per-title list (shifting the later +# subject recycling exactly as the legacy pipeline did). +legacy_prune_quirks <- function(per_title, stops) { + per_title <- lapply(per_title, function(grams) { + if (!length(grams)) return(grams) + keep <- vapply(grams, function(g) { + toks <- strsplit(g, "_", fixed = TRUE)[[1]] + !any(stringi::stri_detect_fixed(stops, tolower(toks[1]))) && + !any(stringi::stri_detect_fixed(stops, tolower(toks[length(toks)]))) + }, logical(1), USE.NAMES = FALSE) + grams[keep] + }) + per_title[vapply(per_title, length, integer(1)) > 1] +} + # --- Mode 0 (legacy) corpus + fallback ------------------------------------- # Mode 0 keeps the legacy corpus/selection STRUCTURE: title n-grams are -# generated INLINE (get_title_ngrams) rather than read from the DF-filtered -# min1/min2 columns, and there are no rank_sources. The title stream itself is -# punctuation-aware (punctuation_segments) like every other mode, so Mode-0 -# labels are no longer byte-identical to the pre-ranking pipeline. +# generated INLINE rather than read from the DF-filtered min1/min2 columns, and +# there are no rank_sources. Generation always goes through the shared +# generator (ngram_candidates); at Setting 0 (legacy_quirks = TRUE) the +# legacy_prune_quirks post-filter reproduces the historical prune behaviour on +# top of it, keeping Setting 0 byte-equivalent to the baseline. Settings >= 1 +# pass their own lengths with the quirks off. get_cluster_corpus_legacy <- function(clusters, metadata, stops, taxonomy_separator, - add_title_ngrams = T, custom_clustering=NULL) { + add_title_ngrams = T, custom_clustering=NULL, + ngram_lengths = c(2, 3), legacy_quirks = TRUE) { subjectlist = list() for (k in seq(1, clusters$num_clusters)) { matches = which(unname(clusters$groups == k) == TRUE) @@ -781,8 +812,18 @@ get_cluster_corpus_legacy <- function(clusters, metadata, stops, taxonomy_separa # span a boundary and tight compounds stay whole; the unigram word stream # is built from the same segment tokens title_segments = lapply(titles, punctuation_segments) + # per-title n-grams from the shared generator (raw stream, no dedup — + # mode-0 tf counts duplicates). One vector per length keeps the legacy tf + # property that subjects recycle over the length groups in the later + # paste(subjects, title_ngrams). + group_join <- function(n) { + per_title <- lapply(titles, function(t) + ngram_candidates(t, stops, ngram_lengths = n)) + if (legacy_quirks) per_title <- legacy_prune_quirks(per_title, stops) + vapply(per_title, paste, character(1), collapse = ";", USE.NAMES = FALSE) + } + title_ngrams <- unlist(lapply(ngram_lengths, group_join)) titles = lapply(title_segments, paste, collapse = " ") - title_ngrams <- get_title_ngrams(title_segments, stops, c(2, 3)) batch_size <- 1000 total_length <- length(stops) for (i in seq(1, total_length, batch_size)) { @@ -1021,13 +1062,12 @@ fill_empty_clusters <- function(fallback_corpus){ } -# Extract pruned bi- and tri-grams from a set of titles (see prune_ngrams) and -# return them concatenated. Each title arrives as its punctuation segments (one -# character vector per title, from punctuation_segments); n-grams are formed -# within a segment, so they cannot cross a punctuation boundary. The per-title -# list structure is preserved because callers align the result with per-paper -# subjects. Note: ngram_lengths is currently unused — lengths 2 and 3 are -# hardcoded. +# LEGACY, no production callers (the Mode-0 corpus builder now generates +# through ngram_candidates): extract pruned bi- and tri-grams from a set of +# titles (see prune_ngrams) and return them concatenated. Kept as the +# reference implementation for tests until the cleanup +# Note: ngram_lengths is unused — +# lengths 2 and 3 are hardcoded. get_title_ngrams <- function(titles, stops, ngram_lengths) { # for ngrams: we have to collapse with "_" or else tokenizers will split ngrams again at that point and we'll be left with unigrams per_title <- function(n) lapply(titles, function(segments) { diff --git a/server/preprocessing/other-scripts/test/test_ngram_generator.R b/server/preprocessing/other-scripts/test/test_ngram_generator.R index f585d4904..54054842f 100644 --- a/server/preprocessing/other-scripts/test/test_ngram_generator.R +++ b/server/preprocessing/other-scripts/test/test_ngram_generator.R @@ -263,15 +263,24 @@ test_that("abstract flag feeds title+abstract for flagged papers only", { ITU_FX <- "test/replay/itu_0204881x.inputs.rds" -test_that("sweep: EVERY setting is coerced to the mode-0 baseline under ranking mode 0", { +test_that("mode 0 at setting 0 is byte-equivalent (generator + legacy_quirks flag)", { if (!file.exists(ITU_FX)) { cat(" (itu fixture missing - skipped)\n"); expect_true(TRUE) } else { on.exit(clear_ngram_env(), add = TRUE) + clear_ngram_env() # default -> setting 0 -> quirk-emulated generator path + expect_equal(replay_labels(ITU_FX, mode = "0"), read_expected("itu_0204881x", "0")) + } +}) + +test_that("mode 0 at a non-0 setting switches generation only (quirks off, structure legacy)", { + if (!file.exists(ITU_FX)) { cat(" (itu fixture missing - skipped)\n"); expect_true(TRUE) } else { + on.exit(clear_ngram_env(), add = TRUE) + clear_ngram_env() + Sys.setenv(NGRAM_SETTING = "3") + l3 <- replay_labels(ITU_FX, mode = "0") base <- read_expected("itu_0204881x", "0") - for (s in as.character(1:5)) { - clear_ngram_env() - Sys.setenv(NGRAM_SETTING = s) - expect_equal(replay_labels(ITU_FX, mode = "0"), base) - } + expect_equal(length(l3), length(base)) + expect_true(all(nzchar(as.character(l3)))) + expect_false(identical(as.character(l3), as.character(base))) } }) From 38d018ef747e319f6691b8dd06dc031082baaefc Mon Sep 17 00:00:00 2001 From: chreman Date: Mon, 7 Sep 2026 13:37:51 +0200 Subject: [PATCH 188/205] re-instate overpruning bug in a controlled way for full legacy equivalence of new ngram generation --- server/preprocessing/other-scripts/summarize.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/server/preprocessing/other-scripts/summarize.R b/server/preprocessing/other-scripts/summarize.R index e071eb29c..cfce20410 100644 --- a/server/preprocessing/other-scripts/summarize.R +++ b/server/preprocessing/other-scripts/summarize.R @@ -521,7 +521,7 @@ create_cluster_labels <- function(clusters, metadata, # title segmentation applies in every mode. mode <- ranking_mode(service) cc <- params$custom_clustering - # N-gram setting axis (docs/ngram-generation-simplify.md §7.2/§7.3), resolved + # N-gram setting axis, resolved # per service like the ranking mode. Mode 0 always runs Setting 0 (it keeps # the legacy corpus/selection structure), and the setting is a no-op on the # custom-clustering path (no title n-grams there). Setting 0 leaves every From 81c82c05b8e2f829ea70b3ae55bd9ed8396e7519 Mon Sep 17 00:00:00 2001 From: chreman Date: Mon, 7 Sep 2026 15:21:05 +0200 Subject: [PATCH 189/205] file split for code readability --- .../other-scripts/label_casing.R | 45 + .../other-scripts/label_corpus.R | 317 +++++++ .../preprocessing/other-scripts/label_debug.R | 80 ++ .../other-scripts/ngram_generation.R | 205 +++++ .../preprocessing/other-scripts/summarize.R | 779 +----------------- .../article_exclusion_rebaseline_diff.md | 42 + .../replay/mode0_exclusion_rebaseline_diff.md | 84 ++ .../mode0_punctuation_rebaseline_diff.md | 676 +++++++++++++++ .../replay/punctuation_rebaseline_diff.md | 464 +++++++++++ .../other-scripts/text_hygiene.R | 171 ++++ 10 files changed, 2099 insertions(+), 764 deletions(-) create mode 100644 server/preprocessing/other-scripts/label_casing.R create mode 100644 server/preprocessing/other-scripts/label_corpus.R create mode 100644 server/preprocessing/other-scripts/label_debug.R create mode 100644 server/preprocessing/other-scripts/ngram_generation.R create mode 100644 server/preprocessing/other-scripts/test/replay/article_exclusion_rebaseline_diff.md create mode 100644 server/preprocessing/other-scripts/test/replay/mode0_exclusion_rebaseline_diff.md create mode 100644 server/preprocessing/other-scripts/test/replay/mode0_punctuation_rebaseline_diff.md create mode 100644 server/preprocessing/other-scripts/test/replay/punctuation_rebaseline_diff.md create mode 100644 server/preprocessing/other-scripts/text_hygiene.R diff --git a/server/preprocessing/other-scripts/label_casing.R b/server/preprocessing/other-scripts/label_casing.R new file mode 100644 index 000000000..54a0ef760 --- /dev/null +++ b/server/preprocessing/other-scripts/label_casing.R @@ -0,0 +1,45 @@ +# label_casing.R +# Final label casing: restore corpus casing per word and capitalise each +# comma-separated phrase. Sourced by summarize.R. + + + +# Normalise a vector of cluster labels: restore term casing (fix_keyword_casing) +# and collapse repeated commas. Returns the cleaned labels. +fix_cluster_labels <- function(clusterlabels, type_counts){ + unlist(mclapply(clusterlabels, function(x) { + x <- fix_keyword_casing(x, type_counts) + # clean up titles from format issues + x <- gsub(",+", ",", x) + })) +} + + +# Restore the casing of each word in a single label and capitalise the first +# letter of every comma-separated phrase. Words are matched back to their +# original corpus casing via match_keyword_case(type_counts). +fix_keyword_casing <- function(keyword, type_counts) { + kw = strsplit(keyword, ", ") + kw = lapply(kw, strsplit, " ")[[1]] + kw = lapply(kw, function(x){lapply(x, match_keyword_case, type_counts=type_counts)}) + kw = lapply(kw, paste, collapse = " ") + kw = lapply(kw, function(x) {paste0(toupper(substr(x, 1, 1)), substr(x, 2, nchar(x)))}) + kw = paste(kw, collapse = ", ") + return(paste(kw, collapse = ", ")) +} + + +# Return the canonical (original-corpus) casing of a token: looks it up in +# type_counts case-insensitively. The lookup is exact on everything but case — +# in particular hyphen-preserving: the vocabulary regularly holds a +# de-hyphenated twin of a hyphenated token (source spelling variants), and a +# hyphen-insensitive lookup would respell the token instead of re-casing it. +# Falls back to the input token if there is no match. Edge hyphens are +# separator debris, not part of the token, and are trimmed before the lookup +# (a token that is only hyphens is returned unchanged). +match_keyword_case <- function(x, type_counts) { + stripped <- gsub("^-+|-+$", "", x) + if (nzchar(stripped)) x <- stripped + y <- names(type_counts[which(tolower(names(type_counts)) == tolower(x))][1]) + if (!is.na(y)) return(y) else return(x) +} diff --git a/server/preprocessing/other-scripts/label_corpus.R b/server/preprocessing/other-scripts/label_corpus.R new file mode 100644 index 000000000..4137d58da --- /dev/null +++ b/server/preprocessing/other-scripts/label_corpus.R @@ -0,0 +1,317 @@ +# label_corpus.R +# Per-cluster corpus assembly for labelling: the heuristic keyword columns +# (HEUR_MIN1/MIN2), the G1 bypass, and the three corpus builders (ranked, +# legacy Mode 0, custom clustering) with their empty-cluster fills. +# Sourced by summarize.R. + + +# Metadata columns holding the heuristically generated (title n-gram) keywords, +# pre-binned by MAP-WIDE document frequency (see add_heuristic_keyword_fields): +# _min1 = n-grams appearing in >= 1 resource (all) -> used for the FALLBACK corpus +# _min2 = n-grams appearing in >= 2 resources -> used for the INITIAL corpus +# The DF filter is GLOBAL (map-wide), applied to Modes 1-3 only; Mode 0 is legacy. +HEUR_MIN1 <- "keywords_rank_heuristically_generated_min1" +HEUR_MIN2 <- "keywords_rank_heuristically_generated_min2" + + +# MeSH rank columns, produced upstream by the data client (base.R, BASE only for +# now). Used by Modes 2/3 to split subject tokens into specific/generic. When +# absent (integrations without MeSH), the split is empty and Mode 2/3 degrade to +# Mode-1-like behaviour (see get_cluster_corpus). +KW_MESH_SPECIFIC <- "keywords_rank_mesh_specific" +KW_MESH_GENERIC <- "keywords_rank_mesh_generic" + + +# Tokenizer used by the TermDocumentMatrix: splits a document into terms on ";". +# The corpus joins tokens (words and "_"-joined n-grams) with ";", so this +# recovers them as individual terms without re-splitting the n-grams. +SplitTokenizer <- function(x) { + tokens = unlist(lapply(strsplit(words(x), split=";"), paste), use.names = FALSE) + return(tokens) +} + + +# bypass for the generator settings (>= 1): papers whose `subject` was +# synthesised from the title (subject_is_heuristic) get it blanked, so the +# synthesis reaches neither the corpus nor any rank source — those papers +# contribute exclusively through the generator columns. Setting 0 must NOT +# apply this: the synthesis is part of the replicated baseline. +bypass_heuristic_subjects <- function(metadata) { + if (!is.null(metadata$subject_is_heuristic)) { + metadata$subject[as.logical(metadata$subject_is_heuristic)] <- "" + } + metadata +} + + +# Add the two heuristic-keyword metadata columns (HEUR_MIN1 / HEUR_MIN2) to the +# metadata data frame. Generates each paper's n-grams through the shared +# generator (ngram_candidates, de-duplicated per paper), computes each n-gram's +# MAP-WIDE document frequency (number of distinct resources it appears in), and stores +# per paper, as "; "-joined "_"-n-gram strings: +# HEUR_MIN1 = all of the paper's n-grams (DF >= 1) +# HEUR_MIN2 = the paper's n-grams with map-wide document frequency >= 2 +# Computed once, early in labelling. ngram_lengths comes from the resolved +# n-gram setting (ngram_setting_lengths); the Setting-0 default c(2, 3) +# reproduces paper_title_ngrams exactly (pinned by the drop-in equivalence +# test in test_ngram_generator.R). The generator input is the title — or +# title + abstract for no-keyword (subject_is_heuristic) papers when the +# abstract flag is on: the input is assembled per paper by the data, not a +# code branch. +add_heuristic_keyword_fields <- function(metadata, stops, ngram_lengths = c(2, 3), + include_abstracts = FALSE) { + input_text <- metadata$title + if (include_abstracts && !is.null(metadata$subject_is_heuristic) && + !is.null(metadata$paper_abstract)) { + fl <- which(as.logical(metadata$subject_is_heuristic)) + input_text[fl] <- paste(metadata$title[fl], metadata$paper_abstract[fl]) + } + per_paper <- lapply(input_text, function(t) + unique(ngram_candidates(t, stops, ngram_lengths = ngram_lengths))) + df <- table(unlist(lapply(per_paper, unique))) # map-wide document frequency + min2_set <- names(df)[df >= 2] + metadata[[HEUR_MIN1]] <- vapply(per_paper, + function(g) paste(g, collapse = "; "), character(1)) + metadata[[HEUR_MIN2]] <- vapply(per_paper, + function(g) paste(g[g %in% min2_set], collapse = "; "), character(1)) + metadata +} + + +# Build the per-cluster corpus from a custom metadata field (custom_clustering) +# instead of subjects. Per cluster: removes stopwords, then joins and normalises +# the field values into a single ";"-separated token string. Returns +# list(corpus, rank_sources) with rank_sources = NULL (no provenance split on this +# path, so ranked modes fall back to legacy selection). +get_custom_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, + add_title_ngrams = T, custom_clustering=NULL) { + subjectlist = list() + for (k in seq(1, clusters$num_clusters)) { + matches = which(unname(clusters$groups == k) == TRUE) + custom_input = metadata[[custom_clustering]][matches] + batch_size <- 1000 + total_length <- length(stops) + for (i in seq(1, total_length, batch_size)) { + custom_input = lapply(custom_input, function(x) {removeWords(x, stops[i:min(i+batch_size -1, total_length)])}) + } + custom_input = mapply(gsub, custom_input, pattern = "; ", replacement=";") + custom_input = mapply(gsub, custom_input, pattern=" ", replacement="_") + + all_subjects = paste(custom_input, collapse=" ") + all_subjects <- normalize_corpus_tokens(all_subjects) + subjectlist = c(subjectlist, all_subjects) + } + # Custom clustering has no keyword/heuristic provenance split, so rank_sources is + # NULL: ranked modes fall back to the legacy selection on this path (ranking.R). + nn_corpus <- VCorpus(VectorSource(subjectlist)) + return(list(corpus = nn_corpus, rank_sources = NULL)) +} + + +# --- Mode 0 (legacy) corpus + fallback ------------------------------------- +# Mode 0 keeps the legacy corpus/selection STRUCTURE: title n-grams are +# generated INLINE rather than read from the DF-filtered min1/min2 columns, and +# there are no rank_sources. Generation always goes through the shared +# generator (ngram_candidates); at Setting 0 (legacy_quirks = TRUE) the +# legacy_prune_quirks post-filter reproduces the historical prune behaviour on +# top of it, keeping Setting 0 byte-equivalent to the baseline. Settings >= 1 +# pass their own lengths with the quirks off. +get_cluster_corpus_legacy <- function(clusters, metadata, stops, taxonomy_separator, + add_title_ngrams = T, custom_clustering=NULL, + ngram_lengths = c(2, 3), legacy_quirks = TRUE) { + subjectlist = list() + for (k in seq(1, clusters$num_clusters)) { + matches = which(unname(clusters$groups == k) == TRUE) + titles = metadata$title[matches] + subjects = metadata$subject[matches] + # segment each title at punctuation boundaries so the inline n-grams cannot + # span a boundary and tight compounds stay whole; the unigram word stream + # is built from the same segment tokens + title_segments = lapply(titles, punctuation_segments) + # per-title n-grams from the shared generator (raw stream, no dedup — + # mode-0 tf counts duplicates). One vector per length keeps the legacy tf + # property that subjects recycle over the length groups in the later + # paste(subjects, title_ngrams). + group_join <- function(n) { + per_title <- lapply(titles, function(t) + ngram_candidates(t, stops, ngram_lengths = n)) + if (legacy_quirks) per_title <- legacy_prune_quirks(per_title, stops) + vapply(per_title, paste, character(1), collapse = ";", USE.NAMES = FALSE) + } + title_ngrams <- unlist(lapply(ngram_lengths, group_join)) + titles = lapply(title_segments, paste, collapse = " ") + batch_size <- 1000 + total_length <- length(stops) + for (i in seq(1, total_length, batch_size)) { + titles = lapply(titles, function(x) {removeWords(x, stops[i:min(i+batch_size -1, total_length)])}) + } + subjects = mapply(gsub, subjects, pattern = "; ", replacement=";") + subjects = mapply(gsub, subjects, pattern=" ", replacement="_") + titles = mapply(gsub, titles, pattern=" ", replacement=";") + + if (!is.null(taxonomy_separator)) { + subjects = mapply(function(x){strsplit(x, ";")}, subjects) + taxons = lapply(subjects, function(y){Filter(function(x){grepl(taxonomy_separator, x)}, y)}) + subjects = lapply(subjects, function(y){Filter(function(x){!grepl(taxonomy_separator, x)}, y)}) + taxons = lapply(taxons, function(x){lapply(strsplit(x, taxonomy_separator), function(y){tail(y,1)})}) + taxons = lapply(taxons, function(x){paste(unlist(x), collapse=";")}) + subjects = lapply(subjects, function(x){paste(unlist(x), collapse=";")}) + subjects = mapply(paste, subjects, taxons, collapse=";") + } + if (add_title_ngrams == T) { + all_subjects = paste(subjects, title_ngrams, collapse=" ") + } else { + all_subjects = paste(subjects, collapse=" ") + } + all_subjects <- str_replace_all(all_subjects, "\\?+_\\?+|\\?+|\\?+ ", "") + all_subjects <- str_replace_all(all_subjects, ";+", ";") + all_subjects <- str_replace_all(all_subjects, " ?; ?", ";") + all_subjects <- str_replace_all(all_subjects, " +", ";") + subjectlist = c(subjectlist, all_subjects) + } + nn_corpus <- VCorpus(VectorSource(subjectlist)) + return(nn_corpus) +} + + +# Legacy (Mode 0) fallback: rebuild the tf-idf of the SAME corpus with local bound +# c(1, Inf) for clusters whose initial tf-idf summed to zero. Verbatim legacy +# 2-arg signature — distinct from the Modes 1-3 fill_empty_clusters (min1 corpus). +fill_empty_clusters_legacy <- function(nn_tfidf, nn_corpus){ + replacement_nn_tfidf <- TermDocumentMatrix(nn_corpus, control = list(tokenize = SplitTokenizer, + weighting = function(x) weightSMART(x, spec="ntn"), + bounds = list(local = c(1, Inf)) + )) + replacement_tfidf_top <- apply(replacement_nn_tfidf, 2, function(x) {x2 <- sort(x, TRUE);x2[x2>0]}) + return(replacement_tfidf_top) +} + + +# Build the per-cluster corpus used for tf-idf labelling. Per cluster: combines +# the papers' `subject` keywords with bi-/tri-grams from their titles. Taxonomy +# subjects (containing taxonomy_separator) are reduced to their last path segment. +# Everything is normalised into one ";"-separated token string per cluster. +# Returns list(corpus, rank_sources): a VCorpus with one document per cluster, and +# per-cluster rank sources (cleaned = subject tokens, heuristic = title n-grams, +# lowercased) used by the ranked selection. add_title_ngrams toggles the title +# n-gram contribution. +get_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, + heuristic_col = HEUR_MIN2) { + subjectlist = list() + subject_dbg = list(); heuristic_dbg = list(); heuristic_min1_dbg = list() + replaced_subject_dbg = list() # subjects synthesised from titles (rank 2, not rank 1) + mesh_spec_dbg = list(); mesh_gen_dbg = list() # normalised MeSH tokens (empty if no mesh columns) + has_mesh <- !is.null(metadata[[KW_MESH_SPECIFIC]]) + for (k in seq(1, clusters$num_clusters)) { + matches = which(unname(clusters$groups == k) == TRUE) + subjects = metadata$subject[matches] + # subject_is_heuristic flags papers whose `subject` was synthesised from the + # title by replace_keywords_if_empty (they had no real keywords). Their tokens + # are routed to the HEURISTIC rank source (rank 2), not the cleaned/keyword + # source (rank 1). Absent on fixtures captured before this change -> all FALSE. + flagged = if (!is.null(metadata$subject_is_heuristic)) { + as.logical(metadata$subject_is_heuristic[matches]) + } else { + rep(FALSE, length(matches)) + } + # Heuristic keywords are pre-generated "_"-joined n-grams (see + # add_heuristic_keyword_fields): the corpus uses heuristic_col (map-wide min2 + # initial / min1 fallback); the rank map always uses min1 (the superset), so any + # heuristic term resolves to rank 2 regardless of which pass produced it. + heuristics = as.character(metadata[[heuristic_col]][matches]) + heuristics_min1 = as.character(metadata[[HEUR_MIN1]][matches]) + + subjects = mapply(gsub, subjects, pattern = "; ", replacement=";") + subjects = mapply(gsub, subjects, pattern=" ", replacement="_") + heuristics = gsub("; ", ";", heuristics) + heuristics_min1 = gsub("; ", ";", heuristics_min1) + + if (!is.null(taxonomy_separator)) { + subjects = mapply(function(x){strsplit(x, ";")}, subjects) + taxons = lapply(subjects, function(y){Filter(function(x){grepl(taxonomy_separator, x)}, y)}) + subjects = lapply(subjects, function(y){Filter(function(x){!grepl(taxonomy_separator, x)}, y)}) + taxons = lapply(taxons, function(x){lapply(strsplit(x, taxonomy_separator), function(y){tail(y,1)})}) + taxons = lapply(taxons, function(x){paste(unlist(x), collapse=";")}) + subjects = lapply(subjects, function(x){paste(unlist(x), collapse=";")}) + subjects = mapply(paste, subjects, taxons, collapse=";") + } + # Corpus is unchanged: it uses ALL subjects + heuristics (flagged or not), so + # tf-idf weights are identical. Only the RANK MAP splits them — flagged papers' + # subject tokens go to the heuristic rank source below, not the cleaned one. + subject_dbg[[k]] = paste(unlist(subjects[!flagged]), collapse=";") # rank 1 (real keywords) + replaced_subject_dbg[[k]] = paste(unlist(subjects[flagged]), collapse=";") # rank 2 (title-synthesised) + heuristic_dbg[[k]] = paste(unlist(heuristics), collapse=";") + heuristic_min1_dbg[[k]] = paste(unlist(heuristics_min1), collapse=";") + # MeSH columns (Modes 2/3): aggregate per cluster, normalised the same way as + # subjects so their tokens match the subject tokens they classify. Empty when + # the client did not populate them (-> Mode 2/3 degrade to Mode-1 behaviour). + if (has_mesh) { + msp = gsub(" ", "_", gsub("; ", ";", as.character(metadata[[KW_MESH_SPECIFIC]][matches]))) + mge = gsub(" ", "_", gsub("; ", ";", as.character(metadata[[KW_MESH_GENERIC]][matches]))) + mesh_spec_dbg[[k]] = paste(unlist(msp), collapse=";") + mesh_gen_dbg[[k]] = paste(unlist(mge), collapse=";") + } else { + mesh_spec_dbg[[k]] = ""; mesh_gen_dbg[[k]] = "" + } + all_subjects = paste(subjects, heuristics, collapse=" ") + all_subjects <- normalize_corpus_tokens(all_subjects) + subjectlist = c(subjectlist, all_subjects) + } + # Debug: record, per cluster, the tokens contributed by subjects vs. by the + # heuristic (title n-gram) column, so a label term can be attributed to its source. + dump_data(data.frame(cluster = seq_along(subjectlist), + subject_tokens = unlist(subject_dbg), + title_ngrams = unlist(heuristic_dbg)), + "summarize_04a_corpus_sources") + # Rank sources for the ranked selection (ranking.R): per-cluster token-sets, all + # run through the SAME normalize_corpus_tokens() + lowercasing + edge-strip as the + # TDM terms — so every rank token matches a tf-idf term. The MeSH split is + # derived by MEMBERSHIP on the (already-normalised) subject tokens: a subject token + # is mesh_specific/generic if it is in the corresponding MeSH column, else it is a + # cleaned (ex-mesh) keyword. This keeps every rank token a genuine subject token + # (so it matches the TDM) and needs no separate normalization path. + # cleaned = all subject tokens (keywords + MeSH pooled) — Mode 1 rank 1. + # mesh_specific/generic = subject tokens classified as specific/generic MeSH. + # cleaned_ex_mesh = subject tokens minus MeSH — Modes 2/3 rank 1. + # heuristic = the min1 title-n-gram tokens. + # Used only for rank lookup, never fed to the TDM. + split_tokens <- function(s) { + t <- tolower(unlist(strsplit(normalize_corpus_tokens(s), ";"))) + t <- gsub("^_+|_+$", "", t) # mirror the TDM tokenizer's edge-punctuation strip + t[nzchar(t)] + } + subj_tok <- lapply(subject_dbg, split_tokens) + spec_tok <- lapply(mesh_spec_dbg, split_tokens) + gen_tok <- lapply(mesh_gen_dbg, split_tokens) + mesh_specific <- mapply(intersect, subj_tok, spec_tok, SIMPLIFY = FALSE) + mesh_generic <- mapply(intersect, subj_tok, gen_tok, SIMPLIFY = FALSE) + cleaned_ex_mesh <- mapply(function(all, sp, ge) setdiff(all, c(sp, ge)), + subj_tok, mesh_specific, mesh_generic, SIMPLIFY = FALSE) + # heuristic = the min1 title n-grams PLUS the flagged papers' synthesised subject + # tokens (title-derived, so they belong in rank 2 — see the subject_is_heuristic + # routing above). Both are already in the corpus, so no double-counting. + heuristic_tok <- mapply(function(h, r) unique(c(split_tokens(h), split_tokens(r))), + heuristic_min1_dbg, replaced_subject_dbg, SIMPLIFY = FALSE) + rank_sources <- list(cleaned = subj_tok, + cleaned_ex_mesh = cleaned_ex_mesh, + mesh_specific = mesh_specific, + mesh_generic = mesh_generic, + heuristic = heuristic_tok) + nn_corpus <- VCorpus(VectorSource(subjectlist)) + return(list(corpus = nn_corpus, rank_sources = rank_sources)) +} + + +# Provide fallback top terms for clusters that produced none under the strict +# tf-idf bound, by recomputing the TermDocumentMatrix with a looser local bound +# (terms appearing at least once, instead of at least twice). Returns the +# per-cluster sorted term lists. +fill_empty_clusters <- function(fallback_corpus){ + replacement_nn_tfidf <- TermDocumentMatrix(fallback_corpus, control = list(tokenize = SplitTokenizer, + weighting = function(x) weightSMART(x, spec="ntn"), + bounds = list(local = c(1, Inf)), + tolower = TRUE + )) + replacement_tfidf_top <- apply(replacement_nn_tfidf, 2, function(x) {x2 <- sort(x, TRUE);x2[x2>0]}) + return(replacement_tfidf_top) +} diff --git a/server/preprocessing/other-scripts/label_debug.R b/server/preprocessing/other-scripts/label_debug.R new file mode 100644 index 000000000..7afcef241 --- /dev/null +++ b/server/preprocessing/other-scripts/label_debug.R @@ -0,0 +1,80 @@ +# label_debug.R +# DEBUG-gated dump helpers tracing how each area label is built (no-ops +# unless LOGLEVEL=DEBUG; see utils.R dump_data). Sourced by summarize.R. + + +# --- Label-generation debug dumps ------------------------------------------------ +# Fine-grained, DEBUG-gated traces of how each cluster's area label is built, for +# backtracking a label to its inputs: the corpus text that feeds tf-idf, the tf-idf +# candidate terms with their weights, the rank-source provenance (Modes 1-3), the +# terms removed by the exclusion list, and the per-cluster label provenance (which +# path produced it, plus the label before/after casing). Each keyed on VIS_ID via +# dump_data; all no-ops unless LOGLEVEL=DEBUG, and never fatal. + +# Per-cluster corpus document text (what tf-idf actually tokenizes). One row/cluster. +dump_corpus_text <- function(corpus, stage) { + if (!debug_enabled()) return(invisible(NULL)) + tryCatch({ + txt <- vapply(seq_along(corpus), + function(k) paste(as.character(content(corpus[[k]])), collapse = " "), + character(1)) + dump_data(data.frame(cluster = seq_along(txt), text = txt, stringsAsFactors = FALSE), stage) + }, error = function(e) vslog$warn(paste("dump_corpus_text failed:", conditionMessage(e)))) +} + + +# Per-cluster tf-idf candidate terms, weight-ordered, with their scores. One row per +# (cluster, term): the candidate pool that selection/ranking draws from. +dump_tfidf_candidates <- function(tfidf_top, stage) { + if (!debug_enabled()) return(invisible(NULL)) + tryCatch({ + rows <- lapply(seq_along(tfidf_top), function(k) { + w <- tfidf_top[[k]] + if (is.null(w) || !length(w) || is.null(names(w))) return(NULL) + data.frame(cluster = k, weight_rank = seq_along(w), + term = gsub("_", " ", names(w)), tfidf = as.numeric(w), + stringsAsFactors = FALSE) + }) + rows <- do.call(rbind, rows[!vapply(rows, is.null, logical(1))]) + if (!is.null(rows) && nrow(rows)) dump_data(rows, stage) + }, error = function(e) vslog$warn(paste("dump_tfidf_candidates failed:", conditionMessage(e)))) +} + + +# Per-cluster rank-source token-sets (Modes 1-3): which provenance set (cleaned, +# mesh_specific, ...) each candidate token belongs to — i.e. what fixes its rank. +dump_rank_sources <- function(rank_sources, stage) { + if (!debug_enabled() || is.null(rank_sources)) return(invisible(NULL)) + tryCatch({ + rows <- list() + for (src in names(rank_sources)) { + per_cluster <- rank_sources[[src]] + for (k in seq_along(per_cluster)) { + toks <- per_cluster[[k]] + if (length(toks)) rows[[length(rows) + 1L]] <- + data.frame(cluster = k, source = src, term = gsub("_", " ", toks), + stringsAsFactors = FALSE) + } + } + if (length(rows)) dump_data(do.call(rbind, rows), stage) + }, error = function(e) vslog$warn(paste("dump_rank_sources failed:", conditionMessage(e)))) +} + + +# Per-cluster terms removed by the exclusion list (tf-idf candidates before vs after +# drop_excluded_terms). Makes each exclusion drop explicit. +dump_excluded_terms <- function(before, after, stage) { + if (!debug_enabled()) return(invisible(NULL)) + tryCatch({ + rows <- lapply(seq_along(before), function(k) { + b <- before[[k]]; a <- after[[k]] + if (is.null(b) || !length(b) || is.null(names(b))) return(NULL) + dropped <- setdiff(names(b), names(a)) + if (!length(dropped)) return(NULL) + data.frame(cluster = k, term = gsub("_", " ", dropped), + tfidf = as.numeric(b[dropped]), stringsAsFactors = FALSE) + }) + rows <- do.call(rbind, rows[!vapply(rows, is.null, logical(1))]) + if (!is.null(rows) && nrow(rows)) dump_data(rows, stage) + }, error = function(e) vslog$warn(paste("dump_excluded_terms failed:", conditionMessage(e)))) +} diff --git a/server/preprocessing/other-scripts/ngram_generation.R b/server/preprocessing/other-scripts/ngram_generation.R new file mode 100644 index 000000000..a0dedfba1 --- /dev/null +++ b/server/preprocessing/other-scripts/ngram_generation.R @@ -0,0 +1,205 @@ +# ngram_generation.R +# The shared n-gram generator (ngram_candidates) and its helpers, the +# legacy-quirk emulation (legacy_prune_quirks, Setting 0 in Mode 0), the +# nested-n-gram filter, and the legacy reference implementations kept for +# tests until the cleanup. +# Sourced by summarize.R. + + + +# Generate all contiguous n-grams of length n from each input string. The words +# within an n-gram are joined with "_" so a whitespace tokenizer keeps the n-gram +# intact. Returns, per input string, a single space-separated string of its n-grams. +expand_ngrams <- function(text, n) { + text <- trimws(text) + lapply(lapply(text, function(x)unlist(lapply(ngrams(unlist(strsplit(x, split = " ")), n), paste, collapse = "_"))), paste, collapse = " ") +} + + +# Drop low-value n-grams from a set of "_"-joined n-grams. Removes n-grams that +# start or end with a stopword, whose first and last token are identical, or that +# are shorter than 2 tokens; stopwords are checked in batches for speed. Returns, +# per input, the surviving n-grams joined with ";". +prune_ngrams <- function(ngrams, stops){ + # lapply/SIMPLIFY = FALSE: with mapply's default simplification, equal + # n-gram counts across all inputs collapse the result into a matrix and + # every n-gram is silently lost + ngrams = lapply(ngrams, function(x) strsplit(x[[1]], split = " |;")[[1]]) + tokenized_ngrams = mapply(function(x) { + strsplit(x, split="_") + }, ngrams, SIMPLIFY = FALSE) + # filter out empty tokens + tokenized_ngrams = lapply(tokenized_ngrams, function(ngrams){ngrams[lapply(ngrams, length)>0]}) + # remove ngrams starting with a stopword + batch_size <- 1000 + total_length <- length(stops) + for (i in seq(1, total_length, batch_size)) { + tokenized_ngrams = lapply(tokenized_ngrams, function(x) { + Filter(function(tokens){ + !any(stringi::stri_detect_fixed(stops[i:min(i+batch_size -1, total_length)], tolower(tokens[[1]]))) + }, x)}) + # remove ngrams ending with a stopword + tokenized_ngrams = lapply(tokenized_ngrams, function(x) { + Filter(function(tokens){ + !any(stringi::stri_detect_fixed(stops[i:min(i+batch_size -1, total_length)], tolower(tail(tokens,1)))) + }, x)}) + } + # remove ngrams starting and ending with the same word + tokenized_ngrams = lapply(tokenized_ngrams, function(x) { + Filter(function(tokens){ + !(tokens[[1]]==tail(tokens,1)) + }, x)}) + # keep ngrams with min length 2 + tokenized_ngrams = lapply(tokenized_ngrams, function(x){x[lapply(x, length)>1]}) + tokenized_ngrams = tokenized_ngrams[lapply(tokenized_ngrams, length)>1] + tokenized_ngrams = lapply(tokenized_ngrams, function(x){mapply(paste, x, collapse="_")}) + pruned_ngrams = lapply(tokenized_ngrams, paste, collapse=";") + return (pruned_ngrams) +} + + +# Heuristically generated keywords for a single paper: the bi- and tri-grams of +# its title's punctuation-delimited segments (punctuation_segments), pruned of +# degenerate forms (n-grams that start or end with a stopword, or whose first +# and last token are identical). Words within an n-gram are "_"-joined so the +# tokenizer keeps them intact. Returns a character vector of unique "_"-joined +# n-grams (possibly empty). +paper_title_ngrams <- function(title, stops) { + segments <- punctuation_segments(title) + if (!length(segments)) return(character(0)) + grams <- unlist(c(expand_ngrams(segments, 2), expand_ngrams(segments, 3))) + grams <- unlist(strsplit(paste(grams, collapse = " "), " ")) + grams <- grams[nzchar(grams)] + if (!length(grams)) return(character(0)) + keep <- vapply(grams, function(g) { + toks <- strsplit(g, "_", fixed = TRUE)[[1]] + length(toks) >= 2 && + !(tolower(toks[1]) %in% stops) && + !(tolower(toks[length(toks)]) %in% stops) && + toks[1] != toks[length(toks)] + }, logical(1), USE.NAMES = FALSE) + unique(grams[keep]) +} + + +# Shared n-gram candidate builder for the heuristic-keyword synthesizer +# (replace_keywords_if_empty) and the last-resort fallback label +# (title_abstract_fallback_label). Same method as paper_title_ngrams: segment +# the text at punctuation boundaries (punctuation_segments — tight compounds +# like "covid-19" or "R&D" stay whole), form n-grams per segment on the +# stopword-RETAINING token stream, then drop n-grams that start or end with a +# stopword — so interior stopwords survive ("biomedical big data" stays a +# trigram; the fused "biomedical data" bigram is never formed). Stopword +# matching is case-insensitive so the outcome does not depend on source +# casing. +# text : one string (title, or title + abstract). +# ngram_lengths : which n-gram sizes to form; may include 1 for unigrams. +# include_unigrams: alias for putting 1 in ngram_lengths — returns single +# tokens that are neither stopwords nor purely numeric (a +# lone number/year is noise; digits stay inside tokens). +# Returns a character vector of "_"-joined n-grams / bare tokens, in formation +# order (not de-duplicated - callers count frequencies; a caller that needs +# per-text dedup wraps the call in unique()). +ngram_candidates <- function(text, stops, ngram_lengths = c(2, 3), + include_unigrams = FALSE) { + # length 1 is the same switch as include_unigrams; unigrams take the + # token-filter path below, never the n-gram keep-filter + include_unigrams <- include_unigrams || 1 %in% ngram_lengths + ngram_lengths <- ngram_lengths[ngram_lengths != 1] + # punctuation_segments performs the entity/URL/HTML hygiene itself; forming + # n-grams per segment keeps them from crossing a punctuation boundary + segments <- punctuation_segments(text) + if (!length(segments)) return(character(0)) + stops_lower <- tolower(stops) + grams <- unlist(lapply(ngram_lengths, function(n) expand_ngrams(segments, n))) + grams <- unlist(strsplit(paste(grams, collapse = " "), " ")) + grams <- grams[nzchar(grams)] + keep <- vapply(grams, function(g) { + toks <- strsplit(g, "_", fixed = TRUE)[[1]] + length(toks) >= 2 && + !(tolower(toks[1]) %in% stops_lower) && + !(tolower(toks[length(toks)]) %in% stops_lower) && + toks[1] != toks[length(toks)] + }, logical(1), USE.NAMES = FALSE) + out <- grams[keep] + if (include_unigrams) { + # tokens may now carry tight punctuation, so a purely numeric token can + # contain separators too ("4.0", "350,067", "2013-2023") - all stay noise + words <- unlist(strsplit(segments, " ", fixed = TRUE)) + words <- words[nzchar(words) & !(tolower(words) %in% stops_lower) & + !grepl("^[0-9]+([.,:-][0-9]+)*$", words)] + out <- c(words, out) + } + out +} + + +# Controlled emulation of the legacy prune_ngrams quirks, applied on top of +# the shared generator's per-title output (Setting 0 only — the flag keeps the +# generator as the single code path while reproducing the reference +# behaviour): +# 1. an n-gram is also dropped when an EDGE TOKEN occurs as a substring of +# any stopword (the legacy stri_detect_fixed haystack/needle inversion — +# over-pruning vs the generator's exact matching); +# 2. a title whose surviving n-grams of one length number <= 1 loses them, +# and its slot is removed from the per-title list (shifting the later +# subject recycling exactly as the legacy pipeline did). +legacy_prune_quirks <- function(per_title, stops) { + per_title <- lapply(per_title, function(grams) { + if (!length(grams)) return(grams) + keep <- vapply(grams, function(g) { + toks <- strsplit(g, "_", fixed = TRUE)[[1]] + !any(stringi::stri_detect_fixed(stops, tolower(toks[1]))) && + !any(stringi::stri_detect_fixed(stops, tolower(toks[length(toks)]))) + }, logical(1), USE.NAMES = FALSE) + grams[keep] + }) + per_title[vapply(per_title, length, integer(1)) > 1] +} + + + +# LEGACY, no production callers (the Mode-0 corpus builder now generates +# through ngram_candidates): extract pruned bi- and tri-grams from a set of +# titles (see prune_ngrams) and return them concatenated. Kept as the +# reference implementation for tests until the cleanup +# Note: ngram_lengths is unused — +# lengths 2 and 3 are hardcoded. +get_title_ngrams <- function(titles, stops, ngram_lengths) { + # for ngrams: we have to collapse with "_" or else tokenizers will split ngrams again at that point and we'll be left with unigrams + per_title <- function(n) lapply(titles, function(segments) { + paste(unlist(expand_ngrams(segments, n)), collapse = " ") + }) + titles_bigrams = prune_ngrams(per_title(2), stops) + titles_trigrams = prune_ngrams(per_title(3), stops) + return(c(titles_bigrams, titles_trigrams)) +} + + + +# De-duplicate overlapping n-grams, preferring the more specific phrase: if a +# candidate is a substring of an already-kept name it is skipped; if a kept name +# is a substring of the candidate it is replaced by the candidate; otherwise the +# candidate is added. Returns up to top_n unique names. +filter_out_nested_ngrams <- function(top_ngrams, top_n) { + top_names <- list() + for (ngram in top_ngrams) { + if (ngram == "") + next; + + ngram_in_top_names = stringi::stri_detect_fixed(top_names, ngram) + top_names_with_ngram = sapply(top_names, function(x)(stringi::stri_detect_fixed(ngram, x))) + + # ngram substring of any top_name, and no top_name substring of ngram -> skip ngram + if (any(ngram_in_top_names == TRUE) && all(top_names_with_ngram == FALSE)) {} + # ngram not substring of any top_name, but at least one top_name is a substring of ngram -> replace top_name with ngram + else if (all(ngram_in_top_names == FALSE) && any(top_names_with_ngram == TRUE)) { + top_names[which(top_names_with_ngram)] <- ngram + } + # a not substring of b, b not substring of a -> add b, next + else if (all(ngram_in_top_names == FALSE) && all(top_names_with_ngram == FALSE)) { + top_names <- unlist(c(top_names, ngram)) + } + } + return(head(unique(top_names), top_n)) +} diff --git a/server/preprocessing/other-scripts/summarize.R b/server/preprocessing/other-scripts/summarize.R index cfce20410..53ad0b102 100644 --- a/server/preprocessing/other-scripts/summarize.R +++ b/server/preprocessing/other-scripts/summarize.R @@ -1,365 +1,28 @@ library(stringr) vslog <- getLogger('vis.summarize') -# Metadata columns holding the heuristically generated (title n-gram) keywords, -# pre-binned by MAP-WIDE document frequency (see add_heuristic_keyword_fields): -# _min1 = n-grams appearing in >= 1 resource (all) -> used for the FALLBACK corpus -# _min2 = n-grams appearing in >= 2 resources -> used for the INITIAL corpus -# The DF filter is GLOBAL (map-wide), applied to Modes 1-3 only; Mode 0 is legacy. -HEUR_MIN1 <- "keywords_rank_heuristically_generated_min1" -HEUR_MIN2 <- "keywords_rank_heuristically_generated_min2" - -# MeSH rank columns, produced upstream by the data client (base.R, BASE only for -# now). Used by Modes 2/3 to split subject tokens into specific/generic. When -# absent (integrations without MeSH), the split is empty and Mode 2/3 degrade to -# Mode-1-like behaviour (see get_cluster_corpus). -KW_MESH_SPECIFIC <- "keywords_rank_mesh_specific" -KW_MESH_GENERIC <- "keywords_rank_mesh_generic" - # summarize.R # Cluster labelling for the overview visualisation: turns each cluster of papers # into a short, human-readable area title. create_cluster_labels() is the entry # point (called from vis_layout.R); the helpers build a per-cluster text corpus, # rank candidate terms by tf-idf, prune and de-nest n-grams, and fix casing. # Tokens are kept ";"-separated throughout because SplitTokenizer (the -# TermDocumentMatrix tokenizer) splits terms on ";". - -# Tokenizer used by the TermDocumentMatrix: splits a document into terms on ";". -# The corpus joins tokens (words and "_"-joined n-grams) with ";", so this -# recovers them as individual terms without re-splitting the n-grams. -SplitTokenizer <- function(x) { - tokens = unlist(lapply(strsplit(words(x), split=";"), paste), use.names = FALSE) - return(tokens) -} - -# Strip leading and trailing whitespace from a string. -trim <- function (x) gsub("^\\s+|\\s+$", "", x) - - -# Normalise a combined token string to the ";"-separated form the SplitTokenizer -# consumes: drop "?" artifacts, collapse repeated ";", trim spaces around ";", and -# turn any remaining whitespace into ";". This is the SINGLE source of truth for -# corpus tokenization, used both to build the corpus document and to derive the -# per-cluster rank sources — so a rank token can never drift out of the tf-idf term -# set (see get_cluster_corpus / ranking.R). -normalize_corpus_tokens <- function(s) { - s <- str_replace_all(s, "\\?+_\\?+|\\?+|\\?+ ", "") - s <- str_replace_all(s, ";+", ";") - s <- str_replace_all(s, " ?; ?", ";") - s <- str_replace_all(s, " +", ";") - s -} - -# Remove a leading or trailing MeSH major-topic "*" from each "; "-separated -# keyword (sources place the marker on either side). Only a keyword-edge -# asterisk is a marker; an interior one ("2*2 design") is real content and -# stays. -strip_major_topic_markers <- function(x) { - x <- gsub("(^|;\\s*)\\*+", "\\1", x) - gsub("\\*+(\\s*;|$)", "\\1", x) -} +# TermDocumentMatrix tokenizer, label_corpus.R) splits terms on ";". +# +# Split by concern; the parts load here so both entry points +# (vis_layout.R and test/replay_harness.R) keep working unchanged: +# text_hygiene.R - entity decode, noise stripping, punctuation_segments +# ngram_generation.R - the shared generator + legacy-quirk emulation +# label_corpus.R - heuristic columns, bypass, corpus builders +# label_casing.R - final label casing +# label_debug.R - DEBUG-gated dump helpers +source("text_hygiene.R") +source("ngram_generation.R") +source("label_corpus.R") +source("label_casing.R") +source("label_debug.R") -# Generate all contiguous n-grams of length n from each input string. The words -# within an n-gram are joined with "_" so a whitespace tokenizer keeps the n-gram -# intact. Returns, per input string, a single space-separated string of its n-grams. -expand_ngrams <- function(text, n) { - text <- trimws(text) - lapply(lapply(text, function(x)unlist(lapply(ngrams(unlist(strsplit(x, split = " ")), n), paste, collapse = "_"))), paste, collapse = " ") -} - -# Drop low-value n-grams from a set of "_"-joined n-grams. Removes n-grams that -# start or end with a stopword, whose first and last token are identical, or that -# are shorter than 2 tokens; stopwords are checked in batches for speed. Returns, -# per input, the surviving n-grams joined with ";". -prune_ngrams <- function(ngrams, stops){ - # lapply/SIMPLIFY = FALSE: with mapply's default simplification, equal - # n-gram counts across all inputs collapse the result into a matrix and - # every n-gram is silently lost - ngrams = lapply(ngrams, function(x) strsplit(x[[1]], split = " |;")[[1]]) - tokenized_ngrams = mapply(function(x) { - strsplit(x, split="_") - }, ngrams, SIMPLIFY = FALSE) - # filter out empty tokens - tokenized_ngrams = lapply(tokenized_ngrams, function(ngrams){ngrams[lapply(ngrams, length)>0]}) - # remove ngrams starting with a stopword - batch_size <- 1000 - total_length <- length(stops) - for (i in seq(1, total_length, batch_size)) { - tokenized_ngrams = lapply(tokenized_ngrams, function(x) { - Filter(function(tokens){ - !any(stringi::stri_detect_fixed(stops[i:min(i+batch_size -1, total_length)], tolower(tokens[[1]]))) - }, x)}) - # remove ngrams ending with a stopword - tokenized_ngrams = lapply(tokenized_ngrams, function(x) { - Filter(function(tokens){ - !any(stringi::stri_detect_fixed(stops[i:min(i+batch_size -1, total_length)], tolower(tail(tokens,1)))) - }, x)}) - } - # remove ngrams starting and ending with the same word - tokenized_ngrams = lapply(tokenized_ngrams, function(x) { - Filter(function(tokens){ - !(tokens[[1]]==tail(tokens,1)) - }, x)}) - # keep ngrams with min length 2 - tokenized_ngrams = lapply(tokenized_ngrams, function(x){x[lapply(x, length)>1]}) - tokenized_ngrams = tokenized_ngrams[lapply(tokenized_ngrams, length)>1] - tokenized_ngrams = lapply(tokenized_ngrams, function(x){mapply(paste, x, collapse="_")}) - pruned_ngrams = lapply(tokenized_ngrams, paste, collapse=";") - return (pruned_ngrams) -} - -# Heuristically generated keywords for a single paper: the bi- and tri-grams of -# its title's punctuation-delimited segments (punctuation_segments), pruned of -# degenerate forms (n-grams that start or end with a stopword, or whose first -# and last token are identical). Words within an n-gram are "_"-joined so the -# tokenizer keeps them intact. Returns a character vector of unique "_"-joined -# n-grams (possibly empty). -paper_title_ngrams <- function(title, stops) { - segments <- punctuation_segments(title) - if (!length(segments)) return(character(0)) - grams <- unlist(c(expand_ngrams(segments, 2), expand_ngrams(segments, 3))) - grams <- unlist(strsplit(paste(grams, collapse = " "), " ")) - grams <- grams[nzchar(grams)] - if (!length(grams)) return(character(0)) - keep <- vapply(grams, function(g) { - toks <- strsplit(g, "_", fixed = TRUE)[[1]] - length(toks) >= 2 && - !(tolower(toks[1]) %in% stops) && - !(tolower(toks[length(toks)]) %in% stops) && - toks[1] != toks[length(toks)] - }, logical(1), USE.NAMES = FALSE) - unique(grams[keep]) -} - -# Decode HTML character entities so they cannot fragment into bare digits or -# stray tokens downstream (removePunctuation turns "–" into "8211" fused -# into the surrounding word). Handles numeric decimal and hex forms and the -# common named entities; "&" is decoded last so a double-encoded entity is -# only unwrapped one level. Decoded en-dash/hyphen codepoints are normalised to -# "-" so they do not multiply spelling variants of hyphenated terms. -decode_html_entities <- function(x) { - decode_one <- function(s) { - if (is.na(s) || !grepl("&", s, fixed = TRUE)) return(s) - m <- gregexpr("&#[0-9]{1,7};", s) - regmatches(s, m) <- lapply(regmatches(s, m), function(v) { - if (!length(v)) return(v) - vapply(v, function(e) intToUtf8(as.integer(sub("&#([0-9]+);", "\\1", e))), - character(1), USE.NAMES = FALSE) - }) - m <- gregexpr("&#[xX][0-9a-fA-F]{1,6};", s) - regmatches(s, m) <- lapply(regmatches(s, m), function(v) { - if (!length(v)) return(v) - vapply(v, function(e) intToUtf8(strtoi(sub("&#[xX]([0-9a-fA-F]+);", "\\1", e), 16L)), - character(1), USE.NAMES = FALSE) - }) - s <- gsub(" ", " ", s, fixed = TRUE) - s <- gsub("<", "<", s, fixed = TRUE) - s <- gsub(">", ">", s, fixed = TRUE) - s <- gsub(""", "\"", s, fixed = TRUE) - s <- gsub("'", "'", s, fixed = TRUE) - s <- gsub("&", "&", s, fixed = TRUE) - s - } - x <- vapply(x, decode_one, character(1), USE.NAMES = FALSE) - # No-break/narrow spaces (decoded " "/" " or already present in the - # source) become plain spaces: they render invisibly but count as non-space - # in regex classes, which would let a "no-space run" span whole sentences. - x <- gsub("[\u00a0\u202f]", " ", x) - gsub("[\u2013\u2010]", "-", x) -} - -# Strip text noise that would otherwise surface as corpus terms or label -# candidates: URLs (including signed URLs with their query strings), HTML tags -# and stray closing-tag fragments ("]*>", " ", x, perl = TRUE) - x <- gsub("<\\s*/\\s*[A-Za-z]*>?", " ", x, perl = TRUE) - x <- gsub("\\S{80,}", " ", x, perl = TRUE) - x -} - -# Tight-colon tokens that keep their colon instead of splitting (matched as the -# whole word:word token). Extend as legitimate ratio-style terms are found. -COLON_KEEP_TOKENS <- c("80:20", "50:50") - -# Split a text into punctuation-delimited segments for n-gram formation, so -# that no n-gram crosses a clause/subtitle boundary and no intra-word -# punctuation compound is broken. A punctuation mark splits when whitespace -# (or a string edge, or another boundary) adjoins it; a mark tight between two -# word characters stays inside its token. Deviations: colon, em dash, pipe and -# underscore always split (underscore because it is the n-gram joiner -# character); colon keep-list tokens and multi-period abbreviation chains -# ("U.S.", "e.g." — trailing period included) stay whole; a balanced (word) -# pair fused to a word character on at least one side keeps its parens as -# token content ("(in)justice", "micro(nano)") while its spaced outer side -# still bounds the segment; a run of >= 2 consecutive punctuation marks always -# splits as a unit. Placeholders \x01 (chain periods), \x03 (kept colons), -# \x04/\x05 (kept parens) and \x02 (boundaries) cannot occur in decoded -# titles. Returns a character vector of trimmed, whitespace-collapsed, -# non-empty segments; case is preserved. NA/empty input -> character(0). -punctuation_segments <- function(text) { - text <- if (is.null(text) || is.na(text)) "" else text - text <- sanitize_corpus_noise(decode_html_entities(text)) - # C0 control characters are removed before anything else: \x01-\x05 are this - # function's own placeholders, so a source string containing them would be - # restored as a period/colon/paren or silently split the text - text <- gsub("[\x01-\x08\x0b\x0c\x0e-\x1f]", " ", text) - # soft hyphen and zero-width characters render as nothing but count as - # non-space, so they would be kept inside a token and stop it matching the - # same word spelled without them - text <- gsub("[\u00ad\u200b\u200c\u200d\ufeff]", "", text) - # normalize spelling variants to one token identity (decode already maps - # en dash U+2013 and hyphen U+2010 to "-"); a tight "--" is a TeX en dash - # inside a compound, a spaced "--" is left as a run to split - text <- gsub("\u2011", "-", text) - text <- gsub("[\u2019\u2018\u02bc]", "'", text) - text <- gsub("\uff1a", ":", text) - text <- gsub("(?<=[^\\s-])--(?=[^\\s-])", "-", text, perl = TRUE) - # protect abbreviation chains (>= 2 single-letter.period components) before - # any boundary rule, so their periods - the trailing one included - survive - m <- gregexpr("(?= 2 && - !(tolower(toks[1]) %in% stops_lower) && - !(tolower(toks[length(toks)]) %in% stops_lower) && - toks[1] != toks[length(toks)] - }, logical(1), USE.NAMES = FALSE) - out <- grams[keep] - if (include_unigrams) { - # tokens may now carry tight punctuation, so a purely numeric token can - # contain separators too ("4.0", "350,067", "2013-2023") - all stay noise - words <- unlist(strsplit(segments, " ", fixed = TRUE)) - words <- words[nzchar(words) & !(tolower(words) %in% stops_lower) & - !grepl("^[0-9]+([.,:-][0-9]+)*$", words)] - out <- c(words, out) - } - out -} - -# bypass for the generator settings (>= 1): papers whose `subject` was -# synthesised from the title (subject_is_heuristic) get it blanked, so the -# synthesis reaches neither the corpus nor any rank source — those papers -# contribute exclusively through the generator columns. Setting 0 must NOT -# apply this: the synthesis is part of the replicated baseline. -bypass_heuristic_subjects <- function(metadata) { - if (!is.null(metadata$subject_is_heuristic)) { - metadata$subject[as.logical(metadata$subject_is_heuristic)] <- "" - } - metadata -} - -# Add the two heuristic-keyword metadata columns (HEUR_MIN1 / HEUR_MIN2) to the -# metadata data frame. Generates each paper's n-grams through the shared -# generator (ngram_candidates, de-duplicated per paper), computes each n-gram's -# MAP-WIDE document frequency (number of distinct resources it appears in), and stores -# per paper, as "; "-joined "_"-n-gram strings: -# HEUR_MIN1 = all of the paper's n-grams (DF >= 1) -# HEUR_MIN2 = the paper's n-grams with map-wide document frequency >= 2 -# Computed once, early in labelling. ngram_lengths comes from the resolved -# n-gram setting (ngram_setting_lengths); the Setting-0 default c(2, 3) -# reproduces paper_title_ngrams exactly (pinned by the drop-in equivalence -# test in test_ngram_generator.R). The generator input is the title — or -# title + abstract for no-keyword (subject_is_heuristic) papers when the -# abstract flag is on: the input is assembled per paper by the data, not a -# code branch. -add_heuristic_keyword_fields <- function(metadata, stops, ngram_lengths = c(2, 3), - include_abstracts = FALSE) { - input_text <- metadata$title - if (include_abstracts && !is.null(metadata$subject_is_heuristic) && - !is.null(metadata$paper_abstract)) { - fl <- which(as.logical(metadata$subject_is_heuristic)) - input_text[fl] <- paste(metadata$title[fl], metadata$paper_abstract[fl]) - } - per_paper <- lapply(input_text, function(t) - unique(ngram_candidates(t, stops, ngram_lengths = ngram_lengths))) - df <- table(unlist(lapply(per_paper, unique))) # map-wide document frequency - min2_set <- names(df)[df >= 2] - metadata[[HEUR_MIN1]] <- vapply(per_paper, - function(g) paste(g, collapse = "; "), character(1)) - metadata[[HEUR_MIN2]] <- vapply(per_paper, - function(g) paste(g[g %in% min2_set], collapse = "; "), character(1)) - metadata -} - # Last-resort label for a cluster that produced no tf-idf label (empty even after # the min1 fallback): build one from the most frequent bi-/tri-grams of the # cluster's papers' titles + abstracts. Returns a single ", "-joined label string. @@ -397,78 +60,6 @@ title_abstract_fallback_label <- function(matches, metadata, stops, top_n = 3, l paste(summary, collapse=", ") } -# --- Label-generation debug dumps ------------------------------------------------ -# Fine-grained, DEBUG-gated traces of how each cluster's area label is built, for -# backtracking a label to its inputs: the corpus text that feeds tf-idf, the tf-idf -# candidate terms with their weights, the rank-source provenance (Modes 1-3), the -# terms removed by the exclusion list, and the per-cluster label provenance (which -# path produced it, plus the label before/after casing). Each keyed on VIS_ID via -# dump_data; all no-ops unless LOGLEVEL=DEBUG, and never fatal. - -# Per-cluster corpus document text (what tf-idf actually tokenizes). One row/cluster. -dump_corpus_text <- function(corpus, stage) { - if (!debug_enabled()) return(invisible(NULL)) - tryCatch({ - txt <- vapply(seq_along(corpus), - function(k) paste(as.character(content(corpus[[k]])), collapse = " "), - character(1)) - dump_data(data.frame(cluster = seq_along(txt), text = txt, stringsAsFactors = FALSE), stage) - }, error = function(e) vslog$warn(paste("dump_corpus_text failed:", conditionMessage(e)))) -} - -# Per-cluster tf-idf candidate terms, weight-ordered, with their scores. One row per -# (cluster, term): the candidate pool that selection/ranking draws from. -dump_tfidf_candidates <- function(tfidf_top, stage) { - if (!debug_enabled()) return(invisible(NULL)) - tryCatch({ - rows <- lapply(seq_along(tfidf_top), function(k) { - w <- tfidf_top[[k]] - if (is.null(w) || !length(w) || is.null(names(w))) return(NULL) - data.frame(cluster = k, weight_rank = seq_along(w), - term = gsub("_", " ", names(w)), tfidf = as.numeric(w), - stringsAsFactors = FALSE) - }) - rows <- do.call(rbind, rows[!vapply(rows, is.null, logical(1))]) - if (!is.null(rows) && nrow(rows)) dump_data(rows, stage) - }, error = function(e) vslog$warn(paste("dump_tfidf_candidates failed:", conditionMessage(e)))) -} - -# Per-cluster rank-source token-sets (Modes 1-3): which provenance set (cleaned, -# mesh_specific, ...) each candidate token belongs to — i.e. what fixes its rank. -dump_rank_sources <- function(rank_sources, stage) { - if (!debug_enabled() || is.null(rank_sources)) return(invisible(NULL)) - tryCatch({ - rows <- list() - for (src in names(rank_sources)) { - per_cluster <- rank_sources[[src]] - for (k in seq_along(per_cluster)) { - toks <- per_cluster[[k]] - if (length(toks)) rows[[length(rows) + 1L]] <- - data.frame(cluster = k, source = src, term = gsub("_", " ", toks), - stringsAsFactors = FALSE) - } - } - if (length(rows)) dump_data(do.call(rbind, rows), stage) - }, error = function(e) vslog$warn(paste("dump_rank_sources failed:", conditionMessage(e)))) -} - -# Per-cluster terms removed by the exclusion list (tf-idf candidates before vs after -# drop_excluded_terms). Makes each exclusion drop explicit. -dump_excluded_terms <- function(before, after, stage) { - if (!debug_enabled()) return(invisible(NULL)) - tryCatch({ - rows <- lapply(seq_along(before), function(k) { - b <- before[[k]]; a <- after[[k]] - if (is.null(b) || !length(b) || is.null(names(b))) return(NULL) - dropped <- setdiff(names(b), names(a)) - if (!length(dropped)) return(NULL) - data.frame(cluster = k, term = gsub("_", " ", dropped), - tfidf = as.numeric(b[dropped]), stringsAsFactors = FALSE) - }) - rows <- do.call(rbind, rows[!vapply(rows, is.null, logical(1))]) - if (!is.null(rows) && nrow(rows)) dump_data(rows, stage) - }, error = function(e) vslog$warn(paste("dump_excluded_terms failed:", conditionMessage(e)))) -} # Entry point: assign a short label ("area title") to every cluster. # Builds one pseudo-document per cluster from its papers' subjects + title @@ -702,287 +293,6 @@ create_cluster_labels <- function(clusters, metadata, } -# Normalise a vector of cluster labels: restore term casing (fix_keyword_casing) -# and collapse repeated commas. Returns the cleaned labels. -fix_cluster_labels <- function(clusterlabels, type_counts){ - unlist(mclapply(clusterlabels, function(x) { - x <- fix_keyword_casing(x, type_counts) - # clean up titles from format issues - x <- gsub(",+", ",", x) - })) -} - -# Restore the casing of each word in a single label and capitalise the first -# letter of every comma-separated phrase. Words are matched back to their -# original corpus casing via match_keyword_case(type_counts). -fix_keyword_casing <- function(keyword, type_counts) { - kw = strsplit(keyword, ", ") - kw = lapply(kw, strsplit, " ")[[1]] - kw = lapply(kw, function(x){lapply(x, match_keyword_case, type_counts=type_counts)}) - kw = lapply(kw, paste, collapse = " ") - kw = lapply(kw, function(x) {paste0(toupper(substr(x, 1, 1)), substr(x, 2, nchar(x)))}) - kw = paste(kw, collapse = ", ") - return(paste(kw, collapse = ", ")) -} - -# Return the canonical (original-corpus) casing of a token: looks it up in -# type_counts case-insensitively. The lookup is exact on everything but case — -# in particular hyphen-preserving: the vocabulary regularly holds a -# de-hyphenated twin of a hyphenated token (source spelling variants), and a -# hyphen-insensitive lookup would respell the token instead of re-casing it. -# Falls back to the input token if there is no match. Edge hyphens are -# separator debris, not part of the token, and are trimmed before the lookup -# (a token that is only hyphens is returned unchanged). -match_keyword_case <- function(x, type_counts) { - stripped <- gsub("^-+|-+$", "", x) - if (nzchar(stripped)) x <- stripped - y <- names(type_counts[which(tolower(names(type_counts)) == tolower(x))][1]) - if (!is.na(y)) return(y) else return(x) -} - -# Build the per-cluster corpus from a custom metadata field (custom_clustering) -# instead of subjects. Per cluster: removes stopwords, then joins and normalises -# the field values into a single ";"-separated token string. Returns -# list(corpus, rank_sources) with rank_sources = NULL (no provenance split on this -# path, so ranked modes fall back to legacy selection). -get_custom_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, - add_title_ngrams = T, custom_clustering=NULL) { - subjectlist = list() - for (k in seq(1, clusters$num_clusters)) { - matches = which(unname(clusters$groups == k) == TRUE) - custom_input = metadata[[custom_clustering]][matches] - batch_size <- 1000 - total_length <- length(stops) - for (i in seq(1, total_length, batch_size)) { - custom_input = lapply(custom_input, function(x) {removeWords(x, stops[i:min(i+batch_size -1, total_length)])}) - } - custom_input = mapply(gsub, custom_input, pattern = "; ", replacement=";") - custom_input = mapply(gsub, custom_input, pattern=" ", replacement="_") - - all_subjects = paste(custom_input, collapse=" ") - all_subjects <- normalize_corpus_tokens(all_subjects) - subjectlist = c(subjectlist, all_subjects) - } - # Custom clustering has no keyword/heuristic provenance split, so rank_sources is - # NULL: ranked modes fall back to the legacy selection on this path (ranking.R). - nn_corpus <- VCorpus(VectorSource(subjectlist)) - return(list(corpus = nn_corpus, rank_sources = NULL)) -} - -# Controlled emulation of the legacy prune_ngrams quirks, applied on top of -# the shared generator's per-title output (Setting 0 only — the flag keeps the -# generator as the single code path while reproducing the reference -# behaviour): -# 1. an n-gram is also dropped when an EDGE TOKEN occurs as a substring of -# any stopword (the legacy stri_detect_fixed haystack/needle inversion — -# over-pruning vs the generator's exact matching); -# 2. a title whose surviving n-grams of one length number <= 1 loses them, -# and its slot is removed from the per-title list (shifting the later -# subject recycling exactly as the legacy pipeline did). -legacy_prune_quirks <- function(per_title, stops) { - per_title <- lapply(per_title, function(grams) { - if (!length(grams)) return(grams) - keep <- vapply(grams, function(g) { - toks <- strsplit(g, "_", fixed = TRUE)[[1]] - !any(stringi::stri_detect_fixed(stops, tolower(toks[1]))) && - !any(stringi::stri_detect_fixed(stops, tolower(toks[length(toks)]))) - }, logical(1), USE.NAMES = FALSE) - grams[keep] - }) - per_title[vapply(per_title, length, integer(1)) > 1] -} - -# --- Mode 0 (legacy) corpus + fallback ------------------------------------- -# Mode 0 keeps the legacy corpus/selection STRUCTURE: title n-grams are -# generated INLINE rather than read from the DF-filtered min1/min2 columns, and -# there are no rank_sources. Generation always goes through the shared -# generator (ngram_candidates); at Setting 0 (legacy_quirks = TRUE) the -# legacy_prune_quirks post-filter reproduces the historical prune behaviour on -# top of it, keeping Setting 0 byte-equivalent to the baseline. Settings >= 1 -# pass their own lengths with the quirks off. -get_cluster_corpus_legacy <- function(clusters, metadata, stops, taxonomy_separator, - add_title_ngrams = T, custom_clustering=NULL, - ngram_lengths = c(2, 3), legacy_quirks = TRUE) { - subjectlist = list() - for (k in seq(1, clusters$num_clusters)) { - matches = which(unname(clusters$groups == k) == TRUE) - titles = metadata$title[matches] - subjects = metadata$subject[matches] - # segment each title at punctuation boundaries so the inline n-grams cannot - # span a boundary and tight compounds stay whole; the unigram word stream - # is built from the same segment tokens - title_segments = lapply(titles, punctuation_segments) - # per-title n-grams from the shared generator (raw stream, no dedup — - # mode-0 tf counts duplicates). One vector per length keeps the legacy tf - # property that subjects recycle over the length groups in the later - # paste(subjects, title_ngrams). - group_join <- function(n) { - per_title <- lapply(titles, function(t) - ngram_candidates(t, stops, ngram_lengths = n)) - if (legacy_quirks) per_title <- legacy_prune_quirks(per_title, stops) - vapply(per_title, paste, character(1), collapse = ";", USE.NAMES = FALSE) - } - title_ngrams <- unlist(lapply(ngram_lengths, group_join)) - titles = lapply(title_segments, paste, collapse = " ") - batch_size <- 1000 - total_length <- length(stops) - for (i in seq(1, total_length, batch_size)) { - titles = lapply(titles, function(x) {removeWords(x, stops[i:min(i+batch_size -1, total_length)])}) - } - subjects = mapply(gsub, subjects, pattern = "; ", replacement=";") - subjects = mapply(gsub, subjects, pattern=" ", replacement="_") - titles = mapply(gsub, titles, pattern=" ", replacement=";") - - if (!is.null(taxonomy_separator)) { - subjects = mapply(function(x){strsplit(x, ";")}, subjects) - taxons = lapply(subjects, function(y){Filter(function(x){grepl(taxonomy_separator, x)}, y)}) - subjects = lapply(subjects, function(y){Filter(function(x){!grepl(taxonomy_separator, x)}, y)}) - taxons = lapply(taxons, function(x){lapply(strsplit(x, taxonomy_separator), function(y){tail(y,1)})}) - taxons = lapply(taxons, function(x){paste(unlist(x), collapse=";")}) - subjects = lapply(subjects, function(x){paste(unlist(x), collapse=";")}) - subjects = mapply(paste, subjects, taxons, collapse=";") - } - if (add_title_ngrams == T) { - all_subjects = paste(subjects, title_ngrams, collapse=" ") - } else { - all_subjects = paste(subjects, collapse=" ") - } - all_subjects <- str_replace_all(all_subjects, "\\?+_\\?+|\\?+|\\?+ ", "") - all_subjects <- str_replace_all(all_subjects, ";+", ";") - all_subjects <- str_replace_all(all_subjects, " ?; ?", ";") - all_subjects <- str_replace_all(all_subjects, " +", ";") - subjectlist = c(subjectlist, all_subjects) - } - nn_corpus <- VCorpus(VectorSource(subjectlist)) - return(nn_corpus) -} - -# Legacy (Mode 0) fallback: rebuild the tf-idf of the SAME corpus with local bound -# c(1, Inf) for clusters whose initial tf-idf summed to zero. Verbatim legacy -# 2-arg signature — distinct from the Modes 1-3 fill_empty_clusters (min1 corpus). -fill_empty_clusters_legacy <- function(nn_tfidf, nn_corpus){ - replacement_nn_tfidf <- TermDocumentMatrix(nn_corpus, control = list(tokenize = SplitTokenizer, - weighting = function(x) weightSMART(x, spec="ntn"), - bounds = list(local = c(1, Inf)) - )) - replacement_tfidf_top <- apply(replacement_nn_tfidf, 2, function(x) {x2 <- sort(x, TRUE);x2[x2>0]}) - return(replacement_tfidf_top) -} - -# Build the per-cluster corpus used for tf-idf labelling. Per cluster: combines -# the papers' `subject` keywords with bi-/tri-grams from their titles. Taxonomy -# subjects (containing taxonomy_separator) are reduced to their last path segment. -# Everything is normalised into one ";"-separated token string per cluster. -# Returns list(corpus, rank_sources): a VCorpus with one document per cluster, and -# per-cluster rank sources (cleaned = subject tokens, heuristic = title n-grams, -# lowercased) used by the ranked selection. add_title_ngrams toggles the title -# n-gram contribution. -get_cluster_corpus <- function(clusters, metadata, stops, taxonomy_separator, - heuristic_col = HEUR_MIN2) { - subjectlist = list() - subject_dbg = list(); heuristic_dbg = list(); heuristic_min1_dbg = list() - replaced_subject_dbg = list() # subjects synthesised from titles (rank 2, not rank 1) - mesh_spec_dbg = list(); mesh_gen_dbg = list() # normalised MeSH tokens (empty if no mesh columns) - has_mesh <- !is.null(metadata[[KW_MESH_SPECIFIC]]) - for (k in seq(1, clusters$num_clusters)) { - matches = which(unname(clusters$groups == k) == TRUE) - subjects = metadata$subject[matches] - # subject_is_heuristic flags papers whose `subject` was synthesised from the - # title by replace_keywords_if_empty (they had no real keywords). Their tokens - # are routed to the HEURISTIC rank source (rank 2), not the cleaned/keyword - # source (rank 1). Absent on fixtures captured before this change -> all FALSE. - flagged = if (!is.null(metadata$subject_is_heuristic)) { - as.logical(metadata$subject_is_heuristic[matches]) - } else { - rep(FALSE, length(matches)) - } - # Heuristic keywords are pre-generated "_"-joined n-grams (see - # add_heuristic_keyword_fields): the corpus uses heuristic_col (map-wide min2 - # initial / min1 fallback); the rank map always uses min1 (the superset), so any - # heuristic term resolves to rank 2 regardless of which pass produced it. - heuristics = as.character(metadata[[heuristic_col]][matches]) - heuristics_min1 = as.character(metadata[[HEUR_MIN1]][matches]) - - subjects = mapply(gsub, subjects, pattern = "; ", replacement=";") - subjects = mapply(gsub, subjects, pattern=" ", replacement="_") - heuristics = gsub("; ", ";", heuristics) - heuristics_min1 = gsub("; ", ";", heuristics_min1) - - if (!is.null(taxonomy_separator)) { - subjects = mapply(function(x){strsplit(x, ";")}, subjects) - taxons = lapply(subjects, function(y){Filter(function(x){grepl(taxonomy_separator, x)}, y)}) - subjects = lapply(subjects, function(y){Filter(function(x){!grepl(taxonomy_separator, x)}, y)}) - taxons = lapply(taxons, function(x){lapply(strsplit(x, taxonomy_separator), function(y){tail(y,1)})}) - taxons = lapply(taxons, function(x){paste(unlist(x), collapse=";")}) - subjects = lapply(subjects, function(x){paste(unlist(x), collapse=";")}) - subjects = mapply(paste, subjects, taxons, collapse=";") - } - # Corpus is unchanged: it uses ALL subjects + heuristics (flagged or not), so - # tf-idf weights are identical. Only the RANK MAP splits them — flagged papers' - # subject tokens go to the heuristic rank source below, not the cleaned one. - subject_dbg[[k]] = paste(unlist(subjects[!flagged]), collapse=";") # rank 1 (real keywords) - replaced_subject_dbg[[k]] = paste(unlist(subjects[flagged]), collapse=";") # rank 2 (title-synthesised) - heuristic_dbg[[k]] = paste(unlist(heuristics), collapse=";") - heuristic_min1_dbg[[k]] = paste(unlist(heuristics_min1), collapse=";") - # MeSH columns (Modes 2/3): aggregate per cluster, normalised the same way as - # subjects so their tokens match the subject tokens they classify. Empty when - # the client did not populate them (-> Mode 2/3 degrade to Mode-1 behaviour). - if (has_mesh) { - msp = gsub(" ", "_", gsub("; ", ";", as.character(metadata[[KW_MESH_SPECIFIC]][matches]))) - mge = gsub(" ", "_", gsub("; ", ";", as.character(metadata[[KW_MESH_GENERIC]][matches]))) - mesh_spec_dbg[[k]] = paste(unlist(msp), collapse=";") - mesh_gen_dbg[[k]] = paste(unlist(mge), collapse=";") - } else { - mesh_spec_dbg[[k]] = ""; mesh_gen_dbg[[k]] = "" - } - all_subjects = paste(subjects, heuristics, collapse=" ") - all_subjects <- normalize_corpus_tokens(all_subjects) - subjectlist = c(subjectlist, all_subjects) - } - # Debug: record, per cluster, the tokens contributed by subjects vs. by the - # heuristic (title n-gram) column, so a label term can be attributed to its source. - dump_data(data.frame(cluster = seq_along(subjectlist), - subject_tokens = unlist(subject_dbg), - title_ngrams = unlist(heuristic_dbg)), - "summarize_04a_corpus_sources") - # Rank sources for the ranked selection (ranking.R): per-cluster token-sets, all - # run through the SAME normalize_corpus_tokens() + lowercasing + edge-strip as the - # TDM terms — so every rank token matches a tf-idf term. The MeSH split is - # derived by MEMBERSHIP on the (already-normalised) subject tokens: a subject token - # is mesh_specific/generic if it is in the corresponding MeSH column, else it is a - # cleaned (ex-mesh) keyword. This keeps every rank token a genuine subject token - # (so it matches the TDM) and needs no separate normalization path. - # cleaned = all subject tokens (keywords + MeSH pooled) — Mode 1 rank 1. - # mesh_specific/generic = subject tokens classified as specific/generic MeSH. - # cleaned_ex_mesh = subject tokens minus MeSH — Modes 2/3 rank 1. - # heuristic = the min1 title-n-gram tokens. - # Used only for rank lookup, never fed to the TDM. - split_tokens <- function(s) { - t <- tolower(unlist(strsplit(normalize_corpus_tokens(s), ";"))) - t <- gsub("^_+|_+$", "", t) # mirror the TDM tokenizer's edge-punctuation strip - t[nzchar(t)] - } - subj_tok <- lapply(subject_dbg, split_tokens) - spec_tok <- lapply(mesh_spec_dbg, split_tokens) - gen_tok <- lapply(mesh_gen_dbg, split_tokens) - mesh_specific <- mapply(intersect, subj_tok, spec_tok, SIMPLIFY = FALSE) - mesh_generic <- mapply(intersect, subj_tok, gen_tok, SIMPLIFY = FALSE) - cleaned_ex_mesh <- mapply(function(all, sp, ge) setdiff(all, c(sp, ge)), - subj_tok, mesh_specific, mesh_generic, SIMPLIFY = FALSE) - # heuristic = the min1 title n-grams PLUS the flagged papers' synthesised subject - # tokens (title-derived, so they belong in rank 2 — see the subject_is_heuristic - # routing above). Both are already in the corpus, so no double-counting. - heuristic_tok <- mapply(function(h, r) unique(c(split_tokens(h), split_tokens(r))), - heuristic_min1_dbg, replaced_subject_dbg, SIMPLIFY = FALSE) - rank_sources <- list(cleaned = subj_tok, - cleaned_ex_mesh = cleaned_ex_mesh, - mesh_specific = mesh_specific, - mesh_generic = mesh_generic, - heuristic = heuristic_tok) - nn_corpus <- VCorpus(VectorSource(subjectlist)) - return(list(corpus = nn_corpus, rank_sources = rank_sources)) -} - # Turn the ranked tf-idf terms of each cluster into a display label: prunes # stopword-edged n-grams, removes n-grams nested inside others (keeping the more @@ -997,6 +307,7 @@ get_top_names <- function(tfidf_top, top_n, stops) { return(tfidf_top_names) } + # Variant of prune_ngrams used on tf-idf term names: drops n-grams that start or # end with a stopword or whose first and last token are identical. Tolerant of # empty/NA tokens. Returns the surviving "_"-joined n-grams. @@ -1046,63 +357,3 @@ another_prune_ngrams <- function(ngrams, stops){ tokens = lapply(tokens, function(x){mapply(paste, x, collapse="_")}) return(tokens) } - -# Provide fallback top terms for clusters that produced none under the strict -# tf-idf bound, by recomputing the TermDocumentMatrix with a looser local bound -# (terms appearing at least once, instead of at least twice). Returns the -# per-cluster sorted term lists. -fill_empty_clusters <- function(fallback_corpus){ - replacement_nn_tfidf <- TermDocumentMatrix(fallback_corpus, control = list(tokenize = SplitTokenizer, - weighting = function(x) weightSMART(x, spec="ntn"), - bounds = list(local = c(1, Inf)), - tolower = TRUE - )) - replacement_tfidf_top <- apply(replacement_nn_tfidf, 2, function(x) {x2 <- sort(x, TRUE);x2[x2>0]}) - return(replacement_tfidf_top) -} - - -# LEGACY, no production callers (the Mode-0 corpus builder now generates -# through ngram_candidates): extract pruned bi- and tri-grams from a set of -# titles (see prune_ngrams) and return them concatenated. Kept as the -# reference implementation for tests until the cleanup -# Note: ngram_lengths is unused — -# lengths 2 and 3 are hardcoded. -get_title_ngrams <- function(titles, stops, ngram_lengths) { - # for ngrams: we have to collapse with "_" or else tokenizers will split ngrams again at that point and we'll be left with unigrams - per_title <- function(n) lapply(titles, function(segments) { - paste(unlist(expand_ngrams(segments, n)), collapse = " ") - }) - titles_bigrams = prune_ngrams(per_title(2), stops) - titles_trigrams = prune_ngrams(per_title(3), stops) - return(c(titles_bigrams, titles_trigrams)) -} - - -# De-duplicate overlapping n-grams, preferring the more specific phrase: if a -# candidate is a substring of an already-kept name it is skipped; if a kept name -# is a substring of the candidate it is replaced by the candidate; otherwise the -# candidate is added. Returns up to top_n unique names. -filter_out_nested_ngrams <- function(top_ngrams, top_n) { - top_names <- list() - for (ngram in top_ngrams) { - if (ngram == "") - next; - - ngram_in_top_names = stringi::stri_detect_fixed(top_names, ngram) - top_names_with_ngram = sapply(top_names, function(x)(stringi::stri_detect_fixed(ngram, x))) - - # ngram substring of any top_name, and no top_name substring of ngram -> skip ngram - if (any(ngram_in_top_names == TRUE) && all(top_names_with_ngram == FALSE)) {} - # ngram not substring of any top_name, but at least one top_name is a substring of ngram -> replace top_name with ngram - else if (all(ngram_in_top_names == FALSE) && any(top_names_with_ngram == TRUE)) { - top_names[which(top_names_with_ngram)] <- ngram - } - # a not substring of b, b not substring of a -> add b, next - else if (all(ngram_in_top_names == FALSE) && all(top_names_with_ngram == FALSE)) { - top_names <- unlist(c(top_names, ngram)) - } - } - return(head(unique(top_names), top_n)) -} - diff --git a/server/preprocessing/other-scripts/test/replay/article_exclusion_rebaseline_diff.md b/server/preprocessing/other-scripts/test/replay/article_exclusion_rebaseline_diff.md new file mode 100644 index 000000000..ddd854d06 --- /dev/null +++ b/server/preprocessing/other-scripts/test/replay/article_exclusion_rebaseline_diff.md @@ -0,0 +1,42 @@ +# Replay label diff: "article" label exclusion + +Adding `article` to resources/label_exclusions.txt and applying the list +in Mode 0 as well. Only changed clusters listed; Mode 0 was unchanged on +every fixture (no Mode-0 label was a listed generic term). + +## orcid_42216275 mode 1 + +- cluster 9: + - before: Spatial learning, Automation, ScholarlyArticle + - after: Spatial learning, Automation, Radial arm maze automated testing system animal welfare animal experimentation refinement and replacement + +## orcid_42216275 mode 2 + +- cluster 9: + - before: Spatial learning, Automation, ScholarlyArticle + - after: Spatial learning, Automation, Radial arm maze automated testing system animal welfare animal experimentation refinement and replacement + +## orcid_42216275 mode 3 + +- cluster 9: + - before: ScholarlyArticle, Radial arm maze automated testing system animal welfare animal experimentation refinement and replacement, LPS (lipopolysaccharide) + - after: Radial arm maze automated testing system animal welfare animal experimentation refinement and replacement, LPS (lipopolysaccharide), Maze memory + +## orcid_96127791 mode 1 + +- cluster 9: + - before: Article, Disease trajectories, Health equity + - after: Disease trajectories, Health equity, Patterns + +## orcid_96127791 mode 2 + +- cluster 9: + - before: Article, Disease trajectories, Health equity + - after: Disease trajectories, Health equity, Patterns + +## orcid_96127791 mode 3 + +- cluster 9: + - before: Article, Disease trajectories, Health equity + - after: Disease trajectories, Health equity, Patterns + diff --git a/server/preprocessing/other-scripts/test/replay/mode0_exclusion_rebaseline_diff.md b/server/preprocessing/other-scripts/test/replay/mode0_exclusion_rebaseline_diff.md new file mode 100644 index 000000000..2489fac7e --- /dev/null +++ b/server/preprocessing/other-scripts/test/replay/mode0_exclusion_rebaseline_diff.md @@ -0,0 +1,84 @@ +# Replay label diff: label exclusions in Mode 0 + +drop_excluded_terms now runs in the Mode-0 branch too (post-tf-idf, +pre-selection), so listed generic terms cannot become Mode-0 labels. +Only changed clusters listed; Modes 1-3 unaffected. + +## base_cancer_fallback mode 0 + +- cluster 7: + - before: Diet, Female, Humans + - after: Diet, Female, Colorectal neoplasms +- cluster 13: + - before: Breast cancer, Biotechnology, Medicine + - after: Breast cancer, Biotechnology, Science policy + +## base_cancer_research mode 0 + +- cluster 6: + - before: Humans, Colorectal neoplasms, Diet + - after: Colorectal neoplasms, Diet, Female +- cluster 13: + - before: Breast cancer, Biotechnology, Medicine + - after: Breast cancer, Biotechnology, Science policy + +## orcid_39246636 mode 0 + +- cluster 6: + - before: Cities, Urban population, Humans + - after: Cities, Urban population + +## orcid_42216275 mode 0 + +- cluster 9: + - before: Spatial learning, Automation, ScholarlyArticle + - after: Spatial learning, Automation, Radial arm maze automated testing system animal welfare animal experimentation refinement and replacement +- cluster 12: + - before: Medicine, Science, Placental implantation + - after: Placental implantation, Female, & dosage + +## orcid_45050517 mode 0 + +- cluster 4: + - before: Humans, Medicine, Phase transition + - after: Phase transition, Statistical models, Data analysis, Statistics and probability + +## orcid_5116955x_r2 mode 0 + +- cluster 5: + - before: Science, Prisoner dilemma, Game theory + - after: Prisoner dilemma, Game theory, Psychological models +- cluster 10: + - before: Animals, Birds, Coevolution + - after: Birds, Coevolution, Grooming + +## orcid_5116955x mode 0 + +- cluster 5: + - before: Prisoner dilemma, Game theory, Science + - after: Prisoner dilemma, Game theory, Motivation +- cluster 10: + - before: Animals, Birds, Coevolution + - after: Birds, Coevolution, Grooming + +## orcid_90626039 mode 0 + +- cluster 3: + - before: Entanglement witnesses, Medicine, Mirrored entanglement + - after: Entanglement witnesses, Mirrored entanglement, Bound entanglement + +## orcid_96127791_r2 mode 0 + +- cluster 5: + - before: Cohort studies, Female, Humans + - after: Cohort studies, Female, Late-onset epilepsy + +## orcid_96127791 mode 0 + +- cluster 4: + - before: Cohort studies, Unsupervised machine learning, Humans + - after: Cohort studies, Unsupervised machine learning, Female +- cluster 9: + - before: Article, Nationwide study, COVID outcomes + - after: Nationwide study, COVID outcomes, Disease trajectories + diff --git a/server/preprocessing/other-scripts/test/replay/mode0_punctuation_rebaseline_diff.md b/server/preprocessing/other-scripts/test/replay/mode0_punctuation_rebaseline_diff.md new file mode 100644 index 000000000..3641585d0 --- /dev/null +++ b/server/preprocessing/other-scripts/test/replay/mode0_punctuation_rebaseline_diff.md @@ -0,0 +1,676 @@ +# Replay label diff: punctuation segmentation in Mode 0 + +Mode 0 now shares the punctuation-aware title segmentation (its legacy +corpus structure is unchanged); prune_ngrams also no longer loses all +n-grams when every title yields the same n-gram count. Only changed +clusters listed; Modes 1-3 were unaffected by this change. + +## base_cancer_fallback mode 0 + +- cluster 1: + - before: Cancer screening, Health behavior, Artificial intelligence + - after: Cancer screening, Artificial intelligence, Associates Poll +- cluster 3: + - before: Cancer incidence seer, Population-based cancer registration, Cancer control + - after: Cancer incidence, Population-based cancer registration, Cancer control +- cluster 6: + - before: Challenges opportunities, Computational immuno, Immuno oncology + - after: Challenges and opportunities, Challenges opportunities, Computational immuno +- cluster 7: + - before: Humans, Diet, Female + - after: Diet, Female, Humans +- cluster 11: + - before: Cervix uteri, AIDS disease, Human cancer + - after: Cervix uteri, Human cancer, Biomolecular therapeutics +- cluster 12: + - before: Cancer epidemiological, Clinical concepts, Epidemiological clinical + - after: Konsep Epidemiologis, Penelitian Kanker +- cluster 14: + - before: Fibroblasts protagonists, Tumor microenvironment, Bone neoplasms + - after: Cancer fibroblasts, Fibroblasts protagonists, Protagonists tumor + +## base_cancer_research mode 0 + +- cluster 1: + - before: Cancer screening, Global cancer statistics, Epidemiology + - after: Cancer screening, Epidemiology, Global cancer statistics +- cluster 5: + - before: Challenges opportunities, Computational immuno, Immuno oncology + - after: Challenges and opportunities, Challenges opportunities, Computational immuno +- cluster 10: + - before: Cervix uteri, AIDS disease, Human cancer + - after: Cervix uteri, Human cancer, Biomolecular therapeutics +- cluster 11: + - before: Cancer epidemiological, Clinical concepts, Epidemiological clinical + - after: Konsep Epidemiologis, Penelitian Kanker +- cluster 14: + - before: Fibroblasts protagonists, Tumor microenvironment, Bone neoplasms + - after: Cancer fibroblasts, Fibroblasts protagonists, Protagonists tumor + +## base_digital_education mode 0 + +- cluster 1: + - before: MSc (digital education), Digital Uzbekistan e-learning platforms, Students + - after: Digital educational environment, E-learning, Students +- cluster 7: + - before: Diversified education, Academic Advising, Accessibility + - after: Diversified education, Academic Advising, Adult and continuing education administration +- cluster 10: + - before: Engineering education, Digital leadership, Digital platforms + - after: Engineering education, Digital leadership, Maritime education faculty +- cluster 14: + - before: Digital tablet, Éducation inclusive, Éducation numérique + - after: Éducation numérique, Digital tablet, Éducation inclusive +- cluster 15: + - before: Ethical considerations, Global legal practices, Legal Tech education + - after: Legal Tech education, Ethical considerations, Global legal practices + +## openaire_fight_ncov mode 0 + +- cluster 2: + - before: Assessment and dose-efficacy, Polymers preclinical assessment, 3. good health + - after: Assessment and dose-efficacy, Preclinical assessment, 3. good health +- cluster 4: + - before: Sars-cov-2 variants, Adult, Neutralizing antibody responses + - after: Adult, Sars-cov-2 variants b.1.351, Neutralizing antibody responses +- cluster 5: + - before: Activity month 12-18, Airway epithelia months, Antiviral activity month + - after: Airway epithelia, Determine tropism, Month 12-18 + +## orcid_0204881x mode 0 + +- cluster 1: + - before: Agonistic design, Constructive disagreement, Neurodiverse children + - after: Agonistic design, Constructive disagreement, Critical Realist +- cluster 2: + - before: Values-led participatory design, Autistic children, Science & technology + - after: Values-led participatory design, Autistic children, Cybernetics +- cluster 3: + - before: Computational empowerment, Field grow, Formal education + - after: Computational empowerment, Child-AI Entanglements, Exploring Child-AI +- cluster 4: + - before: Supporting children, Children social, Computing design + - after: Children social, Computing design, Creative modes +- cluster 5: + - before: More-than-human, HCI, Configure decision + - after: More-than-human, HCI, Annan teknik +- cluster 8: + - before: Verhandlung Technologischer Zukünfte + - after: Technologischer Zukünfte, Verhandlung Technologischer +- cluster 9: + - before: Dementia care, Care Materializing, Designing robotic + - after: Dementia care, Designing robotic, Robotic technology + +## orcid_02979614 mode 0 + +- cluster 1: + - before: Attacking rate, Beast Attacking, Network security + - after: Attacking rate, Address shuffling, Beast Attacking +- cluster 4: + - before: Cloudoscopy Services, Coordinated load, Grid shock + - after: Coordinated load, Grid shock, Topology mapping + +## orcid_22336926 mode 0 + +- cluster 2: + - before: Human sensing, Emotion, Mixed methods approach + - after: Emotion, Mixed methods approach, Walkability +- cluster 7: + - before: Physiological wearable sensors, Moments of stress, Stress detection + - after: Moments of stress, Stress detection, Physiological wearable sensors +- cluster 9: + - before: Naher Echtzeit, Data collection, Analysis flood + - after: Data collection, Naher Echtzeit, Analysis flood +- cluster 10: + - before: Bicycle safety, Emotion sensing, Urban emotions benefits + - after: Bicycle safety, Emotion sensing, Urban emotions +- cluster 13: + - before: Public and environmental health, Algorithms, Wearable electronic devices + - after: Wearable electronic devices, Environmental health, Female +- cluster 17: + - before: Leveraging standardized, Standardized near real-time, Integrated geo-sensing + - after: Leveraging standardized, Integrated geo-sensing, Mobile phone usage + +## orcid_24414043 mode 0 + +- cluster 1: + - before: Accountability and oversight, Intelligence, Political science + - after: Accountability, Intelligence, Oversight +- cluster 2: + - before: Ams algorithmus, Bedeutet Freiheit, Epistemologie und Funktionslogik + - after: Epistemologie und Funktionslogik, Funktionslogik von Cyber +- cluster 3: + - before: Data science, Auditing intermediaries, Content + - after: Data science, Auditing intermediaries, Data set +- cluster 4: + - before: Digital services, Google maps, Cultural sovereignty + - after: Google maps, Comparing travel, Cultural sovereignty +- cluster 5: + - before: Ethics global perspectives, Governance and ethics, Algorithms + - after: Artificial intelligence, Escape from regulation, Ethics-washing to ethics-shopping +- cluster 6: + - before: International relations theory + - after: Relations theory, International relations +- cluster 7: + - before: Technologies human rights, Digital technology, Handbook human + - after: Human rights, Digital technology, Digital technologies +- cluster 11: + - before: Surveillance technology, Export controls, International relations + - after: Surveillance technology, Export controls, Global governance +- cluster 14: + - before: European foreign policy, Authority understanding internet, Governance and Nodal + - after: Understanding internet Exchange, European foreign policy, Governance and Nodal + +## orcid_28976075 mode 0 + +- cluster 2: + - before: Argumentation quality assessment, Argument generation, Argumentation knowledge + - after: Argument generation, Argumentation knowledge, Quality assessment +- cluster 6: + - before: Machine learning, Behaviour change interventions, Clinical trial + - after: Clinical trial, Extracting factual, Machine learning +- cluster 7: + - before: Analysis of high-agreement, Content Units, Haystack an analysis + - after: Analysis of high-agreement, Content Units, MTurk for summarization +- cluster 13: + - before: Discourse structure + - after: Discourse Structure-Based framework, Framework for science, Science Journalism +- cluster 15: + - before: Question answering, Objective approach, Anaphora resolution + - after: Question answering, Discourse context, Objective approach + +## orcid_39246636 mode 0 + +- cluster 1: + - before: Estimation drivers, Job displacement + - after: Job displacement, Skill mismatch + +## orcid_42216275 mode 0 + +- cluster 2: + - before: Neuropsychiatry, Deep learning, Differential diagnosis + - after: Observational learning, Neuropsychiatry, Machine learning +- cluster 8: + - before: Hemisphere language + - after: Hemisphere in language, Hemisphere language, Language is Executive +- cluster 13: + - before: EEG, Electrocardiography, Electroencephalography + - after: Brain-computer interface, EEG, Electrocardiography +- cluster 14: + - before: Layer-5 pyramidal neurons, Bursting accelerates STDP, Acetylcholine and noradrenaline + - after: Layer-5 pyramidal neurons, Neuromodulation in layer-5 +- cluster 15: + - before: Predictive processing theorizes, OpenScope program, Address these conflicts + - after: Collaborative community experiment, Mechanisms of predictive, OpenScope program + +## orcid_45050517 mode 0 + +- cluster 1: + - before: Social and information networks, Data analysis, Statistics and probability + - after: Data analysis, Social and information networks, Phase transitions and critical phenomena +- cluster 4: + - before: Humans, Medicine, Statistical models + - after: Humans, Medicine, Phase transition +- cluster 6: + - before: Geophysics + - after: Aftershock sequences, Geophysics, Spatiotemporal correlations +- cluster 7: + - before: Social and information networks, Machine Learning, Physics and Society + - after: Social and information networks, Physics and Society, Machine Learning +- cluster 9: + - before: Biological physics, Disordered systems and neural networks, Random boolean networks + - after: Biological physics, Random boolean networks, Disordered systems and neural networks +- cluster 10: + - before: Bayes theorem, Markov Chains + - after: Bayes theorem, Markov Chains, Temporal networks + +## orcid_49712944 mode 0 + +- cluster 1: + - before: Dietary recommendations, Digital receipts, Adult + - after: Adult, Algorithms, Counseling +- cluster 3: + - before: Cerebral cortex, Intensities in t2, T2 MRI sequences + - after: Cerebral cortex, Intensities in t2/mri, T2/mri sequences +- cluster 13: + - before: Customization Conceptualizing, Informational privacy, Behaviors online + - after: Behaviors online, Smart Products + +## orcid_5116955x_r2 mode 0 + +- cluster 2: + - before: Stochastic processes, Zero-determinant strategies, Biological evolution + - after: Stochastic processes, Zero-determinant strategies, Evolutionary game theory +- cluster 5: + - before: Prisoner dilemma, Science, Game theory + - after: Science, Prisoner dilemma, Game theory +- cluster 8: + - before: Indirect reciprocity, Behavioral Neuroscience, Experimental and cognitive Psychology + - after: Indirect reciprocity, Social dilemmas +- cluster 11: + - before: Building capacity, Cooperation investments, Collective cooperative + - after: Building capacity, Collective cooperative, Cooperation investments +- cluster 12: + - before: Coercion, Alliances, Applied Mathematics + - after: Coercion, Prisoner's dilemma, Alliances +- cluster 15: + - before: Pool punishment, Democracy, Germany + - after: Pool punishment, Tragedy of the commons, Social behavior + +## orcid_5116955x mode 0 + +- cluster 12: + - before: Building capacity, Cooperation investments, Investments Building + - after: Building capacity, Cooperation investments, Ensure cooperation +- cluster 13: + - before: Coercion, Alliances, Applied Mathematics + - after: Coercion, Prisoner's dilemma, Alliances +- cluster 15: + - before: Pool punishment, Democracy, Germany + - after: Pool punishment, Tragedy of the commons, Social behavior + +## orcid_58498137 mode 0 + +- cluster 2: + - before: Valuation languages, Business & economics, Consistent criticism + - after: Valuation languages, Consistent criticism, Cost–benefit analysis + +## orcid_60114382 mode 0 + +- cluster 1: + - before: Hochschulen ein Werkstattbericht, Kommunikationstool Wissensmanagement-Potenziale, Lehre an Hochschulen + - after: Lehre an Hochschulen, Transformation der Lehre +- cluster 6: + - before: Social semantic server, Learning network, Concepts practices + - after: Social semantic server, Learning network, Recommender approach +- cluster 9: + - before: Field experiment, Tracing knowledge, Barcamp + - after: Field experiment, Tracing knowledge + +## orcid_89117832 mode 0 + +- cluster 3: + - before: Artificial Intelligence, Automated review, Automation + - after: Mental health, Scoping review, Artificial Intelligence +- cluster 4: + - before: Assistant Prototype, Consent Assistant, Persuasive XAI + - after: Persuasive XAI, Awareness Persuasive, Compliance Awareness + +## orcid_90626039 mode 0 + +- cluster 3: + - before: Entanglement witnesses, Mirrored entanglement, Medicine + - after: Entanglement witnesses, Medicine, Mirrored entanglement +- cluster 6: + - before: Medical physics, Positron-emission tomography, Instrumentation and detectors + - after: Medical physics, Instrumentation and detectors, Positron-emission tomography + +## orcid_92873770 mode 0 + +- cluster 1: + - before: Human-centered explainable, Explainable ai HCXAI + - after: Human-centered explainable +- cluster 4: + - before: Visual sampling behavior, Data driven, Agent Teams + - after: Visual sampling behavior, Data driven, Automotive Wizard +- cluster 8: + - before: Assistants accountability, Autonomous vehicles, Explanatory dialogues + - after: Autonomous vehicles, Explanatory dialogues, User acceptance +- cluster 9: + - before: Dynamic monitoring, Management systems, Attention management + - after: Dynamic monitoring tasks, Attention management systems, Evaluating attention management +- cluster 11: + - before: Automated driving, Driving hotzenplotz, Vehicle control + - after: Automated driving, Automated vehicles, Vehicle control +- cluster 13: + - before: Automated driving, Augmentation concepts, Chatbots Waiting + - after: Automated driving, Augmentation concepts, Intelligent user + +## orcid_96127791_r2 mode 0 + +- cluster 3: + - before: Venous thromboembolism, Venous thrombosis, COVID cardiovascular diseases + - after: Venous thromboembolism, Venous thrombosis, Stroke +- cluster 4: + - before: Covid-19 vaccination, SARs-CoV-2 infections COVID-RED, COVID adolescent + - after: Covid-19 vaccination, SARs-CoV-2 infections, COVID adolescent +- cluster 7: + - before: Early-stage clinical evaluation, Artificial intelligence DECIDE-AI, Clinical decision support systems + - after: Early-stage clinical evaluation, Artificial intelligence, Clinical decision support systems +- cluster 11: + - before: Challenges overview, Biomedical ontologies, Data opportunities + - after: Biomedical big data, Biomedical ontologies, Challenges overview +- cluster 13: + - before: Population-scale linked data, Temporal sequencing, 20-year follow-up + - after: Population-scale linked data, Temporal sequencing, 1.7 million individuals +- cluster 14: + - before: Association studies + - after: Genome-wide association studies, Positive Unlabelled learning, Learning for genome-wide +- cluster 17: + - before: National electronic, Health records, Electronic health + - after: National electronic health, Predicting excess deaths, Model for predicting + +## orcid_96127791 mode 0 + +- cluster 1: + - before: Phenotyping algorithms, Common data model, COVID electronic health records + - after: Phenotyping algorithms, Common data model, Data harmonisation +- cluster 4: + - before: Cohort studies, Retrospective studies, Late-onset epilepsy + - after: Cohort studies, Unsupervised machine learning, Humans +- cluster 5: + - before: Artificial intelligence DECIDE-AI, Early-stage clinical evaluation, Clinical decision support systems + - after: Early-stage clinical evaluation, Artificial intelligence, Clinical decision support systems +- cluster 8: + - before: Covid-19 vaccination, SARs-CoV-2 infections COVID-RED, Daily symptom diary + - after: Covid-19 vaccination, SARs-CoV-2 infections, COVID adolescent +- cluster 12: + - before: Challenges overview, Biomedical ontologies, Data opportunities + - after: Biomedical big data, Biomedical ontologies, Challenges overview +- cluster 14: + - before: Population-scale linked data, Temporal sequencing, 20-year follow-up + - after: Population-scale linked data, Temporal sequencing, 1.7 million individuals +- cluster 15: + - before: Association studies + - after: Genome-wide association studies, Positive Unlabelled learning, Learning for genome-wide +- cluster 17: + - before: National electronic, Health records, Electronic health + - after: National electronic health, Predicting excess deaths, Model for predicting + +## orcid_98436798 mode 0 + +- cluster 9: + - before: Library for deep, Short-term memory networks, Near-real-time streamflow observations + - after: Short-term memory networks, Near-real-time streamflow observations, Python library +- cluster 16: + - before: Flow routing processes, Involving active student, Demonstrating the unit + - after: Flow routing processes, Involving active student, University lecture experiment + +## pubmed_cancer_datasets mode 0 + +- cluster 4: + - before: Database Poland, Dermoscopic lesions, Glioblastoma dataset + - after: Glioblastoma dataset, Histological Hyperspectral, Images extracted +- cluster 15: + - before: Biomarker discovery, Cancer biomarker, DNA G-quadruplex + - after: Biomarker discovery, Cancer biomarker, Biomuta bioxpress + +## pubmed_climate_change_all mode 0 + +- cluster 2: + - before: Breeding crops, Climate resilience, Gendered vulnerability + - after: Gendered vulnerability, Global climate, Asthma +- cluster 3: + - before: China, Climate change impacts, Consensus + - after: China, Climate change impacts, Crop yield +- cluster 4: + - before: Environmental health, Climate conditions, Children + - after: Climate conditions, Environmental health, Meta analysis +- cluster 7: + - before: Climate security, Dengue transmission, Malaria + - after: Climate security, Rigorous understanding, Avian demography +- cluster 9: + - before: Climate change mitigation, Denial, Public understanding of science + - after: Climate change mitigation, Systematic review +- cluster 11: + - before: Carbon feedback, Meta-analysis, Community pharmacist + - after: Carbon feedback, Community pharmacist, Geo-evolutionary feedbacks + +## pubmed_disease_all mode 0 + +- cluster 4: + - before: Acute dialysis, Historical overview, Jaws revisited + - after: Acute dialysis, Cardio renal, Cardiovascular entity +- cluster 7: + - before: Eponym Kostmann, Genetic disease, Kostmann disease + - after: Genetic disease, Conversation with Kurt, Elizabeth McNally +- cluster 10: + - before: Blood glucose, Certification Influences, Expander pupil + - after: Blood glucose, Certification Influences, Crossover study +- cluster 11: + - before: Analyses human, Babesiosis clinical, Definition diseases + - after: Analyses human, Definition diseases, Disease relationships +- cluster 12: + - before: Admiral Boerhaave-van Wassenaer, Boerhaave syndrome, Boerhaave Wassenaer + - after: Admiral Boerhaave, Boerhaave syndrome, Boerhaave Wassenaer +- cluster 15: + - before: Chronic coronary, Coronary syndromes + - after: Chronic coronary syndromes, Patients with chronic, Vericiguat in combination + +## pubmed_disease mode 0 + +- cluster 2: + - before: ASD schizophrenia, Bipolar disorder, Colonic tissue + - after: Bipolar disorder, Colonic tissue, Diabetic donors +- cluster 3: + - before: Alzheimer disease, ATN biomarkers, Castillian Spanish + - after: Alzheimer disease, Alzheimer's disease, ATN biomarkers +- cluster 5: + - before: China data, Comprehensive dataset, Dementia mortality + - after: Comprehensive dataset, Dementia mortality, Disease outbreaks +- cluster 6: + - before: Parkinson disease, Parkinson s disease, Multimodal dataset + - after: Parkinson disease, Parkinson's disease, Multimodal dataset +- cluster 9: + - before: Blood transcriptome, Data illuminating, Human virus + - after: Blood transcriptome, Transcriptome dataset, Viral infection +- cluster 12: + - before: Access dataset, Arboviral disease, Chikungunya Brazil + - after: Access dataset, Arboviral disease, Count data +- cluster 15: + - before: Asiaticus strains, Candidatus Liberibacter, Genome assembly + - after: Candidatus Liberibacter, Genome assembly, Genome dataset + +## pubmed_infection_all mode 0 + +- cluster 1: + - before: Urinary tract infection, Joint infection, Randomised controlled + - after: Urinary tract infection, Joint infection, 14 days +- cluster 2: + - before: Randomised controlled, Complicated urinary tract infection, Infections caused + - after: Complicated urinary tract infection, Infections caused, Randomised controlled +- cluster 3: + - before: Rifampin-resistant tuberculosis, Month rifapentine, Pragmatic trial + - after: Month rifapentine, Rifampin-resistant tuberculosis, Moxifloxacin tuberculosis +- cluster 6: + - before: Hiv infected, Africa HPTN, Congenital cytomegalovirus + - after: Hiv infected, Congenital cytomegalovirus, Hiv perinatal +- cluster 8: + - before: Autobiography, Clinical practice Update, Infection expert review + - after: Clinical practice Update, Expert review, Helicobacter pylori infection +- cluster 11: + - before: Plasmodium falciparum, Typhoid conjugate vaccine, Acquired immunodeficiency syndrome + - after: Typhoid conjugate vaccine, Acquired immunodeficiency syndrome, Africa South of the Sahara +- cluster 12: + - before: COVID-19, Complement, SARS-CoV-2 + - after: COVID-19, Complement, Randomized controlled trial + +## pubmed_infection_datasets mode 0 + +- cluster 2: + - before: Sequencing dataset, Data Lancaster, Deep sequencing + - after: Sequencing dataset, Deep sequencing, Genetic variation +- cluster 4: + - before: Sub-Saharan Africa, China data, Comprehensive database + - after: Sub-Saharan Africa, Comprehensive database, Disease outbreaks +- cluster 8: + - before: Blood cells, Cellular immune, Death molecular + - after: Blood cells, Cellular immune, Sequencing dataset +- cluster 9: + - before: Access dataset, Arboviral disease, Chikungunya Brazil + - after: Access dataset, Arboviral disease, Count data + +## pubmed_infection mode 0 + +- cluster 1: + - before: Urinary tract infection, Joint infection, Randomised controlled + - after: Urinary tract infection, Joint infection, 14 days +- cluster 3: + - before: Rifampin-resistant tuberculosis, Month rifapentine, Pragmatic trial + - after: Month rifapentine, Rifampin-resistant tuberculosis, Moxifloxacin tuberculosis +- cluster 6: + - before: Hiv infected, Africa HPTN, Congenital cytomegalovirus + - after: Hiv infected, Congenital cytomegalovirus, Hiv perinatal +- cluster 8: + - before: Autobiography, Clinical practice Update, Infection expert review + - after: Clinical practice Update, Expert review, Helicobacter pylori infection +- cluster 12: + - before: Plasmodium falciparum, Typhoid conjugate vaccine, Acquired immunodeficiency syndrome + - after: Typhoid conjugate vaccine, Acquired immunodeficiency syndrome, Africa South of the Sahara + +## pubmed_space_travel_all mode 0 + +- cluster 1: + - before: Mitochondria, Oxidative stress, Aerospace medicine + - after: Mitochondria, Oxidative stress, Effects spaceflight +- cluster 2: + - before: Disrupts murine, Hypergravity disrupts, Intestinal microbiota + - after: Weightlessness, Effects of microgravity, Boyle +- cluster 4: + - before: Human health, Space flight, Space medicine + - after: Human health, Biological effects, Motion sickness +- cluster 5: + - before: Earth benefits, Space exercise, Systematic review + - after: Systematic review, Microgravity, Bacterial infections +- cluster 9: + - before: Acid-base balance, Biochemistry spaceflight, Bone mineral density + - after: Biochemistry spaceflight, NASA Discipline Regulatory physiology, Nutritional biochemistry +- cluster 10: + - before: Stanley white + - after: Stanley white, Flight surgeons +- cluster 12: + - before: Behavioral implications, Radiation behavioral, Reproductive hazards + - after: Reproductive hazards, Dosage forms, Fluid shift +- cluster 14: + - before: Cellular organisms, Engineering human, Duration spaceflight + - after: Engineering human, Duration spaceflight, Space exploration + +## pubmed_space_travel mode 0 + +- cluster 1: + - before: Analysis China, Animal behavior, Brain organoids + - after: Brain organoids, Ground conditions, Analysis China +- cluster 3: + - before: Color satellite, Development phytoplankton, Fingerprint libraries + - after: Color satellite, Fingerprint libraries, Development phytoplankton + +## pubmed_species_all mode 0 + +- cluster 2: + - before: Cutaneous leishmaniasis, Amoxicillin clavulanate, Clostridioides difficile + - after: Cutaneous leishmaniasis, Clostridioides difficile, Difficile infection +- cluster 3: + - before: Christian Rudolph, Francis Galton, III Christian + - after: Francis Galton, Obstetrical hemorrhage, Birth eugenics +- cluster 4: + - before: Alzheimer's disease, Alzheimer s disease, Active immunotherapy + - after: Alzheimer's disease, Active immunotherapy, ACU193 +- cluster 5: + - before: Anhydrase Thomas, Approach carbonic, Avian cardiac + - after: Avian cardiac, Cardiac physiologist, Charcot paradox +- cluster 7: + - before: Randomized clinical trial, Cardiac arrest, Keratitis clinical + - after: Randomized clinical trial, Cardiac arrest, Adenoviral keratoconjunctivitis +- cluster 8: + - before: Charles Darwin, Darwin scientist, Entomological reactions + - after: Charles Darwin, Darwin 1809-2009, Entomological reactions +- cluster 10: + - before: Centennial review, Comet assay, Corneal transplantation + - after: Centennial review, Corneal transplantation, Classics revisited +- cluster 11: + - before: Darwin and Haeckel, Hermann Joseph, Joseph Muller + - after: Population genetics, Darwin and Haeckel, Hermann Joseph +- cluster 14: + - before: Genome evolution, Microarray design, Oligo microarray + - after: Genome evolution, Epigenetics, Microarray design +- cluster 15: + - before: Disease food, Disease risk, Disturbance increase + - after: Disease risk, Disturbance increase, Insects environmental + +## pubmed_species mode 0 + +- cluster 6: + - before: Functional traits, African bats, Amphipoda Niphargidae + - after: Functional traits, African bats, Ecological traits +- cluster 7: + - before: Camera traps, Artificial intelligence, Bipartite networks + - after: Bipartite networks, Camera traps, Conservation +- cluster 8: + - before: Common vampire, Distribution maps, Habitat maps + - after: Aerial surveys, Common vampire, Distribution maps +- cluster 13: + - before: Herbal species, Acoustic communication, Database Vietnamese + - after: Herbal species, Microbial volatiles, Acoustic communication + +## pubmed_vienna_all mode 0 + +- cluster 7: + - before: Randomised controlled trial, Meropenem treatment, Randomised phase + - after: Meropenem treatment, Randomised controlled trial, Chronic lymphocytic +- cluster 15: + - before: Carl Toldt, Historical Contribution, Lipochrome + - after: Carl Toldt, Historical Contribution, Obstetric forceps + +## pubmed_vienna mode 0 + +- cluster 3: + - before: Catalog genome, Genetic variation, Humans primates + - after: Genetic variation, Primate species, Catalog genome +- cluster 5: + - before: Access dataset, Citizen science, Collaboration dependencies + - after: Access dataset, Citizen science, Collaboration networks +- cluster 7: + - before: Cancer Histology, Canine cutaneous, CATCH dataset + - after: Cancer Histology, Canine cutaneous, Characterization data +- cluster 10: + - before: Alder birch, Aragwas catalog, Brain tumour + - after: Aragwas catalog, Brain tumour, DHS countries +- cluster 14: + - before: Genome assembly, Bobtail squid, Cultivar Désirée + - after: Genome assembly, Bobtail squid, Euprymna scolopes + +## pubmed_work_all mode 0 + +- cluster 2: + - before: Care nurse, Nurses perceptions, Patient satisfaction + - after: Care nurse, Patient satisfaction, Stress management +- cluster 4: + - before: Personal profile, Profile interview, Allain Pioneer + - after: Personal profile, Alexandra Stolzing, Annelies Allain +- cluster 7: + - before: Patient safety, Hour reduction, Mortality risk + - after: Patient safety, Hour reduction, Resident physician +- cluster 10: + - before: Health study, Social engagement intervention, Randomized controlled + - after: Health study, Randomized controlled, Quality of life +- cluster 11: + - before: Cardiac demands, Community variations, Display units + - after: Display units, Ergonomic intervention, Health outcomes +- cluster 13: + - before: Clinical practice guidelines, Infographic returning, Jospt Infographic + - after: Clinical practice guidelines, Workers +- cluster 14: + - before: Coronary heart disease, Systematic review, Disease systematic + - after: Coronary heart disease, Systematic review, Review and meta-analysis + +## pubmed_work mode 0 + +- cluster 3: + - before: Dataset image, Banana leaves, Fundus MSHF + - after: Dataset image, Banana leaves, Glioblastoma dataset +- cluster 5: + - before: Arboviral disease, Breast cancers, Building Rooftops + - after: Breast cancers, Building Rooftops, Depression dataset +- cluster 7: + - before: Bird species, Expression dataset, Extended Lombardy + - after: Expression dataset, Facial expression, Reference dataset +- cluster 8: + - before: Accelerometer magnetometer, Advanced melanoma, Atmospheric moisture + - after: Advanced melanoma, Atmospheric moisture, Blood pressure +- cluster 11: + - before: Dementia mortality, Healthcare monitoring, Heat acclimation + - after: Dementia mortality, Heat acclimation, Human heat +- cluster 13: + - before: Curated plant, Elemental crystals, Peptide database + - after: Curated plant, Peptide database, Elemental crystals + diff --git a/server/preprocessing/other-scripts/test/replay/punctuation_rebaseline_diff.md b/server/preprocessing/other-scripts/test/replay/punctuation_rebaseline_diff.md new file mode 100644 index 000000000..1b21586fc --- /dev/null +++ b/server/preprocessing/other-scripts/test/replay/punctuation_rebaseline_diff.md @@ -0,0 +1,464 @@ +# Replay label diff: punctuation segmentation + +Before = committed expected labels; after = labels under the +spacing-rule segmentation. Only changed clusters listed. + +## orcid_0204881x mode 1 + +- cluster 1: + - before: Assistance intervention, Autistic children, Autism and technology + - after: Autistic children, Autism and technology, Critical Realist +- cluster 4: + - before: Supporting children, Social communication, Children social + - after: Social communication, Children social, Supporting children's +- cluster 7: + - before: Auditory interfaces, Developing technology, Interdisciplinary approach + - after: Auditory interfaces, Developing technology, Awareness + +## orcid_0204881x mode 2 + +- cluster 1: + - before: Assistance intervention, Autistic children, Autism and technology + - after: Autistic children, Autism and technology, Critical Realist +- cluster 4: + - before: Supporting children, Social communication, Children social + - after: Social communication, Children social, Supporting children's +- cluster 7: + - before: Auditory interfaces, Developing technology, Interdisciplinary approach + - after: Auditory interfaces, Developing technology, Awareness + +## orcid_0204881x mode 3 + +- cluster 1: + - before: Assistance intervention, Autistic children, Autism and technology + - after: Autistic children, Autism and technology, Critical Realist +- cluster 4: + - before: Supporting children, Social communication, Children social + - after: Social communication, Children social, Supporting children's +- cluster 7: + - before: Auditory interfaces, Developing technology, Interdisciplinary approach + - after: Auditory interfaces, Developing technology, Awareness + +## orcid_02979614 mode 1 + +- cluster 4: + - before: Power grids, Attacks destabilize, Attacks power + - after: Attacks destabilize, Attacks power, Barcode attacks + +## orcid_02979614 mode 2 + +- cluster 4: + - before: Power grids, Attacks destabilize, Attacks power + - after: Attacks destabilize, Attacks power, Barcode attacks + +## orcid_02979614 mode 3 + +- cluster 4: + - before: Power grids, Attacks destabilize, Attacks power + - after: Attacks destabilize, Attacks power, Barcode attacks + +## orcid_22336926 mode 1 + +- cluster 6: + - before: Virtual reality, Urban planning, Field experiment + - after: Urban planning, Virtual reality, Field experiment +- cluster 14: + - before: Urban emotions, Basis von Echtzeit-Humansensorik, Emotions - Kontextuelle + - after: Urban emotions, Basis von Echtzeit-Humansensorik, Kontextuelle Emotionsinformationen +- cluster 17: + - before: Leveraging standardized, Northern Italy, Standardized near real-time + - after: Leveraging standardized, Northern Italy, Mobile phone + +## orcid_22336926 mode 2 + +- cluster 6: + - before: Virtual reality, Urban planning, Field experiment + - after: Urban planning, Virtual reality, Field experiment +- cluster 14: + - before: Urban emotions, Basis von Echtzeit-Humansensorik, Emotions - Kontextuelle + - after: Urban emotions, Basis von Echtzeit-Humansensorik, Kontextuelle Emotionsinformationen +- cluster 17: + - before: Leveraging standardized, Northern Italy, Standardized near real-time + - after: Leveraging standardized, Northern Italy, Mobile phone + +## orcid_22336926 mode 3 + +- cluster 6: + - before: Virtual reality, Urban planning, Field experiment + - after: Urban planning, Virtual reality, Field experiment +- cluster 14: + - before: Urban emotions, Basis von Echtzeit-Humansensorik, Emotions - Kontextuelle + - after: Urban emotions, Basis von Echtzeit-Humansensorik, Kontextuelle Emotionsinformationen +- cluster 17: + - before: Leveraging standardized, Northern Italy, Standardized near real-time + - after: Leveraging standardized, Northern Italy, Mobile phone + +## orcid_24414043 mode 0 + +- cluster 6: + - before: Constructed Cyber Realities, Cyber Realities international, International relations theory + - after: International relations theory +- cluster 13: + - before: Contention the politics, Digital contention, Everyday life + - after: Digital contention, Everyday life, Life in Tunisia + +## orcid_24414043 mode 1 + +- cluster 2: + - before: Cyber Epistemologie, Epistemologie und Funktionslogik, Funktionslogik von Cyber + - after: Epistemologie und Funktionslogik, Funktionslogik von Cyber, Ams algorithmus +- cluster 6: + - before: Constructed Cyber Realities, Cyber Realities international, International relations theory + - after: International relations theory +- cluster 13: + - before: Contention the politics, Digital contention, Everyday life + - after: Digital contention, Everyday life, Life in Tunisia +- cluster 14: + - before: European foreign policy, Authority understanding internet, Governance and Nodal + - after: European foreign policy, Understanding internet Exchange, Governance and Nodal + +## orcid_24414043 mode 2 + +- cluster 2: + - before: Cyber Epistemologie, Epistemologie und Funktionslogik, Funktionslogik von Cyber + - after: Epistemologie und Funktionslogik, Funktionslogik von Cyber, Ams algorithmus +- cluster 6: + - before: Constructed Cyber Realities, Cyber Realities international, International relations theory + - after: International relations theory +- cluster 13: + - before: Contention the politics, Digital contention, Everyday life + - after: Digital contention, Everyday life, Life in Tunisia +- cluster 14: + - before: European foreign policy, Authority understanding internet, Governance and Nodal + - after: European foreign policy, Understanding internet Exchange, Governance and Nodal + +## orcid_24414043 mode 3 + +- cluster 2: + - before: Cyber Epistemologie, Epistemologie und Funktionslogik, Funktionslogik von Cyber + - after: Epistemologie und Funktionslogik, Funktionslogik von Cyber, Ams algorithmus +- cluster 6: + - before: Constructed Cyber Realities, Cyber Realities international, International relations theory + - after: International relations theory +- cluster 13: + - before: Contention the politics, Digital contention, Everyday life + - after: Digital contention, Everyday life, Life in Tunisia +- cluster 14: + - before: European foreign policy, Authority understanding internet, Governance and Nodal + - after: European foreign policy, Understanding internet Exchange, Governance and Nodal + +## orcid_28976075 mode 1 + +- cluster 2: + - before: Quality assessment, Tasks datasets, Argumentation knowledge + - after: Quality assessment, Argumentation knowledge, Fine-grained information status + +## orcid_28976075 mode 2 + +- cluster 2: + - before: Quality assessment, Tasks datasets, Argumentation knowledge + - after: Quality assessment, Argumentation knowledge, Fine-grained information status + +## orcid_28976075 mode 3 + +- cluster 2: + - before: Quality assessment, Tasks datasets, Argumentation knowledge + - after: Quality assessment, Argumentation knowledge, Fine-grained information status + +## orcid_42216275 mode 0 + +- cluster 14: + - before: Layer-5 pyramidal neurons, Accelerates STDP supporting, Acetylcholine and noradrenaline + - after: Layer-5 pyramidal neurons, Bursting accelerates STDP, Acetylcholine and noradrenaline +- cluster 15: + - before: Cortex predictive processing, OpenScope program, Adaptation dendritic computation + - after: Predictive processing theorizes, OpenScope program, Address these conflicts + +## orcid_49712944 mode 1 + +- cluster 3: + - before: T2 MRI + - after: Cerebral cortex, Intensities in t2/mri, T2/mri sequences + +## orcid_49712944 mode 2 + +- cluster 3: + - before: T2 MRI + - after: Cerebral cortex, Intensities in t2/mri, T2/mri sequences + +## orcid_49712944 mode 3 + +- cluster 3: + - before: T2 MRI + - after: Cerebral cortex, Intensities in t2/mri, T2/mri sequences + +## orcid_5116955x_r2 mode 1 + +- cluster 12: + - before: Coercion, Alliances, Applied Mathematics + - after: Coercion, Prisoner's dilemma, Alliances +- cluster 14: + - before: Leadership a Rationality-Based, Rationality-Based approach, Reconciling different views + - after: Rationality-Based approach, Reconciling different views, Responsible leadership + +## orcid_5116955x_r2 mode 2 + +- cluster 12: + - before: Coercion, Alliances, Applied Mathematics + - after: Coercion, Prisoner's dilemma, Alliances +- cluster 14: + - before: Leadership a Rationality-Based, Rationality-Based approach, Reconciling different views + - after: Rationality-Based approach, Reconciling different views, Responsible leadership + +## orcid_5116955x_r2 mode 3 + +- cluster 12: + - before: Alliances, Applied Mathematics, Social behavior + - after: Prisoner's dilemma, Alliances, Applied Mathematics +- cluster 14: + - before: Leadership a Rationality-Based, Rationality-Based approach, Reconciling different views + - after: Rationality-Based approach, Reconciling different views, Responsible leadership + +## orcid_5116955x mode 1 + +- cluster 13: + - before: Coercion, Alliances, Applied Mathematics + - after: Coercion, Prisoner's dilemma, Alliances +- cluster 14: + - before: Leadership a Rationality-Based, Rationality-Based approach, Reconciling different views + - after: Rationality-Based approach, Reconciling different views, Responsible leadership + +## orcid_5116955x mode 2 + +- cluster 13: + - before: Coercion, Alliances, Applied Mathematics + - after: Coercion, Prisoner's dilemma, Alliances +- cluster 14: + - before: Leadership a Rationality-Based, Rationality-Based approach, Reconciling different views + - after: Rationality-Based approach, Reconciling different views, Responsible leadership + +## orcid_5116955x mode 3 + +- cluster 13: + - before: Alliances, Applied Mathematics, Social behavior + - after: Prisoner's dilemma, Alliances, Applied Mathematics +- cluster 14: + - before: Leadership a Rationality-Based, Rationality-Based approach, Reconciling different views + - after: Rationality-Based approach, Reconciling different views, Responsible leadership + +## orcid_60114382 mode 1 + +- cluster 1: + - before: Digitale Transformation, Hochschulen ein Werkstattbericht, Kommunikationstool Wissensmanagement-Potenziale + - after: Digitale Transformation, Lehre an Hochschulen, Social software + +## orcid_60114382 mode 2 + +- cluster 1: + - before: Digitale Transformation, Hochschulen ein Werkstattbericht, Kommunikationstool Wissensmanagement-Potenziale + - after: Digitale Transformation, Lehre an Hochschulen, Social software + +## orcid_60114382 mode 3 + +- cluster 1: + - before: Digitale Transformation, Hochschulen ein Werkstattbericht, Kommunikationstool Wissensmanagement-Potenziale + - after: Digitale Transformation, Lehre an Hochschulen, Social software + +## orcid_92873770 mode 1 + +- cluster 1: + - before: Human-centered explainable, Explainable ai HCXAI + - after: Human-centered explainable +- cluster 13: + - before: Automated driving, User interfaces, Trust in automated + - after: Automated driving, User interfaces, Augmentation concepts + +## orcid_92873770 mode 2 + +- cluster 1: + - before: Human-centered explainable, Explainable ai HCXAI + - after: Human-centered explainable +- cluster 13: + - before: Automated driving, User interfaces, Trust in automated + - after: Automated driving, User interfaces, Augmentation concepts + +## orcid_92873770 mode 3 + +- cluster 1: + - before: Human-centered explainable, Explainable ai HCXAI + - after: Human-centered explainable +- cluster 13: + - before: Automated driving, User interfaces, Trust in automated + - after: Automated driving, User interfaces, Augmentation concepts + +## orcid_96127791_r2 mode 1 + +- cluster 15: + - before: ClustEHR a tool, Data for unsupervised, Generating synthetic + - after: Data for unsupervised, Generating synthetic, Unsupervised learning experiments +- cluster 17: + - before: Predicting excess deaths, Model for predicting, Pandemic preparedness validation + - after: Predicting excess deaths, Model for predicting, Pandemic preparedness + +## orcid_96127791_r2 mode 2 + +- cluster 15: + - before: ClustEHR a tool, Data for unsupervised, Generating synthetic + - after: Data for unsupervised, Generating synthetic, Unsupervised learning experiments +- cluster 17: + - before: Predicting excess deaths, Model for predicting, Pandemic preparedness validation + - after: Predicting excess deaths, Model for predicting, Pandemic preparedness + +## orcid_96127791_r2 mode 3 + +- cluster 15: + - before: ClustEHR a tool, Data for unsupervised, Generating synthetic + - after: Data for unsupervised, Generating synthetic, Unsupervised learning experiments +- cluster 17: + - before: Predicting excess deaths, Model for predicting, Pandemic preparedness validation + - after: Predicting excess deaths, Model for predicting, Pandemic preparedness + +## orcid_96127791 mode 1 + +- cluster 17: + - before: Predicting excess deaths, Model for predicting, Pandemic preparedness validation + - after: Predicting excess deaths, Model for predicting, Pandemic preparedness + +## orcid_96127791 mode 2 + +- cluster 17: + - before: Predicting excess deaths, Model for predicting, Pandemic preparedness validation + - after: Predicting excess deaths, Model for predicting, Pandemic preparedness + +## orcid_96127791 mode 3 + +- cluster 17: + - before: Predicting excess deaths, Model for predicting, Pandemic preparedness validation + - after: Predicting excess deaths, Model for predicting, Pandemic preparedness + +## pubmed_climate_change mode 1 + +- cluster 1: + - before: Global historical, Global monthly, Africa climate + - after: Global monthly, Africa climate, Belowground data +- cluster 12: + - before: Global historical, Chinese cities, Adaptation management + - after: Chinese cities, Adaptation management, Audit carbon + +## pubmed_climate_change mode 2 + +- cluster 1: + - before: Global historical, Global monthly, Africa climate + - after: Global monthly, Africa climate, Belowground data +- cluster 12: + - before: Global historical, Chinese cities, Adaptation management + - after: Chinese cities, Adaptation management, Audit carbon + +## pubmed_climate_change mode 3 + +- cluster 1: + - before: Global historical, Global monthly, Africa climate + - after: Global monthly, Africa climate, Belowground data +- cluster 12: + - before: Global historical, Chinese cities, Adaptation management + - after: Chinese cities, Adaptation management, Audit carbon + +## pubmed_disease mode 1 + +- cluster 3: + - before: Abnormal deviations, Alzheimer's disease, ATN biomarkers + - after: Alzheimer's disease, Abnormal deviations, ATN biomarkers +- cluster 6: + - before: Parkinson disease, Parkinson s disease, Multimodal dataset + - after: Parkinson disease, Parkinson's disease, Multimodal dataset + +## pubmed_disease mode 2 + +- cluster 3: + - before: Abnormal deviations, Alzheimer's disease, ATN biomarkers + - after: Alzheimer's disease, Abnormal deviations, ATN biomarkers +- cluster 6: + - before: Parkinson disease, Parkinson s disease, Multimodal dataset + - after: Parkinson disease, Parkinson's disease, Multimodal dataset + +## pubmed_disease mode 3 + +- cluster 3: + - before: Abnormal deviations, Alzheimer's disease, ATN biomarkers + - after: Alzheimer's disease, Abnormal deviations, ATN biomarkers +- cluster 6: + - before: Parkinson disease, Parkinson s disease, Multimodal dataset + - after: Parkinson disease, Parkinson's disease, Multimodal dataset + +## pubmed_infection_all mode 1 + +- cluster 9: + - before: Emtricitabine and tenofovir, Randomised double-blind multicentre, Tenofovir alafenamide + - after: Tenofovir alafenamide, Alafenamide vs emtricitabine, Tenofovir disoproxil fumarate + +## pubmed_infection_all mode 2 + +- cluster 9: + - before: Emtricitabine and tenofovir, Randomised double-blind multicentre, Tenofovir alafenamide + - after: Tenofovir alafenamide, Alafenamide vs emtricitabine, Tenofovir disoproxil fumarate + +## pubmed_infection_all mode 3 + +- cluster 9: + - before: Emtricitabine and tenofovir, Randomised double-blind multicentre, Tenofovir alafenamide + - after: Tenofovir alafenamide, Alafenamide vs emtricitabine, Tenofovir disoproxil fumarate + +## pubmed_infection mode 1 + +- cluster 9: + - before: Emtricitabine and tenofovir, Randomised double-blind multicentre, Tenofovir alafenamide + - after: Tenofovir alafenamide, Alafenamide vs emtricitabine, Tenofovir disoproxil fumarate + +## pubmed_infection mode 2 + +- cluster 9: + - before: Emtricitabine and tenofovir, Randomised double-blind multicentre, Tenofovir alafenamide + - after: Tenofovir alafenamide, Alafenamide vs emtricitabine, Tenofovir disoproxil fumarate + +## pubmed_infection mode 3 + +- cluster 9: + - before: Emtricitabine and tenofovir, Randomised double-blind multicentre, Tenofovir alafenamide + - after: Tenofovir alafenamide, Alafenamide vs emtricitabine, Tenofovir disoproxil fumarate + +## pubmed_vienna_all mode 1 + +- cluster 7: + - before: Randomised phase, Randomised controlled trial, Meropenem treatment + - after: Randomised controlled trial, Meropenem treatment, Chronic lymphocytic + +## pubmed_vienna_all mode 2 + +- cluster 7: + - before: Randomised phase, Randomised controlled trial, Meropenem treatment + - after: Randomised controlled trial, Meropenem treatment, Chronic lymphocytic + +## pubmed_vienna_all mode 3 + +- cluster 7: + - before: Randomised phase, Randomised controlled trial, Meropenem treatment + - after: Randomised controlled trial, Meropenem treatment, Chronic lymphocytic + +## pubmed_work_all mode 1 + +- cluster 5: + - before: Work-related medical rehabilitation, Return-to-work, Randomized controlled trial + - after: Randomized controlled trial, Work-related medical rehabilitation, Return-to-work + +## pubmed_work_all mode 2 + +- cluster 5: + - before: Work-related medical rehabilitation, Return-to-work, Randomized controlled trial + - after: Randomized controlled trial, Work-related medical rehabilitation, Return-to-work + +## pubmed_work_all mode 3 + +- cluster 5: + - before: Work-related medical rehabilitation, Return-to-work, Randomized controlled trial + - after: Randomized controlled trial, Work-related medical rehabilitation, Return-to-work + diff --git a/server/preprocessing/other-scripts/text_hygiene.R b/server/preprocessing/other-scripts/text_hygiene.R new file mode 100644 index 000000000..1d3111cef --- /dev/null +++ b/server/preprocessing/other-scripts/text_hygiene.R @@ -0,0 +1,171 @@ +# text_hygiene.R +# Corpus/text hygiene and punctuation-aware segmentation: HTML-entity decode, +# URL/noise stripping, MeSH-marker cleanup, and punctuation_segments — the +# shared segmentation every n-gram generation site builds on +# (docs/keyword-punctuation.md). Sourced by summarize.R. + + +# Strip leading and trailing whitespace from a string. +trim <- function (x) gsub("^\\s+|\\s+$", "", x) + + + +# Normalise a combined token string to the ";"-separated form the SplitTokenizer +# consumes: drop "?" artifacts, collapse repeated ";", trim spaces around ";", and +# turn any remaining whitespace into ";". This is the SINGLE source of truth for +# corpus tokenization, used both to build the corpus document and to derive the +# per-cluster rank sources — so a rank token can never drift out of the tf-idf term +# set (see get_cluster_corpus / ranking.R). +normalize_corpus_tokens <- function(s) { + s <- str_replace_all(s, "\\?+_\\?+|\\?+|\\?+ ", "") + s <- str_replace_all(s, ";+", ";") + s <- str_replace_all(s, " ?; ?", ";") + s <- str_replace_all(s, " +", ";") + s +} + + +# Remove a leading or trailing MeSH major-topic "*" from each "; "-separated +# keyword (sources place the marker on either side). Only a keyword-edge +# asterisk is a marker; an interior one ("2*2 design") is real content and +# stays. +strip_major_topic_markers <- function(x) { + x <- gsub("(^|;\\s*)\\*+", "\\1", x) + gsub("\\*+(\\s*;|$)", "\\1", x) +} + + +# Decode HTML character entities so they cannot fragment into bare digits or +# stray tokens downstream (removePunctuation turns "–" into "8211" fused +# into the surrounding word). Handles numeric decimal and hex forms and the +# common named entities; "&" is decoded last so a double-encoded entity is +# only unwrapped one level. Decoded en-dash/hyphen codepoints are normalised to +# "-" so they do not multiply spelling variants of hyphenated terms. +decode_html_entities <- function(x) { + decode_one <- function(s) { + if (is.na(s) || !grepl("&", s, fixed = TRUE)) return(s) + m <- gregexpr("&#[0-9]{1,7};", s) + regmatches(s, m) <- lapply(regmatches(s, m), function(v) { + if (!length(v)) return(v) + vapply(v, function(e) intToUtf8(as.integer(sub("&#([0-9]+);", "\\1", e))), + character(1), USE.NAMES = FALSE) + }) + m <- gregexpr("&#[xX][0-9a-fA-F]{1,6};", s) + regmatches(s, m) <- lapply(regmatches(s, m), function(v) { + if (!length(v)) return(v) + vapply(v, function(e) intToUtf8(strtoi(sub("&#[xX]([0-9a-fA-F]+);", "\\1", e), 16L)), + character(1), USE.NAMES = FALSE) + }) + s <- gsub(" ", " ", s, fixed = TRUE) + s <- gsub("<", "<", s, fixed = TRUE) + s <- gsub(">", ">", s, fixed = TRUE) + s <- gsub(""", "\"", s, fixed = TRUE) + s <- gsub("'", "'", s, fixed = TRUE) + s <- gsub("&", "&", s, fixed = TRUE) + s + } + x <- vapply(x, decode_one, character(1), USE.NAMES = FALSE) + # No-break/narrow spaces (decoded " "/" " or already present in the + # source) become plain spaces: they render invisibly but count as non-space + # in regex classes, which would let a "no-space run" span whole sentences. + x <- gsub("[\u00a0\u202f]", " ", x) + gsub("[\u2013\u2010]", "-", x) +} + + +# Strip text noise that would otherwise surface as corpus terms or label +# candidates: URLs (including signed URLs with their query strings), HTML tags +# and stray closing-tag fragments ("]*>", " ", x, perl = TRUE) + x <- gsub("<\\s*/\\s*[A-Za-z]*>?", " ", x, perl = TRUE) + x <- gsub("\\S{80,}", " ", x, perl = TRUE) + x +} + + +# Tight-colon tokens that keep their colon instead of splitting (matched as the +# whole word:word token). Extend as legitimate ratio-style terms are found. +COLON_KEEP_TOKENS <- c("80:20", "50:50") + + +# Split a text into punctuation-delimited segments for n-gram formation, so +# that no n-gram crosses a clause/subtitle boundary and no intra-word +# punctuation compound is broken. A punctuation mark splits when whitespace +# (or a string edge, or another boundary) adjoins it; a mark tight between two +# word characters stays inside its token. Deviations: colon, em dash, pipe and +# underscore always split (underscore because it is the n-gram joiner +# character); colon keep-list tokens and multi-period abbreviation chains +# ("U.S.", "e.g." — trailing period included) stay whole; a balanced (word) +# pair fused to a word character on at least one side keeps its parens as +# token content ("(in)justice", "micro(nano)") while its spaced outer side +# still bounds the segment; a run of >= 2 consecutive punctuation marks always +# splits as a unit. Placeholders \x01 (chain periods), \x03 (kept colons), +# \x04/\x05 (kept parens) and \x02 (boundaries) cannot occur in decoded +# titles. Returns a character vector of trimmed, whitespace-collapsed, +# non-empty segments; case is preserved. NA/empty input -> character(0). +punctuation_segments <- function(text) { + text <- if (is.null(text) || is.na(text)) "" else text + text <- sanitize_corpus_noise(decode_html_entities(text)) + # C0 control characters are removed before anything else: \x01-\x05 are this + # function's own placeholders, so a source string containing them would be + # restored as a period/colon/paren or silently split the text + text <- gsub("[\x01-\x08\x0b\x0c\x0e-\x1f]", " ", text) + # soft hyphen and zero-width characters render as nothing but count as + # non-space, so they would be kept inside a token and stop it matching the + # same word spelled without them + text <- gsub("[\u00ad\u200b\u200c\u200d\ufeff]", "", text) + # normalize spelling variants to one token identity (decode already maps + # en dash U+2013 and hyphen U+2010 to "-"); a tight "--" is a TeX en dash + # inside a compound, a spaced "--" is left as a run to split + text <- gsub("\u2011", "-", text) + text <- gsub("[\u2019\u2018\u02bc]", "'", text) + text <- gsub("\uff1a", ":", text) + text <- gsub("(?<=[^\\s-])--(?=[^\\s-])", "-", text, perl = TRUE) + # protect abbreviation chains (>= 2 single-letter.period components) before + # any boundary rule, so their periods - the trailing one included - survive + m <- gregexpr("(? Date: Thu, 10 Sep 2026 12:42:28 +0200 Subject: [PATCH 190/205] settings bugfix --- docker-compose.yml | 6 +++++ .../preprocessing/other-scripts/summarize.R | 25 +++++++++++-------- 2 files changed, 21 insertions(+), 10 deletions(-) diff --git a/docker-compose.yml b/docker-compose.yml index ce179ce92..b49aa4bfc 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -134,6 +134,12 @@ services: RANKING_MODE_PUBMED: "${RANKING_MODE_PUBMED}" RANKING_MODE_ORCID: "${RANKING_MODE_ORCID}" RANKING_MODE_OPENAIRE: "${RANKING_MODE_OPENAIRE}" + NGRAM_SETTING: "${NGRAM_SETTING}" + NGRAM_SETTING_BASE: "${NGRAM_SETTING_BASE}" + NGRAM_SETTING_PUBMED: "${NGRAM_SETTING_PUBMED}" + NGRAM_SETTING_ORCID: "${NGRAM_SETTING_ORCID}" + NGRAM_SETTING_OPENAIRE: "${NGRAM_SETTING_OPENAIRE}" + INCLUDE_ABSTRACTS: "${INCLUDE_ABSTRACTS}" RENV_VERSION: 0.14.0-5 CRAN_REPOS: https://cran.wu.ac.at LC_ALL: "en_US.UTF-8" diff --git a/server/preprocessing/other-scripts/summarize.R b/server/preprocessing/other-scripts/summarize.R index 53ad0b102..21be97768 100644 --- a/server/preprocessing/other-scripts/summarize.R +++ b/server/preprocessing/other-scripts/summarize.R @@ -119,12 +119,12 @@ create_cluster_labels <- function(clusters, metadata, # call site on its current behaviour. nset <- ngram_setting(service) if (identical(mode, "0") && !identical(nset, "0")) { - # Mode 0 keeps its legacy corpus/selection structure at every setting; a - # non-0 setting switches ONLY the title n-gram generation step of the - # legacy corpus builder to the shared generator (synthesis stays part - # of the mode-0 subject stream — no bypass on this path). + # Mode 0 keeps its legacy corpus/selection STRUCTURE at every setting (inline + # title n-grams, no DF filter, zero-sum fill); the setting switches the + # generation step to the shared generator and, like every other mode, drops + # the heuristic subjects (see the bypass below). vslog$info(paste("create_cluster_labels: mode 0 with ngram setting", nset, - "- setting switches generation only, structure stays legacy")) + "- generation switched, legacy corpus/selection structure kept")) } if (!(is.null(cc)) && (cc %in% names(metadata)) && !identical(nset, "0")) { vslog$info(paste("create_cluster_labels: ngram setting", nset, @@ -134,6 +134,14 @@ create_cluster_labels <- function(clusters, metadata, nset_abstracts <- !identical(nset, "0") && include_abstracts(service) vslog$debug(paste("create_cluster_labels: ngram setting", nset, "abstracts", nset_abstracts)) + # Bypass of the heuristic subjects (replace_keywords_if_empty), for EVERY + # mode at settings >= 1: papers that had no real keywords contribute through + # the generator columns only, never through the synthesis. Must run before any + # corpus builder or rank column reads `subject`. The custom-clustering path + # labels from its own field, so the bypass does not apply there. + if (!identical(nset, "0") && (is.null(cc) || !(cc %in% names(metadata)))) { + metadata <- bypass_heuristic_subjects(metadata) + } # Curated area-label exclusion list, applied post-tf-idf / pre-ranking at every # candidate-producing tier (initial, fallback, title/abstract) so listed generic # terms can never become a label. Applied in EVERY mode, Mode 0 included: a @@ -183,11 +191,8 @@ create_cluster_labels <- function(clusters, metadata, # - the two heuristic columns (min1/min2), pre-binned by MAP-WIDE document # frequency (add_heuristic_keyword_fields). subject_cleaned (metadata$subject) # is left untouched. - # G1 bypass (settings >= 1, non-custom path only): must run BEFORE the - # heuristic columns and keywords_rank_cleaned are derived from `subject` - if (!identical(nset, "0") && (is.null(cc) || !(cc %in% names(metadata)))) { - metadata <- bypass_heuristic_subjects(metadata) - } + # (the heuristic-keyword bypass for settings >= 1 already ran above, before + # any corpus builder or rank column reads `subject`) metadata <- add_heuristic_keyword_fields(metadata, stops, ngram_lengths = nset_lengths, include_abstracts = nset_abstracts) From 4c56079f0a7c24c5245283ca247251a8095bb590 Mon Sep 17 00:00:00 2001 From: chreman Date: Mon, 14 Sep 2026 23:06:59 +0200 Subject: [PATCH 191/205] text update in ORCID researcher metrics modal --- .../modals/researcher-modal/OrcidResearcherMetricsInfo.tsx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vis/js/templates/modals/researcher-modal/OrcidResearcherMetricsInfo.tsx b/vis/js/templates/modals/researcher-modal/OrcidResearcherMetricsInfo.tsx index a9b1ae3ed..e9184e35a 100644 --- a/vis/js/templates/modals/researcher-modal/OrcidResearcherMetricsInfo.tsx +++ b/vis/js/templates/modals/researcher-modal/OrcidResearcherMetricsInfo.tsx @@ -47,7 +47,7 @@ const ResearcherMetricsInfo = ({

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