diff --git a/DESCRIPTION b/DESCRIPTION index 7c52861..7ec5cc8 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: owdata Title: Index of openwashdata R Data Packages -Version: 0.1.0.9001 +Version: 0.1.0.9002 Authors@R: c( person("Lars", "Schöbitz", , "lschoebitz@ethz.ch", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-2196-5015")), @@ -27,7 +27,7 @@ Imports: Language: en-GB LazyData: true Config/Needs/website: rmarkdown, brand.yml -Date: 2026-11-02 +Date: 2026-11-09 BugReports: https://github.com/openwashdata/owdata/issues Config/Needs/harvest: desc, cffr, gh, jsonlite Depends: diff --git a/R/sysdata.rda b/R/sysdata.rda index 9e565e9..cb2decc 100644 Binary files a/R/sysdata.rda and b/R/sysdata.rda differ diff --git a/data-raw/harvest_report.md b/data-raw/harvest_report.md index 5f0fecd..14e245f 100644 --- a/data-raw/harvest_report.md +++ b/data-raw/harvest_report.md @@ -1,12 +1,12 @@ # Harvest report -Run: 2026-11-02T10:35:55Z (backend: github) +Run: 2026-11-09T10:57:53Z (backend: github) -Packages: 61 (50 published). Datasets: 78. Variables: 2716. +Packages: 62 (51 published). Datasets: 80. Variables: 2817. ## Deltas since previous committed run -- NA rate for doi rose from 17% to 18% +None. ## Problems diff --git a/data-raw/packages.json b/data-raw/packages.json index b52543e..1626fa2 100644 --- a/data-raw/packages.json +++ b/data-raw/packages.json @@ -87,6 +87,10 @@ "package": "grdwtrsmpkwale", "url": "https://github.com/openwashdata/grdwtrsmpkwale" }, + { + "package": "guluswm", + "url": "https://github.com/openwashdata/guluswm" + }, { "package": "gwinfraassess", "url": "https://github.com/openwashdata/gwinfraassess" diff --git a/inst/extdata/owd_datasets.csv b/inst/extdata/owd_datasets.csv index c22d2de..c4adaf8 100644 --- a/inst/extdata/owd_datasets.csv +++ b/inst/extdata/owd_datasets.csv @@ -38,10 +38,12 @@ "glossarywho","themes","themes: Table of Thematic Areas from https://www.who.int/publications/i/item/9789240105485","A table that assigns thematic areas to definitions included in the WHO Glossary (https://www.who.int/publications/i/item/9789240105485)",217,2,2,"https://raw.githubusercontent.com/openwashdata/glossarywho/main/inst/extdata/themes.csv","https://raw.githubusercontent.com/openwashdata/glossarywho/main/inst/extdata/themes.xlsx" "grdwtrsmpkwale","selected_samples","Samples of selected venues collected 3 weeks in a row",,24,69,69,"https://raw.githubusercontent.com/openwashdata/grdwtrsmpkwale/main/inst/extdata/selected_samples.csv","https://raw.githubusercontent.com/openwashdata/grdwtrsmpkwale/main/inst/extdata/selected_samples.xlsx" "grdwtrsmpkwale","water_samples","Samples collected in March and June 2016",,157,80,80,"https://raw.githubusercontent.com/openwashdata/grdwtrsmpkwale/main/inst/extdata/water_samples.csv","https://raw.githubusercontent.com/openwashdata/grdwtrsmpkwale/main/inst/extdata/water_samples.xlsx" +"guluswm","guluswm_survey","Household survey on solid waste management in Gulu City, Uganda, 2026","One row per household interviewed in the socio-economic household survey of the Gulu City household solid waste study, 23 to 26 September 2026: household characteristics, housing and utilities, income and expenditure bands, the types of waste generated, storage and disposal, the collection service, payment for collection, willingness to pay for an improved service and awareness of by-laws. The 424 households live in six areas of the two divisions of Gulu City. The table also holds the survey weight by division and the wealth index computed by the study team. At the authors' decision, the household coordinates (the consent did not cover locations) and the free text answers of the questionnaire are not published, and the enumerators are coded.",424,46,46,"https://raw.githubusercontent.com/openwashdata/guluswm/main/inst/extdata/guluswm_survey.csv","https://raw.githubusercontent.com/openwashdata/guluswm/main/inst/extdata/guluswm_survey.xlsx" +"guluswm","guluswm_waste","Seven day household waste characterisation in Gulu City, Uganda, 2026","One row per household in the waste characterisation study of the Gulu City household solid waste study. Each household stored the waste it generated over seven consecutive days in bags provided by the study team; the bags were weighed on 1 to 3 October 2026 and the waste was sorted by hand into 16 fractions adapted from ASTM D5231. The table holds the total weights, the per capita generation rate, the bulk density measured with a 10 litre bucket and the weight of every fraction before and after the deduction of the container weight, for 95 households of which 94 are included in the averages of the study. The waste records cannot be linked to the households of guluswm_survey.",95,55,55,"https://raw.githubusercontent.com/openwashdata/guluswm/main/inst/extdata/guluswm_waste.csv","https://raw.githubusercontent.com/openwashdata/guluswm/main/inst/extdata/guluswm_waste.xlsx" "gwinfraassess","gwinfraassess","Groundwater Monitoring Wells, Nkhotakota, Malawi 2022-2023","The Groundwater Monitoring Infrastructure Assessment Dataset (2022 to 2023) is an open dataset published by BASEflow Malawi. The dataset contains field observations collected during an assessment of groundwater monitoring infrastructure in Nkhotakota District, Malawi. It includes information on monitoring well characteristics, groundwater level measurements, monitoring equipment, site accessibility, infrastructure condition, community awareness, and recommended maintenance and rehabilitation actions.",123,40,40,"https://raw.githubusercontent.com/openwashdata/gwinfraassess/main/inst/extdata/gwinfraassess.csv","https://raw.githubusercontent.com/openwashdata/gwinfraassess/main/inst/extdata/gwinfraassess.xlsx" "handpumpstatusdata","handpumpstatusdata","Chiradzulu Handpump Functionality Verification Survey","This dataset contains detailed field survey records of borehole and handpump functionality verification exercises conducted in Chiradzulu District, Malawi in February 2020. Data was collected by BASEflow using the mWater mobile data collection platform. Each record represents a single site visit to a water point, capturing GPS coordinates, technical assessments, water availability, environmental conditions, and maintenance history.",57,46,46,"https://raw.githubusercontent.com/openwashdata/handpumpstatusdata/main/inst/extdata/handpumpstatusdata.csv","https://raw.githubusercontent.com/openwashdata/handpumpstatusdata/main/inst/extdata/handpumpstatusdata.xlsx" "hydrogeologicalghana","hydrogeologicalghana","hydrogeologicalghana: Hydrogeological dynamics in the greater Accra and Ashanti regions of Ghana","This dataset contains measurements of pumping tests that were systematically conducted at each borehole to assess crucial hydrogeological parameters, specifically focusing on pumping rates and drawdown.",12,10,10,"https://raw.githubusercontent.com/openwashdata/hydrogeologicalghana/main/inst/extdata/hydrogeologicalghana.csv","https://raw.githubusercontent.com/openwashdata/hydrogeologicalghana/main/inst/extdata/hydrogeologicalghana.xlsx" -"kalaiwash","kalaiwash","kalaiwash: Household water insecurity and drinking water service levels from the KALAI project in Nampula, Mozambique","Baseline (November 2024) and endline (June 2025) household survey data from the KALAI water, sanitation and hygiene project of HELVETAS in Larde, Memba, Moma and Mecuburi districts, Nampula Province, Mozambique. Each row is one household interview. Variables cover the primary drinking water source and its WHO/UNICEF Joint Monitoring Programme (JMP) service level, water collection times and volumes, sanitation and handwashing practices, and the 12 items of the Household Water Insecurity Experiences (HWISE) Scale with the summary score (0 to 36) and insecurity level. The HWISE items are ordered factors whose levels refer to the number of times in the last four weeks: Never (0), Rarely (1 to 2), Sometimes (3 to 10), Often (11 to 20) and Always (more than 20). Derived variables were recalculated in R from the raw survey responses and checked against the original Excel calculations; see `data-raw/data_processing.R`.",275,34,34,"https://raw.githubusercontent.com/openwashdata/kalaiwash/main/inst/extdata/kalaiwash.csv","https://raw.githubusercontent.com/openwashdata/kalaiwash/main/inst/extdata/kalaiwash.xlsx" +"kalaiwash","kalaiwash","kalaiwash: Household water insecurity and drinking water service levels from the Kalai project in Nampula, Mozambique","Baseline (November 2024) and endline (June 2025) household survey data from the Kalai water, sanitation and hygiene project of HELVETAS in Larde, Memba, Moma and Mecuburi districts, Nampula Province, Mozambique. Each row is one household interview. Variables cover the primary drinking water source and its WHO/UNICEF Joint Monitoring Programme (JMP) service level, water collection times and volumes, sanitation and handwashing practices, and the 12 items of the Household Water Insecurity Experiences (HWISE) Scale with the summary score (0 to 36) and insecurity level. The HWISE items are ordered factors whose levels refer to the number of times in the last four weeks: Never (0), Rarely (1 to 2), Sometimes (3 to 10), Often (11 to 20) and Always (more than 20). Derived variables were recalculated in R from the raw survey responses and checked against the original Excel calculations; see `data-raw/data_processing.R`.",275,34,34,"https://raw.githubusercontent.com/openwashdata/kalaiwash/main/inst/extdata/kalaiwash.csv","https://raw.githubusercontent.com/openwashdata/kalaiwash/main/inst/extdata/kalaiwash.xlsx" "mwefloodresponse","mwefloodresponse","mwefloodresponse: Malawi Emergency Flood Response Water Point Survey (2019–2020)","This dataset contains water point assessment data collected during an emergency flood response in Malawi between 2019 and 2020. The survey was conducted under the BASEflow program to evaluate the status, functionality, and safety of water points affected by flooding events.",319,40,40,"https://raw.githubusercontent.com/openwashdata/mwefloodresponse/main/inst/extdata/mwefloodresponse.csv","https://raw.githubusercontent.com/openwashdata/mwefloodresponse/main/inst/extdata/mwefloodresponse.xlsx" "mwgroundwaterdata","mwgroundwaterdata","mwgroundwaterdata: Malawi Groundwater Monitoring Time-Series Dataset (2024–2025)","This dataset contains groundwater monitoring data collected from monitoring wells across 9 districts in Malawi between 2024 and 2025. The data were captured using automated data loggers installed in monitoring wells and were collected and managed by BASEflow. The dataset provides time-series measurements of key groundwater parameters, enabling detailed analysis of aquifer behavior across multiple geographic locations.",1415,9,9,"https://raw.githubusercontent.com/openwashdata/mwgroundwaterdata/main/inst/extdata/mwgroundwaterdata.csv","https://raw.githubusercontent.com/openwashdata/mwgroundwaterdata/main/inst/extdata/mwgroundwaterdata.xlsx" "nchalohhwatersurvey","nchalohhwatersurvey","Nchalo Water Project – Household Survey 2021","This is a community-level household survey conducted as part of the Nchalo Water Project. Its purpose is to assess livelihoods, water usage, community governance, infrastructure access, and opportunities for value addition in rural water supply and WASH-related development intervention.",74,39,39,"https://raw.githubusercontent.com/openwashdata/nchalohhwatersurvey/main/inst/extdata/nchalohhwatersurvey.csv","https://raw.githubusercontent.com/openwashdata/nchalohhwatersurvey/main/inst/extdata/nchalohhwatersurvey.xlsx" diff --git a/inst/extdata/owd_packages.csv b/inst/extdata/owd_packages.csv index 9954843..0f922bd 100644 --- a/inst/extdata/owd_packages.csv +++ b/inst/extdata/owd_packages.csv @@ -27,11 +27,12 @@ "glaas","Complete Data from the Global Analysis and Assessment of Sanitation and Drinking-Water (GLAAS)","An R data package providing comprehensive access to the UN-Water Global Analysis and Assessment of Sanitation and Drinking-water (GLAAS) dataset. The WHO GLAAS survey collects data on water, sanitation, and hygiene (WASH) systems, policies, and financing from countries worldwide.","0.2.0",FALSE,,"Adriana Clavijo Daza","0009-0002-0589-2274","Nicolò Massari; Colin Walder; Adriana Clavijo Daza","CC BY 4.0",2026-02-05,"glaas, r, rpackage, wash",,,,"0.2.0","https://github.com/openwashdata/glaas","https://openwashdata.github.io/glaas/",1,"v0.2.0","main",2025-04-28,2026-11-01,"glaas, r, rpackage, wash",FALSE,FALSE,"1.2.2","v1.1.0",,FALSE,TRUE,FALSE "glossarywho","WHO Glossary","This package provides access to a tidy version of the WHO Glossary (https://www.who.int/publications/i/item/9789240105485) and thematic areas for each term.","1.0.2",TRUE,,"Lars Schöbitz","0000-0003-2196-5015","Yash Dubey; Lars Schöbitz","CC BY 4.0",2025-01-28,"open data, washdata, glossary, health data, health statistics, public health indicators, data governance, WHO","Global","2025-01-01/2025-12-31","10.5281/zenodo.14754016","1.0.2","https://github.com/openwashdata/glossarywho","https://openwashdata.github.io/glossarywho/",2,"v1.0.1","main",2025-01-28,2026-11-01,,TRUE,TRUE,"1.2.2","v1.1.0",,TRUE,TRUE,TRUE "grdwtrsmpkwale","Groundwater analysis from 2016 in Kwale, Kenya","The goal of grdwtrsmpkwale is to provide datasets for research and planning of water and solid waste management in Kwale, Kenya. This package includes water analysis data collected in 2016 combined with the geospatial data from the collection points.","0.0.1",TRUE,,"Lars Schöbitz","0000-0003-2196-5015","Sebastian Camilo Loos; Mian Zhong; Rob Hope; Lars Schöbitz","CC BY 4.0",2023-10-16,"open data, washdata, groundwater, water quality, Kwale, Kenya","Kwale, Kenya","2016-03-01/2016-06-25","10.5281/zenodo.10016874","0.0.1","https://github.com/openwashdata/grdwtrsmpkwale","https://openwashdata.github.io/grdwtrsmpkwale/",2,"v0.0.1","main",2023-08-17,2026-11-01,"groundwater, groundwater-data, kenya, opendata, openwashdata, r",TRUE,TRUE,"1.2.2","v1.1.0",,TRUE,TRUE,TRUE +"guluswm","Household Waste Survey and Characterisation in Gulu City, Uganda","Field data of a household solid waste study in Gulu City, northern Uganda, in September and October 2026: a socio-economic household survey of 424 households in six areas of the two city divisions, covering housing, income and expenditure bands, waste storage and disposal, the collection service, payment for collection and willingness to pay for an improved service, and a seven day waste characterisation of 95 households with the weights of 16 sorted fractions, the per capita generation rate and the bulk density.","1.0.0",TRUE,,"Lars Schöbitz","0000-0003-2196-5015","Ivan Feni; Abubakar Batte; James Omal; Charles Wakhata; Juliet Amindre; Alex Candia; Rebecca Lamwaka; Grace Okello; Charles B. Niwagaba; Lars Schöbitz","CC BY 4.0",2026-11-03,"open data, washdata, solid waste management, household survey, waste characterisation, willingness to pay, Gulu, Uganda","Gulu City, Uganda","2026-09-23/2026-10-03","10.5281/zenodo.23918126","1.0.0","https://github.com/openwashdata/guluswm","https://openwashdata.github.io/guluswm/",2,"v1.0.0","main",2026-10-30,2026-11-04,,TRUE,TRUE,"1.2.2.9000","v1.1.0","1.7.0",TRUE,TRUE,TRUE "gwinfraassess","Groundwater Monitoring Wells, Nkhotakota, Malawi 2022-2023","The Groundwater Monitoring Infrastructure Assessment Dataset (2022 to 2023) is an open dataset published by BASEflow Malawi. The dataset contains field observations collected during an assessment of groundwater monitoring infrastructure in Nkhotakota District, Malawi. It includes information on monitoring well characteristics, groundwater level measurements, monitoring equipment, site accessibility, infrastructure condition, community awareness, and recommended maintenance and rehabilitation actions.","1.0.1",TRUE,,"Emmanuel Mhango","0000-0003-3197-6244","Emmanuel Mhango","CC BY 4.0",2026-08-07,"open data, washdata, groundwater, groundwater monitoring, monitoring wells, water levels, Malawi","Nkhotakota, Malawi","2022-02-08/2023-10-04","10.5281/zenodo.21839342","1.0.1","https://github.com/openwashdata/gwinfraassess","https://openwashdata.github.io/gwinfraassess/",1,"v1.0.0","main",2026-08-07,2026-11-01,,TRUE,TRUE,"1.2.2","v1.1.0",,TRUE,TRUE,TRUE "handpumpstatusdata","Chiradzulu Handpump Functionality Verification Survey","This dataset contains detailed field survey records of borehole and handpump functionality verification exercises conducted in Chiradzulu District, Malawi in February 2020.","1.0.1",TRUE,,"Emmanuel Mhango","0000-0003-3197-6244","Emmanuel Mhango","CC BY 4.0",2026-09-23,"open data, washdata, handpumps, boreholes, water point functionality, water supply, Malawi","Chiradzulu, Malawi","2020-02-06/2020-11-06","10.5281/zenodo.17474593","1.0.1","https://github.com/openwashdata/handpumpstatusdata","https://openwashdata.github.io/handpumpstatusdata/",1,"v1.0.0","main",2025-08-12,2026-11-01,"handpump, wash, waterpoints",TRUE,TRUE,"1.2.2","v1.1.0",,TRUE,TRUE,TRUE "hydrogeologicalghana","Hydrogeological Dynamics Data in Ghana","This package contains data on boreholes collected to study the hydrogeological dynamics in the greater Accra and Ashanti regions of Ghana.","0.0.1",TRUE,,"Adriana Clavijo Daza","0009-0002-0589-2274","Benedict Addae-Mensah; Elizabeth Tilley; Adriana Clavijo Daza","CC BY 4.0",2025-08-05,"open data, washdata, boreholes, groundwater, hydrogeology, pumping tests, aquifers, Ghana","Greater Accra and Ashanti regions, Ghana","2001-01-01/2001-01-03","10.5281/zenodo.16743741","0.0.1","https://github.com/openwashdata/hydrogeologicalghana","https://openwashdata.github.io/hydrogeologicalghana/",1,"v0.0.1","main",2025-07-24,2026-11-01,,TRUE,TRUE,"1.2.2","v1.1.0",,TRUE,TRUE,TRUE "jmpdata","Input Data and Indicator Estimates from the WHO/UNICEF Joint Monitoring Programme","Contains two data resources from the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene. `jmpraw` holds the survey-level input data on sanitation that underlies the JMP estimates, collected from the country files published on . `jmpindicators` holds the modelled JMP indicator estimates for water, sanitation and hygiene from the JMP world file. Both datasets are packaged from the JMP 2025 release.","0.2.0",FALSE,,"Lars Schöbitz","0000-0003-2196-5015","Lars Schöbitz","CC BY 4.0",2026-08-20,,,,,"0.2.0","https://github.com/openwashdata/jmpdata","https://openwashdata.github.io/jmpdata/",2,"v0.1.0","main",2022-04-26,2026-10-03,,TRUE,TRUE,,,,FALSE,FALSE,FALSE -"kalaiwash","Household Water Insecurity and Drinking Water Service Levels from the KALAI Project in Nampula, Mozambique","Baseline (November 2024) and endline (June 2025) household survey data from the KALAI water, sanitation and hygiene project of HELVETAS in Larde, Memba, Moma and Mecuburi districts, Nampula Province, Mozambique. The data cover 275 household interviews and include the primary drinking water source classified with the WHO/UNICEF Joint Monitoring Programme (JMP) service ladder, water collection times and volumes, sanitation and handwashing practices, and the 12-item Household Water Insecurity Experiences (HWISE) Scale with its summary score and insecurity level.","0.1.0",TRUE,,"Adriana Clavijo Daza","0009-0002-0589-2274","John Brogan; Adriana Clavijo Daza; Dercio Obra","CC BY 4.0",2026-10-05,,,,"10.5281/zenodo.23164217","0.1.0","https://github.com/openwashdata/kalaiwash","https://openwashdata.github.io/kalaiwash/",1,"v0.1.0","main",2026-08-24,2026-11-01,,TRUE,TRUE,"1.2.2","v1.1.0",,TRUE,TRUE,TRUE +"kalaiwash","Household Water Insecurity and Drinking Water Service Levels from the Kalai Project in Nampula, Mozambique","Baseline (November 2024) and endline (June 2025) household survey data from the Kalai water, sanitation and hygiene project of HELVETAS in Larde, Memba, Moma and Mecuburi districts, Nampula Province, Mozambique. The data cover 275 household interviews and include the primary drinking water source classified with the WHO/UNICEF Joint Monitoring Programme (JMP) service ladder, water collection times and volumes, sanitation and handwashing practices, and the 12-item Household Water Insecurity Experiences (HWISE) Scale with its summary score and insecurity level.","0.1.0",TRUE,,"Adriana Clavijo Daza","0009-0002-0589-2274","John Brogan; Adriana Clavijo Daza; Dercio Obra","CC BY 4.0",2026-10-05,,,,"10.5281/zenodo.23164217","0.1.0","https://github.com/openwashdata/kalaiwash","https://openwashdata.github.io/kalaiwash/",1,"v0.1.0","main",2026-08-24,2026-11-03,,TRUE,TRUE,"1.2.2","v1.1.0",,TRUE,TRUE,TRUE "mwefloodresponse","Malawi Emergency Flood Response Water Point Survey (2019–2020)","This dataset contains water point assessment data collected during an emergency flood response in Malawi between 2019 and 2020. The survey was conducted under the BASEflow program to evaluate the status, functionality, and safety of water points affected by flooding events.","0.0.2",TRUE,,"Emmanuel Mhango","0000-0003-3197-6244","Emmanuel Mhango","CC BY 4.0",2026-09-23,"open data, washdata, floods, emergency response, water points, water quality, Malawi","Southern Region, Malawi","2019-04-03/2020-02-12","10.5281/zenodo.18755616","0.0.2","https://github.com/openwashdata/mwefloodresponse","https://openwashdata.github.io/mwefloodresponse/",1,"v.0.0.1","main",2026-02-20,2026-11-01,"sanitation, wash, water",TRUE,TRUE,"1.2.2","v1.1.0",,TRUE,TRUE,TRUE "mwgroundwaterdata","Malawi Groundwater Monitoring Time-Series Dataset (2024–2025)","This dataset contains groundwater monitoring data collected from monitoring wells across 9 districts in Malawi between 2024 and 2025. The data were captured using automated data loggers installed in monitoring wells and were collected and managed by BASEflow. The dataset provides time-series measurements of key groundwater parameters, enabling detailed analysis of aquifer behavior across multiple geographic locations.","0.0.2",TRUE,,"Emmanuel Mhango","0000-0003-3197-6244","Emmanuel Mhango","CC BY 4.0",2026-09-23,"open data, washdata, groundwater, groundwater monitoring, water level, water quality, Malawi","Southern Region, Malawi","2024-12-31/2025-02-07","10.5281/zenodo.18798409","0.0.2","https://github.com/openwashdata/mwgroundwaterdata","https://openwashdata.github.io/mwgroundwaterdata/",1,"v.0.0.1","main",2026-02-26,2026-11-01,"groundwater, sanitation, wash, water, watermonitoring",TRUE,TRUE,"1.2.2","v1.1.0",,TRUE,TRUE,TRUE "nchalohhwatersurvey","Nchalo Water Project Household Survey 2021","Household and community survey data from rural communities around Nchalo, Malawi. Includes water access, livelihoods, governance, and infrastructure information collected via mWater platform to support water supply planning and rural development.","1.0.1",TRUE,,"Emmanuel Mhango","0000-0003-3197-6244","Emmanuel Mhango","CC BY 4.0",2026-09-23,"open data, washdata, household survey, water supply, boreholes, livelihoods, Nchalo, Malawi","Nchalo, Malawi","2021-04-07/2021-04-08","10.5281/zenodo.17522182","1.0.1","https://github.com/openwashdata/nchalohhwatersurvey","https://openwashdata.github.io/nchalohhwatersurvey/",1,"v1.0.0","main",2025-06-27,2026-11-01,"hygiene, sanitation, wash",TRUE,TRUE,"1.2.2","v1.1.0",,TRUE,TRUE,TRUE @@ -57,6 +58,6 @@ "watercostaccra","Household Water Costs and Coping Strategies, Accra 2023","A household survey on water costs and coping strategies (116 households) and a water point survey (49 water points, with E. coli and total coliform test results) were conducted in October and November 2023 in two low-income communities in metropolitan Accra, Ghana. These are Korle Gonno, a larger, well-planned coastal area with over 35 household water vendors, and Abuja, a small, densely packed, extralegal settlement with 15 water vendor and bathhouse businesses.","0.0.0.9000",TRUE,,"Lars Schöbitz","0000-0003-2196-5015","Elizabeth Vicario; Margaux Götschmann; Betty Avanu Davidson; Ebenezer F. Amankwaa; Mian Zhong; Lars Schöbitz","CC BY 4.0",2024-06-21,"open data, washdata, water costs, coping strategies, household survey, water points, water vendors, water quality, Accra, Ghana","Korle Gonno and Abuja, Accra, Ghana","2023-10-05/2023-11-23","10.5281/zenodo.13981224","0.0.0.9000","https://github.com/openwashdata/watercostaccra","https://openwashdata.github.io/watercostaccra/",2,"v0.0.0.9000","main",2024-06-18,2026-11-01,"accra, ghana, household-surveys, open-data, openwashdata, r, water-cost",TRUE,TRUE,"1.2.2","v1.1.0",,TRUE,TRUE,TRUE "waterpointstatus","Water Point Functionality and Service Monitoring, Malawi 2023","This dataset contains structured field survey responses collected from water point inspections and interviews with community members or water point committee representatives. Each record represents a single survey event linked to a specific water point, including GPS coordinates and detailed observations on infrastructure condition, functionality, water availability, maintenance practices, and governance arrangements.","1.0.0",TRUE,,"Emmanuel Mhango","0000-0003-3197-6244","Emmanuel Mhango","CC BY 4.0",2026-04-10,"open data, washdata, water points, functionality, water supply, service delivery, Malawi","Southern Region, Malawi","2023-01-01/2023-12-31","10.5281/zenodo.19498016","1.0.0","https://github.com/openwashdata/waterpointstatus","https://openwashdata.github.io/waterpointstatus/",1,"v1.0.0","main",2026-04-10,2026-11-01,"baseflow, boreholes, groundwater, wash, waterpoints",TRUE,TRUE,"1.2.2","v1.1.0",,TRUE,TRUE,TRUE "waterpumpkwale","Weekly Handpump Water Volumes, Kwale County, Kenya 2014-2015","This dataset contains a summary of the weekly volumetric output of handpumps monitored using Smart Handpump sensors in 2014 and 2015 in Kwale County, Kenya, together with the locations of the monitored pumps.","0.0.1",TRUE,,"Lars Schöbitz","0000-0003-2196-5015","Patrick Thomson; Rob Hope; Tim Foster; Mian Zhong; Lars Schöbitz","CC BY 4.0",2023-10-02,"open data, washdata, handpumps, water supply, groundwater, smart handpump, kwale, kenya","Kwale County, Kenya","2014-01-01/2015-12-31","10.5281/zenodo.8399886","0.0.1","https://github.com/openwashdata/waterpumpkwale","https://openwashdata.github.io/waterpumpkwale/",3,"v0.0.1","main",2023-08-16,2026-11-01,"africa, kenya, kwale, open-data, open-datasets, r, underground-water, upgro, water-pump",TRUE,TRUE,"1.2.2","v1.1.0",,TRUE,TRUE,TRUE -"waterqualityuganda","Drinking Water Quality from Boreholes to Household Storage in Uganda","Monthly E. coli monitoring of drinking water along the chain from borehole sources to household storage in four Ugandan districts (Nakaseke, Kaabong, Kumi and Luuka) between November 2024 and September 2025. For each borehole, water was sampled at the source and, in three households using it, from the container used to carry water home and from the drinking water storage container. Samples were analysed by membrane filtration and classified into five contamination levels. The package provides four tidy datasets: field visits to boreholes, field visits to households, water samples with their laboratory results, and laboratory blanks.","0.0.0.9000",FALSE,,"Adriana Clavijo Daza","0009-0002-0589-2274","Cecilia Nansiimbi; Adriana Clavijo Daza","CC BY 4.0",2026-10-16,"water quality, E. coli, boreholes, household water storage, water transport, WASH, Uganda","Nakaseke, Kaabong, Kumi and Luuka districts, Uganda","2024-11-01/2025-09-16",,"0.0.0.9000","https://github.com/openwashdata/waterqualityuganda","https://openwashdata.github.io/waterqualityuganda/",4,,"main",2026-08-24,2026-11-01,,TRUE,TRUE,"1.2.2","v1.1.0",,TRUE,TRUE,TRUE +"waterqualityuganda","Drinking Water Quality from Boreholes to Household Storage in Uganda","Monthly E. coli monitoring of drinking water along the chain from borehole sources to household storage in four Ugandan districts (Nakaseke, Kaabong, Kumi and Luuka) between November 2024 and September 2025. For each borehole, water was sampled at the source and, in three households using it, from the container used to carry water home and from the drinking water storage container. Samples were analysed by membrane filtration and classified into five contamination levels. The package provides four tidy datasets: field visits to boreholes, field visits to households, water samples with their laboratory results, and laboratory blanks.","0.0.0.9000",FALSE,,"Adriana Clavijo Daza","0009-0002-0589-2274","Cecilia Nansiimbi; Adriana Clavijo Daza","CC BY 4.0",2026-10-16,"water quality, E. coli, boreholes, household water storage, water transport, WASH, Uganda","Nakaseke, Kaabong, Kumi and Luuka districts, Uganda","2024-11-01/2025-09-16",,"0.0.0.9000","https://github.com/openwashdata/waterqualityuganda","https://openwashdata.github.io/waterqualityuganda/",4,,"main",2026-08-24,2026-11-06,,TRUE,TRUE,"1.2.2","v1.1.0",,TRUE,TRUE,TRUE "worldhdi","Human Development Index Worldwide 1990-2022 (2024)","Provides Human Development Index (HDI) trends from 1990 to 2022 for 195 countries and territories (193 of them ranked) and 15 UNDP aggregates (development groups, regions and the world), from the UNDP Human Development Report 2023/2024 Statistical Annex. It includes HDI ranks, changes in rank and average annual HDI growth.","1.0.2",TRUE,,"Lars Schöbitz","0000-0003-2196-5015","Yash Dubey; Lars Schöbitz","CC BY 4.0",2024-10-29,"open data, washdata, human development index, hdi, undp, development indicators, sdgs","Worldwide","1990-01-01/2022-12-31","10.5281/zenodo.14006109","1.0.2","https://github.com/openwashdata/worldhdi","https://openwashdata.github.io/worldhdi/",1,"V1.0.1","main",2024-10-28,2026-11-01,,TRUE,TRUE,"1.2.2","v1.1.0",,TRUE,TRUE,TRUE "wsabrazil","Wastewater management and household infrastructure in Brazil","This dataset about wastewater management and household infrastructure from various Brazilian regions provides insights into wastewater disposal habits, water sources, bathroom facilities, and sanitation infrastructure.","0.1.0",FALSE,,"Lars Schöbitz","0000-0003-2196-5015","Margaux Götschmann; Lais Santos; Lars Schöbitz","CC BY 4.0",2026-09-23,"open data, washdata, wastewater, sanitation, water supply, census, Brazil","Brazil","2010-08-01/2010-10-30",,"0.1.0","https://github.com/openwashdata/wsabrazil","https://openwashdata.github.io/wsabrazil/",1,,"main",2024-03-26,2026-11-01,"2010, brazil, infrastructure, open-data, r, sanitation, wash, wastewater, water",TRUE,TRUE,"1.2.2","v1.1.0",,TRUE,TRUE,TRUE diff --git a/inst/extdata/owd_variables.csv b/inst/extdata/owd_variables.csv index af0f09a..fa76c75 100644 --- a/inst/extdata/owd_variables.csv +++ b/inst/extdata/owd_variables.csv @@ -1455,6 +1455,107 @@ "grdwtrsmpkwale","water_samples","U","double","Uranium concentration in parts per billion (ppb)." "grdwtrsmpkwale","water_samples","delta_O_18","double","The ratio of stable isotopes oxygen-18 (18O) and oxygen-16 (16O) as a measure of groundwater/mineral interactions." "grdwtrsmpkwale","water_samples","delta_H_2","double","δ2H, or delta deuterium, is a measure of the relative abundance of deuterium (a stable isotope of hydrogen) in a sample, often used in hydrology and environmental science to trace the origin and movement of water." +"guluswm","guluswm_survey","household_id","character","Identifier of the interviewed household as assigned by the study team: the enumerator code (E1 to E5, replacing the enumerator's initials used in the field), the survey date as day and month (2309 for 23 September 2026) and a running number per enumerator, for example E1-2309-001." +"guluswm","guluswm_survey","enumerator","character","Code of the enumerator who conducted the interview, E1 to E5; the codes replace the mWater user names and are shared with guluswm_waste." +"guluswm","guluswm_survey","survey_date","Date","Date of the interview, between 23 and 26 September 2026." +"guluswm","guluswm_survey","area","character","Study area in which the household lives: Limo, Vanguard or Highland (Laroo-Pece Division), Kanyagoga, Pabbo Quarters or Goans Quarters (Bardege-Layibi Division). Only two interviews were completed in Highland. The raw file spells Pabbo Quarters as Pabwo Quarters in this sheet." +"guluswm","guluswm_survey","division","character","Administrative division of Gulu City in which the household lives: Bardege-Layibi or Laroo-Pece." +"guluswm","guluswm_survey","household_size","integer","Number of people living in the household." +"guluswm","guluswm_survey","respondent_is_head","character","Whether the respondent is the head of the household (Yes or No)." +"guluswm","guluswm_survey","education_head","factor","Highest level of education of the household head, in rising order: None (no formal education), Primary, Secondary, Tertiary/vocational, University and above." +"guluswm","guluswm_survey","livelihood","character","Main source of livelihood of the household: Trading/business, Casual labour, Salaried employment, Remittances/Donations or Other." +"guluswm","guluswm_survey","income_band","factor","Monthly household income in Ugandan shillings, in six bands from None/no regular income to Above UGX 1,000,000." +"guluswm","guluswm_survey","income_class","factor","Income class derived from income_band: Low (no regular income or below UGX 300,000), Middle (UGX 300,000 to 600,000) or High (above UGX 600,000). The proportions of these classes set the cut points of wealth_index_class." +"guluswm","guluswm_survey","expenditure_band","factor","Monthly household expenditure on basic needs in Ugandan shillings, in five bands from Below UGX 100,000 to Above UGX 1,000,000." +"guluswm","guluswm_survey","wall_material","character","Main material of the walls of the dwelling: Mud/wattle, Unburnt bricks, Burnt bricks, unplastered, Burnt bricks, plastered or Other." +"guluswm","guluswm_survey","roof_material","character","Main material of the roof of the dwelling: Grass thatch, Iron sheets (old/rusted), Iron sheets (new) or Tiles/permanent." +"guluswm","guluswm_survey","piped_water","character","Whether the household has access to piped water (Yes or No)." +"guluswm","guluswm_survey","electricity","character","Whether the household has access to electricity (Yes or No)." +"guluswm","guluswm_survey","waste_types","character","Types of waste the household generates, as named by the respondent: Food preparation waste, Food leftovers, Plastic, Paper, Glass or Other; several answers are separated by a semicolon." +"guluswm","guluswm_survey","disposal_container","character","Container in which the household stores its waste before disposal: Kavera (a polyethylene bag), Sack, Bucket or Bag; several answers are separated by a semicolon." +"guluswm","guluswm_survey","disposal_method","character","How the household disposes of its waste: Burning, Collected by service provider, Taken to collection point/skip, Backyard dumping, Rubbish pit, Roadside dumping, Open drain, Burying, Garden/compost or Other; several answers are separated by a semicolon." +"guluswm","guluswm_survey","waste_collector","character","Who collects the household's waste: City/Division Council, Private Operator, Informal Collector, Community Group, Self (household), No one or Other; asked of households with a collection arrangement." +"guluswm","guluswm_survey","preferred_frequency","character","Collection frequency the household would prefer: Daily, More than once a week, Three times a week, Twice a week, Weekly or Other; the answer Collection points every month was recorded under Other as an open answer." +"guluswm","guluswm_survey","preferred_time","character","Time of day the household considers best for collection: Morning, Day time, Evening, No specific time or Other." +"guluswm","guluswm_survey","service_reliable","character","Whether the household considers waste collection in its area reliable (Yes or No). The question was independent of waste_collector: some households whose waste is not collected take it to the municipal skips." +"guluswm","guluswm_survey","irregular_collection_problem","character","Main problem the household experiences from irregular collection: Dirty neighborhood, Unhealthy environment, Spread of diseases, Bad reputation of the area or Other; asked of households that call their service unreliable." +"guluswm","guluswm_survey","enough_collection_points","character","Whether the household considers the number of skips or collection points in its area enough (Yes or No)." +"guluswm","guluswm_survey","distance_disposal_point","factor","Distance from the household to the nearest disposal point, in bands: Less than 50 Metres, 50 - 100 Metres, 200 - 300 Metres or More than 500 Metres." +"guluswm","guluswm_survey","pays_for_collection","character","Whether the household pays for waste collection (Yes or No)." +"guluswm","guluswm_survey","current_fee_band","factor","Monthly fee the household pays for collection in Ugandan shillings, in five bands from Below UGX 1,000 to UGX 15,000 and above; asked of paying households." +"guluswm","guluswm_survey","current_fee_rating","factor","How the household rates the fee it pays: Affordable, Manageable or Expensive; asked of paying households." +"guluswm","guluswm_survey","payment_channel","character","How the household pays for collection: Direct to City/Division Council, Through the landlord or Other. The question was shown to every household; for households that do not pay (pays_for_collection is No) the answer is not applicable, and for paying households Other covers channels not listed." +"guluswm","guluswm_survey","payment_schedule","character","How often the household pays for collection: Daily, Whenever collected, Bi-weekly or Monthly. The question was shown to every household; for households that do not pay (pays_for_collection is No) the answer is not applicable." +"guluswm","guluswm_survey","willing_to_pay","character","Whether the household is willing to pay for an improved and regular collection service, or to pay more than now if it pays already (Yes or No)." +"guluswm","guluswm_survey","reason_willing","character","Main reason for being willing to pay: Desire for a cleaner environment, Reduced risk of disease, Convenience/saves time, Improves property value or Other." +"guluswm","guluswm_survey","reason_not_willing","character","Main reason for not being willing to pay: Income is too low/irregular, Government/City should bear the cost, Already pay enough / rent includes it, Current service is unreliable so won't pay more, or Other." +"guluswm","guluswm_survey","wtp_band","factor","Monthly amount the household is willing to pay in Ugandan shillings, in five bands from Below UGX 1,000 to UGX 15,000 and above; asked of willing households." +"guluswm","guluswm_survey","preferred_payment_structure","character","Payment structure the household prefers: Flat monthly fee, Pay-per-visit/per-collection, Pay-per-bag/per-bin collected, Included in property rent/rates or No preference." +"guluswm","guluswm_survey","participates_cleanups","character","Whether the household takes part in community clean-up exercises (Yes or No)." +"guluswm","guluswm_survey","ever_complained","character","Whether the household has ever complained about solid waste management (Yes or No)." +"guluswm","guluswm_survey","complained_to","character","To whom the household complained: LCI Chairman (the chairperson of the Local Council I), Land Lord, City/Division Council, Private Operator or Other." +"guluswm","guluswm_survey","receives_swm_information","character","Whether the household receives information on solid waste management from the City (Yes or No)." +"guluswm","guluswm_survey","aware_of_bylaws","character","Whether the household is aware of any solid waste management by-law (Yes or No)." +"guluswm","guluswm_survey","swm_challenges","character","Challenges of solid waste management in the area, coded by the study team from the respondent's answer; several codes are separated by a semicolon." +"guluswm","guluswm_survey","improvement_themes","character","Improvements suggested by the respondent, coded by the study team from the free text answer; several codes are separated by a semicolon." +"guluswm","guluswm_survey","survey_weight","numeric","Post-stratification weight by division: the division's share of the population of Gulu City divided by its share of the sample. Bardege-Layibi 0.680 (population 93,848, sample 251) and Laroo-Pece 1.465 (population 139,423, sample 173), population figures from UBOS (2024)." +"guluswm","guluswm_survey","wealth_index_score","numeric","Wealth index score computed by the study team: the first principal component of five standardised household indicators (roof of new iron sheets or tiles, plastered burnt brick walls, electricity, head with tertiary/vocational or university education, salaried employment as main livelihood); higher values mean wealthier. Missing for 18 households with a missing indicator." +"guluswm","guluswm_survey","wealth_index_class","factor","Wealth class from wealth_index_score: households ranked by score and cut into Low, Middle and High in the proportions of income_class (63.4, 25.2 and 11.4 percent). Ties were ranked by their order in the raw file, so 33 households with the same score of 0.4246 are split between Low (4) and Middle (29). Missing for 18 households." +"guluswm","guluswm_waste","record_id","character","Identifier of the characterisation record: CH, a batch number and a running number, for example CH-1001-001." +"guluswm","guluswm_waste","enumerator","character","Code of the enumerator who recorded the measurement, one of the codes E1 to E5 shared with guluswm_survey." +"guluswm","guluswm_waste","measurement_date","Date","Date on which the household's waste was weighed and sorted, between 1 and 3 October 2026, at the end of the storage period." +"guluswm","guluswm_waste","area","character","Study area of the household after correction by the study team: Limo, Vanguard, Highland, Kanyagoga, Pabbo Quarters or Goans Quarters." +"guluswm","guluswm_waste","area_recorded","character","Study area as recorded on the field form. The field kept the previous entry when two enumerators switched areas; the study team corrected 14 records against the enumerators' daily area logs and the field supervision records (see area)." +"guluswm","guluswm_waste","division","character","Administrative division of Gulu City in which the household lives: Bardege-Layibi or Laroo-Pece." +"guluswm","guluswm_waste","household_form_id","character","Household number written on the field form and the bags, for example HH-01, used for field supervision; it restarts per enumerator and area and is not unique, record_id identifies the household." +"guluswm","guluswm_waste","occupants","integer","Number of people living in the household." +"guluswm","guluswm_waste","people_home_daytime","integer","Number of household members at home during the day." +"guluswm","guluswm_waste","days_stored","integer","Number of days over which the household stored its waste in the bags provided: 7 for every household but one in the test run, which stored for 14 days." +"guluswm","guluswm_waste","person_days","integer","Occupants multiplied by days_stored, the denominator of the per capita generation rate." +"guluswm","guluswm_waste","weight_recorded_kg","numeric","Total weight of the household's waste as recorded in the field, in kilograms, before the correction of keying errors." +"guluswm","guluswm_waste","weight_fractions_sum_kg","numeric","Sum of the weights of the 16 sorted fractions after the container deduction, in kilograms." +"guluswm","guluswm_waste","weight_used_kg","numeric","Total weight of the household's waste used for the generation rate, in kilograms: the recorded total, except for one keying error (CH-1001-012, grams entered as kilograms), where the sum of the sorted fractions is used." +"guluswm","guluswm_waste","mass_balance_diff_pct","numeric","Difference between the recorded total weight and the sum of the sorted fractions, as a percentage of the recorded total weight. Differences come from container weights, moisture loss and spillage during sorting; no record was excluded because of it." +"guluswm","guluswm_waste","generation_kg_cap_day","numeric","Per capita waste generation rate in kilograms per person and day: weight_used_kg divided by person_days." +"guluswm","guluswm_waste","generation_excl_inert_kg_cap_day","numeric","Per capita waste generation rate without the inert fines (sand, soil and stones), in kilograms per person and day: weight_used_kg minus inert_fines_kg, divided by person_days." +"guluswm","guluswm_waste","included_in_averages","character","Whether the household is included in the averages of the study: the rule of at most 2.0 kilograms per person and day was applied to every record, and only one household exceeded it (No (outlier > 2.0))." +"guluswm","guluswm_waste","bucket_empty_kg","numeric","Weight of the empty 10 litre bucket used to measure the bulk density, in kilograms; one bucket was used and cleaned between households." +"guluswm","guluswm_waste","bucket_filled_kg","numeric","Weight of the 10 litre bucket filled loosely with the household's mixed waste before sorting, in kilograms; the mean of at least three weighings." +"guluswm","guluswm_waste","bulk_density_kg_m3","numeric","Bulk density of the loose waste in kilograms per cubic metre: the weight of waste in the 10 litre bucket divided by its volume." +"guluswm","guluswm_waste","container_deduction_kg","numeric","Total weight deducted for the containers (weighing basins and bags) in which the fractions were weighed, in kilograms, summed over the fractions." +"guluswm","guluswm_waste","container_weight_per_fraction_kg","numeric","Container weight deducted from each fraction with a recorded weight, in kilograms. Where the raw fractions summed to more than the recorded total, the study team estimated it as the excess divided by the number of fractions weighed." +"guluswm","guluswm_waste","food_kitchen_kg","numeric","Weight of food and kitchen waste, in kilograms, after the container deduction." +"guluswm","guluswm_waste","garden_kg","numeric","Weight of garden, yard and park waste (green waste), in kilograms, after the container deduction." +"guluswm","guluswm_waste","wood_kg","numeric","Weight of wood and wood products, in kilograms, after the container deduction." +"guluswm","guluswm_waste","textiles_kg","numeric","Weight of textiles and wool, in kilograms, after the container deduction." +"guluswm","guluswm_waste","paper_kg","numeric","Weight of mixed paper and pulp, in kilograms, after the container deduction." +"guluswm","guluswm_waste","plastics_kg","numeric","Weight of mixed plastics, in kilograms, after the container deduction." +"guluswm","guluswm_waste","rubber_leather_kg","numeric","Weight of rubber and leather, in kilograms, after the container deduction." +"guluswm","guluswm_waste","glass_kg","numeric","Weight of glass, in kilograms, after the container deduction." +"guluswm","guluswm_waste","metals_kg","numeric","Weight of metals, in kilograms, after the container deduction." +"guluswm","guluswm_waste","diapers_kg","numeric","Weight of disposable diapers (recorded as Pampers), in kilograms, after the container deduction." +"guluswm","guluswm_waste","polyethylene_bags_kg","numeric","Weight of polyethylene bags, in kilograms, after the container deduction." +"guluswm","guluswm_waste","cardboard_kg","numeric","Weight of cardboard, in kilograms, after the container deduction." +"guluswm","guluswm_waste","ewaste_kg","numeric","Weight of electronic waste, in kilograms, after the container deduction." +"guluswm","guluswm_waste","aluminium_kg","numeric","Weight of aluminium, in kilograms, after the container deduction." +"guluswm","guluswm_waste","inert_fines_kg","numeric","Weight of inert fines (sand, soil and stones from compound sweeping, recorded as inorganic non combustible), in kilograms, after the container deduction. In Limo and Vanguard, sand and soil first entered as miscellaneous waste were moved here in the re-export of 30 October 2026." +"guluswm","guluswm_waste","miscellaneous_kg","numeric","Weight of miscellaneous waste (residual fine material that could not be sorted further), in kilograms, after the container deduction." +"guluswm","guluswm_waste","food_kitchen_raw_kg","numeric","Weight of food and kitchen waste as weighed in the field, in kilograms, before the container deduction." +"guluswm","guluswm_waste","garden_raw_kg","numeric","Weight of garden, yard and park waste (green waste) as weighed in the field, in kilograms, before the container deduction." +"guluswm","guluswm_waste","wood_raw_kg","numeric","Weight of wood and wood products as weighed in the field, in kilograms, before the container deduction." +"guluswm","guluswm_waste","textiles_raw_kg","numeric","Weight of textiles and wool as weighed in the field, in kilograms, before the container deduction." +"guluswm","guluswm_waste","paper_raw_kg","numeric","Weight of mixed paper and pulp as weighed in the field, in kilograms, before the container deduction." +"guluswm","guluswm_waste","plastics_raw_kg","numeric","Weight of mixed plastics as weighed in the field, in kilograms, before the container deduction." +"guluswm","guluswm_waste","rubber_leather_raw_kg","numeric","Weight of rubber and leather as weighed in the field, in kilograms, before the container deduction." +"guluswm","guluswm_waste","glass_raw_kg","numeric","Weight of glass as weighed in the field, in kilograms, before the container deduction." +"guluswm","guluswm_waste","metals_raw_kg","numeric","Weight of metals as weighed in the field, in kilograms, before the container deduction." +"guluswm","guluswm_waste","diapers_raw_kg","numeric","Weight of disposable diapers (recorded as Pampers) as weighed in the field, in kilograms, before the container deduction." +"guluswm","guluswm_waste","polyethylene_bags_raw_kg","numeric","Weight of polyethylene bags as weighed in the field, in kilograms, before the container deduction." +"guluswm","guluswm_waste","cardboard_raw_kg","numeric","Weight of cardboard as weighed in the field, in kilograms, before the container deduction." +"guluswm","guluswm_waste","ewaste_raw_kg","numeric","Weight of electronic waste as weighed in the field, in kilograms, before the container deduction." +"guluswm","guluswm_waste","aluminium_raw_kg","numeric","Weight of aluminium as weighed in the field, in kilograms, before the container deduction." +"guluswm","guluswm_waste","inert_fines_raw_kg","numeric","Weight of inert fines (sand, soil and stones, recorded as inorganic non combustible) as weighed in the field, in kilograms, before the container deduction." +"guluswm","guluswm_waste","miscellaneous_raw_kg","numeric","Weight of miscellaneous waste (residual fine material) as weighed in the field, in kilograms, before the container deduction." "gwinfraassess","gwinfraassess","submitted_on","character","Date and time the assessment record was submitted." "gwinfraassess","gwinfraassess","level_measurable","character","Indicates whether the groundwater level could be measured." "gwinfraassess","gwinfraassess","measure_reason","character","Reason why the groundwater level could not be measured." @@ -1565,7 +1666,7 @@ "kalaiwash","kalaiwash","total_collect_time","numeric","Total collection time in minutes: twice the one-way walk (oneway_travel) plus the wait time (wait_time)" "kalaiwash","kalaiwash","satisfied","factor","Whether the respondent is satisfied with the water service" "kalaiwash","kalaiwash","notsatisfied_why","factor","Why not satisfied with your water service" -"kalaiwash","kalaiwash","defecation_place","factor","The places that adult men and women in this household defecate" +"kalaiwash","kalaiwash","defecation_place","factor","The places where adult men and women defecate and where children's faeces are disposed of within the household" "kalaiwash","kalaiwash","handwash_demo","factor","Willing to show where and how handwashing happens" "kalaiwash","kalaiwash","soap_ash","factor","Household demo uses soap or ash or another cleanser to wash hands" "kalaiwash","kalaiwash","water_wash","factor","Household demo uses water to wash hands"