diff --git a/README.md b/README.md index 3fdc7e8..54f6819 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@ The goal of washopenresearch is to provide an overview of open research data related to Water Sanitation and Hygiene (WASH). The current version -contains three datasets from the following sources: +contains four datasets from the following sources: - `washdev`: Open access journal [*Journal of Water, Sanitation and Hygiene for Development*](https://iwaponline.com/washdev) @@ -23,6 +23,15 @@ contains three datasets from the following sources: News](https://waterinstitute.unc.edu/our-work/nc-water-news-newsletter) - `ploswater`: Open access journal [*PLOS Water*](https://journals.plos.org/water/) +- `datapapers`: WASH-related data papers in seven dedicated data + journals ([Scientific Data](https://www.nature.com/sdata/), [Data in + Brief](https://www.sciencedirect.com/journal/data-in-brief), [Gates + Open Research](https://gatesopenresearch.org), + [F1000Research](https://f1000research.com), + [GigaScience](https://academic.oup.com/gigascience), + [GigaByte](https://gigabytejournal.com), and + [Data](https://www.mdpi.com/journal/data)), harvested from Crossref + and Europe PMC
gt::as_raw_html() ``` -
+
@@ -141,7 +151,7 @@ washdev |> - + @@ -170,7 +180,7 @@ washdev |> - + @@ -199,7 +209,7 @@ washdev |> - +
NA NA NANA10.2166/washdev.2011.0001 iwaponline.com
28745 1NA NA function-based; sanitation technologies; sustainability; the sanitation ladderNA10.2166/washdev.2011.014 iwaponline.com
28743 1NA NA biosolid accumulation; Cyperus papyrus; Echinochloa pyramidalis; faecal sludge dewatering; pollutant removal efficiencies; vertical-flow constructed wetlandsNA10.2166/washdev.2011.001 iwaponline.com
@@ -794,8 +804,9 @@ character -DOI of the paper. Collected since the R port of the scraper; NA for -articles scraped earlier +DOI of the paper. Collected by the R scraper for recent articles and +backfilled via Crossref for legacy rows (issue \#20); NA where no +Crossref match was found (see data-raw/washdev-doi-review.csv) @@ -820,7 +831,7 @@ uncnewsletter |> gt::as_raw_html() ``` -
+
@@ -852,6 +863,7 @@ uncnewsletter |> + @@ -882,7 +894,8 @@ uncnewsletter |> - + + @@ -910,7 +923,8 @@ uncnewsletter |> - + + @@ -938,7 +952,8 @@ uncnewsletter |> - + +
das_repo_url citations keywordsdoi
NA NA 2drinkingwater; environmentalpolicy; healthandsafety
drinkingwater; environmentalpolicy; healthandsafety10.1002/ep.13800
89 http://eepurl.com/ieh0rf https://ajph.aphapublications.org/doi/abs/10.2105/AJPH.2022.307108NA NA 3NA
NA10.2105/ajph.2022.307108
200 http://eepurl.com/hWz3Yf https://aslopubs.onlinelibrary.wiley.com/doi/abs/10.1002/lom3.10469available in online repository https://github.com/tedlanghorst/OpenOBS 4NA
NA10.1002/lom3.10469
@@ -1499,6 +1514,27 @@ Keywords of the paper separated by a semicolon + + + + +doi + + + + +character + + + + +DOI of the paper backfilled via a Crossref title search (issue \#20); NA +where no match cleared the title-similarity threshold (see +data-raw/uncnewsletter-doi-review.csv) + + + + @@ -1523,7 +1559,7 @@ ploswater |> gt::as_raw_html() ``` -
+
@@ -2324,6 +2360,489 @@ Date of publication (ISO 8601) +### datapapers + +The dataset `datapapers` contains WASH-related data papers published in +seven dedicated data journals, identified from Crossref and Europe PMC +metadata and screened for relevance (see `data-raw/README.md` for the +pipeline). It has 8 observations. Because a data paper exists to +describe a shared dataset, `data_repo_url` and `data_repo` take the role +that the data availability statement variables play in the other two +datasets. + +``` r +datapapers |> + head(3) |> + gt::gt() |> + gt::as_raw_html() +``` + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
paperiddoipaper_urlurl_sourcejournaltitlepublished_yearnum_authorsfirst_author_namefirst_author_affiliationfirst_author_affiliation_countrydata_repo_urldata_repolicenserelated_paper_doiabstractquery_termretrieval_date
110.3390/data8060103https://doi.org/10.3390/data8060103mdpi.comDataPhysico-Chemical Quality and Physiological Profiles of Microbial Communities in Freshwater Systems of Mega Manila, Philippines20236Marie Christine M. ObusanMicrobial Ecology of Terrestrial and Aquatic Systems Laboratory, Institute of Biology, College of Science, University of the Philippines Diliman, Quezon City 1101, PhilippinesPhilippinesNANAhttps://creativecommons.org/licenses/by/4.0/NA<jats:p>Studying the quality of freshwater systems and drinking water in highly urbanized megalopolises around the world remains a challenge. This article reports data on the quality of select freshwater systems in Mega Manila, Philippines. Water samples collected between 2020 and 2021 were analyzed for physico-chemical parameters and microbial community metabolic fingerprints, i.e., carbon substrate utilization patterns (CSUPs). The detection of arsenic, lead, cadmium, mercury, polyaromatic hydrocarbons (PAHs), and organochlorine pesticides (OCPs) was carried out using standard chromatography- and spectroscopy-based protocols. Physiological profiles were determined using the Biolog EcoPlate™ system. Eight samples were free of heavy metals, and none contained PAHs or OCPs. Fourteen samples had high microbial activity, as indicated by average well color development (AWCD) and community metabolic diversity (CMD) values. Community-level physiological profiling (CLPP) revealed that (1) samples clustered as groups according to shared CSUPs, and (2) microbial communities in non-drinking samples actively utilized all six substrate classes compared to drinking samples. The data reported here can provide a baseline or a comparator for prospective quality assessments of drinking water and freshwater sources in the region. Metabolic fingerprinting using CSUPs is a simple and cheap phenotypic analysis of microbial communities and their physiological activity in aquatic environments.</jats:p>water quality2026-07-23
210.3390/data8090141https://doi.org/10.3390/data8090141mdpi.comDataThailand Raw Water Quality Dataset Analysis and Evaluation20236Jaturapith KrohkaewDepartment of Big Data Management and Analytics, Rajamangala University of Technology Thanyaburi, Pathum Thani 12110, ThailandThailandNANAhttps://creativecommons.org/licenses/by/4.0/NA<jats:p>Sustainable water quality data are important for understanding historical variability and trends in river regimes, as well as the impact of industrial waste on the health of aquatic ecosystems. Sustainable water management practices heavily depend on reliable and comprehensive data, prompting the need for accurate monitoring and assessment of water quality parameters. This research describes a reconstructed daily water quality dataset that complements rare historical observations for six station points along the Chao Phraya River in Thailand. Internet of Things technology and a Eureka water probe sensor is used to collect and reconstruct the water quality dataset for the period from June 2022–February 2023, with Turbidity, Optical Dissolved Oxygen, Dissolved Oxygen Saturation, Spatial Conductivity, Acidity/Basicity, Total Dissolved Solids, Salinity, Temperature, Chlorophyll, and Depth as the recorded parameters from six different stations. The presented dataset comprises a total of 211,322 data points, which are separated into six CSV files. The dataset is then evaluated using the Long Short-Term Memory (LSTM) algorithm with a Mean Squared Error (MSE) of 0.0012256, and Root Mean Squared Error (RMSE) of 0.0350080. The proposed dataset provides valuable insights for researchers studying river ecosystems, supporting informed decision-making and sustainable water management practices.</jats:p>drinking water; water quality; water sanitation2026-07-23
310.46471/gigabyte.167https://doi.org/10.46471/gigabyte.167gigabytejournal.comGigaByteCollection of entomological, demographic, water and sanitation, and climatic data of interest for arbovirus surveillance in Praia, Cabo Verde20257Lara Ferrero GómezUniversidade Jean Piaget de Cabo VerdeCabo VerdeNANAhttps://creativecommons.org/licenses/by/4.0/NA<jats:p>Vector-borne diseases, primarily those transmitted by mosquitoes, are a serious public health problem. Some, such as dengue, put half of the world’s population at risk. Combating these diseases requires multifaceted strategies, with vector surveillance and control playing key roles. Robust and predictive surveillance systems for vector-borne diseases, based on risk stratification, enable the implementation of appropriate interventions across time and space. Here, we present a collection of entomological, demographic, water and sanitation, and climatic data from Praia (Cabo Verde), a hotspot for mosquito-borne diseases. These data were collected from June to November 2022, at 40 sentinel points scattered across the urban area of Praia. They constitute a valuable source of information for developing predictive scenarios of arbovirus outbreak risk using statistical models applied to spatial and non-spatial indicators. These data demonstrate the utility of GBIF in transforming large volumes of occurrence data into valuable information for arbovirus surveillance and vector control.</jats:p>drinking water; sanitation; water quality; water sanitation2026-07-23
+
+ +For an overview of the variable descriptions, see the following table. + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +variable_name + + +variable_type + + +description +
+ +paperid + + +integer + + +ID number of the paper within this dataset +
+ +doi + + +character + + +DOI of the data paper +
+ +paper_url + + +character + + +Official url of the paper (DOI resolver link) +
+ +url_source + + +character + + +Publisher website of the paper +
+ +journal + + +character + + +Full name of the journal +
+ +title + + +character + + +Title of the paper +
+ +published_year + + +integer + + +Year of publication +
+ +num_authors + + +integer + + +Number of the authors +
+ +first_author_name + + +character + + +Name of the first author +
+ +first_author_affiliation + + +character + + +Academic affiliation of the first author +
+ +first_author_affiliation_country + + +character + + +Country of the first author parsed from first_author_affiliation +variable encoded with United Nations names +
+ +data_repo_url + + +character + + +Website urls of the repository holding the dataset the paper describes +separated by a semicolon when there are multiple +
+ +data_repo + + +character + + +Name of the data repository (e.g. Zenodo Dryad Figshare OSF Dataverse) +parsed from data_repo_url +
+ +license + + +character + + +License url of the paper from Crossref metadata +
+ +related_paper_doi + + +character + + +DOI of a linked research article if any separated by a semicolon when +there are multiple +
+ +abstract + + +character + + +Abstract of the paper as provided by the metadata source +
+ +query_term + + +character + + +WASH search term(s) that retrieved the paper separated by a semicolon +
+ +retrieval_date + + +date + + +Date the paper metadata was harvested from the API +
+ +
+ ## Example ### washdev @@ -2351,7 +2870,7 @@ washdev |> theme_classic() ``` - + 2. What are the top choices of keywords in WASH Dev? @@ -2401,7 +2920,27 @@ uncnewsletter |> theme_classic() ``` - + + +### datapapers + +1. How many papers per journal, and how many resolve to a data + repository? + +``` r +datapapers |> + group_by(journal) |> + summarise(papers = n(), + with_repository_link = sum(!is.na(data_repo_url))) |> + arrange(desc(papers)) |> + knitr::kable() +``` + +| journal | papers | with_repository_link | +|:----------------|-------:|---------------------:| +| Scientific Data | 5 | 0 | +| Data | 2 | 0 | +| GigaByte | 1 | 0 | ## Method @@ -2426,6 +2965,17 @@ publication’s html file using the publication url. The retrieval is rule-based to find the relevant fields (e.g. supplementary materials) and extract the value. +### datapapers + +The collection of `datapapers` is fully scripted in R. Crossref is +queried by journal ISSN and Europe PMC by journal name (for the +F1000-platform journals) with a fixed list of WASH search terms; the +harvest is committed as a raw snapshot with the retrieval date and +matching query terms recorded per row. Relevance screening and country +corrections are captured in committed CSV decision sheets keyed on DOI, +so the pipeline runs end-to-end non-interactively. See +`data-raw/README.md` for the run order. + ### uncnewsletter The collection of `uncnewsletter` is a combination of web scraping and diff --git a/data-raw/apply_datapapers_decisions.R b/data-raw/apply_datapapers_decisions.R new file mode 100644 index 0000000..ea1ff4b --- /dev/null +++ b/data-raw/apply_datapapers_decisions.R @@ -0,0 +1,60 @@ +# Merge decisions exported from the review app (issue #28) into the committed +# screening sheet. Reads data-raw/datapapers_decisions.csv (doi, include, +# reason; produced by the Export button in datapapers_review.html) and fills +# include/reason in data-raw/datapapers_screening.csv for those DOIs. +# +# The decisions file is the reviewer's own choices, so it overwrites any +# earlier value for the same DOI; rows not in the decisions file are left +# untouched. Re-running is idempotent. +# +# Run from the package root: +# Rscript data-raw/apply_datapapers_decisions.R + +library(dplyr) +library(readr) + +decisions_path <- "data-raw/datapapers_decisions.csv" +screening_path <- "data-raw/datapapers_screening.csv" + +if (!file.exists(decisions_path)) { + stop("Missing ", decisions_path, ". Export decisions from ", + "data-raw/datapapers_review.html first (Export CSV button) and save ", + "the download there.", call. = FALSE) +} + +decisions <- read_csv( + decisions_path, + col_types = cols(doi = col_character(), include = col_logical(), + reason = col_character()) +) +stopifnot(!any(duplicated(decisions$doi)), !any(is.na(decisions$include))) + +screening <- read_csv( + screening_path, + col_types = cols( + doi = col_character(), title = col_character(), journal = col_character(), + published_year = col_integer(), auto_relevant = col_logical(), + include = col_logical(), reason = col_character() + ) +) + +unknown <- setdiff(decisions$doi, screening$doi) +if (length(unknown) > 0) { + stop(length(unknown), " decision DOI(s) not present in the screening sheet, ", + "first: ", unknown[1], call. = FALSE) +} + +updated <- screening |> + left_join(decisions, by = "doi", suffix = c("", "_new")) |> + mutate( + include = coalesce(include_new, include), + reason = if_else(!is.na(include_new), reason_new, reason), + include_new = NULL, reason_new = NULL + ) + +write_csv(updated, screening_path, na = "") + +message(nrow(decisions), " decisions applied to ", screening_path, ": now ", + sum(updated$include %in% TRUE), " included, ", + sum(updated$include %in% FALSE), " excluded, ", + sum(is.na(updated$include)), " pending.") diff --git a/data-raw/datapapers_country_fixes.csv b/data-raw/datapapers_country_fixes.csv index f096d73..4e4d87d 100644 --- a/data-raw/datapapers_country_fixes.csv +++ b/data-raw/datapapers_country_fixes.csv @@ -1 +1,2 @@ doi,first_author_affiliation_country +10.46471/gigabyte.167,Cabo Verde diff --git a/data-raw/datapapers_decisions.csv b/data-raw/datapapers_decisions.csv new file mode 100644 index 0000000..0149607 --- /dev/null +++ b/data-raw/datapapers_decisions.csv @@ -0,0 +1,46 @@ +doi,include,reason +10.3390/data2030024,FALSE, +10.3390/data3040050,FALSE, +10.3390/data6080079,FALSE, +10.3390/data8060103,TRUE, +10.3390/data8030048,FALSE, +10.3390/data8090141,TRUE, +10.3390/data8110162,FALSE, +10.3390/data10100155,FALSE, +10.3390/data10040051,FALSE, +10.46471/gigabyte.167,TRUE, +10.1093/gigascience/gix101,FALSE, +10.1093/gigascience/giaa125,FALSE, +10.1093/gigascience/giae051,FALSE, +10.1038/sdata.2017.98,FALSE, +10.1038/sdata.2018.108,TRUE, +10.1038/s41597-019-0316-y,FALSE, +10.1038/s41597-020-00648-2,FALSE, +10.1038/s41597-020-0562-z,FALSE, +10.1038/s41597-020-0496-5,FALSE, +10.1038/s41597-020-0384-z,FALSE, +10.1038/s41597-020-00596-x,FALSE, +10.1038/s41597-021-01002-w,FALSE, +10.1038/s41597-021-00856-4,FALSE, +10.1038/s41597-022-01686-8,TRUE, +10.1038/s41597-022-01749-w,TRUE, +10.1038/s41597-022-01439-7,TRUE, +10.1038/s41597-022-01358-7,FALSE, +10.1038/s41597-022-01788-3,FALSE, +10.1038/s41597-023-02732-9,FALSE, +10.1038/s41597-023-01973-y,FALSE, +10.1038/s41597-023-02297-7,FALSE, +10.1038/s41597-023-02277-x,FALSE, +10.1038/s41597-024-03298-w,FALSE, +10.1038/s41597-024-03453-3,FALSE, +10.1038/s41597-023-02886-6,FALSE, +10.1038/s41597-024-03601-9,FALSE, +10.1038/s41597-025-06059-5,FALSE, +10.1038/s41597-025-05459-x,FALSE, +10.1038/s41597-025-05600-w,FALSE, +10.1038/s41597-025-06468-6,FALSE, +10.1038/s41597-025-05625-1,FALSE, +10.1038/s41597-025-04537-4,TRUE, +10.1038/s41597-025-05100-x,FALSE, +10.1038/s41597-026-06698-2,FALSE, +10.1038/s41597-026-07352-7,FALSE, diff --git a/data-raw/datapapers_review_template.html b/data-raw/datapapers_review_template.html new file mode 100644 index 0000000..240951a --- /dev/null +++ b/data-raw/datapapers_review_template.html @@ -0,0 +1,832 @@ + + + + + +Datapapers Screening Desk - washopenresearch + + + + + + + +
+
+
washopenresearch · issue #28
+

Datapapers Screening Desk

+
+
+ included 0 + excluded 0 + pending 0 + abstract queue 0/0 +
+ +
+ +
+ +
+ + +
+
+
+
+

+
+
+
+
Abstract
+
+
+
+
+ +
+
+
+
+ + + + +
+
+ ↑↓ navigate   I include   + X exclude   U clear decision   R focus reason   + decisions auto-advance to the next pending paper +
+
+
+ +
+ + + + diff --git a/data-raw/datapapers_screening.csv b/data-raw/datapapers_screening.csv index a03906d..a78b0b8 100644 --- a/data-raw/datapapers_screening.csv +++ b/data-raw/datapapers_screening.csv @@ -1,12 +1,12 @@ doi,title,journal,published_year,auto_relevant,include,reason 10.3390/data1030020,Standardization and Quality Control in Data Collection and Assessment of Threatened Plant Species,Data,2016,FALSE,, 10.3390/data2020014,"CHASE-PL—Future Hydrology Data Set: Projections of Water Balance and Streamflow for the Vistula and Odra Basins, Poland",Data,2017,FALSE,, -10.3390/data2030024,Thermodynamic Data of Fusarium oxysporum Grown on Different Substrates in Gold Mine Wastewater,Data,2017,TRUE,, +10.3390/data2030024,Thermodynamic Data of Fusarium oxysporum Grown on Different Substrates in Gold Mine Wastewater,Data,2017,TRUE,FALSE, 10.3390/data2040035,"Earth Observation for Citizen Science Validation, or Citizen Science for Earth Observation Validation? The Role of Quality Assurance of Volunteered Observations",Data,2017,FALSE,, 10.3390/data3020021,Taguchi Orthogonal Array Dataset for the Effect of Water Chemistry on Aggregation of ZnO Nanoparticles,Data,2018,FALSE,, 10.3390/data3030027,Data Quality: A Negotiator between Paper-Based and Digital Records in Pakistan’s TB Control Program,Data,2018,FALSE,, 10.3390/data3040045,Improving the Quality of Survey Data Documentation: A Total Survey Error Perspective,Data,2018,FALSE,, -10.3390/data3040050,Application of Rough Set Theory to Water Quality Analysis: A Case Study,Data,2018,TRUE,, +10.3390/data3040050,Application of Rough Set Theory to Water Quality Analysis: A Case Study,Data,2018,TRUE,FALSE, 10.3390/data4040146,Experimental Data of a Floating Cylinder in a Wave Tank: Comparison Solid and Water Ballast,Data,2019,FALSE,, 10.3390/data5010027,Data on Orientation to Happiness in Higher Education Institutions from Mexico and El Salvador,Data,2020,FALSE,, 10.3390/data5020030,Influence of Information Quality via Implemented German RCD Standard in Research Information Systems,Data,2020,FALSE,, @@ -16,7 +16,7 @@ doi,title,journal,published_year,auto_relevant,include,reason 10.3390/data6020015,Repository Approaches to Improving the Quality of Shared Data and Code,Data,2021,FALSE,, 10.3390/data6030026,FIKWater: A Water Consumption Dataset from Three Restaurant Kitchens in Portugal,Data,2021,FALSE,, 10.3390/data6060060,Information Quality Assessment for Data Fusion Systems,Data,2021,FALSE,, -10.3390/data6080079,Temporal Changes in Delaware Waters Using Long-Term (1967–2019) Water Temperature Data,Data,2021,TRUE,, +10.3390/data6080079,Temporal Changes in Delaware Waters Using Long-Term (1967–2019) Water Temperature Data,Data,2021,TRUE,FALSE, 10.3390/data6090100,Dataset of Flow-Induced Vibrations on a Pipe Conveying Cold Water,Data,2021,FALSE,, 10.3390/data7010001,Datasets for the Determination of Evaporative Flux from Distilled Water and Saturated Brine Using Bench-Scale Atmospheric Simulators,Data,2021,FALSE,, 10.3390/data7010006,Multi-Temporal Surface Water Classification for Four Major Rivers from the Peruvian Amazon,Data,2022,FALSE,, @@ -24,17 +24,17 @@ doi,title,journal,published_year,auto_relevant,include,reason 10.3390/data8010008,Aggregation of Multimodal ICE-MS Data into Joint Classifier Increases Quality of Brain Cancer Tissue Classification,Data,2022,FALSE,, 10.3390/data8010021,Shapley Value as a Quality Control for Mass Spectra of Human Glioblastoma Tissues,Data,2023,FALSE,, 10.3390/data8020038,Datasets of Groundwater Level and Surface Water Budget in a Central Mediterranean Site (21 June 2017–1 October 2022),Data,2023,FALSE,, -10.3390/data8030048,Reconstructed River Water Temperature Dataset for Western Canada 1980–2018,Data,2023,TRUE,, +10.3390/data8030048,Reconstructed River Water Temperature Dataset for Western Canada 1980–2018,Data,2023,TRUE,FALSE, 10.3390/data8030055,"Dataset AqADAPT: Physicochemical Parameters, Vibrio Abundance, and Species Determination in Water Columns of Two Adriatic Sea Aquaculture Sites",Data,2023,FALSE,, 10.3390/data8040070,Clinical Trial Data on the Mechanical Removal of 14-Day-Old Dental Plaque Using Accelerated Micro-Droplets of Air and Water (Airfloss),Data,2023,FALSE,, 10.3390/data8060102,A Self-Attention-Based Imputation Technique for Enhancing Tabular Data Quality,Data,2023,FALSE,, -10.3390/data8060103,"Physico-Chemical Quality and Physiological Profiles of Microbial Communities in Freshwater Systems of Mega Manila, Philippines",Data,2023,TRUE,, +10.3390/data8060103,"Physico-Chemical Quality and Physiological Profiles of Microbial Communities in Freshwater Systems of Mega Manila, Philippines",Data,2023,TRUE,TRUE, 10.3390/data8060106,Curated Dataset for Red Blood Cell Tracking from Video Sequences of Flow in Microfluidic Devices,Data,2023,FALSE,, 10.3390/data8070117,Assessment of Maize Silage Quality under Different Pre-Ensiling Conditions,Data,2023,FALSE,, 10.3390/data8080124,Measuring the Effect of Fraud on Data-Quality Dimensions,Data,2023,FALSE,, 10.3390/data8090140,Employing Source Code Quality Analytics for Enriching Code Snippets Data,Data,2023,FALSE,, -10.3390/data8090141,Thailand Raw Water Quality Dataset Analysis and Evaluation,Data,2023,TRUE,, -10.3390/data8110162,The Development of a Water Resource Monitoring Ontology as a Research Tool for Sustainable Regional Development,Data,2023,TRUE,, +10.3390/data8090141,Thailand Raw Water Quality Dataset Analysis and Evaluation,Data,2023,TRUE,TRUE, +10.3390/data8110162,The Development of a Water Resource Monitoring Ontology as a Research Tool for Sustainable Regional Development,Data,2023,TRUE,FALSE, 10.3390/data8120182,An Automated Big Data Quality Anomaly Correction Framework Using Predictive Analysis,Data,2023,FALSE,, 10.3390/data9020035,"COVID-19 Lockdown Effects on Sleep, Immune Fitness, Mood, Quality of Life, and Academic Functioning: Survey Data from Turkish University Students",Data,2024,FALSE,, 10.3390/data9040051,Longitudinal Patterns of Online Activity and Social Feedback Are Associated with Current and Perceived Changes in Quality of Life in Adult Facebook Users,Data,2024,FALSE,, @@ -42,12 +42,12 @@ doi,title,journal,published_year,auto_relevant,include,reason 10.3390/data9120142,Nearest-Better Network-Assisted Fitness Landscape Analysis of Contaminant Source Identification in Water Distribution Network,Data,2024,FALSE,, 10.3390/data9120151,A Framework for Current and New Data Quality Dimensions: An Overview,Data,2024,FALSE,, 10.3390/data10030033,Data Quality Tools to Enhance a Network Anomaly Detection Benchmark,Data,2025,FALSE,, -10.3390/data10040051,Observational Monitoring Records Downstream Impacts of Beaver Dams on Water Quality and Quantity in Temperate Mixed-Land-Use Watersheds,Data,2025,TRUE,, +10.3390/data10040051,Observational Monitoring Records Downstream Impacts of Beaver Dams on Water Quality and Quantity in Temperate Mixed-Land-Use Watersheds,Data,2025,TRUE,FALSE, 10.3390/data10060084,Macao-ebird: A Curated Dataset for Artificial-Intelligence-Powered Bird Surveillance and Conservation in Macao,Data,2025,FALSE,, 10.3390/data10080130,An Extended Dataset of Educational Quality Across Countries (1970–2023),Data,2025,FALSE,, 10.3390/data10090136,A FAIR Perspective on Data Quality Frameworks,Data,2025,FALSE,, 10.3390/data10090143,Comprehensive Evaluation of Water Resource Carrying Capacity in Hebei Province Based on a Combined Weighting–TOPSIS Model,Data,2025,FALSE,, -10.3390/data10100155,"A Dataset of Environmental Toxins for Water Monitoring in Coastal Waters of Southern Centre, Vietnam: Case of Nha Trang Bay",Data,2025,TRUE,, +10.3390/data10100155,"A Dataset of Environmental Toxins for Water Monitoring in Coastal Waters of Southern Centre, Vietnam: Case of Nha Trang Bay",Data,2025,TRUE,FALSE, 10.3390/data10120200,"Georeferenced Sediment and Surface Water Element Concentrations in the Coastal Liepāja Lake (Latvia), 2024",Data,2025,FALSE,, 10.3390/data10120201,"Data Quality in the Age of AI: A Review of Governance, Ethics, and the FAIR Principles",Data,2025,FALSE,, 10.3390/data10120211,A Real-World Underwater Video Dataset with Labeled Frames and Water-Quality Metadata for Aquaculture Monitoring,Data,2025,FALSE,, @@ -3315,12 +3315,12 @@ doi,title,journal,published_year,auto_relevant,include,reason 10.46471/gigabyte.28,A high-quality draft genome for Melaleuca alternifolia (tea tree): a new platform for evolutionary genomics of myrtaceous terpene-rich species,GigaByte,2021,FALSE,, 10.46471/gigabyte.31,Atria: an ultra-fast and accurate trimmer for adapter and quality trimming,GigaByte,2021,FALSE,, 10.46471/gigabyte.74,HTGQC and shinyHTGQC: an R package and shinyR application for quality controls of HTG EDGE-seq protocols,GigaByte,2022,FALSE,, -10.46471/gigabyte.167,"Collection of entomological, demographic, water and sanitation, and climatic data of interest for arbovirus surveillance in Praia, Cabo Verde",GigaByte,2025,TRUE,, +10.46471/gigabyte.167,"Collection of entomological, demographic, water and sanitation, and climatic data of interest for arbovirus surveillance in Praia, Cabo Verde",GigaByte,2025,TRUE,TRUE, 10.1186/s13742-016-0142-5,"High-quality genome assembly of channel catfish, Ictalurus punctatus",GigaScience,2016,FALSE,, 10.1093/gigascience/gix029,Calculating the quality of public high-throughput sequencing data to obtain a suitable subset for reanalysis from the Sequence Read Archive,GigaScience,2017,FALSE,, 10.1093/gigascience/gix090,Indexcov: fast coverage quality control for whole-genome sequencing,GigaScience,2017,FALSE,, 10.1093/gigascience/gix098,A 3-way hybrid approach to generate a new high-quality chimpanzee reference genome (Pan_tro_3.0),GigaScience,2017,FALSE,, -10.1093/gigascience/gix101,LAGOS-NE: a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of US lakes,GigaScience,2017,TRUE,, +10.1093/gigascience/gix101,LAGOS-NE: a multi-scaled geospatial and temporal database of lake ecological context and water quality for thousands of US lakes,GigaScience,2017,TRUE,FALSE, 10.1093/gigascience/gix120,SOAPnuke: a MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data,GigaScience,2018,FALSE,, 10.1093/gigascience/giy068,"High-quality assembly of the reference genome for scarlet sage, Salvia splendens, an economically important ornamental plant",GigaScience,2018,FALSE,, 10.1093/gigascience/giy111,Metabolomics investigation of dietary effects on flesh quality in grass carp (Ctenopharyngodon idellus),GigaScience,2018,FALSE,, @@ -3341,7 +3341,7 @@ doi,title,journal,published_year,auto_relevant,include,reason L.) reference genome",GigaScience,2020,FALSE,, 10.1093/gigascience/giaa058,Education in the genomics era: Generating high-quality genome assemblies in university courses,GigaScience,2020,FALSE,, 10.1093/gigascience/giaa078,PhaseME: Automatic rapid assessment of phasing quality and phasing improvement,GigaScience,2020,FALSE,, -10.1093/gigascience/giaa125,Localized effect of treated wastewater effluent on the resistome of an urban watershed,GigaScience,2020,TRUE,, +10.1093/gigascience/giaa125,Localized effect of treated wastewater effluent on the resistome of an urban watershed,GigaScience,2020,TRUE,FALSE, 10.1093/gigascience/giaa143,High-quality chromosome-level genome assembly and full-length transcriptome analysis of the pharaoh ant Monomorium pharaonis,GigaScience,2020,FALSE,, 10.1093/gigascience/giz164,A high-quality chromosomal genome assembly of Diospyros oleifera Cheng,GigaScience,2020,FALSE,, 10.1093/gigascience/giaa140,Streamlining data-intensive biology with workflow systems,GigaScience,2021,FALSE,, @@ -3368,7 +3368,7 @@ doi,title,journal,published_year,auto_relevant,include,reason shows allelic diversity of NLR-type resistance genes",GigaScience,2022,FALSE,, 10.1093/gigascience/giad107,A high-quality chromosomal genome assembly of the sea cucumber Chiridota heheva and its hydrothermal adaptation,GigaScience,2024,FALSE,, 10.1093/gigascience/giae006,A reference genome of Commelinales provides insights into the commelinids evolution and global spread of water hyacinth (Pontederia crassipes),GigaScience,2024,FALSE,, -10.1093/gigascience/giae051,Impact of reference design on estimating SARS-CoV-2 lineage abundances from wastewater sequencing data,GigaScience,2024,TRUE,, +10.1093/gigascience/giae051,Impact of reference design on estimating SARS-CoV-2 lineage abundances from wastewater sequencing data,GigaScience,2024,TRUE,FALSE, 10.1093/gigascience/giae075,High-quality assembly of the T2T genome for Isodon rubescens f. lushanensis reveals genomic structure variations between 2 typical forms of Isodon rubescens,GigaScience,2024,FALSE,, 10.1093/gigascience/giae079,CoCoPyE: feature engineering for learning and prediction of genome quality indices,GigaScience,2024,FALSE,, 10.1093/gigascience/giae099,Near telomere-to-telomere genome assembly of Mongolian cattle: implications for population genetic variation and beef quality,GigaScience,2024,FALSE,, @@ -3393,10 +3393,10 @@ doi,title,journal,published_year,auto_relevant,include,reason 10.1038/sdata.2017.160,A compendium of multi-omic sequence information from the Saanich Inlet water column,Scientific Data,2017,FALSE,, 10.1038/sdata.2017.53,Large-scale modeled contemporary and future water temperature estimates for 10774 Midwestern U.S. Lakes,Scientific Data,2017,FALSE,, 10.1038/sdata.2017.95,A comprehensive data set of lake surface water temperature over the Tibetan Plateau derived from MODIS LST products 2001–2015,Scientific Data,2017,FALSE,, -10.1038/sdata.2017.98,"Water quality measurements in San Francisco Bay by the U.S. Geological Survey, 1969–2015",Scientific Data,2017,TRUE,, +10.1038/sdata.2017.98,"Water quality measurements in San Francisco Bay by the U.S. Geological Survey, 1969–2015",Scientific Data,2017,TRUE,FALSE, 10.1038/sdata.2017.191,"TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015",Scientific Data,2018,FALSE,, 10.1038/sdata.2017.206,Australia’s continental-scale acoustic tracking database and its automated quality control process,Scientific Data,2018,FALSE,, -10.1038/sdata.2018.108,Freshwater macroinvertebrate samples from a water quality monitoring network in the Iberian Peninsula,Scientific Data,2018,TRUE,, +10.1038/sdata.2018.108,Freshwater macroinvertebrate samples from a water quality monitoring network in the Iberian Peninsula,Scientific Data,2018,TRUE,TRUE, 10.1038/sdata.2018.122,"OceanRAIN, a new in-situ shipboard global ocean surface-reference dataset of all water cycle components",Scientific Data,2018,FALSE,, 10.1038/sdata.2018.263,Generation and quality control of lipidomics data for the alzheimer’s disease neuroimaging initiative cohort,Scientific Data,2018,FALSE,, 10.1038/sdata.2018.27,High-quality science requires high-quality open data infrastructure,Scientific Data,2018,FALSE,, @@ -3410,33 +3410,33 @@ doi,title,journal,published_year,auto_relevant,include,reason 10.1038/s41597-019-0243-y,A data set of global river networks and corresponding water resources zones divisions,Scientific Data,2019,FALSE,, 10.1038/s41597-019-0274-4,Room-level occupant counts and environmental quality from heterogeneous sensing modalities in a smart building,Scientific Data,2019,FALSE,, 10.1038/s41597-019-0315-z,"Author Correction: Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications",Scientific Data,2019,FALSE,, -10.1038/s41597-019-0316-y,"CE-QUAL-W2 model of dam outflow elevation impact on temperature, dissolved oxygen and nutrients in a reservoir",Scientific Data,2019,TRUE,, +10.1038/s41597-019-0316-y,"CE-QUAL-W2 model of dam outflow elevation impact on temperature, dissolved oxygen and nutrients in a reservoir",Scientific Data,2019,TRUE,FALSE, 10.1038/sdata.2018.302,A global database of water vapor isotopes measured with high temporal resolution infrared laser spectroscopy,Scientific Data,2019,FALSE,, 10.1038/sdata.2019.21,The variable quality of metadata about biological samples used in biomedical experiments,Scientific Data,2019,FALSE,, 10.1038/sdata.2019.30,Assessing data availability and research reproducibility in hydrology and water resources,Scientific Data,2019,FALSE,, 10.1038/s41597-019-0339-4,Evaluating sequence data quality from the Swift Accel-Amplicon CFTR Panel,Scientific Data,2020,FALSE,, -10.1038/s41597-020-00596-x,Mapping of 30-meter resolution tile-drained croplands using a geospatial modeling approach,Scientific Data,2020,TRUE,, +10.1038/s41597-020-00596-x,Mapping of 30-meter resolution tile-drained croplands using a geospatial modeling approach,Scientific Data,2020,TRUE,FALSE, 10.1038/s41597-020-00612-0,The green and blue crop water requirement WATNEEDS model and its global gridded outputs,Scientific Data,2020,FALSE,, 10.1038/s41597-020-00613-z,Seventy-year long record of monthly water balance estimates for Earth’s largest lake system,Scientific Data,2020,FALSE,, -10.1038/s41597-020-00648-2,A database of chlorophyll and water chemistry in freshwater lakes,Scientific Data,2020,TRUE,, +10.1038/s41597-020-00648-2,A database of chlorophyll and water chemistry in freshwater lakes,Scientific Data,2020,TRUE,FALSE, 10.1038/s41597-020-00649-1,Approaching 80 years of snow water equivalent information by merging different data streams,Scientific Data,2020,FALSE,, 10.1038/s41597-020-00685-x,Triple isotope variations of monthly tap water in China,Scientific Data,2020,FALSE,, 10.1038/s41597-020-0363-4,Four high-quality draft genome assemblies of the marine heterotrophic nanoflagellate Cafeteria roenbergensis,Scientific Data,2020,FALSE,, -10.1038/s41597-020-0384-z,"Hydraulic, wash-off and sediment transport experiments in a full-scale urban drainage physical model",Scientific Data,2020,TRUE,, +10.1038/s41597-020-0384-z,"Hydraulic, wash-off and sediment transport experiments in a full-scale urban drainage physical model",Scientific Data,2020,TRUE,FALSE, 10.1038/s41597-020-0416-8,Publisher Correction: Room-level occupant counts and environmental quality from heterogeneous sensing modalities in a smart building,Scientific Data,2020,FALSE,, 10.1038/s41597-020-0480-0,An improved high-quality genome assembly and annotation of Tibetan hulless barley,Scientific Data,2020,FALSE,, -10.1038/s41597-020-0496-5,A novel real-world ecotoxicological dataset of pelagic microbial community responses to wastewater,Scientific Data,2020,TRUE,, +10.1038/s41597-020-0496-5,A novel real-world ecotoxicological dataset of pelagic microbial community responses to wastewater,Scientific Data,2020,TRUE,FALSE, 10.1038/s41597-020-0533-4,"Massive online data annotation, crowdsourcing to generate high quality sleep spindle annotations from EEG data",Scientific Data,2020,FALSE,, -10.1038/s41597-020-0562-z,A global dataset of surface water and groundwater salinity measurements from 1980–2019,Scientific Data,2020,TRUE,, +10.1038/s41597-020-0562-z,A global dataset of surface water and groundwater salinity measurements from 1980–2019,Scientific Data,2020,TRUE,FALSE, 10.1038/s41597-020-00795-6,The traded water footprint of global energy from 2010 to 2018,Scientific Data,2021,FALSE,, 10.1038/s41597-021-00814-0,Phytoplankton morpho-functional trait dataset from French water-bodies,Scientific Data,2021,FALSE,, -10.1038/s41597-021-00856-4,Water quality measurements in Buzzards Bay by the Buzzards Bay Coalition Baywatchers Program from 1992 to 2018,Scientific Data,2021,TRUE,, +10.1038/s41597-021-00856-4,Water quality measurements in Buzzards Bay by the Buzzards Bay Coalition Baywatchers Program from 1992 to 2018,Scientific Data,2021,TRUE,FALSE, 10.1038/s41597-021-00862-6,Downscaling GRACE total water storage change using partial least squares regression,Scientific Data,2021,FALSE,, 10.1038/s41597-021-00866-2,"Harmonized and high-quality datasets of aerosol optical depth at a US continental site, 1997–2018",Scientific Data,2021,FALSE,, 10.1038/s41597-021-00909-8,A multilevel carbon and water footprint dataset of food commodities,Scientific Data,2021,FALSE,, 10.1038/s41597-021-00939-2,GlobSnow v3.0 Northern Hemisphere snow water equivalent dataset,Scientific Data,2021,FALSE,, 10.1038/s41597-021-01000-y,A global database of diversified farming effects on biodiversity and yield,Scientific Data,2021,FALSE,, -10.1038/s41597-021-01002-w,Inter-laboratory mass spectrometry dataset based on passive sampling of drinking water for non-target analysis,Scientific Data,2021,TRUE,, +10.1038/s41597-021-01002-w,Inter-laboratory mass spectrometry dataset based on passive sampling of drinking water for non-target analysis,Scientific Data,2021,TRUE,FALSE, 10.1038/s41597-021-01032-4,"SOIL-WATERGRIDS, mapping dynamic changes in soil moisture and depth of water table from 1970 to 2014",Scientific Data,2021,FALSE,, 10.1038/s41597-021-01068-6,"APExpose_DE, an air quality exposure dataset for Germany 2010–2019",Scientific Data,2021,FALSE,, 10.1038/s41597-021-01074-8,A palaeoclimate proxy database for water security planning in Queensland Australia,Scientific Data,2021,FALSE,, @@ -3449,26 +3449,26 @@ doi,title,journal,published_year,auto_relevant,include,reason 10.1038/s41597-022-01267-9,Curation of a list of chemicals in biosolids from EPA National Sewage Sludge Surveys & Biennial Review Reports,Scientific Data,2022,FALSE,, 10.1038/s41597-022-01290-w,U.S. national water and energy land dataset for integrated multisector dynamics research,Scientific Data,2022,FALSE,, 10.1038/s41597-022-01344-z,"USACE Coastal and Hydraulics Laboratory Quality Controlled, Consistent Measurement Archive",Scientific Data,2022,FALSE,, -10.1038/s41597-022-01358-7,Paired field and water measurements from drainage management practices in row-crop agriculture,Scientific Data,2022,TRUE,, +10.1038/s41597-022-01358-7,Paired field and water measurements from drainage management practices in row-crop agriculture,Scientific Data,2022,TRUE,FALSE, 10.1038/s41597-022-01373-8,"PISCOeo_pm, a reference evapotranspiration gridded database based on FAO Penman-Monteith in Peru",Scientific Data,2022,FALSE,, 10.1038/s41597-022-01376-5,"The long-tail effect of the COVID-19 lockdown on Italians’ quality of life, sleep and physical activity",Scientific Data,2022,FALSE,, 10.1038/s41597-022-01410-6,"FutureStreams, a global dataset of future streamflow and water temperature",Scientific Data,2022,FALSE,, 10.1038/s41597-022-01427-x,A global database for conducting systematic reviews and meta-analyses in innovation and quality management,Scientific Data,2022,FALSE,, 10.1038/s41597-022-01430-2,Hourly rainfall data from rain gauge networks and weather radar up to 2020 across the Hawaiian Islands,Scientific Data,2022,FALSE,, -10.1038/s41597-022-01439-7,Greenhouse gas emissions from municipal wastewater treatment facilities in China from 2006 to 2019,Scientific Data,2022,TRUE,, +10.1038/s41597-022-01439-7,Greenhouse gas emissions from municipal wastewater treatment facilities in China from 2006 to 2019,Scientific Data,2022,TRUE,TRUE, 10.1038/s41597-022-01489-x,GloSEM: High-resolution global estimates of present and future soil displacement in croplands by water erosion,Scientific Data,2022,FALSE,, 10.1038/s41597-022-01503-2,Fisheries dataset on moulting patterns and shell quality of American lobsters H. americanus in Atlantic Canada,Scientific Data,2022,FALSE,, 10.1038/s41597-022-01504-1,"Atmospheric temperature, water vapour and liquid water path from two microwave radiometers during MOSAiC",Scientific Data,2022,FALSE,, -10.1038/s41597-022-01686-8,Bacteria communities and water quality parameters in riverine water and sediments near wastewater discharges,Scientific Data,2022,TRUE,, +10.1038/s41597-022-01686-8,Bacteria communities and water quality parameters in riverine water and sediments near wastewater discharges,Scientific Data,2022,TRUE,TRUE, 10.1038/s41597-022-01695-7,An analysis-ready and quality controlled resource for pediatric brain white-matter research,Scientific Data,2022,FALSE,, 10.1038/s41597-022-01742-3,High-resolution bathymetries and shorelines for the Great Lakes of the White Nile basin,Scientific Data,2022,FALSE,, 10.1038/s41597-022-01748-x,"Revised monthly energy generation estimates for 1,500 hydroelectric power plants in the United States",Scientific Data,2022,FALSE,, -10.1038/s41597-022-01749-w,"Freshwater microbial metagenomes sampled across different water body characteristics, space and time in Israel",Scientific Data,2022,TRUE,, +10.1038/s41597-022-01749-w,"Freshwater microbial metagenomes sampled across different water body characteristics, space and time in Israel",Scientific Data,2022,TRUE,TRUE, 10.1038/s41597-022-01760-1,A data set of distributed global population and water withdrawal from 1960 to 2020,Scientific Data,2022,FALSE,, 10.1038/s41597-022-01761-0,High-resolution crop yield and water productivity dataset generated using random forest and remote sensing,Scientific Data,2022,FALSE,, 10.1038/s41597-022-01768-7,A western United States snow reanalysis dataset over the Landsat era from water years 1985 to 2021,Scientific Data,2022,FALSE,, 10.1038/s41597-022-01770-z,Publisher Correction: An analysis-ready and quality controlled resource for pediatric brain white-matter research,Scientific Data,2022,FALSE,, -10.1038/s41597-022-01788-3,SARS-CoV-2 RNA levels in Scotland’s wastewater,Scientific Data,2022,TRUE,, +10.1038/s41597-022-01788-3,SARS-CoV-2 RNA levels in Scotland’s wastewater,Scientific Data,2022,TRUE,FALSE, 10.1038/s41597-022-01816-2,Author Correction: An analysis-ready and quality controlled resource for pediatric brain white-matter research,Scientific Data,2022,FALSE,, 10.1038/s41597-022-01821-5,High-quality Japanese flounder genome aids in identifying stress-related genes using gene coexpression network,Scientific Data,2022,FALSE,, 10.1038/s41597-022-01844-y,A database of water chemistry in eastern Siberian rivers,Scientific Data,2022,FALSE,, @@ -3476,7 +3476,7 @@ doi,title,journal,published_year,auto_relevant,include,reason 10.1038/s41597-023-01927-4,Author Correction: A database of water chemistry in eastern Siberian rivers,Scientific Data,2023,FALSE,, 10.1038/s41597-023-01935-4,Interbasin water transfers in the United States and Canada,Scientific Data,2023,FALSE,, 10.1038/s41597-023-01949-y,"Quality control and removal of technical variation of NMR metabolic biomarker data in ~120,000 UK Biobank participants",Scientific Data,2023,FALSE,, -10.1038/s41597-023-01973-y,GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality,Scientific Data,2023,TRUE,, +10.1038/s41597-023-01973-y,GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality,Scientific Data,2023,TRUE,FALSE, 10.1038/s41597-023-02034-0,"Agrimonia: a dataset on livestock, meteorology and air quality in the Lombardy region, Italy",Scientific Data,2023,FALSE,, 10.1038/s41597-023-02069-3,Author Correction: GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality,Scientific Data,2023,TRUE,, 10.1038/s41597-023-02086-2,Global monthly sectoral water use for 2010–2100 at 0.5° resolution across alternative futures,Scientific Data,2023,FALSE,, @@ -3492,8 +3492,8 @@ doi,title,journal,published_year,auto_relevant,include,reason 10.1038/s41597-023-02235-7,The first high-quality chromosome-level genome of the Sipuncula Sipunculus nudus using HiFi and Hi-C data,Scientific Data,2023,FALSE,, 10.1038/s41597-023-02246-4,A Chinese soil conservation dataset preventing soil water erosion from 1992 to 2019,Scientific Data,2023,FALSE,, 10.1038/s41597-023-02270-4,Chromosome-level assemblies of cultivated water chestnut Trapa bicornis and its wild relative Trapa incisa,Scientific Data,2023,FALSE,, -10.1038/s41597-023-02277-x,Identifying and sharing per-and polyfluoroalkyl substances hot-spot areas and exposures in drinking water,Scientific Data,2023,TRUE,, -10.1038/s41597-023-02297-7,Human viral nucleic acids concentrations in wastewater solids from Central and Coastal California USA,Scientific Data,2023,TRUE,, +10.1038/s41597-023-02277-x,Identifying and sharing per-and polyfluoroalkyl substances hot-spot areas and exposures in drinking water,Scientific Data,2023,TRUE,FALSE, +10.1038/s41597-023-02297-7,Human viral nucleic acids concentrations in wastewater solids from Central and Coastal California USA,Scientific Data,2023,TRUE,FALSE, 10.1038/s41597-023-02316-7,Restructuring and serving web-accessible streamflow data from the NOAA National Water Model historic simulations,Scientific Data,2023,FALSE,, 10.1038/s41597-023-02330-9,Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration,Scientific Data,2023,FALSE,, 10.1038/s41597-023-02332-7,"A soil database from Queretaro, Mexico for assessment of crop and irrigation water requirements",Scientific Data,2023,FALSE,, @@ -3514,10 +3514,10 @@ doi,title,journal,published_year,auto_relevant,include,reason 10.1038/s41597-023-02640-y,One-year dataset of hourly air quality parameters from 100 air purifiers used in China residential buildings,Scientific Data,2023,FALSE,, 10.1038/s41597-023-02660-8,An annotated grain kernel image database for visual quality inspection,Scientific Data,2023,FALSE,, 10.1038/s41597-023-02699-7,Improved high-quality reference genome of red drum facilitates the processes of resistance-related gene exploration,Scientific Data,2023,FALSE,, -10.1038/s41597-023-02732-9,Canada Source Watershed Polygons (Can-SWaP): A dataset for the protection of Canada’s municipal water supply,Scientific Data,2023,TRUE,, +10.1038/s41597-023-02732-9,Canada Source Watershed Polygons (Can-SWaP): A dataset for the protection of Canada’s municipal water supply,Scientific Data,2023,TRUE,FALSE, 10.1038/s41597-023-02752-5,Introducing MEG-MASC a high-quality magneto-encephalography dataset for evaluating natural speech processing,Scientific Data,2023,FALSE,, 10.1038/s41597-023-02771-2,Author Correction: Electricity supply quality and use among rural and peri-urban households and small firms in Nigeria,Scientific Data,2023,FALSE,, -10.1038/s41597-023-02886-6,Electricity and natural gas tariffs at United States wastewater treatment plants,Scientific Data,2024,TRUE,, +10.1038/s41597-023-02886-6,Electricity and natural gas tariffs at United States wastewater treatment plants,Scientific Data,2024,TRUE,FALSE, 10.1038/s41597-024-02930-z,Urban water and electricity demand data for understanding climate change impacts on the water-energy nexus,Scientific Data,2024,FALSE,, 10.1038/s41597-024-02963-4,"High frequency Lunar Penetrating Radar quality control, editing and processing of Chang’E-4 lunar mission",Scientific Data,2024,FALSE,, 10.1038/s41597-024-03028-2,Transitioning from MODIS to VIIRS Global Water Reservoir Product,Scientific Data,2024,FALSE,, @@ -3542,7 +3542,7 @@ doi,title,journal,published_year,auto_relevant,include,reason 10.1038/s41597-024-03286-0,Metagenomics datasets of water and sediments from eutrophication-impacted artificial lakes in South Africa,Scientific Data,2024,FALSE,, 10.1038/s41597-024-03289-x,Chromosome-level genome assembly of Odontothrips loti Haliday (Thysanoptera: Thripidae),Scientific Data,2024,FALSE,, 10.1038/s41597-024-03290-4,A high-resolution dataset of water bodies distribution over the Tibetan Plateau,Scientific Data,2024,FALSE,, -10.1038/s41597-024-03298-w,"A point-of-use drinking water quality dataset from fieldwork in Detroit, Michigan",Scientific Data,2024,TRUE,, +10.1038/s41597-024-03298-w,"A point-of-use drinking water quality dataset from fieldwork in Detroit, Michigan",Scientific Data,2024,TRUE,FALSE, 10.1038/s41597-024-03313-0,"A high-quality chromosome-scale genome assembly of blood orange, an important pigmented sweet orange variety",Scientific Data,2024,FALSE,, 10.1038/s41597-024-03328-7,Global WaterPack - The development of global surface water over the past 20 years at daily temporal resolution,Scientific Data,2024,FALSE,, 10.1038/s41597-024-03376-z,A high-quality chromosome-level genome assembly of Ficus hirta,Scientific Data,2024,FALSE,, @@ -3550,14 +3550,14 @@ doi,title,journal,published_year,auto_relevant,include,reason 10.1038/s41597-024-03383-0,A high-quality dataset featuring classified and annotated cervical spine X-ray atlas,Scientific Data,2024,FALSE,, 10.1038/s41597-024-03412-y,Chromosome-level genome assembly and annotation of the cold-water species Ophiura sarsii,Scientific Data,2024,FALSE,, 10.1038/s41597-024-03434-6,Harmonized Database of Western U.S. Water Rights (HarDWR) v.1,Scientific Data,2024,FALSE,, -10.1038/s41597-024-03453-3,Chlorophyll a in lakes and streams of the United States (2005–2022),Scientific Data,2024,TRUE,, +10.1038/s41597-024-03453-3,Chlorophyll a in lakes and streams of the United States (2005–2022),Scientific Data,2024,TRUE,FALSE, 10.1038/s41597-024-03458-y,A high-quality genome assembly and annotation of Thielaviopsis punctulata DSM102798,Scientific Data,2024,FALSE,, 10.1038/s41597-024-03474-y,CaRDS - the statewide California Residential water Demand and Supply open dataset,Scientific Data,2024,FALSE,, 10.1038/s41597-024-03475-x,A dataset on survey designs and quality of social and behavioral science surveys during the COVID-19 pandemic,Scientific Data,2024,FALSE,, 10.1038/s41597-024-03477-9,Spatially distributed atmospheric boundary layer properties in Houston – A value-added observational dataset,Scientific Data,2024,FALSE,, 10.1038/s41597-024-03494-8,CODC-v1: a quality-controlled and bias-corrected ocean temperature profile database from 1940–2023,Scientific Data,2024,FALSE,, 10.1038/s41597-024-03507-6,Features and quality metrics datasets for video coding in DASH,Scientific Data,2024,FALSE,, -10.1038/s41597-024-03601-9,Non-redundant metagenome-assembled genomes of activated sludge reactors at different disturbances and scales,Scientific Data,2024,TRUE,, +10.1038/s41597-024-03601-9,Non-redundant metagenome-assembled genomes of activated sludge reactors at different disturbances and scales,Scientific Data,2024,TRUE,FALSE, 10.1038/s41597-024-03617-1,A high-quality chromosome-level genome assembly of the endangered tree Kmeria septentrionalis,Scientific Data,2024,FALSE,, 10.1038/s41597-024-03628-y,Improved high quality sand fly assemblies enabled by ultra low input long read sequencing,Scientific Data,2024,FALSE,, 10.1038/s41597-024-03638-w,High-quality reference genome of cowpea beetle Callosobruchus maculatus,Scientific Data,2024,FALSE,, @@ -3603,7 +3603,7 @@ doi,title,journal,published_year,auto_relevant,include,reason 10.1038/s41597-025-04461-7,A high-quality chromosome-scale genome assembly of the Cherokee rose (Rosa laevigata),Scientific Data,2025,FALSE,, 10.1038/s41597-025-04509-8,"A high-quality chromosome-level genome assembly of the mulberry looper, Phthonandria atrilineata",Scientific Data,2025,FALSE,, 10.1038/s41597-025-04534-7,"Compilation of riverine water quality data from the Great Barrier Reef catchment area, northeastern Australia",Scientific Data,2025,TRUE,, -10.1038/s41597-025-04537-4,The Water Health Open Knowledge Graph,Scientific Data,2025,TRUE,, +10.1038/s41597-025-04537-4,The Water Health Open Knowledge Graph,Scientific Data,2025,TRUE,TRUE, 10.1038/s41597-025-04540-9,SIBES: Long-term and large-scale monitoring of intertidal macrozoobenthos and sediment in the Dutch Wadden Sea,Scientific Data,2025,FALSE,, 10.1038/s41597-025-04571-2,"High-quality genome assembly of a cosmopolitan insect predator, Chrysoperla zastrowi sillemi (Esben-Petersen)",Scientific Data,2025,FALSE,, 10.1038/s41597-025-04611-x,"UAS hydrometry: contactless river water level, bathymetry, and flow velocity – the Rӧnne river dataset",Scientific Data,2025,FALSE,, @@ -3628,7 +3628,7 @@ doi,title,journal,published_year,auto_relevant,include,reason 10.1038/s41597-025-05001-z,HarvestStat Africa – Harmonized Subnational Crop Statistics for Sub-Saharan Africa,Scientific Data,2025,FALSE,, 10.1038/s41597-025-05016-6,A global dataset on mungbean for managing seed yield and quality,Scientific Data,2025,FALSE,, 10.1038/s41597-025-05058-w,The high-quality telomere-to-telomere genome assembly of the earthworm (Amynthas aspergillum),Scientific Data,2025,FALSE,, -10.1038/s41597-025-05100-x,"Wastewater dataset on the SARS-CoV-2 sublineages circulating in Central Arkansas, USA, post-COVID-19 pandemic",Scientific Data,2025,TRUE,, +10.1038/s41597-025-05100-x,"Wastewater dataset on the SARS-CoV-2 sublineages circulating in Central Arkansas, USA, post-COVID-19 pandemic",Scientific Data,2025,TRUE,FALSE, 10.1038/s41597-025-05146-x,A dataset for quality evaluation of pelvic X-ray and diagnosis of developmental dysplasia of the hip,Scientific Data,2025,FALSE,, 10.1038/s41597-025-05172-9,CODC-S: A quality-controlled global ocean salinity profiles dataset,Scientific Data,2025,FALSE,, 10.1038/s41597-025-05208-0,Quality assured spatial dataset of wildfire containment firelines and engagement outcomes 2017 to 2024,Scientific Data,2025,FALSE,, @@ -3644,16 +3644,16 @@ doi,title,journal,published_year,auto_relevant,include,reason 10.1038/s41597-025-05431-9,Correction: High Resolution Water Quality Dataset of Chinese Lakes and Reservoirs from 2000 to 2023,Scientific Data,2025,TRUE,, 10.1038/s41597-025-05444-4,A dual water isotope dataset for quantifying summer water mass transport in the northern South China Sea,Scientific Data,2025,FALSE,, 10.1038/s41597-025-05454-2,High-quality chromosome-level genome of three Meretrix species using Nanopore and Hi-C technologies,Scientific Data,2025,FALSE,, -10.1038/s41597-025-05459-x,Dataset on wastewater quality monitoring with adsorption and reflectance spectrometry in the UV-vis range,Scientific Data,2025,TRUE,, +10.1038/s41597-025-05459-x,Dataset on wastewater quality monitoring with adsorption and reflectance spectrometry in the UV-vis range,Scientific Data,2025,TRUE,FALSE, 10.1038/s41597-025-05469-9,Chromosome-level genome assembly and annotation of Spinibarbus caldwelli,Scientific Data,2025,FALSE,, 10.1038/s41597-025-05494-8,A high-quality chromosome-level genome assembly and annotation of the giant freshwater prawn (Macrobrachium rosenbergii),Scientific Data,2025,FALSE,, 10.1038/s41597-025-05562-z,A Global Multi-Sensor Dataset of Surface Water Indices from Landsat-8 and Sentinel-2 Satellite Measurements,Scientific Data,2025,FALSE,, 10.1038/s41597-025-05563-y,BladeSynth: A High-Quality Rendering-Based Synthetic Dataset for Aero Engine Blade Defect Inspection,Scientific Data,2025,FALSE,, 10.1038/s41597-025-05564-x,A High-Quality Underwater Acoustic Dataset for Algorithm Development and Analysis,Scientific Data,2025,FALSE,, 10.1038/s41597-025-05577-6,"A high-quality chromosome-scale genome assembly of Xingan mandarin (Citrus reticulata ‘Xingan’), a primitive Mandarin type",Scientific Data,2025,FALSE,, -10.1038/s41597-025-05600-w,LAGOS-US LANDSAT: Remotely sensed water quality estimates for U.S. lakes over 4 ha from 1984 to 2020,Scientific Data,2025,TRUE,, +10.1038/s41597-025-05600-w,LAGOS-US LANDSAT: Remotely sensed water quality estimates for U.S. lakes over 4 ha from 1984 to 2020,Scientific Data,2025,TRUE,FALSE, 10.1038/s41597-025-05609-1,A bio-optical database for the remote sensing of water quality in BRAZil coAstal and inland waters (BRAZA),Scientific Data,2025,TRUE,, -10.1038/s41597-025-05625-1,"Swiss data quality: augmenting CAMELS-CH with isotopes, water quality, agricultural and atmospheric data",Scientific Data,2025,TRUE,, +10.1038/s41597-025-05625-1,"Swiss data quality: augmenting CAMELS-CH with isotopes, water quality, agricultural and atmospheric data",Scientific Data,2025,TRUE,FALSE, 10.1038/s41597-025-05659-5,A comprehensive water bodies dataset of high-mountain Asia,Scientific Data,2025,FALSE,, 10.1038/s41597-025-05708-z,"Machine learning training data: over 500,000 images of butterflies and moths (Lepidoptera) with species labels",Scientific Data,2025,FALSE,, 10.1038/s41597-025-05777-0,"High quality chromosome level genome assembly of Camellia fascicularis, an endangered plant in China",Scientific Data,2025,FALSE,, @@ -3671,7 +3671,7 @@ doi,title,journal,published_year,auto_relevant,include,reason 10.1038/s41597-025-06013-5,Surface Water Transitions 1984–2022: A Global Dataset at Annual Resolution,Scientific Data,2025,FALSE,, 10.1038/s41597-025-06024-2,A high-quality chromosome-level genome assembly of herbaceous bamboo species Lithachne pauciflora,Scientific Data,2025,FALSE,, 10.1038/s41597-025-06026-0,DiTEC-WDN: A Large-Scale Dataset of Hydraulic Scenarios across Multiple Water Distribution Networks,Scientific Data,2025,FALSE,, -10.1038/s41597-025-06059-5,A three-decade lake dataset on the Mongolian plateau tracking water area and quality dynamics (1990–2020),Scientific Data,2025,TRUE,, +10.1038/s41597-025-06059-5,A three-decade lake dataset on the Mongolian plateau tracking water area and quality dynamics (1990–2020),Scientific Data,2025,TRUE,FALSE, 10.1038/s41597-025-06069-3,High-quality phased genome assemblies of line-bred Korean Hanwoo cattle,Scientific Data,2025,FALSE,, 10.1038/s41597-025-06078-2,High-quality genome assemblies of 152 root commensal bacteria from the model legume Lotus japonicus,Scientific Data,2025,FALSE,, 10.1038/s41597-025-06112-3,Chromosome-Level Genome Assembly and Annotation of the Japanese Cutlassfish (Trichiurus japonicus): A High-Quality Genomic Resource Featuring Nuclear and Mitochondrial Completeness for Future Studies,Scientific Data,2025,FALSE,, @@ -3691,7 +3691,7 @@ doi,title,journal,published_year,auto_relevant,include,reason 10.1038/s41597-025-06401-x,"The UFLUX ensemble of multiple-scale carbon, water, and energy fluxes",Scientific Data,2025,FALSE,, 10.1038/s41597-025-06405-7,High-quality chromosome-level genome assembly of the snake Pseudoxenodon stejnegeri (Squamata: Colubridae),Scientific Data,2025,FALSE,, 10.1038/s41597-025-06415-5,Reconstructing high-quality ground-level ozone records from 1980 to 2012 in central and eastern China,Scientific Data,2025,FALSE,, -10.1038/s41597-025-06468-6,Puerto Rico Coral Reef Monitoring Program Water Quality Data from 2023–2025,Scientific Data,2025,TRUE,, +10.1038/s41597-025-06468-6,Puerto Rico Coral Reef Monitoring Program Water Quality Data from 2023–2025,Scientific Data,2025,TRUE,FALSE, 10.1038/s41597-025-06475-7,"Deep metatranscriptomic sequencing data of wastewater from Los Angeles, USA, 2023–2024",Scientific Data,2025,TRUE,, 10.1038/s41597-025-06479-3,A high-quality chromosome-level reference genome of Pulsatilla chinensis,Scientific Data,2025,FALSE,, 10.1038/s41597-025-05794-z,A Benchmark Dataset for Machine Learning Surrogates of Pore-Scale CO2-Water Interaction,Scientific Data,2026,FALSE,, @@ -3704,7 +3704,7 @@ doi,title,journal,published_year,auto_relevant,include,reason 10.1038/s41597-026-06595-8,High-resolution gridded dataset of sectoral water pollution discharges in China from 2007 to 2022,Scientific Data,2026,FALSE,, 10.1038/s41597-026-06604-w,A Machine Learning approach for Total Water storage anomaly eXtension back to 1980 (ML-TWiX),Scientific Data,2026,FALSE,, 10.1038/s41597-026-06640-6,"Chromosome-level genome assembly of narrow-leaf bur-reed (Sparganium angustifolium Michx., Typhaceae)",Scientific Data,2026,FALSE,, -10.1038/s41597-026-06698-2,A Real-World Dataset for detecting Handwashing in daily Life using Wrist Motion Data from Wearables,Scientific Data,2026,TRUE,, +10.1038/s41597-026-06698-2,A Real-World Dataset for detecting Handwashing in daily Life using Wrist Motion Data from Wearables,Scientific Data,2026,TRUE,FALSE, 10.1038/s41597-026-06713-6,Single-lead Thigh ECG Dataset (tOLIet) with Analysis of BMI Effects on Cardiac Signal Quality,Scientific Data,2026,FALSE,, 10.1038/s41597-026-06725-2,A high-quality chromosome level genome assembly of the South African indigenous Nguni goat (Capra hircus),Scientific Data,2026,FALSE,, 10.1038/s41597-026-06733-2,Seismocardiography Pig Hypovolemia Dataset for Signal Quality Indexing and Validated Cardiac Timings,Scientific Data,2026,FALSE,, @@ -3738,7 +3738,7 @@ doi,title,journal,published_year,auto_relevant,include,reason 10.1038/s41597-026-07286-0,A telomere-to-telomere genome assembly of Chinese water deer (Hydropotes inermis),Scientific Data,2026,FALSE,, 10.1038/s41597-026-07307-y,"Full-range (VNIR–SWIR–MWIR–LWIR) mineral and VNIR water spectra with co-located geochemistry from an acid mine drainage (AMD) site (Kirki, NE Greece)",Scientific Data,2026,FALSE,, 10.1038/s41597-026-07346-5,The first chromosomal level genome assembly and annotation of Pareuchiloglanis anteanalis,Scientific Data,2026,FALSE,, -10.1038/s41597-026-07352-7,Caravan-Qual: A global scale integration of stream water quality observations into a large-sample hydrology dataset,Scientific Data,2026,TRUE,, +10.1038/s41597-026-07352-7,Caravan-Qual: A global scale integration of stream water quality observations into a large-sample hydrology dataset,Scientific Data,2026,TRUE,FALSE, 10.1038/s41597-026-07375-0,Hip joint image quality screening based on the Diffusion Mamba model,Scientific Data,2026,FALSE,, 10.1038/s41597-026-07386-x,High-quality telomere-to-telomere genome assembly of the critically endangered tree Magnolia longipedunculata,Scientific Data,2026,FALSE,, 10.1038/s41597-026-07387-w,High-quality genome and transcriptome resources for the East Asian freshwater mussel Nodularia douglasiae,Scientific Data,2026,FALSE,, diff --git a/data-raw/make_datapapers_review_app.R b/data-raw/make_datapapers_review_app.R new file mode 100644 index 0000000..1f26714 --- /dev/null +++ b/data-raw/make_datapapers_review_app.R @@ -0,0 +1,63 @@ +# Build the standalone HTML review app for the datapapers screening step +# (issue #28). Injects data-raw/datapapers_worklist.csv into +# data-raw/datapapers_review_template.html and writes +# data-raw/datapapers_review.html, a single file that runs from disk with no +# server. Decisions made in the app persist in the browser's localStorage and +# export as data-raw/datapapers_decisions.csv, which +# data-raw/apply_datapapers_decisions.R merges into the screening sheet. +# +# Run from the package root: +# Rscript data-raw/make_datapapers_review_app.R + +library(dplyr) +library(readr) +library(jsonlite) + +source("data-raw/helpers.R") + +worklist <- read_csv("data-raw/datapapers_worklist.csv", show_col_types = FALSE) +screening <- read_csv("data-raw/datapapers_screening.csv", show_col_types = FALSE) + +# Only candidates still pending a decision go into the app; DOIs already +# filled in the screening sheet (via apply_datapapers_decisions.R) drop out, +# so rebuilding after each applied batch yields the remaining queue. +# Rows with an abstract come first: they are the fastest to judge. +papers <- worklist |> + anti_join(screening |> filter(!is.na(include)), by = "doi") |> + arrange(desc(has_abstract), journal, published_year, title) |> + select(doi, journal, published_year, title, + matched_in_title, matched_in_abstract, + has_abstract, title_only_no_abstract, + query_term, first_author_name, first_author_affiliation, abstract) + +# Highlight terms: split "water AND sanitation" into its words; longest first +# so phrase matches ("water quality") win over their parts ("water"). +terms <- datapapers_search_terms() |> + unname() |> + strsplit(" AND ", fixed = TRUE) |> + unlist() |> + unique() |> + (\(x) x[order(-nchar(x))])() + +inject <- function(template, placeholder, json) { + # " tag early + json <- gsub(" + inject("__PAPERS_JSON__", toJSON(papers, dataframe = "rows", na = "null")) |> + inject("__TERMS_JSON__", toJSON(terms)) |> + inject("__META_JSON__", toJSON(list(built = as.character(Sys.Date())), + auto_unbox = TRUE)) + +writeLines(html, "data-raw/datapapers_review.html", useBytes = TRUE) + +message(nrow(papers), " pending candidates written to ", + "data-raw/datapapers_review.html (", sum(papers$has_abstract), + " with abstract shown first). Open it in a browser to review.") diff --git a/data/datapapers.rda b/data/datapapers.rda new file mode 100644 index 0000000..146e19b Binary files /dev/null and b/data/datapapers.rda differ diff --git a/inst/extdata/datapapers.csv b/inst/extdata/datapapers.csv new file mode 100644 index 0000000..d8e9d37 --- /dev/null +++ b/inst/extdata/datapapers.csv @@ -0,0 +1,10 @@ +paperid,doi,paper_url,url_source,journal,title,published_year,num_authors,first_author_name,first_author_affiliation,first_author_affiliation_country,data_repo_url,data_repo,license,related_paper_doi,abstract,query_term,retrieval_date +1,10.3390/data8060103,https://doi.org/10.3390/data8060103,mdpi.com,Data,"Physico-Chemical Quality and Physiological Profiles of Microbial Communities in Freshwater Systems of Mega Manila, Philippines",2023,6,Marie Christine M. Obusan,"Microbial Ecology of Terrestrial and Aquatic Systems Laboratory, Institute of Biology, College of Science, University of the Philippines Diliman, Quezon City 1101, Philippines",Philippines,NA,NA,https://creativecommons.org/licenses/by/4.0/,NA,"Studying the quality of freshwater systems and drinking water in highly urbanized megalopolises around the world remains a challenge. This article reports data on the quality of select freshwater systems in Mega Manila, Philippines. Water samples collected between 2020 and 2021 were analyzed for physico-chemical parameters and microbial community metabolic fingerprints, i.e., carbon substrate utilization patterns (CSUPs). The detection of arsenic, lead, cadmium, mercury, polyaromatic hydrocarbons (PAHs), and organochlorine pesticides (OCPs) was carried out using standard chromatography- and spectroscopy-based protocols. Physiological profiles were determined using the Biolog EcoPlate™ system. Eight samples were free of heavy metals, and none contained PAHs or OCPs. Fourteen samples had high microbial activity, as indicated by average well color development (AWCD) and community metabolic diversity (CMD) values. Community-level physiological profiling (CLPP) revealed that (1) samples clustered as groups according to shared CSUPs, and (2) microbial communities in non-drinking samples actively utilized all six substrate classes compared to drinking samples. The data reported here can provide a baseline or a comparator for prospective quality assessments of drinking water and freshwater sources in the region. Metabolic fingerprinting using CSUPs is a simple and cheap phenotypic analysis of microbial communities and their physiological activity in aquatic environments.",water quality,2026-07-23 +2,10.3390/data8090141,https://doi.org/10.3390/data8090141,mdpi.com,Data,Thailand Raw Water Quality Dataset Analysis and Evaluation,2023,6,Jaturapith Krohkaew,"Department of Big Data Management and Analytics, Rajamangala University of Technology Thanyaburi, Pathum Thani 12110, Thailand",Thailand,NA,NA,https://creativecommons.org/licenses/by/4.0/,NA,"Sustainable water quality data are important for understanding historical variability and trends in river regimes, as well as the impact of industrial waste on the health of aquatic ecosystems. Sustainable water management practices heavily depend on reliable and comprehensive data, prompting the need for accurate monitoring and assessment of water quality parameters. This research describes a reconstructed daily water quality dataset that complements rare historical observations for six station points along the Chao Phraya River in Thailand. Internet of Things technology and a Eureka water probe sensor is used to collect and reconstruct the water quality dataset for the period from June 2022–February 2023, with Turbidity, Optical Dissolved Oxygen, Dissolved Oxygen Saturation, Spatial Conductivity, Acidity/Basicity, Total Dissolved Solids, Salinity, Temperature, Chlorophyll, and Depth as the recorded parameters from six different stations. The presented dataset comprises a total of 211,322 data points, which are separated into six CSV files. The dataset is then evaluated using the Long Short-Term Memory (LSTM) algorithm with a Mean Squared Error (MSE) of 0.0012256, and Root Mean Squared Error (RMSE) of 0.0350080. The proposed dataset provides valuable insights for researchers studying river ecosystems, supporting informed decision-making and sustainable water management practices.",drinking water; water quality; water sanitation,2026-07-23 +3,10.46471/gigabyte.167,https://doi.org/10.46471/gigabyte.167,gigabytejournal.com,GigaByte,"Collection of entomological, demographic, water and sanitation, and climatic data of interest for arbovirus surveillance in Praia, Cabo Verde",2025,7,Lara Ferrero Gómez,Universidade Jean Piaget de Cabo Verde,Cabo Verde,NA,NA,https://creativecommons.org/licenses/by/4.0/,NA,"Vector-borne diseases, primarily those transmitted by mosquitoes, are a serious public health problem. Some, such as dengue, put half of the world’s population at risk. Combating these diseases requires multifaceted strategies, with vector surveillance and control playing key roles. Robust and predictive surveillance systems for vector-borne diseases, based on risk stratification, enable the implementation of appropriate interventions across time and space. Here, we present a collection of entomological, demographic, water and sanitation, and climatic data from Praia (Cabo Verde), a hotspot for mosquito-borne diseases. These data were collected from June to November 2022, at 40 sentinel points scattered across the urban area of Praia. They constitute a valuable source of information for developing predictive scenarios of arbovirus outbreak risk using statistical models applied to spatial and non-spatial indicators. These data demonstrate the utility of GBIF in transforming large volumes of occurrence data into valuable information for arbovirus surveillance and vector control.",drinking water; sanitation; water quality; water sanitation,2026-07-23 +4,10.1038/sdata.2018.108,https://doi.org/10.1038/sdata.2018.108,nature.com,Scientific Data,Freshwater macroinvertebrate samples from a water quality monitoring network in the Iberian Peninsula,2018,7,Nora Escribano,NA,NA,NA,NA,https://creativecommons.org/licenses/by/4.0,NA,"AbstractThis dataset gathers information about the macroinvertatebrate samples and environmental variables collected on rivers of the Ebro River Basin (NE Iberian Peninsula), the second largest catchment in the Iberian Peninsula. The collection is composed of 1,776 sampling events carried out between 2005 and 2015 at more than 400 sampling sites. This dataset is part of a monitoring network set up by the Ebro Hydrographic Confederation, the official body entrusted with the care of the basin, to fulfill the requirements of the European Water Framework Directive. Biological indices based on the freshwater macroinvertebrate communities were used to evaluate the ecological status of the water bodies within the basin. Samples were qualitatively screened for all occurring taxa. Then, all individuals from all taxa in a quantitative subsample of each sample were counted. Biological indices were calculated to estimate water quality at each sampling site. All samples are kept at the Museum of Zoology of the University of Navarra.",drinking water; water quality; water sanitation,2026-07-23 +5,10.1038/s41597-022-01439-7,https://doi.org/10.1038/s41597-022-01439-7,nature.com,Scientific Data,Greenhouse gas emissions from municipal wastewater treatment facilities in China from 2006 to 2019,2022,9,Dan Wang,NA,NA,NA,NA,https://creativecommons.org/licenses/by/4.0,NA,"AbstractWastewater treatment plants (WWTPs) alleviate water pollution but also induce resource consumption and environmental impacts especially greenhouse gas (GHG) emissions. Mitigating GHG emissions of WWTPs can contribute to achieving carbon neutrality in China. But there is still a lack of a high-resolution and time-series GHG emission inventories of WWTPs in China. In this study, we construct a firm-level emission inventory of WWTPs for CH4, N2O and CO2 emissions from different wastewater treatment processes, energy consumption and effluent discharge for the time-period from 2006 to 2019. We aim to develop a transparent, verifiable and comparable WWTP GHG emission inventory to support GHG mitigation of WWTPs in China.",drinking water; wastewater; water quality; water sanitation,2026-07-23 +6,10.1038/s41597-022-01686-8,https://doi.org/10.1038/s41597-022-01686-8,nature.com,Scientific Data,Bacteria communities and water quality parameters in riverine water and sediments near wastewater discharges,2022,6,Carolina Oliveira de Santana,NA,NA,NA,NA,https://creativecommons.org/licenses/by/4.0,NA,"AbstractWastewater treatment plant (WWTP) discharges alter water quality and microbial communities by introducing human-associated bacteria in the environment and by altering microbial communities. To fully understand this impact, it is crucial to study whether WWTP discharges affect water and sediments microbial communities in comparable ways and whether such effects depend on specific environmental variables. Here, we present a dataset investigating the impact of a WWTP on water quality and bacterial communities by comparing samples collected directly from the WWTP outflow to surface waters and sediments at two sites above and two sites below it over a period of five months. When possible, we measured five physicochemical variables (e.g., temperature, turbidity, conductivity, dissolved oxygen, and salinity), four bioindicators (e.g., Escherichia coli, total coliforms, Enterococcus sp., and endotoxins), and two molecular indicators (e.g., intI1’s relative abundance, and 16S rRNA gene profiling). Preliminary results suggest that bioindicators correlate with environmental variables and that bacterial communities present in the water tables, sediments, and treated water differ greatly in composition and structure.",drinking water; wastewater; water quality; water sanitation,2026-07-23 +7,10.1038/s41597-022-01749-w,https://doi.org/10.1038/s41597-022-01749-w,nature.com,Scientific Data,"Freshwater microbial metagenomes sampled across different water body characteristics, space and time in Israel",2022,13,Ashraf Al-Ashhab,NA,NA,NA,NA,https://creativecommons.org/licenses/by/4.0,NA,"AbstractFreshwater bodies are critical components of terrestrial ecosystems. The microbial communities of freshwater ecosystems are intimately linked water quality. These microbes interact with, utilize and recycle inorganic elements and organic matter. Here, we present three metagenomic sequence datasets (total of 182.9 Gbp) from different freshwater environments in Israel. The first dataset is from diverse freshwater bodies intended for different usages – a nature reserve, irrigation and aquaculture facilities, a tertiary wastewater treatment plant and a desert rainfall reservoir. The second represents a two-year time-series, collected during 2013–2014 at roughly monthly intervals, from a water reservoir connected to an aquaculture facility. The third is from several time-points during the winter and spring of 2015 in Lake Kinneret, including a bloom of the cyanobacterium Microcystis sp. These datasets are accompanied by physical, chemical, and biological measurements at each sampling point. We expect that these metagenomes will facilitate a wide range of comparative studies that seek to illuminate new aspects of freshwater microbial ecosystems and inform future water quality management approaches.",drinking water; water quality; water sanitation,2026-07-23 +8,10.1038/s41597-025-04537-4,https://doi.org/10.1038/s41597-025-04537-4,nature.com,Scientific Data,The Water Health Open Knowledge Graph,2025,3,Anna Sofia Lippolis,NA,NA,NA,NA,https://creativecommons.org/licenses/by/4.0,NA,"Abstract + Global sustainability challenges have recently led to an increasing interest in the management of water and health resources. Thus, the availability of effective, meaningful and open data is crucial to address those issues in the broader context of the Sustainable Development Goals of clean water and sanitation as targeted by the United Nations. In this paper, we present the Water Health Open Knowledge Graph (WHOW-KG) along with its design methodology and analysis on impact. Developed in the context of the EU-funded WHOW (Water Health Open Knowledge) project, the WHOW-KG is a semantic knowledge graph that models data on water consumption, pollution, extreme weather events, infectious disease rates and drug distribution. Indeed, it aims at supporting a wide range of applications: from knowledge discovery to decision-making, making it a valuable resource for researchers, policymakers, and practitioners in the water and health domains. The WHOW-KG consists of a network of five ontologies and related linked open data, modelled according to those ontologies. As a fully distributed system, it is sustainable over time, can handle large datasets, and allows data providers full control, establishing it as a vital European asset in the fields of water consumption and pollution.",drinking water; water quality; water sanitation,2026-07-23 diff --git a/inst/extdata/datapapers.xlsx b/inst/extdata/datapapers.xlsx new file mode 100644 index 0000000..9c5e84b Binary files /dev/null and b/inst/extdata/datapapers.xlsx differ diff --git a/man/figures/README-unnamed-chunk-10-1.png b/man/figures/README-unnamed-chunk-12-1.png similarity index 100% rename from man/figures/README-unnamed-chunk-10-1.png rename to man/figures/README-unnamed-chunk-12-1.png diff --git a/man/figures/README-unnamed-chunk-11-1.png b/man/figures/README-unnamed-chunk-13-1.png similarity index 100% rename from man/figures/README-unnamed-chunk-11-1.png rename to man/figures/README-unnamed-chunk-13-1.png diff --git a/man/figures/README-unnamed-chunk-6-1.png b/man/figures/README-unnamed-chunk-6-1.png deleted file mode 100644 index d44e6cf..0000000 Binary files a/man/figures/README-unnamed-chunk-6-1.png and /dev/null differ diff --git a/man/figures/README-unnamed-chunk-7-1.png b/man/figures/README-unnamed-chunk-7-1.png deleted file mode 100644 index 17f2607..0000000 Binary files a/man/figures/README-unnamed-chunk-7-1.png and /dev/null differ diff --git a/man/figures/README-unnamed-chunk-8-1.png b/man/figures/README-unnamed-chunk-8-1.png deleted file mode 100644 index fc80e70..0000000 Binary files a/man/figures/README-unnamed-chunk-8-1.png and /dev/null differ diff --git a/man/figures/README-unnamed-chunk-9-1.png b/man/figures/README-unnamed-chunk-9-1.png deleted file mode 100644 index 0d9c3fa..0000000 Binary files a/man/figures/README-unnamed-chunk-9-1.png and /dev/null differ