The Surface Water Chemistry (SWatCh) database: a standardized global database of water chemistry to facilitate large-sample hydrological research
Bibliographic record
Abstract
Abstract. Openly accessible global-scale surface water chemistry datasets are urgently needed to detect widespread trends and problems, to help identify their possible solutions, and to determine critical spatial data gaps where more monitoring is required. Existing datasets are limited with respect to availability, sample size and/or sampling frequency, and geographic scope. These limitations inhibit researchers from tackling emerging transboundary water chemistry issues – for example, the detection and understanding of delayed recovery from freshwater acidification. Here, we begin to address these limitations by compiling the global Surface Water Chemistry (SWatCh) database, available on Zenodo (https://doi.org/10.5281/zenodo.6484939; Rotteveel and Heubach, 2021). We collect, clean, standardize, and aggregate open-access data provided by six national and international programs and research groups (United Nations Environment Programme; Hartmann et al., 2019; Environment and Climate Change Canada; the United States of America National Water Quality Monitoring Council; the European Environment Agency; and the United States National Science Foundation McMurdo Dry Valleys Long-Term Ecological Research Network) in order to compile a database containing information on sites, methods, and samples, and a geospatial information system (GIS) shapefile of site locations. We remove poor-quality data (e.g., values flagged as “suspect” or “rejected”), standardize variable naming conventions and units, and perform other data cleaning steps required for statistical analysis. The database contains water chemistry data for streams, rivers, canals, ponds, lakes, and reservoirs across seven continents, 24 variables, 33 722 sites, and over 5 million samples collected between 1960 and 2022. Similar to prior research, we identify critical spatial data gaps on the African and Asian continents, highlighting the need for more data collection and sharing initiatives in these areas, especially considering that freshwater ecosystems in these environs are predicted to be among the most heavily impacted by climate change. We identify the main challenges associated with compiling global databases – limited data availability, dissimilar sample collection and analysis methodology, and reporting ambiguity – and provide recommended solutions. By addressing these challenges and consolidating data from various sources into one standardized, openly available, high-quality, and transboundary database, SWatCh allows users to conduct powerful and robust statistical analyses of global surface water chemistry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".