A review of open data for studying global groundwater in social–ecological systems
Bibliographic record
Abstract
Abstract Global data have served an integral role in characterizing large-scale groundwater systems, identifying their sustainability challenges, and informing on socioeconomic and ecological dimensions of groundwater. These insights have revealed groundwater as a dynamic component of the water cycle and social–ecological systems, leading to an expansion in groundwater science that increasingly focuses on groundwater’s interactions with ecological, socioeconomic, and Earth systems. This shift presents many opportunities that are conditional on broader, more interdisciplinary system conceptualizations, models, and methods that require the integration of a greater diversity of data in contrast to conventional hydrogeological investigations. Here, we catalogue 144 global open access datasets and dataset collections relevant to groundwater science that span elements of the hydrosphere, biosphere, atmosphere, lithosphere, food systems, governance, management, and other socioeconomic system dimensions. The assembled catalogue offers a reference of available data for use in interdisciplinary assessments, and we summarize these data across their primary system, spatial resolution, temporal range, data type, generation method, level of groundwater representation, and institutional location of lead authorship. The catalogue includes 15 groundwater datasets, 23 datasets derived in relation to groundwater, and 106 datasets associated with groundwater. We find the majority of datasets are temporally static and that temporally dynamic data peak in availability during the 2000–2010 decade. Only a small fraction of temporally dynamic data is derived with any direct representation of groundwater, highlighting the need for greater incorporation of groundwater in Earth system models and data collection initiatives across socioeconomic, governance, and environmental science research communities. A small number of countries, led by the USA, Germany, the Netherlands, and Canada, generate most global groundwater data, reflecting a global North bias in the institutional leadership of these data generation activities. We raise three priority themes for future global groundwater data initiatives, which include: data improvements through prioritizing observed and temporally dynamic data; elevating regional and local scale data and perspectives to address challenges relating to equity and bias; and advancing data sharing initiatives founded on reciprocal benefits between global initiatives and data providers.
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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 | 0.036 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.046 | 0.077 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".