A review of open data for studying global groundwater in social-ecological systems
Bibliographic record
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 both the water cycle and social-ecological systems, leading to an expansion in groundwater science that increasingly focuses on interactions between groundwater 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 identify and review over 140 global open access datasets and dataset collections that span elements of the hydrosphere, biosphere, climate, lithosphere, food systems, governance, management, in addition to other human dimensions and socioeconomic systems relevant to groundwater science. This initiative offers a reference of existing data for use in interdisciplinary groundwater assessments, and summarizes these data across the primary system to which the dataset relates, spatial resolution, temporal range, data type, generation method, level of groundwater representation, and institutional location of lead authorship. At present, our review includes 15 groundwater datasets, 23 datasets explicitly linked with groundwater, and 106 datasets with implicit or potential groundwater connections. The majority of datasets are temporally static, and we find that temporally dynamic data availability peaked over the 2000-2010 decade and has declined since. Furthermore, only a small fraction of temporally dynamic data are explicitly linked to groundwater. We find that most groundwater datasets are generated by a small subset of countries, including the USA, Germany, the Netherlands, and Canada and that many countries facing acute groundwater sustainability challenges are not leading global data collection efforts. We conclude with four potential priorities for future global groundwater data collection, including: elevating regional and local scale perspectives, needs, and data in global initiatives, developing data sharing initiatives providing reciprocal benefits to data providers, more explicit representation of groundwater and uncertainty in global datasets, and the development of groundwater or freshwater system-wide essential variables.
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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 | 0.015 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.031 | 0.051 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".