GROW: A Global Time Series Dataset for Large-Sample Groundwater Studies
Bibliographic record
Abstract
Groundwater is a central component of social-ecological systems. However, our understanding of how it is dynamically interlinked with the atmosphere, hydrosphere, cryosphere, biosphere, geosphere, and anthroposphere is limited. Existing datasets lack features that enable us to better understand groundwater functions and how they are affected by anthropogenic change. Specifically, there remains no large-scale groundwater dataset that provides analysis-ready groundwater time series alongside groundwater-associated variables and attributes. In the pursuit of understanding the planet's groundwater dynamics, we present GROW (global GROundWater analysis package). This user-friendly, quality-controlled dataset combines groundwater depth and level time series from around the world with associated social-ecological variables. GROW is designed to enable large-sample spatio-temporal groundwater analysis without much further preprocessing. The dataset contains more than 180,000 time series from 41 countries – whereby over 90 % of the time series are from either North America, Australia or Europe - in a daily, monthly, or yearly temporal resolution. Most of them are between 10 and 20 years long, from 01/1888 to 04/2024, and have a median depth to the water table of 8 metres. Groundwater data is paired with a total of 37 time series or attributes of meteorological, hydrological, geophysical, botanical, and anthropogenic variables (e.g., precipitation, ground elevation, aquifer type, NDVI, land use). More than 20 data flags about well features (e.g., location coordinates and license), as well as time series characteristics (e.g., gap fraction or length), simplify a quick data filtering tailored to specific needs. GROW provides an essential foundation understanding large-scale groundwater processes and provides a robust resource for calibrating and validating models that address groundwater dynamics in social-ecological systems. Gaining an enhanced insight in these processes is essential for managing groundwater resources and ensuring their long-term sustainability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".