This repository provides the dataset accompanying the GEMS-GER (Groundwater Levels, Environment, Meteorology, Site Properties – Germany) benchmark for machine learning-based groundwater modeling. The dataset includes long-term groundwater level time series, meteorological and hydrological forcing data, site-specific environmental properties, and benchmark model evaluation results. All data originate from official public sources and have been harmonized across the 16 German federal states (Bundesländer). Contents of this repository: Groundwater level time series (GEMS-GER_data/dynamic/*.csv): Weekly aggregated groundwater levels (GWL) from 3,207 monitoring wells (1991–2022), including: Daily temperature (mean, min, max) Precipitation and humidity (HYRAS/DWD) Real, potential, and reference evapotranspiration Soil moisture and soil temperature (5 m) Snow water equivalent, snowmelt, and runoff (ERA5-Land) GWL_flag indicating observed vs. imputed values Site-specific static descriptors (GEMS-GER_data/static/static_features.csv): Hydrogeology and soil type Land use and climate classification Elevation and derived topographic parameters (e.g. slope, TWI) Benchmark model evaluation results (GEMS-GER_data/static/model_performance.csv): NSE, RMSE, R², and Bias scores for ML models applied to each well Pre-generated time series plots (GEMS-GER_data_figures/*.pdf): Visualizations of groundwater levels and selected forcing variables for all wells Provided separately to reduce the size of the main dataset download Directory structure: GEMS-GER_data/├── dynamic/ # 3,207 individual CSV files, one per well│ ├── MW_1.csv│ ├── MW_2.csv│ └── ...├── static/│ ├── static_features.csv # Site-specific static descriptors (e.g. geology, land use, climate)│ └── model_performance.csv # ML model evaluation metrics (NSE, RMSE, R², Bias)├── license_information.txt # Licensing details for groundwater level data from federal state sources└── README.md # Dataset description and usage notes GEMS-GER_data_figures/├── DYN_Feat_MW_1.pdf├── DYN_Feat_MW_2.pdf└── ... The dataset is intended for research and benchmarking in hydrogeology, data-driven groundwater modeling, and environmental machine learning. It forms the basis of the GEMS-GER benchmark, as described in the associated preprint. All data originate from public sources and have been harmonized across administrative and institutional boundaries to enable consistent large-scale analysis.