## Dataset Variables
- Erase - Non related: "X" for erased from Database. Empty for not erased from Database.
- Erase - Criteria GWM: "X" for erased from Database. Empty for not erased from Database.
- Erase - Criteria ABM: "X" for erased from Database. Empty for not erased from Database.
- Erase - Criteria Complementary: "X" for erased from Database. Empty for not erased from Database. 
- Reference: Text with: first author, year
- DOI: Text with DOI if applicable (empty otherwise).
- ISSN: Text with ISSN if applicable (empty otherwise).
- Year: Text with article's publication year.
- Title: Text with article's title.
- Journal: Text with article's journal or "N.A." (not applicable).
- General Application: AG:Agriculture. WS: Water Supply. OT: Other.
- Case Study Location: Text with location of the study.
- Modelling Purposes: (i) Prediction, (ii) Explanation, (iii) Description, (iv) Theoretical Exploration, (v) Illustration, (vi) Analogy, (vii) Social Learning, (viii) New Modelling Software
- Subjects of decisions: (i) Agricultural water user, (ii) Urban/Domestic water user, (iii) Industrial water user, (iv) Regulator, (v) Water Utility, (vi) Reservoir Manager, (vii) Interest Group, (viii) Economic Institution, (ix) Other.
- ODD: Whether the ODD protocol was used or not for documentation. (i) Fully Used, (ii) Partially Used, (iii) Not Used.
- ODD Details: Details on the version of the ODD used if applicable.
- Rationality level: (i) Fully-rational, (ii) Boundedly Rational, (iii) Mixed.
- Agents rationality details: Text-based description of assigned agents' rationality levels
- Diversity across: Boolean indicating whether there are multiple agent types simulated (Yes), or not (No).
- Aggregated Individuals: Boolean indicating whether simulated agents represented aggregated entities (Yes) or not (No).
- Model coupling: (i) Loosely coupled, (ii) Tightly/Closely coupled, (iii) Integrated.
- GWM Dimensions: Number of dimensions simulated within the GWM.
- Cell Size : Size of the GWM cell if available.
- GWM Extent: Extent of the simulated space within the GWM.
- GWM Extent Classification: Assigned subjective classification for the extent of the simulated space within the GWM.
- GWM Treatment of Space: (i) Distributed, (ii) Semi-distributed, (iii) Lumped/Aggregated, (iv) Other.	 
- ABM Space Details: Details on the simulation of space within the ABM.
- ABM Treatment of Space - General: (i) Spatially-Explicit, (ii) Spatially-Implicit.	 
- ABM Treatment of Space - Specific: (i) Continuously, (ii) Grid-based, (iii) Network-Based, (iv) Geographical Information System.
- Policies Analysed: Policies simulated/analysed with the model.
- GWM - Treatment of time: (i) Transient, (ii) Steady-state, (iii) Not Applicable.
- GWM Time-steps: Time step of the GWM. "H" for hours, "D" for days, "W" for weeks, "M" for months, "Y" for years.
- ABM Time-steps: Time-steps of the ABM. "H" for hours, "D" for days, "W" for weeks, "M" for months, "Y" for years.
- Learning/Adaptation: Boolean indicating whether learning/adaptation aspects were included (Yes) or not (No).
- GWM Types: (i) Process or Physically-based, (ii) Data-driven or Black-box, (iii) Hybrid, (iv) Other.
- GWM Sub-types: (i) Numerical, (ii) Analytical, (iii) Other.
- ABM Model Output Verification: (i) Performed (calibrated), (ii) Not Performed (not calibrated), (iii) Not clear, (iv) Not mentioned.	 
- ABM Model Output Verification Sub-classification: Technique used for Model Output Verification of the ABM.
- GWM Model Output Verification: (i) Performed (calibrated), (ii) Not Performed (not calibrated), (iii) Not clear, (iv) Not mentioned.	 
- GWM Model Output Verification Type: (i) Automated, (ii) Manual, (iii) Not clear, (iv) Not applicable.	 
- ABM Model Output Corroboration: (i) Performed (validated), (ii) Not Performed (not validated), (iii) Not clear, (iv) Not mentioned.
- GWM Model Output Corroboration: (i) Performed (validated), (ii) Not Performed (not validated), (iii) Not clear.
- ABM Model Output Corroboration Technique: (i) Structural Validation, (ii) Extreme and Sensitivity Tests, (iii) Participatory Modelling, (iv) Pattern-Oriented Modelling, and/or (v) Empirical Output Validation.	 
- SA Sampling strategy: Local sensitivity analysis (LSA): The SA explored how deviations in a single input affect the variability of the results while holding all other inputs constant. This includes the traditional One-At-A-Time (OAT) design. Global sensitivity analysis (GSA): Explores the whole input space, analysing outcome variability both due to single input and interactions (Ligmann-Zielinska et al., 2020, Saltelli, 2004). Hybrid: This category captures those Sensitivity Analysis that do not fit on the previous ones.	 
- Sensitivity Analysis: (i) Performed, (ii), Not Performed.
- SA Purpose: Purpose of the Sensitivity Analysis. Factor prioritisation and screening: Aimed to either obtain insights about the most important input factors (i.e., the factor prioritisation/ranking setting), or find those least influential ones (i.e., the factor fixing/screening setting). Model building: Aimed to guide model development in any of the various phases of the modelling cycle (e.g., model output corroboration, model output verification, etc.), thereby improving the model’s credibility and reliability. Model exploration: Aimed to identify critical or interesting regions in the space of the input factors, or trace how an output of interest is generated, or study which values of the input factors lead to model realisations in a given range of the output space (i.e., the factor mapping setting). Other: This category captures any other purpose not included in the previous ones. N.A.: Not Applicable
- SA Method Type: Name of the method used for Sensitivity Analysis if applicable. Quantitative: Each input factor is associated with a quantitative and reproducible evaluation of its relative influence, normally through a set of sensitivity indices. Qualitative: The sensitivity was assessed qualitatively by visual inspection of model predictions or by specific graphs (e.g., tornado plots, scatter visualisations, etc.). N.A.: Not applicable.
- SA Inputs Targeted: Physical system: The input factors varied during the SA were associated with the hydrology or the groundwater system (or target outcome measures associated to it). Social system: The input factors varied during the SA were associated with the social system (or target outcome measures associated to it). Both: The input factors varied during the SA were associated with both the groundwater system and the social system (or target outcome measures associated to both).
- SA Outputs Measured: Physical system: The selected outcomes varied during the SA were associated with the hydrology or the groundwater system (or target outcome measures associated to it). Social system: The selected outcomes varied during the SA were associated with the social system (or target outcome measures associated to it). Both: The selected outcomes varied during the SA were associated with both the groundwater system and the social system (or target outcome measures associated to both).
- Software-ABM: Name of the software used to build the ABM
- Software-ABM-Type: Type of software used to build the ABM. (i) Proprietary, (ii) Open-source, (iii) Not Clear.	 
- Software-GWM: Name of the software used to build the GWM.
- Software-GWM-Type: Type of software used to build the GWM. (i) Proprietary, (ii) Open-source, (iii) Not Clear.



## Intended Use
This dataset underpins a scientific article that critically examines the integration of Groundwater Models (GWMs) and Agent-Based Models (ABMs) to navigate and mitigate the complexities of groundwater sustainability threats in the Anthropocene era. It is specifically tailored for:

Academic Research: Scholars and researchers in hydrogeology, environmental science, and socio-hydrology can leverage this dataset to explore the nuances of human-water interactions, focusing on the representation of human behaviour, spatial-temporal dynamics, feedback mechanisms between social and hydrological systems, and water governance frameworks.

Model Development: This dataset is instrumental for developing and refining GW-ABMs that aim to address the outlined challenges. It is particularly valuable for those looking to incorporate bounded rationality in human decision-making processes, enhance the representation of policy impacts on groundwater systems.

Policy Analysis and Decision Making: Policymakers and water resource managers can use insights derived from analyses based on this dataset to inform and shape effective and sustainable groundwater management policies. The dataset's focus on water governance structures and policy applications makes it a critical tool for understanding and implementing best practices in water resource management.

Education and Training: Educators and trainers can utilize this dataset in curricula and workshops to illustrate the complexity of coupling social and physical systems in groundwater management, fostering a deeper understanding of interdisciplinary approaches in environmental science and hydrogeology.

## Requirements to Use the Data
The dataset in database.csv can be accessed using any software that supports CSV file formats.