Comparative review of recent developments in archaeological predictive models and automatic structure detection.
This repository contains the data and code for our paper:
Bellat, Soriano-Elias (2026). Testing the water: Importance of surface water resources for settlement installation in various climatic contexts toward a machine learning approach. OSF https://doi.org/10.17605/OSF.IO/CTW2U
Please cite this compendium as:
Bellat, Soriano-Elias (2026). Compendium of R code and data for Testing the water: Importance of surface water resources for settlement installation in various climatic contexts toward a machine learning approach. Accessed 18 juin 2026. Online at https://doi.org/10.17605/OSF.IO/CTW2U
The analysis directory contains:
- 📁 code: Code/scripts in R and JavaScript languages.
- 📁 derived data: Data produced in the analysis, available on OSF.
- 📁 raw data: Data used in the analysis extracted from API and library, available on OSF..
- 📁 supplementary materials: Supplementary file document.
This research compendium has been developed using the statistical programming language R. To work with the compendium, you will need installed on your computer the R software itself and optionally RStudio Desktop.
You can download the compendium as a zip from from this URL:
master.zip. After unzipping: - built the docker
file from your bash with docker compose up - directly compute the R
files from the 📁 code
Text data and figures : CC-BY-4.0
Code : See the MIT
We welcome contributions from everyone. Before you get started, please see our contributor guidelines. Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.