Data_Geitlinger 2026_Their Final Resting Place
Abstract
This repository contains data necessary to reproduce the modelling workflow of the paper: Their Final Resting Place:A machine-learning-based predictive model approach towards the geospatial location of burial mounds in western Switzerland Timo GeitlingerPrehistory Division, Institute for Archaeology, Philology, and Ancient Studies, University of Zurich, Zurich, Switzerland The paper presents a framework for a machine-learning-based predictive model approach for burial mound site locations in western Switzerland. By combining multiple machine-learning techniques with iterative Monte Carlo simulations and a diverse set of covariates approximating past human experience, the empirical basis and robustness of the model are substantially enhanced. The workflow provides a transferable framework for (predictive) modelling in other archaeological contexts.
Fields of Study & Subjects
Archaeological Research and Protection
Ancient and Medieval Archaeology Studies
Archaeological and Geological Studies
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 1 March 2026
- Language
- EN
- Document type
- Dataset