Data_Geitlinger 2026_Their Final Resting Place

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Authors: Timo Geitlinger
Cited by: 0

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