Replication Data for: A Systematic Mapping Study on Automatic Simulation Model Generation for Digital Twins

DOI

This dataset contains the extracted, classified, and analyzed data collected for the systematic mapping study titled “Automatic Simulation Model Generation for Digital Twins.” The dataset supports the investigation of existing approaches, methods, tools, and research trends related to the automated generation of simulation models within Digital Twin environments. It includes bibliographic metadata of the selected studies, study classifications, application domains, modeling and simulation techniques, automation approaches, validation methods, and identified research challenges and gaps. The data was compiled through a systematic literature review process following established systematic mapping study guidelines to ensure transparency and reproducibility. The dataset is provided in Microsoft Excel format and is intended to support replication, secondary analyses, and future research in the fields of Digital Twins, simulation engineering, and automated model generation.

Identifier
DOI https://doi.org/10.18419/DARUS-6111
Metadata Access https://darus.uni-stuttgart.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18419/DARUS-6111
Provenance
Creator Chen, Shengjian Patrick ORCID logo; Schaper, Sascha ORCID logo; Brandt, Nico ORCID logo; Lozic, Josip ORCID logo
Publisher DaRUS
Contributor Chen, Shengjian Patrick
Publication Year 2026
Funding Reference DFG 563998230 ; German Federal Ministry for Economic Affairs and Energy ; German Federal Ministry for Economic Affairs and Energy 13IPC036G
Rights CC BY 4.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/licenses/by/4.0
OpenAccess true
Contact Chen, Shengjian Patrick (University of Stuttgart)
Representation
Resource Type Dataset
Format text/csv; application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
Size 13073827; 8688480
Version 1.0
Discipline Construction Engineering and Architecture; Engineering; Engineering Sciences