How to Make Bespoke Experiments FAIR: Modular Dynamic Semantic Digital Twin and Open Source Information Infrastructure

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.


Manuel Rexer  , Nils Preuß, Sebastian Neumeier, Peter F. Pelz


In this study, we apply the FAIR principles to enhance data management within a modular test environment. By focusing on experimental data collected with various measuring equipment, we develop and implement tailored information models of physical objectes used in the experiments. These models are based on the Resource Description Framework (RDF) and ontologies. Our objectives are to improve data searchability and usability, ensure data traceability, and facilitate comparisons across studies. The practical application of these models results in semantically enriched, detailed digital representations of physical objects, demonstrating significant advancements in data processing efficiency and metadata management reliability. By integrating persistent identifiers to link real-world and digital descriptions, along with standardized vocabularies, we address challenges related to data interoperability and reusability in scientific research. This paper highlights the benefits of adopting FAIR principles and RDF for linked data proposing potential expansions for broader experimental applications., Our approach aims to accelerate innovation and enhance the scientific community’s ability to manage complex datasets effectively.


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  • Published: 2024-06-03
  • Last Updated: 2024-06-03
  • License: Creative Commons Attribution 4.0
  • Subjects: Data Infrastructure, Data Sets
  • Keywords: FAIR, linked data, modular test environment, information model, experimental data, information infrastructure
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