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

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.

Linking theory with empirical data: Improving prediction through mechanistic understanding of lake ecosystem complexity under global change - Fundamental and Applied Limnology Volume 196 Nr. 3-4 — Schweizerbart science publishers