Tensile Test Ontology (TTO)

efficiency and accessibility of research efforts. Traditional practices in testing laboratories are usually inadequate for efficient data acquisition and utilization as they lead to local storage and difficulty in publication and correlation with other results. Electronic laboratory notebooks (ELNs) are promising prospects in this respect. Semantic concepts and ontologies enhance interoperability by standardizing experimental data representation. An in‐laboratory pipeline seamlessly integrating an ELN with transformation scripts to convert experimental into interoperable data in a machine‐actionable format is created in this study as a proof of concept. Tensile test results and the corresponding tensile test ontology are used exemplary. Linking ELN data to semantic concepts enriches the stored information while improving interpretability and reusability. Involving undergraduate students builds a bridge between theory and practice during their training and promotes their digital skills. This study underscores the potential of ELNs and knowledge representations as beneficial means toward improved data management practices that enhance collaborative research and education while ensuring compatibility with evolving standards and technologies.

presents the development of a domain ontology specifically aligned with standard-compliant tensile testing, created within the framework of the Platform MaterialDigital (PMD) initiative. The publication offers insights into the development process of domain ontologies, using the tensile test as a primary example. It demonstrates how semantic technologies and the FAIR (Findable, Accessible, Interoperable, and Reusable) principles can be applied to explore and find the best digitization approaches for materials data handling. By achieving semantic interoperability across different material domains, the ontology enables a structured, machine-readable representation of experimental mechanical testing data, facilitating better data integration and reuse in industrial data spaces.