A case study on providing FAIR and metrologically traceable data sets

In recent years, data science and engineering have faced many challenges concerning the increasing amount of data. In order to ensure findability, accessibility, interoperability, and reusability (FAIRness) of digital resources, digital objects as a synthesis of data and metadata with persistent and unique identifiers should be used. In this context, the FAIR data principles formulate requirements that research data and, ideally, also industrial data should fulfill to make full use of them, particularly when Machine Learning or other data-driven methods are under consideration. In this contribution, the process of providing scientific data of an industrial testbed in a traceable and FAIR manner is documented as an example.