Datenqualitätssicherung im Forschungsprozess am Beispiel von Zugversuchen

Abstract

Progressive digitization throughout the entire product data life cycle requires a more sensitive handling and understanding of data within engineering processes. Regarding engineering research data, the aim is to implement the FAIR data principles (Findable, Accessible, Interoperable, Reusable) to guarantee the post-usability of research data. To ensure the quality of data throughout the entire research process a methodical approach had been developed. Based on the quality categories Intrinsic, Representative, Contextual and Available, the related quality dimensions are considered differentiated along the research data life cycle and presented in a concept. As a use case, this concept is carried out on a tensile test with documentation of results in a research data management system.