Data quality assurance in the research process using the example of tensile tests
- verfasst von
- Norman Mohnfeld, Laura Muller, Max Leo Wawer, Johanna Uhe, Oliver Koepler, Soren Auer, Roland Lachmeyer, Iryna Mozgova
- 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.
- Organisationseinheit(en)
-
Institut für Umformtechnik und Umformmaschinen
Institut für Produktentwicklung und Gerätebau
Data Driven Design (D³)
Fachgebiet Data Science and Digital Libraries
- Externe Organisation(en)
-
Universität Paderborn
Technische Informationsbibliothek (TIB) Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek
- Typ
- Aufsatz in Konferenzband
- Seiten
- 143-152
- Publikationsdatum
- 2023
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Maschinelles Sehen und Mustererkennung, Wirtschaftsingenieurwesen und Fertigungstechnik, Angewandte Informatik
- Elektronische Version(en)
-
https://doi.org/10.35199/dfx2023.15 (Zugang:
Geschlossen)
