Data quality assurance in the research process using the example of tensile tests
- authored by
- 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.
- Organisation(s)
-
Institute of Metal Forming and Metal Forming Machines
Institute of Motion Engineering and Mechanism Design
Data Driven Design (D³)
Data Science and Digital Libraries Section
- External Organisation(s)
-
Paderborn University
German National Library of Science and Technology (TIB)
- Type
- Conference contribution
- Pages
- 143-152
- Publication date
- 2023
- Publication status
- Published
- Peer reviewed
- Yes
- ASJC Scopus subject areas
- Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering, Computer Science Applications
- Electronic version(s)
-
https://doi.org/10.35199/dfx2023.15 (Access:
Closed)
-
Details in the research portal "Research@Leibniz University"
