Data quality assurance in the research process using the example of tensile tests

DS 125: Proceedings of the 34th Symposium Design for X (DFX2023)

Year: 2023
Editor: Dieter Krause, Kristin Paetzold-Byhain, Sandro Wartzack
Author: Laura Muller (1), Max Leo Wawer (2), Norman Heimes (3), Johanna Uhe (3), Oliver Koepler (4), Soren Auer (4), Roland Lachmayer (2), Iryna Mozgova (1)
Series: DfX
Institution: 1: Data Management in Mechanical Engineering, Paderborn University; 2: Institute of Product Development, Leibniz University Hannover; 3: Institute of Forming Technology and Machines, Leibniz University Hannover; 4: TIB - Leibniz Information Centre for Science and Technology and University Library
Page(s): 143-152
DOI number: 10.35199/dfx2023.15


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.

Keywords: data quality assurance, quality dimension, research data management, research data life cycle


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