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"

BibTeX

@inproceedings{bc1c286f372145c3992779afbfd36ea2,
title = "Data quality assurance in the research process using the example of tensile tests",
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.",
keywords = "data quality assurance, quality dimension, research data life cycle, research data management",
author = "Norman Mohnfeld and Laura Muller and Wawer, {Max Leo} and Johanna Uhe and Oliver Koepler and Soren Auer and Roland Lachmeyer and Iryna Mozgova",
note = "Publisher Copyright: {\textcopyright} 2023 die Autoren.",
year = "2023",
doi = "10.35199/dfx2023.15",
language = "English",
pages = "143--152",
booktitle = "DS 125: Proceedings of the 34th Symposium Design for X (DFX2023)",
}