Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | DS 125: Proceedings of the 34th Symposium Design for X (DFX2023) |
| Seiten | 143-152 |
| Publikationsstatus | Veröffentlicht - 2023 |
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.
Schlagwörter
- data quality assurance, quality dimension, research data life cycle, research data management
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Maschinelles Sehen und Mustererkennung
- Ingenieurwesen (insg.)
- Wirtschaftsingenieurwesen und Fertigungstechnik
- Informatik (insg.)
- Angewandte Informatik
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
DS 125: Proceedings of the 34th Symposium Design for X (DFX2023). 2023. S. 143-152.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Data quality assurance in the research process using the example of tensile tests
AU - Mohnfeld, Norman
AU - Muller, Laura
AU - Wawer, Max Leo
AU - Uhe, Johanna
AU - Koepler, Oliver
AU - Auer, Soren
AU - Lachmeyer, Roland
AU - Mozgova, Iryna
N1 - Publisher Copyright: © 2023 die Autoren.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - data quality assurance
KW - quality dimension
KW - research data life cycle
KW - research data management
UR - http://www.scopus.com/inward/record.url?scp=85185827501&partnerID=8YFLogxK
U2 - 10.35199/dfx2023.15
DO - 10.35199/dfx2023.15
M3 - Conference contribution
SP - 143
EP - 152
BT - DS 125: Proceedings of the 34th Symposium Design for X (DFX2023)
ER -