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Leveraging measurement data quality by adoption of the FAIR guiding principles

  • Robert H. Schmitt ORCID logo , Matthias Bodenbenner ORCID logo EMAIL logo , Tobias Hamann ORCID logo , Mark P. Sanders ORCID logo , Mario Moser ORCID logo and Anas Abdelrazeq ORCID logo
Published/Copyright: July 9, 2024

Abstract

The analysis and reuse of measured process data are enablers for sustainable and resilient manufacturing in the future. Maintaining high measurement data quality is vital for maximising the usage and value of the data at hand. To ensure this data quality, the data management must be applied consequently throughout the complete Data Life-Cycle (DLC) and adhere to the FAIR guiding principles. In the two research consortia NFDI4Ing and the Cluster of Excellence “Internet of Production,” we investigate approaches to increase the measurement of data quality by integrating the FAIR guiding principles in all data management activities of the DLC. To facilitate the uptake of the FAIR guiding principles, we underline the significance of FAIR data for the reuse of high-quality data. Second, we are introducing a harmonised DLC to streamline data management activities. Third, we concisely review current trends and best practices in FAIR-aware data management and give suggestions for implementing the FAIR guiding principles.

Zusammenfassung

Die Analyse und Wiederverwendung von gemessenen Prozessdaten sind die Voraussetzung für eine nachhaltige und resiliente Fertigung der Zukunft. Die Gewährleistung einer hohen Messdatenqualität ist von entscheidender Bedeutung, um das Wertschöpfungs- und Nachnutzungspotential der vorliegenden Daten zu erhöhen. Um diese Datenqualität zu gewährleisten, muss das Datenmanagement während des gesamten Datenlebenszyklus (DLZ) konsequent angewendet werden und den Vorgaben der FAIR Guiding Principles entsprechen. In den beiden Forschungskonsortien NFDI4Ing und dem Exzellenzcluster “Internet of Production” untersuchen wir Ansätze zur Steigerung der Datenqualität durch Integration der FAIR Guiding Principles in alle Datenmanagementaktivitäten des DLZ. Um die Umsetzung der FAIR Guiding Principles zu erleichtern, unterstreichen wir die Bedeutung der Prinzipien für die Nachnutzung von qualitativ hochwertigen Daten. Zweitens führen wir einen harmonisierten DLZ ein, um die Datenmanagementaktivitäten zu rationalisieren. Drittens geben wir einen kurzen Überblick über aktuelle Trends und bewährte Praktiken im FAIRen Datenmanagement und machen Vorschläge zur Umsetzung der FAIR Guiding Principles.


Corresponding author: Matthias Bodenbenner, WZL | RWTH Aachen University, Aachen, Germany, E-mail:

Award Identifier / Grant number: 390621612

Award Identifier / Grant number: 442146713

  1. Research ethics: Not applicable.

  2. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: The authors states no competing interests.

  4. Research funding: Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC-2023 Internet of Production – 390621612. The authors would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their funding and support within the framework of the NFDI4Ing consortium. Funded by the German Research Foundation (DFG) – project number 442146713.

  5. Data availability: Not applicable.

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Received: 2024-03-28
Accepted: 2024-06-20
Published Online: 2024-07-09
Published in Print: 2024-09-25

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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