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A Metadata Model for Harmonising Engineering Research Data Across Process and Laboratory Boundaries

Authors:
Martin Zinner
Felix Conrad
Kim Feldhoff
Hajo Wiemer
Jens Weller
Steffen Ihlenfeldt

Keywords: Metadata Model; FAIR Principles; Research Data Management; Ontology; Machine Learning; Domain-Specific Technical Languages.

Abstract:
The availability of precise and comprehensive experimental data in science and technology is crucial for the usability of Artificial Intelligence (AI) models. To enable the deployment of data-driven applications across different platforms, a digitally analysable, system-independent representation of datasets is essential. We propose a metadata model based on domain-specific languages and terminologies, which allows researchers to focus on data provision by reducing routine activities rather than attempting to align with other research groups. Furthermore, it enables a fast and efficient integration of new partners from different laboratories and different disciplines. To conclude, our approach supports a paradigm shift away from more or less subjectively designed individualistic conceptions in handling research data towards objectively established harmonised solutions. The approach is illustrated for an Interdisciplinary Research Training Group, in which researchers from more than 10 different departments are involved with main research profiles, such as textile and polymer technology and material sciences.

Pages: 30 to 39

Copyright: Copyright (c) IARIA, 2024

Publication date: April 14, 2024

Published in: conference

ISSN: 2308-4197

ISBN: 978-1-68558-157-2

Location: Venice, Italy

Dates: from April 14, 2024 to April 18, 2024