A Metadata Model for Harmonising Engineering Research Data Across Process and Laboratory Boundaries
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
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.
Details
Original language | English |
---|---|
Title of host publication | COGNITIVE 2024, The Sixteenth International Conference on Advanced Cognitive Technologies and Applications |
Editors | Jérôme Dinet |
Pages | 30-39 |
Number of pages | 10 |
ISBN (electronic) | 978-1-68558-157-2 |
Publication status | Published - 14 Apr 2024 |
Peer-reviewed | Yes |
External IDs
ORCID | /0000-0001-7540-4235/work/173051978 |
---|