Toolchain for Dataset Description with Contextual Recommendation from Machine-Actionable Data Management Plans

Mohamed-Anis Koubaa, Nan Liu, Fabia Martens, Andreas Schmidt, Andreas Schmidt, Karl-Uwe Stucky, Wolfgang Süß

2025

Abstract

By harnessing the power of interconnected research project information, this position paper introduces a novel system designed to automate and enhance the metadata description process for research data. The system effectively leverages existing structured data from RDMO (Research Data Management Organiser), drawing insights from research projects, measurement equipment, sensors, and simulations to provide context-aware suggestions for metadata fields. We argue that this system significantly reduces the manual burden on researchers, improves the quality and consistency of metadata, and ultimately champions the FAIR principles (Findable, Accessible, Interoperable, Reusable) for all research data.

Paper Citation

in Harvard Style

Koubaa M., Liu N., Martens F., Schmidt A., Stucky K. and Süß W. (2025). Toolchain for Dataset Description with Contextual Recommendation from Machine-Actionable Data Management Plans. In Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KMIS; ISBN 978-989-758-769-6, SciTePress, pages 506-513. DOI: 10.5220/0013786300004000

in Bibtex Style

@conference{kmis25,
author={Mohamed-Anis Koubaa and Nan Liu and Fabia Martens and Andreas Schmidt and Karl-Uwe Stucky and Wolfgang Süß},
title={Toolchain for Dataset Description with Contextual Recommendation from Machine-Actionable Data Management Plans},
booktitle={Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KMIS},
year={2025},
pages={506-513},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013786300004000},
isbn={978-989-758-769-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KMIS
TI - Toolchain for Dataset Description with Contextual Recommendation from Machine-Actionable Data Management Plans
SN - 978-989-758-769-6
AU - Koubaa M.
AU - Liu N.
AU - Martens F.
AU - Schmidt A.
AU - Stucky K.
AU - Süß W.
PY - 2025
SP - 506
EP - 513
DO - 10.5220/0013786300004000
PB - SciTePress