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

Topics: Intelligent Information Systems; KM Strategies and Implementations ; Management and Organisational Issues in Information Systems; Ontologies; Tools and Technologies for Knowledge Management

Authors: Mohamed-Anis Koubaa 1 ; Nan Liu 1 ; Fabia Martens 1 ; Andreas Schmidt 2 ; 1 ; Karl-Uwe Stucky 1 and Wolfgang Süß 1

Affiliations: 1 Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Germany ; 2 University of Applied Sciences, Karlsruhe, Germany

Keyword(s): Research Data Management, Metadata, Automated Metadata Generation, Metadata Suggestions, Data Description, FAIR Principles, RDMO, Data Management Plans, Ontologies, Semantic Web, Scientific Data, Metadata Quality, Metadata Consistency, Digital Repositories, Information Systems, Knowledge Graphs.

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.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Koubaa, M.-A., Liu, N., Martens, F., Schmidt, A., Stucky, K.-U. 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 - KMIS; ISBN 978-989-758-769-6; ISSN 2184-3228, SciTePress, pages 506-513. DOI: 10.5220/0013786300004000

@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 - KMIS},
year={2025},
pages={506-513},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013786300004000},
isbn={978-989-758-769-6},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KMIS
TI - Toolchain for Dataset Description with Contextual Recommendation from Machine-Actionable Data Management Plans
SN - 978-989-758-769-6
IS - 2184-3228
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

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