Automating the assessment of machine-actionable data management plans (maDMPs)

28. October 2021

Automating the assessment of machine-actionable data management plans (maDMPs)

Students from TU Wien presented their findings at the second workshop "Data and research objects management for Linked Open Science" (DaMaLOS 2021).

Three persons in a meeting room — Raffael Foidl, Lea Brugger and Tomasz Miksa

On 24 October 2021, Lea Brugger and Raffael Foidl, students participating in the Data Stewardship course offered within the Data Science curriculum at the TU Wien and co-supervised by Tomasz Miksa, presented results from their assignment to the international scientific community during the 2^nd Workshop on Data and research objects management for Linked Open Science, opens an external URL in a new window. Their work deals with automating the assessment of machine-actionable data management plans (maDMPs) and is of relevance to the FAIR Data Austria, opens an external URL in a new window project that develops a tool for maDMPs.

Review support

Machine-actionable data management plans (maDMPs) have, by their very nature, potential to bring advantages over data management plans that are written in text form. By employing maDMPs, not only researchers should be able to benefit from their merits, but also research funders receiving and assessing the DMPs.

As a basis for the automated DMP review, Lea Brugger and Raffael Foidl use the "Practical Guide to the International Alignment of Research Data Management - Extended Edition, opens an external URL in a new window" from Science Europe, an association of major European research funders. The guide contains the "Guidance for reviewers", an evaluation rubric that provides a common basis to support evaluation of DMPs. By stating a set of criteria, it helps to ensure submitted DMPs cover required aspects and support FAIR data management.

In this paper, a semi-automatic approach to leverage the benefits of maDMPs by providing SPARQL queries that represent requirements of Science Europe is presented. The goal is to support reviewers in the assessment of DMPs expressed as maDMPs. The results show that semantic web technologies can help in providing customised views to reviewers, but human inspection and interpretation is still needed.

Materials

Publication, slides and video recording will soon become available: http://doi.org/10.4126/FRL01-006429413, opens an external URL in a new window

Contact

TU Wien
Center for Research Data Management
Favoritenstraße 16 (top floor), 1040 Vienna

research.data@tuwien.ac.at

Twitter: @RDMTUWien

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