Towards a Toolbox for Automated Assessment of Machine-Actionable Data Management Plans

Tomasz Miksa*, Marek Suchánek, Jan Slifka, Vojtech Knaisl, Fajar J. Ekaputra, Filip Kovacevic, Annisa Maulida Ningtyas, Alaa El-Ebshihy, Robert Pergl

*Korrespondierende*r Autor*in für diese Arbeit

Publikation: Wissenschaftliche FachzeitschriftOriginalbeitrag in FachzeitschriftBegutachtung

Abstract

Most research funders require Data Management Plans (DMPs). The review process can be time consuming, since reviewers read text documents submitted by researchers and provide their feedback. Moreover, it requires specific expert knowledge in data stewardship, which is scarce. Machine-actionable Data Management Plans (maDMPs) and semantic technologies increase the potential for automatic assessment of information contained in DMPs. However, the level of automation and new possibilities are still not well-explored and leveraged. This paper discusses methods for the automation of DMP assessment. It goes beyond generating human-readable reports. It explores how the information contained in maDMPs can be used to provide automated pre-assessment or to fetch further information, allowing reviewers to better judge the content. We map the identified methods to various reviewer goals.

OriginalspracheEnglisch
Aufsatznummer28
FachzeitschriftData Science Journal
Jahrgang22
Ausgabenummer1
DOIs
PublikationsstatusVeröffentlicht - 2023

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