Towards a Toolbox for Automated Assessment of Machine-Actionable Data Management Plans
The article discusses potential strategies for automation of assessment of data management plans so that researchers and funders can receive quick feedback on the quality and FAIRness of planned or executed actions. The results presented on the paper will have impact on the new features developed in the DAMAP.org tool that is in use by TU Wien and TU Graz researchers. It will also provide inputs to the EC Horizon OS Trails project starting from 2024.
Title
Towards a Toolbox for Automated Assessment of Machine-Actionable Data Management Plans
Authors
Tomasz Miksa, Marek Suchánek, Jan Slifka, Vojtech Knaisl, Fajar J. Ekaputra, Filip Kovacevic, Annisa Maulida Ningtyas, Alaa El-Ebshihy, Robert Pergl
Journal
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.