Machine-actionable data management plans (maDMPs) have, by their very nature, advantages over data management plans that are written exclusively 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.
In its Practical Guide to the International Alignment of Research Data Management , Science Europe has published an evaluation rubric (section Guidance for Reviewers) that provides a solid basis to support research (funding) organizations in evaluating DMPs. By stating a set of criteria, it helps to ensure submitted DMPs cover required aspects and support FAIR data management.
This project aims to facilitate leveraging the machine-actionability of DMPs by providing SPARQL queries that are meant to automatically give an initial assessment of the respective data management plan’s quality.