Making Data Management Plans Machine Actionable: Templates and Tools

Authors

JOAKIM PHILIPSON, Stockholm University
ADIL HASAN, Sigma2 AS
HANNE MOA, Norwegian Agency for Shared Services in Education and Research

Document Type

Article

Date of this Version

8-28-2023

Citation

Philipson, J, Hasan, A and Moa, H. 2023. Making Data Management Plans Machine Actionable: Templates and Tools. Data Science Journal, 22: 29, pp. 1–13. DOI: https://doi. org/10.5334/dsj-2023-029

Comments

Open access.

Abstract

Since September of 2019, a task group within the European Open Science Cloud - EOSC Nordic Project, work-package 5 (T5.3.2), has focused its attention on machine-actionable Data Management Plans (maDMPs). A delivery working-paper from the group (Hasan et al. 2021) concluded in summary that extracting useful information from traditional free-text based DMPs is problematic. While maDMPs are generally more FAIR compliant, and as such accessible to both humans and machines, more interoperable with other systems, and serving different stakeholders for processing, sharing, evaluation and reuse. Different DMP tools and templates have developed independently, to a varying degree, allowing for the creation of genuinely machine actionable DMPs. Here we will describe the first three tools or projects for creating maDMPs that were central parts of the original task group mission. We will get into a more detailed account of one of these, specifically the Stockholm University – EOSC Nordic maDMP project using the DMP Online tool, as described by Philipson (2021). We will also briefly touch upon some other current tools and projects for creating maDMPs that are compliant with the RDA DMP Common Standard (RDCS), aiming for integration with other research information systems or research data repositories. A possible conclusion from this overview is that the development of tools for maDMPs is progressing fast and seems to converge towards a common standard. Nonetheless, there remains an immense amount of work to get there.