Abstract
Many research funders mandate researchers to create and maintain data management plans (DMPs) for research projects that describe how research data is managed to ensure its reusability. A DMP, being a static textual document, is difficult to act upon and can quickly become obsolete and impractical to maintain. A new generation of machine-actionable DMPs (maDMPs) was therefore proposed by the Research Data Alliance to enable automated integration of information and updates. maDMPs open up a variety of use cases enabling interoperability of research systems and automation of data management tasks. In this article, we describe a system for machine-actionable data management planning in an institutional context. We identify common use cases within research that can be automated to benefit from machine-actionability of DMPs. We propose a reference architecture of an maDMP support system that can be embedded into an institutional research data management infrastructure. The system semi-automates creation and maintenance of DMPs, and thus eases the burden for the stakeholders responsible for various DMP elements. We evaluate the proposed system in a case study conducted at the largest technical university in Austria and quantify to what extent the DMP templates provided by the European Commission and a national funding body can be pre-filled. The proof-of-concept implementation shows that maDMP workflows can be semi-automated, thus workload on involved parties can be reduced and quality of information increased. The results are especially relevant to decision makers and infrastructure operators who want to design information systems in a systematic way that can utilize the full potential of maDMPs.
Highlights
The data revolution continues to transform every sector of science, industry, and government [1]
DMap can export a machine-actionable DMPs (maDMPs) as JavaScript Object Notation (JSON) that complies with the Research Data Alliance (RDA) data management plans (DMPs) Common Standard
Research institutions can build their research data infrastructure around maDMPs to bring researchers together with departments and services. They can integrate all stakeholders involved in research data management (RDM) by connecting their information systems to manage and exchange information in an automated way
Summary
The data revolution continues to transform every sector of science, industry, and government [1]. The RDA established a common way to model information that is typically described in DMPs. to fully realize the potential of maDMPs as a way to exchange and act on information about data used and produced by researchers, all stakeholders involved in RDM must be connected by information systems that manage and exchange information in an automated way. The recommendation was developed as a collaborative effort by consulting stakeholders and collecting user stories as described in the work of Miksa et al [26] It is capable of describing various entities involved in data management planning such as the DMP itself, projects, funding, contributors, costs, datasets, and their relations. This article breaks down these use cases into specific processes and describes how they can be realized in an institutional context
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