Machine-actionable data management plans (maDMPs)

Simms, Stephanie; Jones, Sarah; Mietchen, Daniel; Miksa, Tomasz

doi:10.3897/rio.3.e13086

Cited by 24 publications

(16 citation statements)

References 3 publications

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“…The concept of Machine-Actionable DMP (maDMP) [13] (sometimes referred as "active", "dynamic", or "machine-readable" DMP), aims at addressing some of these issues by making the DMP machine-readable without compromising its human-readability. The adoption of an open, shared and interoperable concept of maDMP could bring multiple benefits, such as facilitating data discovery and reuse, and enabling automated evaluation and monitoring, etc.…”

Section: Introductionmentioning

confidence: 99%

Machine-Actionable Data Management Plans: A Knowledge Retrieval Approach to Automate the Assessment of Funders’ Requirements

Cardoso

Proença

Borbinha

2020

Lecture Notes in Computer Science

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Funding bodies and other policy-makers are increasingly more concerned with Research Data Management (RDM). The Data Management Plan (DMP) is one of the tools available to perform RDM tasks, however it is not a perfect concept. The Machine-Actionable Data Management Plan (maDMP) is a concept that aims to make the DMP interoperable, automated and increasingly standardised. In this paper we showcase that through the usage of semantic technologies, it is possible to both express and exploit the features of the maDMP. In particular, we focus on showing how a maDMP formalised as an ontology can be used automate the assessment of a funder's requirements for a given organisation.

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Section: Introductionmentioning

confidence: 99%

Machine-Actionable Data Management Plans: A Knowledge Retrieval Approach to Automate the Assessment of Funders’ Requirements

Cardoso

Proença

Borbinha

2020

Lecture Notes in Computer Science

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“…We want to emphasize that maDMPs are part of a global community effort to improve traditional DMPs and the quality of research data (and metadata) more generally through automation while also reducing administrative overhead. The substance and inspiration for the principles is based on community-generated use cases from a workshop held at the International Digital Curation Conference (IDCC) in Edinburgh in 2017 that gathered almost 50 participants from Africa, America, Australia, and Europe [3]. The 10 principles themselves have gone through multiple drafts since then via consultations with Research Data Alliance (RDA) and FORCE11 groups focused on DMPs.…”

Section: Introductionmentioning

confidence: 99%

Ten principles for machine-actionable data management plans

et al. 2019

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Data management plans (DMPs) are documents accompanying research proposals and project outputs. DMPs are created as free-form text and describe the data and tools employed in scientific investigations. They are often seen as an administrative exercise and not as an integral part of research practice. There is now widespread recognition that the DMP can have more thematic, machine-actionable richness with added value for all stakeholders: researchers, funders, repository managers, research administrators, data librarians, and others. The research community is moving toward a shared goal of making DMPs machine-actionable to improve the experience for all involved by exchanging information across research tools and systems and embedding DMPs in existing workflows. This will enable parts of the DMP to be automatically generated and shared, thus reducing administrative burdens and improving the quality of information within a DMP. This paper presents 10 principles to put machine-actionable DMPs (maDMPs) into practice and realize their benefits. The principles contain specific actions that various stakeholders are already undertaking or should undertake in order to work together across research communities to achieve the larger aims of the principles themselves. We describe existing initiatives to highlight how much progress has already been made toward achieving the goals of maDMPs as well as a call to action for those who wish to get involved.

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“…The economic and reputational rewards are profound and various: from retention of corporate knowledge through the tracking and reporting of data, integration with workflows dealing with staff and student departures, an evidence trail in the case of research integrity investigations, greater business intelligence around data management and guidance around storage provisioning. Many of these benefits are yet to be realised due to the static and disconnected nature of DMPs, which the move to machine-actionable DMPs may address (Simms, Jones, Mietchen, & Miksa, 2017).…”

Section: Institutional Benefitsmentioning

confidence: 99%