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Supporting Data Management Planning: ACME-FAIR Issue#4

Authors: Whyte, Angus; Molloy, Laura; Grootveld, Marjan; Thorley, Mark;

Supporting Data Management Planning: ACME-FAIR Issue#4

Abstract

Data management plans (DMPs) are recognised as an important element of good practice in research management, including by the European Commission and Science Europe. Especially since the beginning of the EC Horizon 2020 programme, funders at national and international level expect research grant holders to complete a DMP demonstrating they have planned how data will be managed from the outset of a research project. Research Producing Organisations (RPOs) are expected to play their part, to help their researchers in producing data that is FAIR, and in depositing it in a trustworthy repository that can keep it in FAIR condition. And in some cases including the EC Horizon Europe programme, there is a need for DMPs to cover all research outputs (data, code, models, samples etc.), to be updated throughout the project, and ultimately made available as a project deliverable. ACME-FAIR is a 7-part guide developed in the FAIRsFAIR project, whose main purpose is to help managers of Research Data Management and related professional services to self-assess how they are enabling researchers, and the professional staff who support them, to put the FAIR data principles into practice (for short we refer to this as ‘FAIR-enabling practice’). This part addresses the key issue of Supporting data management planning. The guide aims to help Research Performing Organisations assess their own needs to support DMPs, taking into account what they currently have in place and where improvements may be needed. Please give us your comments in any of the ways detailed on p.5.

Country
Netherlands
Keywords

FAIR principles, FAIR implementation, DMP, research data management, machine-actionable DMP, Data Management Plans, Data Management Planning, DMP, Data Management Plans, FAIR principles, research data management, machine-actionable DMP

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citations
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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