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Machine-Actionable Data Management Plans

Authors: Simms, Stephanie R; Jones, Sarah; Mietchen, Daniel; Miksa, Tomasz;

Machine-Actionable Data Management Plans

Abstract

Data management plans (DMPs) are becoming commonplace across the globe as a result of funders requiring them with grant proposals, but they are not being employed in ways that truly support the research enterprise. The current manifestation of a DMP—a static document often created before a project begins—only contributes to the perception that they are an annoying administrative exercise. What they really are—or at least should be—is an integral part of research practice, since today most research across all disciplines involves data, code, and other digital components. We are galvanizing the community to reimagine DMPs in this context. One goal is to enhance the DMPTool and DMPonline services, but the larger vision is to improve the experience for everyone involved (researchers, funders, institutions, repositories, libraries, IT...) and support the production of FAIR data. We invite you to read the machine-actionable DMP use cases, contribute your ideas, and take action with us by exploring the resourcelinks below. The project is part of the RDA/US Data Share fellowship sponsored through a grant from the Alfred P. Sloan Foundation #G-2014-13746.

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Keywords

data management plan; machine-actionable; research data management; open data; data policy

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selected citations
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This is an alternative to the "Influence" indicator, which also 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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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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