Data Science Journal (Aug 2023)

A Programmatic and Scalable Approach to making Data Management Machine-Actionable

  • Maria Praetzellis,
  • Matthew Buys,
  • Xiaoli Chen,
  • John Chodacki,
  • Neil Davies,
  • Kristian Garza,
  • Catherine Nancarrow,
  • Brian Riley,
  • Erin Robinson

DOI
https://doi.org/10.5334/dsj-2023-026
Journal volume & issue
Vol. 22
pp. 26 – 26

Abstract

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The data management plan (DMP), while seen by many as an ancillary document during a grant application, is a rich source of contextual information that is key to ensuring researchers, funders, and institutions follow the best possible and most appropriate research data management (RDM) practices. Unfortunately, the current practice is to transmit this information to the funder as a PDF or Word file through their web portals. As optimizing internal workflows and information sharing is a priority across the research space, retooling DMPs as machine-readable and machine-actionable will enable leveraging of key information to build RDM strategies collectively. Similarly, there is a growing need to streamline workflows, reuse information and reduce the burden on researchers.

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