• DOI:10.1371/journal.pcbi.1006750
• Corpus ID: 85563774

Ten principles for machine-actionable data management plans

@article{Miksa2019TenPF,
  title={Ten principles for machine-actionable data management plans},
  author={Tomasz Miksa and Stephanie Renee Simms and Daniel Mietchen and Sarah Jones},
  journal={PLoS Computational Biology},
  year={2019},
  volume={15}
}

• Tomasz Miksa, S. Simms, +1 author Sarah Jones
• Published 1 March 2019
• Medicine
• PLoS Computational Biology

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.