Ten principles for machine-actionable data management plans.

View/ Open
Average rating
votes
Date
2019Author
Miksa, T.
Simms, S.
Mietchen, D.
Jones, Sarah
Metadata
Show full item recordAbstract
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, machineactionable
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-.....
Journal
PLOS Computational BiologyVolume
15Issue
3, Article e1006750Page Range
15pp.Document Language
enSustainable Development Goals (SDG)
14.aEssential Ocean Variables (EOV)
N/ADOI Original
https://doi.org/10.1371/journal. pcbi.1006750Citation
Miksa, T., Simms, S., Mietchen, D. and Jones, S. (2019) Ten principles for machine-actionable data management plans. PLoS Computational Biology, 5(3): e1006750, 15pp. DOI: https://doi.org/10.1371/journal. pcbi.1006750Collections
The following license files are associated with this item: