standards > reporting guideline > DOI:10.25504/FAIRsharing.WWI10U


ready The FAIR Principles

Abbreviation: FAIR


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General Information
One of the grand challenges of data-intensive science is to facilitate knowledge discovery by assisting humans and machines in their discovery of, access to, integration and analysis of, task-appropriate scientific data and their associated algorithms and workflows. The term "FAIR" was launched at a Lorentz workshop in 2014, attended by a wide range of academic, corporate, and governmental stakeholders. The resulting draft FAIR Principles were initially made available for public comment via the websites of peer-initiatives such as, for example, Force11. Based on this feedback, the final Principles were published in 2016 (https://www.nature.com/articles/sdata201618). FAIR is a set of guiding principles to make data Findable, Accessible, Interoperable, and Re-usable. These guidelines provide advice for those wishing to enhance the (re)usability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals.


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How to cite this record FAIRsharing.org: FAIR; The FAIR Principles; DOI: https://doi.org/10.25504/FAIRsharing.WWI10U; Last edited: Oct. 24, 2019, 2:56 p.m.; Last accessed: Oct 24 2020 10:32 a.m.


This record is maintained by MarkWilkinson  ORCID

Record added: March 16, 2018, 10:45 a.m.
Record updated: Oct. 24, 2019, 1:08 p.m. by The FAIRsharing Team.

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The FAIR Guiding Principles for scientific data management and stewardship.

Wilkinson MD,Dumontier M,Aalbersberg IJ,Appleton G,Axton M,Baak A,Blomberg N,Boiten JW,da Silva Santos LB,Bourne PE,Bouwman J,Brookes AJ,Clark T,Crosas M,Dillo I,Dumon O,Edmunds S,Evelo CT,Finkers R,Gonzalez-Beltran A,Gray AJ,Groth P,Goble C,Grethe JS,Heringa J,'t Hoen PA,Hooft R,Kuhn T,Kok R,Kok J,Lusher SJ,Martone ME,Mons A,Packer AL,Persson B,Rocca-Serra P,Roos M,van Schaik R,Sansone SA,Schultes E,Sengstag T,Slater T,Strawn G,Swertz MA,Thompson M,van der Lei J,van Mulligen E,Velterop J,Waagmeester A,Wittenburg P,Wolstencroft K,Zhao J,Mons B
Sci Data 2016

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FAIRsharing is a FAIR-supporting resource that provides an informative and educational registry on data standards, databases, repositories and policy, alongside search and visualization tools and services that interoperate with other FAIR-enabling resources. FAIRsharing guides consumers to discover, select and use standards, databases, repositories and policy with confidence, and producers to make their resources more discoverable, more widely adopted and cited. Each record in FAIRsharing is curated in collaboration with the maintainers of the resource themselves, ensuring that the metadata in the FAIRsharing registry is accurate and timely. Every record is manually reviewed at least once a year. Records can be collated into collections, based on a project, society or organisation, or Recommendations, where they are collated around a policy, such as a journal or funder data policy.

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DataverseNO
DataverseNO (https://dataverse.no/) is a national, generic repository for open research data, owned and operated by UiT The Arctic University of Norway. DataverseNO is aligned with the FAIR Guiding Principles for scientific data management and stewardship. The technical infrastructure of the repository is based on the open source application Dataverse, which is developed by an international developer and user community led by Harvard University. DataverseNO is CoreTrustSeal certified.

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