The FAIR Guiding Principles for scientific data management and stewardship

Руководящие принципы FAIR для управления научными данными и их сопровождения
Mark D. Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, Jan‐Willem Boiten, Luiz Olavo Bonino da Silva Santos, Philip E. Bourne, Jildau Bouwman, Anthony J. Brookes, Tim W. Clark, Mercè Crosas, Ingrid Dillo, Olivier Dumon, Scott Edmunds, Chris T. Evelo, Richard Finkers, Alejandra González-Beltrán, Alasdair J. G. Gray, Paul Groth, Carole Goble, Jeffrey S. Grethe, Jaap Heringa, Peter A.C. ’t Hoen, Rob Hooft, Tobias Kuhn, Ruben Kok, Joost N. Kok, Scott J. Lusher, Maryann E. Martone, Albert Mons, Abel L. Packer, Bengt Persson, Philippe Rocca‐Serra, Marco Roos, René van Schaik, Susanna‐Assunta Sansone, Erik Schultes, Thierry Sengstag, Ted Slater, George Strawn, Morris A. Swertz, Mark Thompson, Johan van der Lei, Erik M. van Mulligen, Jan Velterop, Andra Waagmeester, Peter Wittenburg, Katherine Wolstencroft, Jun Zhao, Barend Mons
2016-03-15
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FAIR Data Principlesdata reusedata stewardshipmachine-actionable datascientific data management
Abstract (AI)
There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders-representing academia, industry, funding agencies, and scholarly publishers-have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability 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. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
Key Findings
1
FAIR principles are intended to guide stakeholders seeking to enhance the reusability of existing data holdings.
2
FAIR principles are jointly endorsed by stakeholders from academia, industry, funding agencies, and scholarly publishing.
3
The framework emphasizes machine-actionable data discovery and reuse alongside supporting human scholars.
4
The paper formally introduces the FAIR Data Principles as a concise, measurable framework for improving scholarly data management and reuse.
5
The paper provides rationale for the principles and presents exemplar community implementations.
Research Object

Scholarly data holdings and the infrastructure supporting their reuse

Research Subject

FAIR principles for improving the findability, accessibility, interoperability, and machine- and human-reusability of scientific data

Publication Details
Publication Date
2016-03-15
Journal
Publisher
ISSN
Cited by
18765
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Author Information
Authors
Mark D. Wilkinson
Michel Dumontier
IJsbrand Jan Aalbersberg
Gabrielle Appleton
Myles Axton
Arie Baak
Niklas Blomberg
Jan‐Willem Boiten
Luiz Olavo Bonino da Silva Santos
Philip E. Bourne
Jildau Bouwman
Anthony J. Brookes
Tim W. Clark
Mercè Crosas
Ingrid Dillo
Olivier Dumon
Scott Edmunds
Chris T. Evelo
Richard Finkers
Alejandra González-Beltrán
Alasdair J. G. Gray
Paul Groth
Carole Goble
Jeffrey S. Grethe
Jaap Heringa
Peter A.C. ’t Hoen
Rob Hooft
Tobias Kuhn
Ruben Kok
Joost N. Kok
Scott J. Lusher
Maryann E. Martone
Albert Mons
Abel L. Packer
Bengt Persson
Philippe Rocca‐Serra
Marco Roos
René van Schaik
Susanna‐Assunta Sansone
Erik Schultes
Thierry Sengstag
Ted Slater
George Strawn
Morris A. Swertz
Mark Thompson
Johan van der Lei
Erik M. van Mulligen
Jan Velterop
Andra Waagmeester
Peter Wittenburg
Katherine Wolstencroft
Jun Zhao
Barend Mons
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