Mark D Wilkinson 1 , Michel Dumontier 2 , I Jsbrand Jan Aalbersberg , Gabrielle Appleton , Myles Axton 3 , Arie Baak 4 , Niklas Blomberg 5 , Jan-Willem Boiten 6 , Luiz Bonino da Silva Santos 7 , Philip E Bourne 8 , Jildau Bouwman 9 , Anthony J Brookes 10 , Tim Clark 11 , Mercè Crosas 12 , Ingrid Dillo 13 , Olivier Dumon , Scott Edmunds 14 , Chris T Evelo 15 , Richard Finkers 16 , Alejandra Gonzalez-Beltran 17 , Alasdair J G Gray 18 , Paul Groth , Carole Goble 19 , Jeffrey S Grethe 20 , Jaap Heringa 21 , Peter A C 't Hoen 22 , Rob Hooft 23 , Tobias Kuhn 24 , Ruben Kok 21 , Joost Kok 25 , Scott J Lusher 26 , Maryann E Martone 27 , Albert Mons 28 , Abel L Packer 29 , Bengt Persson 30 , Philippe Rocca-Serra 17 , Marco Roos 31 , Rene van Schaik 32 , Susanna-Assunta Sansone 17 , Erik Schultes 33 , Thierry Sengstag 34 , Ted Slater 35 , George Strawn , Morris A Swertz 36 , Mark Thompson 31 , Johan van der Lei 37 , Erik van Mulligen 37 , Jan Velterop 38 , Andra Waagmeester 39 , Peter Wittenburg 40 , Katherine Wolstencroft 41 , Jun Zhao 42 , Barend Mons 43 , 26 , 37
Mar 15 2016
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.