Chemical ITAcademic PublishingAutomatic ProcessingData ManagementDigital Object IdentifierChemical Sciences

The “Accessible” in FAIR (data).

https://doi.org/10.59350/r8djg-gkj29
Published in Henry Rzepa's Blog

In a previous post, I looked at the Findability of FAIR data in common chemistry journals. Here I move on to the next letter, the A = Accessible. The attributes of A[cite]10.1038/sdata.2016.18[/cite] include: (meta)data are retrievable by their identifier using a standardized communication protocol. the protocol is open, free and universally implementable.  the protocol allows for an authentication and authorization procedure.

References

Library and Information SciencesStatistics, Probability and UncertaintyComputer Science ApplicationsEducationInformation Systems

The FAIR Guiding Principles for scientific data management and stewardship

https://doi.org/10.1038/sdata.2016.18
Published in Scientific Data
Authors Mark D. Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, Jan-Willem Boiten, Luiz Bonino da Silva Santos, Philip E. Bourne, Jildau Bouwman, Anthony J. Brookes, Tim Clark, Mercè Crosas, Ingrid Dillo, Olivier Dumon, Scott Edmunds, Chris T. Evelo, Richard Finkers, Alejandra Gonzalez-Beltran, 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 Kok, Scott J. Lusher, Maryann E. Martone, Albert Mons, Abel L. Packer, Bengt Persson, Philippe Rocca-Serra, Marco Roos, Rene van Schaik, Susanna-Assunta Sansone, Erik Schultes, Thierry Sengstag, Ted Slater, George Strawn, Morris A. Swertz, Mark Thompson, Johan van der Lei, Erik van Mulligen, Jan Velterop, Andra Waagmeester, Peter Wittenburg, Katherine Wolstencroft, Jun Zhao, Barend Mons

AbstractThere 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.