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The FAIR Guiding Principles for scientific data management and stewardship

Overview of attention for article published in Scientific Data, March 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#1 of 1,767)
  • High Attention Score compared to outputs of the same age (99th percentile)

Citations

dimensions_citation
4005 Dimensions

Readers on

mendeley
4344 Mendeley
citeulike
20 CiteULike
Title
The FAIR Guiding Principles for scientific data management and stewardship
Published in
Scientific Data, March 2016
DOI 10.1038/sdata.2016.18
Pubmed ID
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

Abstract

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.

Twitter Demographics

The data shown below were collected from the profiles of 992 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 4,344 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 21 <1%
United States 18 <1%
Spain 11 <1%
United Kingdom 9 <1%
Germany 4 <1%
Canada 4 <1%
Italy 4 <1%
Australia 4 <1%
Japan 3 <1%
Other 28 <1%
Unknown 4238 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 977 22%
Student > Ph. D. Student 766 18%
Student > Master 555 13%
Other 289 7%
Student > Bachelor 270 6%
Other 813 19%
Unknown 674 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 559 13%
Computer Science 529 12%
Biochemistry, Genetics and Molecular Biology 329 8%
Social Sciences 249 6%
Medicine and Dentistry 248 6%
Other 1434 33%
Unknown 996 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 1929. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 December 2021.
All research outputs
#3,108
of 19,613,889 outputs
Outputs from Scientific Data
#1
of 1,767 outputs
Outputs of similar age
#42
of 274,662 outputs
Outputs of similar age from Scientific Data
#1
of 1 outputs
Altmetric has tracked 19,613,889 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,767 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.8. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 274,662 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them