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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 (#2 of 2,418)
  • High Attention Score compared to outputs of the same age (99th percentile)

Citations

dimensions_citation
6170 Dimensions

Readers on

mendeley
5617 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 980 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 5,617 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 8 <1%
Australia 4 <1%
Canada 4 <1%
Germany 4 <1%
Switzerland 3 <1%
Japan 3 <1%
Other 26 <1%
Unknown 5515 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 1197 21%
Student > Ph. D. Student 961 17%
Student > Master 665 12%
Other 349 6%
Student > Bachelor 340 6%
Other 1003 18%
Unknown 1102 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 644 11%
Computer Science 621 11%
Biochemistry, Genetics and Molecular Biology 407 7%
Medicine and Dentistry 307 5%
Social Sciences 288 5%
Other 1863 33%
Unknown 1487 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 2038. 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 02 December 2022.
All research outputs
#3,650
of 22,605,515 outputs
Outputs from Scientific Data
#2
of 2,418 outputs
Outputs of similar age
#35
of 279,826 outputs
Outputs of similar age from Scientific Data
#1
of 1 outputs
Altmetric has tracked 22,605,515 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 2,418 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 24.7. 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 279,826 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