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Learning from Hackers: Open-Source Clinical Trials

Overview of attention for article published in Science Translational Medicine, May 2012
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

blogs
3 blogs
twitter
37 tweeters
facebook
1 Facebook page
googleplus
1 Google+ user
reddit
1 Redditor

Citations

dimensions_citation
13 Dimensions

Readers on

mendeley
50 Mendeley
citeulike
1 CiteULike
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Title
Learning from Hackers: Open-Source Clinical Trials
Published in
Science Translational Medicine, May 2012
DOI 10.1126/scitranslmed.3003682
Pubmed ID
Authors

Adam G. Dunn, Richard O. Day, Kenneth D. Mandl, Enrico Coiera

Abstract

Open sharing of clinical trial data has been proposed as a way to address the gap between the production of clinical evidence and the decision-making of physicians. A similar gap was addressed in the software industry by their open-source software movement. Here, we examine how the social and technical principles of the movement can guide the growth of an open-source clinical trial community.

Twitter Demographics

The data shown below were collected from the profiles of 37 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 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 6%
Spain 2 4%
United Kingdom 1 2%
Finland 1 2%
Unknown 43 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 32%
Other 6 12%
Student > Ph. D. Student 5 10%
Librarian 3 6%
Professor 3 6%
Other 14 28%
Unknown 3 6%
Readers by discipline Count As %
Medicine and Dentistry 14 28%
Agricultural and Biological Sciences 9 18%
Biochemistry, Genetics and Molecular Biology 6 12%
Social Sciences 5 10%
Computer Science 3 6%
Other 9 18%
Unknown 4 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 44. 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 03 July 2017.
All research outputs
#760,197
of 22,075,848 outputs
Outputs from Science Translational Medicine
#1,742
of 4,997 outputs
Outputs of similar age
#3,644
of 143,685 outputs
Outputs of similar age from Science Translational Medicine
#19
of 92 outputs
Altmetric has tracked 22,075,848 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,997 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 80.0. This one has gotten more attention than average, scoring higher than 65% 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 143,685 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 97% of its contemporaries.
We're also able to compare this research output to 92 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.