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An efficient system to fund science: from proposal review to peer-to-peer distributions

Overview of attention for article published in Scientometrics, September 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#13 of 1,695)
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

news
2 news outlets
blogs
3 blogs
twitter
146 tweeters
facebook
2 Facebook pages
googleplus
1 Google+ user

Citations

dimensions_citation
17 Dimensions

Readers on

mendeley
55 Mendeley
citeulike
2 CiteULike
Title
An efficient system to fund science: from proposal review to peer-to-peer distributions
Published in
Scientometrics, September 2016
DOI 10.1007/s11192-016-2110-3
Pubmed ID
Authors

Johan Bollen, David Crandall, Damion Junk, Ying Ding, Katy Börner

Abstract

This paper presents a novel model of science funding that exploits the wisdom of the scientific crowd. Each researcher receives an equal, unconditional part of all available science funding on a yearly basis, but is required to individually donate to other scientists a given fraction of all they receive. Science funding thus moves from one scientist to the next in such a way that scientists who receive many donations must also redistribute the most. As the funding circulates through the scientific community it is mathematically expected to converge on a funding distribution favored by the entire scientific community. This is achieved without any proposal submissions or reviews. The model furthermore funds scientists instead of projects, reducing much of the overhead and bias of the present grant peer review system. Model validation using large-scale citation data and funding records over the past 20 years show that the proposed model could yield funding distributions that are similar to those of the NSF and NIH, and the model could potentially be more fair and more equitable. We discuss possible extensions of this approach as well as science policy implications.

Twitter Demographics

The data shown below were collected from the profiles of 146 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 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 2%
Switzerland 1 2%
Portugal 1 2%
Taiwan 1 2%
Luxembourg 1 2%
Unknown 50 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 25%
Student > Ph. D. Student 11 20%
Professor 5 9%
Librarian 4 7%
Student > Master 4 7%
Other 17 31%
Readers by discipline Count As %
Social Sciences 11 20%
Agricultural and Biological Sciences 8 15%
Unspecified 6 11%
Business, Management and Accounting 3 5%
Computer Science 3 5%
Other 24 44%

Attention Score in Context

This research output has an Altmetric Attention Score of 125. 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 28 August 2019.
All research outputs
#123,319
of 13,606,256 outputs
Outputs from Scientometrics
#13
of 1,695 outputs
Outputs of similar age
#4,888
of 262,240 outputs
Outputs of similar age from Scientometrics
#1
of 50 outputs
Altmetric has tracked 13,606,256 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 1,695 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.3. 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 262,240 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 98% of its contemporaries.
We're also able to compare this research output to 50 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.