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Open Peer Review by a Selected-Papers Network

Overview of attention for article published in Frontiers in Computational Neuroscience, January 2012
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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 (#27 of 1,414)
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

blogs
6 blogs
twitter
14 X users
facebook
1 Facebook page
wikipedia
1 Wikipedia page
googleplus
5 Google+ users
q&a
1 Q&A thread

Citations

dimensions_citation
56 Dimensions

Readers on

mendeley
60 Mendeley
citeulike
5 CiteULike
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Title
Open Peer Review by a Selected-Papers Network
Published in
Frontiers in Computational Neuroscience, January 2012
DOI 10.3389/fncom.2012.00001
Pubmed ID
Authors

Christopher Lee

Abstract

A selected-papers (SP) network is a network in which researchers who read, write, and review articles subscribe to each other based on common interests. Instead of reviewing a manuscript in secret for the Editor of a journal, each reviewer simply publishes his review (typically of a paper he wishes to recommend) to his SP network subscribers. Once the SP network reviewers complete their review decisions, the authors can invite any journal editor they want to consider these reviews and initial audience size, and make a publication decision. Since all impact assessment, reviews, and revisions are complete, this decision process should be short. I show how the SP network can provide a new way of measuring impact, catalyze the emergence of new subfields, and accelerate discovery in existing fields, by providing each reader a fine-grained filter for high-impact. I present a three phase plan for building a basic SP network, and making it an effective peer review platform that can be used by journals, conferences, users of repositories such as arXiv, and users of search engines such as PubMed. I show how the SP network can greatly improve review and dissemination of research articles in areas that are not well-supported by existing journals. Finally, I illustrate how the SP network concept can work well with existing publication services such as journals, conferences, arXiv, PubMed, and online citation management sites.

X Demographics

X Demographics

The data shown below were collected from the profiles of 14 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 8%
Chile 1 2%
Norway 1 2%
Germany 1 2%
Belgium 1 2%
United Kingdom 1 2%
Spain 1 2%
Croatia 1 2%
Unknown 48 80%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 23%
Student > Ph. D. Student 9 15%
Student > Master 7 12%
Librarian 6 10%
Other 5 8%
Other 13 22%
Unknown 6 10%
Readers by discipline Count As %
Social Sciences 14 23%
Computer Science 11 18%
Agricultural and Biological Sciences 7 12%
Psychology 7 12%
Philosophy 2 3%
Other 12 20%
Unknown 7 12%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 64. 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 24 September 2018.
All research outputs
#624,148
of 24,378,498 outputs
Outputs from Frontiers in Computational Neuroscience
#27
of 1,414 outputs
Outputs of similar age
#3,507
of 252,143 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#8
of 71 outputs
Altmetric has tracked 24,378,498 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,414 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. This one has done particularly well, scoring higher than 98% 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 252,143 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 71 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 90% of its contemporaries.