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Facilitated Variation: How Evolution Learns from Past Environments To Generalize to New Environments

Overview of attention for article published in PLoS Computational Biology, November 2008
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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 (95th percentile)

Mentioned by

blogs
5 blogs
twitter
3 X users
wikipedia
3 Wikipedia pages

Citations

dimensions_citation
157 Dimensions

Readers on

mendeley
332 Mendeley
citeulike
17 CiteULike
connotea
4 Connotea
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Title
Facilitated Variation: How Evolution Learns from Past Environments To Generalize to New Environments
Published in
PLoS Computational Biology, November 2008
DOI 10.1371/journal.pcbi.1000206
Pubmed ID
Authors

Merav Parter, Nadav Kashtan, Uri Alon

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 332 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 19 6%
United Kingdom 5 2%
Portugal 4 1%
Italy 4 1%
Switzerland 4 1%
Spain 4 1%
Netherlands 3 <1%
Japan 3 <1%
Mexico 3 <1%
Other 11 3%
Unknown 272 82%

Demographic breakdown

Readers by professional status Count As %
Researcher 101 30%
Student > Ph. D. Student 71 21%
Student > Master 31 9%
Professor > Associate Professor 29 9%
Student > Bachelor 23 7%
Other 51 15%
Unknown 26 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 153 46%
Computer Science 36 11%
Biochemistry, Genetics and Molecular Biology 34 10%
Physics and Astronomy 19 6%
Engineering 11 3%
Other 44 13%
Unknown 35 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 36. 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 19 July 2022.
All research outputs
#1,118,910
of 25,394,764 outputs
Outputs from PLoS Computational Biology
#903
of 8,964 outputs
Outputs of similar age
#2,591
of 104,481 outputs
Outputs of similar age from PLoS Computational Biology
#2
of 47 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,964 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has done well, scoring higher than 89% 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 104,481 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 47 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 95% of its contemporaries.