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BEAST: Bayesian evolutionary analysis by sampling trees

Overview of attention for article published in BMC Evolutionary Biology, January 2007
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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 (95th percentile)

Mentioned by

news
1 news outlet
blogs
2 blogs
policy
1 policy source
twitter
1 tweeter
wikipedia
1 Wikipedia page
q&a
1 Q&A thread

Citations

dimensions_citation
9967 Dimensions

Readers on

mendeley
3736 Mendeley
citeulike
18 CiteULike
connotea
6 Connotea
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Title
BEAST: Bayesian evolutionary analysis by sampling trees
Published in
BMC Evolutionary Biology, January 2007
DOI 10.1186/1471-2148-7-214
Pubmed ID
Authors

Alexei J Drummond, Andrew Rambaut

Abstract

The evolutionary analysis of molecular sequence variation is a statistical enterprise. This is reflected in the increased use of probabilistic models for phylogenetic inference, multiple sequence alignment, and molecular population genetics. Here we present BEAST: a fast, flexible software architecture for Bayesian analysis of molecular sequences related by an evolutionary tree. A large number of popular stochastic models of sequence evolution are provided and tree-based models suitable for both within- and between-species sequence data are implemented.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 3,736 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 75 2%
Brazil 49 1%
Germany 32 <1%
United Kingdom 31 <1%
France 18 <1%
Spain 12 <1%
Canada 10 <1%
Switzerland 9 <1%
Mexico 8 <1%
Other 107 3%
Unknown 3385 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 936 25%
Researcher 758 20%
Student > Master 573 15%
Student > Bachelor 310 8%
Student > Doctoral Student 219 6%
Other 635 17%
Unknown 305 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 2268 61%
Biochemistry, Genetics and Molecular Biology 443 12%
Environmental Science 169 5%
Medicine and Dentistry 77 2%
Computer Science 63 2%
Other 328 9%
Unknown 388 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 32. 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 27 April 2022.
All research outputs
#953,372
of 21,036,846 outputs
Outputs from BMC Evolutionary Biology
#190
of 2,893 outputs
Outputs of similar age
#7,520
of 173,927 outputs
Outputs of similar age from BMC Evolutionary Biology
#1
of 1 outputs
Altmetric has tracked 21,036,846 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 2,893 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one has done particularly well, scoring higher than 93% 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 173,927 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 95% 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