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Selecting optimal partitioning schemes for phylogenomic datasets

Overview of attention for article published in BMC Evolutionary Biology, January 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

Mentioned by

twitter
15 tweeters

Citations

dimensions_citation
430 Dimensions

Readers on

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338 Mendeley
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Title
Selecting optimal partitioning schemes for phylogenomic datasets
Published in
BMC Evolutionary Biology, January 2014
DOI 10.1186/1471-2148-14-82
Pubmed ID
Authors

Robert Lanfear, Brett Calcott, David Kainer, Christoph Mayer, Alexandros Stamatakis

Abstract

Partitioning involves estimating independent models of molecular evolution for different subsets of sites in a sequence alignment, and has been shown to improve phylogenetic inference. Current methods for estimating best-fit partitioning schemes, however, are only computationally feasible with datasets of fewer than 100 loci. This is a problem because datasets with thousands of loci are increasingly common in phylogenetics.

Twitter Demographics

The data shown below were collected from the profiles of 15 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 338 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 2%
Brazil 4 1%
Germany 3 <1%
Czechia 2 <1%
Canada 2 <1%
United Kingdom 2 <1%
Austria 1 <1%
Sweden 1 <1%
Australia 1 <1%
Other 5 1%
Unknown 311 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 90 27%
Researcher 58 17%
Student > Master 53 16%
Student > Doctoral Student 26 8%
Student > Bachelor 20 6%
Other 65 19%
Unknown 26 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 218 64%
Biochemistry, Genetics and Molecular Biology 54 16%
Environmental Science 14 4%
Computer Science 10 3%
Earth and Planetary Sciences 3 <1%
Other 7 2%
Unknown 32 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 04 December 2015.
All research outputs
#1,628,664
of 12,373,386 outputs
Outputs from BMC Evolutionary Biology
#585
of 2,341 outputs
Outputs of similar age
#27,306
of 193,296 outputs
Outputs of similar age from BMC Evolutionary Biology
#6
of 15 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,341 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one has done well, scoring higher than 75% 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 193,296 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.