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treeman: an R package for efficient and intuitive manipulation of phylogenetic trees

Overview of attention for article published in BMC Research Notes, January 2017
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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 (81st percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

Mentioned by

twitter
18 tweeters

Citations

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14 Dimensions

Readers on

mendeley
74 Mendeley
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Title
treeman: an R package for efficient and intuitive manipulation of phylogenetic trees
Published in
BMC Research Notes, January 2017
DOI 10.1186/s13104-016-2340-8
Pubmed ID
Authors

Dominic J. Bennett, Mark D. Sutton, Samuel T. Turvey

Abstract

Phylogenetic trees are hierarchical structures used for representing the inter-relationships between biological entities. They are the most common tool for representing evolution and are essential to a range of fields across the life sciences. The manipulation of phylogenetic trees-in terms of adding or removing tips-is often performed by researchers not just for reasons of management but also for performing simulations in order to understand the processes of evolution. Despite this, the most common programming language among biologists, R, has few class structures well suited to these tasks. We present an R package that contains a new class, called TreeMan, for representing the phylogenetic tree. This class has a list structure allowing phylogenetic trees to be manipulated more efficiently. Computational running times are reduced because of the ready ability to vectorise and parallelise methods. Development is also improved due to fewer lines of code being required for performing manipulation processes. We present three use cases-pinning missing taxa to a supertree, simulating evolution with a tree-growth model and detecting significant phylogenetic turnover-that demonstrate the new package's speed and simplicity.

Twitter Demographics

The data shown below were collected from the profiles of 18 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 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
United States 1 1%
Unknown 72 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 27%
Researcher 13 18%
Student > Bachelor 12 16%
Student > Master 10 14%
Student > Doctoral Student 6 8%
Other 7 9%
Unknown 6 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 38 51%
Biochemistry, Genetics and Molecular Biology 8 11%
Environmental Science 6 8%
Immunology and Microbiology 4 5%
Earth and Planetary Sciences 3 4%
Other 5 7%
Unknown 10 14%

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 15 July 2021.
All research outputs
#2,916,044
of 18,910,941 outputs
Outputs from BMC Research Notes
#411
of 3,868 outputs
Outputs of similar age
#74,194
of 403,280 outputs
Outputs of similar age from BMC Research Notes
#42
of 306 outputs
Altmetric has tracked 18,910,941 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,868 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. 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 403,280 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 81% of its contemporaries.
We're also able to compare this research output to 306 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.