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PopPlanner: visually constructing demographic models for simulation

Overview of attention for article published in Frontiers in Genetics, April 2015
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3 X users

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27 Mendeley
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Title
PopPlanner: visually constructing demographic models for simulation
Published in
Frontiers in Genetics, April 2015
DOI 10.3389/fgene.2015.00150
Pubmed ID
Authors

Gregory B. Ewing, Pauline A. Reiff, Jeffrey D. Jensen

Abstract

Currently there are a number of coalescent simulation programs that support a wide range of features, such as arbitrary demographic models, migration, and sub structure. Defining the model is done typically with either text files or command line switches. Although this has proven to be a powerful method of defining models of high complexity, it is often error prone and difficult to read without familiarity both with command lines and the program in question. A intuitive GUI based population structure program that can both read and write applicable command lines would dramatically simplify the construction, modification, and error checking of such models by a wider user base. PopPlanner is a tool to both construct and inspect complicated demographic models visually with a GUI where the user's primary interaction is through mouse gestures. Because of their popularity, we focus on ms and by extension msms, command line coalescent simulation programs. Our program can be used to find errors with existing command lines, or to build original command lines. Furthermore, the graphical output supports a number of editing and output features including export of publication quality figures.

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 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 30%
Researcher 5 19%
Student > Bachelor 3 11%
Student > Master 3 11%
Other 2 7%
Other 5 19%
Unknown 1 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 63%
Biochemistry, Genetics and Molecular Biology 5 19%
Unspecified 1 4%
Computer Science 1 4%
Arts and Humanities 1 4%
Other 0 0%
Unknown 2 7%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 2015.
All research outputs
#13,941,015
of 22,800,560 outputs
Outputs from Frontiers in Genetics
#3,516
of 11,762 outputs
Outputs of similar age
#134,701
of 265,380 outputs
Outputs of similar age from Frontiers in Genetics
#84
of 118 outputs
Altmetric has tracked 22,800,560 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,762 research outputs from this source. They receive a mean Attention Score of 3.7. This one has gotten more attention than average, scoring higher than 67% 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 265,380 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 118 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.