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Simulating a population genomics data set using FlowSim

Overview of attention for article published in BMC Research Notes, January 2014
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Title
Simulating a population genomics data set using FlowSim
Published in
BMC Research Notes, January 2014
DOI 10.1186/1756-0500-7-68
Pubmed ID
Authors

Ketil Malde

Abstract

The field of population genetics use the genetic composition of populations to study the effects of ecological and evolutionary factors, including selection, genetic drift, mating structure, and migration. Until recently, these studies were usually based upon the analysis of relatively few (typically 10-20) DNA markers on samples from multiple populations. In contrast, high-throughput sequencing provides large amounts of data and consequently very high resolution genetic information. Recent technological developments are rapidly making this a cost-effective alternative. In addition, sequencing allows both the direct study of genomic differences between population, and the discovery of single nucleotide polymorphism marker that can be subsequently used in high-throughput genotyping. Much of the analysis in population genetics was developed before large scale sequencing became feasible. Methods often do not take into account the characteristics of the different sequencing technologies, and consequently, may not always be well suited to this kind of data.

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The data shown below were collected from the profiles of 2 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 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 7%
Netherlands 1 7%
Sweden 1 7%
Unknown 12 80%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 33%
Student > Ph. D. Student 5 33%
Lecturer > Senior Lecturer 1 7%
Professor 1 7%
Professor > Associate Professor 1 7%
Other 1 7%
Unknown 1 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 73%
Environmental Science 1 7%
Computer Science 1 7%
Medicine and Dentistry 1 7%
Unknown 1 7%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 16 February 2014.
All research outputs
#15,293,290
of 22,743,667 outputs
Outputs from BMC Research Notes
#2,314
of 4,261 outputs
Outputs of similar age
#189,442
of 306,968 outputs
Outputs of similar age from BMC Research Notes
#70
of 121 outputs
Altmetric has tracked 22,743,667 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,261 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 33rd percentile – i.e., 33% of its peers scored the same or lower than it.
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 306,968 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 121 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.