You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
Mendeley readers
Attention Score in Context
| Title |
MTML-msBayes: Approximate Bayesian comparative phylogeographic inference from multiple taxa and multiple loci with rate heterogeneity
|
|---|---|
| Published in |
BMC Bioinformatics, January 2011
|
| DOI | 10.1186/1471-2105-12-1 |
| Pubmed ID | |
| Authors |
Wen Huang, Naoki Takebayashi, Yan Qi, Michael J Hickerson |
| Abstract |
MTML-msBayes uses hierarchical approximate Bayesian computation (HABC) under a coalescent model to infer temporal patterns of divergence and gene flow across codistributed taxon-pairs. Under a model of multiple codistributed taxa that diverge into taxon-pairs with subsequent gene flow or isolation, one can estimate hyper-parameters that quantify the mean and variability in divergence times or test models of migration and isolation. The software uses multi-locus DNA sequence data collected from multiple taxon-pairs and allows variation across taxa in demographic parameters as well as heterogeneity in DNA mutation rates across loci. The method also allows a flexible sampling scheme: different numbers of loci of varying length can be sampled from different taxon-pairs. |
Mendeley readers
The data shown below were compiled from readership statistics for 258 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 10 | 4% |
| Brazil | 4 | 2% |
| France | 3 | 1% |
| United Kingdom | 3 | 1% |
| Mexico | 2 | <1% |
| Germany | 1 | <1% |
| Sweden | 1 | <1% |
| Czechia | 1 | <1% |
| Netherlands | 1 | <1% |
| Other | 4 | 2% |
| Unknown | 228 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 60 | 23% |
| Student > Ph. D. Student | 59 | 23% |
| Student > Master | 32 | 12% |
| Professor > Associate Professor | 20 | 8% |
| Student > Bachelor | 19 | 7% |
| Other | 50 | 19% |
| Unknown | 18 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 190 | 74% |
| Biochemistry, Genetics and Molecular Biology | 19 | 7% |
| Environmental Science | 7 | 3% |
| Linguistics | 2 | <1% |
| Nursing and Health Professions | 2 | <1% |
| Other | 9 | 3% |
| Unknown | 29 | 11% |
Attention Score in Context
This research output has an Altmetric Attention Score of 15. 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 March 2014.
All research outputs
#2,128,157
of 22,749,166 outputs
Outputs from BMC Bioinformatics
#573
of 7,268 outputs
Outputs of similar age
#12,624
of 180,466 outputs
Outputs of similar age from BMC Bioinformatics
#4
of 54 outputs
Altmetric has tracked 22,749,166 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,268 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done particularly well, scoring higher than 92% 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 180,466 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 93% of its contemporaries.
We're also able to compare this research output to 54 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.