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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Recursive algorithms for phylogenetic tree counting
|
|---|---|
| Published in |
Algorithms for Molecular Biology, October 2013
|
| DOI | 10.1186/1748-7188-8-26 |
| Pubmed ID | |
| Authors |
Alexandra Gavryushkina, David Welch, Alexei J Drummond |
| Abstract |
In Bayesian phylogenetic inference we are interested in distributions over a space of trees. The number of trees in a tree space is an important characteristic of the space and is useful for specifying prior distributions. When all samples come from the same time point and no prior information available on divergence times, the tree counting problem is easy. However, when fossil evidence is used in the inference to constrain the tree or data are sampled serially, new tree spaces arise and counting the number of trees is more difficult. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 3% |
| Brazil | 1 | 3% |
| United Kingdom | 1 | 3% |
| Iran, Islamic Republic of | 1 | 3% |
| United States | 1 | 3% |
| Unknown | 26 | 84% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 26% |
| Researcher | 7 | 23% |
| Student > Master | 5 | 16% |
| Professor > Associate Professor | 3 | 10% |
| Professor | 3 | 10% |
| Other | 4 | 13% |
| Unknown | 1 | 3% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 12 | 39% |
| Computer Science | 8 | 26% |
| Mathematics | 4 | 13% |
| Biochemistry, Genetics and Molecular Biology | 3 | 10% |
| Environmental Science | 1 | 3% |
| Other | 2 | 6% |
| Unknown | 1 | 3% |
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 18 November 2013.
All research outputs
#18,354,532
of 22,731,677 outputs
Outputs from Algorithms for Molecular Biology
#197
of 264 outputs
Outputs of similar age
#158,167
of 212,683 outputs
Outputs of similar age from Algorithms for Molecular Biology
#6
of 6 outputs
Altmetric has tracked 22,731,677 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 264 research outputs from this source. They receive a mean Attention Score of 3.2. This one is in the 12th percentile – i.e., 12% 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 212,683 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one.