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Mendeley readers
Attention Score in Context
| Title |
ABCtoolbox: a versatile toolkit for approximate Bayesian computations
|
|---|---|
| Published in |
BMC Bioinformatics, March 2010
|
| DOI | 10.1186/1471-2105-11-116 |
| Pubmed ID | |
| Authors |
Daniel Wegmann, Christoph Leuenberger, Samuel Neuenschwander, Laurent Excoffier |
| Abstract |
The estimation of demographic parameters from genetic data often requires the computation of likelihoods. However, the likelihood function is computationally intractable for many realistic evolutionary models, and the use of Bayesian inference has therefore been limited to very simple models. The situation changed recently with the advent of Approximate Bayesian Computation (ABC) algorithms allowing one to obtain parameter posterior distributions based on simulations not requiring likelihood computations. |
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 588 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 18 | 3% |
| France | 7 | 1% |
| United Kingdom | 6 | 1% |
| Germany | 5 | <1% |
| Spain | 4 | <1% |
| Switzerland | 4 | <1% |
| Australia | 3 | <1% |
| Brazil | 3 | <1% |
| Italy | 3 | <1% |
| Other | 12 | 2% |
| Unknown | 523 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 156 | 27% |
| Student > Ph. D. Student | 147 | 25% |
| Student > Master | 53 | 9% |
| Student > Bachelor | 39 | 7% |
| Professor > Associate Professor | 35 | 6% |
| Other | 103 | 18% |
| Unknown | 55 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 381 | 65% |
| Biochemistry, Genetics and Molecular Biology | 54 | 9% |
| Environmental Science | 16 | 3% |
| Mathematics | 13 | 2% |
| Computer Science | 11 | 2% |
| Other | 52 | 9% |
| Unknown | 61 | 10% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 01 January 2023.
All research outputs
#4,722,336
of 23,462,326 outputs
Outputs from BMC Bioinformatics
#1,740
of 7,391 outputs
Outputs of similar age
#19,808
of 94,962 outputs
Outputs of similar age from BMC Bioinformatics
#11
of 70 outputs
Altmetric has tracked 23,462,326 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,391 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 well, scoring higher than 76% 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 94,962 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 78% of its contemporaries.
We're also able to compare this research output to 70 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.