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Mendeley readers
Attention Score in Context
| Title |
Clotho: addressing the scalability of forward time population genetic simulation
|
|---|---|
| Published in |
BMC Bioinformatics, June 2015
|
| DOI | 10.1186/s12859-015-0631-z |
| Pubmed ID | |
| Authors |
Patrick P. Putnam, Philip A. Wilsey, Ge Zhang |
| Abstract |
Forward Time Population Genetic Simulations offer a flexible framework for modeling the various evolutionary processes occurring in nature. Often this model expressibility is countered by an increased memory usage or computational overhead. With the complexity of simulation scenarios continuing to increase, addressing the scalability of the underlying simulation framework is a growing consideration. We propose a general method for representing in silico genetic sequences using implicit data structures. We provide a generalized implementation as a C++ template library called Clotho. We compare the performance and scalability of our approach with those taken in other simulation frameworks, namely: FWDPP and simuPOP. We show that this technique offers a 4x reduction in memory utilization. Additionally, with larger scale simulation scenarios we are able to offer a speedup of 6x - 46x. |
X Demographics
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.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 14% |
| Unknown | 6 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 2 | 29% |
| Student > Ph. D. Student | 2 | 29% |
| Student > Doctoral Student | 1 | 14% |
| Student > Master | 1 | 14% |
| Professor > Associate Professor | 1 | 14% |
| Other | 0 | 0% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 4 | 57% |
| Biochemistry, Genetics and Molecular Biology | 2 | 29% |
| Computer Science | 1 | 14% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 04 July 2015.
All research outputs
#7,217,691
of 22,813,792 outputs
Outputs from BMC Bioinformatics
#2,863
of 7,284 outputs
Outputs of similar age
#86,361
of 266,642 outputs
Outputs of similar age from BMC Bioinformatics
#59
of 114 outputs
Altmetric has tracked 22,813,792 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 7,284 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 gotten more attention than average, scoring higher than 59% 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 266,642 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 114 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.