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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Spartan: A Comprehensive Tool for Understanding Uncertainty in Simulations of Biological Systems
|
|---|---|
| Published in |
PLoS Computational Biology, February 2013
|
| DOI | 10.1371/journal.pcbi.1002916 |
| Pubmed ID | |
| Authors |
Kieran Alden, Mark Read, Jon Timmis, Paul S. Andrews, Henrique Veiga-Fernandes, Mark Coles |
| Abstract |
Integrating computer simulation with conventional wet-lab research has proven to have much potential in furthering the understanding of biological systems. Success requires the relationship between simulation and the real-world system to be established: substantial aspects of the biological system are typically unknown, and the abstract nature of simulation can complicate interpretation of in silico results in terms of the biology. Here we present spartan (Simulation Parameter Analysis RToolkit ApplicatioN), a package of statistical techniques specifically designed to help researchers understand this relationship and provide novel biological insight. The tools comprising spartan help identify which simulation results can be attributed to the dynamics of the modelled biological system, rather than artefacts of biological uncertainty or parametrisation, or simulation stochasticity. Statistical analyses reveal the influence that pathways and components have on simulation behaviour, offering valuable biological insight into aspects of the system under study. We demonstrate the power of spartan in providing critical insight into aspects of lymphoid tissue development in the small intestine through simulation. Spartan is released under a GPLv2 license, implemented within the open source R statistical environment, and freely available from both the Comprehensive R Archive Network (CRAN) and http://www.cs.york.ac.uk/spartan. The techniques within the package can be applied to traditional ordinary or partial differential equation simulations as well as agent-based implementations. Manuals, comprehensive tutorials, and example simulation data upon which spartan can be applied are available from the website. |
X Demographics
The data shown below were collected from the profiles of 14 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 2 | 14% |
| United Kingdom | 2 | 14% |
| Spain | 1 | 7% |
| Germany | 1 | 7% |
| Ecuador | 1 | 7% |
| United States | 1 | 7% |
| Sweden | 1 | 7% |
| Norway | 1 | 7% |
| Unknown | 4 | 29% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 9 | 64% |
| Scientists | 4 | 29% |
| Science communicators (journalists, bloggers, editors) | 1 | 7% |
Mendeley readers
The data shown below were compiled from readership statistics for 150 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 6 | 4% |
| United States | 3 | 2% |
| Brazil | 2 | 1% |
| Germany | 2 | 1% |
| Norway | 1 | <1% |
| Chile | 1 | <1% |
| Sweden | 1 | <1% |
| Netherlands | 1 | <1% |
| Benin | 1 | <1% |
| Other | 1 | <1% |
| Unknown | 131 | 87% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 40 | 27% |
| Researcher | 39 | 26% |
| Student > Master | 11 | 7% |
| Student > Doctoral Student | 8 | 5% |
| Professor | 6 | 4% |
| Other | 27 | 18% |
| Unknown | 19 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 47 | 31% |
| Computer Science | 15 | 10% |
| Engineering | 9 | 6% |
| Mathematics | 8 | 5% |
| Environmental Science | 7 | 5% |
| Other | 35 | 23% |
| Unknown | 29 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 24 January 2018.
All research outputs
#4,563,208
of 25,576,801 outputs
Outputs from PLoS Computational Biology
#3,624
of 9,003 outputs
Outputs of similar age
#35,858
of 205,479 outputs
Outputs of similar age from PLoS Computational Biology
#36
of 157 outputs
Altmetric has tracked 25,576,801 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,003 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. 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 205,479 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 82% of its contemporaries.
We're also able to compare this research output to 157 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.