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X Demographics
Mendeley readers
Attention Score in Context
| Title |
sybil – Efficient constraint-based modelling in R
|
|---|---|
| Published in |
BMC Systems Biology, November 2013
|
| DOI | 10.1186/1752-0509-7-125 |
| Pubmed ID | |
| Authors |
Gabriel Gelius-Dietrich, Abdelmoneim Amer Desouki, Claus Jonathan Fritzemeier, Martin J Lercher |
| Abstract |
Constraint-based analyses of metabolic networks are widely used to simulate the properties of genome-scale metabolic networks. Publicly available implementations tend to be slow, impeding large scale analyses such as the genome-wide computation of pairwise gene knock-outs, or the automated search for model improvements. Furthermore, available implementations cannot easily be extended or adapted by users. |
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 % |
|---|---|---|
| Japan | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 155 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 2% |
| Colombia | 2 | 1% |
| Germany | 2 | 1% |
| France | 2 | 1% |
| United Kingdom | 2 | 1% |
| Norway | 1 | <1% |
| Brazil | 1 | <1% |
| Israel | 1 | <1% |
| Hungary | 1 | <1% |
| Other | 2 | 1% |
| Unknown | 138 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 34 | 22% |
| Researcher | 34 | 22% |
| Student > Master | 22 | 14% |
| Student > Bachelor | 15 | 10% |
| Student > Doctoral Student | 6 | 4% |
| Other | 25 | 16% |
| Unknown | 19 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 48 | 31% |
| Biochemistry, Genetics and Molecular Biology | 33 | 21% |
| Computer Science | 16 | 10% |
| Engineering | 9 | 6% |
| Environmental Science | 4 | 3% |
| Other | 22 | 14% |
| Unknown | 23 | 15% |
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 23 February 2015.
All research outputs
#8,158,001
of 25,837,817 outputs
Outputs from BMC Systems Biology
#281
of 1,133 outputs
Outputs of similar age
#69,949
of 226,926 outputs
Outputs of similar age from BMC Systems Biology
#13
of 58 outputs
Altmetric has tracked 25,837,817 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 1,133 research outputs from this source. They receive a mean Attention Score of 3.7. This one has gotten more attention than average, scoring higher than 72% 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 226,926 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 67% of its contemporaries.
We're also able to compare this research output to 58 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.