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Mendeley readers
Attention Score in Context
| Title |
BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models
|
|---|---|
| Published in |
BMC Systems Biology, June 2010
|
| DOI | 10.1186/1752-0509-4-92 |
| Pubmed ID | |
| Authors |
Chen Li, Marco Donizelli, Nicolas Rodriguez, Harish Dharuri, Lukas Endler, Vijayalakshmi Chelliah, Lu Li, Enuo He, Arnaud Henry, Melanie I Stefan, Jacky L Snoep, Michael Hucka, Nicolas Le Novère, Camille Laibe |
| Abstract |
Quantitative models of biochemical and cellular systems are used to answer a variety of questions in the biological sciences. The number of published quantitative models is growing steadily thanks to increasing interest in the use of models as well as the development of improved software systems and the availability of better, cheaper computer hardware. To maximise the benefits of this growing body of models, the field needs centralised model repositories that will encourage, facilitate and promote model dissemination and reuse. Ideally, the models stored in these repositories should be extensively tested and encoded in community-supported and standardised formats. In addition, the models and their components should be cross-referenced with other resources in order to allow their unambiguous identification. |
Mendeley readers
The data shown below were compiled from readership statistics for 363 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 13 | 4% |
| United Kingdom | 11 | 3% |
| Germany | 9 | 2% |
| Spain | 3 | <1% |
| Austria | 2 | <1% |
| Portugal | 2 | <1% |
| Russia | 2 | <1% |
| Mexico | 2 | <1% |
| Australia | 1 | <1% |
| Other | 8 | 2% |
| Unknown | 310 | 85% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 102 | 28% |
| Student > Ph. D. Student | 93 | 26% |
| Student > Bachelor | 28 | 8% |
| Student > Master | 25 | 7% |
| Professor | 18 | 5% |
| Other | 58 | 16% |
| Unknown | 39 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 137 | 38% |
| Computer Science | 61 | 17% |
| Biochemistry, Genetics and Molecular Biology | 47 | 13% |
| Engineering | 19 | 5% |
| Medicine and Dentistry | 14 | 4% |
| Other | 40 | 11% |
| Unknown | 45 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 17 January 2022.
All research outputs
#7,490,851
of 22,896,955 outputs
Outputs from BMC Systems Biology
#314
of 1,143 outputs
Outputs of similar age
#33,493
of 93,693 outputs
Outputs of similar age from BMC Systems Biology
#5
of 18 outputs
Altmetric has tracked 22,896,955 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,143 research outputs from this source. They receive a mean Attention Score of 3.6. This one has gotten more attention than average, scoring higher than 64% 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 93,693 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.