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Mendeley readers
Attention Score in Context
| Title |
CellML: its future, present and past
|
|---|---|
| Published in |
Progress in Biophysics & Molecular Biology, June 2004
|
| DOI | 10.1016/j.pbiomolbio.2004.01.004 |
| Pubmed ID | |
| Authors |
Catherine M. Lloyd, Matt D.B. Halstead, Poul F. Nielsen |
| Abstract |
Advances in biotechnology and experimental techniques have lead to the elucidation of vast amounts of biological data. Mathematical models provide a method of analysing this data; however, there are two issues that need to be addressed: (1) the need for standards for defining cell models so they can, for example, be exchanged across the World Wide Web, and also read into simulation software in a consistent format and (2) eliminating the errors which arise with the current method of model publication. CellML has evolved to meet these needs of the modelling community. CellML is a free, open-source, eXtensible markup language based standard for defining mathematical models of cellular function. In this paper we summarise the structure of CellML, its current applications (including biological pathway and electrophysiological models), and its future development--in particular, the development of toolsets and the integration of ontologies. |
Mendeley readers
The data shown below were compiled from readership statistics for 190 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 8 | 4% |
| United Kingdom | 6 | 3% |
| France | 3 | 2% |
| Italy | 3 | 2% |
| New Zealand | 3 | 2% |
| Brazil | 2 | 1% |
| Canada | 2 | 1% |
| Australia | 1 | <1% |
| Sweden | 1 | <1% |
| Other | 4 | 2% |
| Unknown | 157 | 83% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 57 | 30% |
| Student > Ph. D. Student | 51 | 27% |
| Student > Master | 14 | 7% |
| Professor | 11 | 6% |
| Student > Doctoral Student | 10 | 5% |
| Other | 33 | 17% |
| Unknown | 14 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 63 | 33% |
| Computer Science | 41 | 22% |
| Engineering | 24 | 13% |
| Biochemistry, Genetics and Molecular Biology | 14 | 7% |
| Medicine and Dentistry | 9 | 5% |
| Other | 17 | 9% |
| Unknown | 22 | 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 27 March 2019.
All research outputs
#8,535,472
of 25,374,917 outputs
Outputs from Progress in Biophysics & Molecular Biology
#334
of 925 outputs
Outputs of similar age
#20,859
of 62,303 outputs
Outputs of similar age from Progress in Biophysics & Molecular Biology
#5
of 9 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 925 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.4. This one is in the 43rd percentile – i.e., 43% of its peers scored the same or lower than it.
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 62,303 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.