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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Design of a flexible component gathering algorithm for converting cell-based models to graph representations for use in evolutionary search
|
|---|---|
| Published in |
BMC Bioinformatics, June 2014
|
| DOI | 10.1186/1471-2105-15-178 |
| Pubmed ID | |
| Authors |
Marianna Budnikova, Jeffrey W Habig, Daniel Lobo, Nicolas Cornia, Michael Levin, Tim Andersen |
| Abstract |
The ability of science to produce experimental data has outpaced the ability to effectively visualize and integrate the data into a conceptual framework that can further higher order understanding. Multidimensional and shape-based observational data of regenerative biology presents a particularly daunting challenge in this regard. Large amounts of data are available in regenerative biology, but little progress has been made in understanding how organisms such as planaria robustly achieve and maintain body form. An example of this kind of data can be found in a new repository (PlanformDB) that encodes descriptions of planaria experiments and morphological outcomes using a graph formalism. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Norway | 1 | 33% |
| United Kingdom | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 28 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 10 | 36% |
| Researcher | 4 | 14% |
| Professor > Associate Professor | 3 | 11% |
| Student > Postgraduate | 2 | 7% |
| Student > Master | 2 | 7% |
| Other | 4 | 14% |
| Unknown | 3 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 11 | 39% |
| Biochemistry, Genetics and Molecular Biology | 7 | 25% |
| Computer Science | 3 | 11% |
| Nursing and Health Professions | 1 | 4% |
| Physics and Astronomy | 1 | 4% |
| Other | 2 | 7% |
| Unknown | 3 | 11% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 19 November 2014.
All research outputs
#17,722,094
of 22,757,090 outputs
Outputs from BMC Bioinformatics
#5,926
of 7,272 outputs
Outputs of similar age
#156,277
of 229,145 outputs
Outputs of similar age from BMC Bioinformatics
#109
of 158 outputs
Altmetric has tracked 22,757,090 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,272 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 13th percentile – i.e., 13% 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 229,145 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 158 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.