You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
CAVER 3.0: A Tool for the Analysis of Transport Pathways in Dynamic Protein Structures
|
|---|---|
| Published in |
PLoS Computational Biology, October 2012
|
| DOI | 10.1371/journal.pcbi.1002708 |
| Pubmed ID | |
| Authors |
Eva Chovancova, Antonin Pavelka, Petr Benes, Ondrej Strnad, Jan Brezovsky, Barbora Kozlikova, Artur Gora, Vilem Sustr, Martin Klvana, Petr Medek, Lada Biedermannova, Jiri Sochor, Jiri Damborsky |
X Demographics
The data shown below were collected from the profiles of 34 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 9 | 26% |
| France | 4 | 12% |
| Netherlands | 1 | 3% |
| China | 1 | 3% |
| Chile | 1 | 3% |
| Belgium | 1 | 3% |
| Mexico | 1 | 3% |
| Unknown | 16 | 47% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 21 | 62% |
| Scientists | 12 | 35% |
| Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 681 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 9 | 1% |
| Germany | 6 | <1% |
| Czechia | 4 | <1% |
| United Kingdom | 3 | <1% |
| France | 1 | <1% |
| Vietnam | 1 | <1% |
| Portugal | 1 | <1% |
| Canada | 1 | <1% |
| Brazil | 1 | <1% |
| Other | 4 | <1% |
| Unknown | 650 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 177 | 26% |
| Researcher | 114 | 17% |
| Student > Master | 77 | 11% |
| Student > Bachelor | 55 | 8% |
| Student > Doctoral Student | 34 | 5% |
| Other | 82 | 12% |
| Unknown | 142 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 172 | 25% |
| Agricultural and Biological Sciences | 142 | 21% |
| Chemistry | 116 | 17% |
| Computer Science | 19 | 3% |
| Pharmacology, Toxicology and Pharmaceutical Science | 14 | 2% |
| Other | 65 | 10% |
| Unknown | 153 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 38. 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 18 May 2022.
All research outputs
#1,097,534
of 26,017,215 outputs
Outputs from PLoS Computational Biology
#869
of 9,038 outputs
Outputs of similar age
#6,493
of 197,810 outputs
Outputs of similar age from PLoS Computational Biology
#12
of 109 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 9,038 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 done particularly well, scoring higher than 90% 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 197,810 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 96% of its contemporaries.
We're also able to compare this research output to 109 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.