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X Demographics
Mendeley readers
Attention Score in Context
| Title |
SVFX: a machine learning framework to quantify the pathogenicity of structural variants
|
|---|---|
| Published in |
Genome Biology, November 2020
|
| DOI | 10.1186/s13059-020-02178-x |
| Pubmed ID | |
| Authors |
Sushant Kumar, Arif Harmanci, Jagath Vytheeswaran, Mark B. Gerstein |
X Demographics
The data shown below were collected from the profiles of 31 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 | 6 | 19% |
| United Kingdom | 5 | 16% |
| Spain | 2 | 6% |
| Germany | 2 | 6% |
| Switzerland | 1 | 3% |
| United Arab Emirates | 1 | 3% |
| India | 1 | 3% |
| Australia | 1 | 3% |
| Ukraine | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 10 | 32% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 15 | 48% |
| Scientists | 13 | 42% |
| Science communicators (journalists, bloggers, editors) | 2 | 6% |
| Practitioners (doctors, other healthcare professionals) | 1 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 57 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 21% |
| Researcher | 7 | 12% |
| Student > Master | 7 | 12% |
| Student > Bachelor | 5 | 9% |
| Student > Postgraduate | 3 | 5% |
| Other | 7 | 12% |
| Unknown | 16 | 28% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 22 | 39% |
| Agricultural and Biological Sciences | 6 | 11% |
| Computer Science | 6 | 11% |
| Medicine and Dentistry | 2 | 4% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 2% |
| Other | 2 | 4% |
| Unknown | 18 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 23. 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 26 August 2021.
All research outputs
#1,655,920
of 25,757,133 outputs
Outputs from Genome Biology
#1,333
of 4,513 outputs
Outputs of similar age
#42,073
of 439,578 outputs
Outputs of similar age from Genome Biology
#25
of 49 outputs
Altmetric has tracked 25,757,133 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,513 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.5. This one has gotten more attention than average, scoring higher than 70% 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 439,578 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 90% of its contemporaries.
We're also able to compare this research output to 49 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.