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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Improving variant calling using population data and deep learning
|
|---|---|
| Published in |
BMC Bioinformatics, May 2023
|
| DOI | 10.1186/s12859-023-05294-0 |
| Pubmed ID | |
| Authors |
Nae-Chyun Chen, Alexey Kolesnikov, Sidharth Goel, Taedong Yun, Pi-Chuan Chang, Andrew Carroll |
X Demographics
The data shown below were collected from the profiles of 39 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 | 7 | 18% |
| United Kingdom | 3 | 8% |
| Germany | 2 | 5% |
| China | 2 | 5% |
| Netherlands | 2 | 5% |
| Italy | 2 | 5% |
| Spain | 2 | 5% |
| South Africa | 1 | 3% |
| Nigeria | 1 | 3% |
| Other | 5 | 13% |
| Unknown | 12 | 31% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 26 | 67% |
| Members of the public | 13 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 38 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 21% |
| Student > Master | 6 | 16% |
| Student > Bachelor | 4 | 11% |
| Student > Doctoral Student | 3 | 8% |
| Professor > Associate Professor | 3 | 8% |
| Other | 3 | 8% |
| Unknown | 11 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 11 | 29% |
| Biochemistry, Genetics and Molecular Biology | 9 | 24% |
| Computer Science | 3 | 8% |
| Nursing and Health Professions | 1 | 3% |
| Chemical Engineering | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 12 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 19. 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 05 June 2023.
All research outputs
#1,912,770
of 25,315,460 outputs
Outputs from BMC Bioinformatics
#389
of 7,672 outputs
Outputs of similar age
#36,930
of 386,288 outputs
Outputs of similar age from BMC Bioinformatics
#7
of 124 outputs
Altmetric has tracked 25,315,460 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,672 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 94% 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 386,288 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 124 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.