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X Demographics
Mendeley readers
Attention Score in Context
| Title |
CNV Radar: an improved method for somatic copy number alteration characterization in oncology
|
|---|---|
| Published in |
BMC Bioinformatics, March 2020
|
| DOI | 10.1186/s12859-020-3397-x |
| Pubmed ID | |
| Authors |
David Soong, Jeran Stratford, Herve Avet-Loiseau, Nizar Bahlis, Faith Davies, Angela Dispenzieri, A. Kate Sasser, Jordan M. Schecter, Ming Qi, Chad Brown, Wendell Jones, Jonathan J. Keats, Daniel Auclair, Christopher Chiu, Jason Powers, Michael Schaffer |
X Demographics
The data shown below were collected from the profiles of 8 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 Kingdom | 3 | 38% |
| Ireland | 1 | 13% |
| Italy | 1 | 13% |
| United States | 1 | 13% |
| India | 1 | 13% |
| Unknown | 1 | 13% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 50% |
| Members of the public | 3 | 38% |
| Practitioners (doctors, other healthcare professionals) | 1 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 71 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 14 | 20% |
| Researcher | 13 | 18% |
| Student > Bachelor | 9 | 13% |
| Student > Ph. D. Student | 6 | 8% |
| Student > Doctoral Student | 3 | 4% |
| Other | 10 | 14% |
| Unknown | 16 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 21 | 30% |
| Medicine and Dentistry | 13 | 18% |
| Agricultural and Biological Sciences | 6 | 8% |
| Computer Science | 6 | 8% |
| Immunology and Microbiology | 2 | 3% |
| Other | 8 | 11% |
| Unknown | 15 | 21% |
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 09 May 2022.
All research outputs
#8,882,501
of 26,017,215 outputs
Outputs from BMC Bioinformatics
#3,313
of 7,793 outputs
Outputs of similar age
#159,586
of 390,378 outputs
Outputs of similar age from BMC Bioinformatics
#46
of 112 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,793 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one is in the 49th percentile – i.e., 49% 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 390,378 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 112 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.