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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives
|
|---|---|
| Published in |
BMC Bioinformatics, September 2013
|
| DOI | 10.1186/1471-2105-14-s11-s1 |
| Pubmed ID | |
| Authors |
Min Zhao, Qingguo Wang, Quan Wang, Peilin Jia, Zhongming Zhao |
X Demographics
The data shown below were collected from the profiles of 7 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 | 3 | 43% |
| Spain | 2 | 29% |
| United Kingdom | 1 | 14% |
| Unknown | 1 | 14% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 57% |
| Members of the public | 3 | 43% |
Mendeley readers
The data shown below were compiled from readership statistics for 1,101 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 15 | 1% |
| United Kingdom | 10 | <1% |
| France | 4 | <1% |
| Norway | 4 | <1% |
| Germany | 3 | <1% |
| Brazil | 3 | <1% |
| Sweden | 2 | <1% |
| Netherlands | 2 | <1% |
| Italy | 2 | <1% |
| Other | 15 | 1% |
| Unknown | 1041 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 262 | 24% |
| Researcher | 235 | 21% |
| Student > Master | 171 | 16% |
| Student > Bachelor | 89 | 8% |
| Student > Doctoral Student | 62 | 6% |
| Other | 133 | 12% |
| Unknown | 149 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 437 | 40% |
| Biochemistry, Genetics and Molecular Biology | 288 | 26% |
| Medicine and Dentistry | 68 | 6% |
| Computer Science | 67 | 6% |
| Engineering | 15 | 1% |
| Other | 58 | 5% |
| Unknown | 168 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 13. 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 22 November 2023.
All research outputs
#2,757,088
of 25,837,817 outputs
Outputs from BMC Bioinformatics
#742
of 7,763 outputs
Outputs of similar age
#23,383
of 212,306 outputs
Outputs of similar age from BMC Bioinformatics
#10
of 99 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,763 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. 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 212,306 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 99 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.