| Title |
DECA: scalable XHMM exome copy-number variant calling with ADAM and Apache Spark
|
|---|---|
| Published in |
BMC Bioinformatics, October 2019
|
| DOI | 10.1186/s12859-019-3108-7 |
| Pubmed ID | |
| Authors |
Michael D. Linderman, Davin Chia, Forrest Wallace, Frank A. Nothaft |
X Demographics
The data shown below were collected from the profiles of 9 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 | 2 | 22% |
| United States | 1 | 11% |
| Brazil | 1 | 11% |
| India | 1 | 11% |
| Unknown | 4 | 44% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 5 | 56% |
| Members of the public | 4 | 44% |
Mendeley readers
The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 11 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 5 | 45% |
| Student > Ph. D. Student | 2 | 18% |
| Student > Bachelor | 1 | 9% |
| Student > Master | 1 | 9% |
| Other | 1 | 9% |
| Other | 0 | 0% |
| Unknown | 1 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 3 | 27% |
| Agricultural and Biological Sciences | 3 | 27% |
| Computer Science | 2 | 18% |
| Economics, Econometrics and Finance | 1 | 9% |
| Medicine and Dentistry | 1 | 9% |
| Other | 0 | 0% |
| Unknown | 1 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. 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 08 September 2020.
All research outputs
#3,115,198
of 25,998,826 outputs
Outputs from BMC Bioinformatics
#925
of 7,793 outputs
Outputs of similar age
#62,092
of 371,297 outputs
Outputs of similar age from BMC Bioinformatics
#24
of 135 outputs
Altmetric has tracked 25,998,826 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 7,793 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one has done well, scoring higher than 88% 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 371,297 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 83% of its contemporaries.
We're also able to compare this research output to 135 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.