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X Demographics
Mendeley readers
Attention Score in Context
| Title |
BERMUDA: a novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes
|
|---|---|
| Published in |
Genome Biology, August 2019
|
| DOI | 10.1186/s13059-019-1764-6 |
| Pubmed ID | |
| Authors |
Tongxin Wang, Travis S. Johnson, Wei Shao, Zixiao Lu, Bryan R. Helm, Jie Zhang, Kun Huang |
X Demographics
The data shown below were collected from the profiles of 35 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 | 15 | 43% |
| United Kingdom | 2 | 6% |
| Germany | 2 | 6% |
| Korea, Republic of | 1 | 3% |
| Australia | 1 | 3% |
| Hong Kong | 1 | 3% |
| Japan | 1 | 3% |
| Russia | 1 | 3% |
| Spain | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 9 | 26% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 18 | 51% |
| Members of the public | 16 | 46% |
| Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 175 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 175 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 40 | 23% |
| Researcher | 36 | 21% |
| Student > Master | 13 | 7% |
| Student > Bachelor | 11 | 6% |
| Student > Doctoral Student | 7 | 4% |
| Other | 20 | 11% |
| Unknown | 48 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 40 | 23% |
| Agricultural and Biological Sciences | 23 | 13% |
| Computer Science | 21 | 12% |
| Medicine and Dentistry | 9 | 5% |
| Neuroscience | 6 | 3% |
| Other | 22 | 13% |
| Unknown | 54 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 31. 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 13 November 2020.
All research outputs
#1,259,043
of 25,385,509 outputs
Outputs from Genome Biology
#951
of 4,470 outputs
Outputs of similar age
#26,439
of 354,607 outputs
Outputs of similar age from Genome Biology
#22
of 68 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,470 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has done well, scoring higher than 78% 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 354,607 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 92% of its contemporaries.
We're also able to compare this research output to 68 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 67% of its contemporaries.