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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A deep generative model for multi-view profiling of single-cell RNA-seq and ATAC-seq data
|
|---|---|
| Published in |
Genome Biology, January 2022
|
| DOI | 10.1186/s13059-021-02595-6 |
| Pubmed ID | |
| Authors |
Gaoyang Li, Shaliu Fu, Shuguang Wang, Chenyu Zhu, Bin Duan, Chen Tang, Xiaohan Chen, Guohui Chuai, Ping Wang, Qi Liu |
X Demographics
The data shown below were collected from the profiles of 90 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 | 13 | 14% |
| India | 9 | 10% |
| United Kingdom | 4 | 4% |
| Germany | 3 | 3% |
| Canada | 2 | 2% |
| Italy | 2 | 2% |
| Sweden | 2 | 2% |
| South Africa | 2 | 2% |
| Brazil | 2 | 2% |
| Other | 15 | 17% |
| Unknown | 36 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 65 | 72% |
| Scientists | 24 | 27% |
| Science communicators (journalists, bloggers, editors) | 1 | 1% |
Mendeley readers
The data shown below were compiled from readership statistics for 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 83 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 14 | 17% |
| Researcher | 14 | 17% |
| Student > Master | 8 | 10% |
| Professor | 3 | 4% |
| Student > Postgraduate | 3 | 4% |
| Other | 8 | 10% |
| Unknown | 33 | 40% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 22 | 27% |
| Computer Science | 12 | 14% |
| Agricultural and Biological Sciences | 9 | 11% |
| Immunology and Microbiology | 2 | 2% |
| Business, Management and Accounting | 1 | 1% |
| Other | 5 | 6% |
| Unknown | 32 | 39% |
Attention Score in Context
This research output has an Altmetric Attention Score of 49. 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 15 January 2024.
All research outputs
#861,266
of 25,515,042 outputs
Outputs from Genome Biology
#567
of 4,484 outputs
Outputs of similar age
#21,953
of 518,934 outputs
Outputs of similar age from Genome Biology
#21
of 90 outputs
Altmetric has tracked 25,515,042 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,484 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 87% 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 518,934 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 95% of its contemporaries.
We're also able to compare this research output to 90 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.