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X Demographics
Mendeley readers
Attention Score in Context
Title |
DISC: a highly scalable and accurate inference of gene expression and structure for single-cell transcriptomes using semi-supervised deep learning
|
---|---|
Published in |
Genome Biology, July 2020
|
DOI | 10.1186/s13059-020-02083-3 |
Pubmed ID | |
Authors |
Yao He, Hao Yuan, Cheng Wu, Zhi Xie |
X Demographics
The data shown below were collected from the profiles of 111 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 | 20 | 18% |
India | 6 | 5% |
Nigeria | 4 | 4% |
United Kingdom | 4 | 4% |
Hong Kong | 2 | 2% |
Italy | 2 | 2% |
Spain | 2 | 2% |
France | 2 | 2% |
Brazil | 2 | 2% |
Other | 16 | 14% |
Unknown | 51 | 46% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 84 | 76% |
Scientists | 25 | 23% |
Science communicators (journalists, bloggers, editors) | 2 | 2% |
Mendeley readers
The data shown below were compiled from readership statistics for 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 87 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 21 | 24% |
Researcher | 11 | 13% |
Student > Bachelor | 10 | 11% |
Student > Master | 6 | 7% |
Student > Doctoral Student | 5 | 6% |
Other | 7 | 8% |
Unknown | 27 | 31% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 20 | 23% |
Biochemistry, Genetics and Molecular Biology | 14 | 16% |
Agricultural and Biological Sciences | 8 | 9% |
Mathematics | 3 | 3% |
Medicine and Dentistry | 2 | 2% |
Other | 6 | 7% |
Unknown | 34 | 39% |
Attention Score in Context
This research output has an Altmetric Attention Score of 62. 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 02 August 2020.
All research outputs
#702,489
of 25,750,437 outputs
Outputs from Genome Biology
#448
of 4,510 outputs
Outputs of similar age
#21,099
of 431,749 outputs
Outputs of similar age from Genome Biology
#15
of 89 outputs
Altmetric has tracked 25,750,437 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,510 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 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 431,749 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 89 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.