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X Demographics
Mendeley readers
Attention Score in Context
| Title |
AutoCoV: tracking the early spread of COVID-19 in terms of the spatial and temporal patterns from embedding space by K-mer based deep learning
|
|---|---|
| Published in |
BMC Bioinformatics, April 2022
|
| DOI | 10.1186/s12859-022-04679-x |
| Pubmed ID | |
| Authors |
Inyoung Sung, Sangseon Lee, Minwoo Pak, Yunyol Shin, Sun Kim |
X Demographics
The data shown below were collected from the profiles of 25 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 4 | 16% |
| United States | 4 | 16% |
| Mexico | 1 | 4% |
| Sri Lanka | 1 | 4% |
| Kenya | 1 | 4% |
| Sweden | 1 | 4% |
| United Kingdom | 1 | 4% |
| Unknown | 12 | 48% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 21 | 84% |
| Scientists | 4 | 16% |
Mendeley readers
The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 12 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Librarian | 1 | 8% |
| Other | 1 | 8% |
| Lecturer | 1 | 8% |
| Student > Doctoral Student | 1 | 8% |
| Student > Bachelor | 1 | 8% |
| Other | 3 | 25% |
| Unknown | 4 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 2 | 17% |
| Medicine and Dentistry | 2 | 17% |
| Social Sciences | 1 | 8% |
| Immunology and Microbiology | 1 | 8% |
| Unknown | 6 | 50% |
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 09 May 2022.
All research outputs
#2,688,364
of 23,726,221 outputs
Outputs from BMC Bioinformatics
#801
of 7,426 outputs
Outputs of similar age
#59,509
of 444,720 outputs
Outputs of similar age from BMC Bioinformatics
#11
of 139 outputs
Altmetric has tracked 23,726,221 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 7,426 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 89% 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 444,720 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 86% of its contemporaries.
We're also able to compare this research output to 139 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.