| Title |
Characterization of the SARS-CoV-2 B.1.621 (Mu) variant
|
|---|---|
| Published in |
Science Translational Medicine, August 2022
|
| DOI | 10.1126/scitranslmed.abm4908 |
| Pubmed ID | |
| Authors |
Peter J Halfmann, Makoto Kuroda, Tammy Armbrust, James Theiler, Ariane Balaram, Gage K Moreno, Molly A Accola, Kiyoko Iwatsuki-Horimoto, Riccardo Valdez, Emily Stoneman, Katarina Braun, Seiya Yamayoshi, Elizabeth Somsen, John J Baczenas, Keiko Mitamura, Masao Hagihara, Eisuke Adachi, Michiko Koga, Matthew McLaughlin, William Rehrauer, Masaki Imai, Shinya Yamamoto, Takeya Tsutsumi, Makoto Saito, Thomas C Friedrich, Shelby L O'Connor, David H O'Connor, Aubree Gordon, Bette Korber, Yoshihiro Kawaoka |
X Demographics
The data shown below were collected from the profiles of 33 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Turkey | 5 | 15% |
| United States | 2 | 6% |
| United Arab Emirates | 1 | 3% |
| Sweden | 1 | 3% |
| United Kingdom | 1 | 3% |
| Unknown | 23 | 70% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 31 | 94% |
| Scientists | 1 | 3% |
| Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 28 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 5 | 18% |
| Student > Doctoral Student | 2 | 7% |
| Professor > Associate Professor | 2 | 7% |
| Student > Master | 2 | 7% |
| Student > Bachelor | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 16 | 57% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 4 | 14% |
| Medicine and Dentistry | 2 | 7% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 4% |
| Linguistics | 1 | 4% |
| Agricultural and Biological Sciences | 1 | 4% |
| Other | 3 | 11% |
| Unknown | 16 | 57% |
Attention Score in Context
This research output has an Altmetric Attention Score of 22. 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 August 2022.
All research outputs
#1,706,394
of 25,466,764 outputs
Outputs from Science Translational Medicine
#2,674
of 5,445 outputs
Outputs of similar age
#37,047
of 432,257 outputs
Outputs of similar age from Science Translational Medicine
#73
of 85 outputs
Altmetric has tracked 25,466,764 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,445 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 86.6. This one has gotten more attention than average, scoring higher than 50% 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 432,257 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 91% of its contemporaries.
We're also able to compare this research output to 85 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.