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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Fast and Accurate Bacterial Species Identification in Urine Specimens Using LC-MS/MS Mass Spectrometry and Machine Learning*[S]
|
|---|---|
| Published in |
Molecular and Cellular Proteomics, October 2019
|
| DOI | 10.1074/mcp.tir119.001559 |
| Pubmed ID | |
| Authors |
Florence Roux-Dalvai, Clarisse Gotti, Mickaël Leclercq, Marie-Claude Hélie, Maurice Boissinot, Tabiwang N. Arrey, Claire Dauly, Frédéric Fournier, Isabelle Kelly, Judith Marcoux, Julie Bestman-Smith, Michel G. Bergeron, Arnaud Droit |
X Demographics
The data shown below were collected from the profiles of 31 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 | 7 | 23% |
| Canada | 4 | 13% |
| Australia | 2 | 6% |
| Denmark | 1 | 3% |
| Switzerland | 1 | 3% |
| United Kingdom | 1 | 3% |
| India | 1 | 3% |
| Thailand | 1 | 3% |
| Malaysia | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 11 | 35% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 17 | 55% |
| Scientists | 11 | 35% |
| Practitioners (doctors, other healthcare professionals) | 2 | 6% |
| Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 115 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 115 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 19 | 17% |
| Student > Ph. D. Student | 18 | 16% |
| Student > Master | 15 | 13% |
| Student > Bachelor | 9 | 8% |
| Student > Doctoral Student | 4 | 3% |
| Other | 17 | 15% |
| Unknown | 33 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 23 | 20% |
| Computer Science | 9 | 8% |
| Immunology and Microbiology | 7 | 6% |
| Agricultural and Biological Sciences | 7 | 6% |
| Medicine and Dentistry | 6 | 5% |
| Other | 21 | 18% |
| Unknown | 42 | 37% |
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 16 February 2023.
All research outputs
#1,263,676
of 25,420,980 outputs
Outputs from Molecular and Cellular Proteomics
#110
of 3,223 outputs
Outputs of similar age
#27,429
of 363,521 outputs
Outputs of similar age from Molecular and Cellular Proteomics
#4
of 37 outputs
Altmetric has tracked 25,420,980 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 3,223 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one has done particularly well, scoring higher than 96% 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 363,521 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 37 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.