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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Combining Precursor and Fragment Information for Improved Detection of Differential Abundance in Data Independent Acquisition*
|
|---|---|
| Published in |
Molecular and Cellular Proteomics, December 2019
|
| DOI | 10.1074/mcp.ra119.001705 |
| Pubmed ID | |
| Authors |
Ting Huang, Roland Bruderer, Jan Muntel, Yue Xuan, Olga Vitek, Lukas Reiter |
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 % |
|---|---|---|
| United States | 10 | 30% |
| Switzerland | 3 | 9% |
| India | 2 | 6% |
| Mexico | 1 | 3% |
| Spain | 1 | 3% |
| Australia | 1 | 3% |
| United Kingdom | 1 | 3% |
| Unknown | 14 | 42% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 25 | 76% |
| Scientists | 6 | 18% |
| Science communicators (journalists, bloggers, editors) | 1 | 3% |
| Practitioners (doctors, other healthcare professionals) | 1 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 121 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 121 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 29 | 24% |
| Student > Ph. D. Student | 24 | 20% |
| Student > Master | 11 | 9% |
| Other | 7 | 6% |
| Student > Bachelor | 6 | 5% |
| Other | 14 | 12% |
| Unknown | 30 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 34 | 28% |
| Agricultural and Biological Sciences | 17 | 14% |
| Chemistry | 9 | 7% |
| Pharmacology, Toxicology and Pharmaceutical Science | 8 | 7% |
| Computer Science | 7 | 6% |
| Other | 12 | 10% |
| Unknown | 34 | 28% |
Attention Score in Context
This research output has an Altmetric Attention Score of 20. 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 14 October 2022.
All research outputs
#1,905,114
of 25,621,213 outputs
Outputs from Molecular and Cellular Proteomics
#233
of 3,233 outputs
Outputs of similar age
#45,785
of 478,932 outputs
Outputs of similar age from Molecular and Cellular Proteomics
#6
of 38 outputs
Altmetric has tracked 25,621,213 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,233 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one has done particularly well, scoring higher than 92% 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 478,932 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 90% of its contemporaries.
We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.