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Attention Score in Context
Title |
Hydra: a scalable proteomic search engine which utilizes the Hadoop distributed computing framework
|
---|---|
Published in |
BMC Bioinformatics, December 2012
|
DOI | 10.1186/1471-2105-13-324 |
Pubmed ID | |
Authors |
Steven Lewis, Attila Csordas, Sarah Killcoyne, Henning Hermjakob, Michael R Hoopmann, Robert L Moritz, Eric W Deutsch, John Boyle |
Abstract |
For shotgun mass spectrometry based proteomics the most computationally expensive step is in matching the spectra against an increasingly large database of sequences and their post-translational modifications with known masses. Each mass spectrometer can generate data at an astonishingly high rate, and the scope of what is searched for is continually increasing. Therefore solutions for improving our ability to perform these searches are needed. |
X Demographics
The data shown below were collected from the profiles of 22 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 | 32% |
United Kingdom | 5 | 23% |
Canada | 2 | 9% |
Sweden | 1 | 5% |
France | 1 | 5% |
Netherlands | 1 | 5% |
Sierra Leone | 1 | 5% |
Brazil | 1 | 5% |
Unknown | 3 | 14% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 13 | 59% |
Scientists | 7 | 32% |
Practitioners (doctors, other healthcare professionals) | 1 | 5% |
Unknown | 1 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 4% |
United Kingdom | 2 | 3% |
Germany | 2 | 3% |
Unknown | 73 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 22 | 28% |
Student > Master | 12 | 15% |
Student > Ph. D. Student | 11 | 14% |
Student > Bachelor | 9 | 11% |
Other | 6 | 8% |
Other | 12 | 15% |
Unknown | 8 | 10% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 31 | 39% |
Agricultural and Biological Sciences | 23 | 29% |
Biochemistry, Genetics and Molecular Biology | 5 | 6% |
Engineering | 5 | 6% |
Chemistry | 2 | 3% |
Other | 4 | 5% |
Unknown | 10 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 13. 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 08 October 2014.
All research outputs
#2,790,736
of 25,706,302 outputs
Outputs from BMC Bioinformatics
#774
of 7,735 outputs
Outputs of similar age
#25,814
of 288,037 outputs
Outputs of similar age from BMC Bioinformatics
#10
of 124 outputs
Altmetric has tracked 25,706,302 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,735 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. 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 288,037 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 124 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 91% of its contemporaries.