↓ Skip to main content

Highly sensitive feature detection for high resolution LC/MS

Overview of attention for article published in BMC Bioinformatics, November 2008
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

news
1 news outlet
twitter
19 tweeters
patent
2 patents

Citations

dimensions_citation
597 Dimensions

Readers on

mendeley
677 Mendeley
citeulike
7 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Highly sensitive feature detection for high resolution LC/MS
Published in
BMC Bioinformatics, November 2008
DOI 10.1186/1471-2105-9-504
Pubmed ID
Authors

Ralf Tautenhahn, Christoph Böttcher, Steffen Neumann

Twitter Demographics

The data shown below were collected from the profiles of 19 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 677 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 8 1%
Germany 6 <1%
United States 4 <1%
South Africa 4 <1%
Brazil 4 <1%
Austria 3 <1%
Portugal 2 <1%
Italy 2 <1%
Belgium 2 <1%
Other 8 1%
Unknown 634 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 204 30%
Researcher 153 23%
Student > Master 74 11%
Student > Bachelor 63 9%
Student > Doctoral Student 37 5%
Other 95 14%
Unknown 51 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 187 28%
Chemistry 157 23%
Biochemistry, Genetics and Molecular Biology 76 11%
Medicine and Dentistry 33 5%
Computer Science 30 4%
Other 103 15%
Unknown 91 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 26. 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 21 January 2021.
All research outputs
#891,065
of 17,027,788 outputs
Outputs from BMC Bioinformatics
#135
of 6,063 outputs
Outputs of similar age
#32,628
of 396,536 outputs
Outputs of similar age from BMC Bioinformatics
#11
of 413 outputs
Altmetric has tracked 17,027,788 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,063 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done particularly well, scoring higher than 97% 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 396,536 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 413 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 97% of its contemporaries.