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The M5nr: a novel non-redundant database containing protein sequences and annotations from multiple sources and associated tools

Overview of attention for article published in BMC Bioinformatics, June 2012
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (70th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

twitter
2 tweeters
wikipedia
1 Wikipedia page

Citations

dimensions_citation
244 Dimensions

Readers on

mendeley
281 Mendeley
citeulike
4 CiteULike
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Title
The M5nr: a novel non-redundant database containing protein sequences and annotations from multiple sources and associated tools
Published in
BMC Bioinformatics, June 2012
DOI 10.1186/1471-2105-13-141
Pubmed ID
Authors

Andreas Wilke, Travis Harrison, Jared Wilkening, Dawn Field, Elizabeth M Glass, Nikos Kyrpides, Konstantinos Mavrommatis, Folker Meyer

Abstract

Computing of sequence similarity results is becoming a limiting factor in metagenome analysis. Sequence similarity search results encoded in an open, exchangeable format have the potential to limit the needs for computational reanalysis of these data sets. A prerequisite for sharing of similarity results is a common reference.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 281 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 9 3%
Brazil 3 1%
Sweden 2 <1%
Germany 1 <1%
Argentina 1 <1%
United Kingdom 1 <1%
Spain 1 <1%
Denmark 1 <1%
Unknown 262 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 75 27%
Researcher 59 21%
Student > Master 39 14%
Student > Bachelor 27 10%
Student > Doctoral Student 15 5%
Other 37 13%
Unknown 29 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 133 47%
Biochemistry, Genetics and Molecular Biology 43 15%
Environmental Science 13 5%
Computer Science 10 4%
Immunology and Microbiology 9 3%
Other 27 10%
Unknown 46 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 31 December 2016.
All research outputs
#6,298,375
of 21,334,388 outputs
Outputs from BMC Bioinformatics
#2,459
of 6,922 outputs
Outputs of similar age
#41,556
of 142,324 outputs
Outputs of similar age from BMC Bioinformatics
#2
of 16 outputs
Altmetric has tracked 21,334,388 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 6,922 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 63% 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 142,324 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 16 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 93% of its contemporaries.