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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 (75th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

twitter
2 tweeters
wikipedia
1 Wikipedia page

Citations

dimensions_citation
212 Dimensions

Readers on

mendeley
262 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 262 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%
United Kingdom 1 <1%
Argentina 1 <1%
Denmark 1 <1%
Spain 1 <1%
Germany 1 <1%
Unknown 243 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 72 27%
Researcher 56 21%
Student > Master 38 15%
Student > Bachelor 27 10%
Student > Doctoral Student 13 5%
Other 33 13%
Unknown 23 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 129 49%
Biochemistry, Genetics and Molecular Biology 40 15%
Environmental Science 11 4%
Computer Science 9 3%
Immunology and Microbiology 8 3%
Other 24 9%
Unknown 41 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
#3,122,281
of 12,373,386 outputs
Outputs from BMC Bioinformatics
#1,407
of 4,576 outputs
Outputs of similar age
#28,508
of 119,889 outputs
Outputs of similar age from BMC Bioinformatics
#3
of 14 outputs
Altmetric has tracked 12,373,386 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 4,576 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 68% 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 119,889 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.