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De novo clustering methods outperform reference-based methods for assigning 16S rRNA gene sequences to operational taxonomic units

Overview of attention for article published in PeerJ, December 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

blogs
1 blog
twitter
116 X users
facebook
1 Facebook page

Citations

dimensions_citation
236 Dimensions

Readers on

mendeley
573 Mendeley
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Title
De novo clustering methods outperform reference-based methods for assigning 16S rRNA gene sequences to operational taxonomic units
Published in
PeerJ, December 2015
DOI 10.7717/peerj.1487
Pubmed ID
Authors

Sarah L. Westcott, Patrick D. Schloss

X Demographics

X Demographics

The data shown below were collected from the profiles of 116 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 573 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 13 2%
Canada 2 <1%
Estonia 2 <1%
Belgium 2 <1%
Sweden 2 <1%
Brazil 1 <1%
United Kingdom 1 <1%
France 1 <1%
Italy 1 <1%
Other 3 <1%
Unknown 545 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 136 24%
Researcher 116 20%
Student > Master 85 15%
Student > Bachelor 52 9%
Student > Doctoral Student 43 8%
Other 65 11%
Unknown 76 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 220 38%
Biochemistry, Genetics and Molecular Biology 106 18%
Environmental Science 43 8%
Immunology and Microbiology 33 6%
Computer Science 21 4%
Other 57 10%
Unknown 93 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 71. 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 28 March 2019.
All research outputs
#611,554
of 25,732,188 outputs
Outputs from PeerJ
#621
of 15,308 outputs
Outputs of similar age
#10,044
of 397,373 outputs
Outputs of similar age from PeerJ
#10
of 250 outputs
Altmetric has tracked 25,732,188 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 15,308 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.0. This one has done particularly well, scoring higher than 95% 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 397,373 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 97% of its contemporaries.
We're also able to compare this research output to 250 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 96% of its contemporaries.