↓ Skip to main content

NBZIMM: negative binomial and zero-inflated mixed models, with application to microbiome/metagenomics data analysis

Overview of attention for article published in BMC Bioinformatics, October 2020
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 (82nd percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

blogs
1 blog
twitter
14 tweeters

Citations

dimensions_citation
23 Dimensions

Readers on

mendeley
52 Mendeley
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
NBZIMM: negative binomial and zero-inflated mixed models, with application to microbiome/metagenomics data analysis
Published in
BMC Bioinformatics, October 2020
DOI 10.1186/s12859-020-03803-z
Pubmed ID
Authors

Xinyan Zhang, Nengjun Yi

Twitter Demographics

The data shown below were collected from the profiles of 14 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 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 52 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 17%
Student > Ph. D. Student 7 13%
Student > Bachelor 7 13%
Student > Doctoral Student 5 10%
Student > Master 4 8%
Other 6 12%
Unknown 14 27%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 25%
Biochemistry, Genetics and Molecular Biology 5 10%
Environmental Science 4 8%
Business, Management and Accounting 2 4%
Computer Science 2 4%
Other 11 21%
Unknown 15 29%

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 20 May 2021.
All research outputs
#2,152,708
of 21,232,578 outputs
Outputs from BMC Bioinformatics
#692
of 6,900 outputs
Outputs of similar age
#67,546
of 397,448 outputs
Outputs of similar age from BMC Bioinformatics
#59
of 484 outputs
Altmetric has tracked 21,232,578 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 6,900 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 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 397,448 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 82% of its contemporaries.
We're also able to compare this research output to 484 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.