| Title |
metamicrobiomeR: an R package for analysis of microbiome relative abundance data using zero-inflated beta GAMLSS and meta-analysis across studies using random effects models
|
|---|---|
| Published in |
BMC Bioinformatics, April 2019
|
| DOI | 10.1186/s12859-019-2744-2 |
| Pubmed ID | |
| Authors |
Nhan Thi Ho, Fan Li, Shuang Wang, Louise Kuhn |
X Demographics
The data shown below were collected from the profiles of 67 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 8 | 12% |
| United Kingdom | 6 | 9% |
| Spain | 5 | 7% |
| Canada | 5 | 7% |
| France | 3 | 4% |
| Australia | 3 | 4% |
| Germany | 2 | 3% |
| Japan | 1 | 1% |
| China | 1 | 1% |
| Other | 9 | 13% |
| Unknown | 24 | 36% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 33 | 49% |
| Members of the public | 32 | 48% |
| Science communicators (journalists, bloggers, editors) | 1 | 1% |
| Practitioners (doctors, other healthcare professionals) | 1 | 1% |
Mendeley readers
The data shown below were compiled from readership statistics for 187 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 187 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 47 | 25% |
| Student > Ph. D. Student | 35 | 19% |
| Student > Master | 23 | 12% |
| Student > Bachelor | 15 | 8% |
| Student > Doctoral Student | 10 | 5% |
| Other | 29 | 16% |
| Unknown | 28 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 54 | 29% |
| Biochemistry, Genetics and Molecular Biology | 37 | 20% |
| Immunology and Microbiology | 17 | 9% |
| Environmental Science | 8 | 4% |
| Medicine and Dentistry | 8 | 4% |
| Other | 26 | 14% |
| Unknown | 37 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 36. 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 16 July 2021.
All research outputs
#1,147,683
of 25,712,965 outputs
Outputs from BMC Bioinformatics
#102
of 7,735 outputs
Outputs of similar age
#25,012
of 348,809 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 176 outputs
Altmetric has tracked 25,712,965 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,735 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one has done particularly well, scoring higher than 98% 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 348,809 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 92% of its contemporaries.
We're also able to compare this research output to 176 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 99% of its contemporaries.