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X Demographics
Mendeley readers
Attention Score in Context
| Title |
LinDA: linear models for differential abundance analysis of microbiome compositional data
|
|---|---|
| Published in |
Genome Biology, April 2022
|
| DOI | 10.1186/s13059-022-02655-5 |
| Pubmed ID | |
| Authors |
Huijuan Zhou, Kejun He, Jun Chen, Xianyang Zhang |
X Demographics
The data shown below were collected from the profiles of 19 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 | 4 | 21% |
| United Kingdom | 2 | 11% |
| India | 2 | 11% |
| Sweden | 1 | 5% |
| Canada | 1 | 5% |
| Singapore | 1 | 5% |
| Unknown | 8 | 42% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 9 | 47% |
| Scientists | 8 | 42% |
| Science communicators (journalists, bloggers, editors) | 2 | 11% |
Mendeley readers
The data shown below were compiled from readership statistics for 129 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 129 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 29 | 22% |
| Researcher | 15 | 12% |
| Student > Master | 12 | 9% |
| Student > Doctoral Student | 9 | 7% |
| Student > Bachelor | 8 | 6% |
| Other | 13 | 10% |
| Unknown | 43 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 25 | 19% |
| Agricultural and Biological Sciences | 19 | 15% |
| Immunology and Microbiology | 10 | 8% |
| Medicine and Dentistry | 6 | 5% |
| Computer Science | 4 | 3% |
| Other | 20 | 16% |
| Unknown | 45 | 35% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 01 March 2023.
All research outputs
#4,266,191
of 25,392,582 outputs
Outputs from Genome Biology
#2,656
of 4,470 outputs
Outputs of similar age
#90,168
of 447,085 outputs
Outputs of similar age from Genome Biology
#46
of 57 outputs
Altmetric has tracked 25,392,582 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,470 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 40th percentile – i.e., 40% of its peers scored the same or lower than it.
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 447,085 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 79% of its contemporaries.
We're also able to compare this research output to 57 others from the same source and published within six weeks on either side of this one. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.