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X Demographics
Mendeley readers
Attention Score in Context
| Title |
The unifrac significance test is sensitive to tree topology
|
|---|---|
| Published in |
BMC Bioinformatics, July 2015
|
| DOI | 10.1186/s12859-015-0640-y |
| Pubmed ID | |
| Authors |
Catherine A. Lozupone, Rob Knight |
| Abstract |
Long et al. (BMC Bioinformatics 2014, 15(1):278) describe a "discrepancy" in using UniFrac to assess statistical significance of community differences. Specifically, they find that weighted UniFrac results differ between input trees where (a) replicate sequences each have their own tip, or (b) all replicates are assigned to one tip with an associated count. We argue that these are two distinct cases that differ in the probability distribution on which the statistical test is based, because of the differences in tree topology. Further study is needed to understand which randomization procedure best detects different aspects of community dissimilarities. |
X Demographics
The data shown below were collected from the profiles of 14 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 | 29% |
| Spain | 1 | 7% |
| Germany | 1 | 7% |
| Cameroon | 1 | 7% |
| Unknown | 7 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 11 | 79% |
| Members of the public | 3 | 21% |
Mendeley readers
The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 5% |
| Brazil | 2 | 3% |
| Canada | 1 | 2% |
| Unknown | 57 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 16 | 25% |
| Student > Ph. D. Student | 13 | 21% |
| Student > Master | 10 | 16% |
| Professor > Associate Professor | 5 | 8% |
| Student > Bachelor | 4 | 6% |
| Other | 11 | 17% |
| Unknown | 4 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 26 | 41% |
| Environmental Science | 6 | 10% |
| Biochemistry, Genetics and Molecular Biology | 6 | 10% |
| Computer Science | 5 | 8% |
| Immunology and Microbiology | 4 | 6% |
| Other | 10 | 16% |
| Unknown | 6 | 10% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 July 2015.
All research outputs
#5,093,665
of 24,885,505 outputs
Outputs from BMC Bioinformatics
#1,799
of 7,601 outputs
Outputs of similar age
#58,981
of 267,683 outputs
Outputs of similar age from BMC Bioinformatics
#34
of 114 outputs
Altmetric has tracked 24,885,505 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,601 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 75% 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 267,683 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 77% of its contemporaries.
We're also able to compare this research output to 114 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.