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Mendeley readers
Attention Score in Context
Title |
DetectiV: visualization, normalization and significance testing for pathogen-detection microarray data
|
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Published in |
Genome Biology, September 2007
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DOI | 10.1186/gb-2007-8-9-r190 |
Pubmed ID | |
Authors |
Michael Watson, Juliet Dukes, Abu-Bakr Abu-Median, Donald P King, Paul Britton |
Abstract |
DNA microarrays offer the possibility of testing for the presence of thousands of micro-organisms in a single experiment. However, there is a lack of reliable bioinformatics tools for the analysis of such data. We have developed DetectiV, a package for the statistical software R. DetectiV offers powerful yet simple visualization, normalization and significance testing tools. We show that DetectiV performs better than previously published software on a large, publicly available dataset. |
X Demographics
The data shown below were collected from the profiles of 26 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 | 5 | 19% |
United Kingdom | 4 | 15% |
India | 1 | 4% |
Spain | 1 | 4% |
France | 1 | 4% |
Italy | 1 | 4% |
Puerto Rico | 1 | 4% |
Ukraine | 1 | 4% |
Mexico | 1 | 4% |
Other | 1 | 4% |
Unknown | 9 | 35% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 16 | 62% |
Members of the public | 9 | 35% |
Science communicators (journalists, bloggers, editors) | 1 | 4% |
Mendeley readers
The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 2 | 7% |
United States | 1 | 4% |
Australia | 1 | 4% |
Unknown | 23 | 85% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 9 | 33% |
Student > Ph. D. Student | 6 | 22% |
Professor | 4 | 15% |
Professor > Associate Professor | 2 | 7% |
Librarian | 1 | 4% |
Other | 2 | 7% |
Unknown | 3 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 13 | 48% |
Veterinary Science and Veterinary Medicine | 2 | 7% |
Computer Science | 2 | 7% |
Engineering | 2 | 7% |
Chemical Engineering | 1 | 4% |
Other | 4 | 15% |
Unknown | 3 | 11% |
Attention Score in Context
This research output has an Altmetric Attention Score of 14. 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 04 June 2021.
All research outputs
#2,590,266
of 25,374,917 outputs
Outputs from Genome Biology
#2,071
of 4,467 outputs
Outputs of similar age
#5,909
of 82,572 outputs
Outputs of similar age from Genome Biology
#5
of 38 outputs
Altmetric has tracked 25,374,917 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 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has gotten more attention than average, scoring higher than 53% 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 82,572 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 38 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.