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X Demographics
Mendeley readers
Attention Score in Context
| Title |
ShrinkBayes: a versatile R-package for analysis of count-based sequencing data in complex study designs
|
|---|---|
| Published in |
BMC Bioinformatics, April 2014
|
| DOI | 10.1186/1471-2105-15-116 |
| Pubmed ID | |
| Authors |
Mark A van de Wiel, Maarten Neerincx, Tineke E Buffart, Daoud Sie, Henk MW Verheul |
| Abstract |
Complex designs are common in (observational) clinical studies. Sequencing data for such studies are produced more and more often, implying challenges for the analysis, such as excess of zeros, presence of random effects and multi-parameter inference. Moreover, when sample sizes are small, inference is likely to be too liberal when, in a Bayesian setting, applying a non-appropriate prior or to lack power when not carefully borrowing information across features. |
X Demographics
The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 3 | 27% |
| United Kingdom | 1 | 9% |
| United States | 1 | 9% |
| Switzerland | 1 | 9% |
| Norway | 1 | 9% |
| Unknown | 4 | 36% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 7 | 64% |
| Members of the public | 4 | 36% |
Mendeley readers
The data shown below were compiled from readership statistics for 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 4% |
| Netherlands | 2 | 3% |
| Australia | 1 | 1% |
| Finland | 1 | 1% |
| Sweden | 1 | 1% |
| Spain | 1 | 1% |
| United Kingdom | 1 | 1% |
| Unknown | 66 | 87% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 25 | 33% |
| Researcher | 18 | 24% |
| Student > Master | 5 | 7% |
| Student > Bachelor | 4 | 5% |
| Professor | 4 | 5% |
| Other | 10 | 13% |
| Unknown | 10 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 34 | 45% |
| Biochemistry, Genetics and Molecular Biology | 8 | 11% |
| Mathematics | 7 | 9% |
| Medicine and Dentistry | 4 | 5% |
| Engineering | 4 | 5% |
| Other | 8 | 11% |
| Unknown | 11 | 14% |
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 14 June 2014.
All research outputs
#2,623,760
of 24,562,945 outputs
Outputs from BMC Bioinformatics
#755
of 7,554 outputs
Outputs of similar age
#26,049
of 231,904 outputs
Outputs of similar age from BMC Bioinformatics
#18
of 134 outputs
Altmetric has tracked 24,562,945 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 7,554 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 particularly well, scoring higher than 90% 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 231,904 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 88% of its contemporaries.
We're also able to compare this research output to 134 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.