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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Assessing Computational Methods for Transcription Factor Target Gene Identification Based on ChIP-seq Data
|
|---|---|
| Published in |
PLoS Computational Biology, November 2013
|
| DOI | 10.1371/journal.pcbi.1003342 |
| Pubmed ID | |
| Authors |
Weronika Sikora-Wohlfeld, Marit Ackermann, Eleni G. Christodoulou, Kalaimathy Singaravelu, Andreas Beyer |
X Demographics
The data shown below were collected from the profiles of 15 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 | 27% |
| Germany | 1 | 7% |
| New Zealand | 1 | 7% |
| Norway | 1 | 7% |
| Australia | 1 | 7% |
| China | 1 | 7% |
| Netherlands | 1 | 7% |
| India | 1 | 7% |
| Unknown | 4 | 27% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 8 | 53% |
| Members of the public | 7 | 47% |
Mendeley readers
The data shown below were compiled from readership statistics for 171 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 8 | 5% |
| United Kingdom | 5 | 3% |
| Germany | 2 | 1% |
| Australia | 1 | <1% |
| Singapore | 1 | <1% |
| Brazil | 1 | <1% |
| Greece | 1 | <1% |
| Korea, Republic of | 1 | <1% |
| Unknown | 151 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 47 | 27% |
| Researcher | 40 | 23% |
| Student > Master | 23 | 13% |
| Professor > Associate Professor | 11 | 6% |
| Student > Doctoral Student | 10 | 6% |
| Other | 28 | 16% |
| Unknown | 12 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 82 | 48% |
| Biochemistry, Genetics and Molecular Biology | 42 | 25% |
| Computer Science | 17 | 10% |
| Medicine and Dentistry | 5 | 3% |
| Neuroscience | 4 | 2% |
| Other | 6 | 4% |
| Unknown | 15 | 9% |
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 02 November 2016.
All research outputs
#4,280,834
of 26,017,215 outputs
Outputs from PLoS Computational Biology
#3,414
of 9,038 outputs
Outputs of similar age
#45,885
of 321,685 outputs
Outputs of similar age from PLoS Computational Biology
#53
of 146 outputs
Altmetric has tracked 26,017,215 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 9,038 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 62% 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 321,685 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 85% of its contemporaries.
We're also able to compare this research output to 146 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 63% of its contemporaries.