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Mendeley readers
Attention Score in Context
| Title |
R/parallel – speeding up bioinformatics analysis with R
|
|---|---|
| Published in |
BMC Bioinformatics, September 2008
|
| DOI | 10.1186/1471-2105-9-390 |
| Pubmed ID | |
| Authors |
Gonzalo Vera, Ritsert C Jansen, Remo L Suppi |
| Abstract |
R is the preferred tool for statistical analysis of many bioinformaticians due in part to the increasing number of freely available analytical methods. Such methods can be quickly reused and adapted to each particular experiment. However, in experiments where large amounts of data are generated, for example using high-throughput screening devices, the processing time required to analyze data is often quite long. A solution to reduce the processing time is the use of parallel computing technologies. Because R does not support parallel computations, several tools have been developed to enable such technologies. However, these tools require multiple modications to the way R programs are usually written or run. Although these tools can finally speed up the calculations, the time, skills and additional resources required to use them are an obstacle for most bioinformaticians. |
Mendeley readers
The data shown below were compiled from readership statistics for 162 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 7 | 4% |
| United Kingdom | 6 | 4% |
| France | 3 | 2% |
| Brazil | 3 | 2% |
| Germany | 3 | 2% |
| Netherlands | 2 | 1% |
| Switzerland | 1 | <1% |
| Sweden | 1 | <1% |
| Colombia | 1 | <1% |
| Other | 7 | 4% |
| Unknown | 128 | 79% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 57 | 35% |
| Student > Ph. D. Student | 41 | 25% |
| Professor > Associate Professor | 13 | 8% |
| Student > Master | 12 | 7% |
| Professor | 12 | 7% |
| Other | 21 | 13% |
| Unknown | 6 | 4% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 87 | 54% |
| Computer Science | 22 | 14% |
| Biochemistry, Genetics and Molecular Biology | 11 | 7% |
| Environmental Science | 6 | 4% |
| Mathematics | 6 | 4% |
| Other | 21 | 13% |
| Unknown | 9 | 6% |
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 09 July 2019.
All research outputs
#3,541,632
of 22,653,392 outputs
Outputs from BMC Bioinformatics
#1,287
of 7,236 outputs
Outputs of similar age
#13,101
of 87,490 outputs
Outputs of similar age from BMC Bioinformatics
#6
of 35 outputs
Altmetric has tracked 22,653,392 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,236 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 82% 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 87,490 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 35 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.