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Attention Score in Context
| Title |
MrsRF: an efficient MapReduce algorithm for analyzing large collections of evolutionary trees
|
|---|---|
| Published in |
BMC Bioinformatics, January 2010
|
| DOI | 10.1186/1471-2105-11-s1-s15 |
| Pubmed ID | |
| Authors |
Suzanne J Matthews, Tiffani L Williams |
| Abstract |
MapReduce is a parallel framework that has been used effectively to design large-scale parallel applications for large computing clusters. In this paper, we evaluate the viability of the MapReduce framework for designing phylogenetic applications. The problem of interest is generating the all-to-all Robinson-Foulds distance matrix, which has many applications for visualizing and clustering large collections of evolutionary trees. We introduce MrsRF (MapReduce Speeds up RF), a multi-core algorithm to generate a t x t Robinson-Foulds distance matrix between t trees using the MapReduce paradigm. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 6% |
| France | 3 | 4% |
| Germany | 2 | 3% |
| Australia | 1 | 1% |
| Austria | 1 | 1% |
| China | 1 | 1% |
| New Zealand | 1 | 1% |
| Unknown | 65 | 82% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 22 | 28% |
| Researcher | 21 | 27% |
| Student > Master | 7 | 9% |
| Student > Bachelor | 6 | 8% |
| Other | 6 | 8% |
| Other | 16 | 20% |
| Unknown | 1 | 1% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 33 | 42% |
| Agricultural and Biological Sciences | 30 | 38% |
| Biochemistry, Genetics and Molecular Biology | 5 | 6% |
| Mathematics | 1 | 1% |
| Business, Management and Accounting | 1 | 1% |
| Other | 2 | 3% |
| Unknown | 7 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 11 August 2013.
All research outputs
#18,343,746
of 22,716,996 outputs
Outputs from BMC Bioinformatics
#6,294
of 7,260 outputs
Outputs of similar age
#150,078
of 163,976 outputs
Outputs of similar age from BMC Bioinformatics
#43
of 58 outputs
Altmetric has tracked 22,716,996 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,260 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% of its peers scored the same or lower than it.
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 163,976 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 4th percentile – i.e., 4% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 58 others from the same source and published within six weeks on either side of this one. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.