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Mendeley readers
Attention Score in Context
| Title |
Robinson-Foulds Supertrees
|
|---|---|
| Published in |
Algorithms for Molecular Biology, February 2010
|
| DOI | 10.1186/1748-7188-5-18 |
| Pubmed ID | |
| Authors |
Mukul S Bansal, J Gordon Burleigh, Oliver Eulenstein, David Fernández-Baca |
| Abstract |
Supertree methods synthesize collections of small phylogenetic trees with incomplete taxon overlap into comprehensive trees, or supertrees, that include all taxa found in the input trees. Supertree methods based on the well established Robinson-Foulds (RF) distance have the potential to build supertrees that retain much information from the input trees. Specifically, the RF supertree problem seeks a binary supertree that minimizes the sum of the RF distances from the supertree to the input trees. Thus, an RF supertree is a supertree that is consistent with the largest number of clusters (or clades) from the input trees. |
Mendeley readers
The data shown below were compiled from readership statistics for 95 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 3 | 3% |
| United States | 3 | 3% |
| Germany | 1 | 1% |
| Denmark | 1 | 1% |
| Canada | 1 | 1% |
| Spain | 1 | 1% |
| Estonia | 1 | 1% |
| Unknown | 84 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 24 | 25% |
| Researcher | 21 | 22% |
| Student > Master | 10 | 11% |
| Professor > Associate Professor | 9 | 9% |
| Student > Bachelor | 8 | 8% |
| Other | 19 | 20% |
| Unknown | 4 | 4% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 46 | 48% |
| Computer Science | 15 | 16% |
| Biochemistry, Genetics and Molecular Biology | 10 | 11% |
| Earth and Planetary Sciences | 4 | 4% |
| Environmental Science | 3 | 3% |
| Other | 10 | 11% |
| Unknown | 7 | 7% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 10 July 2020.
All research outputs
#7,454,427
of 22,790,780 outputs
Outputs from Algorithms for Molecular Biology
#73
of 264 outputs
Outputs of similar age
#34,664
of 93,835 outputs
Outputs of similar age from Algorithms for Molecular Biology
#2
of 2 outputs
Altmetric has tracked 22,790,780 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 264 research outputs from this source. They receive a mean Attention Score of 3.2. This one has gotten more attention than average, scoring higher than 68% 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 93,835 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one.