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Mendeley readers
Attention Score in Context
| Title |
Open Biomedical Ontology-based Medline exploration
|
|---|---|
| Published in |
BMC Bioinformatics, May 2009
|
| DOI | 10.1186/1471-2105-10-s5-s6 |
| Pubmed ID | |
| Authors |
Weijian Xuan, Manhong Dai, Barbara Mirel, Jean Song, Brian Athey, Stanley J Watson, Fan Meng |
| Abstract |
Effective Medline database exploration is critical for the understanding of high throughput experimental results and the development of novel hypotheses about the mechanisms underlying the targeted biological processes. While existing solutions enhance Medline exploration through different approaches such as document clustering, network presentations of underlying conceptual relationships and the mapping of search results to MeSH and Gene Ontology trees, we believe the use of multiple ontologies from the Open Biomedical Ontology can greatly help researchers to explore literature from different perspectives as well as to quickly locate the most relevant Medline records for further investigation. |
Mendeley readers
The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 6 | 12% |
| United Kingdom | 4 | 8% |
| Portugal | 1 | 2% |
| Canada | 1 | 2% |
| Switzerland | 1 | 2% |
| Spain | 1 | 2% |
| New Zealand | 1 | 2% |
| Unknown | 34 | 69% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 19 | 39% |
| Student > Ph. D. Student | 9 | 18% |
| Student > Master | 6 | 12% |
| Other | 5 | 10% |
| Student > Bachelor | 3 | 6% |
| Other | 6 | 12% |
| Unknown | 1 | 2% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 16 | 33% |
| Agricultural and Biological Sciences | 12 | 24% |
| Medicine and Dentistry | 8 | 16% |
| Mathematics | 2 | 4% |
| Arts and Humanities | 2 | 4% |
| Other | 5 | 10% |
| Unknown | 4 | 8% |
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 02 November 2010.
All research outputs
#7,454,427
of 22,789,566 outputs
Outputs from BMC Bioinformatics
#3,023
of 7,280 outputs
Outputs of similar age
#32,607
of 92,928 outputs
Outputs of similar age from BMC Bioinformatics
#14
of 36 outputs
Altmetric has tracked 22,789,566 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 7,280 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 gotten more attention than average, scoring higher than 50% 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 92,928 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.