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X Demographics
Mendeley readers
Attention Score in Context
Title |
OPPL-Galaxy, a Galaxy tool for enhancing ontology exploitation as part of bioinformatics workflows
|
---|---|
Published in |
Journal of Biomedical Semantics, January 2013
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DOI | 10.1186/2041-1480-4-2 |
Pubmed ID | |
Authors |
Mikel Egaña Aranguren, Jesualdo Tomás Fernández-Breis, Chris Mungall, Erick Antezana, Alejandro Rodríguez González, Mark D Wilkinson |
Abstract |
Biomedical ontologies are key elements for building up the Life Sciences Semantic Web. Reusing and building biomedical ontologies requires flexible and versatile tools to manipulate them efficiently, in particular for enriching their axiomatic content. The Ontology Pre Processor Language (OPPL) is an OWL-based language for automating the changes to be performed in an ontology. OPPL augments the ontologists' toolbox by providing a more efficient, and less error-prone, mechanism for enriching a biomedical ontology than that obtained by a manual treatment. |
X Demographics
The data shown below were collected from the profiles of 4 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 Kingdom | 1 | 25% |
France | 1 | 25% |
United States | 1 | 25% |
Unknown | 1 | 25% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 2 | 50% |
Members of the public | 2 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 6% |
Japan | 2 | 4% |
Netherlands | 1 | 2% |
Canada | 1 | 2% |
Australia | 1 | 2% |
Sweden | 1 | 2% |
Spain | 1 | 2% |
Unknown | 40 | 80% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 14 | 28% |
Student > Ph. D. Student | 9 | 18% |
Student > Master | 7 | 14% |
Student > Doctoral Student | 4 | 8% |
Professor > Associate Professor | 4 | 8% |
Other | 11 | 22% |
Unknown | 1 | 2% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 24 | 48% |
Computer Science | 14 | 28% |
Biochemistry, Genetics and Molecular Biology | 4 | 8% |
Engineering | 3 | 6% |
Arts and Humanities | 1 | 2% |
Other | 0 | 0% |
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 24 January 2013.
All research outputs
#13,027,733
of 22,691,736 outputs
Outputs from Journal of Biomedical Semantics
#185
of 364 outputs
Outputs of similar age
#154,419
of 280,650 outputs
Outputs of similar age from Journal of Biomedical Semantics
#18
of 32 outputs
Altmetric has tracked 22,691,736 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 364 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 48th percentile – i.e., 48% 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 280,650 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 32 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.