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Attention Score in Context
Title |
Large-scale biomedical concept recognition: an evaluation of current automatic annotators and their parameters
|
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Published in |
BMC Bioinformatics, February 2014
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DOI | 10.1186/1471-2105-15-59 |
Pubmed ID | |
Authors |
Christopher Funk, William Baumgartner, Benjamin Garcia, Christophe Roeder, Michael Bada, K Bretonnel Cohen, Lawrence E Hunter, Karin Verspoor |
Abstract |
Ontological concepts are useful for many different biomedical tasks. Concepts are difficult to recognize in text due to a disconnect between what is captured in an ontology and how the concepts are expressed in text. There are many recognizers for specific ontologies, but a general approach for concept recognition is an open problem. |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
China | 1 | 17% |
Australia | 1 | 17% |
Switzerland | 1 | 17% |
United States | 1 | 17% |
Norway | 1 | 17% |
Unknown | 1 | 17% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 4 | 67% |
Scientists | 1 | 17% |
Practitioners (doctors, other healthcare professionals) | 1 | 17% |
Mendeley readers
The data shown below were compiled from readership statistics for 121 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 4 | 3% |
Australia | 2 | 2% |
United Kingdom | 2 | 2% |
Spain | 2 | 2% |
Brazil | 1 | <1% |
Switzerland | 1 | <1% |
Netherlands | 1 | <1% |
Portugal | 1 | <1% |
Unknown | 107 | 88% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 35 | 29% |
Researcher | 26 | 21% |
Student > Master | 16 | 13% |
Professor | 7 | 6% |
Other | 7 | 6% |
Other | 18 | 15% |
Unknown | 12 | 10% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 58 | 48% |
Agricultural and Biological Sciences | 20 | 17% |
Medicine and Dentistry | 9 | 7% |
Social Sciences | 4 | 3% |
Engineering | 3 | 2% |
Other | 11 | 9% |
Unknown | 16 | 13% |
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 21 December 2017.
All research outputs
#13,364,855
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#3,690
of 7,454 outputs
Outputs of similar age
#105,730
of 223,739 outputs
Outputs of similar age from BMC Bioinformatics
#50
of 107 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,454 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. 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 223,739 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 107 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.