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PubFocus: semantic MEDLINE/PubMed citations analytics through integration of controlled biomedical dictionaries and ranking algorithm

Overview of attention for article published in BMC Bioinformatics, October 2006
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About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
1 tweeter

Citations

dimensions_citation
45 Dimensions

Readers on

mendeley
86 Mendeley
citeulike
16 CiteULike
connotea
5 Connotea
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Title
PubFocus: semantic MEDLINE/PubMed citations analytics through integration of controlled biomedical dictionaries and ranking algorithm
Published in
BMC Bioinformatics, October 2006
DOI 10.1186/1471-2105-7-424
Pubmed ID
Authors

Maksim V Plikus, Zina Zhang, Cheng-Ming Chuong

Abstract

Understanding research activity within any given biomedical field is important. Search outputs generated by MEDLINE/PubMed are not well classified and require lengthy manual citation analysis. Automation of citation analytics can be very useful and timesaving for both novices and experts.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 8%
Netherlands 3 3%
Mexico 3 3%
United Kingdom 2 2%
Brazil 2 2%
Spain 2 2%
Denmark 1 1%
Norway 1 1%
Italy 1 1%
Other 2 2%
Unknown 62 72%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 20%
Student > Ph. D. Student 14 16%
Student > Master 13 15%
Librarian 9 10%
Professor > Associate Professor 8 9%
Other 23 27%
Unknown 2 2%
Readers by discipline Count As %
Computer Science 30 35%
Agricultural and Biological Sciences 15 17%
Medicine and Dentistry 13 15%
Social Sciences 6 7%
Arts and Humanities 5 6%
Other 15 17%
Unknown 2 2%

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 10 March 2013.
All research outputs
#2,364,471
of 4,600,266 outputs
Outputs from BMC Bioinformatics
#1,832
of 2,678 outputs
Outputs of similar age
#43,794
of 89,657 outputs
Outputs of similar age from BMC Bioinformatics
#104
of 137 outputs
Altmetric has tracked 4,600,266 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,678 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 19th percentile – i.e., 19% 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 89,657 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 137 others from the same source and published within six weeks on either side of this one. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.