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Biomedical semantics in the Semantic Web

Overview of attention for article published in Journal of Biomedical Semantics, March 2011
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Mentioned by

googleplus
1 Google+ user

Citations

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11 Dimensions

Readers on

mendeley
67 Mendeley
citeulike
15 CiteULike
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Title
Biomedical semantics in the Semantic Web
Published in
Journal of Biomedical Semantics, March 2011
DOI 10.1186/2041-1480-2-s1-s1
Pubmed ID
Authors

Andrea Splendiani, Albert Burger, Adrian Paschke, Paolo Romano, M Scott Marshall, M Marshall

Abstract

The Semantic Web offers an ideal platform for representing and linking biomedical information, which is a prerequisite for the development and application of analytical tools to address problems in data-intensive areas such as systems biology and translational medicine. As for any new paradigm, the adoption of the Semantic Web offers opportunities and poses questions and challenges to the life sciences scientific community: which technologies in the Semantic Web stack will be more beneficial for the life sciences? Is biomedical information too complex to benefit from simple interlinked representations? What are the implications of adopting a new paradigm for knowledge representation? What are the incentives for the adoption of the Semantic Web, and who are the facilitators? Is there going to be a Semantic Web revolution in the life sciences?We report here a few reflections on these questions, following discussions at the SWAT4LS (Semantic Web Applications and Tools for Life Sciences) workshop series, of which this Journal of Biomedical Semantics special issue presents selected papers from the 2009 edition, held in Amsterdam on November 20th.

Mendeley readers

The data shown below were compiled from readership statistics for 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 6%
Mexico 2 3%
Belgium 2 3%
Brazil 2 3%
Netherlands 1 1%
Sweden 1 1%
Germany 1 1%
Italy 1 1%
Denmark 1 1%
Other 4 6%
Unknown 48 72%

Demographic breakdown

Readers by professional status Count As %
Researcher 22 33%
Student > Ph. D. Student 10 15%
Student > Master 10 15%
Professor > Associate Professor 6 9%
Other 5 7%
Other 13 19%
Unknown 1 1%
Readers by discipline Count As %
Computer Science 27 40%
Agricultural and Biological Sciences 20 30%
Social Sciences 6 9%
Medicine and Dentistry 5 7%
Physics and Astronomy 1 1%
Other 7 10%
Unknown 1 1%

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 16 July 2013.
All research outputs
#2,521,705
of 4,745,703 outputs
Outputs from Journal of Biomedical Semantics
#126
of 193 outputs
Outputs of similar age
#41,817
of 82,868 outputs
Outputs of similar age from Journal of Biomedical Semantics
#2
of 4 outputs
Altmetric has tracked 4,745,703 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 193 research outputs from this source. They receive a mean Attention Score of 4.4. 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 82,868 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.