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Metadata mapping and reuse in caBIG™

Overview of attention for article published in BMC Bioinformatics, February 2009
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

Mentioned by

wikipedia
3 Wikipedia pages

Citations

dimensions_citation
18 Dimensions

Readers on

mendeley
48 Mendeley
citeulike
5 CiteULike
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Title
Metadata mapping and reuse in caBIG™
Published in
BMC Bioinformatics, February 2009
DOI 10.1186/1471-2105-10-s2-s4
Pubmed ID
Authors

Isaac Kunz, Ming-Chin Lin, Lewis Frey

Abstract

This paper proposes that interoperability across biomedical databases can be improved by utilizing a repository of Common Data Elements (CDEs), UML model class-attributes and simple lexical algorithms to facilitate the building domain models. This is examined in the context of an existing system, the National Cancer Institute (NCI)'s cancer Biomedical Informatics Grid (caBIG). The goal is to demonstrate the deployment of open source tools that can be used to effectively map models and enable the reuse of existing information objects and CDEs in the development of new models for translational research applications. This effort is intended to help developers reuse appropriate CDEs to enable interoperability of their systems when developing within the caBIG framework or other frameworks that use metadata repositories.

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 15%
United Kingdom 1 2%
Sweden 1 2%
Canada 1 2%
Saudi Arabia 1 2%
Brazil 1 2%
Unknown 36 75%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 29%
Student > Ph. D. Student 9 19%
Professor > Associate Professor 5 10%
Student > Postgraduate 4 8%
Student > Bachelor 4 8%
Other 10 21%
Unknown 2 4%
Readers by discipline Count As %
Computer Science 18 38%
Medicine and Dentistry 10 21%
Agricultural and Biological Sciences 6 13%
Engineering 4 8%
Social Sciences 3 6%
Other 5 10%
Unknown 2 4%

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 14 March 2011.
All research outputs
#816,143
of 3,631,090 outputs
Outputs from BMC Bioinformatics
#787
of 2,289 outputs
Outputs of similar age
#24,461
of 94,193 outputs
Outputs of similar age from BMC Bioinformatics
#44
of 127 outputs
Altmetric has tracked 3,631,090 research outputs across all sources so far. This one has received more attention than most of these and is in the 63rd percentile.
So far Altmetric has tracked 2,289 research outputs from this source. They receive a mean Attention Score of 4.3. This one has gotten more attention than average, scoring higher than 58% 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 94,193 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 72% of its contemporaries.
We're also able to compare this research output to 127 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 62% of its contemporaries.