You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
Mendeley readers
Attention Score in Context
| 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 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 7 | 14% |
| Brazil | 1 | 2% |
| United Kingdom | 1 | 2% |
| Sweden | 1 | 2% |
| Saudi Arabia | 1 | 2% |
| Canada | 1 | 2% |
| Unknown | 39 | 76% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 15 | 29% |
| Student > Ph. D. Student | 9 | 18% |
| Professor > Associate Professor | 5 | 10% |
| Student > Bachelor | 4 | 8% |
| Student > Postgraduate | 4 | 8% |
| Other | 10 | 20% |
| Unknown | 4 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 19 | 37% |
| Medicine and Dentistry | 10 | 20% |
| Agricultural and Biological Sciences | 6 | 12% |
| Engineering | 4 | 8% |
| Social Sciences | 3 | 6% |
| Other | 5 | 10% |
| 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 14 March 2011.
All research outputs
#7,453,350
of 22,786,087 outputs
Outputs from BMC Bioinformatics
#3,023
of 7,279 outputs
Outputs of similar age
#49,576
of 170,210 outputs
Outputs of similar age from BMC Bioinformatics
#20
of 56 outputs
Altmetric has tracked 22,786,087 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,279 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 50% 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 170,210 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 56 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.