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Applications and methods utilizing the Simple Semantic Web Architecture and Protocol (SSWAP) for bioinformatics resource discovery and disparate data and service integration

Overview of attention for article published in BioData Mining, June 2010
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

wikipedia
1 Wikipedia page

Citations

dimensions_citation
10 Dimensions

Readers on

mendeley
59 Mendeley
citeulike
9 CiteULike
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Title
Applications and methods utilizing the Simple Semantic Web Architecture and Protocol (SSWAP) for bioinformatics resource discovery and disparate data and service integration
Published in
BioData Mining, June 2010
DOI 10.1186/1756-0381-3-3
Pubmed ID
Authors

Rex T Nelson, Shulamit Avraham, Randy C Shoemaker, Gregory D May, Doreen Ware, Damian DG Gessler

Abstract

Scientific data integration and computational service discovery are challenges for the bioinformatic community. This process is made more difficult by the separate and independent construction of biological databases, which makes the exchange of data between information resources difficult and labor intensive. A recently described semantic web protocol, the Simple Semantic Web Architecture and Protocol (SSWAP; pronounced "swap") offers the ability to describe data and services in a semantically meaningful way. We report how three major information resources (Gramene, SoyBase and the Legume Information System [LIS]) used SSWAP to semantically describe selected data and web services.

Mendeley readers

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 5%
United Kingdom 2 3%
Australia 1 2%
Germany 1 2%
Malaysia 1 2%
Sweden 1 2%
Unknown 50 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 25%
Student > Ph. D. Student 7 12%
Student > Bachelor 6 10%
Other 5 8%
Student > Postgraduate 5 8%
Other 18 31%
Unknown 3 5%
Readers by discipline Count As %
Computer Science 15 25%
Agricultural and Biological Sciences 13 22%
Medicine and Dentistry 6 10%
Biochemistry, Genetics and Molecular Biology 5 8%
Arts and Humanities 3 5%
Other 12 20%
Unknown 5 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 September 2011.
All research outputs
#817,319
of 3,628,014 outputs
Outputs from BioData Mining
#46
of 90 outputs
Outputs of similar age
#25,220
of 97,348 outputs
Outputs of similar age from BioData Mining
#3
of 9 outputs
Altmetric has tracked 3,628,014 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 90 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one is in the 47th percentile – i.e., 47% 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 97,348 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 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.