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BioMart – biological queries made easy

Overview of attention for article published in BMC Genomics, January 2009
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

Mentioned by

twitter
2 tweeters
patent
1 patent

Citations

dimensions_citation
444 Dimensions

Readers on

mendeley
445 Mendeley
citeulike
56 CiteULike
connotea
3 Connotea
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Title
BioMart – biological queries made easy
Published in
BMC Genomics, January 2009
DOI 10.1186/1471-2164-10-22
Pubmed ID
Authors

Damian Smedley, Syed Haider, Benoit Ballester, Richard Holland, Darin London, Gudmundur Thorisson, Arek Kasprzyk

Abstract

Biologists need to perform complex queries, often across a variety of databases. Typically, each data resource provides an advanced query interface, each of which must be learnt by the biologist before they can begin to query them. Frequently, more than one data source is required and for high-throughput analysis, cutting and pasting results between websites is certainly very time consuming. Therefore, many groups rely on local bioinformatics support to process queries by accessing the resource's programmatic interfaces if they exist. This is not an efficient solution in terms of cost and time. Instead, it would be better if the biologist only had to learn one generic interface. BioMart provides such a solution.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters 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 445 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 17 4%
United Kingdom 12 3%
Germany 5 1%
France 5 1%
Canada 4 <1%
Spain 4 <1%
Belgium 3 <1%
Brazil 3 <1%
Mexico 3 <1%
Other 26 6%
Unknown 363 82%

Demographic breakdown

Readers by professional status Count As %
Researcher 138 31%
Student > Ph. D. Student 98 22%
Student > Master 54 12%
Student > Bachelor 32 7%
Professor > Associate Professor 24 5%
Other 67 15%
Unknown 32 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 242 54%
Biochemistry, Genetics and Molecular Biology 60 13%
Computer Science 59 13%
Medicine and Dentistry 19 4%
Engineering 7 2%
Other 20 4%
Unknown 38 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 20 December 2018.
All research outputs
#4,057,991
of 15,220,640 outputs
Outputs from BMC Genomics
#2,122
of 8,652 outputs
Outputs of similar age
#37,385
of 153,243 outputs
Outputs of similar age from BMC Genomics
#5
of 20 outputs
Altmetric has tracked 15,220,640 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 8,652 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 74% 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 153,243 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.