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BIGSdb: Scalable analysis of bacterial genome variation at the population level

Overview of attention for article published in BMC Bioinformatics, December 2010
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#45 of 7,793)
  • High Attention Score compared to outputs of the same age (98th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

blogs
2 blogs
policy
2 policy sources
twitter
27 X users
patent
6 patents
wikipedia
1 Wikipedia page

Citations

dimensions_citation
1926 Dimensions

Readers on

mendeley
979 Mendeley
citeulike
4 CiteULike
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Title
BIGSdb: Scalable analysis of bacterial genome variation at the population level
Published in
BMC Bioinformatics, December 2010
DOI 10.1186/1471-2105-11-595
Pubmed ID
Authors

Keith A Jolley, Martin CJ Maiden

X Demographics

X Demographics

The data shown below were collected from the profiles of 27 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 979 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 8 <1%
Germany 6 <1%
Brazil 6 <1%
United States 5 <1%
Australia 3 <1%
France 2 <1%
Sweden 2 <1%
Belgium 2 <1%
Spain 2 <1%
Other 12 1%
Unknown 931 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 218 22%
Student > Ph. D. Student 216 22%
Student > Master 107 11%
Student > Bachelor 76 8%
Student > Doctoral Student 49 5%
Other 142 15%
Unknown 171 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 335 34%
Biochemistry, Genetics and Molecular Biology 174 18%
Immunology and Microbiology 101 10%
Medicine and Dentistry 58 6%
Veterinary Science and Veterinary Medicine 31 3%
Other 75 8%
Unknown 205 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 55. 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 01 March 2023.
All research outputs
#781,771
of 26,017,215 outputs
Outputs from BMC Bioinformatics
#45
of 7,793 outputs
Outputs of similar age
#3,293
of 197,423 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 63 outputs
Altmetric has tracked 26,017,215 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,793 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.6. This one has done particularly well, scoring higher than 99% 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 197,423 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 63 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.