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Sequence squeeze: an open contest for sequence compression

Overview of attention for article published in Giga Science, April 2013
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (96th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

blogs
2 blogs
twitter
28 X users
peer_reviews
1 peer review site
facebook
1 Facebook page
googleplus
3 Google+ users

Citations

dimensions_citation
13 Dimensions

Readers on

mendeley
30 Mendeley
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Title
Sequence squeeze: an open contest for sequence compression
Published in
Giga Science, April 2013
DOI 10.1186/2047-217x-2-5
Pubmed ID
Authors

Richard CG Holland, Nick Lynch

Abstract

Next-generation sequencing machines produce large quantities of data which are becoming increasingly difficult to move between collaborating organisations or even store within a single organisation. Compressing the data to assist with this is vital, but existing techniques do not perform as well as might be expected. The need for a new compression technique was identified by the Pistoia Alliance who commissioned an open innovation contest to find one. The dynamic and interactive nature of the contest led to some novel algorithms and a high level of competition between participants.

X Demographics

X Demographics

The data shown below were collected from the profiles of 28 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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 2 7%
Hong Kong 2 7%
United States 2 7%
United Kingdom 1 3%
Sweden 1 3%
Unknown 22 73%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 37%
Researcher 6 20%
Other 3 10%
Student > Master 3 10%
Student > Postgraduate 2 7%
Other 3 10%
Unknown 2 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 50%
Computer Science 5 17%
Medicine and Dentistry 2 7%
Biochemistry, Genetics and Molecular Biology 1 3%
Pharmacology, Toxicology and Pharmaceutical Science 1 3%
Other 3 10%
Unknown 3 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 37. 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 08 July 2018.
All research outputs
#1,085,089
of 25,374,917 outputs
Outputs from Giga Science
#167
of 1,168 outputs
Outputs of similar age
#8,048
of 209,841 outputs
Outputs of similar age from Giga Science
#2
of 6 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,168 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 21.8. This one has done well, scoring higher than 85% 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 209,841 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 96% of its contemporaries.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.