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Long-read sequencing settings for efficient structural variation detection based on comprehensive evaluation

Overview of attention for article published in BMC Bioinformatics, November 2021
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

blogs
1 blog
twitter
14 X users

Citations

dimensions_citation
17 Dimensions

Readers on

mendeley
41 Mendeley
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Title
Long-read sequencing settings for efficient structural variation detection based on comprehensive evaluation
Published in
BMC Bioinformatics, November 2021
DOI 10.1186/s12859-021-04422-y
Pubmed ID
Authors

Tao Jiang, Shiqi Liu, Shuqi Cao, Yadong Liu, Zhe Cui, Yadong Wang, Hongzhe Guo

X Demographics

X Demographics

The data shown below were collected from the profiles of 14 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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 41 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 17%
Student > Bachelor 3 7%
Student > Master 3 7%
Student > Ph. D. Student 2 5%
Unspecified 2 5%
Other 2 5%
Unknown 22 54%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 17%
Biochemistry, Genetics and Molecular Biology 6 15%
Unspecified 2 5%
Business, Management and Accounting 1 2%
Computer Science 1 2%
Other 1 2%
Unknown 23 56%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 13 December 2021.
All research outputs
#2,740,545
of 24,998,746 outputs
Outputs from BMC Bioinformatics
#790
of 7,630 outputs
Outputs of similar age
#59,484
of 429,542 outputs
Outputs of similar age from BMC Bioinformatics
#18
of 164 outputs
Altmetric has tracked 24,998,746 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,630 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 89% 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 429,542 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 86% of its contemporaries.
We're also able to compare this research output to 164 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.