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Testing the assumptions of parametric linear models: the need for biological data mining in disciplines such as human genetics

Overview of attention for article published in BioData Mining, February 2019
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (67th percentile)

Mentioned by

twitter
10 tweeters

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
12 Mendeley
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Title
Testing the assumptions of parametric linear models: the need for biological data mining in disciplines such as human genetics
Published in
BioData Mining, February 2019
DOI 10.1186/s13040-019-0194-z
Authors

Jason H. Moore, Trudy F. C. Mackay, Scott M. Williams

Twitter Demographics

The data shown below were collected from the profiles of 10 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 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 12 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 17%
Student > Bachelor 2 17%
Professor 1 8%
Student > Doctoral Student 1 8%
Lecturer 1 8%
Other 3 25%
Unknown 2 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 25%
Agricultural and Biological Sciences 3 25%
Computer Science 3 25%
Engineering 1 8%
Unknown 2 17%

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 31 March 2019.
All research outputs
#3,773,971
of 14,570,779 outputs
Outputs from BioData Mining
#107
of 241 outputs
Outputs of similar age
#107,057
of 332,005 outputs
Outputs of similar age from BioData Mining
#1
of 1 outputs
Altmetric has tracked 14,570,779 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 241 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.5. This one has gotten more attention than average, scoring higher than 55% 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 332,005 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 67% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them