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Generalized box-plot for root growth ensembles

Overview of attention for article published in BMC Bioinformatics, February 2017
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Title
Generalized box-plot for root growth ensembles
Published in
BMC Bioinformatics, February 2017
DOI 10.1186/s12859-016-1445-3
Pubmed ID
Authors

Viktor Vad, Douglas Cedrim, Wolfgang Busch, Peter Filzmoser, Ivan Viola

Abstract

In the field of root biology there has been a remarkable progress in root phenotyping, which is the efficient acquisition and quantitative description of root morphology. What is currently missing are means to efficiently explore, exchange and present the massive amount of acquired, and often time dependent root phenotypes. In this work, we present visual summaries of root ensembles by aggregating root images with identical genetic characteristics. We use the generalized box plot concept with a new formulation of data depth. In addition to spatial distributions, we created a visual representation to encode temporal distributions associated with the development of root individuals. The new formulation of data depth allows for much faster implementation close to interactive frame rates. This allows us to present the statistics from bootstrapping that characterize the root sample set quality. As a positive side effect of the new data-depth formulation we are able to define the geometric median for the curve ensemble, which was well received by the domain experts.

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X Demographics

The data shown below were collected from the profile of 1 X user 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 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 19%
Professor > Associate Professor 3 19%
Student > Ph. D. Student 2 13%
Researcher 2 13%
Student > Master 2 13%
Other 3 19%
Unknown 1 6%
Readers by discipline Count As %
Computer Science 5 31%
Engineering 3 19%
Agricultural and Biological Sciences 3 19%
Biochemistry, Genetics and Molecular Biology 2 13%
Earth and Planetary Sciences 1 6%
Other 1 6%
Unknown 1 6%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 07 September 2017.
All research outputs
#20,446,373
of 23,001,641 outputs
Outputs from BMC Bioinformatics
#6,887
of 7,312 outputs
Outputs of similar age
#385,365
of 454,579 outputs
Outputs of similar age from BMC Bioinformatics
#128
of 149 outputs
Altmetric has tracked 23,001,641 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,312 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 149 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.