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Digital imaging of root traits (DIRT): a high-throughput computing and collaboration platform for field-based root phenomics

Overview of attention for article published in Plant Methods, November 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

news
1 news outlet
twitter
9 X users
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

dimensions_citation
151 Dimensions

Readers on

mendeley
292 Mendeley
citeulike
1 CiteULike
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Title
Digital imaging of root traits (DIRT): a high-throughput computing and collaboration platform for field-based root phenomics
Published in
Plant Methods, November 2015
DOI 10.1186/s13007-015-0093-3
Pubmed ID
Authors

Abhiram Das, Hannah Schneider, James Burridge, Ana Karine Martinez Ascanio, Tobias Wojciechowski, Christopher N. Topp, Jonathan P. Lynch, Joshua S. Weitz, Alexander Bucksch

Abstract

Plant root systems are key drivers of plant function and yield. They are also under-explored targets to meet global food and energy demands. Many new technologies have been developed to characterize crop root system architecture (CRSA). These technologies have the potential to accelerate the progress in understanding the genetic control and environmental response of CRSA. Putting this potential into practice requires new methods and algorithms to analyze CRSA in digital images. Most prior approaches have solely focused on the estimation of root traits from images, yet no integrated platform exists that allows easy and intuitive access to trait extraction and analysis methods from images combined with storage solutions linked to metadata. Automated high-throughput phenotyping methods are increasingly used in laboratory-based efforts to link plant genotype with phenotype, whereas similar field-based studies remain predominantly manual low-throughput. Here, we present an open-source phenomics platform "DIRT", as a means to integrate scalable supercomputing architectures into field experiments and analysis pipelines. DIRT is an online platform that enables researchers to store images of plant roots, measure dicot and monocot root traits under field conditions, and share data and results within collaborative teams and the broader community. The DIRT platform seamlessly connects end-users with large-scale compute "commons" enabling the estimation and analysis of root phenotypes from field experiments of unprecedented size. DIRT is an automated high-throughput computing and collaboration platform for field based crop root phenomics. The platform is accessible at http://www.dirt.iplantcollaborative.org/ and hosted on the iPlant cyber-infrastructure using high-throughput grid computing resources of the Texas Advanced Computing Center (TACC). DIRT is a high volume central depository and high-throughput RSA trait computation platform for plant scientists working on crop roots. It enables scientists to store, manage and share crop root images with metadata and compute RSA traits from thousands of images in parallel. It makes high-throughput RSA trait computation available to the community with just a few button clicks. As such it enables plant scientists to spend more time on science rather than on technology. All stored and computed data is easily accessible to the public and broader scientific community. We hope that easy data accessibility will attract new tool developers and spur creative data usage that may even be applied to other fields of science.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 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 292 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 <1%
Chile 1 <1%
Germany 1 <1%
Belgium 1 <1%
New Zealand 1 <1%
Unknown 286 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 75 26%
Researcher 52 18%
Student > Master 43 15%
Student > Doctoral Student 16 5%
Student > Bachelor 16 5%
Other 45 15%
Unknown 45 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 166 57%
Biochemistry, Genetics and Molecular Biology 20 7%
Environmental Science 10 3%
Engineering 9 3%
Social Sciences 4 1%
Other 24 8%
Unknown 59 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 28 August 2020.
All research outputs
#2,194,663
of 24,669,628 outputs
Outputs from Plant Methods
#96
of 1,192 outputs
Outputs of similar age
#31,542
of 290,841 outputs
Outputs of similar age from Plant Methods
#1
of 11 outputs
Altmetric has tracked 24,669,628 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,192 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.5. This one has done particularly well, scoring higher than 92% 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 290,841 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 89% of its contemporaries.
We're also able to compare this research output to 11 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 99% of its contemporaries.