Evidence map›Paper›PMID 26535051›Full record

ArticlePlant methods2015

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

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

Open access · goldAbstract read
In one paragraph

Article in Plant methods, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
78citing papers in PubMed, 1 pooled it
18.6field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

78 citing papers in PubMed, 1 synthesis or guideline pooled it, 206 citations in OpenAlex.

  1. Pearl millet response to drought: A review.Frontiers in plant science · 2023
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  19. Genetic resources and breeding of maize forFrontiers in plant science · 2023
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18 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 5 institutions in 3 countries.

Abhiram DasSchool of Biology, Georgia Institute of Technology, Atlanta, GA USA.
Hannah SchneiderDepartment of Plant Science, Pennsylvania State University, State College, PA USA.
James BurridgeDepartment of Plant Science, Pennsylvania State University, State College, PA USA.
Ana Karine Martinez AscanioSchool of Agriculture and Veterinary Medicine, University of Bologna, Bologna, Italy.
Tobias WojciechowskiForschungszentrum Jülich IBG-2 Pflanzenwissenschaften, Jülich, Germany.
Christopher N ToppDonald Danforth Plant Science Center, St. Louis, MO USA.
Jonathan P LynchDepartment of Plant Science, Pennsylvania State University, State College, PA USA.
Joshua S WeitzSchool of Biology, Georgia Institute of Technology, Atlanta, GA USA ; School of Physics, Georgia Institute of Technology, Atlanta, GA USA.
Alexander BuckschSchool of Biology, Georgia Institute of Technology, Atlanta, GA USA ; School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA USA.ORCID 0000-0002-1071-5355
Georgia Institute of Technology · USPennsylvania State University · USDonald Danforth Plant Science Center · USForschungszentrum Jülich · DEUniversity of Bologna · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPlant 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. DESCRIPTION: 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.

conclusionDIRT 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.

Identifiers

PMID26535051
PMCPMC4630929
OpenAlexW2136828565

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.