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@ARTICLE{Das:281176,
      author       = {Das, Abhiram and Schneider, Hannah and Burridge, James and
                      Ascanio, Karine Martinez Ana and Wojciechowski, Tobias and
                      Topp, Christopher N and Lynch, Jonathan P and Weitz, Joshua
                      S and Bucksch, Alexander},
      title        = {{D}igital imaging of root traits ({DIRT}): a
                      high-throughput computing and collaboration platform for
                      field-based root phenomics},
      journal      = {Plant methods},
      volume       = {11},
      number       = {51},
      issn         = {1746-4811},
      address      = {London},
      publisher    = {BioMed Central},
      reportid     = {FZJ-2016-00875},
      pages        = {1-12},
      year         = {2015},
      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.DescriptionHere,
                      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://​dirt.​iplantcollaborat​ive.​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.},
      cin          = {IBG-2},
      ddc          = {580},
      cid          = {I:(DE-Juel1)IBG-2-20101118},
      pnm          = {582 - Plant Science (POF3-582)},
      pid          = {G:(DE-HGF)POF3-582},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000364283700001},
      pubmed       = {pmid:26535051},
      doi          = {10.1186/s13007-015-0093-3},
      url          = {https://juser.fz-juelich.de/record/281176},
}