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      SensorDB: a virtual laboratory for the integration, visualization and analysis of varied biological sensor data

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          To our knowledge, there is no software or database solution that supports large volumes of biological time series sensor data efficiently and enables data visualization and analysis in real time. Existing solutions for managing data typically use unstructured file systems or relational databases. These systems are not designed to provide instantaneous response to user queries. Furthermore, they do not support rapid data analysis and visualization to enable interactive experiments. In large scale experiments, this behaviour slows research discovery, discourages the widespread sharing and reuse of data that could otherwise inform critical decisions in a timely manner and encourage effective collaboration between groups.


          In this paper we present SensorDB, a web based virtual laboratory that can manage large volumes of biological time series sensor data while supporting rapid data queries and real-time user interaction. SensorDB is sensor agnostic and uses web-based, state-of-the-art cloud and storage technologies to efficiently gather, analyse and visualize data.


          Collaboration and data sharing between different agencies and groups is thereby facilitated. SensorDB is available online at http://sensordb.csiro.au.

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          Most cited references 4

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          Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement

          More accurate and precise phenotyping strategies are necessary to empower high-resolution linkage mapping and genome-wide association studies and for training genomic selection models in plant improvement. Within this framework, the objective of modern phenotyping is to increase the accuracy, precision and throughput of phenotypic estimation at all levels of biological organization while reducing costs and minimizing labor through automation, remote sensing, improved data integration and experimental design. Much like the efforts to optimize genotyping during the 1980s and 1990s, designing effective phenotyping initiatives today requires multi-faceted collaborations between biologists, computer scientists, statisticians and engineers. Robust phenotyping systems are needed to characterize the full suite of genetic factors that contribute to quantitative phenotypic variation across cells, organs and tissues, developmental stages, years, environments, species and research programs. Next-generation phenotyping generates significantly more data than previously and requires novel data management, access and storage systems, increased use of ontologies to facilitate data integration, and new statistical tools for enhancing experimental design and extracting biologically meaningful signal from environmental and experimental noise. To ensure relevance, the implementation of efficient and informative phenotyping experiments also requires familiarity with diverse germplasm resources, population structures, and target populations of environments. Today, phenotyping is quickly emerging as the major operational bottleneck limiting the power of genetic analysis and genomic prediction. The challenge for the next generation of quantitative geneticists and plant breeders is not only to understand the genetic basis of complex trait variation, but also to use that knowledge to efficiently synthesize twenty-first century crop varieties.
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            A multisite managed environment facility for targeted trait and germplasm phenotyping

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              Evaluation of a wireless infrared thermometer with a narrow field of view


                Author and article information

                Plant Methods
                Plant Methods
                Plant Methods
                BioMed Central (London )
                8 December 2015
                8 December 2015
                : 11
                [ ]CSIRO Agriculture, Clunies Ross St, Canberra, 2601 Australia
                [ ]CSIRO Data61, Clunies Ross St, Canberra, 2601 Australia
                [ ]CSIRO Agriculture, 306 Carmody Road, Brisbane, 4067 Australia
                [ ]School of Computer Science and Information Technology, RMIT University, 124 La Trobe Street, Melbourne, 3000 Australia
                [ ]Centre of Excellence for Translational Photosynthesis, Australian National University, Canberra, 0200 Australia
                © Salehi et al. 2015

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                Funded by: FundRef http://dx.doi.org/10.13039/501100000980, Grains Research and Development Corporation;
                Award ID: CSP00148
                Award Recipient :
                Funded by: National Collaborative Research Infrastructure Strategy
                Award ID: Australian Plant Phenomics Facility
                Award Recipient :
                Custom metadata
                © The Author(s) 2015

                Plant science & Botany

                phenomics, high frequency data, big data, nosql, real-time statistics


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