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GridScore: a tool for accurate, cross-platform phenotypic data collection and visualization

BACKGROUND: Plant breeding and crop research rely on experimental phenotyping trials. These trials generate data for large numbers of traits and plant varieties that needs to be captured efficiently and accurately to support further research and downstream analysis. Traditionally scored by hand, phe...

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Autores principales: Raubach, Sebastian, Schreiber, Miriam, Shaw, Paul D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9169276/
https://www.ncbi.nlm.nih.gov/pubmed/35668357
http://dx.doi.org/10.1186/s12859-022-04755-2
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author Raubach, Sebastian
Schreiber, Miriam
Shaw, Paul D.
author_facet Raubach, Sebastian
Schreiber, Miriam
Shaw, Paul D.
author_sort Raubach, Sebastian
collection PubMed
description BACKGROUND: Plant breeding and crop research rely on experimental phenotyping trials. These trials generate data for large numbers of traits and plant varieties that needs to be captured efficiently and accurately to support further research and downstream analysis. Traditionally scored by hand, phenotypic data is nowadays collected using spreadsheets or specialized apps. While many solutions exist, which increase efficiency and reduce errors, none offer the same familiarity as printed field plans which have been used for decades and offer an intuitive overview over the trial setup, previously recorded data and plots still requiring scoring. RESULTS: We introduce GridScore which utilizes cutting-edge web technologies to reproduce the familiarity of printed field plans while enhancing the phenotypic data collection process by adding advanced features like georeferencing, image tagging and speech recognition. GridScore is a cross-platform open-source plant phenotyping app that combines barcode-based systems with a guided data collection approach while offering a top-down view onto the data collected in a field layout. GridScore is compared to existing tools across a wide spectrum of criteria including support for barcodes, multiple platforms, and visualizations. CONCLUSION: Compared to its competition, GridScore shows strong performance across the board offering a complete manual phenotyping experience.
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spelling pubmed-91692762022-06-07 GridScore: a tool for accurate, cross-platform phenotypic data collection and visualization Raubach, Sebastian Schreiber, Miriam Shaw, Paul D. BMC Bioinformatics Software BACKGROUND: Plant breeding and crop research rely on experimental phenotyping trials. These trials generate data for large numbers of traits and plant varieties that needs to be captured efficiently and accurately to support further research and downstream analysis. Traditionally scored by hand, phenotypic data is nowadays collected using spreadsheets or specialized apps. While many solutions exist, which increase efficiency and reduce errors, none offer the same familiarity as printed field plans which have been used for decades and offer an intuitive overview over the trial setup, previously recorded data and plots still requiring scoring. RESULTS: We introduce GridScore which utilizes cutting-edge web technologies to reproduce the familiarity of printed field plans while enhancing the phenotypic data collection process by adding advanced features like georeferencing, image tagging and speech recognition. GridScore is a cross-platform open-source plant phenotyping app that combines barcode-based systems with a guided data collection approach while offering a top-down view onto the data collected in a field layout. GridScore is compared to existing tools across a wide spectrum of criteria including support for barcodes, multiple platforms, and visualizations. CONCLUSION: Compared to its competition, GridScore shows strong performance across the board offering a complete manual phenotyping experience. BioMed Central 2022-06-06 /pmc/articles/PMC9169276/ /pubmed/35668357 http://dx.doi.org/10.1186/s12859-022-04755-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Raubach, Sebastian
Schreiber, Miriam
Shaw, Paul D.
GridScore: a tool for accurate, cross-platform phenotypic data collection and visualization
title GridScore: a tool for accurate, cross-platform phenotypic data collection and visualization
title_full GridScore: a tool for accurate, cross-platform phenotypic data collection and visualization
title_fullStr GridScore: a tool for accurate, cross-platform phenotypic data collection and visualization
title_full_unstemmed GridScore: a tool for accurate, cross-platform phenotypic data collection and visualization
title_short GridScore: a tool for accurate, cross-platform phenotypic data collection and visualization
title_sort gridscore: a tool for accurate, cross-platform phenotypic data collection and visualization
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9169276/
https://www.ncbi.nlm.nih.gov/pubmed/35668357
http://dx.doi.org/10.1186/s12859-022-04755-2
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