GridScore NEXT

GridScore is a field trial phenotyping app for trait data. It lets you keep track of what's happening in the field on a plot-level basis. This could be anything from plant emergence, flowering date, plant height, flower colour, etc.

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Description

GridScore is a cutting-edge, cross-platform open-source application designed to optimize the collection of phenotypic data in plant breeding and crop research. By leveraging modern web technologies, GridScore replicates the intuitive layout of traditional printed field plans while introducing advanced features that enhance the data collection process.

Key Features:

  • Familiar Layout: Reflects the intuitive design of printed field plans for easy navigation.
  • Georeferencing: Allows for precise location tracking of data points within the field.
  • Image Tagging: Enables users to associate images with specific data entries for better context.
  • Speech Recognition: Facilitates hands-free data entry, improving efficiency and user experience.
  • Barcode Integration: Supports barcode-based systems for quick identification and data capture.
  • Guided Data Collection: Provides a structured approach to ensure comprehensive data acquisition.
  • Top-Down View: Offers an overview of data organized within the field layout for easy monitoring.
  • Cross-Platform Compatibility: Accessible on various devices to accommodate diverse user needs.

Overall, GridScore enhances efficiency and accuracy in phenotypic data collection, making it a valuable asset for researchers conducting experimental phenotyping trials.

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Programming languages
  • Vue 85%
  • JavaScript 7%
  • TypeScript 7%
  • Other 1%
License
</>Source code
Packages
hub.docker.com

Participating organisations

James Hutton Institute

Reference papers

Mentions

Contributors

SR
Sebastian Raubach
Lead developer
The James Hutton Institute
PS
Paul Shaw
Project manager
The James Hutton Institute

Member of community

BrAPI

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brapi2arc

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This API server allows researchers to submit phenotyping observations to a dataset stored as an Annotated Research Context (ARC). The ARC provides a standardized structure for capturing and sharing research data, enabling reproducibility and transparency in scientific investigations.

Updated 11 months ago
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