URL: https://github.com/The-Zhou-Lab/SeedGerm-VIG
Proper Citation: SeedGerm-VIG (RRID:SCR_027483)
Description: Software pipeline to quantify seed vigour in wheat and other cereal crops using deep learning powered dynamic phenotypic analysis.
Resource Type: data analysis software, data processing software, software application, software resource, source code, time-series analysis software
Defining Citation: DOI:10.1093/gigascience/giaf129
Keywords: seed vigour, germination, vision-based deep learning, dynamic trait analysis, wheat
Funding: Allan & Gill Gray Foundation’ Sustainable Productivity for Crop Improvement G118688; BBSRC’s ALERT grant BB/X019683/1; BBSRC’s International Partnership Grant BB/Y514081/1; National Natural Science Foundation of China 32070400 & U24A20402; the United Kingdom Research and Innovation's (UKRI) Biotechnology and Biological Sciences Research Council (BBSRC) AI in Bioscience Grant BB/Y513969/1
Availability: Free, Available for download, Freely available
Resource Name: SeedGerm-VIG
Resource ID: SCR_027483
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Source: SciCrunch Registry (RRID:SCR_005400)
Description: Interactive portal for finding and submitting biomedical resources. Resources within SciCrunch have assigned RRIDs which are used to cite resources in scientific manuscripts. SciCrunch Registry, formerly NIF Registry, provides resources catalog. Allows to add new resources. Allows edit existing resources after registration. Curators are tasked with identifying and registering resources, examining data, writing configuration files to index and display data and keeping contents current.
URL: https://rrid.site/rin/sources/SCR_005400
