Ontology for Experimental Phenotypic Objects
Names
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Ontology for Experimental Phenotypic Objects
metasource: Fairsharing.org
version: None
OEPO
metasource: Fairsharing.org
version: None
Names
More detailed information about this field from each metasource.
Ontology for Experimental Phenotypic Objects
metasource: Fairsharing.orgversion: None
OEPO
metasource: Fairsharing.orgversion: None
Names
More detailed information about this field from each metasource.
Ontology for Experimental Phenotypic Objects
metasource: Fairsharing.orgversion: None
OEPO
metasource: Fairsharing.orgversion: None
The Ontology for Experimental Phenotypic Objects (OEPO) describes objects involved in phenotyping experiments (e.g. infrastructure, devices, germplasm, scientific objects) and defines a hierarchy between them according to the specificities of the installations and experiments. The OEPO ontology contains three main groups of objects, namely Experimental Condition, Scientific Object, and Germplasm Descriptor.
Description
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The Ontology for Experimental Phenotypic Objects (OEPO) describes objects involved in phenotyping experiments (e.g. infrastructure, devices, germplasm, scientific objects) and defines a hierarchy between them according to the specificities of the installations and experiments. The OEPO ontology contains three main groups of objects, namely Experimental Condition, Scientific Object, and Germplasm Descriptor.
metasource: Fairsharing.orgversion: None
Webpage:
https://github.com/OpenSILEX/ontology-phis-oepo-fieldWebpage
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Licence:
Name: Creative Commons Attribution 4.0 International (CC BY 4.0)Licence name
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Creative Commons Attribution 4.0 International (CC BY 4.0)
metasource: Fairsharing.orgversion: None
Licence URL
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Publications:
Publications
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Dealing with multi-source and multi-scale information in plant phenomics: the ontology-driven Phenotyping Hybrid Information System
PMID: 30152011
metasource: Fairsharing.org
version: None
Publications
More detailed information about this field from each metasource.
Dealing with multi-source and multi-scale information in plant phenomics: the ontology-driven Phenotyping Hybrid Information System
PMID: 30152011
metasource: Fairsharing.org
version: None
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Dealing with multi-source and multi-scale information in plant phenomics: the ontology-driven Phenotyping Hybrid Information System
PubMed citations: 19.
19 articles citing: Dealing with multi-source and multi-scale information in plant phenomics: the ontology-driven Phenotyping Hybrid Information System
Applications of Multi-Omics Technologies for Crop Improvement. PMID:34539683
Opportunities and limits of controlled-environment plant phenotyping for climate response traits. PMID:34302493
UAS-Based Plant Phenotyping for Research and Breeding Applications. PMID:34195621
Crop breeding for a changing climate: integrating phenomics and genomics with bioinformatics. PMID:33852055
Editorial: Phenotyping; From Plant, to Data, to Impact and Highlights of the International Plant Phenotyping Symposium - IPPS 2018. PMID:33343612
Applying FAIR Principles to Plant Phenotypic Data Management in GnpIS. PMID:33313522
A systems genetics approach reveals environment-dependent associations between SNPs, protein coexpression, and drought-related traits in maize. PMID:33060172
Phenomics-based GWAS analysis reveals the genetic architecture for drought resistance in cotton. PMID:32558152
Low-Cost Automated Vectors and Modular Environmental Sensors for Plant Phenotyping. PMID:32545168
Gloxinia-An Open-Source Sensing Platform to Monitor the Dynamic Responses of Plants. PMID:32481619
Decoding Plant-Environment Interactions That Influence Crop Agronomic Traits. PMID:32392328
Plant, space and time - linked together in an integrative and scalable data management system for phenomic approaches in agronomic field trials. PMID:32336978
Emerging Technologies in Algal Biotechnology: Toward the Establishment of a Sustainable, Algae-Based Bioeconomy. PMID:32256509
Enabling reusability of plant phenomic datasets with MIAPPE 1.1. PMID:32171029
Semantic concept schema of the linear mixed model of experimental observations. PMID:32109232
Combining computer vision and deep learning to enable ultra-scale aerial phenotyping and precision agriculture: A case study of lettuce production. PMID:31231528
To clean or not to clean phenotypic datasets for outlier plants in genetic analyses? PMID:31020325
Review: New sensors and data-driven approaches-A path to next generation phenomics. PMID:31003608
CropSight: a scalable and open-source information management system for distributed plant phenotyping and IoT-based crop management. PMID:30715329
Tags:
Tags
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experimental measurement
metasource: Fairsharing.org
version: None
experimentally determined
metasource: Fairsharing.org
version: None
germplasm
metasource: Fairsharing.org
version: None
phenotype
metasource: Fairsharing.org
version: None
protocol
metasource: Fairsharing.org
version: None
study design
metasource: Fairsharing.org
version: None
Tags
More detailed information about this field from each metasource.
experimental measurement
metasource: Fairsharing.orgversion: None
experimentally determined
metasource: Fairsharing.orgversion: None
germplasm
metasource: Fairsharing.orgversion: None
phenotype
metasource: Fairsharing.orgversion: None
protocol
metasource: Fairsharing.orgversion: None
study design
metasource: Fairsharing.orgversion: None
experimental measurement experimentally determined germplasm phenotype protocol study design
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