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Dealing with multi‐source and multi‐scale information in plant phenomics: the ontology‐driven Phenotyping Hybrid Information System
Author(s) -
Neveu Pascal,
Tireau Anne,
Hilgert Nadine,
Nègre Vincent,
MineauCesari Jonathan,
Brichet Nicolas,
Chapuis Romain,
Sanchez Isabelle,
Pommier Cyril,
Charnomordic Brigitte,
Tardieu François,
CabreraBosquet Llorenç
Publication year - 2019
Publication title -
new phytologist
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.742
H-Index - 244
eISSN - 1469-8137
pISSN - 0028-646X
DOI - 10.1111/nph.15385
Subject(s) - phenomics , computer science , metadata , ontology , tracing , field (mathematics) , data science , information retrieval , data mining , scale (ratio) , cluster analysis , semantic integration , data integration , semantic heterogeneity , semantic web , ontology based data integration , artificial intelligence , world wide web , genomics , biology , geography , philosophy , mathematics , operating system , biochemistry , epistemology , genome , pure mathematics , gene , semantic web stack , cartography
Summary Phenomic datasets need to be accessible to the scientific community. Their reanalysis requires tracing relevant information on thousands of plants, sensors and events. The open‐source Phenotyping Hybrid Information System ( PHIS ) is proposed for plant phenotyping experiments in various categories of installations (field, glasshouse). It unambiguously identifies all objects and traits in an experiment and establishes their relations via ontologies and semantics that apply to both field and controlled conditions. For instance, the genotype is declared for a plant or plot and is associated with all objects related to it. Events such as successive plant positions, anomalies and annotations are associated with objects so they can be easily retrieved. Its ontology‐driven architecture is a powerful tool for integrating and managing data from multiple experiments and platforms, for creating relationships between objects and enriching datasets with knowledge and metadata. It interoperates with external resources via web services, thereby allowing data integration into other systems; for example, modelling platforms or external databases. It has the potential for rapid diffusion because of its ability to integrate, manage and visualize multi‐source and multi‐scale data, but also because it is based on 10 yr of trial and error in our groups.