2 research outputs found
Dealing with multi-source and multi-scale information in plant phenomics: the ontology-driven Phenotyping Hybrid Information System
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
PHIS, a plant science ontology-driven Phenotyping Hybrid Information System
International audiencePlant phenomics datasets are unprecedented resources for identifying and testing novel mechanisms and models. These datasets need to be reusable to the scientific community. Their analysis requires the understanding of relevant information on thousands of plants, sensors and events. The open-source Phenotyping Hybrid Information System (PHIS) is proposed for the smart management of plant phenotyping experimental data. It allows the unambiguous identification and management of all agronomical objects and traits in an experiment and establishes their relations thanks to semantics resources such as reference ontologies. PHIS deals with various experimental context, e.g. field and greenhouse conditions