Plant DataDiscovery

Plant DataDiscovery indexes and semantically integrates public plant biology datasets to improve findability, interoperability, and reuse for integrative studies of genotypic, phenotypic, agronomical, and oenological data.

Key Features:

• Indexing and discovery: Indexes diverse plant-related datasets and exposes metadata to improve dataset findability.
• DataDiscovery framework: Implements the DataDiscovery framework for dataset metadata aggregation and registration.
• FAIR compliance: Adheres to the FAIR principles (Findable, Accessible, Interoperable, Reusable) to enhance dataset reuse and interoperability.
• Semantic integration: Performs semantic integration and advanced data modeling to enable meaningful cross-dataset queries.
• Data mining support: Supports next-generation data mining tools for analysis across integrated datasets.
• Standards for annotation: Develops and adopts standards for data annotation and formatting to standardize dataset representation.
• Federated integration (GrapeIS): Integrates into the GrapeIS federation under the International Grapevine Genome Program to link genotypic, phenotypic, agronomical, and oenological data.

Scientific Applications:

• Dataset discovery for plant research: Enables researchers to locate and integrate public plant biology datasets across repositories.
• Integrative genomics and phenomics: Facilitates integration of genotypic and phenotypic data for grape biology and viticulture studies.
• Breeding and varietal development: Supports creation of new grape varieties by linking genomic, phenotypic, agronomical, and oenological data.
• Stress and environmental studies: Enables analysis of biotic and abiotic stress responses and impacts of environmental change.
• Cross-dataset data mining: Enables mining across integrated datasets to generate hypotheses about genotype–phenotype associations.

Methodology:

Uses the DataDiscovery framework, semantic integration and advanced data modeling, adoption of annotation and formatting standards, and next-generation data mining tools within the GrapeIS federation.