Delivered¶
- Definition of an ontology and namespace describing the EMO BON data model
- Development of sampling event (meta)data validation (Pydantic library framework) and quality control procedures using explicit rules and GitHub actions
- Procedures and software creation to perform semantic uplift of EMO BON data using the data model to RDF triples
RO-cratespecification and generation software for the metaGOflow data products- Jupyter notebooks of data visualisations of taxonomic and genetic such as
alphaandbetadiversities, workflow for biosynthetic gene cluster identification using software on the Galaxy back-end via their API, and others. - PyPI distributed marine
-omics -methods package of reusable methods implemented for the Jupyter notebooks - Metadata RDF triple generation
- DCAT catalogue templating descriptions of EMO BON data assets, i.e. IDDAS ingestion framework for the metadata triple store
- Storage solution for MGF data products - S3 Object Store in testbed infrastructure
- IDDAS ingestion framework for the metadata triple database and the MGF data product RO-crates
- RO-crate viewer to browse and download specific files from the sample and MGF RO-crates
- Incorporate our RO-crates and triple store into the UDAL code, provide named queries Uniform Data Access Layer (UDAL)
- Initial VRE construction on the BlueCloud platform
To be delivered¶
- SPARQL endpoint construction for the RDF triples of sample metadata and metaGOflow data products
- Documentation and landing pages from the VRE on BlueCloud2026 infrastructure.
- Build FAIR metadata records for all our datasets/triple store and have them assessed through tools like F-UJI
- Ensure provenance is provided out of our Jupyter Notebooks in the VRE