AI programmes stall on research data nobody can find
Every AI programme in discovery runs into the same wall: the data exists, but nobody can find it, nothing says what it means, and the ontology that would explain it lives in a project folder. TopQuadrant gives R&D a governed ontology registry and a metadata layer that makes datasets findable, described, and reusable across programmes.
Trusted to govern the data behind the world's most regulated decisions.
There is no FAIR data standard, which is why you need governance
FAIR is a set of principles, not a specification, and no external body certifies compliance. That leaves every organisation self-assessing, and it leaves data standards and ontologies as the most commonly cited gap. The practical response is an internal registry: governed vocabularies, tracked external versions, and dataset metadata a scientist can search without filing a ticket.
Of labs cite data standards and ontologies as a major gap preventing their data from being FAIR.
Prebuilt ontologies and vocabularies to adopt and extend, rather than curating from scratch.
One registry holding external ontologies, internal extensions, and the version relationship between them.
Internal ontology registry
A registry for the ontologies you actually use
Public registries help you discover an ontology. They do not manage your proprietary models, your access controls, or the local extensions your programmes depend on. This is the internal equivalent, holding external standards and your own work in one governed place.
- Import and track. Adopt ontologies from OLS, BioPortal, or a standards body and record which version you are on.
- Governed extensions. Local terms are separable from the imported standard, so upstream releases apply cleanly.
- Access control. Proprietary models are governed with the same rigour and restricted appropriately.
Version drift management
Know when an upstream release affects your work
External ontologies release on their own cadence. When one does, see which of your extensions, mappings, and annotated datasets reference concepts that changed, and decide deliberately rather than discovering it in a failed analysis.
- Upstream diffs. New, retired, and modified concepts in each external release.
- Affected extensions. Your local terms and mappings that depend on changed upstream concepts.
- Conflict detection. Where an internal term now collides with a new public concept.
Findable, described datasets
Make a dataset findable by what it is about
Dataset metadata is annotated against governed ontologies rather than filled in as free text, so a scientist can search by assay, target, modality, or study type and find work from another programme. Provenance travels with it.
- Ontology-annotated metadata. Datasets described using governed concepts, so search is precise.
- Provenance retained. Instrument, protocol, study, and processing history recorded and queryable.
- Standards support. Works with ISA-style study metadata and Allotrope instrument data descriptions.
Measurable FAIR maturity
Report on FAIR maturity with evidence
Because the metadata is governed data, maturity is a query. Report coverage by programme, identify which datasets lack ontology annotation, and show progress against a maturity framework with the underlying evidence rather than a self-assessment slide.
- Coverage reporting. Annotation and metadata completeness by programme, site, or data type.
- Gap lists that are actionable. Named datasets and named owners, not a percentage.
- Evidence on demand. The assessment and the data behind it are the same artefact.