lamindb

Track data provenance for biological workflows by defining Artifacts, Runs, and Transforms to capture lineage. This approach uses guardrails like versioning and track/finish commands to ensure reproducible, auditable pipelines across local and cloud storage.

Ontology-enabled annotation for reproducible bioinformatics applies standardized schemas and ontologies to biological metadata. This mechanism validates features against defined ontologies, ensuring consistent data curation and standardized lineage tracking across pipelines.

You can integrate data provenance tracking across notebooks, pipelines, and storage backends like cloud storage. This capability captures end-to-end lineage and tracks artifacts seamlessly across both local and cloud environments for reproducible science.

To validate schemas and save curated biological datasets, start a session, load your artifacts, and apply ontology-driven schema validation. Finish by saving the curated outputs to maintain strict lineage tracking and data provenance.

Yes, you need to define transforms and runs to ensure auditable biological pipelines. These definitions capture end-to-end data lineage, linking specific transformations to their input and output artifacts for full provenance tracking.