lamindb

To track biological data lineage for reproducible workflows, this Skill automatically captures dataset provenance from raw files to final results. It provides a unified framework ensuring data remains queryable, traceable, and FAIR compliant throughout complex research pipelines.

FAIR biological data management is a framework making datasets Findable, Accessible, Interoperable, and Reusable. You need it for genomics data to solve fragmentation issues, standardize metadata using biological ontologies, and ensure long-term reproducibility of multi-modal data curation.

To curate single-cell RNA-seq data and validate cell type annotations, you can use this Skill to standardize metadata against public biological ontologies like Cell Ontology. It integrates with scRNA-seq analysis workflows to track the entire analysis pipeline.

Yes, this data management Skill supports building a queryable lakehouse using pandas and anndata. It enables complex filtering and searching across large-scale biological datasets and metadata while maintaining automatic lineage tracking.

To integrate biological ontologies like Ensembl and UniProt for metadata standardization, this Skill uses the bionty dependency. It standardizes research metadata against public ontologies to validate annotations and maintain FAIR compliance across curated datasets.