Track dataset versions, lineage, and provenance for FAIR biological data management.
npx skills add https://github.com/shushuzn/Rairos --skill lamindb-shushuzn
Please help me install this Agent Skill. Skill: lamindb Source: https://github.com/shushuzn/Rairos/tree/main/skills/lamindb Command: npx skills add https://github.com/shushuzn/Rairos --skill lamindb-shushuzn
LaminDB helps research teams turn biological datasets and analysis pipelines into queryable, traceable, and reproducible assets with FAIR-aligned metadata and lineage tracking.
Use the lamindb skill to curate and validate an scRNA-seq AnnData file by defining schemas and linking ontology terms, then save the validated artifact with preserved provenance.
High-intent search queries and answers about installing and using this skill.
Track single-cell data lineage by recording which inputs and code produced specific outputs across notebook or script workflows. This preserves provenance by keeping experiments, code, and datasets in sync for reproducible computational transforms.
Ontology-based annotation standardizes biological entities like genes, cell types, and tissues using controlled vocabularies. This schema validation reduces metadata inconsistency and makes curated datasets queryable across local or cloud storage.
Yes, curated artifacts can connect with workflow managers like Nextflow, Snakemake, and Redun, plus experiment tracking platforms such as W&B and MLflow. This integration keeps data, code, and experiments in sync.
Organize artifacts using scalable data lakehouse patterns to query biological datasets by metadata without repeatedly reloading raw files. This requires defining typed schemas and features to validate and standardize values across storage backends.
Validate AnnData files by defining typed schemas, linking ontology terms, and standardizing values via controlled vocabularies. This FAIR curation process ensures reliable querying and saves the artifact with preserved provenance.
Yes, defining typed schemas and features is required to validate and standardize values via ontologies before tracking dataset versions. This ensures metadata consistency for reliable querying across your data lakehouse workflows.
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