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lamindb

Organize biological datasets with provenance and FAIR metadata using LaminDB artifacts.

21|2|Updated Dec 8, 2025
Search Tags:#bioinformatics#lineage tracking#schema validation#reproducible research#data curation#lamindb#ontology annotation
One-click install
npx skills add https://github.com/silverstein/claude-scientific-skills-desktop --skill lamindb-silverstein
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: lamindb
Source: https://github.com/silverstein/claude-scientific-skills-desktop/tree/main/corpus/lamindb
Command: npx skills add https://github.com/silverstein/claude-scientific-skills-desktop --skill lamindb-silverstein

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

LaminDB helps you manage biological datasets so they become queryable, traceable, reproducible, and FAIR by capturing lineage from inputs to outputs and enforcing structured metadata through typed features and schema validation.

Core Features & Use Cases

  • Data lineage tracking: Record computational workflows (runs and transforms) so results can be traced back to inputs, code, parameters, and environments.
  • Queryable artifacts with typed metadata: Store datasets as versioned artifacts and annotate them with typed features for efficient filtering and discovery.
  • Schema validation & ontology-based curation: Validate datasets against flexible/minimal/strict schemas and standardize annotations using biological ontologies (e.g., via bionty).
  • Common use cases: scRNA-seq and spatial transcriptomics curation, data lakehouse-style querying across experiments, and integration with workflow managers (Nextflow/Snakemake) and MLOps tools (W&B/MLflow).

Quick Start

Use LaminDB to validate and ontology-standardize your dataset by curating your DataFrame with a schema and saving it as a linked, versioned artifact.

Frequently Asked Questions about lamindb

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I track data lineage and provenance for scRNA-seq workflows?▼

Data lineage tracking for scRNA-seq workflows is handled by recording computational runs and transforms, linking outputs back to inputs, code, and parameters to ensure results are fully traceable and reproducible.

What is schema validation for biological datasets and how does it enforce FAIR metadata?▼

Schema validation for biological datasets enforces FAIR metadata by checking DataFrames against flexible or strict schemas, standardizing ontology annotations, and saving curated datasets as queryable, versioned artifacts.

Can I integrate LaminDB with existing workflow managers and MLOps tools?▼

LaminDB integrates with workflow managers like Nextflow and Snakemake, as well as MLOps tools including W&B and MLflow, enabling lineage-aware tracking across pipeline runs and machine learning transforms.

How do I ontology-annotate and curate spatial transcriptomics data?▼

Ontology annotation for spatial transcriptomics data is performed by validating datasets against defined schemas and standardizing biological annotations using bionty, saving results as versioned artifacts with full lineage links.

Do I need predefined schema and feature definitions to use LaminDB for data curation?▼

Yes, LaminDB requires schema and feature definitions upfront to validate datasets, enforce typed metadata, and ensure biological data is standardized, queryable, and traceable across experimental runs.

What is the best way to make flow cytometry datasets queryable and traceable?▼

Make flow cytometry datasets queryable and traceable by storing them as versioned artifacts, annotating with typed features for filtering, and capturing lineage links between data transforms and pipeline runs.

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