lamindb

Manage biological datasets with LaminDB for traceability and reproducibility.

21|1|Updated Mar 19, 2026
One-click install
npx skills add https://github.com/OwnLabAI/ownlab --skill lamindb-ownlabai
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: lamindb
Source: https://github.com/OwnLabAI/ownlab/tree/main/mart/skills/scientific-skills/lamindb
Command: npx skills add https://github.com/OwnLabAI/ownlab --skill lamindb-ownlabai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

LaminDB provides a unified framework to manage biological data with full traceability, reproducibility, and FAIR principles, solving the overhead of coordinating diverse datasets and analyses across projects.

Core Features & Use Cases

  • Artifact management, lineage tracking, and versioning across datasets and computational runs
  • Ontology-driven annotation and standardization using biological ontologies via Bionty
  • Workflow integrations with Nextflow, Snakemake, Redun, and MLOps platforms (W&B, MLflow) plus cloud storage
  • Use cases include building queryable data lakes, reproducible pipelines, and provenance-aware analyses in omics research

Quick Start

Install LaminDB, initialize a local instance, and run a minimal tracked workflow to create and annotate an artifact

Frequently Asked Questions about lamindb

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

FAQPage Schema
How do I track biological data lineage and ensure reproducibility for omics workflows?

You can track biological data lineage by managing datasets as artifacts with versioned records and transforms, ensuring full traceability and reproducibility across computational runs in omics workflows.

Can I integrate LaminDB with Nextflow, Snakemake, or MLOps platforms like W&B and MLflow?

Yes, LaminDB integrates with workflow orchestrators like Nextflow and Snakemake, alongside MLOps platforms including W&B and MLflow, enabling provenance-aware pipelines and tracked computational runs.

What is the best way to annotate scRNA-seq datasets using biological ontologies?

The best way to annotate scRNA-seq datasets is by using ontology-driven standardization via Bionty, which applies biological ontologies to manage and query features within your data lake.

Does LaminDB support managing spatial transcriptomics and other omics data in cloud storage?

Yes, LaminDB supports managing spatial transcriptomics and other omics data with cloud storage integrations, allowing you to build queryable data lakes with full provenance and FAIR compliance.

How do I version biological datasets and manage artifacts across different projects?

You can version biological datasets by registering them as artifacts with tracked records and runs, coordinating diverse data across projects while maintaining a unified, reproducible data management framework.

Why do I need ontology-driven annotation for bioinformatics data management?

Ontology-driven annotation is needed to standardize biological metadata and features, solving the overhead of coordinating diverse datasets by ensuring queryability and FAIR principles across projects.