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.