lamindb

Coordinate biological data management with LaminDB for traceability and reproducibility.

22|4|Updated May 25, 2026
One-click install
npx skills add https://github.com/crazymsn/academic-skills --skill lamindb-crazymsn
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: lamindb
Source: https://github.com/crazymsn/academic-skills/tree/main/academic-skills/lamindb
Command: npx skills add https://github.com/crazymsn/academic-skills --skill lamindb-crazymsn

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

LaminDB enables researchers to organize, validate, and annotate large biological datasets with automatic lineage tracking and ontology integration, providing a single source of truth for reproducible analyses.

Core Features & Use Cases

  • Centralized artifact management and provenance tracking of experiments (Artifacts, Runs, Transforms)
  • Ontology-aware annotation and validation using public biological ontologies
  • Seamless integration with popular ML, workflow, and storage tools for scalable deployments
  • Use Case: curate scRNA-seq datasets with ontology-backed annotations and query across experiments

Quick Start

Track a new analysis, load an annotated artifact, and query lineage with LaminDB to begin reproducible research.

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 for reproducible scRNA-seq analysis?

Track biological data lineage for scRNA-seq analysis by managing artifacts, runs, and transforms with LaminDB. It provides automatic provenance tracking to maintain a single source of truth for reproducible research across experiments.

What is the best way to validate biological data schemas using public ontologies?

The best way to validate biological data schemas is through ontology-aware annotation using public biological ontologies. LaminDB integrates these ontologies to validate and annotate large datasets, ensuring consistency and accuracy across experiments.

Can I use LaminDB data management for multi-storage deployment from local to cloud?

Yes, you can use LaminDB data management for multi-storage deployment from local to cloud. It seamlessly integrates with popular storage tools to enable scalable deployments and flexible data configuration across environments.

Does LaminDB workflow integration support connecting with ML and workflow tools?

Yes, LaminDB workflow integration supports connecting with popular ML and workflow tools. This integration allows researchers to coordinate end-to-end biological data management within their existing scalable analysis pipelines.

How do I curate scRNA-seq datasets with ontology-backed annotations?

Curate scRNA-seq datasets with ontology-backed annotations by applying public biological ontologies through LaminDB. This process validates data artifacts and enables researchers to query across experiments with traceable lineage.

Why do I need data lineage tracking for biological data management?

You need data lineage tracking for biological data management to ensure traceability and reproducibility. LaminDB automatically tracks experiments, transforms, and artifacts, preventing lost provenance and maintaining a reliable analysis history.