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lamindb

Manage biological data assets with lineage tracking in Python.

33.0k|3.2k|Updated Oct 19, 2025
Search Tags:#ontology#bioinformatics#artifact#data-management#scRNA-seq#lamindb#lineage-tracking
One-click install
npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill lamindb-k-dense-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: lamindb
Source: https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/scientific-skills/lamindb
Command: npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill lamindb-k-dense-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

LaminDB provides a Python-based framework to manage biological data assets with full data lineage, provenance tracking, and FAIR metadata, enabling researchers to organize, validate, and reproduce analyses across multi-omics workflows.

Core Features & Use Cases

  • Core concepts and data lineage (Artifacts, Records, Runs, Transforms) for traceable research.
  • Data management, querying, validation, and ontology-driven annotation to enable FAIR data across scRNA-seq, spatial transcriptomics, proteomics, and clinical data.
  • Integrations with workflow managers and MLOps platforms (Nextflow, Snakemake, Weights & Biases, MLflow) plus deployment strategies for local and cloud environments.

Quick Start

Install LaminDB, initialize your instance, and start tracking data lineage.

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 in Python?▼

You can track scRNA-seq data lineage using a Python framework that captures artifacts, records, runs, and transforms. This ensures full provenance and reproducibility across biological workflows.

What is ontology-driven annotation for biological data management?▼

Ontology-driven annotation applies standardized vocabularies to biological data assets, enabling FAIR metadata. It allows researchers to query, validate, and organize multi-omics datasets consistently.

How do I make biological data queryable and reproducible across multi-omics pipelines?▼

To make biological data queryable and reproducible, use a framework that supports schema validation, versioning, and end-to-end lineage capture across spatial omics, proteomics, and clinical data.

Does this data management framework work with existing MLOps and workflow tools?▼

Yes, this biological data framework integrates with workflow managers and MLOps platforms like Nextflow, Snakemake, Weights & Biases, and MLflow for seamless pipeline tracking.

Can I use this lineage tracking system for both local and cloud environments?▼

Yes, this data lineage tracking system supports deployment strategies for both local and cloud environments, ensuring traceable research and interoperability across different infrastructures.

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