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lamindb

Manage FAIR biological datasets and track workflows via a Python API.

8|Updated Jan 13, 2026
Search Tags:#ontology#data management#data lineage#reproducibility#lamindb#biological data#FAIR data
One-click install
npx skills add https://github.com/hxk622/TokenDance --skill lamindb-hxk622
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: lamindb
Source: https://github.com/hxk622/TokenDance/tree/main/backend/app/skills/builtin/scientific/lab-automation/lamindb
Command: npx skills add https://github.com/hxk622/TokenDance --skill lamindb-hxk622

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenges of managing, tracking, and reproducing biological data by providing a unified framework for data queryability, lineage tracking, and FAIR compliance.

Core Features & Use Cases

  • Data Management: Organize and query diverse biological datasets (scRNA-seq, spatial, etc.).
  • Lineage Tracking: Automatically track computational workflows and data provenance.
  • Ontology Integration: Standardize metadata using biological ontologies (genes, cell types, etc.).
  • Use Case: A researcher can use this Skill to manage their scRNA-seq datasets, track the analysis pipeline from raw reads to processed counts, annotate cell types using standardized ontologies, and ensure all steps are reproducible.

Quick Start

Use the lamindb skill to import the cell type ontology and standardize cell type annotations in the 'my_data.h5ad' file.

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 for scRNA-seq analysis pipelines?▼

You can standardize biological metadata using the Bionty ontology integration, which maps cell types, genes, and other entities against established biological ontologies. This ensures your scRNA-seq and spatial transcriptomics datasets remain queryable and FAIR-compliant.

Can I query and manage spatial transcriptomics data using FAIR principles?▼

You can validate biological data against predefined schemas using the Python API. It enforces data validation and annotation to maintain queryability, traceability, and reproducibility across scRNA-seq, spatial transcriptomics, and flow cytometry datasets.

Does this framework support flow cytometry data management?▼

This approach differs from basic data storage by providing a unified framework that actively tracks computational workflows, integrates biological ontologies via Bionty, and enforces FAIR compliance. It ensures your biological data remains queryable, traceable, and reproducible.

How do I standardize cell type annotations using biological ontologies?▼

You can standardize cell type annotations by importing the cell type ontology through the Python API. It integrates biological ontologies via Bionty to map and validate metadata in files like 'my_data.h5ad'.

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