lamindb

Manage, query, and track provenance for complex biological datasets.

1|Updated Mar 19, 2026

Search Tags:#ontology #reproducibility #workflow-integration #data-lineage #lamindb #biological-data #ontology-management

Ready to run

npx skills add https://github.com/JosephWoodall/noosphere --skill lamindb-josephwoodall

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: lamindb
Source: https://github.com/JosephWoodall/noosphere/tree/main/.agent/skills/lamindb
Command: npx skills add https://github.com/JosephWoodall/noosphere --skill lamindb-josephwoodall

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

LaminDB provides a unified framework for managing, querying, and tracing complex biological datasets, enabling reproducible analyses and provenance-aware workflows across experiments.

Core Features & Use Cases

Artifacts, Records, Runs, Transforms, and Features for end-to-end data lineage and provenance tracking.
Data management and querying across registries with feature-based search, streaming, and cross-registry traversal.
Ontology integration with the Bionty plugin for standardized annotation, validation, and cross-domain interoperability.
Integrations with machine learning platforms, workflow managers, and cloud/storage backends to build scalable data pipelines.

Quick Start

Track your first dataset with LaminDB by calling ln.track() at the start and saving an artifact to capture provenance.

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 experiments?▼

Track scRNA-seq data lineage by calling ln.track() before processing and saving outputs as artifacts. This captures runs, transforms, and features, ensuring end-to-end reproducibility for biological datasets across complex experimental workflows.

What is the best way to manage biological data ontologies for reproducibility?▼

Manage biological data ontologies using the Bionty plugin for standardized annotation and schema validation. It enables cross-domain interoperability and reproducibility by mapping complex experimental metadata to controlled vocabularies across multi-modal datasets.

How do I integrate biological data management with Nextflow or Snakemake workflows?▼

Integrate biological data management with Nextflow or Snakemake by tracking workflow runs as transforms and saving inputs and outputs as artifacts. This bridges pipeline execution with provenance tracking and cloud storage backends for scalable data analysis.

Does LaminDB support querying across multiple registries for spatial transcriptomics data?▼

LaminDB supports querying across multiple registries for spatial transcriptomics data. It provides feature-based search, streaming, and cross-registry traversal to manage and retrieve complex biological datasets efficiently.

Can I validate biological dataset schemas against standard ontologies?▼

You can validate biological dataset schemas against standard ontologies using the Bionty plugin. This enforces standardized annotation and cross-domain interoperability, ensuring complex experimental metadata conforms to controlled vocabulary definitions.

What limitations exist when streaming multi-modal biological data across cloud storage backends?▼

Streaming multi-modal biological data across cloud storage backends requires compatible local or cloud infrastructure configurations. While LaminDB supports streaming and cross-registry traversal, performance depends on the underlying storage backend and network bandwidth.