lamindb

name	lamindb
description	This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
license	Apache-2.0 license
metadata	{"skill-author":"K-Dense Inc."}
verified	false
lastVerifiedAt	"2026-02-19T05:29:09.098Z"
source	builtin
trust_score	100
provenance_sha	27918a98a3bfde90

LaminDB

Overview

LaminDB is an open-source data framework for biology designed to make data queryable, traceable, reproducible, and FAIR (Findable, Accessible, Interoperable, Reusable). It provides a unified platform that combines lakehouse architecture, lineage tracking, feature stores, biological ontologies, LIMS (Laboratory Information Management System), and ELN (Electronic Lab Notebook) capabilities through a single Python API.

Core Value Proposition:

• Queryability: Search and filter datasets by metadata, features, and ontology terms
• Traceability: Automatic lineage tracking from raw data through analysis to results
• Reproducibility: Version control for data, code, and environment
• FAIR Compliance: Standardized annotations using biological ontologies

When to Use This Skill

Use this skill when:

• Managing biological datasets: scRNA-seq, bulk RNA-seq, spatial transcriptomics, flow cytometry, multi-modal data, EHR data
• Tracking computational workflows: Notebooks, scripts, pipeline execution (Nextflow, Snakemake, Redun)
• Curating and validating data: Schema validation, standardization, ontology-based annotation
• Working with biological ontologies: Genes, proteins, cell types, tissues, diseases, pathways (via Bionty)
• Building data lakehouses: Unified query interface across multiple datasets
• Ensuring reproducibility: Automatic versioning, lineage tracking, environment capture
• Integrating ML pipelines: Connecting with Weights & Biases, MLflow, HuggingFace, scVI-tools
• Deploying data infrastructure: Setting up local or cloud-based data management systems
• Collaborating on datasets: Sharing curated, annotated data with standardized metadata

Core Capabilities

LaminDB provides six interconnected capability areas, each documented in detail in the references folder.

1. Core Concepts and Data Lineage

Core entities:

• Artifacts: Versioned datasets (DataFrame, AnnData, Parquet, Zarr, etc.)
• Records: Experimental entities (samples, perturbations, instruments)
• Runs & Transforms: Computational lineage tracking (what code produced what data)
• Features: Typed metadata fields for annotation and querying