lamindb

# LaminDB

## Overview

LaminDB is an open-source, lineage-native lakehouse for biology. It makes datasets and models queryable, traceable, validated, reproducible, and FAIR (Findable, Accessible, Interoperable, Reusable) while storing data in open formats across local filesystems, S3, GCS, Hugging Face, SQLite, and Postgres.

**Core Value Proposition:**
- **Queryability**: Search and filter artifacts, records, runs, features, schemas, and collections
- **Traceability**: Track inputs, outputs, parameters, source code, and environments for notebooks, scripts, functions, and pipelines
- **Validation**: Curate DataFrame, AnnData, SpatialData, TileDB-SOMA, Parquet, Zarr, and other biological formats with schemas
- **FAIR Compliance**: Standardize annotations with Bionty-backed ontologies and custom registries
- **Change management**: Organize work with projects, branches, spaces, collections, and saved notes or plans

## 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, functions, shell scripts, and 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, Hugging Face, Lightning, 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 & ULabels**: Experimental entities, typed records, and simple labels
- **Collections**: Versioned, immutable sets of artifacts
- **Runs & Transforms**: Computational lineage tracking (what code produced what data)
- **Features**: Typed metadata fields for annotation and querying
- **Projects, Branches & Spaces**: Project grouping, change management, and access boundaries

**Key workflows:**
- Create and version artifacts from files or Python objects
- Track notebook/script execution with `ln.track()` and `ln.finish()`
- Track function workflows with `@ln.flow()` and `@ln.step()`
- Annotate artifacts with records, ulabels, projects, and typed features
- Visualize data lineage graphs with `artifact.view_lineage()`
- Query by provenance (find all outputs from specific code/inputs)

**Reference:** `references/core-concepts.md` - Read this for detailed information on artifacts, records, runs, transforms, features, versioning, and lineage tracking.

### 2. Data Management and Querying

**Query capabilities:**
- Registry exploration and lookup with auto-complete
- Single record retrieval with `get()`, `one()`, `one_or_none()`
- Filtering with comparison operators (`__gt`, `__lte`, `__contains`, `__startswith`)
- Feature-based queries, including expression-style queries with `Feature` objects
- Cross-registry traversal with double-underscore syntax
- Full-text search across registries
- Advanced logical queries with `ln.Q` objects (AND, OR, NOT)
- Streaming large datasets without loading into memory

**Key workflows:**
- Browse artifacts with filters and ordering
- Query by features, creation date, creator, size, etc.
- Stream large files in chunks or with array slicing
- Organize data with hierarchical keys
- Group artifacts into collections

**Reference:** `references/data-management.md` - Read this for comprehensive query patterns, filtering examples, streaming strategies, and data organization best practices.

### 3. Annotation and Validation

**Curation process:**
1. **Validation**: Confirm datasets match desired schemas
2. **Standardization**: Fix typos, map synonyms to canonical terms
3. **Annotation**: Link datasets to metadata entities for queryability

**Schema types:**
- **Flexible schemas**: Validate only known columns, allow additional metadata
- **Minimal required schemas**: Specify essential columns, permit extras
- **Strict schemas**: Complete control over structure and values

**Supported data types:**
- DataFrames (Parquet, CSV)
- AnnData (single-cell genomics)
- MuData (multi-modal)
- SpatialData (spatial transcriptomics)
- TileDB-SOMA (scalable arrays)

**Key workflows:**
- Define features and schemas for data validation
- Use `DataFrameCurator`, `AnnDataCurator`, `SpatialDataCurator`, or `TiledbsomaExperimentCurator` for validation
- Standardize values with `.cat.standardize()`
- Map to ontologies with `.cat.add_ontology()`
- Save curated artifacts with schema linkage
- Query validated datasets by features

**Reference:** `references/annotation-validation.md` - Read this for detailed curation workflows, schema design patterns, handling validation errors, and best practices.

### 4. Biological Ontologies

**Available ontologies (via Bionty):**
- Genes (Ensembl), Proteins (UniProt)
- Cell types (CL), Cell lines (CLO)
- Tissues (Uberon), Diseases (Mondo, DOID)
- Phenotypes (HPO), Pathways (GO)
- Experimental factors (EFO), Developmental stages
- Organisms (NCBItaxon), Drugs (DrugBank)

**Key workflows:**
- Import public ontologies with `bt.CellType.import_source()`
- Search ontologies with keyword or exact matching
- Standardize terms using synonym mapping
- Explore hierarchical relationships (parents, children, ancestors)
- Validate data against ontology terms
- Annotate datasets with ontology records
- Create custom t