Add support to LaminDB

docs/scientific-packages.md

@@ -12,6 +12,9 @@
 - **Scanpy** - Single-cell RNA-seq analysis with clustering, marker genes, and UMAP/t-SNE visualization
 - **scvi-tools** - Probabilistic deep learning models for single-cell omics analysis. PyTorch-based framework providing variational autoencoders (VAEs) for dimensionality reduction, batch correction, differential expression, and data integration across modalities. Includes 25+ models: scVI/scANVI (RNA-seq integration and cell type annotation), totalVI (CITE-seq protein+RNA), MultiVI (multiome RNA+ATAC integration), PeakVI (ATAC-seq analysis), DestVI/Stereoscope/Tangram (spatial transcriptomics deconvolution), MethylVI (methylation), CytoVI (flow/mass cytometry), VeloVI (RNA velocity), contrastiveVI (perturbation studies), and Solo (doublet detection). Supports seamless integration with Scanpy/AnnData ecosystem, GPU acceleration, reference mapping (scArches), and probabilistic differential expression with uncertainty quantification
 
+## Data Management & Infrastructure
+- **LaminDB** - Open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR (Findable, Accessible, Interoperable, Reusable). Provides unified platform combining lakehouse architecture, lineage tracking, feature stores, biological ontologies (via Bionty plugin with 20+ ontologies: genes, proteins, cell types, tissues, diseases, pathways), LIMS, and ELN capabilities through a single Python API. Key features include: automatic data lineage tracking (code, inputs, outputs, environment), versioned artifacts (DataFrame, AnnData, SpatialData, Parquet, Zarr), schema validation and data curation with standardization/synonym mapping, queryable metadata with feature-based filtering, cross-registry traversal, and streaming for large datasets. Supports integrations with workflow managers (Nextflow, Snakemake, Redun), MLOps platforms (Weights & Biases, MLflow, HuggingFace, scVI-tools), cloud storage (S3, GCS, S3-compatible), array stores (TileDB-SOMA, DuckDB), and visualization (Vitessce). Deployment options: local SQLite, cloud storage with SQLite, or cloud storage with PostgreSQL for production. Use cases: scRNA-seq standardization and analysis, flow cytometry/spatial data management, multi-modal dataset integration, computational workflow tracking with reproducibility, biological ontology-based annotation, data lakehouse construction for unified queries, ML pipeline integration with experiment tracking, and FAIR-compliant dataset publishing
+
 ## Cheminformatics & Drug Discovery
 - **Datamol** - Molecular manipulation and featurization with enhanced RDKit workflows
 - **DeepChem** - Molecular machine learning, graph neural networks, and MoleculeNet benchmarks