- ⸻ 2026-03-04
A data lakehouse for biology's sparse measurements
One avenue into the future of biotech is scaled learning from multi-modal datasets. Given that the union of these datasets can easily span millions of sparse features, they can’t be queried through any established data infrastructure. Warehouses are too rigid, data lakes can’t be queried, and tabular lakehouses don’t understand the formats. Biology needs a data lakehouse with support for bio-formats and registries.
- ⸻ 2026-03-02
Interactive visualization of multimodal and spatial data with Vitessce
The open-source tool Vitessce and Lamin now work together to manage & visualize multimodal and spatial single-cell data. It’s simple: define a Vitessce config in code, save it as an artifact, and share the interactive visualization along with your datasets on LaminHub.
- ⸻ 2026-02-27
Symbolic memory for biological R&D
What should the shared memory layer for agents and humans look like? Will it live in embeddings or in records? A high-level note.
- ⸻ 2024-04-03
MappedCollection: Weighted random sampling from large collections of scRNA-seq datasets
A few labs and companies now train models on large-scale scRNA-seq count matrices and related data modalities. But unlike for many other data types, there isn’t yet a playbook for data scales that don’t fit into memory.
- ⸻ 2022-08-29
nbproject: Manage Jupyter notebooks
nbproject is an open-source Python tool to help manage Jupyter notebooks with metadata, dependency, and integrity tracking. A draft-to-publish workflow creates more reproducible notebooks with context.
- ⸻ 2022-08-27
readfcs: Read FCS files
readfcs is a lightweight open-source Python package that loads data and metadata from Flow Cytometry Standard (FCS) files into DataFrame and AnnData objects, allowing users to flexibly use downstream analytical tools.
- ⸻ 2022-07-31
Key problems of data-heavy R&D
The complexity of modern R&D data often blocks realizing the scientific progress it promises.
- ⸻ 2022-05-04
Hello world!
We just launched lamin.ai as a place for sharing prototypes with our beta customers and collaborators. Over time, we’ll add public releases and use this blog to explain our work.