SciPy 2026

The problem. Modern bioimaging instruments produce datasets that are large, multidimensional, and stored in vendor-specific proprietary formats. These monolithic files are not cloud-ready, are difficult to stream or share, and hinder reproducible, collaborative science. The community needs an open, chunked, cloud-native format backed by robust, accessible tooling.

OME-Zarr and the community. OME-Zarr (OME-NGFF) addresses this need as a community-driven open standard built on Zarr's chunked, compressed, n-dimensional array storage. The specification and its ecosystem are described in Moore et al., "OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies," Nature Methods, 2021; Moore et al., "OME-Zarr: a cloud-optimized bioimaging file format with international community support," Histochemistry and Cell Biology, 2023; and Lüthi et al., "2024 OME-NGFF workflows hackathon," BioHackrXiv, 2025. ngff-zarr is developed within and for this community.

ngff-zarr features. ngff-zarr is a lean, minimal-dependency implementation that is lazy, parallel, and web-ready -- no local filesystem required. Its features include:

• A simple Python interface following a four-step pipeline: array to NgffImage to Multiscales to OME-Zarr store, accepting any Python Array API Standard input (NumPy, Dask, CuPy, PyTorch).
• Out-of-core multiscale generation via Dask for processing extremely large datasets that exceed available memory.
• Multiple downscaling methods: SIMD-accelerated Gaussian filtering via ITK-Wasm (default), bin shrink, label-image mode, and scipy-based fallbacks.
• OME-Zarr Zip (.ozx) single-file archives for easy sharing and archival (RFC-9).
• RFC-4 anatomical orientation metadata for medical and neuroimaging interoperability.
• High Content Screening (HCS) plate/well support, TIFF/OME-TIFF and Leica LIF conversion, Zarr v3 sharding, and a command-line interface for batch workflows.

Python usage. A typical conversion requires just a few lines:

import ngff_zarr as nz

image = nz.to_ngff_image(array, dims=["z", "y", "x"], scale={"z": 2.0, "y": 0.5, "x": 0.5})
multiscales = nz.to_multiscales(image, scale_factors=[2, 4], chunks=64)
nz.to_ngff_zarr("output.ome.zarr", multiscales)

Cloud stores (S3, GCS, Azure) are supported via fsspec, and the CLI (ngff-zarr -i input.nrrd -o output.ome.zarr) handles common batch workflows with memory-aware scheduling.

MCP server and lessons learned. The ngff-zarr-mcp package exposes conversion, validation, inspection, and optimization tools to AI coding agents via the Model Context Protocol (MCP). Researchers interact in natural language -- asking an AI assistant to convert a file, examine OME-Zarr metadata, validate spec compliance, or generate a batch processing script -- and the MCP server handles execution. Lessons learned include the importance of structured tool parameters for reliable agent interaction, designing functions that map to researcher intent rather than low-level API calls, and how natural language interfaces lower the barrier for scientists to adopt cloud-native formats and reproducible workflows.

Audience and takeaways. Attendees will learn how to convert and manage bioimaging data with ngff-zarr's Python API and CLI, understand the OME-Zarr ecosystem, and see how MCP servers can bring AI-assisted automation to scientific data workflows.

Source code: github.com/fideus-labs/ngff-zarr | Documentation: ngff-zarr.readthedocs.io