Matt McCormick
I am a research software engineer who helps scientists perform computational image analysis for reproducible research.
Session
Bioimaging generates massive datasets in fragmented, proprietary formats that are difficult to share and align with FAIR principles. ngff-zarr is a lightweight, open-source Python toolkit implementing the OME-Zarr specification -- the community-driven, cloud-native bioimaging standard. With minimal dependencies and a simple pipeline interface, ngff-zarr converts, validates, and generates multiscale representations of extremely large images out-of-core via Dask. Features include multiple downscaling methods, OME-Zarr Zip archives (.ozx), RFC-4 anatomical orientation, and High Content Screening support. This talk also covers ngff-zarr's Model Context Protocol (MCP) server, which enables AI agents to perform bioimaging tasks through natural language, and lessons learned from its deployment at EMBL.