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Working with OME-Zarr NGFF

Instructors

• Matt McCormick (Kitware)

Tutorial Description

The Open Microscopy Environment (OME) organization has long guided the bioimaging community in the establishment of open standard formats such as OME-TIFF. Recently, the Zarr format emerged as an underlying format for multi-dimensional scientific arrays + metadata that is compatible with the modern, cloud-based computing ecosystem. Furthermore, this format addresses the scalability needs for large-scale microscopy volumes generated by state-of-the-art systems.

Neuroimaging community standards such as Microscopy-BIDS, and BRAIN Initiative archives, such as BIL and DANDI, are working on adoption of the format. A suite of visualization and analysis tools are evolving to support the format.

This tutorial provides a basic, working understanding the structure of OME-Zarr and explains how this relates to findable, accessible, interoperable, and reusable (FAIR) objectives.

Duration: 0.5 hours.

Learning Outcomes

1. Outcome 1. Understand the structure of the OME-Zarr format.
2. Outcome 2. Understand how an OME-Zarr image can provide the same interface but be stored in multiple different backend storage systems.
3. Outcome 3. Learn how to read pixel values and image metadata from an OME-Zarr volume.
4. Outcome 4. Learn how to visualize OME-Zarr volumes with web-based visualization tools.

Approach and Materials

• OME-Zarr Structure
• OME-Zarr Storage Backends
• Reading OME-Zarr Data
• Web-based OME-Zarr Visualization

For workshop attendees, reproduce in Code Ocean with this link.

Background and References

• OME-Zarr: a cloud-optimized bioimaging file format with international community support
• OME-NGFF: a next-generation file format for expanding bioimaging data-access strategies
• Microscopy-BIDS: An Extension to the Brain Imaging Data Structure for Microscopy Data
• Brain Imaging Archive (BIL)
• Distributed Archives for Neurophysiology Data Integration (DANDI)
• NEUBIAS OME-Zarr Training Modules

Licensing

This repository uses dual licensing:

• All source code is licensed under the Apache License 2.0
• Documentation, images, and other non-code assets are licensed under Creative Commons Attribution 4.0 International (CC BY 4.0)