These illustrations were contracted by Zarr, a NumFOCUS sponsored project, from Henning Falk and are free to re-use under a CC-BY license.
Please include an attribution similar to: "Adam uploads" by Henning Falk, ©2022 NumFOCUS, is used under a CC BY 4.0 license. Modifications to this photo include cropping.
Falk, H. zarr-developers/zarr-illustrations-falk-2022 | Zenodo [WWW Document], 2022. URL https://doi.org/10.5281/zenodo.7037367 (accessed 8.31.22).
- Adam uploads
- Bea computes
- Clara shares
- FAIR re-use
- Kit's deluge
- Monolithic vs. chunked
- Multiple clients
- Navid zooms
- Qui downloads
Adam staring at his screen waiting for the upload to complete.
Bea running processing jobs from her laptop on a high-performance compute cluster.
Clara transporting physical media to share her dataset with collaborators.
FAIR sharing of data is beneficial for both data producers and consumers. Consumers gain access to interesting datasets that would otherwise be out of reach. Producers get citations to their work, when consumers publish their derivative work. OME-Zarr is the technology basis for enabling effective FAIR sharing of large image datasets.
Kit talking to their therapist about data handling nightmares.
Technical differences between monolithic and chunked file formats.
Several visualization tools already support OME-Zarr, including Fiji/Bigdataviewer/MoBIE, webKnossos and Neuroglancer. With OME-Zarr a selection of software tools and devices are able to access the same datasets from centralized storage.
Navid quickly zooming through a large dataset on his computer.
Qui talking about his experiences working with published data.
