A browser-based visualization framework for exploring and analyzing Zarr and NetCDF datasets.

Browzarr is a powerful, browser-native framework for visualizing, exploring and analyzing Zarr and NetCDF datasets.
Load multi-dimensional datasets directly in the browser, interactively slice and inspect chunks, and gain insights without any backend or server setup.

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Usage 🚀

🌐 Online

Visit browzarr.io - no installation required.

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📦 npm (Offline)

Install once, run anytime - no internet required after install:

npm install -g browzarr

Then launch:

browzarr

On a custom port:

browzarr --port 8080
# or via environment variable (macOS/Linux)
PORT=8080 browzarr
# Windows PowerShell
$env:PORT=8080; browzarr
# Windows CMD
set "PORT=8080" && browzarr

By default, Browzarr starts on port 3000. If that port is already in use, it will automatically try the next available one.

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Julia - Browzarr.jl (Offline)

Installation

using Pkg; Pkg.add("Browzarr")

Usage

Launch with a local file, a remote store, or no arguments to get started:

using Browzarr
# default (any available port)
browzarr()
# custom port
browzarr(; port=3000)
# local NetCDF file
browzarr(; store="/absolute/path/to/file.nc")
# local Zarr directory
browzarr(; store="/absolute/path/to/zarr_file.zarr")
# remote Zarr store
browzarr(; store="https://s3.bucket.de:67/misc/out.zarr")

Tip

💡 Have suggestions or feature requests? Feel free to open an issue or submit a PR!

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Stay tuned - major updates and new visualization features are on the way! 🚀

Version 1.0.0 - Progress/Features

We are still in active development but plan to release a stable 1.0.0 when all of the desired features are in place and a final stability and bug pass has been completed

• Temporal and spatial slicing
• Larger texture support
• NetCDF Support
• Customizable export
• Increase flexibility for personal datasets
• Animation export
• Docs/Manual
• Stability pass

Acknowledgments & Funding

• This project has received funding from the European Union's Horizon Europe research and innovation programme under grant agreement no. 101137682 (AI4PEX – Artificial Intelligence and Machine Learning for Enhanced Representation of Processes and Extremes in Earth System Models).

• This work builds on developments from the European Space Agency (ESA) SeasFire project, funded under the ESA Future EO-1 Science for Society Call, and supported by ESA’s Network of Resources Initiative. Browzarr is not currently funded under this programme.

Funded by the European Union. The views expressed are those of the authors and do not necessarily reflect those of the European Union, the European Research Executive Agency or the European Space Agency. Neither granting authority can be held responsible for them.