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magland committed Feb 29, 2024
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The main technical challenge in developing Neurosift was the requirement to lazy-load data objects from remote NWB files, which are built on the complex HDF5 format. While HDF5's efficient data organization is ideal for the large, multidimensional datasets typical in neurophysiology, its primary implementations are in the C language. This necessitates a creative solution to enable efficient web-based access to these files. To bridge this gap, Neurosift leverages WebAssembly to run compiled C code in the browser, specifically utilizing a modified version of the h5wasm [@h5wasm] library. Unlike the original h5wasm, which was built primarily to handle fully downloaded files, Neurosift's fork introduces an innovative approach to efficiently read data chunks from remote files. This allows for synchronous data reads without the need for a prior download of the entire file. This solution not only makes Neurosift a powerful tool for neuroscience research but also showcases the potential of WebAssembly in overcoming challenges associated with web-based data analysis tools.

In addition to HDF5-backed NWB files, Neurosift also supports NWB datasets stored using [Zarr][https://zarr.dev], which is a more efficient, cloud-friendly format, but which can be less convenient to manage since NWB-Zarr datasets can contain thousands of individual files in contrast to a single HDF5 file.

# Conclusion

Neurosift makes neurophysiology data more accessible to scientists. By facilitating the exploration of complex datasets directly within a browser, it lowers the barrier to entry for data analysis and fosters collaborative research efforts. Looking forward, there is potential for Neurosift to expand its capabilities, with enhanced visualizations and support for additional data types.
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