AqNWB - Enabling data acquisition in the Neurodata Without Borders data standard

About This Grant

PROJECT SUMMARY The goal of this project is to enhance the sustainability and impact of AqNWB, an open C/C++ API for enabling direct data acquisition in the Neurodata Without Borders (NWB) data standard. With the increasing adoption of NWB as the leading format for sharing neurophysiology data, there is a pressing need to support its use throughout the entire data lifecycle—from acquisition and processing to analysis, publication, and reuse. Currently, researchers typically acquire data in system-specific formats and convert their data to NWB later for analysis and sharing, a process which is inefficient and error-prone. AqNWB aims to address this challenge by allowing direct data acquisition in NWB, thus streamlining laboratory practices and promoting FAIR data principles. This proposal will enable us to ensure AqNWB’s readiness for production use (Aim1) and to promote its adoption through integration with leading neurophysiology data acquisition software (Aim 2). Specifically, Aim 1 focuses on enhancing production readiness of AqNWB by improving reliability through enhanced testing workflows, simplifying installation via standardized deployment processes, and increasing usability with better documentation and tutorials. Additionally, Aim 1 will enhance support for multi-modal neurophysiology data acquisition by enhancing AqNWB to support optical physiology and behavioral NWB data types. Aim 2 aims to facilitate the integration of AqNWB with leading neurophysiology data acquisition systems to promote broader adoption. This work will involve the development of advanced I/O features for large-scale data acquisition, including customization of chunking and compression settings and integration of Zarr as a new storage backend to enhance parallel write and cloud support. AqNWB will be integrated with OpenEphys in the first year, and further integration targets (e.g., SpikeGadgets, ScanImage, and BPOD) will be prioritized in the second year. By leveraging best practices in open-source software development and collaborations with leading data acquisition systems, this project will enhance the sustainability and impact of AqNWB as a valuable tool for neuroscience research and promote the widespread adoption of NWB. Beyond the innovations in the AqNWB software, this project will remove the need for post-acquisition data conversion, thus streamlining laboratory data management practices and promoting FAIR data practices by enabling use of NWB throughout the data lifecycle.