NeuroConv: Streamlining Neurophysiology Data Conversion to the NWB Standard

Modern neurophysiology generates increasingly complex, multimodal datasets that require standardized formats for effective sharing and reuse. The Neurodata Without Borders (NWB) format has emerged as a solution for data standardization, but data conversion remains a significant bottleneck due to format heterogeneity, metadata complexity, and required technical expertise. We present NeuroConv, an open-source Python library that automates the conversion of neurophysiology data from 47 distinct formats into NWB through a unified, modular architecture. Developed through collaboration with over 50 neurophysiology laboratories, NeuroConv addresses key challenges through three core components: format-specific DataInterfaces that abstract parsing complexity, multi-stream Converters that integrate heterogeneous data modalities, and optimized writing strategies for large-scale datasets including chunked operations and cloud-compatible storage. NeuroConv's design enables researchers to convert complex, multi-modal experimental sessions with minimal code while preserving critical metadata and temporal alignment across recording systems. By removing technical barriers NeuroConv thus advances the transformation of neurophysiology toward FAIR (Findable, Accessible, Interoperable, and Reusable) data practices, facilitating reproducible research and accelerating scientific discovery.