Neurophysiology research heavily relies on acquiring and analyzing complex, multimodal, and increasingly large datasets. The Neurodata Without Borders (NWB) format and Distributed Archives for Neurophysiology Data Integration (DANDI) platform play a pivotal role in organizing, sharing, and archiving these datasets. NWB standardizes data storage by integrating contextual metadata with primary data, facilitating comprehensive long-term data interpretability. It supports various experimental modalities within a single file format, promoting robust reanalysis and interoperability. DANDI enhances collaboration by hosting NWB datasets, supporting NIH data sharing requirements, and providing free data archival and access to advanced visualization tools.Despite their benefits, the learning curve associated with understanding NWB specification and the technical demands of using the associated software APIs (PyNWB and MatNWB) poses significant barriers to the adoption of the NWB standard. Furthermore, converting historical data to NWB format is a labor-intensive process that discourages widespread adoption due to the sheer volume and diversity of legacy datasets.To address these challenges, we developed a sophisticated software suite to ease NWB adoption. This suite includes NeuroConv, which facilitates data conversion to NWB format; NWB Inspector, a Python tool verifying NWB file validity; and NWB GUIDE, an interactive application guiding users through data conversion without programming requirements. These tools aim to improve data integrity, streamline conversion processes, and make NWB adoption more accessible to neurophysiologists regardless of their coding expertise. By simplifying the adoption process, our software suite seeks to encourage widespread use of NWB standards, enhancing data sharing, collaboration, and the advancement of neurophysiological research.