DANDI is a cellular neurophysiology data archive and collaboration hub built to support multiscale, multispecies, and multimodal neuroscience research. The data and resources related to DANDI support theoretical neuroscience, drive biological applications, and help develop new analytic tools through standardized annotation and dissemination. The DANDI infrastructure is built on open-source technologies and in the cloud, to support dissemination, search, visualization, computation, collaboration, and coordination in neurophysiology research projects to promote FAIRness and efficiency. DANDI currently contains over 370TB of data from over 172 datasets, across 6 species, and multiple recording modalities including electrophysiology, optophysiology, optogenetic, and behavioral experiments, as well as multimodal MRI, OCT and immunostaining data from human ex vivo brain tissue samples. DANDI is a Web platform that provides data storage for the purposes of collaboration and dissemination of neurophysiology data with an option for embargoed/private access, easy to use tools for data submission and access, a Jupyter-based computation hub to introspect data in the archive, and integration with other archives and analytic platforms. DANDI is addressing challenges in standardization, data transfer, storage and access, and the culture of sharing through a community of practice. DANDI uses and participates in development of the BRAIN Initiative community data standards such as NWB and BIDS, and data formats such as Zarr and NGFF. DANDI provides an application programming interface server to attract software developers to interact with the archive programmatically and has also contributed to community efforts to provide efficient access to large data stored in the cloud. With DANDI, researchers can now validate, share, collaborate on, and publish citable datasets, and thereby increase rigor and reproducibility of cellular neurophysiology research. Work is ongoing to establish versatile search and further improve usability and efficiency of data deposition and access.