‘Open Science’ is a current movement to make scientific research and its dissemination accessible to all and to share scientific data across laboratories. Despite of the recent technological development allowing fast networking and data sharing, there are still large disparities in the current state-of-the art of data- and resource-sharing in various academic fields. In particular, several barriers hinder currently electrophysiological data sharing: multiple, incompatible or incomplete data formats (proprietary or many custom-made); a tradition of sharing raw data only for the purpose of a well-defined collaboration. Here, we propose to seed a data-sharing effort in the research community studying the link between neuronal activity and behavior. Starting among the high-impact but relatively small community working on songbirds as an animal model, we will develop a public database platform and open-source toolbox for data analysis initially consisting in recordings from a few labs (including on-going collaborations) and growing as more labs join in. The data will first be exported and stored in a common open data format that includes all meta-data necessary for the interpretation of neuronal and behavioral recordings. We will build an open-source song-sorting and neural analysis toolbox with a proper graphical user interface for the investigation of the relationship between neuronal activity and singing behavior. The promotion of the platform will first be launched in the songbird-related research community and will progressively include large data sets of neural recordings during song. With datasets from multiple labs meta-analysis aiming at testing current hypotheses in the field and to reproduce important previously published results will become possible. We will publish results from these meta-analyses as reproducible articles (see https://repro.elifesciences.org/example.html#) based on a shared database and open-source and publicly-available analysis tools. Ultimately, we propose to open the platform to other research communities investigating the link between neuronal activity and behavior in other experimental contexts, and to develop the analysis tools accordingly. We believe that seeding data-sharing in small communities around tangible collaboration opportunities will accelerate the maturation of open science in our discipline and help structuring the tools for reuse, interoperability, meta-analysis and openness of research data.