This proposed session will guide researchers and developers through the practical steps of creating truly open, reproducible, and collaborative AI research repositories to make their work transparent, accessible, and reproducible for the entire community.

Participants will learn practical strategies for organizing experiments, code, datasets, and documentation in ways that maximize collaboration and trust. The main topics include structuring repositories for clarity, crafting effective README files, adding citation metadata for automatic referencing, selecting appropriate open licenses, and ensuring all steps and results can be easily reproduced by others.

Attendees will also explore the importance of FAIR (Findable, Accessible, Interoperable, Reusable) data practices and discover tools and templates to streamline the process. The real significance of this session is that, by adopting these approaches, researchers can ensure their work is not only open but also robust, verifiable, and ready to serve as a foundation for future research. This will help accelerate scientific discovery and foster a culture of shared progress in AI and beyond.