As researchers, we make complex choices and decisions within our research teams throughout the lifecycle of our projects. We are expected to ensure that each individual in the context of team science shares responsibility for making our research objects are easily accessed, openly examined and built upon by others in future work. Although open research and transparent reporting help make sure that scientific work can be trusted, we should also establish ways to integrate considerations of the societal and ethical implications of our work – especially when these considerations impact people's lives. Furthermore, reproducibility alongside open research practices is important for enabling independent verification of research methods, underlying data, analysis code and workflows. All these require that everyone in the research team understands what their responsibilities are, where they can learn about research best practices and acquire skills that are often not widely taught or explored among academic researchers. In this talk, we introduce The Turing Way - an open source, open collaboration and community-driven guide to reproducible, ethical and inclusive data science and research. Drawing insights from ~250 chapters across 5 guides of this handbook, we highlight best practices that researchers should integrate to advance the ethos of team science while ensuring the highest reproducible and ethical standards in their work. Finally, we demonstrate the guides to help researchers, PhD students, postdocs, people in research infrastructure roles and other stakeholders in research identify what responsibility for reproducibility they can take at different stages of development in team science. We will understand the many dimensions of openness and how they can participate in an inclusive, kind and inspiring open source ecosystem as they collaboratively seek to improve research culture. All questions and contributions are welcome at the GitHub repository: https://github.com/alan-turing-institute/the-turing-way.