Open source software is an established practice in many industry domains, especially when it comes to platforms and tools. However, the value of open source is more than the code; the community is essential for the successful interplay between the proprietary and the open. This talk is based on research on governance of contributions and collaborations on open tools and platforms. Further, we look out beyond the code. What happens when system features are shaped through data in machine learning algorithms? Is there a case for open or shared data? What tools, licences, and governance does it take, and can privacy and integrity be protected in the open?