Data sharing is a long-established practice. There are several general-purpose cloud platforms (google, dropbox, etc.) as well as more specialized sharing services such as, for example, those provided by xenodo, OSF (open science foundation) which focus on scientific data sharing. Finally, there are also sharing platforms that specialize in building applications (for example the Odyssee- Mure database in the EU, the ‘building datasets’ from the Department of Energy in the USA). The innovative points of the platform presented in this paper are its focus on open, real-time, dataset sharing, as well its ability to also expand over to building-related app sharing. The purpose of real-time data sharing would be multifold and could create value for both the research as well as the business community. For example, it could empower data-intensive and driven applications developed by third parties. It could also provide for factual and data-driven validation of new building-related products and services as well as data-driven benchmarking services. In this paper, we discuss the types of real-time data shared, the compliance to standards and established approaches (e.g. Brick, Haystack), the implementation of pull and push sharing APIs, the supported sharing policies as well as important conditioning, privacy, and data quality issues. We also describe the provisions made for extending this environment to a collaborative platform where just as data, apps can also be shared along with well-defined policies and sharing options. A specific use case related to XAI (explainable artificial intelligence ) based demand forecasting will be presented and discussed in order to highlight exactly how data and application sharing features interplay and integrate. The work has been performed in the framework of the TRUST AI Horizon 2020/ FET project (2020- 2024).