In this talk, I will focus on an exceptional way of doing data-driven research employing networked community. Many examples of collaboration with the data-science community within competitions organised on Kaggle or Coda Lab platforms usually get limited by restrictions on those platforms. Common metrics do not necessarily correspond to the goal of the original research. Constraints imposed by the problem statement typically look artificial for ML-community. Preparing a perfect competition takes a considerable amount of efforts. On the contrary, research process requires a lot of flexibility and ability to look at the problem from different angles. I will describe the alternative research collaboration process can bridge the gap between domain-specific research and data science community. Particularly, it can involve academic researchers, younger practitioners and all enthusiasts who are willing to contribute. Such research process can be supported by an open computational platform that is will be described along with meaningful examples and discussed amongst the audience of the track.