Biomedical research becomes increasingly interdisciplinary and collaborative in nature. Researchers need to effectively collaborate and make decisions by meaningfully assembling, mining and analyzing available large-scale volumes of complex multi-faceted data residing in different sources. Through a real scenario, this chapter reports on the practical use of the Dicode solution in the above context. Evaluation results show that the proposed solution enables a meaningful aggregation and analysis of large-scale data in complex biomedical research settings. Moreover, it allows for new working practices that turn the problem of information overload and cognitive complexity into the benefit of knowledge discovery.