Data visualization is becoming increasingly important in biomedical research as a means to explore data for patterns and relationships. Such exploration, driven by the graphical representation of information, is the critical first step to inform subsequent computational and machine-driven approaches to data analysis. The process requires us to further define clear objectives and improved implementation to be successful. Data for the most part have no natural form or “look” - we have to make choices about how they are displayed. Each decision can bring out certain kinds of patterns in data while hiding others. We rely heavily on our intuition, common sense, and precedent in published material when we visually depict data. This is largely an unscientific process.
In this workshop we will explore systematic approaches that rely on core graphic design principles and vision science (i.e. how we decode information encoded in graphical form) to develop effective visualizations of data.