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Visualization of Biomedical Data

The rapid increase in volume and complexity of biomedical data requires changes in research, communication, and clinical practices. This includes learning how to effectively present and visualize data sets and research outcomes that clearly express complex phenomena. In this review, the authors we summarize key principles and resources from data visualization research that help address this difficult challenge and discuss 4 common misconceptions: 1) “The goal of data visualization is to impress.” 2) “Data visualization is easy.” 3) “Studying data visualization is unnecessary.” 4) “Visualization is just a synonym for imaging.”The authors then survey how visualization is being used in a selection of emerging biomedical research areas and highlight common poor visualization practices. O’Donoghue and colleagues also outline ongoing initiatives aimed at improving visualization practices in biomedical research via better tools, peer-to-peer learning, and interdisciplinary collaboration with computer scientists, science communicators, and graphic designers. These changes are revolutionizing how scientists see and think about our data.

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