Abstract
Data visualization at large can be described as a process that reduces data to mentally comprehensible visual products. Many visualizations are based on very large and complex data, often integrating multiple data sources and complex measures and concepts. Communicating to broad audiences involves drastically simplifying the message, extracting salient concepts and often omitting low-level details. In this chapter we give two examples for data visualizations in the field of climate research that proved to be successful in supporting communication to broad audiences. In a research institute setting, striking a balance between scientific correctness and comprehensibility is key. We describe how a careful design of the visual encoding such as reducing data dimensionality, dealing with data issues (e.g. uncertainty), the number of colors, and choice of visual elements is important to achieve simplicity. Finally, we describe two technical settings that we use for face-to-face communication of climate research results to broad audiences.
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Böttinger, M. (2020). Reaching Broad Audiences from a Research Institute Setting. In: Chen, M., Hauser, H., Rheingans, P., Scheuermann, G. (eds) Foundations of Data Visualization. Springer, Cham. https://doi.org/10.1007/978-3-030-34444-3_17
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