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Challenges and Open Issues in Visualization for Broad Audiences

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Foundations of Data Visualization

Abstract

As discussed in the last chapters, a lot of work has already been done by academia and practitioners with respect to developing and producing visualization tools and visual products specifically designed for broad audiences. While there have been significant efforts in the field, there are still open issues and new exciting areas to explore for visualization researchers and practitioners, such as the need to develop guidelines for engaging visualization and visual interfaces where humans are in the loop, and how to prepare the next generation of data-driven storytellers. Toward the end of this chapter, as we contemplate the next era of visualization, we reflect on past accomplishments and share a few thoughts about the future of our practice.

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Acknowledgements

Helen-Nicole Kostis would like to thank Universities Space Research Association (USRA) and Goddard Earth Sciences Technology and Research (GESTAR), for supporting her participation in the Dagsthul seminar and time to contribute to this book through two Learning and Development (L&D) Awards. Kostis would like to express her gratitude to the late Dr. Bill Corso (former director of USRA/GESTAR) for his immense support over the years and for being a role model of a servant leader. A big heartfelt thank you to mentor and Superhero of Synthesis Dan Sandin for providing images of two groundbreaking works of art: Spiral5 PTL and Particle Dreams in Spherical Hormonics. Special thanks to all her colleagues at the Scientific Visualization Studio (SVS), and Dr. Nicholas White, Dr. Scott Miller, Dagmar Morgan, Jefferson Beck, Daria Tsoupikova, John Fujii, Evan Hirsch, Dr. Jessica Hodgins, and Dr. Dorothy Zukor. And last but not least, many thanks to Anastasios and Ellie Golnas and for their patience and love.

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Correspondence to Michael Böttinger .

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Böttinger, M., Kostis, HN., Ynnermann, A. (2020). Challenges and Open Issues in Visualization for Broad Audiences. 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_21

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  • DOI: https://doi.org/10.1007/978-3-030-34444-3_21

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