Article published In:
Information Design JournalVol. 27:3 (2022) ► pp.295–308
Immersive architectures for visual data literacy
The datafication process transforming society enables us to witness the pandemic from a global perspective. This article provides an example of immersive architecture in which coronavirus-related scientific literature was revealed during Ars Electronica 2021. Like a starry sky, a network visualization representing more than 600,000 articles was showcased in the Deep Space 8K theater, where spectators were accompanied in reading insights. The case study of 3D Cartography of COVID-19 illustrates a novel way to present data in public spaces to foster conversations and reflects on how visual data literacy can be addressed in museums.
Keywords: Ars Electronica, COVID-19, data literacy, dialogic practice, visual literacy
Article outline
- Introduction
- One First Response to COVID-19
- A Successive, More Complex Response to COVID-19
- Design Process
- Data Literacy
- Conclusions
- Acknowledgments
-
Bibliography
Available under the Creative Commons Attribution (CC BY) 4.0 license.
For any use beyond this license, please contact the publisher at [email protected].
References (43)
Arnheim, R. (1969). Visual thinking. University of California Press.
Ars Electronica. (2021, September 9). 3D Cartography of COVID-19 Research. Ars Electronica, a New Digital Deal. [URL]
Balazka, D., & Rodighiero, D. (2020). Big data and the little big bang: An epistemological (r)evolution. Frontiers in Big Data, 31, 31. 
Börner, K. (2010). Atlas of science: Visualizing what we know. MIT Press.
Börner, K., Maltese, A., Balliet, R. N., & Heimlich, J. (2016). Investigating aspects of data visualization literacy using 20 information visualizations and 273 science museum visitors. Information Visualization, 15(3), 198–213.
Bostock, M., Ogievetsky, V., & Heer, J. (2011). D3: Data-Driven Documents. IEEE Transactions on Visualization and Computer Graphics, 17(12), 2301–2309.
Coleman, N. (2021, February 21). On the front page, a wall of grief. The New York Times. [URL]
Cooper, M. (1989). Computers and Design. Design Quarterly, 1421, 1.
D’Ignazio, C. (2017). Creative data literacy: Bridging the gap between the data-haves and data-have nots. Information Design Journal, 23(1), 6–18.
D’Ignazio, C., & Bhargava, R. (2015). Approaches to building big data literacy. Proceedings of the Bloomberg Data for Good Exchange Conference.
Dondis, D. A. (1975). A primer of visual literacy. MIT Press. (Original work published 1973)
Feng, D., de Vlas, S. J., Fang, L.-Q., Han, X.-N., Zhao, W.-J., Sheng, S., Yang, H., Jia, Z.-W., Richardus, J. H., & Cao, W.-C. (2009). The SARS epidemic in mainland China: Bringing together all epidemiological data. Tropical Medicine & International Health, 141, 4–13.
Freire, P. (2000). Pedagogy of the oppressed (M. Bergman Ramos, Trans.; 30th-anniversary edition ed.). Continuum. (Original work published 1970)
Galison, P. (Director). (2020). Black holes: The edge of all we know [Documentary]. Netflix. [URL]
Gray, J., Gerlitz, C., & Bounegru, L. (2018). Data infrastructure literacy. Big Data & Society, 5(2), 205395171878631–13.
Hocking, J., & Schell, J. (2022). Unity in action: Multiplatform game development in C# (Third edition). Manning Publications Co.
Jandsl, M., & Stocker, G. (Eds.). (2021). Ars Electronica 2021. Festival for art, technology and society. Hatje Cantz Verlag.
Kanas, N. (2012). Star maps: History, artistry, and cartography (Second edition). Springer.
Kaplan, F., & Lenardo, I. di. (2017). Big data of the past. Frontiers in Digital Humanities, 41, 769.
Kenderdine, S. (2010). Immersive visualization architectures and situated embodiments of culture and heritage. 14th International Conference Information Visualisation, 408–414.
Kenderdine, S., Mason, I., & Hibberd, L. (2021). Computational archives for experimental museology. 3–18.
Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. SAGE Publications.
Latour, B. (2005). From realpolitik to dingpolitik: Or how to make the things public. In B. Latour & P. Weibel (Eds.), Making things public: Atmospheres of democracy. MIT Press.
Latour, B., & Weibel, P. (Eds.). (2005). Making things public: Atmospheres of democracy. MIT Press.
Loukissas, Y. A. (2019). All data are local: Thinking critically in a data-driven society. [URL].
Maaten, L. van der, & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9(86), 2579–2605. [URL]
Manning, C. D., & Schütze, H. (1999). Foundations of statistical natural language processing. MIT Press.
Manovich, L. (2008). Data visualization as new abstraction and anti-sublime. In B. Hawk, D. M. Rieder, & O. O. Oviedo (Eds.), Small tech: The culture of digital tools. University of Minnesota Press.
Meirelles, I. (2013). Design for information: An introduction to the histories, theories, and best practices behind effective information visualizations. Rockport.
Moon, C. Y. E., & Rodighiero, D. (2020). Mapping as a contemporary instrument for orientation in conferences. Atti Del IX Convegno Annuale AIUCD.
Papaki, E. (2020, December 10). DARIAH theme call 2020/2021: Meet the winning projects. DARIAH. [URL]
Petrovich, E. (2020). Science mapping. Encyclopedia of Knowledge Organization. [URL]
Rigal, A., & Joseph-Goteiner, D. (2021). The globalization of an interaction ritual chain: “Clapping for carers” during the conflict against COVID-19. Sociology of Religion, 82(4), 471–493.
Rodighiero, D., & Romele, A. (2022, February 4). Reading network diagrams by using contour lines and word clouds. Proceeding of Graphs and Networks in the Humanities.
Rodighiero, D., Wandl-Vogt, E., & Carsenat, E. (2021). Making visible the invisible work of scientists during the COVID-19 pandemic. Visual Culture Studies, 21, 143–165.
(2022). A visual translation of the pandemic. Leonardo, 55(3), 297–303.
Sick-Leitner, M. (2015, November 8). Deep Space 8K: the next generation. Ars Electronica Blog. [URL]
Sismondo, S. (2010). An introduction to science and technology studies (Second edition). Wiley-Blackwell. (Original work published 2004)
Van Der Spuy, R. (2015). Learn Pixi.js. Apress.
Van Dijck, J. (2014). Datafication, dataism and dataveillance: Big data between scientific paradigm and ideology. Surveillance & Society, 12(2), 197–208.
Vanderplas, J. T. (2016). Python data science handbook: Essential tools for working with data. O’Reilly Media.
Wang, L. L., Lo, K., Chandrasekhar, Y., Reas, R., Yang, J., Burdick, D., Eide, D., Funk, K., Katsis, Y., Kinney, R., Li, Y., Liu, Z., Merrill, W., Mooney, P., Murdick, D., Rishi, D., Sheehan, J., Shen, Z., Stilson, B., … Kohlmeier, S. (2020). CORD-19: The COVID-19 Open Research Dataset. Proceedings of the Workshop on NLP for COVID-19 at ACL 2020. [URL]
Wilkinson, M. D., Dumontier, M., Aalbersberg, Ij. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., Santos, L. B. da S., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3(1), e1002295–10.