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Article published In: Information Design Journal
Vol. 27:3 (2022) ► pp.295–308

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Immersive architectures for visual data literacy

ORCiD logo with link | University of Groningen
ORCiD logo with link | Austrian Academy of Sciences
ORCiD logo with link | Namsor
 | Garamantis
 | Garamantis
 | Ars Electronica Solutions
 | Ars Electronica Solutions
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Available under the Creative Commons Attribution (CC BY) 4.0 license.

For any use beyond this license, please contact the publisher at rights@benjamins.nl.

Published online: 2 December 2022
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https://doi.org/10.1075/idj.22016.rod
Abstract
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, September 9). 3D Cartography of COVID-19 Research. Ars Electronica, a New Digital Deal. [URL]. 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.
Article outline
  • Introduction
  • One First Response to COVID-19
  • A Successive, More Complex Response to COVID-19
  • Design Process
  • Data Literacy
  • Conclusions
  • Acknowledgments
  • Bibliography
References (43)
Bibliography
Arnheim, R. (1969). Visual thinking. University of California Press.Google Scholar logo with link to Google Scholar
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. Google Scholar logo with link to Google Scholar
Börner, K. (2010). Atlas of science: Visualizing what we know. MIT Press.Google Scholar logo with link to Google Scholar
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. Google Scholar logo with link to Google Scholar
Bostock, M., Ogievetsky, V., & Heer, J. (2011). D3: Data-Driven Documents. IEEE Transactions on Visualization and Computer Graphics, 17(12), 2301–2309. Google Scholar logo with link to Google Scholar
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. Google Scholar logo with link to Google Scholar
D’Ignazio, C., & Bhargava, R. (2015). Approaches to building big data literacy. Proceedings of the Bloomberg Data for Good Exchange Conference.Google Scholar logo with link to Google Scholar
Dondis, D. A. (1975). A primer of visual literacy. MIT Press. (Original work published 1973)Google Scholar logo with link to Google Scholar
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. Google Scholar logo with link to Google Scholar
Freire, P. (2000). Pedagogy of the oppressed (M. Bergman Ramos, Trans.; 30th-anniversary edition ed.). Continuum. (Original work published 1970)Google Scholar logo with link to Google Scholar
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. Google Scholar logo with link to Google Scholar
Hocking, J., & Schell, J. (2022). Unity in action: Multiplatform game development in C# (Third edition). Manning Publications Co.Google Scholar logo with link to Google Scholar
Jandsl, M., & Stocker, G. (Eds.). (2021). Ars Electronica 2021. Festival for art, technology and society. Hatje Cantz Verlag.Google Scholar logo with link to Google Scholar
Kanas, N. (2012). Star maps: History, artistry, and cartography (Second edition). Springer. Google Scholar logo with link to Google Scholar
Kaplan, F., & Lenardo, I. di. (2017). Big data of the past. Frontiers in Digital Humanities, 41, 769. Google Scholar logo with link to Google Scholar
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. Google Scholar logo with link to Google Scholar
Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. SAGE Publications.Google Scholar logo with link to Google Scholar
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.Google Scholar logo with link to Google Scholar
Latour, B., & Weibel, P. (Eds.). (2005). Making things public: Atmospheres of democracy. MIT Press.Google Scholar logo with link to Google Scholar
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.Google Scholar logo with link to Google Scholar
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.Google Scholar logo with link to Google Scholar
Meirelles, I. (2013). Design for information: An introduction to the histories, theories, and best practices behind effective information visualizations. Rockport.Google Scholar logo with link to Google Scholar
Moon, C. Y. E., & Rodighiero, D. (2020). Mapping as a contemporary instrument for orientation in conferences. Atti Del IX Convegno Annuale AIUCD. Google Scholar logo with link to Google Scholar
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. Google Scholar logo with link to Google Scholar
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. Google Scholar logo with link to Google Scholar
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. Google Scholar logo with link to Google Scholar
(2022). A visual translation of the pandemic. Leonardo, 55(3), 297–303. Google Scholar logo with link to Google Scholar
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)Google Scholar logo with link to Google Scholar
Van Der Spuy, R. (2015). Learn Pixi.js. Apress. Google Scholar logo with link to Google Scholar
Van Dijck, J. (2014). Datafication, dataism and dataveillance: Big data between scientific paradigm and ideology. Surveillance & Society, 12(2), 197–208. Google Scholar logo with link to Google Scholar
Vanderplas, J. T. (2016). Python data science handbook: Essential tools for working with data. O’Reilly Media.Google Scholar logo with link to Google Scholar
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. Google Scholar logo with link to Google Scholar
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