#Topics on Aesthetic Data Visualization: Viewpoints, Interpretation, and Alternative Senses.
This paper explores different types of data visualization projects in artistic projects. The author mainly divided several data visualization projects into three different parts: (1) Viewpoints, (2) Interpretation, (3) Enhancement and transition of senses, and development of sensory organs.
I agree with the first two points in which artists transfer the dataset based on the number to visual graphics. However, the third point seems like the author breaks away from targets of data visualization, which is a way used to reflect the information hidden in a dataset. The third one is more like how artists present the final visual effects. In my mind, I do not think it still belongs to the topics in data visualization. At least, it is not the main care in data visualization projects. If we follow that root, people would like to consider all artistic forms in media art, such as audiovisual, immersive environment, and so on.
No matter the topic of the viewpoint of interpretation, artists just departure different perspectives of the material characteristics. For example, the example of the Korean family book mentioned in this paper has the property of narrative in itself.
(picture was captured from https://vimeo.com/ryojiikeda)
When it comes up with data visualization, I have to mention one of my favorite artists and his project, data-verse 1, by Ryoji Ikeda. I went to Venice Biennale and get into a huge dark space where this project was located. He collected different types of scientific datasets. Unlike the data visualization projects, where the artist chooses only one dataset, we used to see, he used the dataset no matter what it is to represent everything in the universe. During the 10 minutes, I sat in a dark environment. Most frames of the project consisted of white and red color and some abstract graphics, such as particle system, flashing labels, bio models, and some lines scanning through the whole screens. Especially, the effect of the sound, it does not like the massive dataset. The sonic effect is clean which is like the sounds from bells. According to the paper aforementioned, Ryoji extracted the property of the universe in all types of datasets, and stimulate the audience's senses by the way of an immersive environment.
Ref:
Kim, Hyoyoung, and Jin Wan Park. Topics on Aesthetic Data Visualization: Viewpoints, Interpretation, and Alternative Senses. p. 7.
Evers, Lucas, and Frank Nack. ‘Data Aesthetics: The Ethics and Aesthetics of Big Data Gathering Seen from the Artists Eye’. Proceedings of the 2016 ACM on Multimedia Conference - MM ’16, ACM Press, 2016, pp. 779–80. DOI.org (Crossref), doi:10.1145/2964284.2993205.
Data-verse 1, by Ryoji Ikeda: https://vimeo.com/ryojiikeda