Tuesday, March 24, 2015

#DietDH 04

            For this weeks #DietDH, the topic is visualization – as in Data Visualization. I do not have much experience with developing displays for quantitative data. My current art project and research is by means of qualitative methods or categorical data. I have viewed different styles of visualization through a couple of websites.
            For me, these readings have offered additional insight to the process of developing data visualization. Tooling Up for the Digital Humanities, from Stanford goes over the basic technical aspects and forms. From the visual complexity (VC Blog) a 2009 posting of Information Visualization Manifesto by Manual Lima investigates the ten requirements that developed his manifesto.
            I find Lima’s argument ambiguous about coexisting ideas and methods with informational visualization and information art. Especially, with the previous paragraph confirming his ten requirements aiding in the structure of projects, while if the choice is made to pursue outside of his requirements he labels it as experimental. He continues by specifying terms for these experimental types of visualizations, such as new media art, computer art, algorithimic art, and the “term” he recommends Information Art. It is worth reading through the comments of this blog post about the lively discussion on art and science, experimentation, and the role of aesthetics.
            The third reading is a more recent blog posting that places a more human side to research and offers insight to how other humanity disciplines are (gender and ethnic studies) using visualization. Opaque is Being Polite: On Algorithms,Violence, & Awesomeness in Data Visualization a 2013 posting on Jen Jack Gieseking website details the process of gathering data, learning new software with an outcome, and digital media’s placement with social inequalities. Gieseking points out the human element of coding that influences (along with corporate marketing and government groups) algorithms. Then these preprogrammed algorithms sort through databases for specified information. Some examples from the blog post are facial recognition software and language as data. I will be keeping both of these readings as future reference to explore. 

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