With so many resources, software, and applications at our disposal, the possibilities for digital projects seems infinite. Moreover, the integration of technology in almost every factor of our lives can easily turn any novice to a computer wiz (at least self-proclaimed). Yet, as Diane Zorich states, a Digital Humanist or Digital Art historian is not one who can effectively use Google or who knows their way around the Met’s online collection. A Digital scholar is one who “adopt(s) the computational methodologies and analytical techniques that are enabled by new technologies” to their own research. I’ve mentioned in previous blog posts the utilitarianism of digital art history/humanities– that it is one method within the scholars tool belt. But Zorich poses a question–and its a big one– where is the Art in Digital Art History?
It’s a good question, right? Something to really keep in mind. As I’ve been researching different projects and initiatives, I’ve asked myself it a few times– where is the art? I want to spend this post unpacking this question because ultimately art historical research does not always have to focus on an artwork(s), yet art should be at its center, if only indirectly. Let me elaborate….
I was taught that art history should always focus on the object– that the art guides your scholarly discussion. Any questions or insights should come from within the object as it is the portal to new ways of understanding realities. With this, any method employed should serve the art. It should provide a frame in which the art takes on new understanding. If digital art history is an alternative method, then it should work the same way and for the same purpose (that is to provide a new understanding of works of art).
I should note that my skepticism is in response to digital projects that deal with large data sets– data visualization, cultural and network analytics, and text mining . At the foundation of the many types of data visualization projects is the transformation of data (which can include numerical data to even text documents) into visual forms that may provide new ways of engaging with the information. It seems that these projects help us comprehend trends over time and place and popularity of terms, styles, artists, schools, etc. Of course, there are so many other insights that these projects could present, but i am basing my understanding from the examples I’ve explored.
Zorich presents a few examples of Art Historical projects that deal with large data sets. Lev Manovich at the City University of New York employed a statistical technique called Principal Component Analysis (PCA) to analyze 60 visual features (or ‘image features’ such as color, texture, lines, shapes, etc.) In one project, Manovich applied this model to 128 paintings by Mondrian, creating a scatter plot organized by visual similarity among the works. From this cultural analysis, he explains how this visualization allows you to see, “the parts of the space of visual possibilities (that the artist) explored, the relative distributions of their works– the dense areas, the sparser areas, the presence or absence of clusters, etc.” (Zorich)
As we can see, the presentation of data provides new insights that allow for further inquiry. It prompts a scholarly discussion that centers on works of art. Even with this comparative image of Mondrian and Rothko, we can begin to recognize similarities and ask questions— all of which originate from the paintings themselves. In my opinion, this is a strong Digital Art History project because it applies a new way to engage with works of art that allow for new avenues of research. I doubt that one could notice the comparisons between Mondrian and Rothko through analog methods. We can page and page through catalogues, but even this might not spark such big insights! Manovich’s Cultural Analytics seems to be an effective way to center works of art within a digital project.
Zorich also includes a topic or textual mining project in which large corpuses of texts are mined and visualized for popularity of words and themes. She includes Dr. Robert Nelson’s project “Mining the Dispatch,” as an example. “Mining the Dispatch” examines the print run of the Richmond Daily Paper from 1860-1865. A number of topics were mined from over 112,000 papers, including Negro, years, reward, boy, man, jail, delivery, black, ran, and color. Like Manovich’s “Cultural Analytics”, the results from this project allowing for new questions– specifically in thinking why these terms might be so popular. Text mining projects like this are extremely interesting, no doubt, but its place of origin– its site of creation– has shifted from image to word. Zorich suggests ways in which Art Historians could use text mining for their work (such as scanning over Academic Journals or even the oeuvres of some of the leading theorists in the field) and I agree these would be extremely insightful and useful contextualizations to any research project. Still, though, I am not totally convinced this would result in an art historical scholarship. Sure, it would be illuminating to topic-mine African Art journals and major publications from the past 70 years to see which countries or ethnic groups are most popular (though I think most Africanists would already have some good guesses), but this seems like a historiography inquiry of the field. Historiography is important and often can be a much-needed addition to an art historical study, but should it be the foundation for said study? Should an art historical project come out of a mining of textual data from the field? Should it be the site of creation?
I suppose I will end with this thought– an Art Historian should practice visual primacy. Our discipline utilizes images and objects to understand our world and its histories. And so, I have some trouble approaching art history without a focus on the visual. Am I discrediting textual evidence, historical documents, and theoretical writings? Obviously not! But to ground a discussion on these is not art historical. With this, data visualization projects should follow this hierarchy. If a project, like Cultural Analytics, examines image sets, then we could use this as a foundation for inquiry. If a project does not, such as text mining, then it should be used as supplement. Text mining could definitely unearth new avenues of discussion, but I think should be used selectively and to assist the image. It is one digital method that I can see being employed during the process and not at its beginning. Most of these projects should be situated in a sequence of research (and some might be able to exist at multiple points). But regardless of method, digital or not, the beginning of any sequence must be an image or object. It must be the art.
Bibliography:
Nelson, Robert K. “Mining the Dispatch.” Digital Research Lab, the University of Richmond. http://dsl.richmond.edu/dispatch/
Software Studies Initiative. “Mondrian vs Rothko: footprints and evolution in style space.” 2011. http://lab.softwarestudies.com/2011/06/mondrian-vs-rothko-footprints-and.html
Zorich, Diane M. “The ‘Art’ of Digital Art History” (presented at The Digital World of Art History, Princeton University, June 26, 2013), https://ima.princeton.edu/pubs/2013Zorich.pdf