Untitled: Art Datathon was a most exciting data analysis and visualization workshop that took place at the Museum of Modern Art (MoMA), New York, on February 19-20, 2016. I was one of the judges who evaluated the projects. The event was organized by Laura Norén (Moore-Sloan Research Scientist, NYU), Fiona Romeo (Director of Digital Content and Strategy, MoMA), and Jackie Thomas (Department Manager, Digital Media, MoMA). Untitled: Art Datathon was announced on the MoMA calendar of events as "a two-day workshop in which multidisciplinary teams detect art world trends using data about art, including MoMA collection data that was released on GitHub last year. Datathons challenge participants to come up with research designs that can utilize specific data—in this case, collection data from museums—to create models, figures, maps, and other presentations of findings."
As one of the judges of these "research designs" that the participants created during the two busy days, I was thrilled to find myself among a stellar line-up of experts. Here's the Art Datathon judges at work (from the left):
Matt Lincoln (PhD Candidate in Art History, University of Maryland, College Park), Ramona Bronkar Bannayan (Senior Deputy Director of Exhibitions and Collections, MoMA), myself, and Mark Hansen (Director, David and Helen Gurley Brown Institute for Media Innovation, and Professor of Journalism, Columbia University).
The Art Datathon was focused on a dataset describing the collection of the MoMA, and the participants were encouraged to engage also other museums’ publicly available datasets, such as the data provided by the Cooper Hewitt museum or the Carnegie Museum of Art. The workshop participants also had a privileged access to some additional collections that are not currently available on GitHub.
The participants’ task was to explore and visualize data in order to see what we can learn from data about the museum, its institutional history, and its collection. The seven teams of participants explored topics as diverse as:
- the changing preferences in the use of color in paintings over the 20th century,
- the relationship between dominant colors in paintings and the geographical background of the painters,
- a semantic analysis of the descriptions of various MoMA collections,
- an inquiry into the exhibition history of MoMA,
- an insight into the history of MoMA’s acquisition policy,
- the dynamics of artist inclusion in MoMA’s exhibitions,
- and the background and gender breakdown of artists whose works are in MoMA’s collections.
While all the teams worked hard and brought up valid and relevant questions, the jury unanimously favored projects presented by Team 4 and Team 6.
Team 4 in their project “Creators and Concepts: A Computational Analysis of Curatorial Approaches” used some visualization tools that may appear simple and obvious, such as the word cloud, but in their project, these simple tools actually brought into focus some surprising trends, such as the fact that the word “photographs” has appeared in the titles of MoMA’s exhibitions even more often than, for example, words like "painting" or “Picasso.” Team 4 are: Joan Beaudoin (Wayne State, Indiana), Michael Fehrenbach (MoMA), Shira Feldman (NYU), Juliet Fong (BBDO), and Aiyi Zhang (NYU).
Team 6 in their study of the most often exhibited artists in MoMA, “MoMA through Time,” offered a meaningful application of data visualization tools: their analysis of the exhibition history data made visible interesting patterns that otherwise would not stand out so clearly. The team made their beautifully designed project available online: MoMA through Time (and see a screenshot below). Team 6 are: Woojin Kim (Columbia), Marily Konstantinopoulou (independent scholar), Nomaduma Masilela (MoMA Intern), A'Nisa Megginson (NYU), and Manuel Rueda (Columbia University).