It’s always a great start to a day when the first two links you click inspire a flurry of fresh thought. I have been getting stuck into some PhD writing this week, and fast losing myself in the doldrums of theory. So waking this morning to a little bit of inspiration was just what I needed.
The first shot of inspiration, that woke me far more than a coffee would, was this super-cool research on What Makes Paris Look Like Paris? (on Openculture via Jasper Visser & Seb Chan). I remember as a child someone telling me that all cities had colours; that some cities were grey, and some brown. Some were blue. These dominant colours reflected the materials that had been used in construction, the fashions that had shaped the way the city was constructed, the natural resources that were available to the builders. And so I often look out at new cities as I approach them, and watch to see what colour they are. This research reminds me of that, for it analyses Google Street View imagery to look for the common visual elements of a city, like its architectural features. It turns out that not only do cities have colours, they also have distinguishing architectural features.
Imagine what kind of new information and understand lies within our collections, if similar techniques were deployed. What kind of features are common to paintings of Paris? Are there common colours used to depict Paris, or are the architectural elements captured in the research above visible? And what else can we learn about our collections through these kinds of techniques? It is easy to think of this (for me) in terms of art collections, but I am sure there is much that can be found in archeological data and other museological data. (Not that most museum data is that great, as Mia Ridge recently discovered when playing in the Cooper Hewitt datasets.)
There is some work being done in this area, of course, but I’m interested in what else we can find in our collections using these kinds of techniques. This morning, I also watched What do they have? Alternate Visualizations of Museum Collections in which Piotr Adamczyk, when he was at the Met, talks about the possibilities for new information that might be possible in collections data. During the question time, he speaks of his interest in using data to look at provenance and figure out the history of the object in order to visualise who had it, when and where. For me, this is exactly the sort of flow of information that I would find so interesting about collections. I am really interested in the power structures and power makers in any sector. When I met curator Helen Molesworth recently, I asked her what I would discover about her influence on permanent collections, were I to look across the course of her career; who and what she had collected consistently or in different institutions over her life as a curator. It was a question that floored her, because it was one no one had ever asked before. But to me, this is the interesting stuff of museums. Who are the individuals that change the shape of our collections, and indirectly then, the shape of our material wants and expectations? Who has shaped the art market by collecting the works of an individual and increasing their value for other collections? Which individuals have really changed the shape of our cultural heritage, its value and its impact? Who has championed the work of previously unknown artists, and turned them into a hot commodity?
So my other early moment of inspiration in starting the day was watching Koven Smith’s MuseumNext talk, which was just gone online. In it, Koven speaks about curators using algorithms to produce collection narratives – interpretive algorithms. Now it seems to me that this idea starts to coincide with the work being done above, whereby collection researchers and communicators working in a museum could have a focus on a whole collection, and how it relates to the rest of the world, rather than only having curators (or researchers) whose focus is on exhibitions and material culture.
When I last wrote about big data and museums, I quoted from Mia Ridge, who mentioned that there are probably lots of other people who can do great things with museum data, much more than museums can and potentially should. And I agree with that. But I also wonder if making sense of our collections at a macro level with these sorts of techniques and possibilities isn’t also something museums should be doing. I don’t know about that, but I do think it’s something to think about.
What do you think? What would you like to see visualised using museum collections? Are there new ways of looking at the work we do that technology is making possible in ways that weren’t previously available? And should this work be done within the museum, or is it just the responsibility of the museum to enable others to do it?