The Art Historian’s Macroscope: Museum Data and the Academy

Tonight I’d like to talk about the art historian’s macroscope:

1. What it is.
2. Why art historians need it.
3. An example of how to use it.
4. My hopes for how it will impact the way that museums and academics work with each other

Jöel de Rosnay called the macroscope “a symbolic instrument” comprising interdisciplinary theories and methods for distilling patterns from infinite complexity.

More recently Shawn Graham, Ian Milligan, and Scott Weingart have adopted the term for their book “The Historian’s Macroscope”, and I like their perspective on data-driven analysis as just one tool in the historian’s workbelt, to be deployed in concert with traditional historiographical methods.

Now, there’s actually an argument that data analysis is not new to art historians - Roger de Piles was already quantifying style in 1708. But I’d argue that the increasingly digitized museum world should reignite interest in these methods b/c it could address a longstanding tension in art history.

Art historians pride ourselves on our skill at close looking at artworks, considering them from every visual, material, historical, and philosophical angle.

But one of our biggest challenges - especially for historians of engravings and etchings - is the sheer number of artworks that we have to deal with! It’s hard to claim an understanding of, say, Dutch printmaking as a whole, when your book only looks at fifty artworks.

So how can we honor the specificity of individual artists and artworks, while also doing justice to the complexity of the large-scale, dynamic structures these artists worked within?

Enter the digital museum. Museums are repositories of artworks, yes, but also of repositories of knowledge structured as data. More than a century of curatorial work describing collections’ history is finally starting to make it into publicly-accessible databases.

The finest example of this is the British Museum, which has described their collections as Linked Open Data based on the CIDOC Conceptual Reference Model that links together objects, people, events, and concepts in a rich data model. Turns out, you can do some exciting things with this!

Take prints, for example. These are often collaborative artworks, with an artist who makes a design, a engraver who actually makes the printing plate, and a publisher who prints and markets impressions. These prints become an index of professional relationships at a certain point in time.

This kind of data is a perfect candidate for computational network analysis. Because we have so many dated prints, it’s possible to construct a dynamic representation of this printmaking network. We can trace the shape and structure of network itself, over time.

This lets us ask some very new questions. E.g. did different regional networks experience their own patterns of centralization and decentralization? A new artist trying to join the market might employ very different strategies depending on the character of the professional networks already in place.

And this is already leading to new questions about individuals’ place in these networks. Printmaking networks are a lot like other networks found in both society and in nature: just a few actors receive the lion’s share of the connections (following Zipf’s law).

But who sits at those central positions can be surprising! Measuring who was most central to the Dutch printmaking network in 1630, we expect to see Visscher and Rembrandt. Right up there with them is Jonas Suyderhoef - a total unknown. The last thing written about him was in 1861.

Suyderhoef was everywhere, making reproductions after:

• Italianate landscapist Nicolaes Berchem
• peasant scene specialist Adrian van Ostade
• The polymathic master Peter Paul Rubens
• Refined scenes Gerard ter Borch
• Vivacious portraits of Frans Hals

He was no virtuoso engraver, and this is surely why he has dropped off the art historical radar. And yet he was incredibly active in the Dutch network of print producers. Without some data-driven analysis, his story would remain lost, and along with it a more accurate picture of Dutch printmaking in this period.

So how do we encourage more of this work?

What can museums do?

1. Extraordinary amount of curatorial knowledge is stowed in coll. mng’t systems - expose it AND work to build even more struct. data
2. Our dig. database shouldn’t be designed ONLY with web or mobile apps in mind. Inventive work needs bulk datasets built for complexity, not just for speed and convenience.
3. Make our data interoperable between museums. Don’t overcustomize or prematurely optimize for individual projects. This is easily the hardest goal!

What can universities do?

1. We’ve built art historical data for centuries (catalogues raisonné) - let’s reimagine how we can describe and permute our knowledge in digital formats.
2. Macroscopic research can support hypothesis-driven experimentation
3. See Sheila Brennan’s “DH Centered in Museums”: Museums have done DH for a long time, and they have their own priorities.