Here’s a roundup of how people have used MoMA’s data so far.
Oliver Roeder of FiveThirtyEight took us on a data-driven dérive through MoMA’s galleries, with A Nerd’s Guide To The 2,229 Paintings At MoMA.
Scientist, teacher, and programmer Eamon Caddigan visualized MoMA acquisitions by artist gender. Our dataset didn’t actually include artist gender (we’re working on it!), so Eamon used genderize.io.
Looking at acquisition dates, humanities professor Steven Lubar asked “how modern is modern?” and Eamon Caddigan concluded that “MoMA keeps things fresh.” Design researcher Florian Kräutli used the data to produce timelines of artwork production and acquisition. (Thumbnail URLs are coming soon, Florian.)
Writer and technologist Allison Parrish used our data to create the Twitter bot @ModernArt.exe. It randomly recombines data from MoMA to generate new titles and medium descriptions for artworks. Fusion wrote about it here.
Another Twitter bot, @BizarroMoMA by technologist Ross Goodwin, posts alternate versions of MoMA collection items every two minutes.
Srini Kadamati of Dataquest.io used a small subset of the data to illustrate how the programming language Python can be used for data cleanup. Technologist and O’Reilly author Bob DuCharme took his data wrangling further into what he calls “feature engineering”, making our dimensions and nationality data much more usable. It’s no secret that our data is a work-in-progress, featuring many quirks and gaps. We loved Bob’s reference to the Five Stages of Data Grief.
Finally, for now, MoMA’s collection data is being incorporated into the Digital Public Library of America, and Wikipedians made Wikidata properties P2174 and P2014, creating links from Wikipedia to more than 3,500 artists and 2,ooo artworks.
What did I miss?
[Updated October 26 2015 in response to comments.]
