I was thrilled to see several articles on data-manipulation this week, since I am considering working on an exploration of museum data for the final project. I also had a lot of fun exploring the different examples- it was very interesting to see how one data set from the Tate could be interpreted and presented in many different ways by a single person.
Big data is a hot topic in many circles now, in part because people seem to view it as a good way of challenging the power structures and relations that exist between curators and viewers, as discussed in the Exhibitionary Complex last week. The fact that more institutions are now becoming open to sharing their data online also seems like a win for democratization of information, although it would be difficult to deny that the release also comes with questions about what kind of data they have (selectively) chosen to reveal to us, and what (if anything) they are hiding from us.
On the positive side, though, Helen Wall mentions in her data visualization study of MoMA’s online collection that having access to such data and the opportunity to explore it with visualization tools gives us a new perspective of the artworks that we would not be able to glean from simply visiting the gallery or reading catalogues. I agree that the insight we get from well-designed data visualizations changes the way we view artworks- I would even argue that it gives us more of a bird’s eye view perspective via which to identify historical trends and artist preferences that might not be as apparent when we are viewing the works individually.
Apart from the art world, where data visualizations are used primarily to reimagine information and chart historical trends, other professions have used available datasets to assess current situations and predict future trends. For instance, datasets have been used by environmentalists to conduct climate change vulnerability assessments, and by geographers to track population density. More casually, it was also used by a friend of mine who was interested in identifying trends in 800 Degrees’ Instagram phenomenon Pizza of the Day, where he attempted to predict what toppings would be put on the pizzas before they posted the picture. Although he ended up not being able to develop a model with sufficient accuracy, he was able to extract information from 800 Degrees’ account using the Instagram API to create several interesting visualizations that revealed the most frequently used toppings, and which pizza bases were most popular (by assuming that the number of likes and comments on a given photo reflected more interest in the pizza of that day). I leave you with some screenshots of his work, but if pizza is of any importance in your life, you should definitely check out the original post here for some real insight and good fun.
Cool! (I don’t approve of corn on pizza, though.)
Wow! I really enjoyed your post. From museum collections to pizza toppings, I agree that people are increasingly interested both professionally and personally in data and data manipulations.
Interesting that you mention in the second paragraph about how museums may selectively deciding which information to release to the public. Is that not, in part, what a museum already does by choosing what or what not to put on display? For instance, at the Broad, it is very well known that the Broads still have many works away in storage, as you can see the storage room as you exit the main museum area. But still, with museums deciding to make more and more of their materials available via open source, it is still one step closer to democratization of information.
Also, your friend’s project seems so interesting! (It’s also made me hungry, although I agree with Dr. Posner — corn on pizza? Blasphemous!)
I really liked your post!! It’s funny that someone made the potd into an open data set. I like all the variables they use with the comments included, and some of the results weren’t what I expected, like egg and bacon being so low.
One of the controversies with museum data and data visualizations is that they detract from the wonder of the object. I think Yoav’s project does a great job of illustrating this phenomenon: while he presented some beautiful graphs and provided insights into the POTD, consumption of that information is a completely different experience than consumption of an actual pizza. Perhaps there’s a way to marry the two?