Explore WCMA

Here's a chance to discover more about the collection of the Williams College Museum of Art!

You will be presented with five exhibits from which you can choose from to start your exploration. A social network of artworks and exhibitions will be generated based on your choices. Each exhibition is linked to artworks that were shown, and each artwork is linked to exhibitions that it was shown in.

Please feel free to see their connections, find a random piece you like, or even start over from the beginning to get a new set of exhibits!

Help x

The Network

Each node (circle) represents either an artwork or an exhibit. The exhibit nodes are pink, while the artwork nodes will have a color that corresponds to its thumbnail.

A connection between two nodes means that an artwork was shown in the connected exhibition.

Interactions

Hover over a node to view its details.
Click on a node to select it.
Scroll to zoom in or out.
Drag a node to move it around.
Search WCMA will search the WCMA collection for the currently selected node.
Add Neighbors will look for all nodes that are connected to the currently selected node, and add them to the graph.
Load Random Node will randomly select a node and add it to the graph.

Misc

The data is incomplete, so not every node will have neighbors. Some exhibits might be empty because artwork data is missing. Not every artwork has a thumbnail. Apologies!

About x

I am Lester Lee, a Computer Science major at Williams College.

In Fall'18, I did an independent study on information visualization with Professor Iris Howley. More information about my independent study can be found on my blog. This blog also contains my process for working on this project!

Much thanks to the Williams College Museum of Art (WCMA) for providing me with data and helping me out with this project!

Explore WCMA is a way to find pieces in the collection that you haven't seen before by following an artwork's connections through exhibits. You can also get a quick sense of the color scheme of each exhibit!

The code for this visualization is available on Github.

Sample

Artist,