Exploration and Innovation: New Digital Tools Increase Engagement
In 2017 the Princeton University Art Museum launched an Application Programming Interface (API) with the goal of making the Museum’s collections of more than 113,000 works of art readily available for students, instructors, curators, and developers around the world. For each work that enters the collections, the Museum records a set of information documenting core characteristics, such as the artist who created the work and the date when it was created; physical characteristics such as its size and medium; and narrative summaries of any analysis of the work that has been carried out by Museum curators or other scholars. For the majority of works, this text-based data is accompanied by digital images produced by the Museum that provide a visual representation of the work. The API aggregates these resources and makes them available for developers to integrate into their own applications, opening up a broad range of possibilities for engagement with the collections. While the API’s potential uses are still to be imagined and explored, it has become an important tool for students, who have developed remarkable projects with widely different applications.
As juniors, Nina He and Ezra Zinberg, both Class of 2021, created an interface to explore the use of color in the Art Museum’s collections. PU Palette (pupalette.herokuapp.com) allows users to select any hue from the visible color spectrum and see works of art from across the collections in which that color appears. As someone who loves to view and create art, Nina wanted to incorporate the arts into her independent computer science work at Princeton: “I’ve always found the history of colors in art fascinating; in painting classes, I had learned about the origins of various paint pigments and seen how different cultures utilized color over certain time periods.” The countless arrangements of artworks based on their color palettes are possible due to the way the metadata is made accessible through the API.
Similarly, a group of students in a 2019 computer science class, tasked with creating software that solved a campus problem, used the API to create an interactive map for campus art. The Princeton Artfinder allows anyone on campus to find the nearest work of public art. Meanwhile, others have used the API to generate new approaches to understanding art: for her prize-winning senior thesis, Alice Xue, Class of 2020, combined her fascination with East Asian culture and her major in Computer Science by creating a machine-learning algorithm that generates paintings in the style of traditional Chinese landscape painting. In order to do this, Alice used the dataset of the Museum’s collection of Chinese art, which was easy to access and integrate into the project thanks to the API.
Daniel T. Brennan, who has been the lead developer responsible for managing this digital tool and responding to the dozens of requests for access that come in from around the world, notes that, above all else, the API provides the possibility for what any successful teaching and learning experience needs: experimentation. The API is there to be used in creative and innovative ways by anyone in the University or global community who has a new idea of how to engage with the Museum’s collections.
Brian Kernighan, professor and director of undergraduate studies in the Department of Computer Science, emphasizes the significance of accessing and studying the collection as data—something that his students have done regularly in recent years, using the Museum’s API as part of their capstone course project. Moving away from an emphasis on individual works of art and looking for larger trends in data across time and around the globe, with the help of automatized and digital processes, offers new and exciting possibilities for understanding the Museum’s collections.
This project was made possible in part by the Institute of Museum and Library Services.