Making the Desert Island Discs Dataset: Data Visceralization and How We Don’t Know What We Know (MDIDD) explores the use of alternative modes of representing data such as materialization and sonification as a means of connecting critical making to a more visceral understanding of the data itself. This project was conducted as coursework for the class “Advanced Projects in Digital Humanities” and is a collaboration of Master’s students in the Pratt School of Information and the professor. As a group of mainly students entering the Information Science field, we were interested in the experimental, reflexive process of working with data to enhance our individual and collective practice as digital humanists.
The Desert Island Discs Dataset, compiled and posted by Andrew James Gustar of the Open University (UK) on Humanities Commons in 2020, served as the underlying information for our research (Gustar, 2020). Desert Island Discs is a long-running BBC radio program that was first “recorded in the BBC’s bomb-damaged Maida Vale studio on 27th January 1942 and aired in the Forces Programme at 8pm two days later. It was introduced to the listening public as ‘a programme in which a well-known person is asked the question, if you were to be cast away alone on a desert island, which eight gramophone records would you choose to have with you, assuming of course, that you had a gramophone and an inexhaustible supply of needles.’”(BBC Radio 4 - Desert Island Discs - The History of Desert Island Discs, n.d.)
We explored five different methods of representing this resource: a 1:1 map, a textile, a musical composition, a physicalization of the data as weights, and a material network with integrated sound. Together these projects, which attend to critical concerns of data literacy and data feminism, were driven by the hypothesis that such experimental approaches to data representation can demonstrate the importance of process in determining an output and highlight the often invisible, feminized labor of processing. Through MDIDD, our “visceralizations” convey information in a variety of forms that engage more of our senses. Making these representations gave us the opportunity to engage more thoughtfully with the dataset, connecting our decisions with our insights as we worked. We hope that by presenting these different modalities, our audience can engage with, reflect, and discuss the experience of data, furthering a collective understanding of how these experimental formats might enrich digital humanities research and inquiry.
While each sub-project is a unique transformation of the data, together they illustrate how modes of critical making enrich knowledge production. We approached the rows, columns, and cells of the Desert Island Discs dataset as a terrain of absent, present, and problematic data to visualize as a map to convey the extent of processing labor and failures of data collection common in data research. From there, each of us pursued a further line of inquiry: How can we make the dataset into a textile that conveys its scale and nature? How can we quantify and materialize the labor that goes into data cleaning? Can sound be used to better understand the age of the radio program’s guests? How can we depict patterns of connection in the musical choices across the dataset through a multi-sensory network graph experience? Working through these questions, we discovered the limits and possibilities of our chosen methods: a process not just of making, but of making mistakes and failing–aspects of academic research that are traditionally overshadowed by the value put on a solid conclusion or visual output. This project foregrounds the continual labor of processing data and the discoveries made when using nontraditional methods to represent information. By both illuminating the human intervention needed in computational processes and engaging the audience through different modalities, we aim to expand the developing canon of Digital Humanities and encourage critical approaches to knowledge creation across the field.
https://studentwork.prattsi.org/data-visceralization/introduction/
