Critical Data Methods: Theory & Praxis
February 6, 2020 @ 3:00 pm - 5:00 pm EST| Free
Whether in the classroom or archive, humanities scholars and students often encounter data methods as means to an end. Processes like data modeling, analysis, and visualization — sometimes represented by particular applications or technologies — populate the proverbial DH toolbox, equipping practitioners to pursue data-driven research and project-based learning curricula. But, while these data-oriented skills and tools frequently facilitate incredible research and classroom practice, they aren’t always accompanied by a robust critical framework that centers historical, ethical, and justice-oriented concerns.
In this workshop, we will approach basic concepts in data (including data taxonomies and applications) from a critical data studies perspective. Rather than taking a tool- or software-oriented approach, we will collaborate on ways to “do” and teach data that are informed by feminist, critical race, and indigenous theories of information. Keeping in mind this year’s theme — “Histories and Representations of Communities Across the Five Boroughs” — we will engage with local archival materials and other humanities content in order to develop data praxes that are situated and self-reflective.
Participants can expect to:
- become familiar with types of data, including structured and unstructured data
- think critically about ways to model their research or teaching data
- begin to explore key theorists and concepts in critical data studies, including data feminism
- participate in an exercise that enacts critical data pedagogy by bringing humanities methods to data modeling
- situate their own use of data within historical and epistemological matrices
- collaborate on a shared document featuring critical data resources
This workshop is designed for humanities scholars and students who are interested in pursuing data-driven work and who want to develop critical — rather than purely instrumental — data practices. Instructors and researchers who already work extensively with data are also welcome, regardless of discipline!
Equipment Requirements: Laptop recommended (Chromebooks OK)