Book Club Meetup (Data Feminism)

Description

This interactive, dialogue-driven meetup will explore applying feminist theory and critique to how we work with data and perform research. How do structures of power and oppression shape the data we collect, analyses we perform, and research insights we develop? Together we will reflect on selected chapters from the (freely available) book Data Feminism by Catherine D'Ignazio and Lauren Klein. The book can be freely accessed online here: https://data-feminism.mitpress.mit.edu/. In advance of the meetup participants should read Chapter 6: The Numbers Don't Speak for Themselves and at least skim the book's introduction. Come prepared to apply takeaways from the readings to unpacking provided case studies from the health sciences, and your own research processes. Researchers from all domains are welcome to participate. Together we’ll brainstorm how as individuals and a community we can contribute to more equitable data-driven research.

Prerequisites

Participants should read Chapter 6: The Data Don't Speak for Themselves as well as at least skim the introduction from Data Feminism (https://data-feminism.mitpress.mit.edu/) in advance of the session. It is recommended, but not required, to also watch the Data Feminism Symposium recording by the UC Davis DataLab featuring a talk by the book's authors (https://datalab.ucdavis.edu/2021/04/14/save-the-date-for-the-data-feminism-symposium-may-12-2021/).

Instructors: Pamela Reynolds, Ariel Deardorff

Instructors’ Biographies

Pamela Reynolds, PhD, is the co-lead of the Data Feminism Research and Learning Cluster at the UC Davis DataLab and an experimental ecologist with a background in project management and team science. At DataLab she works to connect and train students, faculty and staff with computational tools and thinking to accelerate innovative research. Reynolds received her PhD in Biology from the University of North Carolina at Chapel Hill where she studied the biodiversity of the world’s oceans.

Ariel Deardorff, MLIS, is the Data Services Librarian at the University of California, San Francisco. In her role she teaches classes and provides support for research data management, open science, and reproducibility in the health sciences. She is an expert in research data publishing, and has led the UCSF Library’s involvement with several data infrastructure and policy projects. Ariel is also an open science advocate who performs research on the role of the Library in enabling open and reproducible research.