Epistemologies of missing data COVID dashboard builders and the production and maintenance of marginalized COVID data

During COVID-19, countless dashboards served as the central media for people to learn critical information about the pandemic. Varied actors, including news organizations, government agencies, universities, and nongovernmental organizations, created and maintained these dashboards, through the onerous labor of collecting, categorizing, and circulating COVID data. This study uncovers different forms of labor and data practices—the work of “COVID data builders”—behind the construction of these dashboards based on in-depth interviews with volunteers and practitioners across the United States and India who participated in COVID dashboard projects. Specifically, we examine projects focused on marginalized and missing COVID data that aimed to show the pandemic's disproportionate and unjust impact. Through an investigation of data builders’ encounters and experiences with missing COVID data in mediating the pandemic, we ask: What data problems did COVID data builders encounter? How did they produce missing COVID data while questioning its representational capacity? And lastly, what “alternative epistemologies of data” beyond representation do their data practices suggest? Through our analysis, we surfaced three types of epistemological ambiguities COVID data builders encountered within their datasets: disappearing and ephemeral data, obscuring data, and disregarded data. By highlighting these different epistemological dimensions of missing data, we conclude that focusing on the performative and infrastructural aspects of what makes datasets “work” builds a new vocabulary for addressing missing data beyond representation. We argue that the politics of counting COVID cases is grounded in the material and affective labor of confronting, navigating, and negotiating with data's epistemological ambiguities.