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During COVID-19, countless dashboards have served as central media where people learn critical information about the pandemic. Varied actors, including news organizations, government agencies, universities, and NGOs created and maintained these dashboards, conducting the onerous labor of collecting, categorizing, and taking care of COVID data. This study uncovers different forms of data practices and labor behind the building of these dashboards, based on in-depth interviews with volunteers and practitioners across India and the United States who have participated in COVID dashboard projects.Specifically, we are interested in projects that have focused on underrepresented or missing COVID data such as COVID cases in prisons and long-term care facilities, racial/ethnic breakdown of cases, as well as deaths due to COVID enforcement. These data builders employed sometimes creative, sometimes mundane and laborious data practices to not simply collect, but to produce these data that are often invisible in the official COVID dataset. In this process of data production, dashboard builders grappled with the questions of how certain data is collected, who/what is missing from the dataset, and how these data voids shape and manipulate our understanding of the pandemic. Interviewing 74 data builders who participated in COVID dashboard projects, this paper demonstrates the range of underrepresented and messy COVID data that these data builders have identified, fixed, and maintained to render them useful: disappearing data, lumped data, and absent data. Such critical engagement with messy COVID data reveals different data injustices that have tremendous potential to affect future pandemic preparation and management. 

ABSTRACT The goals of open science are driven by policies requiring data management, sharing, and accessibility. One way of measuring the impact of open science policies on scientific knowledge is to access data that has been prepared for re‐use. But how accessible/available are data resources? In this paper, we discuss a method for exploring and locating datasets made available by scientists from federally funded projects in the US. The data pathways method was tested on federal awards. Here we describe the method and the results from analyzing fifty federal awards granted by the National Science Foundation to pursue data resources and their availability in publications, data repositories, or institutional repositories. The data pathways approach contributes to the development of a practical approach on availability that captures the current ways in which data are accessible from federally funded science projects –ranging from institutional repositories, journal data deposit, PI and project web pages, and science data platforms, among other found possibilities. This paper discusses some background and motivations for such a method, the method, research design, barriers encountered when searching for data resources from projects, and how this method can be useful to future studies of data availability. 

Lessons from the recent past. 

What is the relationship between Data Management Plans (DMPs), DMP guidance documents, and the reality of end-of-project data preservation and access? In this short paper we report on some preliminary findings of a 3-year investigation into the impact of DMPs on federally funded science in the United States. We investigated a small sample of publicly accessible DMPs (N=14) published using DMPTool. We found that while DMPs followed the National Science Foundation's guidelines, the pathways to the resulting research data are often obscure, vague, or not obvious. We define two “data pathways” as the search tactics and strategies deployed in order to find datasets. 

This poster reports on ongoing research into the National Science Foundation’s Data Management Plan guidelines and its impact on science data lifecycles. We ask two research questions (RQs): 1) How does guidance about the formulation of DMPs vary across different research areas? And 2) How has guidance about the management of data changed since the first DMP policies were published in 2011? To this end, we collected, examined, and compared 37 DMP guidance policies from 15 different research areas. We identify the following three themes during document analysis: 1) Responsibility for the future of data; 2) Data maintenance changes over time; and 3) The use of data repositories. Based on these preliminary findings we believe that National Science Foundation guidance policies represent a unique view into changes in data management practices over the last decade.