Abstract This study uses a theoretical lens of infrastructural dimensions to examine stakeholders' perceptions of the value of curation, focusing on the social science data repository, the Inter‐university Consortium for Political and Social Research (ICPSR). Drawing on 67 interviews with both internal (ICPSR staff) and external (funders, data producers, and reusers) stakeholders, we analyze how value is ascribed to curation across technical, organizational, and social components of infrastructure. We identify five key ways interviewees conceptualized the value of curation infrastructures: supporting sustainability and durability, enabling research efficiency, fostering trust, building community, and advancing data equity. Our findings highlight the role of curation in knowledge generation by reframing curation as infrastructure rather than a set of discrete practices. We clarify how transparency operates in dual—and sometimes conflicting—ways: as both understandability and invisibility, shaping trust in and access to data repositories. Second, we demonstrate how data equity is increasingly perceived by stakeholders as a core infrastructural value, enacted through practices that lower barriers to access. Finally, we surface the persistent challenges in evaluating and funding curation infrastructures due to their long time horizons and often‐invisible nature. This work advocates recognizing and funding curation infrastructures as essential for long‐term scientific and societal progress.

Abstract Despite increasing expectations that researchers and funding agencies release their data for reuse, concerns about data misuse hinder the open sharing of data. The COVID‐19 crisis brought urgency to these concerns, yet we are currently missing a theoretical framework to understand, prevent, and respond to research data misuse. In the article, we emphasize the challenge of defining misuse broadly and identify various forms that misuse can take, including methodological mistakes, unauthorized reuse, and intentional misrepresentation. We pay particular attention to underscoring the complexity of defining misuse, considering different epistemological perspectives and the evolving nature of scientific methodologies. We propose a theoretical framework grounded in the critical analysis of interdisciplinary literature on the topic of misusing research data, identifying similarities and differences in how data misuse is defined across a variety of fields, and propose a working definition of what it means “to misuse” research data. Finally, we speculate about possible curatorial interventions that data intermediaries can adopt to prevent or respond to instances of misuse.

Abstract A 5‐year project to study scientific data uses in geography, starting in 1999, evolved into 20 years of research on data practices in sensor networks, environmental sciences, biology, seismology, undersea science, biomedicine, astronomy, and other fields. By emulating the “team science” approaches of the scientists studied, the UCLA Center for Knowledge Infrastructures accumulated a comprehensive collection of qualitative data about how scientists generate, manage, use, and reuse data across domains. Building upon Paul N. Edwards's model of “making global data”—collecting signals via consistent methods, technologies, and policies—to “make data global”—comparing and integrating those data, the research team has managed and exploited these data as a collaborative resource. This article reflects on the social, technical, organizational, economic, and policy challenges the team has encountered in creating new knowledge from data old and new. We reflect on continuity over generations of students and staff, transitions between grants, transfer of legacy data between software tools, research methods, and the role of professional data managers in the social sciences.