Abstract In this paper, we examine the role digital curation practices and practitioners played in facilitating open science (OS) initiatives amid the COVID‐19 pandemic. In Summer 2023, we conducted a content analysis of available information regarding 50 OS initiatives that emerged—or substantially shifted their focus—between 2020 and 2022 to address COVID‐19 related challenges. Despite growing recognition of the value of digital curation for the organization, dissemination, and preservation of scientific knowledge, our study reveals that digital curatorial work often remains invisible in pandemic OS initiatives. In particular, we find that, even among those initiatives that greatly invested in digital curation work, digital curation is seldom mentioned in mission statements, and little is known about the rationales behind curatorial choices and the individuals responsible for the implementation of curatorial strategies. Given the important yet persistent invisibility of digital curatorial work, we propose a shift in how we conceptualize digital curation from a practice that merely “adds value” to research outputs to a practice of knowledge production. We conclude with reflections on how iSchools can lead in professionalizing the field and offer suggestions for initial steps in that direction.

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.

In this paper, we argue that WhatsApp can play an important role in correcting misinformation. We show how specific WhatsApp affordances (flexibility in format and audience selection) and existing social capital (prevalence of strong ties; homophily in political groups) can be leveraged to maximize the re-sharing of debunking messages, such as those accessed by WhatsApp users via ChatBots and Tip-Lines. Debunking messages received in the format of audio files generated more interest and were more effective in correcting beliefs than text- or image-based messages. In addition, we found clear evidence that users re-share debunks at higher rates when they received them from people close to them (strong ties), from individuals who generally agree with them politically (in-group members), or when both conditions are met. We suggest that WhatsApp leverages our findings to maximize the re-share of those fact-checks that are already circulating on the platform by using the existing social capital in the network, unlocking the potential for such debunks to reach a larger audience on WhatsApp.

Building from sociotechnical studies of disinformation and of information infrastructures, we examine how - over a period of eleven months - Italian QAnon supporters designed and maintained a distributed, multi-layered "infrastructure of disinformation" that spans multiple social media platforms, messaging apps, online forums, alternative media channels, as well as websites, databases, and content aggregators. Examining disinformation from an infrastructural lens reveals how QAnon disinformation operations extend well-beyond the use of social media and the construction of false narratives. While QAnon conspiracy theories continue to evolve and adapt, the overarching (dis)information infrastructure through which "epistemic evidence" is constructed and constantly updated is rather stable and has increased in size and complexity over time. Most importantly, we also found that deplatforming is a time-sensitive effort. The longer platforms wait to intervene, the harder it is to eradicate infrastructures as they develop new layers, get distributed across the Internet, and can rely on a critical mass of loyal followers. More research is needed to examine whether the key characteristics of the disinformation infrastructure that we identified extend to other disinformation infrastructures, which might include infrastructures put together by climate change denialists, vaccine skeptics, or voter fraud advocates.

Data-centered participatory design research projects—wherein researchers collaborate with community members for the purpose of gathering, generating, or communicating data about the community or their causes—can place epistemic burdens on minoritized or racialized groups, even in projects focused on social justice outcomes. Analysis of epistemic burden encourages researchers to rethink the purpose and value of data in community organizing and activism more generally. This paper describes three varieties of epistemic burden drawn from two case studies based on the authors’ previous work with anti-police brutality community organizations. The authors conclude with a discussion of ways to alleviate and avoid these issues through a series of questions about participatory research design. Ultimately, we call for a reorientation of knowledge production away from putative design solutions to community problems and toward a more robust interrogation of the power dynamics of research itself.

International audience

Open access to data is commonly required by funding agencies, journals, and public policy, despite the lack of agreement on the concept of "open data." We present findings from two longitudinal case studies of major scientific collaborations, the Sloan Digital Sky Survey in astronomy and the Center for Dark Energy Biosphere Investigations in deep subseafloor biosphere studies. These sites offer comparisons in rationales and policy interpretations of open data, which are shaped by their differing scientific objectives. While policy rationales and implementations shape infrastructures for scientific data, these rationales also are shaped by pre-existing infrastructure. Meanings of the term "open data" are contingent on project objectives and on the infrastructures to which they have access.