RAPID: Linking institutional logics and data sharing to research outputs before and after SARS-CoV-2 peak infection

科学家访问和共享与COVID-19研究相关的数据的能力（例如，基因组学数据、序列和毒株、监测数据、蛋白质结构）对于了解病毒并最终开发治疗剂和疫苗至关重要。数据的获取和共享将对知识产生的数量和发现的速度产生重大影响，这两者都会影响美国的公共卫生。早期的报告证明了创纪录的科学合作和数据共享，但从长远来看，数据访问和共享将在很大程度上取决于科学家的价值观、信仰和实践的融合，以及对数据储存库的治理和管理。这项研究调查了科学家的数据访问和共享偏好和行为在COVID-19大流行期间如何变化，因为研究从早期危机应对转向可见性，声誉和创新的具体机会。在危机的早期，我们预计研究界将使用具有最大限度地开放数据共享的治理模型的存储库。但随着知识的积累，对病毒的普遍了解的增加，以及病例数量的稳定，我们预计会看到一个转变，研究人员在如何，何时以及在何处分享他们的数据方面变得更加有选择性。本项目研究这些变化是如何以及为什么发生的，以便设计数据治理解决方案，最大限度地提高社区对公共危机的反应，最大限度地减少全球合作和数据共享的延迟，并最终导致研究生产和创新。这项研究利用了COVID提供的独特机会窗口，19危机，以了解数据访问和共享的机构决定因素如何以及为什么在公共卫生危机过程中演变，以及数据存储库如何发挥促进作用（或障碍）的快速研究响应社会的挑战。我们问：研究人员如何看待和整合不同的，往往是相互冲突的，数据共享的理由时，面对突发公共事件？随着危机的发展，研究人员如何调整他们的行动和决定？数据存储库如何通过调整、整合或破坏有关数据共享的相互冲突的制度逻辑来应对全球挑战？为什么研究人员会集中在某些数据库上，而不是其他数据库？什么样的治理模式能够更成功地整合相互冲突的逻辑，从而最大限度地提高研究人员对公共危机的反应？为了构建调查框架，我们整合了机构工作和开放社区理论，将1）关于数据访问，交换和使用的个人决策和行动与2）数据存储库的集体治理系统联系起来。该研究采用混合方法研究设计，使用访谈和调查数据来了解研究人员在感染高峰期前后的决策，行动和产出。通过展示研究人员和数据存储库如何应对全球范围内快速研究和数据共享的需求，研究结果将为资助机构、数据存储库管理团队、大学和研究社区提供实用的见解。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The ability of scientists to access and share data relevant for COVID-19 research (e.g., genomics data, sequences and strains, surveillance data, protein structures) is critical for understanding the virus and ultimately developing therapeutics and vaccines. Access to and sharing of data will have a significant effect on the amount of knowledge produced and the speed of discovery, both of which impact public health in the US. Early reports attest to record-breaking scientific collaboration and data sharing, but in the long run data access and sharing will greatly depend on the confluence of scientists’ values, beliefs and practices, as well as the governance and management of the repositories curating the data. This research investigates how scientists’ data access and sharing preferences and behaviors change over the course of the COVID-19 pandemic, as research moves from early crisis response to concrete opportunities for visibility, reputation, and innovation. Early in the crisis, we expect that the research community will use repositories with governance models that maximize open sharing of data. But as knowledge builds, general understanding of the virus increases, and the number of cases plateaus, we expect to see a shift, with researchers becoming more selective in how, when, and where they share their data. This project investigates how and why these change occur in order to design data governance solutions that maximize the community response to public crises, minimize delays in global collaboration and data sharing, and ultimately lead to research production and innovation.This research takes advantage of the unique opportunity window offered by the COVID-19 crisis to understand how and why institutional determinants of data access and sharing evolve over the course of a public health crisis and how data repositories can act as facilitators (or barriers) for rapid research response to societal challenges. We ask: How do researchers perceive and integrate different, often conflicting, rationales for data sharing when faced with public emergencies? How do researchers adjust their actions and decisions as the crisis evolves? How do data repositories react to global challenges by adjusting, integrating, or disrupting conflicting institutional logics regarding data sharing? Why do researchers converge around certain data repositories but not others? What governance models are more successful in integrating conflicting logics in a way that maximize researchers’ response to public crises? To frame the inquiry, we integrate institutional work and open community theories to link 1) individual-level decisions and actions regarding data access, exchange, and use with 2) collective governance systems of data repositories. The study undertakes a mixed-method research design that uses interviews and survey data to understand the decisions, actions, and outputs of researchers before and after peak infection levels. Findings will contribute practical insights to funding agencies, data repository management teams, universities, and research communities by showing how researchers and data repositories respond to the need for rapid research and data sharing at a global level.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.