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

科学家能够获取和共享与COVID-19研究相关的数据（例如基因组学数据、序列和毒株、监测数据、蛋白质结构），这对于了解病毒并最终开发治疗方法和疫苗至关重要。获取和共享数据将对产生的知识数量和发现的速度产生重大影响，这两者都影响到美国的公共卫生。早期的报告证明了破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，以及对数据存储库的治理和管理。本研究调查了在2019冠状病毒病大流行期间，随着研究从早期危机应对转向能见度、声誉和创新的具体机会，科学家的数据访问和共享偏好和行为是如何变化的。在危机早期，我们期望研究社区将使用带有治理模型的存储库，以最大限度地开放数据共享。但随着知识的积累，对病毒的普遍了解的增加，以及病例数量的停滞，我们预计会看到一种转变，研究人员在如何、何时、何地分享数据方面变得更加有选择性。该项目调查了这些变化发生的方式和原因，以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协作和数据共享的延迟，并最终导致研究成果和创新。本研究利用2019冠状病毒病危机提供的独特机会窗口，了解在公共卫生危机过程中，数据获取和共享的制度决定因素如何以及为何演变，以及数据存储库如何促进（或阻碍）快速研究应对社会挑战。我们的问题是：面对突发公共事件，研究人员如何看待和整合不同的、往往相互冲突的数据共享理由？随着危机的发展，研究人员如何调整他们的行动和决策？数据存储库如何通过调整、整合或破坏与数据共享相关的相互冲突的制度逻辑来应对全球挑战？为什么研究人员集中在某些数据存储库而不是其他数据存储库？什么样的治理模式更成功地整合了相互冲突的逻辑，从而最大限度地提高了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、交换和使用的个人层面决策和行动与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前后的决策、行动和产出。研究结果将通过展示研究人员和数据存储库如何响应全球快速研究和数据共享的需求，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。该奖项反映了美国国家科学基金会的法定使命，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.