RAPID: COVID Information Commons (CIC)

该项目将创建一个COVID信息共享（CIC）网站，以促进各种COVID研究工作之间的知识共享和协作，特别是关注所有nsf资助的COVID快速响应研究（Rapid）项目。该中心将为来自政府、学术界、非营利组织和行业的研究人员以及决策者提供资源，以利用彼此的研究成果，投资并加速最有希望的研究，以减轻COVID-19大流行的广泛社会影响。它还将成为就其他公共卫生挑战进行综合知识共享和协作的典范，造福社会。对于来自学术界、工业界、政府和非营利部门的各种潜在利益相关者来说，项目将能够以最相关和用户友好的方式输入和发布有关其努力的信息。信息将以多种方式组织，例如，按研究主题、领域和地理。除了来自NSF COVID-19 RAPID项目的信息外，COVID信息共享平台还将纳入来自NSF开放知识网络项目以及其他NSF研究项目的冠状病毒相关信息。COVID信息共享将利用信息科学方法汇集由国家科学基金会资助的COVID-19 RAPID项目收集的信息。目前正在开展一系列广泛的研究工作，以研究大流行在生物物理学、社会正义/不平等、行为科学、公共卫生、供应链和风险管理等领域的影响。CIC将在语义上链接跨项目的信息，以提供跨不同工作的更全面的视图，包括NSF开放知识网络中的COVID项目等工作。由此产生的简洁、精心策划的综合资源将为nsf资助的COVID - RAPID项目提供见解，并促进这些项目之间的合作。这些目标将使用信息科学方法来实现：1)编制NSF COVID RAPID奖项的综合列表，以及每个项目的相关详细信息；2)链接到任何公开可用的数据集和数据源；3)使用为资源和现有分类法和语义框架开发的元数据模式，按研究领域和/或地理类别组织信息和数据源；4)设计和开发一个门户网站，允许项目团队发布他们的数据或数据链接，并以最相关和用户友好的方式为学术界、工业界和政府的研究人员呈现项目信息；5)整合schema.org的COVID-19注释数据，以便更有效地识别、检索和整合相关数据。首先将为网站开发一个最小可行产品，与社区的利益相关者合作，优先考虑功能并添加新功能。除了信息共享之外，该项目还将评估实施数据和模型共享的努力和可行性，以共享数据集和数据驱动模型，如与COVID-19相关的机器学习模型。该RAPID奖由综合活动办公室的融合加速器项目在《冠状病毒援助、救济和经济安全法案》的资助下颁发。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。

This project will create a COVID Information Commons (CIC) website to facilitate knowledge sharing and collaboration across various COVID research efforts, especially focusing on all the NSF-funded COVID Rapid Response Research (RAPID) projects. The CIC will serve as a resource for researchers as well as decision-makers from government, academia, not-for-profit and industry to leverage each other's findings, and invest in and accelerate the most promising research to mitigate the broad societal impacts of the COVID-19 pandemic. It will also serve as a model for integrated knowledge sharing and collaboration on other public health challenges, in benefit to society. Projects will be able to enter and publish information about their efforts in ways that are most relevant and user-friendly for a variety of potential stakeholders from academia, industry, government, and non-profit sectors. Information will be organized in multiple ways, for example, by research topics areas and by geography. In addition to information from NSF COVID-19 RAPID projects, the COVID Information Commons will incorporate coronavirus-related information from NSF Open Knowledge Network projects, as well as from other NSF research projects in general. The COVID Information Commons will utilize information science methods to bring together information about the collection of COVID-19 RAPID projects funded by the National Science Foundation. A wide array of research efforts are underway to study the impacts of the pandemic in fields as far ranging as biophysics, social justice/inequity, behavioral science, public health, supply chains, and risk management. The CIC will semantically link information across projects to provide a more holistic view across distinct efforts, including efforts such as the COVID projects in the NSF Open Knowledge Network. The resulting, concise, curated, integrated resource will provide insight into NSF-funded COVID RAPID projects and facilitate collaborations among such efforts. These objectives will be achieved using information science approaches to 1) compile a comprehensive list of NSF COVID RAPID awards, along with relevant details for each project, 2) link to any publicly available data sets and data feeds, 3) organize the information and data feeds, for example, by categories of research areas and/or geography, using a meta-data schema developed for the resource and existing taxonomy and semantic frameworks; 4) design and develop a web portal to allow project teams to publish their data, or links to the data, and present project information in ways that are most relevant and user friendly for researchers in academia, industry, and government; 5) integrate the schema.org COVID-19 annotated data to enable more effective identification, retrieval, and integration of relevant data. A Minimum Viable Product for the website will be developed first, working with stakeholders in the community to prioritize features and add new functionality. In addition to the Information Commons, the project will also assess the effort and feasibility of implementing a data and model commons—to share datasets as well as data-driven models, such as machine learning models related to COVID-19.This RAPID award is made by the Convergence Accelerator program in the Office of Integrative Activities with funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.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.