RAPID: Dashboard for COVID-19 Scientific Development

科学发现依赖于知识的积累。任何一个主题都有成千上万的文章，但没有一个人能读完它们。这种局限性在COVID-19时代更为重要，可靠的知识可能意味着生与死的区别。该项目致力于通过开发可视化仪表板来总结COVID-19相关研究工作，从而改进与科学知识综合相关的方法。主要目标是将来自怀特豪斯的当前COVID-19文献数据集与来自PubMed的知识图和Data 2Discovery开发的药物发现知识图整合在一起。这将有助于创建“抗击COVID-19仪表板”，这是一个可视化工具，将集中和可视化与COVID相关的关键、最新数据和科学信息。这个仪表板将帮助科学家和临床医生访问和可视化有关COVID的最新信息。这些信息对于矿业出版物产生研究假设以及确定科学传播中的合作和创新模式以阻止COVID的传播也至关重要。该项目旨在开发Fight COVID-19 Dataset和可视化仪表板，以推动信息科学的发展，并帮助抗击COVID-19。这是通过整合来自白宫的当前COVID-19文献数据集与来自PubMed的知识图谱以及在临床前药物发现中互连数十个公开数据库的药物发现知识图谱来实现的。此仪表板将显示COVID-19相关信息，包括：1）当前最常提及的生物实体（例如，药物、疾病、疫苗、基因）; 2）PubMed文献和临床试验中相关生物实体的演变; 3）相关生物实体的网络连接; 4）活跃科学家、团队和机构及其研究主题的列表;及5）科学团队的合作，以建立网络并激发潜在的合作以对抗COVID-19。这项研究将超越关键词分析，进一步理解关键词所指示的对象，并推动文本方法转向基于知识图的分析，从而推进文本分析方法。该奖项还为最近对与生物实体相关的COVID-19科学研究的演变途径的调查增加了纵向元素，并以新的和潜在的创新方式将科学科学与相关研究领域联系起来。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。

Scientific discovery depends on the accumulation of knowledge. There are thousands of articles on any given topic, but no one person can read them all. This limitation is more important in the COVID-19 era, where dependable knowledge can mean the difference between life and death. This project works to improve methods related to the synthesis of scientific knowledge by developing a visual dashboard to summarize COVID-19 related research efforts. The main goal is to integrate a current COVID-19 literature dataset from the Whitehouse with a knowledge graph from PubMed and a drug discovery knowledge graph developed by Data2Discovery. This would enable the creation of the “Fight COVID-19 Dashboard,” a visualization tool that would centralize and visualize crucial, up to date data and scientific information related to COVID. This dashboard will help scientists and clinicians access and visualize the most recent information about COVID. Such information is also crucial for mining publications to generate research hypotheses and for identifying patterns of collaboration and innovation in scientific communication working to stop the spread of COVID. The PIs will make their data and the codes for constructing the dashboard open to the public to enable future efforts and enhance public trust in science through transparency.This project develops the Fight COVID-19 Dataset and visual dashboard to advance information science and aid in the fight against COVID-19. This is accomplished by integrating a current COVID-19 literature dataset from the White House with a knowledge graph from PubMed and a drug discovery knowledge graph interlinking dozens of publicly available databases in pre-clinical drug discovery. This dashboard will display COVID-19 related information, including: 1) the currently most mentioned biological entities (e.g., drugs, diseases, vaccines, genes) in PubMed and clinical trials; 2) the evolution of related biological entities according to PubMed literature and clinical trials; 3) the network connections of related biological entities; 4) the lists of active scientists, teams, and institutions and their research topics; and 5) collaborations of scientific teams to enable networking and inspire potential collaborations to fight against COVID-19. This research will advance textual analysis methods by moving beyond keyword analysis towards advanced understanding of the objects that the keywords indicate and propelling textual methods towards knowledge graph-based analysis. It also adds a longitudinal element to recent investigations of the evolving pathway of COVID-19 scientific studies related to bio entities and links the science of science to related research domains in new and potentially innovative ways.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.