RAPID: Dashboard for COVID-19 Scientific Development

RAPID：COVID-19 科学发展仪表板

• 批准号: 2028717
• 负责人: Ying Ding
• 金额: $ 19.86万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028717&HistoricalAwards=false
• 关键词: RAPID; Dashboard; COVID; 19; Scientific

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.

科学发现依赖于知识的积累。关于任何特定主题，都有数以千计的文章，但没有人能够阅读全部文章，这一限制在 COVID-19 时代更为重要，在这个时代，依赖知识可能意味着生命与生命之间的差异。该项目致力于通过开发可视化仪表板来总结与 COVID-19 相关的研究工作，从而改进与科学知识综合相关的方法。主要目标是将白宫当前的 COVID-19 文献数据集与来自的知识图整合起来。 PubMed 和开发的药物发现知识图Data2Discovery 将能够创建“抗击 COVID-19 仪表板”，这是一种可视化工具，可以集中和可视化与 COVID 相关的关键、最新数据和科学信息。该仪表板将帮助科学家和拥护者访问和可视化最重要的信息。有关新冠病毒的最新信息对于挖掘出版物以生成研究假设以及确定科学传播的合作和创新模式以阻止新冠病毒的传播也至关重要。PI 将开放其数据和构建仪表板的代码。向公众开放，以实现未来通过透明度增强公众对科学的信任。该项目开发了“抗击 COVID-19 数据集”和可视化仪表板，以推进信息科学并帮助抗击 COVID-19。这是通过整合当前的 COVID-19 文献数据集来实现的。白宫提供了 PubMed 的知识图和药物发现知识图，该知识图将临床前药物发现中的数十个公开可用数据库相互连接。该仪表板将显示 COVID-19 相关信息，包括：1）当前最常被提及的生物实体（例如，毒品， 2) 根据 PubMed 文献和临床试验的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；以及 5) 科学团队的合作；这项研究将超越关键词分析，进一步深入理解关键词所涉及的对象，并推动文本方法向知识方向发展，从而推动建立网络并激发潜在的合作，以对抗 COVID-19。它还为最近与生物实体相关的 COVID-19 科学研究的演变路径的调查添加了纵向元素，并以新的和潜在的创新方式将科学与相关研究领域联系起来。该奖项反映了 NSF 的法定规定。使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Are we there yet? Analyzing scientific research related to COVID-19 drug repurposing

我们到了吗？

• DOI: 10.21203/rs.3.rs-80893/v1
• 发表时间: 2021-01
• 期刊: 2021.
• 作者: Park, N.;Ryu, H.;Ding, Y.;Yu, Q.;Bu, Y.;Wang, Q.;Yang, J.;& Song, M.
• 通讯作者: & Song, M.

COVID-19 Portal: Profiling Researchers, Bio-entities, and Institutions

COVID-19 门户：研究人员、生物实体和机构概况

• 发表时间: 2022-01
• 期刊: 2022
• 作者: Wan; A.
• 通讯作者: A.

Building the COVID-19 portal by integrating literature, clinical trials, and knowledge graphs

通过整合文献、临床试验和知识图构建 COVID-19 门户

• DOI: 10.1109/jcdl52503.2021.00040
• 发表时间: 2021-01
• 期刊: 2021
• 作者: Wan; A.
• 通讯作者: A.

Analyzing knowledge entities about COVID-19 using entitymetrics

• DOI: 10.1007/s11192-021-03933-y
• 发表时间: 2021
• 期刊: Scientometrics
• 影响因子: 3.9
• 作者: Yu Q;Wang Q;Zhang Y;Chen C;Ryu H;Park N;Baek JE;Li K;Wu Y;Li D;Xu J;Liu M;Yang JJ;Zhang C;Lu C;Zhang P;Li X;Chen B;Ebeid IA;Fensel J;Min C;Zhai Y;Song M;Ding Y;Bu Y
• 通讯作者: Bu Y

Toward a Coronavirus Knowledge Graph

走向冠状病毒知识图谱

• DOI: 10.3390/genes12070998
• 发表时间: 2021-06-29
• 期刊: Genes
• 影响因子: 3.5
• 作者: Zhang P;Bu Y;Jiang P;Shi X;Lun B;Chen C;Syafiandini AF;Ding Y;Song M
• 通讯作者: Song M