Collaborative Research: CyberTraining: Implementation: Small: Broadening Adoption of Cyberinfrastructure and Research Workforce Development for Disaster Management

灾害是一个突出的全球性问题，同时对多个国家或地区构成威胁。随着地理空间大数据意识的增强和计算能力的提高，灾害管理能力逐渐增强，从而产生空间脆弱性和对情况的理解，从而支持及时决策。该项目将建立一个国际灾害管理网络培训（CTDM）网络，灾害研究界可以通过参与拟议的培训活动来扩大其网络基础设施和地理空间技能。该项目将在学术机构、政府机构、灾害研究中心、工业和教育组织之间建立一个基于ci的地理空间灾害科学网络，以利用相关社区的专业知识开发培训材料，为下一代劳动力做好准备。开发了一个新的培训课程，包括各种培训模式，如暑期学校、研讨会和在线网络研讨会，利用CI和可扩展的地理空间分析进行有效的灾害管理实践。目标是通过多样化的合作网络培训2000多名学生、研究人员和教育工作者。该项目将扩大灾害研究界使用CI的机会，并有助于加强灾害科学、地球科学、交通、工程、社会、行为和经济科学等不同学科的劳动力发展。通过与西班牙裔服务机构（如德克萨斯农工大学）和传统黑人学院和大学（如摩根州立大学）的合作，将向代表性不足的社区提供各种灾难数据、培训材料和CI资源。该项目将帮助灾害研究团体扩大其基于ci的灾害管理和计算技能，从而提高决策能力，增强社区的抗灾能力。CTDM旨在极大地改善受气候变化和相关灾害严重影响的社会弱势群体的福祉。该项目将通过开发基于CI的灾害管理课程，向灾害研究界介绍先进的CI和地理空间分析。一个关键的方法是通过引入从基础到高级的四个相互关联的培训模块：CI计算模块、灾害数据模块、地理空间分析模块和灾害解决问题模块，将CI和地理空间分析应用于灾害管理。灾难数据模块提供了FAIR（可查找性、可访问性、互操作性和可重用性）原则的最佳实践以及前沿的地理空间数据分析和可视化技术。CI- enabled Computing Module介绍CI和高性能计算的基本概念和技能，以降低在灾害管理研究中利用CI的障碍。通过地理空间分析模块，学习者将具备先进的地理空间数据分析和可视化技术，以更好地了解不同时空尺度的灾害模式。最后，灾害问题解决模块作为一个集成框架，确保灾害管理概念和实践与其他三个模块良好地联系起来，从而全面了解高级CI所解决的灾害管理挑战。该奖项由高级网络基础设施办公室颁发，并得到社会、行为和经济科学理事会的联合支持。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。

Disasters are prominent global issues which simultaneously pose threats to multiple countries or regions. Disaster management is gradually empowered by increasing geospatial big data awareness and growing computing capabilities to produce spatial vulnerability and situational understanding for supporting timely decisions. This project will establish an international CyberTraining for Disaster Management (CTDM) network in which disaster research communities can broaden their cyberinfrastructure (CI) and geospatial skills by participating in the proposed training activities. The project will establish a CI-enabled geospatial disaster science network among academic institutions, governmental agencies, hazards research centers, industry, and educational organizations to leverage the expertise of pertinent communities in developing training materials for preparing the next-generation workforce. A novel training curriculum is developed to consist of various training modalities such as summer schools, workshop sessions, and online webinars, which utilize CI and scalable geospatial analytics for effective disaster management practice. The goal is to train over 2000 students, researchers, and educators through diverse collaboration networks. The project will broaden access to CI for disaster research communities and help enhance workforce development among diverse disciplines such as disaster science, geosciences, transportation, engineering, social, behavioral, and economic sciences. A variety of disaster data, training materials, and CI resources will be provided to underrepresented communities through partnerships with Hispanic Serving Institutions (e.g., Texas A&M University) and Historically Black Colleges & Universities (e.g., Morgan State University). The project will help disaster research communities broaden their CI-enabled disaster management and computational skills, thus improving decision-making capabilities for enhancing community resilience. CTDM is designed to greatly improve the well-being of socially vulnerable communities significantly impacted by climate change and related disasters.The project will introduce advanced CI and geospatial analytics to disaster research communities by developing a CI-enabled disaster management curriculum. A key approach is to apply CI and geospatial analytics in disaster management by introducing four interconnected training modules from basic to advanced learning levels: CI-Enabled Computing Module, Disaster Data Module, Geospatial Analytics Module, and Disaster Problem-Solving Module. The Disaster Data Module provides best practices of the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles and cutting-edge geospatial data analysis and visualization techniques. The CI-Enabled Computing Module Introduces fundamental concepts and skills of CI and high-performance computing to lower the barriers to taking advantage of CI in disaster management research. Through the Geospatial Analytics Module, learners will be equipped with advanced geospatial data analysis and visualization techniques to better understand disaster patterns across various spatiotemporal scales. Finally, the Disaster Problem-Solving Module serves as an integration framework to ensure disaster management concepts and practices will be well connected with the other three modules for a holistic understanding of disaster management challenges addressed by advanced CI. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Directorate for Social, Behavioral, and Economic Sciences.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.