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

灾害是突出的全球性问题，同时对多个国家或地区构成威胁。通过不断提高对地理空间大数据的认识和不断增强的计算能力，逐步增强灾害管理的能力，以产生空间脆弱性和情况了解，以支持及时决策。该项目将建立一个国际灾害管理网络培训(CTDM)网络，在该网络中，灾害研究界可以通过参加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由传播和信息技术支持的地球空间灾害科学网络，以利用有关社区的专业知识编写培训材料，为下一代劳动力做准备。开发了一种新的培训课程，包括各种培训形式，如暑期学校、讲习班和在线网络研讨会，利用传播与信息和可扩展的地理空间分析进行有效的灾害管理实践。我们的目标是通过我们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社区接触传播与信息的渠道，并有助于加强灾害科学、地球科学、交通运输、工程、社会、行为和经济科学等不同学科的劳动力发展。将通过与拉美裔服务机构(例如德克萨斯农工大学)和历史上的黑人学院和大学(例如摩根州立大学)的伙伴关系，向代表性不足的社区提供各种灾害数据、培训材料和CI资源。该项目将帮助灾害研究社区扩大其传播信息支持的灾害管理和计算技能，从而提高决策能力，以增强社区的复原力。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社区的福祉。该项目将通过开发支持CI的灾害管理课程，向灾害研究社区介绍先进的CI和地理空间分析。一个关键方法是将CI和地理空间分析应用于灾害管理，从基础到高级引入四个相互关联的培训模块：启用CI的计算模块、灾害数据模块、地理空间分析模块和灾害问题解决模块。灾害数据模块提供公平(可发现性、可访问性、互操作性和可重用性)原则以及尖端地理空间数据分析和可视化技术的最佳实践。支持CI的计算模块引入了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，学员将掌握先进的地理空间数据分析和可视化技术，以便更好地了解各种时空尺度上的灾害模式。最后，灾害问题解决模块作为一个综合框架，确保灾害管理的概念和做法与其他三个模块很好地结合在一起，以便全面了解高级传播与信息部门应对的灾害管理挑战。这项由高级网络基础设施办公室颁发的奖项由社会、行为和经济科学理事会共同支持。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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. We aim to train over 2000 students, researchers, and educators through our 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.