Collaborative Research: CyberTraining: Implementation: Small: Inclusive Cyberinfrastructure and Machine Learning Training to Advance Water Science Research

合作研究：网络培训：实施：小型：包容性网络基础设施和机器学习培训，以推进水科学研究

• 批准号: 2320980
• 负责人: Ibrahim Demir
• 金额: $ 12.5万
• 依托单位: University of Iowa
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2320980&HistoricalAwards=false
• 关键词: Collaborative; Research; CyberTraining; Implementation; Small

Advanced cyberinfrastructure (CI) that emphasizes machine learning modeling, data analytics, cloud computing, and open and reproducible software practices is rapidly transforming water science. However, the water science user community and the current undergraduate and graduate curricula have not kept pace with this transformation. To address this gap, this project will train CI users and CI contributors through a recurring, two-week-long, immersive hackathon during a three-year project. By effectively integrating CI tools with analytics, machine learning, and cloud computing this project will create new, open-source CI modules and curriculum that can serve as templates to increase the adoption of advanced CI computational and data-driven models by a broad range of water science disciplines. Furthermore, this project will promote open, interoperable, reproducible, and accessible CI tools, allowing the scientific community to quickly integrate new findings and develop robust workflows. Faculty and scientists with complementary expertise in water science, engineering, computer science, and education will develop new CI training materials that are applicable to various water science disciplines. This project further aims to extend the data access and collaboration capabilities of the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) by promoting and distributing the curriculum to the consortium’s 130-plus member universities. The cyber-training activities will increase water science research activities by (i) promoting holistic and open-source CI and analytics approaches, and (ii) empowering the next generation of researchers to overcome major bottlenecks in CI applications for water science. This project will increase diversity in water science, create state-of-the-art educational tools and curricula, and empower researchers and students to move from CI technology consumption to CI technology creation. It will use multiple diversity programs to recruit and broaden the participation of CI users and CI contributors from underrepresented groups across a broad range of educational institutions (HBCUs, PUIs, HSIs, R2s, and R1s).This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Directorate for Geosciences.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.

强调机器学习建模、数据分析、云计算以及开放和可复制软件实践的先进网络基础设施（CI）正在迅速改变水科学，然而，水科学用户社区以及当前的本科生和研究生课程却未能跟上这一步伐。为了解决这一差距，该项目将在为期三年的项目中通过定期、为期两周的沉浸式黑客马拉松来培训 CI 用户和 CI 贡献者，从而有效地将 CI 工具与分析、机器学习和云计算相集成。项目将创建新的开源 CI 模块和课程，作为模板，以增加广泛的水科学学科对先进 CI 计算和数据驱动模型的采用。此外，该项目将促进开放、可互操作、可重复和可重复性。 CI 工具使科学界能够快速整合新发现并开发强大的工作流程，在水科学、工程、计算机科学和教育方面具有互补专业知识的教师和科学家将开发适用于各种水科学学科的新 CI 培训材料。该项目进一步旨在扩展数据访问通过向联盟的 130 多所成员大学推广和分发课程，网络培训活动将通过以下方式增加水科学研究活动：促进整体和开源 CI 和分析方法，以及 (ii) 帮助下一代研究人员克服水科学 CI 应用的主要瓶颈。该项目将增加水科学的多样性，创造。最先进的教育工具和课程，并使研究人员和学生能够从 CI 技术消费转向 CI 技术创造。它将利用多种多元化计划来招募和扩大来自代表性不足群体的 CI 用户和 CI 贡献者的参与。广泛的教育机构（HBCU、PUI、HSI、R2 和 R1）。该奖项由先进网络基础设施办公室颁发，并得到地球科学理事会的共同支持。该奖项反映了 NSF 的法定使命，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。