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: 2320979
• 负责人: Vidya Samadi
• 金额: $ 36.76万
• 依托单位: Clemson University
• 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=2320979&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 types 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重现的软件实践的高级网络基础结构（CI）正在迅速改变水科学。但是，水科学用户社区以及当前的本科和研究生课程并没有跟上这种转变。为了解决这一差距，该项目将在为期三年的项目中通过经常出现的两周沉浸式黑客马拉松培训CI用户和CI贡献者。通过有效地将CI工具与分析，机器学习和云计算集成在一起，该项目将创建新的开源CI模块和课程，可以用作模板，以通过广泛的水科学学科来增加采用高级CI计算和数据驱动模型的模型。此外，该项目将促进开放，可互操作，可重现和可访问的CI工具，从而使科学界能够快速整合新发现并发展强大的工作流程。具有完整专业知识的教师和科学家在水科学，工程，计算机科学和教育方面将开发适用于各种水科学学科的新的CI培训材料。该项目进一步旨在通过将课程推广和分配到该联盟的130多名成员大学，扩大大学联盟联盟的数据访问和协作能力。网络培训活动将通过（i）促进整体和开源CI和分析方法来增加水科学研究活动，以及（ii）赋予下一代研究人员以克服CI水科学应用程序中的主要瓶颈。该项目将增加水科学的多样性，创建最先进的教育工具和课程，并赋予研究人员和学生从CI技术消费转变为CI技术创造。它将使用多种多样性计划来招募和扩大CI用户和CI的参与，来自多种多样的教育机构（HBCUS，PUIS，HSIS，R2S和R1）的代表性不足的群体的参与。该奖项由高级基础设施的统计范围内的统计范围内的统计信息。值得通过基金会的智力优点和更广泛的影响审查标准来通过评估来支持。