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
基本信息
- 批准号:2320979
- 负责人:
- 金额:$ 36.76万
- 依托单位:
- 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:
- 关键词:
项目摘要
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复使用的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,水科学用户群体以及当前的本科生和研究生课程没有跟上这一转变的步伐。为了弥补这一差距,该项目将在为期三年的项目期间,通过为期两周、身临其境的反复黑客松来培训CI用户和CI贡献者。通过有效地将传播与信息工具与分析、机器学习和云计算相结合,该项目将创建新的、开源的传播与信息模块和课程,这些模块和课程可以作为模板,以促进各种水科学学科采用先进的传播与信息计算和数据驱动模型。此外,该项目将促进开放、可互操作、可复制和可访问的传播与信息工具,使科学界能够迅速整合新的发现并制定强有力的工作流程。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的CI培训材料。该项目旨在通过向该联盟的130多所成员大学推广和分发课程,进一步扩大该联盟(CUAHSI)的数据访问和协作能力。网络培训活动将通过以下方式增加水科学研究活动:(I)推广全面和开源的传播与信息和分析方法,(Ii)增强下一代研究人员克服传播与信息应用于水科学方面的主要瓶颈的能力。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从CI技术消费转向CI技术创造。它将使用多个多样性计划来招募和扩大CI用户和CI贡献者的参与,这些用户和贡献者来自广泛类型的教育机构(HBCU、PUI、HSI、R2和R1)。这项由高级网络基础设施办公室颁发的奖项由地球科学局联合支持。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势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
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Vidya Samadi其他文献
Challenges and opportunities when bringing machines onto the team: Human-AI teaming and flood evacuation decisions
将机器引入团队时的挑战和机遇:人机协作和洪水疏散决策
- DOI:
10.1016/j.envsoft.2024.105976 - 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Vidya Samadi;Keri K. Stephens;A. Hughes;Pamela Murray - 通讯作者:
Pamela Murray
Decoding time: Unraveling the power of N-BEATS and N-HiTS vs. LSTM for accurate soil moisture prediction
解码时间:揭示N - BEATS和N - HiTS相较于LSTM在精准土壤湿度预测方面的能力
- DOI:
10.1016/j.compag.2025.110614 - 发表时间:
2025-10-01 - 期刊:
- 影响因子:8.900
- 作者:
Lisa Umutoni;Vidya Samadi;George Vellidis;Charles Privette III;Jose Payero;Bulent Koc - 通讯作者:
Bulent Koc
Can large language models effectively reason about adverse weather conditions?
大型语言模型能否有效地对恶劣天气状况进行推理?
- DOI:
10.1016/j.envsoft.2025.106421 - 发表时间:
2025-04-01 - 期刊:
- 影响因子:4.600
- 作者:
Nima Zafarmomen;Vidya Samadi - 通讯作者:
Vidya Samadi
Vidya Samadi的其他文献
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{{ truncateString('Vidya Samadi', 18)}}的其他基金
SCC-PG : Human-AI Teaming for Flood Evacuation Decision Making
SCC-PG:人机协作进行洪水疏散决策
- 批准号:
2125283 - 财政年份:2021
- 资助金额:
$ 36.76万 - 项目类别:
Standard Grant
RAPID: Reconstruction of Hurricane Florence Flood Hydrographs (HF2Hs) for South Carolina's Critical Infrastructures Using Data Analytics Algorithms and In-situ Field Measurements
RAPID:使用数据分析算法和现场现场测量重建南卡罗来纳州关键基础设施的飓风弗洛伦斯洪水过程线 (HF2Hs)
- 批准号:
2035685 - 财政年份:2020
- 资助金额:
$ 36.76万 - 项目类别:
Standard Grant
RAPID: Reconstruction of Hurricane Florence Flood Hydrographs (HF2Hs) for South Carolina's Critical Infrastructures Using Data Analytics Algorithms and In-situ Field Measurements
RAPID:使用数据分析算法和现场现场测量重建南卡罗来纳州关键基础设施的飓风弗洛伦斯洪水过程线 (HF2Hs)
- 批准号:
1901646 - 财政年份:2018
- 资助金额:
$ 36.76万 - 项目类别:
Standard Grant
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