Collaborative Research: CyberTraining: Pilot: A Cybertraining Program to Advance Data Acquisition, Processing, and Machine Learning-based Modeling in Marine Science

合作研究：网络培训：试点：一项网络培训计划，旨在推进海洋科学中的数据采集、处理和基于机器学习的建模

• 批准号: 2230046
• 负责人: Qianqian Liu
• 金额: $ 20.57万
• 依托单位: University of North Carolina at Wilmington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2230046&HistoricalAwards=false
• 关键词: Collaborative; Research; CyberTraining; Pilot; Cybertraining

Marine science researchers have access to a variety of cyberinfrastructure (CI), including software applications, facilities to host large datasets and provide computational power, and scientific tools that enable them to explore and predict the ever-changing ocean. However, the use existing CI resources are often not intuitive for new researchers. Furthermore, early-career marine science researchers have limited experience with advanced computing and data engineering techniques, which makes adoption of advanced research CI resources difficult. This project will carry out two summer training programs to educate and train early career researchers in marine science to promote CI awareness and related technical skills development to facilitate and support their research. The project team includes oceanographers and machine learning scientists. This project will create a publicly available training material repository on core CI skills for marine science researchers and provide two summer training programs in 2023 and 2024. The training will introduce the design principles of a data workflow for research projects on CI. To support researchers in the workflow-building process, this project will create a drag-and-drop tool that helps researchers generate scripts for common data workflow components such as data acquisition, data processing, and data visualization. The scripts generated can be added as components of a data workflow system and then deployed to advanced CI platforms. This project will train early-career researchers in marine science to integrate and use CI in their research through hands-on projects and hackathons to solve open-ended research questions. The training programs will be broadly disseminated through various outreach programs to encourage participation from researchers at community colleges and minority-serving institutions that have marine science or related programs.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.

海洋科学研究人员可以使用各种网络基础设施（CI），包括软件应用程序，托管大型数据集并提供计算能力的设施以及使他们能够探索和预测不断变化的海洋的科学工具。但是，现有的CI资源对于新研究人员通常不直观。此外，早期的海洋科学研究人员在高级计算和数据工程技术方面的经验有限，这使得采用先进的研究CI资源。该项目将开展两个夏季培训计划，以教育和培训海洋科学的早期职业研究人员，以促进CI意识和相关的技术技能开发，以促进和支持他们的研究。该项目团队包括海洋学家和机器学习科学家。该项目将创建有关海洋科学研究人员核心CI技能的公开培训材料存储库，并在2023年和2024年提供两个夏季培训计划。培训将介绍CI研究项目的数据工作流程的设计原理。为了支持研究人员在工作流构建过程中，该项目将创建一个拖放工具，可帮助研究人员生成用于常见数据工作流程组件的脚本，例如数据获取，数据处理和数据可视化。生成的脚本可以作为数据工作流程系统的组件添加，然后部署到高级CI平台。该项目将培训海洋科学领域的早期研究人员，通过动手项目和黑客马拉松在研究中整合和使用CI，以解决开放式的研究问题。这些培训计划将通过各种宣传计划广泛传播，以鼓励社区学院和具有海洋科学或相关计划的少数族裔服务机构的参与。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准通过评估来通过评估来支持的。

项目成果

Temporal Prediction of Coastal Water Quality Based on Environmental Factors with Machine Learning

• DOI: 10.3390/jmse11081608
• 发表时间: 2023-08
• 期刊: Journal of Marine Science and Engineering
• 影响因子: 2.9
• 作者: Junan Lin;Qianqian Liu;Yang Song;Jiting Liu;Yixue Yin;N. Hall