CyberTraining: Implementation: Medium: Machine Learning Training and Curriculum Development for Earth Science Studies

地球系统科学发现越来越多地受到使用强大的机器学习（ML）技术的数据管理、分析和推理的影响。然而，执行这些任务所需的技能，以及构建机器学习模型和管道、大数据和云计算的尖端开源技术培训，并不包括在传统的地球科学研究生课程中。为了填补这些空白，该项目将开发地球科学机器学习资源和培训（GeoSMART）框架，该框架将为开源科学生态系统和通用机器学习理论、工具包以及云计算平台上的部署奠定基础。该项目将包括一个由地球科学和机器学习教育者组成的团队，他们将创建一个新的机器学习课程，重点是地震学、冰冻圈和水文学应用。培训材料将包含在增强课程中，这将扩大对新兴ML社区的影响。该项目的实施计划将提供开源机器学习工具包和数据科学技能方面的培训。此外，该项目将培养特定学科的机器学习库、工作流程和实践社区的发展，以维持机器学习网络培训机会的未来增长。通过使用开源和云访问平台构建工具，并与缺乏ML工作流程计算资源的大学和机构合作，该项目将增加对网络培训材料的访问，并帮助解决地球科学挑战。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Earth system science discoveries are increasingly affected by data management, analysis, and inference using powerful machine learning (ML) techniques. Yet, the skills required to perform these tasks, and training in cutting-edge, open-source technologies to build ML models and pipelines, big data, and cloud computing, are not covered by the traditional graduate curriculum in the geosciences. To fill these gaps, this project will develop the GeoScience MAchine Learning Resources and Training (GeoSMART) framework that will build a foundation in open-source scientific ecosystems and general ML theory, toolkits, and deployment on Cloud computing platforms. This project will include a team of geoscience and ML educators to create a novel ML curriculum with focus on seismology, cryosphere and hydrology applications. The training materials will be included in an enhanced curriculum that will broaden impact on emerging ML communities. The project’s implementation plan will provide training in open-source ML toolkits and data science skills. Further, the project will cultivate the development of discipline-specific ML libraries, workflows, and communities of practice to sustain future growth of ML cybertraining opportunities. By building tools using open-source and cloud-accessible platforms, and by partnering with colleges and institutions that lack computing resources for ML workflows, the project will increase access to cybertraining materials and help to solve geoscience challenges.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.