Cybertraining: Pilot: Collaborative Research: Cybertraining for Earth Surface Processes Modelers

在快速变化的地球上可持续地生活是现代科学和社会面临的最大挑战之一。全球变化的一个关键方面涉及地球表面本身：地貌、土壤和沉积物因滑坡、泥石流、洪水和海岸侵蚀等过程而重新排列。社区表面动力学建模系统（CSDMS）创建网络基础设施，以实现地球表面的高级数值模型，其随时间的变化以及人类活动的影响。然而，传统的地球科学教育通常不能使学生掌握成为有效的网络基础设施用户和网络基础设施贡献者的技能。为了开发用于分析和预测地球表面如何响应环境变化和人类影响的创新模型，地球表面过程（ESP）建模社区需要一个平台来教授现代编程实践和高性能计算方法。该项目实施了为期10天的网络基础设施在地球表面过程研究所（ESPIn）的研究生，博士后研究员和早期职业教师在CSDMS集成设施在科罗拉多大学博尔德分校在2020-2021年的夏天培养下一代成为创新者。ESPIn旨在通过跨学科、以问题为基础的“及时教学”模式的使用和发展，超越传统的以系为基础的研究生教育模式。超过40名参与者，从不同的学科背景与明确的插槽预留给代表性不足的少数民族，获得直接的经验，将他们的研究代码转化为开源分布式软件。ESPIn主机在在线开放获取教育资源库中开发了课程材料。ESPIn有助于培养新一代精通计算的综合科学家，同时完成主要的社区科学优先事项。因此，正如NSF的使命所述，该项目符合国家利益：促进科学进步;通过建立一支有能力的地球科学工作队伍来促进国家的繁荣和福利。地球表面过程研究所（ESPIn）是研究生，博士后研究员和早期职业教师为期10天的沉浸式体验，使他们能够利用最先进的建模工具在关键的地球表面过程研究问题上取得进展。该项目的目标是学习者谁将受益于关键的知识，技能和工具，成为更好的网络基础设施的用户和开发人员通过仔细，包容性的选择程序。该项目旨在帮助在地球表面过程（ESP）的研究中取得科学进步，这些研究利用了新的网络工具（如Python建模工具）的强大和先进功能。为此，主要目标是通过培训扩大ESP研究社区成员对网络基础设施的使用，（1）提高他们使用网络基础设施工具，方法和资源的能力和信心，（2）推动更大的ESP社区更广泛地采用工具来推进预测地表变化的基础科学。 经验丰富的科学家，访问教师和软件工程师协助培训和指导参与者。ESPIn提供最佳编程实践，数值方法，开源软件开发，版本控制系统的高级使用，编写单元测试，基于HPC的灵敏度测试和模型不确定性量化技术的实践培训。有几天致力于研究和编码项目的合作。参与者致力于开发自己的代码，目的是使代码更加强大，并符合现有的ESP CI框架。对暑期学院的学习效果进行定量评估，并利用评估结果来衡量课程材料的质量。ESPIn提供所有开发的课程材料作为在线学习和教学模块，并广泛地向地球科学界提供这些资源。该奖项反映了NSF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。

Living sustainably on a rapidly changing planet is one of the greatest modern scientific and societal challenges. One critical aspect of global change involves the earth's surface itself: the rearrangement of its landforms, soils, and sediments by processes such as landslides, debris flows, floods, and coastal erosion. The Community Surface Dynamics Modeling System, CSDMS, creates cyberinfrastructure to enable advanced numerical models of the earth's surface, its changes through time, and the influence of human activity. However, traditional earth science education does not usually equip students with skills to become effective cyberinfrastructure users and cyberinfrastructure contributors. In order to develop innovative models for analyzing and predicting how the earth's surface responds to environmental change and human influence, the earth surface processes (ESP) modeling community needs a platform to teach modern programming practices and High Performance Computing methods. This project implements a 10-day Cyberinfrastructure in Earth Surface Processes Institute (ESPIn) for graduate students, postdoctoral fellows and early career faculty at the CSDMS Integration Facility at the University of Colorado in Boulder in the summers of 2020-2021 trains the next generation to be innovators. ESPIn aims to transcend the traditional model of department-based graduate education through interdisciplinary, problem-based, "Just in Time Teaching" of model use and development. Over forty participants, selected from diverse disciplinary backgrounds with explicit slots reserved for underrepresented minorities, gain direct experience in converting their research codes into open-source distributed software. ESPIn hosts developed lesson material in online open access educational repositories. ESPIn helps to train a new generation of computationally savvy, integrative scientists, while accomplishing major community science priorities. This project thus serves the national interest, as stated by NSF's mission: to promote the progress of science; to advance the national prosperity and welfare by building a capable geoscience workforce.The Earth Surface Processes Institute (ESPIn) is a 10-day immersive experience for graduate students, postdoctoral fellows and early career faculty, allowing them to make advances on critical earth surface processes research questions with state-of-the-art modeling tools. This project targets learners who would benefit from critical knowledge, skills, and tools to become better cyberinfrastructure users and developers through a careful, inclusive selection procedure. This project aims to help make scientific advances in the study of Earth Surface Processes (ESP) that leverage the powerful and advanced capabilities of new cybertools, such as the Python Modeling Tool. To these ends, the primary objective is to expand the use of cyberinfrastructure among members of the ESP research community with training that (1) increases their competence and confidence with using cyberinfrastructure tools, methods, and resources and (2) moves the larger ESP community towards more widely adopting tools to advance the fundamental science of predicting surface change. Experienced scientists, visiting faculty, and software engineers assist with training and mentoring of the participants. ESPIn offers hands-on training in best programming practices, numerical methods, open source software development, advanced use of version control systems, writing unit tests, HPC-based sensitivity testing and model uncertainty quantification techniques. Several days are dedicated to working collaboratively on research and coding projects. Participants work on developing their own codes, with the intent of making codes more robust and compliant with existing ESP CI frameworks. The Summer Institute is quantitatively evaluated for learning efficacy and evaluations are used to iterate on lesson material quality. ESPIn provides all developed lesson material as online learning and teaching modules and broadly advertises these resources to the geoscience community.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.