Collaborative Research: GEO OSE Track 1: Facilitating Reproducible Open GeoScience

所有科学工件（源数据、代码和工作流）的共享是可再现和可复制研究的基础。地球科学社区现在才开始利用开源模型的力量，大多数地球科学课程中仍然缺少关于该模型的好处和正确使用的正式培训。该项目提出了一系列讲习班，旨在教授不同职业水平的地球科学家如何将相关的开源工具集成到他们的工作中，并使这些工具成为他们出版过程中不可或缺的一部分。此外，参与者还将学习如何为开源研究软件的生产和维护做出贡献。通过这样做，该项目将促进具有不同观点、教育背景、技能水平和计算机熟练程度的科学家之间的合作，为所有参与者创造一个公平的竞争环境。该项目将利用现有的社区努力来创建简明的培训课程，重点关注开源社区中有效使用开源资源和科学出版的最佳实践。该项目将扩展支持开放科学资源的人力基础设施，同时促进地球科学研究可重复性的最佳实践。获得参与开放科学社区的技能将提供超越纯学术研究领域的机会，这将提高美国数据科学劳动力的能力，并有助于弥合学术研究与市场就绪解决方案之间的差距。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。

The sharing of all science artifacts (source data, code, and workflow) is fundamental to reproducible and replicable research. The geoscience community is only now beginning to harness the power of the open source model, and formal training on the benefits and proper use of this model is still missing from most geoscience curricula. This project proposes a series of workshops aimed at teaching geoscientists at different career levels how to integrate relevant open source tools into their work, and make these tools an integral part of their publishing process. Additionally, participants will learn how to contribute to the production and maintenance of open-source research software. By doing so, this project will facilitate collaboration among scientists with diverse perspectives, educational backgrounds, skill levels, and computer proficiency, promoting a level playing field for all participants.This project will leverage existing community endeavors to create concise training sessions focused on the effective use of open source resources and best practices in science publishing within the open source community. This project will expand on the human infrastructure supporting open sciences resources while promoting best practices for the reproducibility of geoscience research. Acquiring the skills to participate in the open science community will offer opportunities beyond the realm of pure academic research, which will build capacity in the American data science workforce and help bridge the gap between academic research and market-ready solutions.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.