EarthCube Capabilities: PaleoCube: Enabling Cloud-Based Paleoclimatology

地球过去的气候记录（例如，来自树木年轮、海洋沉积物、冰和珊瑚）是理解气候系统能力和测试用于预测未来气候的气候模型的关键。目前，有一些社会和技术障碍，阻止充分利用这些古气候观测信息建模。PaleoCube建议通过将不同观点的科学家带到云中工作来降低这些障碍。这些数据和代码将通过黑客马拉松、网络研讨会和YouTube教程进行共享，旨在让参与气候科学不同方面的科学家在公平的竞争环境中参与进来，同时培养地球科学工作者的能力。（LinkedIn、Pangeo、Google Yter）、新兴数据标准、和科学Python生态系统（包括对pandas库的扩展，以适应更通用的时间表示），将科学家带到存储在云中的数据和相关的可复制工作流。拟议的活动将使这些工具易于访问，促进与科学Python堆栈的互操作性，并建立一个可复制的科学工作流的大型库，没有经验的用户可以模仿和修改以满足自己的目的。通过提供方便和免费的大规模交互式计算，再加上教学实例和黑客松，PaleoCube将扩大对地球科学的参与，使代表性不足的群体，丰富STEM管道，该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的评估被认为值得支持。影响审查标准。

Records of Earth’s past climates (e.g., from tree rings, marine sediments, ice, and corals) are key to understanding what the climate system is capable of and for testing the climate models used to project future climate. At present, there are a number of social and technical barriers that prevent the full use of these paleoclimate observations to inform modeling. PaleoCube proposes to lower these barriers by bringing scientists of diverse perspectives to work in the Cloud. The data and code will be shared through hackathons, webinars, and YouTube tutorials designed to engage scientists involved in different aspects of climate science on an even playing field while building capacity among the geoscientific workforce.PaleoCube will use and extend upon existing cyberinfrastructure (LinkedEarth, Pangeo, Jupyter), emerging data standards, and the scientific Python ecosystem (including an extension to the pandas library to accommodate more general time representations) to bring scientists to the data and associated reproducible workflows stored in the cloud. The proposed activities will make these tools accessible, facilitate interoperability with the Scientific Python Stack, and build a large library of reproducible scientific workflows that inexperienced users can emulate and modify to serve their own purposes. By providing easy and free access to interactive computing at scale, coupled with didactic examples and hackathons, PaleoCube will broaden participation in the geosciences to under-represented groups, enrich the STEM pipeline, and provide transferable data science skills to geoscience practitioners and enthusiasts.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.