Collaborative Research: EarthCube RCN: "What About Model Data?": Determining Best Practices for Archiving and Reproducibility

协作研究：EarthCube RCN：“模型数据怎么样？”：确定存档和可重复性的最佳实践

• 批准号: 1929773
• 负责人: Gretchen Mullendore
• 金额: $ 14.32万
• 依托单位: University of North Dakota Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1929773&HistoricalAwards=false
• 关键词: Collaborative; Research; EarthCube; RCN; What

Much of the research in the geosciences, such as projecting future changes in the environment and improving weather and flood forecasting, is conducted using computational models that simulate the Earth's atmosphere, oceans, and land surfaces. These geoscience models are part of the full research workflow that leads to scientific discovery. There is strong agreement across the sciences that reproducible workflows are needed. Open and reproducible workflows not only strengthen public confidence in the sciences, but also result in more efficient community science, leading to faster time to science. However, recent efforts to standardize data sharing and archiving guidelines within research institutions, professional societies, and academic publishers make clear that the scientific community does not know what to do about data produced as output from the computational models. To date, the rule for reproducibility is to "save all the data", but model data can be prohibitively large, particularly in a field like atmospheric science. The massive size of the model outputs, as well as the large computational cost to produce these outputs, makes this not only a problem of reproducibility, but also a "big data" problem. To achieve open and reproducible workflows in geoscience modeling research, this project will bring together modelers representing diverse research areas and application types, and representing modeling efforts from large to small.Discussion across different modeling communities suggests that the answer to "what to do about model data" will look different depending on model descriptors. Examples of important model descriptors include reproducibility, storage vs. computational costs, and value to the community. Since the atmospheric model community is incredibly diverse, this project will organize community workshops to tackle the problem. These workshops will involve representatives from across the geoscience modeling spectrum, including both operations and research, and ranging across complexity and size. The ultimate goal of these workshops is to provide model data best practices to the community, including scientific journal publishers, and funding agencies. To achieve this goal, this team of researchers suggests to craft rubrics based on the model descriptors that will help researchers and centers describe their model data in consistent terms so that proper decisions are made regarding archiving and retention.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.

地球科学中的许多研究，例如预测环境的未来变化并改善天气和洪水预测，是使用模拟地球大气，海洋和陆地表面的计算模型进行的。这些地球科学模型是导致科学发现的完整研究工作流程的一部分。 在整个科学方面有强烈的共识，需要可重现的工作流程。 开放且可重复的工作流不仅增强了公众对科学的信心，而且会导致更有效的社区科学，从而导致了更快的科学时间。 但是，在研究机构，专业社会和学术出版商中标准化数据共享和归档指南的最新努力明确指出，科学界不知道该如何处理计算模型产生的数据。 迄今为止，可重复性的规则是“保存所有数据”，但是模型数据可以过多地大，尤其是在大气科学等领域。 模型输出的巨大尺寸以及产生这些输出的大量计算成本，不仅使这是可重复性的问题，而且使其成为“大数据”问题。 为了实现地球科学建模研究中的开放且可重复的工作流程，该项目将汇集代表各种研究领域和应用程序类型的建模者，并代表从大型到小的建模工作。不同建模社区之间的讨论表明，根据模型描述符，“对模型数据做什么”的答案将不同。 重要模型描述符的示例包括可重复性，存储与计算成本以及对社区的价值。 由于大气模型社区非常多样化，因此该项目将组织社区研讨会以解决问题。 这些研讨会将涉及来自整个地球科学建模范围的代表，包括操作和研究，以及跨越复杂性和规模的范围。 这些研讨会的最终目标是向社区（包括科学期刊出版商和资助机构）提供模型数据最佳实践。 为了实现这一目标，这支研究人员建议根据模型描述符来制作专栏文章，以帮助研究人员和中心以一致的方式描述他们的模型数据，以便在归档和保留方面做出正确的决定。该奖项反映了NSF的法定任务，并认为通过基金会的智力功能和广泛的影响来评估NSF的支持，并被认为是值得的。