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.

地球科学研究的许多研究，例如预测未来环境的变化以及改进天气和洪水预报，都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。 科学界一致认为需要可重复的工作流程。 开放且可重复的工作流程不仅增强了公众对科学的信心，而且还能提高社区科学的效率，从而加快科学发展的速度。 然而，最近研究机构、专业协会和学术出版商内部标准化数据共享和归档指南的努力清楚地表明，科学界不知道如何处理计算模型输出产生的数据。 迄今为止，可重复性的规则是“保存所有数据”，但模型数据可能非常大，特别是在大气科学等领域。 模型输出的巨大规模，以及产生这些输出的大量计算成本，使得这不仅是再现性问题，而且也是一个“大数据”问题。 为了在地球科学建模研究中实现开放和可重复的工作流程，该项目将汇集代表不同研究领域和应用类型的建模人员，并代表从大到小的建模工作。不同建模社区的讨论表明，“如何处理模型”的答案根据模型描述符的不同，“数据”看起来会有所不同。 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。 由于大气模型社区极其多样化，该项目将组织社区研讨会来解决这个问题。 这些研讨会将涉及地球科学建模领域的代表，包括运营和研究，范围涵盖复杂性和规模。 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，该研究团队建议根据模型描述符制定细则，以帮助研究人员和中心以一致的术语描述其模型数据，以便就归档和保留做出正确的决策。该奖项反映了 NSF 的法定使命，并具有通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。