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

GSTDTAP

项目编号	1929757
	Collaborative Research: EarthCube RCN: "What About Model Data?": Determining Best Practices for Archiving and Reproducibility
	Matthew Mayernik (Principal Investigator)
主持机构	University Corporation For Atmospheric Res
项目开始年	2019
	2019-10-01
项目结束日期	2021-09-30
资助机构	US-NSF
项目类别	Standard Grant
项目经费	119386(USD)
国家	美国
语种	英语
英文摘要	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.
文献类型	项目
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/214186
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Matthew Mayernik .Collaborative Research: EarthCube RCN: "What About Model Data?": Determining Best Practices for Archiving and Reproducibility.2019.