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Research Spending & Results

Award Detail

Doing Business As Name:University of North Dakota Main Campus
  • Gretchen L Mullendore
  • (701) 777-4707
Award Date:08/29/2019
Estimated Total Award Amount: $ 143,199
Funds Obligated to Date: $ 143,199
  • FY 2019=$143,199
Start Date:10/01/2019
End Date:09/30/2021
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.050
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:Collaborative Research: EarthCube RCN: "What About Model Data?": Determining Best Practices for Archiving and Reproducibility
Federal Award ID Number:1929773
DUNS ID:102280781
Parent DUNS ID:102280781
Program Officer:
  • Subhashree (Shree) Mishra
  • (703) 292-2979

Awardee Location

Street:264 Centennial Dr Stop 7306
City:Grand Forks
County:Grand Forks
Awardee Cong. District:00

Primary Place of Performance

Organization Name:University of North Dakota Main Campus
Street:4149 University Ave, Stop 9006
City:Grand Forks
County:Grand Forks
Cong. District:00

Abstract at Time of Award

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.

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