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It has become common for researchers to make their data publicly available to meet the data management and accessibility requirements of funding agencies and scientific publishers. However, many researchers face the challenge of determining what data to preserve and share and where to preserve and share those data. This can be especially challenging for those who run dynamical models, which can produce complex, voluminous data outputs, and have not considered what outputs may need to be preserved and shared as part of the project design. This manuscript presents findings from the NSF EarthCube Research Coordination Network project titled “What About Model Data? Best Practices for Preservation and Replicability” (https://modeldatarcn.github.io/). These findings suggest that if the primary goal of sharing data are to communicate knowledge, most simulation-based research projects only need to preserve and share selected model outputs along with the full simulation experiment workflow. One major result of this project has been the development of a rubric, designed to provide guidance for making decisions on what simulation output needs to be preserved and shared in trusted community repositories to achieve the goal of knowledge communication. This rubric, along with use cases for selected projects, provide scientists with guidance on data accessibility requirements in the planning process of research, allowing for more thoughtful development of data management plans and funding requests. Additionally, this rubric can be referred to by publishers for what is expected in terms of data accessibility for publication.

 

Abstract Harsh winters and hazards such as blizzards are synonymous with the northern Great Plains of the United States. Studying these events is difficult; the juxtaposition of cold temperatures and high winds makes microphysical observations of both blowing and falling snow challenging. Historically, these observations have been provided by costly hydrometeor imagers that have been deployed for field campaigns or at select observation sites. This has slowed the development and validation of microphysics parameterizations and remote-sensing retrievals of various properties. If cheaper, more mobile instrumentation can be developed, this progress can be accelerated. Further, lowering price barriers can make deployment of instrumentation feasible for education and outreach purposes. The Blowing Snow Observations at the University of North Dakota: Education through Research (BLOWN-UNDER) Campaign took place during the winter of 2019-2020 to investigate strategies for obtaining microphysical measurements in the harsh North Dakota winter. Student led, the project blended education, outreach, and scientific objectives. While a variety of in-situ and remote-sensing instruments were deployed for the campaign, the most novel aspect of the project was the development and deployment of OSCRE, the Open Snowflake Camera for Research and Education. Images from this instrument were combined with winter weather educational modules to describe properties of snow to the public, K-12 students, and members of indigenous communities through a tribal outreach program. Along with an educational deployment of a Doppler on Wheels mobile radar, nearly 1000 individuals were reached during the project.