Collaborative Research: CyberTraining: Implementation: Small: EMBARK: Developing essential skills to (re)use an ocean of data

Volumes and varieties of data generated by ocean observing systems and long-term research have increased substantially in recent years. Yet, many early career researchers lack essential skills that are needed to find, access, use and reuse these data. The EMBARK collaborative project will address this gap by teaching skills to minimize the need to move large data by accessing and (re)using data with software workflows. A short-format, virtual training course will be developed and delivered for graduate students and postdoctoral researchers in the ocean sciences. The open-access course materials will provide practical, hands-on training to promote flexibility in adopting cyberinfrastructure (CI) tools and resources provided by major facilities and data repositories in the ocean sciences. By improving CI literacy and enabling the (re)use of large, complex, and/or long-term ocean datasets, this project will promote research to better understand the ocean and its role in global environmental and ecological processes. This project serves the national interest by promoting the progress of science, expanding utilization of NSF-funded research infrastructure, and strengthening the American STEM workforce.

The EMBARK collaborative project promotes the adoption of cyberinfrastructure (CI) tools, methods, and resources by early career ocean professionals (ECOPs) as CI Users. Specifically, the goal is to train early-career researchers with skills to use and reuse data from major facilities and data repositories in the ocean sciences. This project will develop and deliver virtual workshops to three cohorts, with a total of ~90 participants, focused on developing key skills including data discovery, access via application programming interfaces (APIs), and software workflows for data harmonization and reuse. Training materials will be developed through an iterative design process with CI experts and ECOPs. The materials will then be packaged into modular learning units inclusive of executable Jupyter Notebooks and practical exercises. The training will be based around four ocean science use cases. The use cases will illustrate workflows for accessing and reusing oceanographic time series, satellite data, model data, and imagery classified by machine learning. The efficacy of the training will be evaluated to assess changes in learners’ CI awareness, confidence, and skill application. All training materials will be openly accessible and disseminated for broader use and future development, ensuring use beyond the participants in the project.

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.

Award Number: 2519659
Principal Investigator: Stace Beaulieu
Funds Obligated: $242,648
State: MA