Cyber Training for FAIR Science
The overall goal of this project is to create a new generation of scientists to manage data-rich and computationally intensive tasks to become globally competitive in the STEM, thus fulfilling NSF's mission to promote the progress of science. It brings cyber-enabled state-of-the-art computational tools into practice by training students and working professionals through courses, workshops and boot camps at multiple institutions, including Purdue University, University of New Hampshire and University of Alabama. The project creates a cyber training curriculum that is driven by the need to acquire expertise in the following six areas: data access, geo-processing, time series analysis, computational simulation, visualization and publication. These areas form the foundation of a modular cyber training framework that supports development and implementation of training materials targeting geoscience learners. The training component of the project will make the science openly available and transparently reproducible using the best practices in Findable, Accessible, Interoperable, and Reusable (FAIR) science.
- Venkatesh Merwade (Civil Engineering and Agricultural & Biological Engineering, Purdue University)
- Matthew Huber (Department of Earth, Atmospheric, and Planetary Sciences, Purdue University)
- Carol Song (Research Computing, Purdue University)
- Wanju Huang (Learning Design and Technology, Purdue University)
- Lan Zhao (Research Computing, Purdue University)