CyberTraining Modules for Findable, Accessible, Interoperable, and Reusable (FAIR) science in Water and Climate

Abstract:

Addressing the grand challenges associated with growing population, food and water security, frequently occurring natural disasters, and changing climate require not only domain expertise, but also computational expertise to deal with big data analytics and simulations. However, formal training is lacking at most institutions to train students to handle big geoscience related data, develop computational workflows, use high performance computing (HPC) for scientific simulations and publish digital products, including data and models. Such training, referred here as cyber training, is critical for addressing the grand challenges such as in sustainability and resilience. Cyber training is also needed to make the science openly available and transparently reproducible by using the best practices in Findable, Accessible, Interoperable, and Reusable (FAIR) science as articulated by many scientific institutions. The overall goal of this work is to create a new generation of geoscientists to produce FAIR science using big data analytics, computational simulations and HPC. Specifically, we are developing a curriculum for cyber training that is driven by the need to acquire expertise in the following areas: data access, processing, visualization and publication. This presentation will discuss the overall water and climate FAIR cyber training curriculum, their implementation at Purdue University and the ongoing work by FAir CyberTraining (FACT) Fellows.