These resources have been contributed and “vetted” by the community of cyberinfrastructure professionals (researchers, research computing facilitators, research software engineers and HPC system administrators) that are participating in programs such as this one, that are supported by the ConnectCI community management platform. Additional Knowledge Base Resources are always welcome!
Cornell Virtual Workshop is a comprehensive training resource for high performance computing topics. The Cornell University Center for Advanced Computing (CAC) is a leader in the development and deployment of Web-based training programs. Our Cornell Virtual Workshop learning platform is designed to enhance the computational science skills of researchers, accelerate the adoption of new and emerging technologies, and broaden the participation of underrepresented groups in science and engineering. Over 350,000 unique visitors have accessed Cornell Virtual Workshop training on programming languages, parallel computing, code improvement, and data analysis. The platform supports learning communities around the world, with code examples from national systems such as Frontera, Stampede2, and Jetstream2.
DeapSECURE is a training program to infuse high-performance computational techniques into cybersecurity research and education. It is an NSF-funded project of the ODU School of Cybersecurity along with the Department of Electrical and Computer Engineering and the Information Technology Services at ODU. The DeapSECURE team has developed six non-degree training modules to expose cybersecurity students to advanced CI platforms and techniques rooted in big data, machine learning, neural networks, and high-performance programming. Techniques taught in DeapSECURE workshops are rather general and transferable to other areas including science, engineering, finance, linguistics, etc. All lesson materials are made available as open-source educational resources.
This webinar series is an orientation to R. We start with an overview of R’s history and place in the larger data science ecosystem. Next, we introduce the R Studio user interface and how to access R’s excellent documentation. Finally, we present the fundamental concepts you need to use the R environment and language for data analysis. Along the way, we compare R script files (.R) to R Notebook (.Rmd) files and show how the features of R Notebook support better communication and encourage more dynamic engagement with statistical analysis and code. It is helpful to be familiar with tabular data analysis using statistical software, database tools, or spreadsheet programs.
Workshop materials, including setup directions and slides are available at https://github.com/CornellCAC/r_for_edu/ The Rstudio Cloud project used in the workshop is https://rstudio.cloud/project/4044219.