Overview of SCINet Training Opportunities
One of SCINet’s main objectives is training scientists in computational methods to empower agricultural research. We are doing this in several ways. We offer online and in person trainings, support topic-specific working groups, and provide help, training, and tutorials for scientists.
Get Started with SCINet Learning Pathway
With the expansive list of free training available online, finding the right training to meet your learning needs can be daunting. Take the first steps in getting started with your introductory learning path to help you get started with SCINet. Learn about SCINet, how to sign up for an account, and what is possible when supported by SCINet infrastructure. Then dive in with hands-on tutorials available across multiple searchable platforms to find the information you need for just-in-time learning.
Additional Training Resources
|Quick Start Guide
||Use this step-by-step guide to walk you through getting started with SCInet and answer common trouble shooting questions
||Resources and links to support data management
|Ceres User Manual
||Guide to getting started with Ceres including a technical overview and system configurations.
|Atlas HPC User Guide
||Guide to accessing Atlas including transferring files and quotas.
|Iowa State HPC Site
||Guide on compute clusters, UNIX command line, building containers, and data transfer with Globus.
|Ag Data Commons
||Public, government, scientific research data catalog and repository to share and discover research data funded by the United States Department of Agriculture and meet Federal open access requirements. Through the Ag Data Commons, the USDA National Agricultural Library (NAL) provides services to make USDA funded research data systems and data products Findable, Accessible, Interoperable, and Reusable (FAIR). Critical functions the Ag Data Commons provides include: Scientific metadata creation and curation, Public access to data through a catalog of USDA funded research and public APIs, Data repository for long-term archiving (as required to support DOI service)