The SCINet initiative is an effort by the USDA Agricultural Research Service to improve the USDA’s research capacity by providing scientists with access to high-performance computing (HPC) clusters, high-speed networking for data transfer, and training in scientific computing.
SCINet supports a growing community of over 2,000 USDA research scientists and university partners to accelerate agricultural discovery through advanced computational infrastructure and scientific computing.
Current uses of SCINet span multiple disciplines, including genomics, plant breeding, hydrology, crop production, plant and animal disease modeling, and natural resource management. SCINet users include ARS and other federal scientists, as well as partners external to the federal government.
SCINet supports a broad range of computational tools, including R (and RStudio), Python (and Jupyter notebooks), and software for bioinformatics, geospatial analyses, machine learning and deep learning, and image processing.
To get started using SCINet, sign up for an account and visit our Quick Start Guide.
Training and Support
- Computational skills
- Data management
- Scientific algorithms for HPC
- Computational workflow design
- Software installation and support
- AI and machine learning
Computing Resources
- 2 High-Performance Computing Clusters
- 320 Standard Compute Nodes
- 41 High-Memory Compute Nodes
- 9 GPU Nodes (more on the way)