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High-Performance Computing.
Training.
High-Speed Networking.

What is SCINet?

The SCINet initiative is an effort by the USDA Agricultural Research Service (ARS) to grow USDA’s research capacity by providing scientists with access to high-performance computing clusters, high-speed networking for data transfer, and training in scientific computing.

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ARS scientists/collaborators:
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Upcoming Trainings and Events

  • Bioinformatics Foundations · Introduction to Modern Bioinformatics

    This workshop is designed for researchers who are new to bioinformatics or who may want a refresher of the basics. Participants will be introduced to biological sequence data, file formats, command-line tools for navigating these file formats, sequence databases, and an overview of HPC environments used in modern genomics research.

    • April 13, 15-16, 2026, 1-5 PM ET
      • Registration: Register Here
      • Prerequisites:
        • Familiarity with basic command-line concepts.

  • Bioinformatics Foundations · Genome Assembly

    In this interactive, hands-on workshop, you will learn how to use SCINet’s computing resources to convert raw DNA sequencing data into a complete genome assembly. Along the way, you will also learn best practices for ensuring that your genome assemblies are robust and useful for downstream analyses.

  • Using large language models (LLMs) on SCINet's supercomputers

    Large language models (LLMs), a key technology behind well-known tools like ChatGPT and Microsoft Copilot, have a multitude of applications in agricultural research. SCINet’s high-performance computing resources provide an excellent environment for research use of LLMs, and this workshop will equip you with the knowledge and tools you need to take advantage of LLM capabilities in your research.

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Explore Use Cases

Genomics

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SCINet for geospatial research

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Advanced computing capabilities to run watershed, erosion, and hydrodynamic simulation models.

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The USDA’s National Plant Germplasm System maintains the world’s foremost collection of crop diversity.