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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.

Get Started with SCINet

ARS scientists/collaborators:
Register for an account

Get Started

Upcoming Trainings and Events

  • Transfer Learning

    This workshop provides the foundational concepts and practical applications of transfer learning, a powerful technique in deep learning that allows AI models to leverage pre-trained knowledge to improve performance on new tasks. The sessions will cover different types of transfer learning techniques, such as feature extraction and fine-tuning. This includes hands-on experience in applying these techniques to computer vision and language models.

  • Animal Behavior AI - Working Group Meeting

    This SCINet working group aims to explore the potential benefits of Artificial Intelligence (AI) in animal behavior research.

    • SCINet discussion, focusing on how to use globus and Juno for hosting data.
    • Brad A. Freking, Research Geneticist from the U.S. Meat Animal Research Center (USMARC) at ARS will present their on-going challenge about detecting sheep maternal behavior from camera images. We will discuss possible AI approaches to address this challenge.
  • Data Preparation and Quality Assessment in Genome Assembly

    This workshop provides a hands-on introduction to data preparation, genome assembly, and quality assessment. Participants will explore different assembly approaches and techniques for evaluating the accuracy and completeness of genome assemblies, helping attendees understand key metrics and statistical methods used to assess the quality of genomic data.

    • Tuesday, April 29, 1 – 5 PM ET
      • Registration: Register Here
      • Prerequisites:
        • Familiarity with basic command-line concepts.