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.
Upcoming Trainings and Events
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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.
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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.
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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.
Featured Stories
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High-Performance Computing Facilitates Improved Understanding of Phenotypic Plasticity in Maize
In maize and other crops, important traits are often complex, affected by genetics, the environment, and their interaction. In addition, different crop varieties exhibit varying degrees of phenotypic plasticity, in which a given genotype displays different phenotype values in different environments.
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SCINet as a Resource for Safeguarding and Advancing ARS's Biological Collections
Across the USDA’s Agricultural Research Service (ARS) there are nealy 100 biological collections containing millions of preserved and viable specimens including animal tissues, seeds, fungal cultures, plant accessions, pinned insects, and viral isolates. These specimens and the data about them document and support ARS research efforts and are an integral part of delivering on the Agency’s mission.
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Monte Carlo simulations on Atlas for soil content determinations
The Monte Carlo Method, or multiple probability simulation, is a mathematical technique used to estimate possible outcomes of uncertain events. The Monte Carlo Method was applied for nuclear problems by John von Neumann and Stanislaw Ulam during work on the Manhattan Project.
Find out how SCINet can enable your research
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Working Groups
Information about how our collaborators currently use SCINet
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Fellowship Opportunities
SCINet-funded research fellowship opportunities for PhD and MS level graduates
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How to Use SCINet
Quick Start guide to getting up and running with SCINet
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Running Analyses
Guides for running different analyses
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Frequently Asked Questions
Answers to common questions asked about SCINet
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Need Help?
Find who you need to contact for specific issues or requests