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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The Carpentries Unix, Git, and Python Workshop
The SCINet Office, in collaboration with ARS’s certified Carpentries instructors, is offering a Carpentries workshop that will teach participants the Unix command line, version control with Git, and Python programming.
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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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Practicum AI · 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 pretrained 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.
Featured Stories
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Humanely deterring birds around aquaculture and development of AI-driven, non-lethal deterrence tactics
Predatory birds such as Great Blue Herons, Canada Geese, and Egrets can cause significant losses at fish ponds. Traditional deterrents like netting and noise cannons are often intrusive or ineffective over time. The project leverages SCINet's high-performance computing resources to train and optimize YOLO-based deep-learning models using large datasets of bird images. These trained models make it possible to implement detection and identification of birds in real time at aquaculture ponds in the field.
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Advancing Irrigation Mapping Through AI and Remote Sensing
Accurate mapping of irrigation methods is essential for tackling water scarcity across large regions, boosting agricultural productivity, and informing conservation practices. Researchers at the USDA-ARS Northwest Irrigation and Soils Research Laboratory at Kimberly, Idaho are spearheading the development of an irrigation methods mapping tool by leveraging Artificial Intelligence (AI) and publicly available satellite imagery.
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Comparative genomics reveals a light-activated phytotoxin that contributes to red leaf blotch disease of soybean
*Coniothyrium glycines* causes red leaf blotch, a major disease of soybean in Africa (Figure 1). It is one of two fungal pathogens listed on the USDA APHIS Plant Protection and Quarantine Select Agents and Toxins list owing to its likely destructive potential if it spreads to major soybean growing regions.
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