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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SCINet Corner · Data transfer using Globus
Data transfer to, from, and among SCINet systems (Ceres, Atlas, and Juno) using Globus
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Computer Vision I: Introduction and Image Classification
This workshop will teach participants the concepts and tools they need to begin applying modern, deep learning-based computer vision methods to their own scientific research.
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Omics Webinar · The Promises and Pitfalls of Deep Learning Methods for Plant Phenotype Prediction
To recieve an invitation to upcoming webinars, fill out the Translational Omics Working Group registration survey.
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