High-Performance Computing. Training. High-Speed Networking.
Transferring Data from Box
If you have data on Box, you can learn how to transfer it to SCINet resources by watching this instructional video or by following the instructions in the SCINet User Guides.
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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Practicum AI · Python for AI
Most hands-on artificial intelligence work is currently done using the Python programming language. As such, some understanding of Python and computer programming is needed to be successful in applying AI. That said, it is truly astounding how much complex AI research can be accomplished with a few lines of code! The content in this workshop is aimed at beginning coders who may have never programmed before. As with the rest of the Practicum AI workshops, we use Jupyter Notebooks for the learning experience.
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SCINet Corner · August SCINet Corner
Introduction to SCINet’s Galaxy interface, a web-based platform for data-intensive bioinformatics analyses.
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Practicum AI · Deep Learning Foundations
Deep learning is the focus of modern AI. Models have many layers and millions, or now approaching a trillion, parameters! This course breaks things down and introduces you to a small AI model to provide a conceptual understanding of how AI models are built, trained, and deployed.
Featured Stories
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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.
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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.
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