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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Computer Vision II: Object Detection and Instance Segmentation
In this workshop, participants will learn the key concepts and techniques needed to use modern, deep learning-based computer vision methods for object detection and instance segmentation. Learners will practice training and evaluating state-of-the-art computer vision models on custom image datasets.
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SCINet Corner · Open Q&A
We invite you to submit any SCINet-related questions, challenges or concerns you’d like us to address in this SCINet Corner by filling out this short form.
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Introduction to Bioinformatics
This workshop provides an overview of bioinformatics covering applications, sequencing technologies, and basic workflows. Participants will also explore various file formats in bioinformatics and gain hands-on experience using NCBI’s BLAST web tool to perform sequence alignments and interpret results.
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