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 · Introduction to plotting with ggplot2
- Accessing RStudio through Open OnDemand
- Graphing concepts in creating basic plots using the gapminder data set
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SCINet · Software Package/Environment Management Workshop
In this workshop, we will cover best practices for managing software packages and computing environments on SCINet’s supercomputers. This will be a hands-on workshop that will provide you with the practical knowledge and skills you need to spend less time worrying about package management and more time focusing on your research!
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Multispectral UAV Imagery Workshop
This workshop will focus on processing multispectral imagery from unoccupied aerial vehicles (UAVs) and extracting zonal statistics for geospatial modeling using OpenDroneMap on SCINet systems.
Featured Stories
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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.
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SCINet Resources for Photogrammetry
Scientists at the NWRC use UAS to research long-term vegetation dynamics in sagebrush-dominated rangelands. Multispectral, hyperspectral, and natural color imagery are collected to study the effects of fire, grazing, and invasive weed encroachment.
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CameraTrapDetectoR: Detecting, Classifying and Counting Animals in Camera Trap Images
The use of camera traps is a popular and cost-effective way to monitor animal populations, evaluate animal behavior, and study ecological processes influencing populations. However, a camera trap dataset for a single site or research question can result in millions of images that require classification to be useful for analysis.
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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Contact Us
Find who you need to contact for specific issues or requests