UVA Research Computing

Research Computing

Creating innovative solutions for researchers

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  • Data Analysis

    Research Computing can help with accessing, preparing, visualizing and analyzing data. We can assist you by implementing your analysis strategy in an appropriate computing language, including R, Python and Matlab. We will work with you to prepare scripts that are are reproducible, efficient and flexible. Manipulation Data analysis generally involves a significant effort to transform, aggregate, subset or otherwise prepare a dataset. That could include dealing with missing values as well as merging or joining multiple datasets. We can help you wrangle your data into a “tidy” format in order to analyze the features and observations relevant to your question.
  • Image Processing & Scientific Visualization

    Image Processing and Scientific Visualization are two separate processes within the scientific research lifecycle, yet the two concepts often play off of one another. Image processing refers to the enhancement and transformation of images to prepare them for quantitative analysis. Scientific visualization is the graphical communication of data so that trends and anomalies can be more easily recognized. UVa Research Computing offers many services and resources to help researchers augment their work with image processing and scientific visualization techniques. Image Processing Overview Image processing encompasses a variety of techniques to prepare images for analysis. Researchers often need to remove noise artifacts from their imaging data, or they need to analyze particular regions of interest.
  • Bioinformatics Resources on Rivanna

    UVA research community has access to numerous bioinformatics software installed and ready-to-use on Rivanna. They are all available via the LMod module system. In addition, Click here for a comprehensive list. Popular Bioinformatics Software Below are some popular tools and useful links for their documentation and usage: .tg {border-collapse:collapse;border-spacing:0;border-color:#ccc;} .tg td{font-family:Arial, sans-serif;font-size:14px;padding:10px 5px;border-style:solid;border-width:0px;overflow:hidden;word-break:normal;border-color:#ccc;color:#333;background-color:#fff;} .tg th{font-family:Arial, sans-serif;font-size:14px;font-weight:normal;padding:10px 5px;border-style:solid;border-width:0px;overflow:hidden;word-break:normal;border-color:#ccc;color:#333;background-color:#f0f0f0;} .tg .tg-hy9w{background-color:#eceeef;border-color:inherit;vertical-align:middle;} .tg .tg-dc35{background-color:#f9f9f9;border-color:inherit;vertical-align:middle;} .tg .tg-hy9w-nw{background-color:#eceeef;border-color:inherit;vertical-align:middle;white-space:nowrap;} .tg .tg-dc35-nw{background-color:#f9f9f9;border-color:inherit;vertical-align:middle;white-space:nowrap;} .tg .tg-0qmj{font-weight:bold;background-color:#eceeef;border-color:inherit;vertical-align:middle;} .scroll thead, .scroll tbody {display: block} .scroll tbody {overflow-y: auto; height: 500px;} .scroll thead tr:after {content: “;overflow-y: scroll; visibility: hidden; height: 0;} Tool Version Description Useful Links BEDTools 2.
  • Bioinformatics User Guides

    Bioinformatics on Rivanna UVA’s High-performance Computing Cluster All faculty, research staff and graduate students of UVA have access to Rivanna, university’s high-performance computing system with 290+ compute nodes (6500+ cores) for high-throughput multithreaded jobs, parallel jobs as well as memmory intensive large-scale data analyses. The architecture is specifically suited for large scale distributed genomic data analysis, with 100+ bioinformatics software packages installed and ready to use. Learn more Bioinformatics using FireCloud FireCloud Home
  • High Performance Computing

    Research Computing supports all UVA researchers who are interested in writing code to address their scientific inquiries. Whether these programming tasks are implemented interactively, in a series of scripts or as an open-source software package, services are available to provide guidance and enable collaborative development. RC has specific expertise in object oriented programming in Matlab, R, and Python. Examples of service areas include: Collaborating on package development Reviewing and debugging code Preparing scripts to automate or expedite tasks Developing web interfaces for interactive data exploration Advising on integration of existing software tools UVA has two local HPC facilities available to researchers: Rivanna and Ivy.