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Visualization refers to the process of rendering information for the purpose of visual analysis or understanding. Case UTech research computing supports visualization activities for researchers throughout the Case community. In addition, research computing has computing systems that provide the means to produce, explore, or share visualization materials.

Scientific data is distinguished by having clear spatial relations amongst the attributes of the information.

  • For High Performance Computing (HPC) users, there is visualization software available for use on the HPC: including (but not limited to) Matlab, gnuplot, Paraview and Schrodinger. Contact with requests for installation of custom software not listed here: cluster/ridermarkov-software/categorical-list/visualization
  • Interactive Visualization display-walls: View high-resolution imagery on large-format visualization walls using a Windows 10-based workstation. Experience a user-customizable environment for viewing and manipulating your data, images and video materials. Share content from your computer through a Firefox or Chrome browser, or use the native Chrome browser with OpenSeadragonizer extension to pan and zoom gracefully through web-sourced or local high-resolution images.
    • Interactive Commons Vizwall: 24 x 8 feet, 18 -- 1920x1080 monitors (6x3 grid)
    • RCCI Vizwall, AW Smith, Rm 209: 12 -- 2560x1600 (4x3 grid).
  • The CWRU software center provides a variety of applications to perform scientific visualization, including: Matlab, MathCAD and Mathematica (perform and visualize calculated quantities), PyMOL (molecular visualization), SAS and SPSS (statistics), ChemOffice Professional (chemical and biological structure visualization), Origin (graphing), and, ESRI ArcGIS (geospatial data).
  • Many people on campus work with open source software such as R, and tools such as iPython are being developed in the broader community to enhance the effectiveness of visualization through the interpreted, object-oriented, high-level programming language python.
  • Learning: Web resources remain an effective starting point to train in visualization methods. In fact, searching the web based on your initial thoughts about the type of information you are working with can help refine your view of what you should look to learn. CWRU maintains an open subscription for those affiliated with the university to LinkedIn Learning, a premiere online resource for training videos and courses. Search with keyword 'data visualization' to find introductory materials that also be helpful if you are just beginning your visualization activities.
  • Consultation: The team engages through consultation and collaboration to identify resources appropriate to researcher needs. Please contact us to learn more about visualization support services, to begin consulting, or simply to let us know what you are doing in the realm of visualization: