Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Large scale visualization on the Powerwall.
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.

Events on September 13, 2017

NERG Seminar

Christopher Butson, Associate Professor, Bioengineering Faculty, Scientific Computing & Imaging Institute Director of Neuromodulation Research, Neurosurgery University of Utah Presents:

Neuromodulation Therapy: How Recent Advances Could Make Patient Management Easier (But Sometimes More Difficult)

September 13, 2017 at 9:00am for 1hr
CNC 1st floor auditorium

Posted by: Nathan Galli

Visualization Seminar

Mukund Raj Presents:

Depth based Visualizations for Ensemble Data and Graphs

September 13, 2017 at 12:00pm for 1hr
Evans Conference Room, WEB 3780
Warnock Engineering Building, 3rd floor.


Ensemble datasets are being increasingly seen in a range of domains. Such datasets often appear as a result of a collection of solutions recorded from simulation runs with different parameters/initial conditions, as well as precision uncertainty associated with repeated measurements of a natural phenomenon. Studying ensembles in terms of the variability between members can provide valuable insight into the generating process; particularly when mathematically modeling the process is complex or infeasible. Ensemble visualization can be a powerful way to study the generating process by analyzing ensembles of solutions or possible outcomes. In ensemble visualization, key interests include understanding the typical/atypical members as well as variability in the ensemble. In absence of any information about the underlying generative model, data depth, a family of nonparametric methods from descriptive statistics, is able to quantify the notion of centrality and provide center-outward order statistics for ensembles. The goal of the proposed dissertation is to explore novel applications for existing depth based visualization methods, and to develop new advantageous visualizations—and associated methods to compute depth—for ensembles of various data types—namely, 3D isocontours, paths on a graph, nodes on a graph, graphs, and data in inner product spaces.

Posted by: Nathan Galli