Visualization

The Health Analytics Consortium (HAC) is an open incubator for collaboration and digital scholarship that emphasizes team-based transdisciplinary data science and advanced health analytics. A core mission of HAC is to foster integration of innovative research, development, education and training, and outreach in data and health sciences.

State-of-the-art clinical learning facility that focus on active learning that fosters greater understanding and more advanced clinical reasoning. The Clinical Learning Center has simulation rooms housed with high-fidelity mannequins for replicating realistic health care situations, to skills labs for honing basic and advanced skills, to staff rich with knowledge, experience and expertise, this environment enables students to apply their knowledge of nursing theory in an interactive and challenging yet safe and supportive environment.

 (Longitudinal Online Research and Imaging System) is a web-based data and project management software for neuroimaging research studies. It is an OPEN SOURCE framework for storing and processing behavioural, clinical, neuroimaging and genetic data. LORIS also makes it easy to manage large datasets acquired over time in a longitudinal study, or at different locations in a large multi-site study.

XNAT is an open source imaging informatics platform, developed by the Neuroinformatics Research Group at Washington University. It facilitates common management, productivity, and quality assurance tasks for imaging and associated data.  Learn more here

 The Data Dashboard webapp provides a mechanism to integrate dispersed multi-source data and service the mashed information via human and machine interfaces in a secure, scalable manner. The Dashboard enables the exploration of subtle associations between variables, population strata, or clusters of data elements, which may be opaque to standard independent inspection of the individual sources. This a new platform includes a device agnostic tool for graphical querying, navigating and exploring the multivariate associations in complex heterogeneous datasets.