Large Data

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.

 The University of Michigan Health System provides leadership and support of “Collaborative Quality Initiatives” (CQIs) which seek to address some of the most common, complex, and costly areas of surgical and medical care. CQI Coordinating Centers, led by UMHS faculty, work collaboratively with health care providers throughout Michigan to collect data to a centralized registry; analyze and share data to identify processes that lead to improved delivery of care and outcomes, and guide quality improvement interventions. 

 This is an open-access archive of a diverse collection of datasets that can be used for demonstrations, algorithm development, instrument testing, exploratory analytics and hands-on experiential practice of data-driven inference. The data are classified by type. Meta-data is provided to frame the information into the scope of specific driving motivational challenges. Observed and simulated data are included in the SOCR Data archive. The data can be redistributed (CC-BY).

 ABL consults and collaborates with investigative teams in nursing, contributing their expertise to the designing of experiments, statistical analysis planning (including sample size justification), statistical analysis in support of grants, and preparing/submitting grant proposals.

UMHS Data Set Catalog is a listing of data sets available to various constituents within the University, along with associated metadata that describes what each data set contains, how it can be accessed, who its stakeholders are, and what its history has been.
An online, searchable directory of data assets (e.g. Disease Registries, Financial Data, Biospecimen Collections, Government Health Data, Quality Data), institutional or departmental, that have the potential for data reuse by the broader UMHS constituency.

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.