Multisite Collaborations
Share data and processes around the corner or around the world
Flywheel accelerates collaboration and scales with your work
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Streamline secure data collection, processing, and sharing
Flywheel supports multisite research collaborations ranging from a few labs working together to national- and international-scale research cooperatives. Our tools for standardization and workflow automation transform your productivity, while security and de-identification features satisfy compliance requirements for IRBs, HIPAA, and GDPR. Flywheel can also help you discover new collaborators, and make your own data available for discovery.
TRACK-TBI: Flywheel Standardizes Data Management for Multisite TBI Research
Collaborators at 19 institutions use Flywheel to centralize, de-identify, and curate data in their shared image repository.
“Our collaboration is really allowing us to achieve things that not one of us individually or one university could do. Flywheel’s automation of data capture and de-identification of patient personal information were critical time-savers.”
UC San Francisco
We work with the best in the field
- Stanford University
- Columbia University
- University of Pennsylvania
- Massachusetts General Hospital
- Boston Children’s Hospital
- University of North Carolina BRIC
- University of Wisconsin-Madison
- UCSF
- University of Texas Health
- St. Jude Children’s Hospital
- University of Michigan
- University of Southern California
- University of California, Irvine
- Carnegie Mellon University
Comprehensive Tools for Multisite Collaborations
- Fully web based to simplify collaboration with anyone, anywhere
- Full support for all major cloud vendors
- Elastic compute scaling
- Rich templating suite for projects and Gears, ensuring standardization across sites
- Compliant, configurable de-identification tools, with data de-identified on the edge
- Support for federated identity management, allowing authentication via each user’s institutional credentials
- Ability to publish your data for discovery, reuse, and compliance with NIH data-sharing requirements
- Discover additional public and private datasets; leverage data through traditional methods or via federated learning
