SPARC: SMPH Data Platform for Accelerating Real-World Data Access and Collaboration

Need a broader research ecosystem rather than a one-time data extract?

SPARC is a secure, scalable research data ecosystem that supports large-scale data-driven research across UW Madison. Managed by CRDS, SPARC brings together clinical, non-clinical, research, and public data within a standardized framework.

Built on the OMOP Common Data Model, SPARC enables efficient cohort discovery, reproducible analytics, multi-site collaboration, and advanced data science applications. By standardizing data across sources, researchers can spend less time preparing data and more time generating scientific insights.

Whether supporting investigator-initiated studies, quality improvement initiatives, machine learning projects, or large collaborative research programs, SPARC provides access to curated data assets, secure computing resources, and a shared analytics ecosystem designed to accelerate discovery and innovation.

What is OMOP?

The Observational Medical Outcomes Partnership (OMOP) Common Data Model is an internationally adopted standard for organizing healthcare data provided by OHDSI. OMOP transforms information from multiple source systems into a consistent structure and vocabulary, making it easier to conduct research across datasets and institutions.

Benefits of OMOP

• Standardized clinical concepts and terminology
• Consistent cohort definitions across studies
• Reusable analytic methods and tools
• Improved reproducibility and transparency
• Easier collaboration with external research networks
• Support for federated and multi-site studies

By standardizing data into OMOP, SPARC enables researchers to focus less on data harmonization and more on scientific discovery, while benefiting from a global ecosystem of shared tools, methods, and collaborative research resources.

Ideal Use Cases

SPARC is particularly valuable for:

• Disease registries and large observational research studies
• Multi-investigator collaborations
• AI and machine learning initiatives
• Multi-site and national research network participation