The Michael J. Fox Foundation for Parkinson’s Research is committed to equipping researchers with data to accelerate discovery and therapeutic development.
We believe data sharing is critical to research progress. Through our sponsored and funded studies, we collaborate with the research community to collect and distribute a multitude of data resources. You can explore these resources through the platforms below or by searching individual datasets.
Parkinson’s Progression Markers Initiative (PPMI) Data Repository
This comprehensive data repository contains de-identified clinical, imaging, and 'omics data collected through PPMI — a longitudinal study launched in 2010 with the mission of identifying biomarkers of Parkinson’s disease onset and progression. Learn more about the PPMI Data Repository
Data Distribution Platforms
The Fox Insight Data Exploration Network (Fox DEN) provides investigators with a tool to explore, download, and analyze patient-reported outcomes and genetic data from the Fox Insight online clinical study.
The Accelerating Medicines Partnership Parkinson’s Disease (AMP PD) Knowledge Platform harmonizes 'omics and clinical data from several large studies to enable biomarker discovery and validation.
This technology platform, developed by Sage Bionetworks, allows research teams to organize data, track analyses, and collaborate across organizational boundaries.
Critical Path for Parkinson’s (CPP) aggregates and analyzes clinical trial data to develop a model for quantitative characterization of Parkinson's progression.
Note: available for Critical Path Institute consortia members only.
The BRAIN Commons is a secure cloud-based data sharing and exploration platform designed to enable team science and harness the power of big data. It provides access to a collaborative community, multi-modal data, and state-of-the-art analytic tools to facilitate the conduct of rigorous and reproducible research. The BRAIN Commons is spearheaded by Cohen Veterans Bioscience, a non-profit dedicated to advancing brain health through data-driven science.