• Create a rich repository of data, omics, and images from multiple large scale cardiovascular disease (CVD) epidemiology studies, multicenter heart failure (HF) clinical trials, and other studies to facilitate open data science for the investigation of heart failure with preserved ejection fraction (HFpEF).

• Define subtypes of HFpEF, and develop algorithms (e.g., biomarkers, image analyses) for identifying the subtypes.

• Discovery and prioritization of targets in mechanistic pathways for diagnosis, risk assessment, and development of new therapies.

• Establish a large, electronic health record (EHR) based cohort of HF patients and comorbidity-matched controls who can be enrolled in a registry for future studies.

• Establish a cohort of deeply-phenotyped patients who can support target validation and facilitate proof-of-concept treatment trials for HFpEF.

• Train new HF data scientists proficient in multi-omics, machine learning/augmented intelligence techniques.