Dementia Data Landscape 1. Cohorts.

INTRODUCTION: Understanding and maximizing complex health data is crucial for accelerating discovery, translational research, funding priorities, and improving data management. Rapid, cost-effective progress can be made by repurposing datasets. This work explores the dementia cohort landscape, identifies cohorts relevant to dementia translation, and highlights areas to strengthen health cohort infrastructure. METHOD: PubMed was searched for publications utilizing dementia-related cohorts (1970-2024), supplemented by international dementia data platforms. A template aligned with the C-Surv data model was used to summarize administrative details and the presence of measurements across 17 themes. RESULTS: From 4596 publications and 11 data platforms, 883 cohorts were identified (558 population and 325 clinical). Of these, 74% indicated data availability for future research, though metadata reporting varied. Cohort metadata are accessible via the landscape tool. DISCUSSION: This work reveals extensive global dementia-related data for repurposing and identifies priority areas for improvement, including metadata transparency, data accessibility, and locations to prioritize for future research. HIGHLIGHTS: A total of 883 cohorts were identified globally (1970 to 2024): 558 population and 325 clinical The Global South is substantially underrepresented Seventy-four percent of cohorts offer data access, but protocols and metadata quality vary widely Only 45% of cohorts were discoverable via existing data platforms The online landscape tool enables strategic discovery and reuse of dementia data.