Core C - Data Core

Icahn School Of Medicine At Mount Sinai, New York NY

Investigators

Linked publications, trials & patents

Abstract

Mount Sinai ADRC (Sano): Data Core (Core C) – Research Summary An effective Data Management and Statistical Core (Data Core) facilitates strong connections across all Cores within the ADRC, serves as a critical resource for ongoing Center activities and for other investigators who are interested in research in Alzheimer’s disease (AD) and AD-related dementias (ADRD), and helps accelerate research and translation in the field. Specifically, our Data Core will provide a data and analytic foundation for Mount Sinai ADRC to explore the relationships between multi-model data types to uncover unique pathways to diagnosis, treatment, and prevention of AD/ADRD. We will build on our newly modernized and standardized electronic data capture (EDC) enabled REDCap database structure that has curated, linked and integrated data across all cores. We will enable new data types including polygenic risk scores (PRS), fluid and imaging biomarkers, recorded speech for natural language processing (NLP), and digital neuropathological profiles associated with vulnerability or resistance to disease in AD/ADRD. The Data Core will provide expert statistical services and training on a variety of topics including study design, data harmonization and integration, and advanced statistical methods within our Center, especially through the Research Education Component (REC) core. The Data Core will continue to strengthen our productive Center- NACC, Center-Center, and Center-NIH partnerships, building on our success with new publications, grant proposals, and training opportunities. We will initiate new efforts in facilitating connection of AI and AD/ADRD researchers to develop new partnerships. Our Specific Aims are (1) Provide a reliable, quality assured, and secure multi-modal data and analytic infrastructure; (2) Provide integrated data and statistical analysis and support; (3) Educate ADRC investigators, trainees, and junior faculty; and (4) Broaden our impact by increasing Findability, Accessibility, Interoperability and Reusability (FAIR) of our data. These activities are well aligned with priorities highlighted in the RFA. Throughout all Data Core activities, we will continue to build a culture of innovation through the application of novel data methods and encouraging open science through data sharing. We also aim to ensure diverse perspectives of our faculty who are from multiple scientific disciplines and backgrounds and also engage and serve our participants who have a wide range of diverse backgrounds.