Study Design and Data Science Facility Core
- Institution
- COLUMBIA UNIVERSITY HEALTH SCIENCES, NY
- Principal investigator
- Pam R Factor-Litvak
- Funding
- $158K
- Duration
- 1998–2028
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Abstract
SUMMARY – STUDY DESIGN AND DATA SCIENCE FACILITY CORE (DATA FC) The overall goal of the Study Design and Data Science Facility Core (Data FC) is to facilitate the use of cutting-edge study design, biostatistical, and data science methods related to laboratory, basic, patient- oriented, and population-based research within the Center for Environmental Health in Northern Manhattan (CEHNM). CEHNM has a long history of robust study design, database design, and data analytics support coordinated by the Data FC. During the ongoing cycle, the Data FC has provided intellectual assistance and computational infrastructure to plan, conduct, and analyze studies via: (a) support for the design of laboratory, basic experimental, clinical, and population-based research; (b) data management expertise and services; (c) facilitation of the use of publicly available large data sources; and (d) biostatistical and epidemiological consultations. The Core collaborates with a wide range of Columbia University (CU) programs to provide scientific training on cutting-edge methods such as causal inference, geospatial analysis, environmental mixtures, statistical genomics, advanced coding, and data science. It will also provide services to support compliance with the current NIH data sharing requirements. The Data FC will continue to help investigators identify, access, and interpret data sources; manage large data sets; create visualizations to aid in understanding such data; and assist in developing and implementing statistical models. The Data FC team has expertise in environmental data science, geospatial analysis, population-based study data analysis, statistical genomics, multi-omics, biomarker data, and complex survey design. We will establish formal ties to multiple CU partners to bring additional resources and expertise in artificial intelligence, advanced machine learning, electronic medical records, systems biology, and data science training. We will also develop a repository of code in statistical programming languages to support the analyses of Center member projects. The Data FC will continue to offer services free of charge for CEHNM projects (depending on consultation level) and free-of-charge consultations on the design and analysis of CEHNM-funded Pilot Projects to ensure high-quality study designs and sound statistical analysis plans. By promoting high-quality, relevant environmental health analyses, the Data FC enables the CEHNM to achieve its mission to understand health concerns caused by environmental exposures and reduce these exposures through translational science and partnerships.
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