Speaker
Description
The National Health and Nutrition Examination Survey (NHANES) provides extensive public data on demographics, health, and nutrition, collected in two-year cycles since 1999. Although invaluable for epidemiological and health-related research, the complexity of NHANES data makes accessing, managing, and analyzing these datasets challenging. We present a reproducible computational environment built upon Docker containers, PostgreSQL databases, and R/RStudio, designed to streamline NHANES data management, facilitate rigorous quality control, and simplify analyses across multiple survey cycles. We envision this effort as part of the broader Epiconnector project, which aims to foster collaborative sharing of code, analytical scripts, and best practices, which taken together can significantly enhance the reproducibility, extensibility, and robustness of scientific research using NHANES data.
Additional Material or Paper
https://doi.org/10.1093/database/baaf073
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| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | reproducible research, docker, database, NHANES |
|---|---|
| Virtual Option | This submission is for onsite presentation only |
| Video Recording | Video sharing is fine |
| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |