Summary: NHANES, the National Health and Nutrition Examination Survey, is a program of studies led by the Centers for Disease Control and Prevention (CDC) designed to assess the health and nutritional status of adults and children in the United States (U.S.). NHANES data is frequently used by biostatisticians and clinical scientists to study health trends across the U.S., but every analysis requires extensive data management and cleaning before use and this repetitive data engineering collectively costs valuable research time and decreases the reproducibility of analyses. Here, we introduce NHANES-GCP, a Cloud Development Kit for Terraform (CDKTF) Infrastructure-as-Code (IaC) and Data Build Tool (dbt) resources built on the Google Cloud Platform (GCP) that automates the data engineering and management aspects of working with NHANES data. With current GCP pricing, NHANES-GCP costs less than $2 to run and less than $15/yr of ongoing costs for hosting the NHANES data, all while providing researchers with clean data tables that can readily be integrated for large-scale analyses. We provide examples of leveraging BigQuery ML to carry out the process of selecting data, integrating data, training machine learning and statistical models, and generating results all from a single SQL-like query. NHANES-GCP is designed to enhance the reproducibility of analyses and create a well-engineered NHANES data resource for statistics, machine learning, and fine-tuning Large Language Models (LLMs). Availability and implementation" NHANES-GCP is available at https://github.com/In-Vivo-Group/NHANES-GCP

NHANES-GCP自动化处理NHANES数据工程和管理，提供干净的数据表，可用于大规模分析，以增强分析的可重复性，为统计学、机器学习和大型语言模型的微调创建良好的NHANES数据资源。

NHANES-GCP: Leveraging the Google Cloud Platform and BigQuery ML for
  reproducible machine learning with data from the National Health and
  Nutrition Examination Survey

NHANES-GCP: 利用谷歌云平台和BigQuery ML进行可复现机器学习，基于美国国家健康和营养调查数据