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On this page

  • 1 Research Methodology & Workflows
    • 1.1 Mac Workflow for Tracking Daily Research Progress
    • 1.2 Setting Up a Comprehensive Research Backup System on macOS
  • 2 Statistical Computing & Development
    • 2.1 RCT Validation Language
    • 2.2 Setting up an R Development Environment on GitHub
  • 3 Data Science Applications
    • 3.1 Making Optimal Use of ChatGPT and Other Chatbots for Data Science
    • 3.2 Minimalist EDC Application Framework
  • 4 Technical Infrastructure
    • 4.1 Containerized R Analysis Workflows with Docker
    • 4.2 AWS Server Configuration for Research Computing

White Papers

Technical reports, methodological frameworks, and research workflows

1 Research Methodology & Workflows

1.1 Mac Workflow for Tracking Daily Research Progress

A comprehensive framework for organizing research activities, maintaining progress logs, and implementing version control for academic projects.

Research management Workflow automation Version control Academic productivity

🔗 Full Report • 📄 PDF

1.2 Setting Up a Comprehensive Research Backup System on macOS

Technical specification for implementing a multi-layered backup strategy for research data, ensuring redundancy and security across local and cloud storage systems.

Data management Backup systems macOS Research infrastructure

🔗 Full Report • 📄 PDF

2 Statistical Computing & Development

2.1 RCT Validation Language

Specification for a domain-specific programming language designed to capture clinical trial database validation logic, with compilation targets for Lua and JavaScript.

Clinical trials Programming languages Data validation DSL design

🔗 Full Report • 📄 PDF

2.2 Setting up an R Development Environment on GitHub

Best practices and step-by-step methodology for establishing reproducible R package development workflows using GitHub integration and continuous integration.

R development GitHub Package development CI/CD

🔗 Full Report • 📄 PDF

3 Data Science Applications

3.1 Making Optimal Use of ChatGPT and Other Chatbots for Data Science

Evaluation framework and practical guidelines for integrating large language models into data science workflows, including prompt engineering and quality assessment.

AI tools Data science LLM integration Prompt engineering

🔗 Full Report • 📄 PDF

3.2 Minimalist EDC Application Framework

Technical architecture for building lightweight electronic data capture systems for clinical research, emphasizing simplicity and regulatory compliance.

EDC systems Clinical research Software architecture Regulatory compliance

🔗 Full Report • 📄 PDF

4 Technical Infrastructure

4.1 Containerized R Analysis Workflows with Docker

Implementation guide for reproducible R analysis environments using Docker containerization, including best practices for sharing and deployment.

Docker Reproducibility R environment Containerization

🔗 Full Report • 📄 PDF

4.2 AWS Server Configuration for Research Computing

Comprehensive guide for setting up and configuring AWS instances for statistical computing and research data analysis.

AWS Cloud computing Server configuration Research computing

🔗 Full Report • 📄 PDF


These white papers represent in-depth technical analyses, methodological frameworks, and implementation guides developed for research and statistical computing applications. Each document provides detailed specifications, best practices, and reproducible workflows.

Copyright 2023-2025, Ronald G. Thomas