Posts tagged: Reproducibility
What Do We Mean by Bias in Health Data Research?
A reference guide to what bias means in health data research and the distinct types you meet in electronic health records and other health datasets.
The Builder Pattern for Complex Records
Learn how to use the builder pattern in Rust to construct complex records in a flexible and readable way. This helps manage optional fields and maintain code clarity in data pipelines, and can help your data science and engineering teams build more reliable and maintainable systems.
Why Synthetic Data is Good for Open Science
Understanding the benefits of synthetic data for open science and reproducibility.
Applying the Open-Closed Principle to Research Code
Applying the Open/Closed Principle is how you stop accidentally changing yesterday's results.
The Single Responsibility Principle for Scientists Who Write Code
Exploring the Single Responsibility Principle in the context of scientific programming and data analysis.
Representativeness in Synthetic Data: What It Means and How to Measure It
Understanding the concept of representativeness in synthetic data and the methods used to measure it.
Why Rust for Data-Intensive Applications
Explores why Rust matters for research data pipelines - not for performance, but for correctness. Learn how Rust's type system prevents data failures.
Accidental Functional Programming in Rust (From an Epidemiologist's Perspective)
Rust quietly pushes you into functional patterns. An epidemiologist explains Result, match, enums, iterators, and when readability beats idioms.
How to Create a Codelist
A practical guide to creating codelists from scratch for health data research.