The FAIRification Of Research In Real-World Evidence: A Practical Introduction To Reproducible Analytic Workflows Using

How can you ensure your data and analytic workflows are reproducible and transparent?

What are the FAIR principles, and why are they crucial for real-world evidence research?

How did a pharmacist and epidemiologist become an expert in real-world data analytics?

In this episode, we explore the practicalities of creating reproducible analytic workflows using Git and R with our special guest, Janick Weberpals. As an instructor in medicine at Brigham and Women's Hospital and Harvard Medical School, Janick shares his journey from pharmacist and epidemiologist to an expert in real-world data analytics and methodology.

He highlights the critical importance of reproducibility in statistical programming and explains how the FAIR principles—making data and code Findable, Accessible, Interoperable, and Reproducible—can transform research practices.

This episode is a must-listen for anyone involved in real-world evidence research, offering hands-on insights and step-by-step guidance to ensure your work is robust and transparent.

Tune in to learn how to harness the power of Git and R for your own projects, ensuring that your data and results are both reliable and reproducible.