Reproducible and Open Research

The participants will be introduced to principles of open research and state of the art techniques of how to enhance the reproducibility of their research.

Topics:

The introductory course provides a broad overview of different aspects of open research and reproducibility. This includes the fields of technical, statistical and computational reproducibility. With a focus on the latter two aspects. The idea of this course is to introduce general concepts of reproducibility and open science to everyone, but with a focus on early career researchers.

Methods:

The course consists of lessons on different aspects of open and reproducible research and offers the opportunity to discuss experiences and expectations on this topic. Furthermore, some hand-on examples of how to implement these methods in your daily work are provided.

Learning goals

Understand Key Concepts of Open Research and Reproducibility

• Define principles of open science and distinguish types of reproducibility (technical, statistical, and computational).
• Explain the importance of reproducibility in enhancing research transparency and credibility.

Learn Techniques for Enhancing Reproducibility

• Discuss tools and strategies to strengthen reproducibility in everyday research tasks.
• Identify methods to improve statistical and computational reproducibility in research.

Discuss Challenges and Benefits of Open Research

• Reflect on the challenges and benefits of open and reproducible research, particularly for early career researchers.
• Share experiences and strategies for integrating open science principles into diverse research settings.

Prerequisites

None. However, basic knowledge in statistics (as taught in the course “Introduction to Statistics”) and programming (as taught in “Kickstart R”) is advantageous.

Target group

Scientists who want to learn about reproducibility of research and open science, with a focus on early career researchers.

This course is free of charge.