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. This course presents common issues in scientific reproducibility. You will be introduced to routines in the research process and principles of open research raising your awareness of common pitfalls and enhancing the robustness of your research output. 

Target Audience

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

Topics

This course provides a practical introduction to the principles of open research and the methods required to make your work more transparent, robust, and reliable. We will cover the different dimensions of reproducibility with a focus on the statistical and computational aspects that are critical to data-driven research. The course is designed as a foundational module, ideally taken at the beginning of your research career, to help you build your research projects on a robust and transparent footing from day one.

Methods

The course is structured as an interactive seminar. It combines presentations on core concepts with facilitated discussions about the real-world challenges and benefits of implementation. Practical demonstrations of select tools and workflows will be provided to illustrate how these principles can be applied in a typical research setting.

Learning Goals

Upon completion of this course, you will be able to:

1. Define and differentiate the key types of research reproducibility.
2. Explain the core principles and motivations of the Open Science movement.
3. Identify practical strategies and tools for improving the statistical and computational reproducibility of your research.
4. Articulate the benefits of these practices for enhancing the integrity, impact, and efficiency of your research.

5. Develop a concrete plan for applying foundational reproducible practices to your own work.

    

Prerequisites

None. However, basic knowledge in statistics and programming is advantageous.

Format

• Duration: 4 hours
• Language: English
• This course will be offered either on campus (in person), or online.

• For online courses we use the software Zoom.

   

Dates and Application

• Courses provided for Helmholtz Munich:
  • You can check the current dates and whether the courses are already fully booked here*.
  • Please read the corresponding FAQ* before applying via the forms of the HR Development department*.
• Courses provided for HIDA:
  • You can check the current dates and whether the courses are already fully booked here.
  • Registrations for these courses are exclusively possible via the provided homepage.

 * Links marked with * are only available for Helmholtz Munich staff.