Reproducible research - methodological principles for transparent science

Course description

You take notes and you want to be able to find them back? You make calculations on your computer, but your results change from day to day? You analyse data, or you work on a new method that you would like to share easily with your colleagues so that they can use it as well?

The authors of this MOOC show you some modern and reliable tools :

• Markdown for taking structured notes
• Desktop search application (DocFetcher et ExifTool)
• GitLab for version control and collaborative working
• Computational notebooks (Jupyter, RStudio, and Org-Mode) for efficiently combining the computation, presentation, and analysis of data

By doing the exercises, you will learn how to use these tools for improving your skills in note taking, data management and computation. To do this, you will have a Gitlab repository and a Jupyter space, which are integrated into the FUN platform and do not require any installation. Those who wish to do the practical work with Rstudio or Org-mode will be able to do so after installing these tools on their machine. All the procedures for installing and configuring the tools are provided in the Mooc, as well as numerous tutorials.

We will also present the challenges and difficulties of reproducible research.

Course objectives

• Understand the challenges and difficulties of replicable research
• Discover tools to improve note-taking, data management, and calculations
• Become familiar with a version control tool (Gitlab)
• Become familiar with replicable computational documents (Jupyter, RStudio or Org-Mode)
• Write a notebook to effectively combine data analysis and documentation

Who is this course for?

This MOOC is aimed at PhD students, researchers, Master's students, teachers and engineers from all disciplines who wish to learn about reliable publishing environments and tools.

Course outline

• Let's set the scene : Reproducibility in crisis? Reproducibility and transparency
• Module 1: Taking notes and finding them back
• Module 2: From the showcase to the full story: computational documents
• Module 3: Diving in: a replicable analysis
• Module 4: The rough road to real-life reproducible research

Pedagogical team

Authors:

• Arnaud Legrand, Computer science researcher, CNRS/LIG, Inria, UGA
• Christophe Pouzat, Neurophysiologist, CNRS/MAP5 Univ. Paris Descartes
• Konrad Hinsen, Biophysicist, CNRS, Centre de biophysique moléculaire, Soleil

Pedagogical support:

• Laurence Farhi, learning engineer, Inria Learning Lab
• Marie-Hélène Comte, learning engineer, Inria Learning Lab
• Benoit Rospart, IT engineer, Inria Learning Lab, Inria Learning Lab

Partners

With the support of Fonds national de la science ouverte