Reproducible research: methodological principles for transparent science
Ref. 41016
This Mooc proposes methodological principles for open and transparent science. It deals in a practical way with note-taking, computational documentation, replicability of analyses.
- Effort: 24 hours
- Pace: Self paced
- Languages: French
What you will learn
At the end of this course, you will be able to:
- 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
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?
This MOOC is for you. We will 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 explain what is at stake and where the challenges lie in reproducible research.
We will also present the challenges and difficulties of reproducible research.
At the end of this MOOC, you will have acquired good habits for preparing replicable documents and for sharing the results of your work in a transparent fashion.
Format
🆕 The 3rd session of this Mooc is open for one year, which will allow you to follow the Mooc at your own pace and to register when you have time. Note that the estimated time to follow this course and do the exercises is 24 hours.
This MOOC consists of four modules that combine video lectures, many resources describing installation and use of the presented tools (in the form of videos or web pages), quizzes an exercises for getting hands-on experience with the tools and methods that are presented.
To illustrate and deepen the concept of laboratory notebooks, you may view interviews with four researchers from different fields (mathematics, modern and contemporary history, neurophysiology).
Practical cases are proposed throughout the course. For example, we suggest that you work on a "historical" dataset, that of analyzing the risk of failure of the O-rings on the space shuttle Challenger, infamous for its disintegration 73 seconds after takeoff, resulting in the death of the crew's seven astronauts. This accident could perhaps have been avoided...
Another exercise, corrected by the other participants, consists in preparing a data analysis in the form of a computational document, with several subjects to choose from based on real cases, on very different subjects.
To perform these exercises, we propose three paths, each of which uses a different notebook technology:
- The first path uses Jupyter notebooks and the Python language. It requires no software installation on your computer.
- The second path uses RStudio and the R language. You will have to install RStudio on your computer, but we will guide you through this process.
- The third path uses the Org-Mode package of the Emacs editor and the languages Python and R. You will have to install Emacs, Python, and R on your computer, but we will guide you through this process.
This course is bilingual French / English. Videos are in French with French and English subtitles. All other content is provided in both languages as well as the quizzes and exercises.
All resources in this Mooc will be accessible in an open Gitlab repository, in Org-mode or Markdown formats.
Prerequisites
The first module assumes no particular prior knowledge. Starting from the second module, a basic knowledge of Python (with the libraries pandas, numpy and matplotlib) or R is required.
In the fourth module, we treat more specialized topic, each of which may require specific competences.
A familiarity with data analysis and statistics is required for some of the exercises in this MOOC.
🆕 New topics with a lower prerequisite in statistics have been added in this 3rd session so that everyone can find exercises suitable for them. However, even if you can't fully complete these exercises, you will be able to learn about many tools and methods for reproducible research.
Assessment and certification
An Open Badge for successful completion of the course will be issued on request to learners who achieve a final mark of 50% or more. Assessment is based on quizzes, practical exercises and an assignment to be assessed by other students.
Course plan
- 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
Course team
Christophe Pouzat
Categories
Christophe Pouzat is a CNRS researcher in the laboratory MAP5 (applied mathematics at Paris-Descartes).
Arnaud Legrand
Categories
Arnaud Legrand is a CNRS researcher at the Laboratoire d'Informatique in Grenoble.
Konrad Hinsen
Categories
Konrad Hinsen is a CNRS researcher at the Centre de Biophysique Moléculaire in Orléans and at the Synchrotron SOLEIL in Saint Aubin.
Organizations
License
License for the course content
Attribution-NonCommercial
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material
Under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- NonCommercial — You may not use the material for commercial purposes.
License for the content created by course participants
Attribution-NonCommercial-NoDerivatives
You are free to:
- Share — copy and redistribute the material in any medium or format
Under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- NonCommercial — You may not use the material for commercial purposes.
- NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.