Teaching open and reproducible research

This document

We gather here a list of topics to teach to student and more advanced researchers. Each topic may/will correspond to a workshop or/and a lecture that could be given. The topics are evaluated for different Goals of the overall project here, from 0 to 2 stars:

  • FASTER: The scientist will work faster or with less effort
  • BETTER: Make science more reproducible and trustworthy
  • COLLABORATIVE: Foster scientific collaboration
  • VISIBLE: attract attention of your science

Data management and version control

FASTER BETTER COLLABORATIVE VISIBLE
** ** ** *

Basics

time(h) skills needed: required workshop
2 none none

This workshop presents basics in data management: use of master files (data inventory), file naming (human and computer readability), repositories and backups, and versioning (manual version control). Git will be mentioned but not introduced.

Git and version control

time(h) skills needed: required workshop
2 none none

This workshop presents basics in version control and introduce the use of Git, with Git UI (tortoise, sourcetree or github desktop). How to use commit, branch and merge and more importantly why. Then we will look into git repositories and the use of push and pull. Github will be shortly mentioned.

Github and collaborative work

time(h) skills needed: required workshop
2 none Git and version control

This workshop will go through github functionlities and their potential for scientific collaboration.

R and Rmarkdown (introduction

FASTER BETTER COLLABORATIVE VISIBLE
** ** ** *

R basics

time(h) skills needed: required workshop
4 none none

In this introduction, student will learn about R and Rstudio, the CRAN package system. They will learn to import data, make simple analysis and make simple plots, statistical analysis and create outputs (using tidyverse packages) . They will also get introducted to stackoverflow and ways to bebug their code.

R markdown basics

time(h) skills needed: required workshop
2 R basics R basics

In this introduction, student will first learn about markdown. Then we will introduce Rmarkdown, the yaml introductions and the use of chunks. They will be asked to create a report using rmarkdown corresponding to the analysis done in the Rbasics course with output in word, html and pdf.

Tidy data

FASTER BETTER COLLABORATIVE VISIBLE
** * *

Basics

time(h) skills needed: required workshop
2 none none

This workshop presents the idea of tidy data and why planning your data gathering correctly will save you and your colleague much time. It also insist on naming files and columns correctly in a human and computer readable way. It takes example from the audience to discuss the building of a tidy table depending on the data to be collected. Ways to tidy up table after data collection, and metadata production is shortly introduced.

Intermediate

time(h) skills needed: required workshop
2 Basic R knowledge Tidy basics

This workshop dig in more details in ways to transform data to make it tidier. We start with tyding up a messy data, and then look at different tricks to work the data, when specific analysis requires it.