README.md



Training on data management
Lidiski project. Tuesday 2023-12-05 9:30 CET.
After the workshop, the participants will have acquired the basic principles for an efficient management of their research data and documents, avoiding duplication, handling multiple versions, producing appropriate documentation and naming practices.

Specific objectives:


Identify the different requirements of data storage, analysis and visualisation.


Implement good practices for naming files and variables on your own data set.


Organise your data according to tidy principles.


Start a data dictionary for your data.


LICENSE : CC-BY-SA.


Preparation
A copy of one or several data sets and project files in order to practice.
If you don't have anything at hand, you can use this one (from Datacarpentry)

Practical exercise
Your mission: apply these principles to one of your projects


Re-organise your project files in a suitable directory structure


Rename files using descriptive and systematic name structures


Restructure a data set in a tidy format


Write the documentation (meta-data) of the data set


Document your own work. Take some screen shots before and after intervention. Explain what you have changed and justify your decisions. This will be your deliverable for the workshop.
Note: work on a copy of your project, so you feel free to experiment and make mistakes. No need to be exhaustive, it is simply an exercise. A few examples of each type of change will suffice.

Some related tools


Stats tips


dataspice: A R package to facilitate the creation of metadata