ReproNim module for dataprocessing

This module will give you an overview of best practices and available tools to set up and conduct a fully reproducible data processing and analysis workflow.

This module is a part of the training curriculum from the ReproNim (Reproducible Neuroimaging) Center.

The template for all ReproNim modules is based on the templates of Neurohackweek, Data Carpentry and Software Carpentry workshops.


09:00 Module overview What do we need to know to set up a reproducible analysis workflow?
09:10 Lesson 1: Core concepts using an analysis workflow example What are the different considerations for reproducible analysis?
10:40 Lesson 2: Annotate, harmonize, clean, and version data How to work with and preserve data of different types?
12:40 Lesson 3: Create and maintain reproducible computational environments Why and how to use containers and Virtual Machines?
15:40 Lesson 4: Create reusable and composable dataflow tools How to use dataflow tools?
15:55 Lesson 5: Use integration testing to revalidate analyses as data and software change Why and how do we use continuous integration?
15:59 Lesson 6: Track provenance from data to results Can we represent the history of an entire analysis?
Can we use this history to repeat the analysis?
16:44 Finish