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.
|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?