OverviewTeaching: 10 min Exercises: 0 minQuestions
What do we need to know to set up a reproducible analysis workflow?Objectives
Understand the conceptual pieces that make up reproducible research.
Learn where to go for information
You can skip this module if you can answer these questions? —>
- What elements should be captured for repeatable analysis?
- How do you annotate a CSV file for others to understand?
- How do you convert a docker container to singularity?
- How can you recreate your analysis environment on any machine?
Typical brain imaging analyses involve data, software, and human interaction to test hypotheses, explore relations in data and/or extract properties in data (e.g., data features). These analyses rely on numerous elements such as data quality, software environment, algorithms, and human input (e.g., assessment and/or curation) that can introduce errors. In order to repeat or reproduce any analysis, it is essential to record the information of each element.
Reproducing an analysis workflow requires knowing the details of:
These steps are essential for the preservation of information for future use, for the correct documentation of methods for widespread dissemination, and for the repeatability/reproducibility of the experiment by third parties.
For this module, we expect the reader to be familiar with
and have a general idea of brain image analysis. We recommend that you go
through the overview lectures of the reproducible basics and FAIR data
You will learn how to set up and conduct reproducible analysis workflows, how to preserve the information, and how to share data and code with others.
This module consists of 6 lessons, each comprising multiple units. Each unit in this module will take you up to 10 hours of work.
Reproducible research requires understanding all pieces of the (data) workflow
You should be familiar with the necessary elements and tools for reproducible analysis.