OverviewTeaching: 10 min Exercises: 0 minQuestions
What do we need to know to conduct reproducible analysis?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?
The typical brain imaging experiment uses data, software, and human interaction to test hypotheses and/or explore relations in data. These analyses can involve many different elements (data quality, software environment, algorithms, human input) that can introduce errors. It is therefore useful to capture the information necessary to repeat or reproduce the analysis.
Reproducing the analysis requires knowing descriptions of:
These steps are needed for the researcher to preserve information for future use, to document the methods for dissemination, and to repeat the experiment.
For this module, we expect the reader to be familiar with unix computing concepts and have a general idea of brain image analysis. It is highly recommended that you go through the overview lectures of the reproducible basics and FAIR data principles modules.
You will learn how to perform reproducible analysis, 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 flow
You should be familiar with the necessary elements and tools for reproducible analysis.