This page under construction, beware...
See this link for a publically-commentable draft of the future page...

5 Steps to More Reproducible Neuroimaging Research

Going whole-hog into completely reproducible neuroimaging is hard. But, there are lots of little things you can do today to increase the reproducibility of your current studies.

Study Design

Data Collection

  • Adopt standards-based data representation from the get go.
  • Use ReproIn to automate conversion of your imaging data to BIDS.
  • Annotate your metadata (all experimental details) as you collect it - see, for example, BrainVerse.
  • Use a version control system for all files (data, code, environments, etc.) - i.e. DataLad.

Data Processing

  • Document your software and computational environment - for example NeuroDocker and ReproMan.
  • Use containerization/virtualization to encapsulate and share analysis workflow (Docker, Singularity, Virtual Machines, etc.).
  • Annotate your results - using NeuroImaging Data Model (NIDM), for example.

Statistical Analysis

  • Most studies are underpowered, combat this by sharing your data and reusing available data to enable creating larger data sets, thereby increasing study power. Understand:
    • Effect size, confidence intervals, power, positive predictive value and significance testing - some training materials can be found here.


  • Include the anonymized raw data and workflow used - using resources such as zenodo, etc.
  • Include complete processed results
  • Make it Findable, Accessible, Interoperable and Reusable (FAIR)