Tutorials

To help you get started with ReproNim, we have created a set of tutorials to show how ReproNim’s tools and services support best practices laid out in ReproNim’s principles of reproducible neuroimaging and ReproNim’s four core actions.

The tutorials are designed in a modular fashion, showing how individual steps to improve reproducibility can be fit together into an overall reproducible workflow.

Tutorials cover basic and more advanced approaches as noted below. Each tutorial is organized in a similar fashion, providing a list of tools, necessary skills and system requirements, and step-by-step instructions to implement and use the tools.

For an alternative approach to finding the right tutorial for you, see our user types and use cases.

Principle 1: Study planning

Actions: Annotation and provenance

• Estimating Costs: A guide to estimating costs and required resources for implementing reproducible practices.
• Creating a Data Management and Sharing Plan: Tips for creating an NIH Data Management and Sharing Plan.

Principle 2: Data and metadata management

Actions: Use of standards, Annotation and provenance, Version control

• Converting DICOM to BIDS: Use ReproNim tools to convert DICOM data to the BIDS standard (basic).
• Creating a Data Dictionary: Create a data dictionary for a BIDS dataset (basic).
• Planning a Distributed Project Using ReproSchema: Set up a data collection and annotation framework for a multi-site study.
• Searching Across Studies Using ReproPond and ReproLake: Search and share metadata through ReproPond and ReproLake (advanced).

Forthcoming:

• Add standards and semantics to data dictionaries to promote data harmonization and findability (*basic+).
• Put your derived data into BIDS derivatives and creating a semantically-enriched data dictionary using NIDM, a standard for annotating neuroimaging data (advanced).

Principle 3: Software management

Actions: Annotation and provenance Version control, Use of containers

• Basic Software Versioning Using Git: Use Git to manage workflow and pipeline versions (basic).
• Streamlining Neuroimaging Processing with Nipoppy: Curate and process a single study to generate standardized, analysis-ready data for distributed projects (basic+).
• Advanced Containerization Using DataLad: Use containers within DataLad for software, data, and metadata management. (advanced).

Principle 4: Publishing everything

Forthcoming:

• Publish all of your work as a “re-executable paper” that includes data, code, and text.