Nipoppy

Nipoppy is a lightweight framework for standardized organization and processing of neuroimaging-clinical datasets. Its goal is to help users adopt the FAIR principles and improve the reproducibility of studies.

The framework includes three components:

1. A protocol for data organization, curation and processing, with steps that include the following:

   • Organization of raw data, including conversion of raw DICOMs (or NIfTIs) to BIDS
   • Processing of imaging data with existing or custom pipelines
   • Tracking of data availability and processing status
   • Extraction of imaging-derived phenotypes (IDPs) for downstream statistical modelling and analysis

   

2. A specification for dataset organization that extends the Brain Imaging Data Structure (BIDS) standard by providing additional guidelines for tabular (e.g., phenotypic) data and imaging derivatives.

   

3. A command-line interface and Python package that provide user-friendly tools for applying the framework. The tools build upon existing technologies such as the Apptainer container platform and the Boutiques descriptor framework. Several existing containerized pipelines are supported out-of-the-box, and new pipelines can be added easily by the user.

   • We have also developed a web dashboard for interactive visualizations of imaging and phenotypic data availability.

See the documentation website for more information!