Use cases¶
Nipoppy can help several types of users and use cases to help address challenges related to data curation and processing.
Users¶
The modular design of Nipoppy offers a variety of entry points for Nipoppy adoption suitable for your workflows and analysis.
In general, Nipoppy workflows are designed to be light-weight and for “self-help” - to simplify common data wrangling tasks.
Below we list several use case scenarios and their touchpoints with Nipoppy for users to get started.
Use cases for individual researchers¶
Imaging Data Curation
Task |
Starting point |
End goal |
Related resources |
|---|---|---|---|
Standardize acquired imaging scans |
Source images (e.g. DICOMs) with an expected list of participants |
Curate BIDSified dataset and assert multimodal data availability |
Tabular Data Curation (under development)
Task |
Starting point |
End goal |
Related resources |
|---|---|---|---|
Organize source demographic, clinical, and other tabular data |
Source tabular data collected in spreadsheets or other data capture software (e.g. RedCAP) |
Assert data availability across data types and link with imaging modalities |
Pandas (Python) or R |
Imaging Data Processing (with common pipelines)
Task |
Starting point |
End goal |
Related resources |
|---|---|---|---|
Process BIDSified data to produce derived imaging output |
Valid BIDS dataset |
Assert availability of processed output |
Imaging Data Processing (with custom pipelines)
Task |
Starting point |
End goal |
Related resources |
|---|---|---|---|
Process BIDSified data to produce custom derived imaging output |
Organized dataset (BIDS or otherwise) required by the custom pipeline |
Assert availability of processed output |
Imaging Data Extraction
Task |
Starting point |
End goal |
Related resources |
|---|---|---|---|
Extract “analysis-ready” imaging-derived-phenotypes from the processed output |
Successful run from a pipeline with extractor support |
Generate tabular files and/or data structures ready for statistical analysis |
Long-term sustainable benefits for institutes and consortia¶
The above use cases target individual researchers and data managers to help adopt best-practices and FAIR data workflows. This can significantly improve reproducibility, reuse, and reduce duplication of effort - particularly in the following two canonical data governance setups.
Centralized Nipoppy adoption of medium and large size datasets in a lab or institute
Provides a single ground truth and inventory of collected and processed data
Streamlines and avoids duplication of compute heavy processing
Keeps provenance of processing configurations
Distributed Nipoppy adoption by participating sites in a consortium
Enables consistent processing across distributed sites
Simplifies tracking of data availability and processing provenance
Accelerate deployment of a new pipeline and version upgrades