Descriptive & neuroImaging data Graphical Explorer for Subject Tracking

• Overview
• Preview
• Quickstart
• Input schema
• Creating a dashboard-ready "digest" file
• Running in a Docker container
• Local development

Overview

digest is a web dashboard for exploring subject-level availability of pipeline derivatives and phenotypic variables in a neuroimaging dataset. It provides user-friendly options for querying data availability, along with interactive visual summaries.

digest supports any dataset TSV file that follows a data modality-specific schema (called a "digest" file). digest is also compatible with the processing status files generated by Nipoppy.

Preview

Quickstart

Try out digest at https://digest.neurobagel.org/!

You can find correctly formatted example input files here to test out dashboard functionality.

Input schema

digest supports long format TSVs that contain the columns specified in the digest schemas (see also the schema README). At the moment, each digest file is expected to correspond to one dataset.

Creating a dashboard-ready "digest" file

While digest accepts any TSV compliant with one of the digest schemas, the easiest way to obtain dashboard-ready files for pipeline derivative availability is to use the Nipoppy specification for organizing your neuroimaging dataset. Nipoppy provides dataset trackers that can automatically extract subjects' imaging data and pipeline output availability, producing digest-compatible processing status files.

For detailed instructions to get started using Nipoppy, see the documentation.

In brief, the (mostly automated!) Nipoppy steps to generate a processing status file can be as simple as:

1. Initializing an empty, Nipoppy-compliant dataset directory tree for your dataset
2. Updating your Nipoppy configuration with the pipeline versions you are using, and creating a manifest file containing all available participants and sessions
3. Populating the directory tree with any existing data and pipeline outputs *
4. Running the tracker for the relevant pipeline(s) to generate a processing status file

*Nipoppy also provides a protocol for running processing pipelines from raw imaging data.

Running in a Docker container

1. To get the most recent changes, pull the neurobagel/digest docker image tagged nightly:

docker pull neurobagel/digest:nightly

2. Currently, digest also relies on a local copy of the qpn_workflows repository, which contains ready-to-use digest files that are automatically generated for the Quebec Parkinson Network data.

git clone https://github.com/neurodatascience/qpn_workflows.git

3. Run digest and mount the qpn_workflows directory into the container:

docker run -d -p 8050:8050 -v ${PWD}/qpn_workflows:/app/qpn_workflows neurobagel/digest:nightly

Now, the dashboard can be accessed at http://127.0.0.1:8050 on your local machine.

Local development

To install digest from the source repository, run the following in a Python environment:

git clone https://github.com/neurobagel/digest.git
cd digest
pip install -r requirements.txt

To launch the app locally:

python -m digest.app

Once the server is running, the dashboard can be accessed at http://127.0.0.1:8050/ in your browser.

Testing

pytest and dash.testing are used for testing dashboard functionality during development.

To run the tests, run the following command from the repository's root:

pytest tests