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Neurobagel
Neurobagel
Type to start searching
GitHub
 * 8
 * 6

 * Welcome
 * User guide
 * Data models
 * Preparing data using Nipoppy
 * Contributing
 * Getting help
 * Glossary
 * Citing Neurobagel

An ecosystem for distributed dataset harmonization and search.


Neurobagel allows you to connect a local neuroimaging dataset with others in a
decentralized framework using linked data principles.

Get started


Federated data integration, local control

Our aim is to allow researchers and data owners to harmonize and integrate their
datasets without uploading them to a central database. Neurobagel provides
reusable data schemas and intuitive tools for local data annotation, enabling
participant-level cohort search across harmonized datasets.



Your data, your rules Dataset files and access control always remain locally
with data owners. This avoids the usual vendor lock-in of centralized databases.

Open standards for data description Subject data are annotated using widely
adopted controlled vocabularies and are represented using semantic web
standards. This allows data to be easily integrated with other resources.

Ease of use Web interfaces as well as a modular, containerized software stack
allow datasets to be annotated and queried with as little as no coding
experience while remaining highly configurable.

Participant-level search Annotated data supports queries of phenotypic and
imaging properties at the individual subject rather than the dataset level.


ANNOTATE YOUR TABULAR DATA

Describe phenotypic data using a harmonized data model completely in the
browser.

Try it out Learn more →



WORK WITH STANDARDIZED TERMINOLOGIES

Reap the benefits of curated vocabularies (e.g., BIDS, SNOMED CT, and the
Cognitive Atlas) and linked data, without the implementation expertise.

Learn more →



CREATE REPRODUCIBLE, SHAREABLE DATA REPRESENTATIONS

Once a dataset is annotated, harmonized subject data can be updated and
regenerated at any time. These subject-level annotations are added to knowledge
graphs, while your local data files remain the only source of truth about the
dataset.

Learn more →



EASY LOCAL DEPLOYMENTS

Want to host a local knowledge graph store at your institute? docker compose up,
and you're in business.

Learn more →



SEARCH FOR SUBJECTS ACROSS DATASETS

Easily define your own cross-dataset cohorts based on clinical-demographic and
imaging parameters.

Try it out Learn more →


Our team

Neurobagel is a collaborative effort based in the ORIGAMI Lab at the Montreal
Neurological Institute in collaboration with the Douglas Research Centre. Visit
this page to learn more about the project team.


THE DEVELOPMENT OF NEUROBAGEL IS SUPPORTED BY


Neurobagel
GitHub
 * 8
 * 6

 * Welcome
 * User guide
   User guide
    * Overview
    * Preparing data for annotation
    * Annotating a dataset
    * Generating harmonized subject-level metadata
    * Set up a neurobagel node
    * Set up local federation
    * Updating a harmonized dataset
    * Using the API directly
    * Running cohort queries

 * Data models
   Data models
    * Augmented BIDS data dictionaries
    * Data files for Neurobagel graphs
    * Naming conventions for terms

 * Preparing data using Nipoppy
   Preparing data using Nipoppy
    * Overview
    * Installation
    * Configs
    * Code organization
    * Data organization
    * Workflow
      Workflow
       * DICOM organization
       * BIDS conversion
       * Pipeline-specific instructions
         Pipeline-specific instructions
          * fmriprep
          * mriqc
   
    * Trackers
    * Glossary

 * Contributing
   Contributing
    * How to contribute
    * Our team

 * Getting help
 * Glossary
 * Citing Neurobagel