bulk_annotations

Retroactively annotate the phenotypic data of a large number of BIDS datasets at once.

At the moment this focuses on datasets with MRI data only.

This takes as input the datasets that are available on the datalad superdataset.

This may not reflect the latest version of all of the datasets on openneuro and openneuro derivatives.

OpenNeuro datasets:

Number of datasets: 790 with 34479 subjects including:

• 649 datasets with MRI data
  • with participants.tsv: 450 (450 with more than 'participant_id' column)
  • with phenotype directory: 22
  • with fmriprep: 17 (1877 subjects)
  • with participants.tsv: 16
  • with phenotype directory: 1
  • with freesurfer: 28 (1834 subjects)
  • with participants.tsv: 26
  • with phenotype directory: 1
  • with mriqc: 65 (5664 subjects)
  • with participants.tsv: 57
  • with phenotype directory: 9

OpenNeuro derivatives datasets:

Number of datasets: 258 with 10582 subjects including:

• 258 datasets with MRI data
  • with participants.tsv: 189 (189 with more than 'participant_id' column)
  • with phenotype directory: 11
  • with fmriprep: 59 (1789 subjects)
  • with participants.tsv: 45
  • with phenotype directory: 1
  • with freesurfer: 59 (1789 subjects)
  • with participants.tsv: 45
  • with phenotype directory: 1
  • with mriqc: 258 (10582 subjects)
  • with participants.tsv: 189
  • with phenotype directory: 11

Requirements

• datalad: http://handbook.datalad.org/en/latest/intro/installation.html
• requires make

Install openneuro and openneuro-derivatives using datalad

openneuro can be installed via (this will take a while):

make openneuro

openneuro derivatives can be installed via (this will take a while):

make openneuro-derivatives

Update datasets to get the latest version

WIP

listing datasets contents

run list_openneuro_dependencies.py and it will create TSV file with basic info for each dataset and its derivatives.

TODO:

• make it able to install on the fly datasets or subdatasets (especially sourcedata/raw for the openneuro-derivatives)

listing the content of the participants.tsv files

Run list_participants_tsv_columns.py.py to get a listing of all the columns present in all the participants.tsv files and a list of all the unique columns across participants.tsv files.

Run list_participants_tsv_levels.py to also get a listing of all the levels in all the columns present in all the participants.tsv files.

Clone the datasets from OpenNeuro-JSONLD

The OpenNeuro-JSONLD org has augmented openneuro datasets. To clone these effectively, you can use the below command:

It uses the GH CLI: https://cli.github.com/manual/installation

And make sure to be logged into the CLI

gh repo list OpenNeuroDatasets-JSONLD --fork -L 500 | awk '{print $1}' | sed 's/OpenNeuroDatasets-JSONLD\///g' | parallel -j 6 git clone git@github.com:OpenNeuroDatasets-JSONLD/{}

Running the bagel-cli on bulk annotated data

The following scripts are used:

• extract_bids_dataset_name.py
• add_description.py
• run_bagel_cli.sh
• parallel_bagel.sh

Steps

1. (Optional) create a new Python environment with python -m venv my_env.

2. Activate your python environment with source ./my_env/bin/activate

3. Install the dependencies with pip install -r requirements.txt

4. Get the latest version of the bagel-cli from Docker Hub: docker pull neurobagel/bagelcli:latest

5. Create a directory called inputs in the repository root that contains all the datasets that will be processed with the CLI.

6. To run the CLI in parallel across the datasets in inputs/, double check that the directory paths used by parallel_bagel.sh and run_bagel_cli.sh are correct, then run:

   ./parallel_bagel.sh