Lightweight BIDS Layouts for all #87
Labels
bay_area_usa
Bay area event
git_skills:1_commit_push
git_skills:2_branches_PRs
git_skills:3_continuous_integration
programming:containerization
Docker, Singularity
programming:documentation
Markdown, Sphinx
programming:Python
project_development_status:2_releases_existing
project_type:coding_methods
project_type:documentation
project
status:published
status:web_ready
tools:BIDS
tools:DIPY
topic:reproducible_scientific_methods
Title
Lightweight BIDS Layouts for all
Leaders
Collaborators
Brainhack Global 2022 Event
Bay Area Brainhack
Project Description
PyBIDS'
BIDSLayout
currently uses a generic database (sqlite) to represent a BIDS dataset. For large datasets of >100 subjects, this can be time-prohibitive to construct.ancpBIDS has written a custom domain-specific model based at least partially on the BIDS schema, which enables a
BIDSLayout
that is orders of magnitude faster than PyBIDS'.During BrainHack Global, we will be working on porting PyBIDS to use ancpBIDS instead of sqlite to represent datasets in memory, and we would be glad to have your help.
Link to project repository/sources
Goals for Brainhack Global
We'll settle for not breaking any apps we test. So let's test some apps.
Good first issues
Evaluate ancpbids as a successor to bids.layout bids-standard/pybids#831
We're aiming to do some additional prep and break this down into smaller issues
Communication channels
https://mattermost.brainhack.org/brainhack/channels/pybids
Skills
If you want to work on the refactor:
Testing:
Onboarding documentation
PyBIDS doesn't have contribution guidelines currently. The DIPY developer guidelines are a pretty useful for general best practices: https://dipy.org/documentation/1.5.0/devel/
To get up to speed on the problem, we have a couple PRs that are worth perusing:
What will participants learn?
This is going to be an exercise in refactoring and testing. You will learn:
We will also need bug reports! Testing our proposal out on other projects will involve:
Data to use
Ideally we'll be working with a variety of BIDS datasets to ensure that we test as many components of BIDSLayout as possible, so feel free to bring your own data.
Another good source of datasets is https://openneuro.org
Number of collaborators
3
Credit to collaborators
PyBIDS contributors are credited in a Zenodo file and listed as authors on Zenodo releases.
Image
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Type
coding_methods
Development status
2_releases_existing
Topic
reproducible_scientific_methods
Tools
BIDS
Programming language
containerization, documentation, Python
Modalities
not_applicable
Git skills
1_commit_push, 2_branches_PRs, 3_continuous_integration
Anything else?
No response
Things to do after the project is submitted and ready to review.
Hi @brainhackorg/project-monitors my project is ready!
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