Synapsy DMP Training @ Campus Biotech, Geneva
March 08, 2021
Results from www.webofknowledge.com
(Date: March 07, 2021)
Databases such as OpenNeuro, LORIS, COINs, XNAT, SciTran and others accept and export datasets organized following BIDS
>>> from bids import BIDSLayout
>>> layout = BIDSLayout('/home/data/ds000117')
>>> layout.get_subjects()
>>> BIDS = bids.layout('/home/data/ds000117');
>>> bids.query(BIDS, 'subjects')
A number of processing pipelines handling BIDS datasets (BIDS Apps) are available, ranging from quality control to preprocessing, connectome mapping, and statistical analysis - and maybe one of yours in the future!
https://github.com/bids-standard/bids-starter-kit/wiki/Tutorials
datalad create (-c yoda, -c text2git)
A dataset has a history to track files and their modificafications that is explored with Git:
git log
datalad save -m "messsage"
Concise commit messages should summarize the change for future you and others.
datalad status
A clean status is good practice.
datalad clone
A dataset (sub-dataset) can be installed inside a super-dataset (dataset nesting):
datalad get
It allows us to efficiently handle large datasets and download file content on demand.
datalad create-sibling
It creates a remote dataset repository and configures it as a dataset sibling to be used as a publication target.
datalad push
It updates all your local changes saved and annexed data to the remote dataset repository.
datalad run
It links datasets (as subdatasets) and source code, records data origin and command execution, and collect and store provenance of all contents of a dataset created.
datalad rerun
datalad run-containers