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A tutorial for decentralized, reproducible processing with DataLad, based on fMRIprep and structural studyforrest data

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processing-w...
Adina Wagner efbb4a6a45 Declare MIT for code, CC-0 for data, CC-BY for writing 3 éve
.datalad a8f4fd4b7c Register pipeline dataset 3 éve
code 3b2010da3e SLURM submission setup 3 éve
fmriprep 56737369a8 Merge results 3 éve
inputs d13c44b1fb Update input data to its latest state 3 éve
.gitattributes ab87c2c2e3 Apply YODA dataset setup 3 éve
.gitignore 3b2010da3e SLURM submission setup 3 éve
.gitmodules cdca5fa093 Register input data dataset as a subdataset 3 éve
CHANGELOG.md ab87c2c2e3 Apply YODA dataset setup 3 éve
LICENSE efbb4a6a45 Declare MIT for code, CC-0 for data, CC-BY for writing 3 éve
README.md b87a00ee2a DOC: add a minimal README as a description 3 éve

README.md

Preprocessed structural data from the studyforrest project

This dataset contains the results of an fMRIprep-based, anatomical preprocessing workflow of structural studyforrest data (studyforrest.org).

All results are fully and automatically reproducible with datalad. The details of this workflow are described at https://github.com/psychoinformatics-de/processing-workflow.

Software requirements for automatic recomputation

How to recompute

First, clone this dataset with DataLad. Next, take a look at the Git history of the data and identify the 40-character long commit shasum of a single-subject computation, then use this shasum in a datalad rerun command:

$ datalad rerun a95484c793793b7274dbef5239e0cc3d315ca0fe

How to obtain data without recomputation

First, cline this dataset with DataLad. Next, retrieve any file(s) of your choice with the datalad get command.