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Title: Human Connectome Project Minimal Preprocessing Pipelines to Nipype      
dateReleased:
10-24-2016
privacy:
not applicable
aggregation:
instance of dataset
dateCreated:
10-19-2016
refinement:
curated
ID:
doi:10.5524/100223
creators:
Earl, Eric
Demeter, Damion V
Mills, Kate
,
Ruzic, Luka
Ketz, Nick
Reineberg, Andrew
Reddan, Marianne C
Gonzalez-Castillo, Javier
Gorgolewski, Krzysztof J
availability:
available
types:
sequence
description:
The goal was to convert the Human Connectome Project (HCP) Minimal Preprocessing Pipelines into Nipype code. The HCP minimal preprocessing pipelines represent a significant advance in image processing pipelines in our time. They provide preprocessed volume and surface data in native and atlas space, for both functional and structural data. Nipype is an open source neuroimaging project for designing imaging pipelines which has been around since 2011 and provides many excellent features for provenance and reliability of processing pipelines. Together, these two pieces of software would allow for a more robust, more flexible synergy of pipeline design and operability. More work is needed to truly contribute back to the HCP Pipelines. The greatest achievement of the hackathon project was forming a collaborative team of interested Nipype developers who were trained and are ready to continue collaborating across seven institutions. Future work will continue trying to achieve the original goals as stated, but may need an organizer to hold the team accountable to deadlines.
accessURL: https://doi.org/10.5524/100223
storedIn:
GigaScience Database
qualifier:
not compressed
format:
HTML
accessType:
landing page
primary:
true
authentication:
none
authorization:
none
abbreviation:
GigaDB
homePage: http://gigadb.org/
ID:
SCR:006565
name:
Giga Science Database

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