TY - DATA T1 - Human Connectome Project Minimal Preprocessing Pipelines to Nipype AU - Earl, Eric AU - Demeter, Damion V AU - Mills, Kate AU - Glad Mihai AU - Ruzic, Luka AU - Ketz, Nick AU - Reineberg, Andrew AU - Reddan, Marianne C AU - Goddings, Anne-Lise AU - Gonzalez-Castillo, Javier AU - Gorgolewski, Krzysztof J DO - 10.5524/100223 UR - http://gigadb.org/dataset/100223 AB - 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. KW - Imaging KW - Neuroscience PY - 2016 PB - GigaScience Database LA - en SN - 100215 ER -