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High performance science - open and reproducible neuroscience image processing that scales?
--client
lca
--show
lca2018
--room medium 13614 --force
Next: 14 Geographically distributed multi-master replication with PostgreSQL and BDR
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Marks
Author(s):
Steffen Bollmann
Location
Medium Lecture Theatre CB11.00.401
Date
jan Thu 25
Days Raw Files
Start
13:40
First Raw Start
13:09
Duration
0:45:0
Offset
0:30:32
End
14:25
Last Raw End
14:39
Chapters
00:00
0:28:51
Total cuts_time
44 min.
http://lca2018.linux.org.au/schedule/presentation/100/
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Today’s neuroscientific results are based on complex image analyses that involve a large variety of open source software. Unfortunately the last years have shown that many neuroscientific findings cannot be replicated and that data sets are not yet shared widely to enable efficient reuse of data and alternative analyses. In this talk I want to give an overview of how the neuroscience field aims for open and reproducible analyses and by this enables better research and more reliable findings. I want to show how the field progressed to use open data formats for storing raw data acquired from MRI scanners (ISMRMRD), how processed images that can reach multiple TB in file size are stored, including data provenance information (MINC), and how these data are analysed in a reproducible way using software containers (SINGULARITY) that allow scaling an analysis from a small development platform to large scale high performance computing systems and enable the efficient managing of different software versions and the reproducibility of analyses. Finally, I want to touch on sharing neuro scientific data in open repositories that allow the re-use and pooling of large datasets to derive new results that would be impossible without data sharing.
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production notes
2018-01-25/13_09_28.ts
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