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FAIRly big: A framework for computationally reproducible processing of large-scale data

View ORCID ProfileAdina S. Wagner, View ORCID ProfileLaura K. Waite, View ORCID ProfileMałgorzata Wierzba, View ORCID ProfileFelix Hoffstaedter, View ORCID ProfileAlexander Q. Waite, View ORCID ProfileBenjamin Poldrack, View ORCID ProfileSimon B. Eickhoff, View ORCID ProfileMichael Hanke
doi: https://doi.org/10.1101/2021.10.12.464122
Adina S. Wagner
1Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
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  • For correspondence: adina.wagner@t-online.de
Laura K. Waite
1Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
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Małgorzata Wierzba
1Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
2Laboratory of Brain Imaging, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
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Felix Hoffstaedter
1Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
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Alexander Q. Waite
1Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
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Benjamin Poldrack
1Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
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Simon B. Eickhoff
1Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
3Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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Michael Hanke
1Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
3Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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Abstract

Large-scale datasets present unique opportunities to perform scientific investigations with unprecedented breadth. However, they also pose considerable challenges for the findability, accessibility, interoperability, and reusability (FAIR) of research outcomes due to infrastructure limitations, data usage constraints, or software license restrictions. Here we introduce a DataLad-based, domain-agnostic framework suitable for reproducible data processing in compliance with open science mandates. The framework attempts to minimize platform idiosyncrasies and performance-related complexities. It affords the capture of machine-actionable computational provenance records that can be used to retrace and verify the origins of research outcomes, as well as be re-executed independent of the original computing infrastructure. We demonstrate the frame-work’s performance using two showcases: one highlighting data sharing and transparency (using the studyforrest.org dataset) and another highlighting scalability (using the largest public brain imaging dataset available: the UK Biobank dataset).

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted October 14, 2021.
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FAIRly big: A framework for computationally reproducible processing of large-scale data
Adina S. Wagner, Laura K. Waite, Małgorzata Wierzba, Felix Hoffstaedter, Alexander Q. Waite, Benjamin Poldrack, Simon B. Eickhoff, Michael Hanke
bioRxiv 2021.10.12.464122; doi: https://doi.org/10.1101/2021.10.12.464122
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FAIRly big: A framework for computationally reproducible processing of large-scale data
Adina S. Wagner, Laura K. Waite, Małgorzata Wierzba, Felix Hoffstaedter, Alexander Q. Waite, Benjamin Poldrack, Simon B. Eickhoff, Michael Hanke
bioRxiv 2021.10.12.464122; doi: https://doi.org/10.1101/2021.10.12.464122

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