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Neuroscience Cloud Analysis As a Service

View ORCID ProfileTaiga Abe, Ian Kinsella, Shreya Saxena, E. Kelly Buchanan, Joao Couto, John Briggs, Sian Lee Kitt, Ryan Glassman, John Zhou, Liam Paninski, John P. Cunningham
doi: https://doi.org/10.1101/2020.06.11.146746
Taiga Abe
1Mortimer B. Zuckerman Mind Brain Behavior Institute
2Center for Theoretical Neuroscience, Columbia University
4Department of Neuroscience, Columbia University
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  • ORCID record for Taiga Abe
Ian Kinsella
1Mortimer B. Zuckerman Mind Brain Behavior Institute
2Center for Theoretical Neuroscience, Columbia University
5Department of Statistics, Columbia University
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Shreya Saxena
1Mortimer B. Zuckerman Mind Brain Behavior Institute
2Center for Theoretical Neuroscience, Columbia University
3Grossman Center for the Statistics of Mind, Columbia University
5Department of Statistics, Columbia University
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E. Kelly Buchanan
1Mortimer B. Zuckerman Mind Brain Behavior Institute
2Center for Theoretical Neuroscience, Columbia University
4Department of Neuroscience, Columbia University
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Joao Couto
7Department of Neurobiology, University of California
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John Briggs
1Mortimer B. Zuckerman Mind Brain Behavior Institute
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Sian Lee Kitt
6Department of Computer Science, Columbia University
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Ryan Glassman
6Department of Computer Science, Columbia University
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John Zhou
6Department of Computer Science, Columbia University
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Liam Paninski
1Mortimer B. Zuckerman Mind Brain Behavior Institute
2Center for Theoretical Neuroscience, Columbia University
3Grossman Center for the Statistics of Mind, Columbia University
4Department of Neuroscience, Columbia University
5Department of Statistics, Columbia University
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John P. Cunningham
1Mortimer B. Zuckerman Mind Brain Behavior Institute
2Center for Theoretical Neuroscience, Columbia University
3Grossman Center for the Statistics of Mind, Columbia University
5Department of Statistics, Columbia University
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  • For correspondence: jpc2181@columbia.edu
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Abstract

A major goal of computational neuroscience is the development of powerful data analyses that operate on large datasets. These analyses form an essential toolset to derive scientific insights from new experiments. Unfortunately, a major obstacle currently impedes progress: novel data analyses have a hidden dependence upon complex computing infrastructure (e.g. software dependencies, hardware), acting as an unaddressed deterrent to potential analysis users. While existing analyses are increasingly shared as open source software, the infrastructure needed to deploy these analyses – at scale, reproducibly, cheaply, and quickly – remains totally inaccessible to all but a minority of expert users. In this work we develop Neuroscience Cloud Analysis As a Service (NeuroCAAS): a fully automated analysis platform that makes state-of-the-art data analysis tools accessible to the neuroscience community. Based on modern large-scale computing advances, NeuroCAAS is an open source platform with a drag-and-drop interface, entirely removing the burden of infrastructure purchase, configuration, deployment, and maintenance from analysis users and developers alike. NeuroCAAS offers two major scientific benefits to any data analysis. First, NeuroCAAS provides automatic reproducibility of analyses at no extra effort to the analysis developer or user. Second, NeuroCAAS cleanly separates tool implementation from usage, allowing for immediate use of arbitrarily complex analyses, at scale. We show how these benefits drive the design of simpler, more powerful data analyses. Furthermore, we show that many popular data analysis tools offered through NeuroCAAS outperform typical analysis solutions (in terms of speed and cost) while improving ease of use, dispelling the myth that cloud compute is prohibitively expensive and technically inaccessible. By removing barriers to fast, efficient cloud computation, NeuroCAAS can dramatically accelerate both the dissemination and the effective use of cutting-edge analysis tools for neuroscientific discovery.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • New section on platform structure and development added; new results concerning widefield imaging analyses and markerless tracking added.

  • http://www.neurocaas.org

  • https://github.com/cunningham-lab/neurocaas

  • https://github.com/cunningham-lab/neurocaas_contrib

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-NC 4.0 International license.
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Posted June 03, 2021.
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Neuroscience Cloud Analysis As a Service
Taiga Abe, Ian Kinsella, Shreya Saxena, E. Kelly Buchanan, Joao Couto, John Briggs, Sian Lee Kitt, Ryan Glassman, John Zhou, Liam Paninski, John P. Cunningham
bioRxiv 2020.06.11.146746; doi: https://doi.org/10.1101/2020.06.11.146746
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Neuroscience Cloud Analysis As a Service
Taiga Abe, Ian Kinsella, Shreya Saxena, E. Kelly Buchanan, Joao Couto, John Briggs, Sian Lee Kitt, Ryan Glassman, John Zhou, Liam Paninski, John P. Cunningham
bioRxiv 2020.06.11.146746; doi: https://doi.org/10.1101/2020.06.11.146746

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