publication . Preprint . 2018

NeuroStorm: Accelerating Brain Science Discovery in the Cloud

Kiar, Gregory; Anderson, Robert J.; Baden, Alex; Badea, Alexandra; Bridgeford, Eric W.; Champion, Andrew; Chandrashekhar, Vikram; Collman, Forrest; Duderstadt, Brandon; Evans, Alan C.; ...
Open Access English
  • Published: 08 Mar 2018
Abstract
Comment: 10 pages, 4 figures, hackathon report
Subjects
free text keywords: Quantitative Biology - Other Quantitative Biology
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[3] Yuste, R. & Bargmann, C. Toward a global BRAIN initiative. Cell 168, 956-959 (2017). [OpenAIRE]

[4] Wilkinson, M. D. et al. The fair guiding principles for scientific data management and stewardship. Scientific data 3, 160018 (2016).

[5] Randlett, O. et al. Whole-brain activity mapping onto a zebrafish brain atlas. Nature methods 12, 1039-1046 (2015). between zebrafish brain atlases using symmetric dieomorphic normaTlization. bioRxiv 081000 [OpenAIRE]

[6] Marquart, G. D., Tabor, K. M., Horstick, E. J., Brown, M. & Burgess, H. A. High precision registration (2016).

[17] Glatard, T. et al. A virtual imaging platform for multi-modality medical image simulation. IEEE Transactions on Medical Imaging 32, 110-118 (2013). [OpenAIRE]

[19] Gorgolewski, K. J. et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data 3, 160044 (2016).

[20] Jain, S. et al. Computational anatomy gateway: leveraging xsede computational resources for shape analysis. In Proceedings of the 2014 Annual Conference on Extreme Science and Engineering Discovery Environment, 54 (ACM, 2014).

[21] Mori, S. et al. Mricloud: Delivering high-throughput mri neuroinformatics as cloud-based software as a service. Computing in Science & Engineering 18, 21-35 (2016).

[22] Beg, M. F., Miller, M. I., Trouvé, A. & Younes, L. Computing large deformation metric mappings via geodesic flows of dieomorphisms. International journal of computer vision 61, 139-157 (2005).

Abstract
Comment: 10 pages, 4 figures, hackathon report
Subjects
free text keywords: Quantitative Biology - Other Quantitative Biology
Related Organizations
Download from

[3] Yuste, R. & Bargmann, C. Toward a global BRAIN initiative. Cell 168, 956-959 (2017). [OpenAIRE]

[4] Wilkinson, M. D. et al. The fair guiding principles for scientific data management and stewardship. Scientific data 3, 160018 (2016).

[5] Randlett, O. et al. Whole-brain activity mapping onto a zebrafish brain atlas. Nature methods 12, 1039-1046 (2015). between zebrafish brain atlases using symmetric dieomorphic normaTlization. bioRxiv 081000 [OpenAIRE]

[6] Marquart, G. D., Tabor, K. M., Horstick, E. J., Brown, M. & Burgess, H. A. High precision registration (2016).

[17] Glatard, T. et al. A virtual imaging platform for multi-modality medical image simulation. IEEE Transactions on Medical Imaging 32, 110-118 (2013). [OpenAIRE]

[19] Gorgolewski, K. J. et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data 3, 160044 (2016).

[20] Jain, S. et al. Computational anatomy gateway: leveraging xsede computational resources for shape analysis. In Proceedings of the 2014 Annual Conference on Extreme Science and Engineering Discovery Environment, 54 (ACM, 2014).

[21] Mori, S. et al. Mricloud: Delivering high-throughput mri neuroinformatics as cloud-based software as a service. Computing in Science & Engineering 18, 21-35 (2016).

[22] Beg, M. F., Miller, M. I., Trouvé, A. & Younes, L. Computing large deformation metric mappings via geodesic flows of dieomorphisms. International journal of computer vision 61, 139-157 (2005).

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