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Suggested Citation:"Appendix A: References." National Academies of Sciences, Engineering, and Medicine. 2020. Neuroscience Data in the Cloud: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25653.
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Appendix A

References

Cousijn, H., A. Kenall, E. Ganley, M. Harrison, D. Kernohan, T. Lemberger, F. Murphy, P. Polischuk, S. Taylor, M. Martone, and T. Clark. 2018. A data citation roadmap for scientific publishers. Scientific Data 5(November):180259. doi: 10.1038/sdata.2018.259.

Glatard, T., G. Kiar, T. Aumentado-Armstrong, N. Beck, P. Bellec, R. Bernard, A. Bonnet, et al. 2018. Boutiques: A flexible framework to integrate command-line applications in computing platforms. GigaScience 7(5). doi: 10.1093/gigascience/giy016.

Gorgolewski, K. J., F. Alfaro-Almagro, T. Auer, P. Bellec, M. Capot , M. Chakravarty, N. W. Churchill, et al. 2017. BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods. PLoS Computational Biology 13(3):e1005209. doi: 10.1371/journal.pcbi.1005209.

King, M. D., D. Wood, B. Miller, R. Kelly, D. Landis, W. Courtney, R. Wang, J. A. Turner, and V. D. Calhoun. 2014. Automated collection of imaging and phenotypic data to centralized and distributed data repositories. Frontiers in Neuroinformatics 8:60. doi: 10.3389/fninf.2014.00060.

Lee, C. A., R. B. Bohn, and M. Michel. 2020. The NIST Cloud Federation Reference Architecture. NIST Special Publication (SP) 500-332. National Institute of Standards and Technology. https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.500-332.pdf (accessed February 28, 2020).

Martone, M. (ed.). 2014. Data Citation Synthesis Group: Joint declaration of data citation principles. FORCE11. doi: 10.25490/a97f-egyk.

Mell, P., and T. Grance. 2011. The NIST definition of cloud computing. NIST Special Publication (SP) 800-145. doi: 10.6028/NIST.SP.800-145.

Pierce, H. H., A. Dev, E. Statham, and B. E. Bierer. 2019. Credit data generators for data reuse. Nature 570(7759):30–32. doi: 10.1038/d41586-019-01715-4.

Poldrack, R. A., D. M. Barch, J. P. Mitchell, T. D. Wager, A. D. Wagner, J. T. Devlin, C. Cumba, O. Koyejo, and M. P. Milham. 2013. Toward open sharing of task-based FMRI data: The OpenfMRI Project. Frontiers in Neuroinformatics 7:12. doi: 10.3389/fninf.2013.00012.

Suggested Citation:"Appendix A: References." National Academies of Sciences, Engineering, and Medicine. 2020. Neuroscience Data in the Cloud: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25653.
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Psychiatric GWAS Consortium Steering Committee. 2009. A framework for interpreting genome-wide association studies of psychiatric disorders. Molecular Psychiatry 14(1):10–17. doi: 10.1038/mp.2008.126.

Rocher, L., J. M. Hendrickx, and Y.-A. de Montjoye. 2019. Estimating the success of re-identifications in incomplete datasets using generative models. Nature Communications 10(1):1–9. doi: 10.1038/s41467-019-10933-3.

Suggested Citation:"Appendix A: References." National Academies of Sciences, Engineering, and Medicine. 2020. Neuroscience Data in the Cloud: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25653.
×
Page 73
Suggested Citation:"Appendix A: References." National Academies of Sciences, Engineering, and Medicine. 2020. Neuroscience Data in the Cloud: Opportunities and Challenges: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/25653.
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The cloud model of data sharing has led to a vast increase in the quantity and complexity of data and expanded access to these data, which has attracted many more researchers, enabled multi-national neuroscience collaborations, and facilitated the development of many new tools. Yet, the cloud model has also produced new challenges related to data storage, organization, and protection. Merely switching the technical infrastructure from local repositories to cloud repositories is not enough to optimize data use.

To explore the burgeoning use of cloud computing in neuroscience, the National Academies Forum on Neuroscience and Nervous System Disorders hosted a workshop on September 24, 2019. A broad range of stakeholders involved in cloud-based neuroscience initiatives and research explored the use of cloud technology to advance neuroscience research and shared approaches to address current barriers. This publication summarizes the presentation and discussion of the workshop.

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