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Science in the cloud (SIC): A use case in MRI connectomics
Modern technologies are enabling scientists to collect extraordinary amounts of complex and sophisticated data across a huge range of scales like never before. With this onslaught of data, we can allow the focal point to shift from data collection to data analysis. Unfortunately, lack of standardize...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5467033/ https://www.ncbi.nlm.nih.gov/pubmed/28327935 http://dx.doi.org/10.1093/gigascience/gix013 |
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author | Kiar, Gregory Gorgolewski, Krzysztof J. Kleissas, Dean Roncal, William Gray Litt, Brian Wandell, Brian Poldrack, Russel A. Wiener, Martin Vogelstein, R. Jacob Burns, Randal Vogelstein, Joshua T. |
author_facet | Kiar, Gregory Gorgolewski, Krzysztof J. Kleissas, Dean Roncal, William Gray Litt, Brian Wandell, Brian Poldrack, Russel A. Wiener, Martin Vogelstein, R. Jacob Burns, Randal Vogelstein, Joshua T. |
author_sort | Kiar, Gregory |
collection | PubMed |
description | Modern technologies are enabling scientists to collect extraordinary amounts of complex and sophisticated data across a huge range of scales like never before. With this onslaught of data, we can allow the focal point to shift from data collection to data analysis. Unfortunately, lack of standardized sharing mechanisms and practices often make reproducing or extending scientific results very difficult. With the creation of data organization structures and tools that drastically improve code portability, we now have the opportunity to design such a framework for communicating extensible scientific discoveries. Our proposed solution leverages these existing technologies and standards, and provides an accessible and extensible model for reproducible research, called ‘science in the cloud’ (SIC). Exploiting scientific containers, cloud computing, and cloud data services, we show the capability to compute in the cloud and run a web service that enables intimate interaction with the tools and data presented. We hope this model will inspire the community to produce reproducible and, importantly, extensible results that will enable us to collectively accelerate the rate at which scientific breakthroughs are discovered, replicated, and extended. |
format | Online Article Text |
id | pubmed-5467033 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-54670332017-06-19 Science in the cloud (SIC): A use case in MRI connectomics Kiar, Gregory Gorgolewski, Krzysztof J. Kleissas, Dean Roncal, William Gray Litt, Brian Wandell, Brian Poldrack, Russel A. Wiener, Martin Vogelstein, R. Jacob Burns, Randal Vogelstein, Joshua T. Gigascience Research Modern technologies are enabling scientists to collect extraordinary amounts of complex and sophisticated data across a huge range of scales like never before. With this onslaught of data, we can allow the focal point to shift from data collection to data analysis. Unfortunately, lack of standardized sharing mechanisms and practices often make reproducing or extending scientific results very difficult. With the creation of data organization structures and tools that drastically improve code portability, we now have the opportunity to design such a framework for communicating extensible scientific discoveries. Our proposed solution leverages these existing technologies and standards, and provides an accessible and extensible model for reproducible research, called ‘science in the cloud’ (SIC). Exploiting scientific containers, cloud computing, and cloud data services, we show the capability to compute in the cloud and run a web service that enables intimate interaction with the tools and data presented. We hope this model will inspire the community to produce reproducible and, importantly, extensible results that will enable us to collectively accelerate the rate at which scientific breakthroughs are discovered, replicated, and extended. Oxford University Press 2017-03-07 /pmc/articles/PMC5467033/ /pubmed/28327935 http://dx.doi.org/10.1093/gigascience/gix013 Text en © The Author 2017. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Kiar, Gregory Gorgolewski, Krzysztof J. Kleissas, Dean Roncal, William Gray Litt, Brian Wandell, Brian Poldrack, Russel A. Wiener, Martin Vogelstein, R. Jacob Burns, Randal Vogelstein, Joshua T. Science in the cloud (SIC): A use case in MRI connectomics |
title | Science in the cloud (SIC): A use case in MRI connectomics |
title_full | Science in the cloud (SIC): A use case in MRI connectomics |
title_fullStr | Science in the cloud (SIC): A use case in MRI connectomics |
title_full_unstemmed | Science in the cloud (SIC): A use case in MRI connectomics |
title_short | Science in the cloud (SIC): A use case in MRI connectomics |
title_sort | science in the cloud (sic): a use case in mri connectomics |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5467033/ https://www.ncbi.nlm.nih.gov/pubmed/28327935 http://dx.doi.org/10.1093/gigascience/gix013 |
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