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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.
Afiliação
  • Kiar G; Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
  • Gorgolewski KJ; Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA.
  • Kleissas D; Department of Psychology, Stanford University, Stanford, CA, USA.
  • Roncal WG; Johns Hopkins University Applied Physics Lab, Columbia, MD, USA.
  • Litt B; Johns Hopkins University Applied Physics Lab, Columbia, MD, USA.
  • Wandell B; Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
  • Poldrack RA; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
  • Wiener M; Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.
  • Vogelstein RJ; Department of Psychology, Stanford University, Stanford, CA, USA.
  • Burns R; Center for Cognitive and Neurobiological Imaging, Stanford University, Stanford, CA, USA.
  • Vogelstein JT; Department of Psychology, Stanford University, Stanford, CA, USA.
Gigascience ; 6(5): 1-10, 2017 05 01.
Article em En | MEDLINE | ID: mdl-28327935
ABSTRACT
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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Ciência / Computação em Nuvem Tipo de estudo: Guideline Limite: Humans Idioma: En Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Ciência / Computação em Nuvem Tipo de estudo: Guideline Limite: Humans Idioma: En Ano de publicação: 2017 Tipo de documento: Article