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
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA.
Gigascience. 2017 May 1;6(5):1-10. doi: 10.1093/gigascience/gix013.
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.
现代技术使科学家们能够以前所未有的方式,在极大的范围内收集大量复杂而精密的数据。面对如此海量的数据,我们可以将重点从数据收集转移到数据分析上。不幸的是,缺乏标准化的共享机制和实践往往使得再现或扩展科学成果变得非常困难。随着数据组织结构和工具的创建极大地提高了代码的可移植性,我们现在有机会设计这样一个框架来交流可扩展的科学发现。我们提出的解决方案利用了这些现有技术和标准,并为可重复研究提供了一个可访问且可扩展的模型,称为“云科学”(SIC)。通过利用科学容器、云计算和云数据服务,我们展示了在云中进行计算并运行一个网络服务的能力,该服务能够与所展示的工具和数据进行紧密交互。我们希望这个模型能够激励科学界产生可重复且重要的是可扩展的成果,从而使我们能够共同加快科学突破被发现、复制和扩展的速度。