Example use case of SIC with the ndmg pipeline (SIC:ndmg)

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 which 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 which will enable us to collectively accelerate the rate at which scientific breakthroughs are discovered, replicated, and extended.
We introduce and document an example use case of SIC with the ndmg pipeline, thus entitled SIC:ndmg. We have developed a capability which enables users to launch a cloud instance and run a container which performs an analysis of a cohort of structural and diffusion magnetic resonance imaging scans by (i) downloading the required data from a public repository in the cloud, (ii) fully processing each subject's data to estimate a connectome for each subject's associated graph statistics, and, optionally, (iii) plot quality control figures of various multivariate graph statistics.

现代技术正助力科学家以前所未有的跨尺度规模，采集海量复杂且精细的数据。面对如此汹涌的数据洪流，我们得以将研究重心从数据采集转向数据分析。然而，由于缺乏标准化的共享机制与实践，复刻或拓展科学研究成果往往困难重重。随着大幅提升代码可移植性的数据组织架构与工具的问世，我们如今有机会构建一套可用于传播可拓展科学发现的框架。

我们提出的解决方案依托现有技术与标准，为可重复研究提供了一套易用且可拓展的模型，命名为「云端科研（science in the cloud, SIC）」。依托科学容器、云计算与云数据服务，我们实现了云端计算能力，并搭建了可让用户与所展示的工具及数据进行深度交互的Web服务。我们期望该模型能够推动科研社区产出可重复且尤为关键的可拓展成果，进而助力我们共同加快科学突破的发现、复刻与拓展速度。

我们介绍并展示了将SIC与ndmg流程结合的示例用例，因此将其命名为SIC:ndmg。我们开发了一项功能，允许用户启动云实例并运行容器，以完成对一批结构磁共振成像与弥散磁共振成像扫描数据的分析：(i) 从云端公共仓库下载所需数据；(ii) 对每位受试者的数据进行全流程处理，为其关联的图统计量估算连接组；以及(iii，可选) 绘制各类多元图统计量的质控图谱。