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NeuronDepot: keeping your colleagues in sync by combining modern cloud storage services the local file system and simple web applications

机译:NeuronDepot:通过结合现代云存储服务本地文件系统和简单的Web应用程序使您的同事保持同步

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摘要

Neuroscience today deals with a “data deluge” derived from the availability of high-throughput sensors of brain structure and brain activity, and increased computational resources for detailed simulations with complex output. We report here (1) a novel approach to data sharing between collaborating scientists that brings together file system tools and cloud technologies, (2) a service implementing this approach, called NeuronDepot, and (3) an example application of the service to a complex use case in the neurosciences. The main drivers for our approach are to facilitate collaborations with a transparent, automated data flow that shields scientists from having to learn new tools or data structuring paradigms. Using NeuronDepot is simple: one-time data assignment from the originator and cloud based syncing—thus making experimental and modeling data available across the collaboration with minimum overhead. Since data sharing is cloud based, our approach opens up the possibility of using new software developments and hardware scalabitliy which are associated with elastic cloud computing. We provide an implementation that relies on existing synchronization services and is usable from all devices via a reactive web interface. We are motivating our solution by solving the practical problems of the GinJang project, a collaboration of three universities across eight time zones with a complex workflow encompassing data from electrophysiological recordings, imaging, morphological reconstructions, and simulations.
机译:如今,神经科学致力于处理“数据泛滥”,这种“数据泛滥”源于大脑结构和大脑活动的高通量传感器的可用性,以及用于复杂输出详细仿真的更多计算资源。我们在这里报告(1)协作科学家之间共享数据系统文件和云技术的新颖方法,(2)实现此方法的服务称为NeuronDepot,以及(3)该服务在复杂环境中的示例应用神经科学中的用例。我们方法的主要驱动力是通过透明,自动化的数据流促进协作,从而使科学家不必学习新工具或数据结构范例。使用NeuronDepot很简单:从发起者和基于云的同步进行一次数据分配-从而以最小的开销在整个协作中提供实验和建模数据。由于数据共享基于云,因此我们的方法为使用与弹性云计算相关的新软件开发和硬件扩展开辟了可能性。我们提供了一个依赖现有同步服务的实现,并且可以通过反应式Web界面在所有设备上使用。我们正在通过解决GinJang项目的实际问题来激励我们的解决方案,该项目是由八个时区的三所大学合作组成的,其复杂的工作流程涵盖了电生理记录,成像,形态重建和模拟中的数据。

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