Collaborative Research: CIBR: Building Capacity for Data-driven Neuroscience Research

合作研究：CIBR：数据驱动神经科学研究能力建设

• 批准号: 1935749
• 负责人: Amitava Majumdar
• 金额: $ 63.05万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1935749&HistoricalAwards=false
• 关键词: Collaborative; Research; CIBR; Building; Capacity

Advances in experimental neuroscience are generating large amounts of high-quality, high-resolution data that must be analyzed in order to reveal new insights into how the brain functions. Dealing with this data avalanche poses a special challenge for research that probes the structure and function of brain circuits and systems with techniques such as large scale high resolution light microscopy, functional magnetic resonance imaging (fMRI), and high density recording of brain electrical activity. The aim of this collaborative project between the University of California San Diego and Yale University is to catalyze such research by enhancing the capabilities of the Neuroscience Gateway (NSG), an existing cyberinfrastructure resource that was originally developed to facilitate projects that need High Performance Computing, such as large scale computational modeling of brain circuits. The current project will enhance NSG by incorporating innovations in high throughput computing (HTC) and data management that are required for research involving large amounts of data, implemented in ways that reduce or eliminate the technical and administrative challenges faced by scientists who need to deal with such data. In addition to enabling data-intensive neuroscience research, these new capabilities will increase NSG's utility in education, where it is already widely used in neuroscience and biology instruction at the undergraduate level and higher. Webinars, workshops, and training classes at various conferences will be presented to students and researchers to learn about NSG's new capabilities. This project will increase NSG's scientific and social value as an open and free resource that democratizes participation in science by enabling access to computing and data resources for students and researchers at all academic institutions. This project adds HTC features to NSG that have been judged most suitable to meet the large scale computing needs for neuroscience data processing, based on actual and projected use cases provided by neuroscientists engaged in data-intensive research. It incorporates commercial cloud computing and Open Science Grid (OSG) resources, integrating them with NSG’s ability to submit appropriate compute workloads to these HTC resources while maintaining the ease of use features of NSG that allow users to seamlessly exploit these compute resources,. Many of the tools that utilize HTC computing mode are made available via NSG to allow processing of input data and retrieval of output results within the existing web based and programmatic user environment of NSG. Flexibility is also provided for users to directly use containerized images of neuroscience modeling and data processing tools on commercial cloud computing resources. Integration of OSG’s data federation capability allows processing of publicly available large neuroscience data which can be distributed in a scalable manner to HTC resources. Incorporation of various data functionalities such as the ability to transfer large data directly to NSG’s storage, share data among NSG users, access and process data by multiple NSG users, enable researchers to perform a wide diversity of data-driven neuroscience research be it processing of electrophysiological (electroencephalography i.e. EEG, magnetoencephalography i.e. MEG), imaging (fMRI) or behavioral (reaction time, test accuracy) data, correlational analysis of multimodal data, or application of machine/deep learning. Throughout the project close interaction with the user community is maintained to gain feedback as new features are added and resources are incorporated. The web site for this project can be found at https://www.nsgportal.org/This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

实验神经科学的进步正在产生大量高质量、高分辨率的数据，必须对这些数据进行分析，才能揭示大脑如何运作的新见解，处理这些数据雪崩对探索大脑结构和功能的研究提出了特殊的挑战。加州大学圣地亚哥分校和耶鲁大学合作项目的目的是利用大规模高分辨率光学显微镜、功能磁共振成像（fMRI）和高密度脑电活动记录等技术来研究大脑回路和系统。到通过增强神经科学网关（NSG）的能力来促进此类研究，神经科学网关是一种现有的网络基础设施资源，最初是为了促进需要高性能计算的项目而开发的，例如脑回路的大规模计算建模。当前的项目将通过合并来增强 NSG。涉及大量数据的研究所需的高通量计算 (HTC) 和数据管理方面的创新，其实施方式除了支持数据之外，还可以减少或消除需要处理此类数据的科学家所面临的技术和管理挑战。 -密集的神经科学研究方面，这些新功能将提高 NSG 在教育中的实用性，它已经广泛应用于本科及以上级别的神经科学和生物学教学，并将在各种会议上向学生和研究人员展示网络研讨会、研讨会和培训课程。该项目将提高 NSG 作为开放和免费资源的科学和社会价值，使所有学术机构的学生和研究人员能够访问计算和数据资源，从而实现科学参与的民主化。该项目为 NSG 添加了 HTC 功能。根据从事数据密集型研究的神经科学家提供的实际和预测用例，它被认为最适合满足神经科学数据处理的大规模计算需求，它结合了商业云计算和开放科学网格（OSG）资源，集成了。 NSG 能够向这些 HTC 资源提交适当的计算工作负载，同时保持 NSG 的易用性，使用户能够无缝地利用这些计算资源。许多利用 HTC 计算模式的工具都可以通过 NSG 进行处理。的在 NSG 现有的基于网络和编程的用户环境中输入数据和检索输出结果还为用户提供了直接使用商业云计算资源上的神经科学建模和数据处理工具的容器化图像的灵活性。处理公开可用的大型神经科学数据，这些数据可以以可扩展的方式分发到 HTC 资源。合并各种数据功能，例如将大数据直接传输到 NSG 存储、在 NSG 用户之间共享数据、访问和共享数据的能力。处理多个 NSG 用户的数据，使研究人员能够执行各种数据驱动的神经科学研究，无论是处理电生理学（脑电图，即 EEG、脑磁图，即 MEG）、成像 (fMRI) 或行为（反应时间、测试准确性）数据，多模态数据的相关分析，或机器/深度学习的应用，在整个项目中保持与用户社区的密切互动，以在添加新功能和整合资源时获得反馈。该项目可在 https://www.nsgportal.org/ 上找到。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。