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.

实验性神经科学的进步正在产生大量高质量的高分辨率数据，必须分析这些数据，以揭示有关大脑功能的新见解。在处理这些数据时，雪崩对研究提出了一个特别的挑战，该研究探讨了脑电路和系统的结构和功能，例如大型高分辨率光显微镜，功能磁共振成像（fMRI）和大脑电活动的高密度记录。加利福尼亚大学圣地亚哥大学和耶鲁大学之间的合作项目的目的是通过增强神经科学网关（NSG）的能力来促进此类研究，这是一种现有的网络基础设施资源，该资源最初是为了促进需要高性能计算的项目，例如大型大脑通用型脑通道的大型计算模型。当前的项目将通过将创新纳入高吞吐量计算（HTC）和数据管理中来增强NSG，这是涉及大量数据所必需的，这些数据以减少或消除需要处理此类数据的科学家面临的技术和行政挑战的方式实施。除了启用数据密集型神经科学研究外，这些新功能还将增加NSG在教育方面的效用，在该教育方面，它已经在本科层面和更高级别的神经科学和生物学教学中广泛使用。在各种会议上的网络研讨会，研讨会和培训课程将向学生和研究人员介绍，以了解NSG的新功能。该项目将增加NSG的科学和社会价值，作为一种开放和免费的资源，通过为所有学术机构的学生和研究人员提供访问计算和数据资源的访问来使参与科学的参与。该项目将HTC功能添加到NSG中，这些功能最适合于基于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，以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，将它们与NSG的能力集成到了这些HTC资源的适当计算工作负载的能力，同时保持NSG的易用性功能，该功能允许用户无缝利用这些计算资源。许多使用HTC计算模式的工具可通过NSG提供，以允许在NSG的现有基于Web和程序化的用户环境中处理输入数据并检索输出结果。还为用户提供了灵活性，可以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。 OSG数据联合能力的集成允许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。 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精度）数据，多模式数据的相关分析或机器/深度学习的应用。通过项目，随着添加新功能并合并了资源，通过与用户社区的密切互动来获得反馈。可以在https://www.nsgportal.org/this奖上找到该项目的网站，以反映NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，通过评估被认为是宝贵的支持。