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: 1935771
• 负责人: Nicholas Carnevale
• 金额: $ 12.11万
• 依托单位: Yale University
• 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 2023-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1935771&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产生了大量高质量、高分辨率的数据，必须对这些数据进行分析，以揭示大脑功能的新见解。处理这种数据雪崩对研究提出了特殊的挑战，这些研究采用大规模高分辨率光学显微镜，功能性磁共振成像（fMRI）和高密度脑电活动记录等技术来探测大脑回路和系统的结构和功能。加州圣地亚哥大学和耶鲁大学之间的这个合作项目的目的是通过增强神经科学网关（NSG）的能力来促进这种研究，NSG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，例如大脑回路的大规模计算建模。目前的项目将通过纳入涉及大量数据的研究所需的高吞吐量计算（HTC）和数据管理方面的创新来加强NSG，其实施方式将减少或消除需要处理此类数据的科学家所面临的技术和行政挑战。除了支持数据密集型神经科学研究外，这些新功能还将增加NSG在教育中的实用性，它已经广泛用于本科及以上层次的神经科学和生物学教学。网络研讨会，研讨会和各种会议的培训课程将呈现给学生和研究人员，以了解NSG的新功能。该项目将提高NSG作为开放和免费资源的科学和社会价值，通过使所有学术机构的学生和研究人员能够获得计算和数据资源，使科学参与民主化。该项目为NSG添加了HTC功能，这些功能被认为最适合满足神经科学数据处理的大规模计算需求，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。它结合了商业云计算和开放科学网格（OSG）资源，将它们与NSG的能力相集成，以将适当的计算工作负载提交给这些HTC资源，同时保持NSG的易用性功能，允许用户无缝利用这些计算资源。利用HTC计算模式的许多工具通过NSG提供，以允许在NSG现有的基于网络的编程用户环境中处理输入数据和检索输出结果。还为用户提供了在商业云计算资源上直接使用神经科学建模和数据处理工具的容器化图像的灵活性。OSG的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。各种数据功能的整合，例如将大数据直接传输到NSG的存储，在NSG用户之间共享数据，由多个NSG用户访问和处理数据的能力，使研究人员能够进行各种各样的数据驱动的神经科学研究，无论是电生理学的处理，（脑电图即EEG、脑磁图即MEG）、成像（fMRI）或行为成像（fMRI）。（反应时间、测试准确度）数据、多模态数据的相关性分析或机器/深度学习的应用。在整个项目中，与用户社区保持密切的互动，以获得新功能的添加和资源的整合反馈。该项目的网站可以在www.example.com上找到https://www.nsgportal.org/This奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。