Neuroscience Gateway to Enable Dissemination of Computational And Data Processing Tools And Software.

神经科学网关能够传播计算和数据处理工具和软件。

• 批准号: 10650338
• 负责人: Amitava Majumdar
• 金额: $ 35.15万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-20 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10650338
• 关键词: Behavioral; Benchmarking; Brain imaging; Cells; Cognitive; Communities; Computer Models; Computer Simulation; Computer software; Data; Data Analyses; Data Science; Data Set; Development; Disease; Education; Education and Outreach; Educational workshop; Electroencephalography; Engineering; Environment; Foundations; Functional Magnetic Resonance Imaging; Funding; Future; Goals; Health; High Performance Computing; Hour; Human Resources; Image; Machine Learning; Magnetic Resonance Imaging; Methods; Modeling; Neurophysiology - biologic function; Neurosciences; Neurosciences Research; Occupations; Output; Peer Review; Persons; Process; Production; Psychologist; Publications; Reaction Time; Research; Research Personnel; Resources; Running; Science; Students; Support System; System; Testing; Time; Training; Training Programs; United States National Institutes of Health; Visualization; Wait Time; Work; Workload; Writing; base; biomedical imaging; brain cell; collaborative environment; computational neuroscience; computerized data processing; computing resources; data sharing; image processing; interest; models and simulation; open data; operation; outreach program; programs; response; simulation; success; supercomputer; tool; tool development; trend; webinar

Abstract (Proposal title: Neuroscience Gateway to Enable Dissemination of Computational and Data Processing Tools and Software.): This proposal presents a focused plan for expanding the capabilities of the Neuroscience Gateway (NSG) to meet the evolving needs of neuroscientists engaged in computationally intensive research. The NSG project began in 2012 with support from the NSF. Its initial goal was to catalyze progress in computational neuroscience by reducing technical and administrative barriers that neuroscientists faced in large scale modeling projects involving tools and software which require and run efficiently on high performance computing (HPC) resources. NSG's success is reflected in the facts that (1) its base of registered users has grown continually since it started operation in early 2013 (more than 800 at present), (2) every year the NSG team successfully acquires ever larger allocations of supercomputer time (recently more than 10,000,000 core hours/year) on academic HPC resources of the Extreme Science and Engineering Discovery (XSEDE – that coordinates NSF supercomputer centers) program by writing proposals that go through an extremely competitive peer review process, and (3) it has contributed to large number of publications and Ph.D thesis. In recent years experimentalists, cognitive neuroscientists and others have begun using NSG for brain image data processing, data analysis and machine learning. NSG now provides over 20 tools on HPC resources for modeling, simulation and data processing. While NSG is currently well used by the neuroscience community, there is increasing interest from that community in applying it to a wider range of tasks than originally conceived. For example, some are trying to use it as an environment for dissemination of lab-developed tools, even though NSG is not suitable for that use because of delays from the batch queue wait times of production HPC resources, and lack of features and resources for an interactive, graphical, and collaborative environment needed for tool development, benchmarking and testing. “Forced” use of NSG for development and dissemination makes NSG's operators a “person-in-the-middle” bottleneck in the process. Another issue is that newly developed data processing tools require high throughput computing (HTC) usage mode, as opposed to HPC, but currently NSG does not provide access to compute resources suitable for HTC. Additionally, data processing workflows require features such as the ability to transfer large size data, process shared data, and visualize output results, which are not currently available on NSG. The work we propose will enhance NSG by adding the features that it needs to be a suitable and efficient dissemination environment for lab-developed neuroscience tools to the broader neuroscience community. This will allow tool developers to disseminate their lab-developed tools on NSG taking advantage of the current functionalities that are being well served on NSG for the last six years such as a growing user base, an easy user interface, an open environment, the ability to access and run jobs on powerful compute resources, availability of free supercomputer time, a well-established training and outreach program, and a functioning user support system. All of these well-functioning features of NSG will make it an ideal environment for dissemination and use of lab-developed computational and data processing neuroscience tools.

摘要(提案标题：神经科学门户，使计算和数据处理工具和 软件。)：这项提案提出了一项重点计划，将神经科学网关(NSG)的能力扩展到 满足从事计算密集型研究的神经科学家不断发展的需求。核供应国集团项目始于2012年 在国家科学基金会的支持下。它最初的目标是通过减少技术含量来促进计算神经科学的进步。 神经学家在大规模建模项目中面临的管理障碍，这些项目涉及工具和软件， 需要高性能计算(HPC)资源并在其上高效运行。NSG的成功体现在以下事实上：(1) 自2013年初开始运营以来，其注册用户基数不断增长(目前已超过800人)，(2) 每年，NSG团队都成功地获得了越来越多的超级计算机时间分配(最近超过 10,000,000个核心小时/年)，用于极端科学与工程发现(XSEDE- 它协调NSF超级计算机中心)计划，通过撰写提案，通过竞争极其激烈的同行 评审过程；(3)发表了大量的出版物和博士论文。近年来，实验者们， 认知神经学家和其他人已经开始使用NSG进行大脑图像数据处理、数据分析和机器 学习。NSG现在在HPC资源上提供了20多个工具，用于建模、仿真和数据处理。而NSG则是 目前被神经科学界很好地使用，该社区对将其应用于 比最初设想的任务范围更广。例如，一些人试图利用它作为传播的环境 实验室开发的工具，即使NSG由于批处理队列等待时间的延迟而不适合使用 生产HPC资源，缺乏用于交互、图形和协作环境的功能和资源 用于工具开发、基准测试和测试。“强迫”使用核供应国集团进行发展和传播 NSG的运营商是这一过程中的“中间人”瓶颈.另一个问题是新开发的数据处理 工具需要高吞吐量计算(HTC)使用模式，而不是HPC，但目前NSG不提供访问 计算适合HTC的资源。此外，数据处理工作流需要以下功能： 传输大型数据、处理共享数据和可视化输出结果，这些都是NSG当前无法提供的。这个 我们提出的工作将通过添加适当和有效的传播所需的功能来增强NSG 为更广泛的神经科学界提供实验室开发的神经科学工具的环境。这将允许工具开发人员 在NSG上传播他们的实验室开发的工具，利用目前得到很好服务的功能 在过去六年的NSG上，例如不断增长的用户群、轻松的用户界面、开放的环境、访问 并在强大的计算资源、可用的空闲超级计算机时间、完善的培训和 外展计划，以及一个正常运作的用户支持系统。NSG的所有这些运行良好的功能将使其成为 传播和使用实验室开发的计算和数据处理神经科学工具的理想环境。