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: 10019388
• 负责人: Amitava Majumdar
• 金额: $ 38.13万
• 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 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10019388
• 关键词: Behavioral; Benchmarking; Brain imaging; Cells; Cognitive; Communities; Computer Models; Computer software; Data; Data Analyses; Data Correlations; 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; Software Tools; Students; Support System; System; Testing; Time; Training; Training Programs; United States National Institutes of Health; Wait Time; Work; Workload; Writing; base; bioimaging; 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; 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：神经科学网关，以实现计算和数据处理工具的传播和 软件。）：该提案提出了一项重点计划，旨在扩展神经科学网关（NSG）的功能 满足从事计算密集型研究的神经科学家不断变化的需求。 NSG项目于2012年启动 在美国国家科学基金会的支持下。其最初的目标是通过减少技术投入来促进计算神经科学的进步 神经科学家在涉及工具和软件的大规模建模项目中面临的行政障碍和管理障碍 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：(1) 自 2013 年初开始运营以来，其注册用户群不断增长（目前超过 800 名），(2) 每年，NSG 团队都会成功获得越来越多的超级计算机时间分配（最近超过 10,000,000 核心小时/年）关于极限科学与工程发现 (XSEDE – 通过撰写经过竞争激烈的同行评审的提案来协调 NSF 超级计算机中心）计划 审查过程，（3）它促成了大量的出版物和博士论文。近年来实验家们， 认知神经科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 学习。 NSG 现在提供 20 多种 HPC 资源工具，用于建模、仿真和数据处理。虽然 NSG 正在 目前已被神经科学界广泛使用，该界越来越有兴趣将其应用到 任务范围比最初设想的更广泛。例如，有些人试图将其用作传播环境 实验室开发的工具，尽管 NSG 不适合该用途，因为批处理队列等待时间的延迟 生产 HPC 资源，缺乏交互式、图形和协作环境的功能和资源 工具开发、基准测试和测试所需的。 “强制”使用 NSG 进行开发和传播使得 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，数据处理工作流程需要诸如能够 传输大尺寸数据、处理共享数据以及可视化输出结果，这些目前在 NSG 上不可用。这 我们建议的工作将通过添加适当且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。这将使工具开发人员 利用当前得到良好服务的功能在 NSG 上传播其实验室开发的工具 过去六年对 NSG 的评价，例如不断增长的用户群、简单的用户界面、开放的环境、访问能力 并在强大的计算资源、可用的免费超级计算机时间、完善的培训和 外展计划和有效的用户支持系统。 NSG 的所有这些运作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。