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
基本信息
- 批准号:1935749
- 负责人:
- 金额:$ 63.05万
- 依托单位:
- 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:
- 关键词:
项目摘要
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是大量的高质量,高分辨率数据,必须在脑电路和系统的脑电路和系统中具有较大的高分辨率光,功能性磁共振成像(FMRI)的脑电路的功能。加利福尼亚大学Sandigo Aale Niversity之间的协作项目的目的是催化神经科学网关(NSG)的能力,以促进需要高性能计算的项目JECT将通过将创新(HTC)纳入涉及大型AMPL所需的数据管理,以减少或消除需要数据的科学家面临的挑战数据性神经科学能力将在地下级别和更高的网络研讨会上增加NSG的教育科学和生物学工具。通过为所有学术机构的学生和研究人员计算和数据资源的科学。向HTC资源G提交APPLIT工作负载,以允许用户无缝利用计算资源。 NSG的LSO在商业云中直接使用神经科学建模和数据处理工具的容器图像各种数据功能将大数据直接传输到NSG的存储中,在NSG用户中共享数据,由多个NSG用户在ND用户ND处理数据中共享,使研究人员能够执行各种各样的数据传统研究(eletroChalographograghy(即磁脑摄影,磁脑摄影) )或对机器/深度学习的应用(反应时间,测试精度)对于该项目,请参见https:// www.nsgportal.org/this奖,反映了NSF'Stutory Mission,值得我们支持索尼的知名度和更广泛的影响标准。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Amitava Majumdar其他文献
Cyberinfrastructure Usage Modalities on the TeraGrid
TeraGrid 上的网络基础设施使用方式
- DOI:
10.1109/ipdps.2011.239 - 发表时间:
2011 - 期刊:
- 影响因子:0
- 作者:
Daniel S. Katz;David L. Hart;C. Jordan;Amitava Majumdar;J. Navarro;Warren Smith;John Towns;Von Welch;Nancy Wilkins - 通讯作者:
Nancy Wilkins
A parallel Monte Carlo code for planar and SPECT imaging: implementation, verification and applications in /sup 131/I SPECT
用于平面和 SPECT 成像的并行蒙特卡罗代码:/sup 131/I SPECT 中的实现、验证和应用
- DOI:
10.1109/nssmic.2000.949310 - 发表时间:
2000 - 期刊:
- 影响因子:0
- 作者:
Y. Dewaraja;Michael Ljungberg;Amitava Majumdar;Abhijit Bose;K. Koral - 通讯作者:
K. Koral
Ground bounce considerations in DC parametric test generation using boundary scan
使用边界扫描生成直流参数测试时的地弹注意事项
- DOI:
10.1109/vtest.1998.670853 - 发表时间:
1998 - 期刊:
- 影响因子:0
- 作者:
Amitava Majumdar;M. Komoda;Tim Ayres - 通讯作者:
Tim Ayres
Creating intelligent cyberinfrastructure for democratizing AI
创建智能网络基础设施以实现人工智能民主化
- DOI:
10.1002/aaai.12166 - 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Dhabaleswar K. Panda;Vipin Chaudhary;Eric Fosler‐Lussier;R. Machiraju;Amitava Majumdar;Beth Plale;R. Ramnath;P. Sadayappan;Neelima Savardekar;Karen Tomko - 通讯作者:
Karen Tomko
The MVAPICH Project: Evolution and Sustainability of an Open Source Production Quality MPI Library for HPC
MVAPICH 项目:HPC 开源生产质量 MPI 库的演变和可持续性
- DOI:
10.6084/m9.figshare.791563.v5 - 发表时间:
2013 - 期刊:
- 影响因子:0
- 作者:
D. Panda;K. Tomko;Karl W. Schulz;Amitava Majumdar - 通讯作者:
Amitava Majumdar
Amitava Majumdar的其他文献
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{{ truncateString('Amitava Majumdar', 18)}}的其他基金
Collaborative Research: Frameworks: hpcGPT: Enhancing Computing Center User Support with HPC-enriched Generative AI
协作研究:框架:hpcGPT:通过 HPC 丰富的生成式 AI 增强计算中心用户支持
- 批准号:
2411297 - 财政年份:2024
- 资助金额:
$ 63.05万 - 项目类别:
Standard Grant
Category II: Exploring Neural Network Processors for AI in Science and Engineering
第二类:探索科学与工程中人工智能的神经网络处理器
- 批准号:
2005369 - 财政年份:2020
- 资助金额:
$ 63.05万 - 项目类别:
Cooperative Agreement
Collaborative Research: Frameworks: Designing Next-Generation MPI Libraries for Emerging Dense GPU Systems
协作研究:框架:为新兴密集 GPU 系统设计下一代 MPI 库
- 批准号:
1931450 - 财政年份:2019
- 资助金额:
$ 63.05万 - 项目类别:
Standard Grant
Promoting International Collaboration on Developing Scalable, Portable & Efficient HPC Software for Modern HPC Platforms
促进开发可扩展、便携的国际合作
- 批准号:
1849519 - 财政年份:2018
- 资助金额:
$ 63.05万 - 项目类别:
Standard Grant
SHF: Large: Collaborative Research: Next Generation Communication Mechanisms exploiting Heterogeneity, Hierarchy and Concurrency for Emerging HPC Systems
SHF:大型:协作研究:利用新兴 HPC 系统的异构性、层次结构和并发性的下一代通信机制
- 批准号:
1565336 - 财政年份:2016
- 资助金额:
$ 63.05万 - 项目类别:
Standard Grant
Bilateral BBSRC-NSF/BIO: Collaborative Research: ABI Development: Seamless Integration of Neuroscience Models and Tools with HPC - Easy Path to Supercomputing for Neuroscience
双边 BBSRC-NSF/BIO:合作研究:ABI 开发:神经科学模型和工具与 HPC 的无缝集成 - 神经科学超级计算的简单途径
- 批准号:
1458840 - 财政年份:2015
- 资助金额:
$ 63.05万 - 项目类别:
Standard Grant
BIGDATA: F: DKM: Collaborative Research: Scalable Middleware for Managing and Processing Big Data on Next Generation HPC Systems
BIGDATA:F:DKM:协作研究:用于在下一代 HPC 系统上管理和处理大数据的可扩展中间件
- 批准号:
1447861 - 财政年份:2014
- 资助金额:
$ 63.05万 - 项目类别:
Standard Grant
SHF: Large: Collaborative Research: Unified Runtime for Supporting Hybrid Programming Models on Heterogeneous Architecture.
SHF:大型:协作研究:支持异构架构上的混合编程模型的统一运行时。
- 批准号:
1213056 - 财政年份:2012
- 资助金额:
$ 63.05万 - 项目类别:
Standard Grant
Collaborative Research: SI2-SSI: A Comprehensive Performance Tuning Framework for the MPI Stack
合作研究:SI2-SSI:MPI 堆栈的综合性能调优框架
- 批准号:
1147926 - 财政年份:2012
- 资助金额:
$ 63.05万 - 项目类别:
Standard Grant
Collaborative Research: ABI Development: Building A Community Resource for Neuroscientists
合作研究:ABI 开发:为神经科学家建立社区资源
- 批准号:
1146949 - 财政年份:2012
- 资助金额:
$ 63.05万 - 项目类别:
Standard Grant
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相似海外基金
Collaborative Research: CIBR: Leaping the Specimen Digitization Gap: Connecting Novel Tools, Machine Learning and Public Participation to Label Digitization Efforts
合作研究:CIBR:跨越标本数字化差距:将新工具、机器学习和公众参与与标签数字化工作联系起来
- 批准号:
2027241 - 财政年份:2021
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