
NSF Org: |
DBI Division of Biological Infrastructure |
Recipient: |
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Initial Amendment Date: | July 23, 2015 |
Latest Amendment Date: | September 14, 2015 |
Award Number: | 1458495 |
Award Instrument: | Standard Grant |
Program Manager: |
Peter McCartney
DBI Division of Biological Infrastructure BIO Directorate for Biological Sciences |
Start Date: | August 1, 2015 |
End Date: | November 30, 2018 (Estimated) |
Total Intended Award Amount: | $129,624.00 |
Total Awarded Amount to Date: | $129,624.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
150 MUNSON ST NEW HAVEN CT US 06511-3572 (203)785-4689 |
Sponsor Congressional District: |
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Primary Place of Performance: |
330 Cedar St. New Haven CT US 06510-3218 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): |
ADVANCES IN BIO INFORMATICS, CYBERINFRASTRUCTURE, Cross-BIO Activities |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.074 |
ABSTRACT
This project is a collaboration between the University of California San Diego and Yale University to develop a science gateway for the computational neuroscience community. A gateway such as this helps improve our understanding of how the brain works by making it easier for neuroscientists to use complex digital models of brain cells and circuits in their research. Powerful software has been developed for building and using models, and on-line resources such as Open Source Brain (OSB), ModelDB, Neuroscience Information Framework (NIF), and OpenWorm have been created to help neuroscientists find existing models, collaborate in developing new ones, and share the results of their work with others. However, models are becoming too complex for the computer hardware that is available to most neuroscientists, resulting in a critical need to use high performance computing resources (HPC). This work extends an existing Neuroscience Gateway (NSG), which was developed with support from NSF to eliminate or reduce many of the technical and administrative difficulties that previously limited neuroscientists' access to HPC (http://www.nsgportal.org/). That said, NSG users must still log in, upload models, launch simulations, and download results--a process that involves many time-consuming, error-prone steps. The expanded NSG-R will eliminate these steps by enabling on-demand, automated communication between itself and familiar working environments including resources like OSB and others mentioned above, and even with neural simulation software running on neuroscientists' own laptop and desktop computers.
This seamless access to HPC is implemented in NSG-R by a software infrastructure that uses REpresentational State Transfer ("REST", the R in NSG-R). NSG-R utilizes set of web services which expose the capabilities of NSG for access via publicly available application programmer interfaces. This will allow users of neuroscience resources such as OSB, ModelDB, NIF and OpenWorm to readily access HPC from their respective websites via NSG-R. This enhances the usefulness of NSG-R, other neuroscience resources like OSB, and widely used neural simulators such as NEURON, GENESIS, PyNN, NEST, Brian and MOOSE. It also results in greater research productivity and enables wider use of large scale computational modeling by scientists and students. NSG-R will accelerate progress in brain science, and have far-reaching beneficial effects on related fields such as robotics and engineering of adaptive and learning systems. It will widen opportunities for educational and career advancement in neuroscience and engineering. Furthermore, by removing barriers that traditionally have limited access to HPC, NSG-R levels the playing field for all students and researchers regardless of their institutional affiliation. NSG-R, a free and open neuroscience gateway infrastructure, will naturally be a ready entry point for students and researchers from historically underrepresented schools and colleges. NSG-R workshops will be hosted at minority serving institutions (MSI) and opportunities for students to do internships with the NSG-R team at the University of California San Diego will be provided.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
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. The NSG project was followed in 2015 by the NSG-R project, which added a programmatic interface to NSG so that neuroscientists could more easily automate the process of setting up and controlling modeling projects. 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. At this time, we know of more than 100 reports of research that has been enabled by NSG. 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.
Last Modified: 02/28/2019
Modified by: Nicholas T Carnevale
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