| NSF Org: |
DBI Division of Biological Infrastructure |
| Recipient: |
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| Initial Amendment Date: | April 16, 2020 |
| Latest Amendment Date: | April 16, 2020 |
| Award Number: | 1935771 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Reed Beaman
rsbeaman@nsf.gov (703)292-7163 DBI Division of Biological Infrastructure BIO Directorate for Biological Sciences |
| Start Date: | April 15, 2020 |
| End Date: | March 31, 2023 (Estimated) |
| Total Intended Award Amount: | $121,058.00 |
| Total Awarded Amount to Date: | $121,058.00 |
| 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 Street 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): | Infrastructure Capacity for Bi |
| Primary Program Source: |
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| Program Reference Code(s): | |
| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.074 |
ABSTRACT
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.
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 aim of this project was to advance brain research by simplifying the task of analyzing the large amounts of high quality data about brain structure and function that are being generated by advanced experimental methods. We had originally developed the Neuroscience Gateway portal as a tool for neuroscientists engaged in computational modeling in order to test hypotheses about how the operation of brain cells and circuits accounts for behavior. That work continues, but a new challenge has arisen from recent experimental advances that made it possible for neuroscientists to study the structure of the nervous system and the chemical and electrical signals that are responsible for its operation at an unprecedented level of detail. Because of these advances, there is a rapidly growing amount of high quality data that must be analyzed so that we can gain new insights into how the nervous system actually works. In this project we addressed this challenge by adding features to NSG that help neuroscientists store, manage, share, and analyze this data on NSF-supported supercomputers, and take advantage of additional computational power available from commercial cloud services. This work was accomplished by integrating and customizing existing open-source software into NSG. This statement efficiently captures the motivation of the project and the essence of what was done.
Last Modified: 05/30/2023
Modified by: Nicholas T Carnevale
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