| 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: | 1935749 |
| Award Instrument: | Standard Grant |
| Program Manager: |
John Steven C. De Belle
jcdebell@nsf.gov (703)292-2975 DBI Division of Biological Infrastructure BIO Directorate for Biological Sciences |
| Start Date: | April 15, 2020 |
| End Date: | March 31, 2025 (Estimated) |
| Total Intended Award Amount: | $630,486.00 |
| Total Awarded Amount to Date: | $630,486.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
9500 GILMAN DR LA JOLLA CA US 92093-0021 (858)534-4896 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
9500 Gilman Drive La Jolla CA US 92093-0934 |
| 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.
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.
Past decades have seen tremendous growth in the use of computational modeling and simulation, large scale data processing/analysis and application of Artificial Intelligence (AI) and Machine Learning (ML) in neuroscience research. It has also revealed a research bottleneck. Most modeling and data processing projects start “small” and many stays “small”, in the sense of being accommodated by individual desktop systems, but many eventually outstrip the speed and storage capabilities of local hardware. This is often the case with projects that involve complex models (especially large-scale neuronal networks) or complex protocols (often involving learning rules), or require high-dimensional optimization or parameter space exploration or processing of large amount of data from fMRI, EEG, MEG etc. In recent years AI/ML is increasingly being applied in neuroscience research. All of these projects have a tremendous need to use high performance (HPC), high throughput (HTC), GPU and cloud computing but only a very few neuroscientists have been able to perform simulations on large HPC/HTC/GPU/Cloud resources.
The Neuroscience Gateway (NSG) project enables neuroscientists to easily use supercomputing resources for large scale data processing/analysis, computational modeling of neurons and networks, and AI/ML work. NSG reduces the administrative and technical barriers to use supercomputers for neuroscientists. As a result neuroscientists can focus on neuroscience research.
NSG provide the following functionalities:
- It offers free and open access to various types of supercomputing resources based on allocations obtained by the NSG team on different types of supercomputing resources on behalf of the neuroscience community
- NSG jobs (workloads) are distributed across appropriate supercomputing resources automatically based on user needs
- Commonly used neuronal simulation codes, image/data processing/analysis tools and AI/ML tools are optimally installed and benchmarked on various supercomputing resources
- Input is solicited from the neuroscientists about new tools to be made available via NSG
- It is an easy process to get an NSG account
- It has a simple web portal-based user environment for uploading neuronal input models or data, accessing and downloading output results or data, specifying number of nodes/cores, and the wallclock time
- It provides programmatic interface for neuroscience community projects and experienced users
- Users may query the status of running jobs, and receive job completion notices via email
- It provides a user environment with ticketing system, and documentation
- It provides a means for code developers to install and test codes before they are released on supercomputers
- It facilitates hosting of workshops and trainings
- The NSG team hosts in-person and virtual training sessions for the community
- NSG is used in classroom teaching
Computational neuroscience, with its focus on modeling neural networks and analyzing complex datasets, benefits significantly from the NSG's capabilities.
Beyond computational modeling, NSG also supports high-throughput computing and cloud resources, which are essential for processing diverse datasets like EEG, MRI, and fMRI data. By simplifying access to large scale computing resources, NSG is fostering a larger community of researchers using these tools, including those who may not have had prior access or expertise in supercomputing.
Computational modeling and data processing offer opportunities for students and researchers at institutions with limited resources for wet lab or experimental infrastructure to participate in leading edge science. Projects such as NSG facilitate access to large scale compute resources, storage resources and data management capabilities, and thus encourages participation in science by all by mitigating the financial barriers to research infrastructure.
NSG enables research for graduate students, postdoctoral researchers, and faculties and allows high school students to use supercomputers as a part of their summer internship programs. NSG has seen a continual growth in number of users who use NSG for neuroscience research. NSG enabled publications, presentations, and MS/PhD thesis have grown over the years. There are neuroscience community projects, especially related to various types of brain imaging data, which use NSG as the compute engine. Thus NSG has a tremendous impact on the neuroscience community.
Last Modified: 07/09/2025
Modified by: Amitava Majumdar
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