| NSF Org: |
OAC Office of Advanced Cyberinfrastructure (OAC) |
| Recipient: |
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| Initial Amendment Date: | July 12, 2017 |
| Latest Amendment Date: | July 12, 2017 |
| Award Number: | 1730655 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Alan Sussman
OAC Office of Advanced Cyberinfrastructure (OAC) CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | September 1, 2017 |
| End Date: | August 31, 2021 (Estimated) |
| Total Intended Award Amount: | $494,651.00 |
| Total Awarded Amount to Date: | $494,651.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
121 UNIVERSITY HALL COLUMBIA MO US 65211-3020 (573)882-7560 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
MO US 65211-0001 |
| 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): | CyberTraining - Training-based |
| 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): | 47.070 |
ABSTRACT
This project will develop cyberinfrastructure-based training modules that advance the existing training methods used for learning and research in data-intensive neuroscience communities. The project outcomes will enhance research into our understanding of both normal and abnormal brains, contributing to NSF's mission of advancing progress in both science and health. The project activities will address important gaps in existing training methods that arise because neuroscience research and education activities are increasingly becoming data-intensive. There is a growing need to integrate and analyze voluminous data being generated at multiple levels to explore the functioning of normal and abnormal brains. Consequently, research and training in the area now necessitates access to distributed resources, including multiple software packages, high-performance computing with large numbers of cores, virtual desktops with data sharing/collaboration capabilities, neuro-data archives, and also requires multi-disciplinary expertise (e.g., engineering, biology, psychology). Computational neuroscience researchers, undergraduate and graduate students and teachers (three targeted communities in this project) face challenges in accessing such resources and expertise in a scalable and extensive manner. Further, they lack the necessary training in the use of advanced cyberinfrastructure (CI) technologies and distributed resources to improve their scientific productivity and to pursue large-scale data-enabled investigations.
The transformative nature of project's training modules is in the "self-service" nature planned for the modules that make them accessible to neuroscience users in an "on-demand" and "personalized" manner. The training modules development will be based on survey of training needs, and will be focused on having students/teachers/multi-disciplinary researchers use, apply and create hands-on laboratory exercises and tools that can be deployed locally (i.e., within institutional CI) and be supplemented with publicly accessible national resources such as the NSF-funded Neuroscience Gateway (NSG). The training modules will considerably enhance existing traditional neuroscience courses covering foundational concepts at undergraduate, graduate and teacher-training levels with hands-on laboratory exercises related to managing scientific workflows, CI middleware and application programming interfaces (APIs) to integrate geographically distributed resources. The proposed activities will leverage existing active training programs in cloud computing and in neuroscience, and will use NSF-supported advanced CI resources that are available locally at University of Missouri and at NSG. Project outcomes will be integrated into on-going courses (with its 50+ neuroscience faculty spanning 10 departments, and 5 colleges), into on-going NSF and NIH summer training programs, which recruit diverse participants including under-served and under-represented students, and into an on-going K-12 outreach program in neuro-robotics. The summer trainees that are being recruited in this project include over 50 students, neuroscience faculty and cyberinfrastructure engineers interested in advanced cyberinfrastructure capabilities for diverse research and education efforts. In addition, over 80 students will benefit from the training modules within formal classroom courses in existing neuroscience and cyberinfrastructure courses at the University of Missouri, and over 150 students will benefit from outreach activities that include webinars and tutorials at conferences.
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.
Neuroscience research and education activities are increasingly becoming data-intensive due to the need to integrate and analyze voluminous data being generated at multiple levels in order to explore the functioning of brains. Consequently, research and training in the area now necessitates access to distributed resources, including varied software packages, and requires multi-disciplinary expertise (e.g., engineering, biology, psychology). Computational neuroscience researchers, undergraduate and graduate students, and teachers (three targeted communities in this project) face challenges in getting trained effectively in both the development and usage of software automation tools.
The specific objectives of our NSF CI project were to:
- Create CI-based neuroscience education modules to enhance cross-disciplinary learning in data-intensive applications requiring diverse software;
- Redesign traditional neuroscience courses to accommodate the changes, including to enhance data-intensive investigations;
- Disseminate the training modules and related CI technologies.
The key outcomes were as follows:
(i) We developed the following modules to enhance training at circuit and systems level in neuroscience, at undergraduate and graduate levels: (i) Colab tutorials focused on neuroscience, (ii) Automated Single Cell tuner for neurobiologist to model single neurons from raw data, (iii) BM-Tools an all-in-one python package with interfaces to facilitate model development using pubic neuro packages bmtk and Neuron, and (iv) SimAgent to submit code to high-performance computers including via Neuroscience Gateway (NSG) and XSEDE. Some others are presently in development including a network modeling tool. Some of these tools are presently available for public download from cyneuro.org, and others will be added after completion.
(ii) We have developed CI-based tools based on the needs assessment, using local (i.e., MU institutional) and National (i.e., NSG, JetStream– an Extreme Science and Engineering Discovery Environment, XSEDE cloud resource) resources and packages such as e.g., NEURON (a computational neuroscience tool), MATLAB, CIPRES (a science gateway development software) and Jupyter Notebooks.
(iii) Two new courses initiated by the grant, one undergraduate and one graduate, incorporate the training modules cited. Additional modules are continuing to be developed. A third of the courses focuses on software automation principles, including development and usage of automation tools. Undergraduate are trained, among other things, to access large neuronal databases (e.g., Allen Database), use APIs, and learn the basics of conversion from python to Notebooks for the open-source neuroscience software used. The interdisciplinary projects-based undergraduate course ECE/CS/BE/BME 4580 Neural Models & Machine Learning that is offered once a year has grown in popularity with enrollments of 10, 29, 35 and 36 students through the 4-year period from Spring 2019 to Spring 2022 (enrolled).
(iv) A total of four on-campus workshops have been conducted to both gather information about use-cases, and to disseminate the training modules to neuroscience researchers and faculty on campus. Also, four online workshops have been conducted to date to disseminate them to over 130+ neural engineering faculty from 2- and 4-year institutions who have been working with us over the past decade. Since the open-source tools are continuing to be developed, their dissemination will continue via on-going and planned free workshops, including for Ph.D. students, medical students, postdocs and faculty.
(v) A total of 75+ undergraduates and 20+ graduate students have been trained to date in software automation concepts, including development and usage of such tools. The two undergrad and grad courses continue to be offered annually.
(vi) To date, the project has resulted in 6 journal, 6 refereed conference articles, and 3 open-source software automation tools for public download (from cyneuro.org)
(vii) Lessons learned included the realization that the CI ‘needs’ of the neuroscience community on campus and those outside that we interacted with (130+ undergraduate faculty and 90+ advanced neuro-researchers; with a majority with background in biological and psychological sciences) were very diverse due to the multiple levels in neuroscience from genetic – molecular – cellular – systems – behavioral and clinical levels. We found that basic software that were still not popular in most levels of neuroscience. Possibly for this reason, usage of the advanced CI tools for research that we developed initially were not as popular as envisaged. However, both graduate and undergraduate students seem to embrace the concepts more readily and so we focused considerable effort on incorporating software automation concepts at undergraduate and graduate levels, and in developing automation tools at single cell and network levels.
(viii) The project has also positively impacted other campus faculty and programs. In addition to campus faculty (some of whom are incorporating our modules into their classes, e.g., Colab tutorials), other on-going projects such as the NSF-REU and NSF-RET programs in neuroscience have benefited from the software automation modules developed as part of the present NSF-OAC project.
Last Modified: 01/07/2022
Modified by: Satish S Nair
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