
NSF Org: |
DUE Division Of Undergraduate Education |
Recipient: |
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Initial Amendment Date: | July 31, 2017 |
Latest Amendment Date: | August 4, 2020 |
Award Number: | 1726532 |
Award Instrument: | Standard Grant |
Program Manager: |
Paul Tymann
DUE Division Of Undergraduate Education EDU Directorate for STEM Education |
Start Date: | September 1, 2017 |
End Date: | August 31, 2021 (Estimated) |
Total Intended Award Amount: | $230,000.00 |
Total Awarded Amount to Date: | $275,999.00 |
Funds Obligated to Date: |
FY 2020 = $45,999.00 |
History of Investigator: |
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Recipient Sponsored Research Office: |
2301 S 3RD ST LOUISVILLE KY US 40208-1838 (502)852-3788 |
Sponsor Congressional District: |
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Primary Place of Performance: |
2301 South Third Street Louisville KY US 40292-0001 |
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): | IUSE |
Primary Program Source: |
04002021DB NSF Education & Human Resource |
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): | 47.076 |
ABSTRACT
Business, government, and science researchers are producing massive amounts of complex data. The availability of these huge datasets fuels a need for both data-driven analytics and a 21st-century workforce that can use data analytics to answer questions and solve problems. This collaborative project will develop a cloud-based virtual platform to train undergraduate students how to use software tools essential to data science. The platform will make state-of-the-art computing resources, including both powerful data analysis tools and parallel hardware systems, more accessible to students and faculty, even if they are at institutions without locally available high-power computing systems. The project aims to help students develop critical workforce skills in data science. The project will also provide professional development opportunities to help faculty use data-analysis tools in their courses and research.
The goal of this project is to develop a cloud-based infrastructure in the form of a virtual science platform with related training modules. First, it will leverage an existing framework for building web applications to provide broad access to open source, high performance computing resources at the collaborating universities and through the NSF Extreme Science and Engineering Discovery Environment. The cloud-based platform will support both training of students and collaboration among students. Second, the project will produce a data science curriculum targeted to undergraduate students. The curriculum will also be suitable for graduate students, post-doctoral researchers, and information technology professionals interested in data science. The project will deliver a full set of interactive documents and video tutorials on using and configuring the platform. The educational activities will use graphical, interactive, simulation-based, and experiential learning components to teach data science concepts and computing skills, accessed through the cloud-based platform. Through the platform, students will have the opportunity to learn how to use powerful data science resources, enabling their potential to transform data-rich computer science and engineering problems into practical solutions. Third, the project will deliver professional development for faculty at multiple institutions, to help them learn how to use data science in their classrooms and their own research. This project addresses national interests by making state-of-the-art computing resources more accessible to students, supporting their development of critical workforce skills.
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.
Project Outcomes Report
Business, government, and research scientists are working with massive amounts of complex data today. The availability of these huge datasets fuels a need for data-driven analytics as well as the next generation of workforce that can use data analytics to answer questions and solve big data problems. To prepare our students towards successful data scientists in different productive sectors, we identified unique challenges and proposed to develop a virtual platform to help train them on how to use essential tools and computing resources to solve data-driven problems where different knowledge fields intersect. Based on our experiences in providing many successful training sessions, we also proposed to design and develop new software and corresponding training modules that can be further utilized to enhance data science education for undergraduate and graduate students.
In the four years of the project, the collaborative partners - the University of Louisville and the Texas Advanced Computing Center (TACC) developed IDOLS, a cloud-based virtual platform (https://idols.tacc.utexas.edu/) allowing students to remotely access analysis and computing tools that are essential to big data sciences, and remotely run their own analytic workflows as a web service in a shareable and re-useable fashion. The platform has also allowed educators to utilize its data-analysis and parallel computing tools as active learning modules in various educational settings. PIs of this project have organized a workshop series on Performance Engineering with Advances in Software and Hardware for Big Data Sciences (PEASH https://cecsresearch.org/vcl/ASH) since 2014, co-located with IEEE BigData conference, to build the community around methodologies and best practices to adopt high performance computing in big data sciences and data science education.
At Louisville site, the project provided substantive educational opportunities for 3 phd students through the research and development of this project, training opportunities for 1 KY high school science teacher through 2018 RET summer program, and 1 non-UofL undergraduate student through 2021 RUE summer program. The parallel computing modules in the platform has been adopted in the lectures of PI Zhang's software engineering class (UofL CSE 550) to introduce parallel processing architecture and parallel computing in scientific software systems. Work under this grant also contributed to 9 publications including 7 conference papers, 2 journal articles, and thesis being developed that contain specific results from the project.
This project addresses national interests by making state-of-the-art computing resources more accessible to students, supporting their development of critical workforce skills. The general project indicates the cloud-based environment can deliver software tools in ways that meet community needs and lower the barriers for students and researchers to use advanced computing systems and can naturally facilitate the integration of education and research. These findings influenced the educational framework and materials developed in the project. The project also introduces a potential new paradigm to perform data-intensive and compute-intensive research, by empowering scientists and researchers with a simple yet powerful web-based interface to perform data-intensive analytics workflow and large-scale simulations by utilizing remote resources.
Project information and materials are available at http://www.cecsresearch.org/vcl/nsf1726532/ .
Last Modified: 09/12/2021
Modified by: Hui Zhang
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