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Award Abstract # 2127548
CI CoE: CI Compass: An NSF Cyberinfrastructure (CI) Center of Excellence for Navigating the Major Facilities Data Lifecycle

NSF Org: OAC
Office of Advanced Cyberinfrastructure (OAC)
Recipient: UNIVERSITY OF SOUTHERN CALIFORNIA
Initial Amendment Date: July 22, 2021
Latest Amendment Date: November 14, 2022
Award Number: 2127548
Award Instrument: Standard Grant
Program Manager: Kevin Thompson
kthompso@nsf.gov
 (703)292-4220
OAC
 Office of Advanced Cyberinfrastructure (OAC)
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: July 15, 2021
End Date: June 30, 2026 (Estimated)
Total Intended Award Amount: $8,000,000.00
Total Awarded Amount to Date: $9,000,000.00
Funds Obligated to Date: FY 2021 = $8,000,000.00
FY 2023 = $1,000,000.00
History of Investigator:
  • Ewa Deelman (Principal Investigator)
    deelman@isi.edu
  • Valerio Pascucci (Co-Principal Investigator)
  • Anirban Mandal (Co-Principal Investigator)
  • Jaroslaw Nabrzyski (Co-Principal Investigator)
  • Angela Murillo (Co-Principal Investigator)
Recipient Sponsored Research Office: University of Southern California
3720 S FLOWER ST FL 3
LOS ANGELES
CA  US  90033
(213)740-7762
Sponsor Congressional District: 34
Primary Place of Performance: USC-Information Sciences Institute
4676 Admiralty Way, Suite 1001
Marina del Rey
CA  US  90292-6611
Primary Place of Performance
Congressional District:
36
Unique Entity Identifier (UEI): G88KLJR3KYT5
Parent UEI:
NSF Program(s): CiCoE-Cyberinfrastructure Cent
Primary Program Source: 01002324DB NSF RESEARCH & RELATED ACTIVIT
01002122DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 020Z, 9102
Program Element Code(s): 139Y00
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

Innovative and robust Cyberinfrastructure (CI) is critical to the science missions of the NSF Major Facilities (MFs), which are at the forefront of science and engineering innovations, enabling pathbreaking discoveries across a broad spectrum of scientific areas. The MFs serve scientists, researchers and the public at large by capturing, curating, and serving data from a variety of scientific instruments (from telescopes to sensors). The amount of data collected and disseminated by the MFs is continuously growing in complexity and size and new software solutions are being developed at an increasing pace. MFs do not always have all the expertise, human resources, or budget to take advantage of the new capabilities or to solve every technological issue themselves. The proposed NSF Cyberinfrastructure Center of Excellence, CI Compass, brings together experts from multiple disciplines, with a common passion for scientific CI, into a problem-solving team that curates the best of what the community knows; shares expertise and experiences; connects communities in response to emerging challenges; and builds on and innovates within the emerging technology landscape. By supporting MFs to enhance and evolve the underlying CI, the proposed CI Compass will amplify the largest of NSF?s science investments, and have a transformative, broad societal impact on a multitude of MF science and engineering areas and the community of scientists, engineers, and educators MFs serve. CI Compass will also impact the broader NSF CI ecosystem through dissemination of CI Compass outcomes, which can be adapted and adopted by other large-scale CI projects and thus empower them to more efficiently serve their user communities.

The goal of the proposed CI Compass is to enhance the CI underlying the MF data lifecycle (DLC) that represents the transformation of raw data captured by state-of-the-art scientific MF instruments into interoperable and integration-ready data products that can be visualized, disseminated, and converted into insights and knowledge. CI Compass will engage with MFs and contribute knowledge and expertise to the MF DLC CI by offering a collection of services that includes evaluating CI plans, helping design new architectures and solutions, developing proofs of concept, and assessing applicability and performance of existing CI solutions. CI Compass will also enable knowledge-sharing across MFs and the CI community, by brokering connections between MF CI professionals, facilitating topical working groups, and organizing community meetings. CI Compass will also disseminate the best practices and lessons learned via online channels, publications, and community events.

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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(Showing: 1 - 10 of 24)
Bhatia, Harsh and Hoang, Duong and Morrical, Nate and Pascucci, Valerio and Bremer, Peer-Timo and Lindstrom, Peter "AMM: Adaptive Multilinear Meshes" IEEE Transactions on Visualization and Computer Graphics , v.28 , 2022 https://doi.org/10.1109/TVCG.2022.3165392 Citation Details
Brower, Don and Butcher, David and Murillo, Angela "FAIR Data for Large Research Facilities" Proceedings of the 2023 ACM/IEEE Joint Conference on Digital Libraries 23 , 2023 https://doi.org/10.1109/jcdl57899.2023.00073 Citation Details
Choudhary, Khushi and Nersisyan, Nona and Lin, Edward and Chandrasekaran, Shobana and Mayani, Rajiv and Pottier, Loic and Murillo, Angela P. and Virdone, Nicole K. and Kee, Kerk and Deelman, Ewa "Application of Edge-to-Cloud Methods Toward Deep Learning" 2022 IEEE 18th International Conference on EScience (EScience) , 2022 https://doi.org/10.1109/eScience55777.2022.00065 Citation Details
Fan, Ke and Gilray, Thomas and Pascucci, Valerio and Huang, Xuan and Micinski, Kristopher and Kumar, Sidharth "Optimizing the Bruck Algorithm for Non-uniform All-to-all Communication" HPDC '22: The 31st International Symposium on High-Performance Parallel and Distributed Computing , 2022 https://doi.org/doi.org/10.1145/3502181.3531468 Citation Details
Fan, Ke and Gilray, Thomas and Pascucci, Valerio and Huang, Xuan and Micinski, Kristopher and Kumar, Sidharth "Optimizing the Bruck Algorithm for Non-uniform All-to-all Communication" The 31st International Symposium on High-Performance Parallel and Distributed Computing , 2022 https://doi.org/10.1145/3502181.3531468 Citation Details
Fan, Ke and Hoang, Duong and Petruzza, Steve and Gilray, Thomas and Pascucci, Valerio and Kumar, Sidharth "Load-balancing Parallel I/O of Compressed Hierarchical Layouts" 2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics (HiPC) , 2021 https://doi.org/10.1109/HiPC53243.2021.00048 Citation Details
Hoang, Duong and Bhatia, Harsh and Lindstrom, Peter and Pascucci, Valerio "Progressive Tree-Based Compression of Large-Scale Particle Data" IEEE Transactions on Visualization and Computer Graphics , 2024 https://doi.org/10.1109/TVCG.2023.3260628 Citation Details
Huang, Xuan and Miao, Haichao and Kim, Hyojin and Townsend, Andrew and Champley, Kyle and Tringe, Joseph and Pascucci, Valerio and Bremer, Peer-Timo "Bimodal Visualization of Industrial X-Ray and Neutron Computed Tomography Data" IEEE Transactions on Visualization and Computer Graphics , 2024 https://doi.org/10.1109/TVCG.2024.3382607 Citation Details
Klacansky, Pavol and Gyulassy, Attila and Bremer, Peer-Timo and Pascucci, Valerio "A Study of the Locality of Persistence-Based Queries and Its Implications for the Efficiency of Localized Data Structures" 2022 IEEE 15th Pacific Visualization Symposium (PacificVis) , 2022 https://doi.org/10.1109/PacificVis53943.2022.00021 Citation Details
Klacansky, Pavol and Miao, Haichao and Gyulassy, Attila and Townsend, Andrew and Champley, Kyle and Tringe, Joseph and Pascucci, Valerio and Bremer, Peer-Timo "Virtual Inspection of Additively Manufactured Parts" 2022 IEEE 15th Pacific Visualization Symposium (PacificVis) , 2022 https://doi.org/10.1109/PacificVis53943.2022.00017 Citation Details
Leventhal, Samuel and Gyulassy, Attila and Heimann, Mark and Pascucci, Valerio "Exploring Classification of Topological Priors with Machine Learning for Feature Extraction" IEEE Transactions on Visualization and Computer Graphics , 2024 https://doi.org/10.1109/TVCG.2023.3248632 Citation Details
(Showing: 1 - 10 of 24)

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