Restricted Research - Award List, Note/Discussion Page
Fiscal Year: 2023
1548 The University of Texas at Arlington (143436)
Principal Investigator: Jessica Eisma,jessica.eisma@uta.edu,(517) 719-1049
Total Amount of Contract, Award, or Gift (Annual before 2011): $ 298,830
Exceeds $250,000 (Is it flagged?): Yes
Start and End Dates: 1/1/23 - 12/31/24
Restricted Research: YES
Academic Discipline: Department of Civil Engineering
Department, Center, School, or Institute: none
Title of Contract, Award, or Gift: CyberTraining: Pilot: Justice in Data: An intensive, mentored online bootcamp developing FAIR data competencies in undergraduate researchers in the water and energy sectors
Name of Granting or Contracting Agency/Entity:
National Science Foundation (NSF)
CFDA Link: NSF
47.041
Program Title:
Training-based Workforce Development for Advanced Cyberinfrastructure (CyberTraining)
CFDA Linked: Engineering Grants
Note:
(SAM Category 1.3.1 and 1.3.2) Undergraduate STEM researchers are well positioned to be the prime catalysts for increasing the development and use of cyberinfrastructure (CI) in research activities due to their high motivation and probability of pursuing a research-based graduate degree. The pilot program proposed here develops an intensive weeklong Findable, Accessible, Interoperable, Reusable (FAIR) data principles and introductorymachine learning (ML) bootcamp for civil engineering undergraduate students conducting summer research in the water or energy sectorsfollowed by two workshops with principal investigators. The short (S) and long-term (L) goals of this program are: (1-S) to develop and test an accessible framework and instructional materials for expanding CI adoption among budding researchers, (2-S.L) to increase the use of FAIR principles and ML to solve civil engineering research problems, (3-L) to increase the diversity of the CI research workforce, and (4-L) to broaden the adoption of CI in established research laboratories. The bootcamp and workshops will be hosted by the University of Texas at Arlington (UTA) but will be held primarily online. Forty bootcamp participants will be recruited chieflyfrom the seventeenTexas R1 and R2 universities that have civil and environmental engineering programs or similar, of which ten are Hispanic-Serving Institutions (HSIs) and two are Historically Black Colleges and Universities (HBCUs). The bootcampwill cover high-impact topics for new CI users, for example large-scale data access, data analytics,and data visualization,and will introduce basic machine learning concepts. Bootcamp instructors will continue to serve as mentors throughout participants’ summer research experiences. At the end of the summer, a competition-based, online research symposium will be held where participants describehow they (1) applied FAIR principles in their summer research experienceand(2) developed workflows and tools for research-related tasks (e.g., data download and organization).Bootcamp instructors and involved faculty will work with interested students to publish their developed workflows and tools through MyGeoHub, a geospatial modeling, data analysis, and visualization hub for research and education communities. Following the conclusion of the end-of-summer symposium, two workshops will be held with separate principal investigator groups. The goals of the workshops are (1) to identify desired FAIR competencies that were unmet by the bootcamp, (2) to identify subject areas beyond water and energy where FAIR principles and ML could immediately impact research productivity, and (3) tobrainstorm challenges and opportunities for scaling up the pilot program, both in terms of reach and effectiveness. Ideas generated by the workshops will be extended to form the basis for a CyberTraining: Medium Implementation proposal. Intellectual Merit: The proposed pilot program develops a unique framework for increasing the CI competency of undergraduate researchers by introducing FAIR principles and targeting the ML skills of summer research students whose degree programs do not traditionally include training in data science and engineering. The proposed program seeks to demonstrate that researchers at the undergraduate level are interested in and capable of incorporating FAIR and ML principles into civil engineering research, whereas current practices typically wait to introducethese topics in high-level graduate level courses, if at all. Broader Impacts:
Discussion: No discussion notes