This program (NSF CISE CyberTraining, OAC 2230054, $298K, 01/01/2023 – 12/31/2024, awarded June, 2022) involves an intensive week-long Findable, Accessible, Interoperable, Reusable (FAIR) data principles and introductory machine learning (ML) bootcamp for undergraduate students conducting summer research in the water or energy sector followed by two workshops with principal investigators. The goals of this program are: (1) to develop and test an accessible framework and instructional materials for expanding CI adoption among budding researchers, (2) to increase the use of FAIR principles and ML to solve civil engineering research problems, (3) to increase the diversity of the CI research workforce, and (4) to broaden the adoption of CI in established research laboratories. The virtual bootcamp and workshops, hosted by the University of Texas at Arlington (UTA), will serve up to 40 participants in two consecutive years. Participants are recruited from Hispanic-Serving Institutions (HSIs) and Historically Black Colleges and Universities (HBCUs). The bootcamp will 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 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 describe how they (1) applied FAIR principles in their summer research experience and (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.
Jessica Eisma – Principal Investigator
June Young Park – Co-Principal Investigator
Cory Forbes – Co-Principal Investigator
Sharma Chakravarthy – Co-Principal Investigator