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| Funder | National Science Foundation (US) |
|---|---|
| Recipient Organization | Texas A&M University Corpus Christi |
| Country | United States |
| Start Date | May 01, 2022 |
| End Date | Apr 30, 2026 |
| Duration | 1,460 days |
| Number of Grantees | 5 |
| Roles | Principal Investigator; Co-Principal Investigator |
| Data Source | National Science Foundation (US) |
| Grant ID | 2152131 |
This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).
This National Science Foundation Research Traineeship (NRT) project at the Texas A&M University-Corpus Christi (TAMU-CC), a Hispanic-Serving Institution (HSI), will train students from groups traditionally underrepresented in their participation in STEM fields of study to be part of a diverse workforce of interdisciplinary environmental scientists capable of harnessing the data revolution (HDR) for Stakeholder-Guided Environmental Science (STAGES). Coastal climate change is a grand challenge, and there is a need to generate actionable new knowledge and solutions to address this challenge, using convergent approaches.
The Coastal and Marine System Science program (CMSS) will house the STAGES program and offer a convergent training experience. This experience will connect innovative research to practitioner and community needs to answer stakeholder-guided questions of national concern. The project anticipates training up to forty (40) Master's and Ph.D. students, including twenty-one (21) funded trainees from the TAMU-CC's Coastal and Marine System Science (CMSS) program, over four years.
STAGES will generate new knowledge: (1) from big data at the nexus of land-water-atmospheric connections to understand complex processes driving coastal environmental systems, and (2) regarding the integration of stakeholders and big data into academic research in ways that boost trainee involvement in convergent research and occupational readiness. CMSS faculty's long-standing relationships with stakeholders will be used for a new training model that is sustainable and scalable.
This model will co-develop research projects suitable for trainee teams to tackle using Machine Learning (ML) methods. Stakeholders include government agencies on all levels, non-governmental organizations, and community groups, with a common goal to improve the resiliency of Gulf Coast communities and environment. STAGES will provide a curricular foundation for convergent environmental science that is data intensive, starting each spring with coursework and a week-long field trip to experience the interaction of land-water-atmospheric events firsthand.
Late Spring Stakeholder Workshops pair trainees and stakeholders to formulate data-focused research questions. Summer Big Data Blitzes prepare trainees for team efforts to answer questions, culminating in Fall Capstones to refine and communicate research results. Trainees will benefit from an interdisciplinary cadre of deeply experienced faculty researchers with extensive field, data science, and student training experience.
Anticipated findings are expected to include best practices for engaging stakeholders to identify data-intensive challenges and co-develop research questions; assimilation of the challenges by trainees with various backgrounds; and identification of problems appropriate for the trainees' different fields of study. During the training process, trainees will become better science communicators who can engage stakeholders and grasp the ethical dimensions of their decisions for long-term collaborations.
STAGES will establish and disseminate transformative STEM training, research, and evaluation advances in coastal and marine system science that produce a sustainable and scalable graduate education model.
The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.
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.
Texas A&M University Corpus Christi
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