NRT-HDR: Finding Signal in the Noise to Enable Science-Based Community Response to Change in Coastal Region

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。由于气候和人为干扰，世界各地的沿海环境正在发生重大变化。自然灾害和气候压力使沿海社区本已普遍存在的不平等和环境不公正更加突出。因此，迫切需要一个能够调查沿海地区如何应对人为和自然压力的劳动力。然后，由此产生的科学可以转化为帮助社区评估这些挑战的风险，并采取行动应对这些挑战。各种机构和利益攸关方定期收集有助于解决这些问题的环境和社会经济数据。然而，传统分析或建模方法所需的数据很少被收集。因此，了解沿海变化的驱动因素需要创造性的方法来理解现有的各种数据集。沿海社区环境数据学者（CCEDS）项目的学员将代表多个研究生学位项目，并开展培训他们如何应用数据科学技能的活动，同时建立他们作为数据科学学者的信心——这是一个重要的问题，因为许多来自地质学或生物学背景的学生可能将数据科学视为成功的障碍，而不是通往职业生涯的途径。受训者将扩展他们的论文研究，运用他们的基础科学技能与当地社区合作，以确定和解决人们关注的问题。这项授予东卡罗莱纳大学（ECU）的国家科学基金会研究培训（NRT）奖将培训跨学科的科学家和工程师，通过数据科学解决对沿海社区至关重要的问题。该项目预计将培养36名博士生，其中包括18名受资助的实习生，他们来自ECU的两个旗舰博士项目，即综合海岸科学项目和生物学、生物医学和化学跨学科博士项目。数据驱动的工具，如经验动态建模和深度学习，在信息不足的情况下，比传统的建模和分析方法有优势，无法建立环境过程及其与人类活动耦合的机制模型。这些数据驱动的工具可以为理解复杂系统带来新的见解，特别是在检测系统行为变化方面，这些变化可以与更传统的研究相补充，以了解变化发生的原因或方式。拟议的培训计划支持专注于两个不同博士项目融合的研究活动，并确保为跨学科数据科学劳动力做好准备，这对经济发展中的沿海地区尤其重要。学员将通过参加为期两年的六个项目活动来积累他们的数据科学经验，这些活动包括支持融合研究、科学传播和社区参与研究的实践。此外，实习是基于并测试培训学生克服与数据科学相关的冒名顶替现象和刻板印象威胁等问题的最佳实践的有效性。体验式活动旨在建立学员的信心和身份，同时为他们在学术界内外的真实数据科学工作做好准备。该项目将通过培训多样化的劳动力与社区合作伙伴合作，并利用数据驱动的活动来缓解影响沿海地区公平和社会正义的环境问题，从而造福社会。美国国家科学基金会研究实习生（NRT）计划旨在鼓励开发和实施大胆的、具有潜在变革性的STEM研究生教育培训新模式。该项目致力于通过创新、循证、适应不断变化的劳动力和研究需求的综合培训模式，在高优先级跨学科或融合研究领域对STEM研究生进行有效培训。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). Coastal environments throughout the world are undergoing substantial changes in response to climate and human-induced disturbances. Natural disasters and climate stressors make already pervasive inequities and environmental injustices in coastal communities even more pronounced. Therefore, there is an imperative for a workforce that can investigate how coastal regions are changing in response to human and natural stressors. The resulting science can then be translated to aid communities in assessing the risks of, and actions to address, these challenges. A variety of agencies and stakeholders routinely collect environmental and socio-economic data that could help to address these problems. However, the data required for traditional analyses or modeling approaches are rarely collected. Therefore, understanding the drivers of coastal change requires creative ways to make sense of available diverse data sets. Trainees in the Coastal Community Environmental Data Scholars (CCEDS) program will represent multiple graduate degree programs and undertake activities that train them in how to apply data science skills while building their confidence as data science scholars – an important issue as many students coming from backgrounds like geology or biology may see data science as a barrier to success rather than a pathway to career enablement. The trainees will extend their dissertation research by applying their basic science skills in partnerships with local communities to identify and solve problems of concern. This National Science Foundation Research Traineeship (NRT) award to East Carolina University (ECU) will train interdisciplinary scientists and engineers to address problems of critical importance to coastal communities via data science. The project anticipates training 36 Ph.D. students, including 18 funded trainees, from two of ECU’s flagship doctoral programs, the Integrated Coastal Science program and the Interdisciplinary Doctoral Program in Biology, Biomedicine, and Chemistry. Data-driven tools like empirical dynamical modeling and deep learning have an advantage over traditional modeling and analysis methods when insufficient information is available to develop mechanistic models of environmental processes and their coupling with human activities. These data-driven tools can bring new insights to the understanding of complex systems, particularly in detecting changes in system behavior that can be complemented with more traditional studies to understand why or how the change occurred. The proposed traineeship supports research activities focused on the convergence of two distinct Ph.D. programs and ensures the preparation of an interdisciplinary data science workforce, especially important for economically developing coastal regions. Trainees will build on their data science experience by taking part in a 2-year sequence of six program activities involving practices supporting convergent research, science communication, and community-engaged research. Further, the traineeship is based on and tests the effectiveness of best practices for training students to overcome issues such as imposter phenomena and stereotype threats related to data science. The experiential activities are structured to build trainees’ confidence and identity while preparing them for real-world data science jobs within and beyond academia. This program will benefit society by training a diverse workforce to collaborate with community partners and use data-driven activities to mitigate environmental issues that impact equity and social justice in coastal regions.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.