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Collaborative teamwork is fundamental to successful research and is a desirable skill set for employers. Yet students receive little training in how to effectively work in teams. This article presents the preliminary design and implementation of course-based undergraduate research experiences (CUREs) in biology, geology, and environmental engineering in which student teams address questions related to their discipline while contributing to a shared research project. Team science training in communication, research planning, and conflict resolution was embedded into CURE classes at a regional R2 university. Although barriers to this approach were present, evidence in the form of writing prompt scores and team science products suggested student understanding of effective teams and the benefits of working with individuals within and across disciplines to solve complex problems increased. 

Flood mitigation and adaptation measures, among other tools to improve resiliency, will be necessary to sustain coastal communities in the face of climate change. Key to successful adaptation will be engineering projects, and critical to the success of those projects will be community engagement and support. Despite the recognized importance of community engagement when addressing complex issues like coastal flooding on which engineers work, most undergraduate engineering programs offer little to no training in community engagement. In this paper, we describe our experiences working with undergraduate engineering students to develop community-driven designs to address flooding and water quality issues in the Lake Mattamuskeet watershed in eastern North Carolina. Through an interdisciplinary approach, student teams learned to engage with local stakeholders to better integrate local knowledge and address issues identified by community members in their designs. Because of the COVID-19 pandemic, all community engagement aspects of the project moved to virtual forums, and we discuss the impact this shift had on the engineering designs as well as student learning outcomes and community connections. 

Abstract Private groundwater wells can be unmonitored sources of contaminated water that can harm human health. Developing models that predict exposure could allow residents to take action to reduce risk. Machine learning models have been successful in predicting nitrate contamination using geospatial information such as proximity to nitrate sources, but previous models have not considered meteorological factors that change temporally. In this study, we test random forest (regression and classification) and linear regression models to predict nitrate contamination using rainfall, temperature, and readily available soil parameters. We trained and tested models for (1) all of North Carolina, (2) each geographic region in North Carolina, (3) a three‐county region with a high density of animal agriculture, and (4) a three‐county region with a low density of animal agriculture. All regression models had poor predictive performance (R² < 0.09). The random forest classification model for the coastal plain showed fair agreement (Cohen'sκ = 0.23) when trying to predict whether contamination occurred. All other classification models had slight or poor predictive performance. Our results show that temporal changes in rainfall and temperature, or in combination with soil data, are not enough to predict nitrate contamination in most areas of North Carolina. The low level of contamination (<25%) measured during the study could have contributed to the poor performance of the models.