NRT-HDR: Integrative Training in Data Science-Enabled Sensing of the Environment for Climate Adaptation (DataSENSE)

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Climate change is one of the most complex issues facing humankind today, and it is having profound impacts on the environment and the quality of land, water, and air we all depend on. Development of new sensor technologies, growth in the amount and availability of sensor data, advances in computational methods, and expansion of computational power will enable new approaches to better understand changes to the environment. These emerging technologies and tools can contribute to solutions for multiple challenging environmental problems. However, to realize this potential, there is an urgent need to equip next-generation scientists and engineers with the ability to understand and apply data science across diverse fields and in culturally responsive and equity minded ways. This National Science Foundation Research Traineeship (NRT) award to Michigan Technological University will address this need by training PhD students in data-science-enabled sensing of the environment. The project will prepare 28 PhD students, including 15 funded trainees, from multiple disciplines including computational and data sciences, forestry, geological engineering, and environmental engineering. The integrative research and training experiences are framed around the data science lifecycle and three knowledge pillars: sensors and sensing technologies, computational analysis, and application domain knowledge. Along with the knowledge generation in scientific research, the data science lifecycle also calls for the professional development and cutting-edge skills and competencies, required by industry, government, and academic employers of these graduates. The goal of this project is to provide integrative, culturally-responsive, and equity minded training, research, and mentoring experiences for graduate students that produce comb-shaped professionals to address these complex, climate-adaptation-related problems. Integrated training elements include: interdisciplinary curricula (course and graduate certificate), interdisciplinary research, summer experiences, research seminars, research symposia, career development training, outreach, and near-peer mentoring. Trainees will have opportunities for leadership positions in organizing aspects of the training activities; advancing professional skills in scientific communication (reading, writing, and presentations); and career development training to grow skills in leadership, negotiation, team building, networking and project management. The project will create a new graduate certificate in Data Science-Enabled Environmental Remote Sensing. Successful and effective training elements will be disseminated as best practices.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.