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

气候变化是当今人类面临的最复杂的问题之一，它正在对我们赖以生存的环境和土地、水、空气的质量产生深远的影响。新传感器技术的发展、传感器数据量和可用性的增加、计算方法的进步和计算能力的扩大，将为更好地了解环境变化提供新的方法。这些新兴技术和工具有助于解决多种具有挑战性的环境问题。然而，为了实现这一潜力，迫切需要让下一代科学家和工程师能够以文化敏感和公平的方式理解和应用不同领域的数据科学。这个国家科学基金会研究培训（NRT）奖给密歇根理工大学将通过培训博士生在数据科学支持的环境传感解决这一需求。该项目将培养28名博士生，其中包括15名受资助的学员，来自多个学科，包括计算和数据科学，林业，地质工程和环境工程。 综合研究和培训经验围绕数据科学生命周期和三大知识支柱：传感器和传感技术，计算分析和应用领域知识。沿着科学研究中的知识生成，数据科学生命周期还要求这些毕业生的行业，政府和学术雇主所需的专业发展和尖端技能和能力。该项目的目标是为研究生提供综合，文化响应和公平的培训，研究和指导经验，培养梳形专业人员，以解决这些复杂的气候适应相关问题。综合培训内容包括：跨学科课程（课程和研究生证书）、跨学科研究、暑期经验、研究研讨会、研究专题讨论会、职业发展培训、外联和近同行指导。 学员将有机会在组织培训活动方面担任领导职务;提高科学交流（阅读，写作和演讲）的专业技能;职业发展培训，以提高领导，谈判，团队建设，网络和项目管理方面的技能。 该项目将创建一个新的数据科学环境遥感研究生证书。成功和有效的培训元素将作为最佳实践进行传播。该奖项反映了NSF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。

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.