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

NRT-HDR：数据科学支持的气候适应环境感知综合培训 (DataSENSE)

• 批准号: 2244403
• 负责人: Laura Brown
• 金额: $ 200万
• 依托单位: Michigan Technological University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2028-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2244403&HistoricalAwards=false
• 关键词: NRT; HDR; Integrative; Training; Data

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.

气候变化是当今人类面临的最复杂问题之一，它对我们都依赖的土地，水和空气的环境和质量产生了深远的影响。开发新的传感器技术，传感器数据的数量和可用性的增长，计算方法的进步以及计算能力的扩展将使新方法能够更好地了解环境的变化。这些新兴技术和工具可以为解决多个具有挑战性的环境问题做出贡献。但是，为了意识到这一潜力，迫切需要为下一代科学家和工程师配备能够在各种领域以及具有文化响应和公平的思维方式上理解和应用数据科学的能力。这项国家科学基金会研究训练（NRT）授予密歇根技术大学将通过培训博士学位的学生对环境的感知来满足这一需求。该项目将从多个学科，包括计算和数据科学，林业，地质工程和环境工程等多个学科中为28名博士生做好准备，包括15名资助的学员。 综合研究和培训经验围绕数据科学生命周期和三个知识支柱进行构建：传感器和传感技术，计算分析和应用领域知识。与科学研究中的知识生成一起，数据科学生命周期还呼吁这些毕业生的行业，政府和学术雇主要求的专业发展以及最先进的技能和能力。该项目的目的是为研究生提供综合性，具有文化响应性和公平性的培训，研究和指导经验，这些研究生生产梳状专业人员，以解决这些复杂，与气候适应有关的问题。综合培训元素包括：跨学科课程（课程和研究生证书），跨学科研究，夏季经验，研究研讨会，研究研讨会，职业发展培训，外展和近培养指导。 学员将有机会在组织培训活动方面的领导职位；提高科学沟通（阅读，写作和演讲）方面的专业技能；以及职业发展培训，以提高领导力，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基于数据科学的环境遥感的研究生证书。成功有效的培训因素将作为最佳实践传播。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评论标准来评估值得支持的。