NRT-HDR: Team training to develop new hardware and software applications for digital plant science across multiple scales

NRT-HDR：团队培训，为跨多个尺度的数字植物科学开发新的硬件和软件应用程序

• 批准号: 1922551
• 负责人: Michael Scanlon
• 金额: $ 299.81万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1922551&HistoricalAwards=false
• 关键词: NRT; HDR; Team; training; develop

There has been an unprecedented explosion of Big Data in the life sciences over the last several years. Likewise, advances in bioengineering promise to see the previously un-seeable and measure the previously immeasurable. This combination of "Big Data" and novel technological advances enables the investigation of fundamental principles in plant science at multiple scales. Interdisciplinary research in Digital Plant Science (DPS) leverages these advances by combining computational science, plant science and systems engineering approaches to respond to agricultural and ecological challenges. Accordingly, this next generation of graduate students must receive cross-disciplinary training in plant science, bioengineering, and computational biology to acquire the foundational skills to sense, capture, and measure information about plant processes in real time and at multiple scales, from microscopic single cells to entire ecosystems. This National Science Foundation Research Traineeship (NRT) award to Cornell University will create a transformative experience that integrates interdisciplinary team learning and professional development to equip future plant scientists with the tools needed to investigate, comprehend, and engineer plant processes to improve plant productivity and sustainability in the 21st century. The project anticipates training 120 graduate students, including 18 funded trainees, from the graduate fields of Plant Biology, Plant Breeding, Plant Pathology, Soil and Crop Sciences, Biological and Environmental Engineering, Chemical Engineering, and Computer Science. This project will create an interactive and innovative core curriculum that integrates plant science, computational science, and bioengineering, while disseminating this curriculum throughout and beyond the Cornell community. Graduate trainee learning through co-instruction will reinforce this core training. In addition, novel, team research rotation experiences will be utilized. Teams of three graduate students, mentored by cross-disciplinary faculty comprising computational scientists, bioengineers, and plant scientists, will collaborate to generate and analyze original "Big Data" in a group environment that simulates the team-based research approaches that typify industrial research and development settings. Graduate student training will be broadened to include the systematic development of communication and team-working skills that are essential for successful careers in multiple endeavors both within and outside of academia. Partnerships between Cornell and the private sector will be exploited and extended to facilitate trainee internships and continuing-education experiences in an industry setting. Ultimately, this graduate training program will develop new tools and strategies to investigate plant genotypes, phenotypes, and processes in real time and at multiple scales. 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.

在过去的几年中，生命科学中的大数据发生了前所未有的爆炸。 同样，生物工程的进步承诺将看到以前不可见的并衡量以前的不可估量。这种“大数据”和新技术进步的结合使得对植物科学中的基本原理进行了多个尺度的研究。数字植物科学（DPS）的跨学科研究通过结合计算科学，植物科学和系统工程方法来应对农业和生态挑战来利用这些进步。因此，下一代研究生必须接受植物科学，生物工程和计算生物学方面的跨学科培训，才能获得基础技能，以实时和在多个尺度上感知，捕获和测量有关植物过程的信息，从微观单细胞到整个生态系统。康奈尔大学（NRT）这项国家科学基金会研究实习生（NRT）奖将创造一种变革性的体验，将跨学科的团队学习和专业发展整合，以使未来的植物科学家提供研究，理解和工程植物工艺所需的工具，以提高21世纪的植物生产力和可持续性。该项目预计，培训了来自植物生物学，植物育种，植物病理学，土壤和作物科学，生物学和环境工程，化学工程和计算机科学的植物生物学，植物病理学，土壤和作物科学的研究生领域的120名研究生，包括18名资助的学员。 该项目将创建一个互动且创新的核心课程，该课程整合了植物科学，计算科学和生物工程，同时在整个康奈尔大学社区范围内传播了该课程。通过共同教学的研究生实习生学习将加强这种核心培训。此外，还将利用新颖的团队研究轮换体验。由包括计算科学家，生物工程师和植物科学家组成的跨学科教师指导的三名研究生团队将在模拟基于团队的研究方法的基于团队的研究方法中合作生成和分析原始的“大数据”，这些方法代表工业研究和开发环境。研究生培训将被扩展，以包括系统的沟通和团队合作技能的系统发展，这对于在学术界内外的多项工作中成功职业至关重要。康奈尔和私营部门之间的伙伴关系将被利用并扩展，以促进受训人员的实习和在行业环境中继续进行教育经验。最终，该研究生培训计划将开发新的工具和策略，以实时和多个尺度研究植物基因型，表型和过程。 NSF研究训练（NRT）计划旨在鼓励开发和实施用于STEM研究生教育培训的大胆，新的潜在变革模型。该计划致力于通过全面的跨学科或收敛性研究领域的STEM研究生进行有效培训，通过全面的培训模型，这些模型具有创新性，基于循证的，并且与不断变化的劳动力和研究需求保持一致。该奖项反映了NSF的法定任务，并通过使用基金会的知识优点和广泛影响来评估NSF的法定任务，并被视为值得的支持。

项目成果

Comosum: An Extensible, Reconfigurable, and Fault-Tolerant IoT Platform for Digital Agriculture

• 发表时间: 2023
• 作者: Gloire Rubambiza;Shiang-Wan Chin;Mueed Rehman;Sachille Atapattu;José-Fernán Martínez;Hakim Weatherspoon

Toward Cloud‐Native, Machine Learning Base Detection of Crop Disease With Imaging Spectroscopy

• DOI: 10.1029/2022jg007342
• 发表时间: 2023-05
• 期刊: Journal of Geophysical Research: Biogeosciences
• 作者: Gloire Rubambiza;Fernando Romero Galvan;R. Pavlick;Hakim Weatherspoon;K. Gold

EdgeRDV: A Framework for Edge Workload Management at Scale

• DOI: 10.1109/edge60047.2023.00051
• 发表时间: 2023-07
• 期刊: 2023 IEEE International Conference on Edge Computing and Communications (EDGE)
• 作者: Gloire Rubambiza;Braulio Dumba;Andrew J. Anderson;Hakim Weatherspoon