NRT-DESE: P3 -- Predictive Phenomics of Plants

为了满足对粮食、饲料、纤维和燃料的预期需求，需要提高作物生产力的新方法。利用现代传感器和数据分析技术，开发基于基因组和环境信息预测植物生长和生产力的方法是可行的。然而，这样做需要植物科学以及计算科学和工程方面的专业知识。这项授予爱荷华州立大学的国家科学基金会研究培训（NRT）奖将把具有不同背景的学生聚集在一起，包括植物科学、统计学和工程学，并为他们提供数据支持的科学和工程培训。通过建立一个支持集体学习的实践社区，学生在这种独特的智力环境中茁壮成长所需的合作精神将得到加强。该培训计划预计将培养48名硕士生和博士生，其中包括28名受资助的博士生，他们将具备设计和构建作物所需性状的知识和工具，这些作物能够在不断变化的环境中茁壮成长。了解特定的遗传性状如何在特定的环境条件下导致特定的植物特性是现代生物学的核心目标，这将促进具有商业用途特性的作物的有效开发。植物特性受到遗传和广泛的环境因素的影响，例如，降雨、温度和土壤类型。利用现有的生物学、统计学和工程学知识，开发有效整合这些不同输入的方法，将是这项研究和培训计划的关键领域，该计划将汇集来自八个系的教员。学员将从事尖端的研究和开发领域，包括从活植物中直接收集和分析数据，包括传感器开发，高通量机器人技术，以及通过图像分析提取生物特征。该培训项目将采用T-training模式，为学生提供广泛学科的培训，同时在一个领域培养深厚的技术专长。这些专业知识与软技能的发展相结合，将使受训者能够跨越组织和文化界限以及科学学科进行工作。为了培养学生对如何与不同群体分享知识的理解，该项目将通过培养高级沟通和创业技能的活动，为学生提供传统课程和研究之外的培训。此外，在工业、国家实验室和其他环境中的实习机会将使学员能够在科学家和工程师可选择的多种职业道路中进行选择。NSF研究培训（NRT）计划旨在鼓励开发和实施大胆的、新的、具有潜在变革性和可扩展的STEM研究生教育培训模式。通过创新、循证、适应不断变化的劳动力和研究需求的综合培训模式，培训项目致力于在高优先级跨学科研究领域对STEM研究生进行有效培训。

NRT- DESE: Predictive Phenomics of Plants (P3)New methods to increase crop productivity are required to meet anticipated demands for food, feed, fiber, and fuel. Using modern sensors and data analysis techniques, it is now feasible to develop methods to predict plant growth and productivity based on information about their genome and environment. However, doing so requires expertise in plant sciences as well as computational sciences and engineering. This National Science Foundation Research Traineeship (NRT) award to Iowa State University will bring together students with diverse backgrounds, including plant sciences, statistics, and engineering, and provide them with data-enabled science and engineering training. The collaborative spirit required for students to thrive in this unique intellectual environment will be strengthened through the establishment of a community of practice to support collective learning. This traineeship anticipates preparing forty-eight (48) master's and doctoral students, including twenty-eight (28) funded doctoral students, with the understanding and tools to design and construct crops with desired traits that can thrive in a changing environment. Understanding how particular genetic traits result in given plant characteristics under specific environmental conditions is a core goal of modern biology that will facilitate the efficient development of crops with commercially useful characteristics. Plant characteristics are influenced by genetics and a wide range of environmental factors, including, for example, rainfall, temperature and soil types. Developing methods to effectively integrate these diverse inputs that take advantage of existing biological, statistical, and engineering knowledge will be a key area in this research and training program that will bring together faculty from eight departments. Trainees will engage in cutting-edge research and development areas involving direct data collection and analysis from living plants, including sensor development, high throughput robotic technology, and biological feature extraction through image analysis. This traineeship will use the T-training model to provide students with training across a broad range of disciplines while developing a deep technical expertise in one area. This expertise, in combination with soft skills development, will enable the trainees to work across organizational and cultural boundaries as well as scientific disciplines. To develop understanding of how to share knowledge with diverse groups, the program will provide students with training beyond traditional coursework and research through activities that will develop advanced communication and entrepreneurship skills. Additionally, internship opportunities in industry, national labs, and other settings will equip trainees to choose among the diverse career paths available to scientists and engineers.The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new, potentially transformative, and scalable models for STEM graduate education training. The Traineeship Track is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas, through the comprehensive traineeship model that is innovative, evidence-based, and aligned with changing workforce and research needs.