NRT-HDR: Intersecting computational and data science to address grand challenges in plant biology

NRT-HDR：交叉计算和数据科学以应对植物生物学的巨大挑战

• 批准号: 1828149
• 负责人: Shin-Han Shiu
• 金额: $ 300万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1828149&HistoricalAwards=false
• 关键词: NRT; HDR; Intersecting; computational; data

Plants are indispensable for life on earth, providing food, energy, and oxygen, as well as the basis for many man-made products. A better understanding of plant science will lead to more secure plant resources, which is even more important given the rapidly increasing global population. Genomics research has significantly advanced our understanding about how plants function, with the application of genomics yielding datasets that could revolutionize plant science and lead to safe, reliable, and sustainable production of food and biofuels. To achieve these outcomes, there is a critical need for scientists with both an understanding of plant biology and computational skills. This National Science Foundation Research Traineeship (NRT) award to Michigan State University will address this demand by training doctoral students who can employ advanced computational and data science approaches to address grand challenges in plant biology. The project anticipates training approximately seventy (70) PhD students, including thirty-eight (38) funded trainees from plant biology and computational data science programs. Trainees will engage in research and coursework that emphasize tackling "grand challenge" questions in plant biology by leveraging computational approaches. Training will go beyond the traditional genomics and bioinformatics approaches in plant biology to include the advanced training in computation and modeling required to handle increasingly heterogeneous, multi-scale data from the molecular to ecosystem levels. This type of training will allow students to tackle complex questions such as investigating genotype-phenotype relationships across the Plant Tree of Life or machine learning for high-dimensional plant data. In addition, the traineeship features professional development opportunities, outreach activities, and industry/governmental internships that serve to broaden trainees' career options while also improving their ability to communicate with a wide range of audiences. Upon completion of the training program, trainees will have a core understanding of plant and computational sciences, excel in interdisciplinary biological and computational research, and possess effective communication, leadership, management, teaching, and mentoring skills. Trainees will be co-advised by experts in plant science and computational/data science. To accomplish the training goals, trainees will participate in a program consisting of: (1) curricular and research activities that will create a cohort of trainees with dual expertise in computational sciences and plant biology, (2) a biweekly forum to encourage scientific interactions, (3) a trainee-led annual symposium that engages a wider scientific audience and builds organizational and leadership skills, (4) internship opportunities in industry and government agencies, (5) professional development activities tailored to individual career goals, including entrepreneurship, and (6) public engagement through outreach activities, further bolstering the ability of trainees to communicate with a wide range of audiences. 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 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.

植物是地球上生命不可或缺的，提供食物、能量和氧气，也是许多人造产品的基础。更好地了解植物科学将导致更安全的植物资源，这在全球人口迅速增长的情况下尤为重要。 基因组学研究大大提高了我们对植物功能的理解，基因组学的应用产生的数据集可以彻底改变植物科学，并导致安全，可靠和可持续的粮食和生物燃料生产。为了实现这些成果，迫切需要既了解植物生物学又掌握计算技能的科学家。密歇根州立大学获得的国家科学基金会研究培训（NRT）将通过培训博士生来满足这一需求，这些博士生可以采用先进的计算和数据科学方法来应对植物生物学的重大挑战。该项目预计将培训大约七十（70）名博士生，其中包括三十八（38）名来自植物生物学和计算数据科学项目的受资助学员。学员将参与研究和课程，强调利用计算方法解决植物生物学中的“重大挑战”问题。培训将超越植物生物学中传统的基因组学和生物信息学方法，包括处理从分子到生态系统层面日益异质化的多尺度数据所需的计算和建模方面的高级培训。 这种类型的培训将使学生能够解决复杂的问题，例如调查整个植物生命树的基因型-表型关系或高维植物数据的机器学习。 此外，培训的特点是专业发展机会、外联活动和行业/政府实习，这有助于扩大学员的职业选择，同时提高他们与广泛受众沟通的能力。完成培训计划后，学员将对植物和计算科学有核心理解，擅长跨学科生物和计算研究，并拥有有效的沟通，领导，管理，教学和指导技能。学员将由植物科学和计算/数据科学专家共同提供咨询。为了实现培训目标，学员将参加一个包括以下内容的计划：（1）课程和研究活动，将培养一批具有计算科学和植物生物学双重专业知识的学员，（2）双周论坛，鼓励科学互动，（3）由学员领导的年度研讨会，吸引更广泛的科学受众，培养组织和领导技能，（4）在行业和政府机构的实习机会，（5）针对个人职业目标的专业发展活动，包括创业精神，以及（6）通过外联活动进行公众参与，进一步加强学员与广泛受众沟通的能力。NSF研究培训（NRT）计划旨在鼓励为STEM研究生教育培训开发和实施大胆的，新的潜在变革模式。该计划致力于通过全面的培训模式，在高优先级的跨学科研究领域对STEM研究生进行有效培训，这些模式具有创新性，以证据为基础，并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的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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• 发表时间: 2020-02-01
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• DOI: 10.1073/pnas.2121531119
• 发表时间: 2022-03-15
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1
• 作者: Xu Y;Wieloch T;Kaste JAM;Shachar-Hill Y;Sharkey TD
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