NRT-HDR: Integrated Data Science (Int dS): Teams for Advancing Bioscience Discovery

NRT-HDR：综合数据科学 (Int dS)：推进生物科学发现的团队

• 批准号: 2022138
• 负责人: Thomas Cech
• 金额: $ 300万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2022138&HistoricalAwards=false
• 关键词: NRT; HDR; Integrated; Data; Science

Enormous datasets have become a major foundation for biological discovery. As one example, the complete DNA codes of thousands of species of bacteria, plants, and animals have been sequenced over the past 20 years, reshaping the course of fields as diverse as biotechnology, ecology and evolutionary biology, genetic counseling, forensics, and medicine. However, providing comprehensive data-science training for the bioscience workforce has been challenged by the interdisciplinary nature of the field. The National Science Foundation (NRT) award to the University of Colorado Boulder will address this need by producing scientists who are skilled at acquiring large datasets, writing code to interrogate them, modeling the inherent biological principles, and collaborating effectively to apply knowledge across a range of domains. The project anticipates providing hands-on, personalized training to 40 PhD students, including 22 funded trainees, from 12 fields of study including computer science, applied math, physics, engineering, and multiple biological disciplines. The program will foster an open, interdisciplinary, and diverse community of researchers. The trainees will also engage industrial and academic partners to strengthen local outreach while they enhance collaborative data-science research. Trainees will tackle interdisciplinary research themes that require harnessing complex genomic, RNA science, proteomic, ecological, and social science datasets. They will learn data-driven approaches (data measurements, manipulations, visualizations), computational approaches (automation and simulation), and scientific approaches (causality and inference). The program will include modular curricular elements, cross-discipline laboratory rotations, and a team practicum. The technical data-science curriculum will be complemented by training in interdisciplinary collaboration, including leadership, ethics, collaborative platforms, and cross-discipline communication. The curriculum is tailored to serve students based on their individual backgrounds and technical knowledge, and it is built to transition students from being mentees and participants to mentors and collaborative research leaders as they advance in their graduate career. NRT-funded trainees will be co-advised, with faculty advisors trained in effective co-mentorship. The overall goal of the Integrated Data Science Traineeship is to train each graduate student to be a data producer, a data modeler, and a data collaborator, proficient in the complete life cycle that is essential to generate and understand complex biological data. 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.

庞大的数据集已经成为生物发现的主要基础。举个例子，过去20年来，数千种细菌、植物和动物的完整DNA代码已被测序，重塑了生物技术、生态学和进化生物学、遗传咨询、法医学和医学等不同领域的进程。然而，为生物科学劳动力提供全面的数据科学培训一直受到该领域跨学科性质的挑战。美国国家科学基金会（NRT）授予科罗拉多大学博尔德分校的奖项将通过培养擅长获取大型数据集的科学家来满足这一需求，编写代码来询问它们，对固有的生物学原理进行建模，并有效地合作以将知识应用于一系列领域。该项目预计将为40名博士生提供动手，个性化的培训，其中包括22名受资助的学员，来自12个研究领域，包括计算机科学，应用数学，物理，工程和多个生物学科。该计划将培养一个开放的，跨学科的和多样化的研究人员社区。学员还将与工业和学术合作伙伴合作，在加强数据科学合作研究的同时，加强当地的外联工作。学员将解决需要利用复杂的基因组学，RNA科学，蛋白质组学，生态学和社会科学数据集的跨学科研究主题。他们将学习数据驱动的方法（数据测量，操作，可视化），计算方法（自动化和模拟）和科学方法（因果关系和推理）。该计划将包括模块化的课程元素，跨学科的实验室轮换，和团队实习。技术数据科学课程将通过跨学科合作的培训来补充，包括领导力，道德，协作平台和跨学科沟通。该课程是根据学生的个人背景和技术知识量身定制的，旨在让学生在研究生生涯中从学员和参与者转变为导师和协作研究领导者。NRT资助的学员将共同建议，与教师顾问在有效的共同指导培训。综合数据科学培训的总体目标是培养每个研究生成为数据生产者，数据建模者和数据协作者，精通生成和理解复杂生物数据所必需的完整生命周期。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。该计划致力于通过创新的、基于证据的、与不断变化的劳动力和研究需求相一致的综合培训模式，在高优先级的跨学科或融合研究领域对STEM研究生进行有效培训。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。

项目成果

Goals for Statistics and Data Science Collaborations

统计和数据科学合作的目标

• 发表时间: 2020
• 期刊: Statistical Consulting Section
• 作者: Vance, Eric A.

Creating Shared Understanding in Statistics and Data Science Collaborations

在统计和数据科学合作中建立共识

• DOI: 10.1080/26939169.2022.2035286
• 发表时间: 2022
• 期刊: Journal of Statistics and Data Science Education
• 影响因子: 1.7
• 作者: Vance, Eric A.;Alzen, Jessica L.;Smith, Heather S.
• 通讯作者: Smith, Heather S.

The NEON Ecological Forecasting Challenge

NEON 生态预测挑战

• DOI: 10.1002/fee.2616
• 发表时间: 2023
• 期刊: Frontiers in Ecology and the Environment
• 影响因子: 10.3
• 作者: Thomas, R Quinn;Boettiger, Carl;Carey, Cayelan C;Dietze, Michael C;Johnson, Leah R;Kenney, Melissa A;McLachlan, Jason S;Peters, Jody A;Sokol, Eric R;Weltzin, Jake F
• 通讯作者: Weltzin, Jake F

Assessing Statistical Consultations and Collaborations

评估统计咨询与合作

• 发表时间: 2020
• 期刊: Statistical Consulting Section
• 作者: Vance, Eric A.;Alzen, Jessica L.;Seref, Michelle M.H.
• 通讯作者: Seref, Michelle M.H.