Spokes: MEDIUM: SOUTH: Collaborative: Integrating Biological Big Data Research into Student Training and Education

辐条：中：南：协作：将生物大数据研究融入学生培训和教育

• 批准号: 1761839
• 负责人: Hong Qin
• 金额: $ 54.99万
• 依托单位: University of Tennessee Chattanooga
• 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: 2018-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1761839&HistoricalAwards=false
• 关键词: Spokes; MEDIUM; SOUTH; Collaborative; Integrating

The project is a collaborative effort among the University of Tennessee Chattanooga, Tuskegee University, Spelman College, and West Virginia University to integrate and automate biological big data into student training and education. Leveraging the team's expertise in computer science and ecology, the project will offer training workshops on using network models to integrate heterogeneous genomic big data and heterogeneous ecological big data to address life sciences questions. The team will engage faculty and students in developing a protocol to automate field data collection. The team also will prototype automated methods to enhance plant digitization, leveraging the collection of digitized plant images and meta-information at the Southeast Regional Network of Expertise and Collections, as well as the ecological datasets in collaboration with the Encyclopedia of Life.The project objectives are to (1) enhance faculty expertise in big biological data through summer workshops; (2) catalyze interdisciplinary collaboration on big biological data research and education through hackathons, working groups, and community-building via a Video Education Faculty Network; and (3) develop hands-on, constructively peer-evaluated learning modules incorporating high-quality video tutorials. The proposed activities will address challenges surrounding the integration and automation of big biological data into education and training at predominantly undergraduate institutions and Historically Black Colleges and Universities. The project will help bridge the gaps between big biological data and the fields of systems biology, ecology and evolution, and environmental sciences. Overall, the project will catalyze collaborations among diverse institutions and disciplines while increasing diversity in big data. This award is co-funded by the Improving Undergraduate STEM Education: Education and Human Resources (IUSE): EHR Program (NSF 17-590). IUSE supports projects that are designed to improve student learning through development of new curricular materials and methods of instruction and development of new assessment tools to measure student learning.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.

该项目是田纳西大学查塔努加分校、塔斯基吉大学、斯佩尔曼学院和西弗吉尼亚大学之间的合作项目，旨在将生物大数据集成并自动化到学生培训和教育中。利用该团队在计算机科学和生态学方面的专业知识，该项目将提供有关使用网络模型整合异构基因组大数据和异构生态大数据以解决生命科学问题的培训研讨会。该团队将让教师和学生参与开发自动化现场数据收集的协议。该团队还将利用东南地区专业知识和收藏网络收集的数字化植物图像和元信息，以及与生命百科全书合作的生态数据集，建立自动化方法的原型，以增强植物数字化。项目目标(1) 通过夏季研讨会增强教师在生物大数据方面的专业知识； (2) 通过黑客马拉松、工作组和视频教育教师网络社区建设，促进生物大数据研究和教育的跨学科合作； (3) 开发包含高质量视频教程的实践性、建设性的同行评估学习模块。拟议的活动将解决围绕将大生物数据集成和自动化纳入以本科院校和传统黑人学院和大学为主的教育和培训中的挑战。该项目将有助于弥合生物大数据与系统生物学、生态学和进化以及环境科学领域之间的差距。总体而言，该项目将促进不同机构和学科之间的合作，同时增加大数据的多样性。该奖项由改善本科 STEM 教育：教育和人力资源 (IUSE)：EHR 计划 (NSF 17-590) 共同资助。 IUSE 支持旨在通过开发新的课程材料和教学方法以及开发新的评估工具来衡量学生学习来提高学生学习的项目。该奖项反映了 NSF 的法定使命，并通过使用基金会的知识进行评估，被认为值得支持。优点和更广泛的影响审查标准。

项目成果

Applying for, Operating, and Renewing a Research Experience for Undergraduates Site Program in Computational Biology

申请、运营和更新计算生物学本科生研究经验现场项目

• DOI: 10.54808/imcic2022.01.171
• 发表时间: 2022
• 期刊: Informatics and Cybernetics
• 作者: Qin, Hong

Cognitive GPR for Subsurface Object Detection Based on Deep Reinforcement Learning

基于深度强化学习的认知探地雷达用于地下物体检测

• DOI: 10.1109/jiot.2021.3059281
• 发表时间: 2021
• 期刊: IEEE Internet of Things Journal
• 影响因子: 10.6
• 作者: Omwenga, Maxwell M.;Wu, Dalei;Liang, Yu;Yang, Li;Huston, Dryver;Xia, Tian
• 通讯作者: Xia, Tian

Fitmix: An R Package for Mixture Modeling of the Budding Yeast S. cerevisiae Replicative Lifespan (RLS) Distributions

Fitmix：用于对芽殖酵母复制寿命 (RLS) 分布进行混合建模的 R 包

• DOI: 10.3390/app11136114
• 发表时间: 2021
• 期刊: Applied Sciences
• 作者: Güven, Emine;Qin, Hong
• 通讯作者: Qin, Hong

Lidar-Based Real-Time Mapping for Digital Twin Development

基于激光雷达的实时测绘数字孪生开发

• DOI: 10.1109/icme51207.2021.9428337
• 发表时间: 2021
• 期刊: Computers and Communications
• 作者: Brock, Evan;Huang, Chengxuan;Wu, Dalei;Liang, Yu
• 通讯作者: Liang, Yu

A quantitative yeast aging proteomics analysis reveals novel aging regulators

• DOI: 10.1007/s11357-021-00412-3
• 发表时间: 2021-07-09
• 期刊: GEROSCIENCE
• 影响因子: 5.6
• 作者: Sun，Yu;Yu，Ruofan;Dang，Weiwei
• 通讯作者: Dang，Weiwei