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: 1761792
• 负责人: Donald Adjeroh
• 金额: $ 15万
• 依托单位: West Virginia University Research Corporation
• 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=1761792&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、塔斯基吉大学、斯佩尔曼学院和西弗吉尼亚大学之间的一项合作努力，旨在将生物大数据集成和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，该项目将提供关于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制定一项协议，以自动化现场数据收集。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。该项目的目标是：（1）通过夏季研讨会提高教师在大生物数据方面的专业知识;（2）通过黑客马拉松，工作组和社区建设，通过视频教育教师网络促进大生物数据研究和教育的跨学科合作;（3）开发实践，建设性的同行评估学习模块，结合高质量的视频教程。拟议的活动将解决围绕大生物数据的整合和自动化到主要是本科院校和历史上的黑人学院和大学的教育和培训的挑战。该项目将有助于弥合大生物数据与系统生物学，生态学和进化以及环境科学领域之间的差距。总的来说，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的项目。该奖项反映了NSF的法定使命，并被认为值得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来支持。

项目成果

Adversarial Latent Autoencoders

• DOI: 10.1109/cvpr42600.2020.01411
• 发表时间: 2020-04
• 期刊: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
• 作者: Stanislav Pidhorskyi;D. Adjeroh;Gianfranco Doretto

Efficient Classification of Very High Resolution Histopathological Images

• DOI: 10.1109/bibm55620.2022.9994942
• 发表时间: 2022-12
• 期刊: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
• 作者: Mohammad Iqbal Nouyed;Gianfranco Doretto;D. Adjeroh

Fine-Grained Visual Classification of Plant Species In The Wild: Object Detection as A Reinforced Means of Attention

• 发表时间: 2021-06
• 期刊: ArXiv
• 作者: M. Keaton;Ram J. Zaveri;Meghana Kovur;Cole Henderson;D. Adjeroh;Gianfranco Doretto