RCN-UBE: Sustainable, nationwide network to promote reproducible big-data analysis in biology programs within community colleges and minority-serving institutions

RCN-UBE：可持续的全国性网络，旨在促进社区大学和少数族裔服务机构内生物学项目的可重复大数据分析

• 批准号: 2316223
• 负责人: Serghei Mangul
• 金额: $ 49.88万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2316223&HistoricalAwards=false
• 关键词: RCN; UBE; Sustainable; nationwide; network

This project aims to serve the national interest by equipping undergraduate students with essential skills in big data analytics, bridging a gap that exists at some community college and minority-serving institutions. By providing training in this highly relevant and sought-after skill set, this project aims to empower students from diverse backgrounds to thrive in the data-driven landscape of modern life sciences and STEM-related fields. To achieve this objective, we will support and train faculty members from community colleges and minority-serving institutions, working with them in integrating computational skills and big data analytic techniques into their existing life science curricula. Furthermore, this project will establish a sustainable and nationwide network dedicated to developing, adopting, curating, and maintaining computational and pedagogical resources, enabling a wide range of undergraduate students to engage in the analysis of real-world biological data. The rapid advancement of high throughput technologies has transformed the way research is conducted in the life sciences, with computational tools playing a crucial role. In order to effectively navigate and gain insights from large datasets, 21st-century biologists heavily rely on big data analytic techniques (BDAT). However, at teaching-focused institutions such as community colleges and some minority-serving institutions, faculty face numerous challenges to incorporating BDAT into the life sciences curricula. To address these barriers, we propose a targeted effort by a dedicated group of faculty members with appointments both in life science research at four-year universities and in life science units at community colleges and minority-serving institutions. This project will develop a sustainable, nationwide network for implementing effective course-based undergraduate research experiences (CUREs) focused on BDAT, with a particular emphasis on best practices for improving reproducibility in life sciences research. To achieve this overarching goal, the project will focus on three main objectives: 1) Develop, adopt, curate, and maintain computational and pedagogical resources for the Consortium of Biological Data Science Education (CBSE); 2) Develop faculty training strategies for life science instructors who lack departmental integration and institutional support for BDAT; and 3) Evaluate teaching, learning, and assessment strategies for BDAT skills and reproducibility concepts in life science education. By addressing these objectives, this effort will provide an effective approach to teaching BDAT skills at community colleges and minority-serving institutions, empowering undergraduate students with upper-division educational and workforce-ready computational and data science skill sets. This project is being jointly funded by the Directorate for Biological Sciences, Division of Biological Infrastructure, and the Directorate for STEM Education, Division of Undergraduate Education as part of their efforts to address the challenges posed in Vision and Change in Undergraduate Biology Education: A Call to Action (http://visionandchange/finalreport/).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.

该项目旨在为国家利益服务，为本科生提供大数据分析方面的基本技能，弥合一些社区大学和为少数族裔服务的机构存在的差距。通过提供这一高度相关和受欢迎的技能集的培训，该项目旨在使来自不同背景的学生能够在现代生命科学和STEM相关领域的数据驱动环境中茁壮成长。为了实现这一目标，我们将支持和培训来自社区大学和少数群体服务机构的教职员工，与他们合作，将计算技能和大数据分析技术整合到他们现有的生命科学课程中。此外，该项目将建立一个可持续的全国性网络，致力于开发、采用、管理和维护计算和教学资源，使广泛的本科生能够从事真实世界生物数据的分析。高通量技术的快速发展改变了生命科学研究的方式，计算工具发挥着关键作用。为了有效地导航并从大数据集中获得洞察力，21世纪的生物学家严重依赖大数据分析技术(BDAT)。然而，在以教学为重点的机构，如社区学院和一些为少数群体服务的机构，教师在将bdat纳入生命科学课程方面面临着许多挑战。为了解决这些障碍，我们建议由一群敬业的教职员工有针对性地努力，任命他们在四年制大学的生命科学研究以及社区学院和少数族裔服务机构的生命科学单位工作。该项目将建立一个可持续的、全国性的网络，以实施以bdat为重点的有效的以课程为基础的本科生研究经验，特别强调提高生命科学研究的重现性的最佳做法。为了实现这一总体目标，该项目将集中于三个主要目标：1)为生物数据科学教育联盟(CBSE)开发、采用、管理和维护计算和教学资源；2)为缺乏BDAT部门整合和机构支持的生命科学教师制定教师培训战略；以及3)评估生命科学教育中BDAT技能和可再生性概念的教、学和评估策略。通过实现这些目标，这一努力将提供一种在社区大学和少数群体服务机构教授bdat技能的有效方法，使本科生获得高级教育和劳动力准备的计算和数据科学技能。该项目由生物科学局生物基础设施司和STEM教育局本科生教育司共同资助，作为他们应对《本科生物学教育的愿景与变革：行动呼吁》中提出的挑战的努力的一部分(http://visionandchange/finalreport/).This奖反映了美国国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。