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

该项目旨在通过为本科生提供大数据分析方面的基本技能来服务于国家利益，弥合一些社区学院和少数民族服务机构存在的差距。通过提供这一高度相关和抢手的技能培训，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，我们将支持和培训社区学院和少数民族服务机构的教师，与他们合作，将计算技能和大数据分析技术融入他们现有的生命科学课程。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，策划和维护计算和教学资源，使广大本科生能够参与分析真实世界的生物数据。高通量技术的快速发展改变了生命科学研究的方式，其中计算工具发挥着至关重要的作用。为了有效地从大型数据集中导航和获得见解，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，如社区学院和一些少数民族服务机构，教师面临着将BDAT纳入生命科学课程的众多挑战。为了解决这些障碍，我们提出了一个有针对性的努力，由一个专门的小组的教师与任命都在生命科学研究在四年制大学和生命科学单位在社区学院和少数民族服务机构。该项目将开发一个可持续的全国性网络，用于实施以BDAT为重点的有效的基于课程的本科生研究经验（CURES），特别强调提高生命科学研究再现性的最佳实践。为了实现这一总体目标，该项目将侧重于三个主要目标：1）为生物数据科学教育联盟（CBSE）开发、采用、策划和维护计算和教学资源; 2）为缺乏部门整合和机构支持的生命科学教师制定教师培训战略;评估生命科学教育中BDAT技能和再现性概念的教学、学习和评估策略。通过解决这些目标，这项工作将提供一种有效的方法，在社区学院和少数民族服务机构教授BDAT技能，使本科生能够获得高年级教育和劳动力准备的计算和数据科学技能。该项目由生物基础设施部生物科学理事会和本科教育部STEM教育理事会共同资助，作为应对本科生物学教育中的愿景和变革所带来的挑战的努力的一部分：行动呼吁（网址：//visionandchange/finalreport/）该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查进行评估，被认为值得支持的搜索.

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.