Biomedical Data Science Training Program

项目总结 杰克逊州立大学(JSU)建议开发生物医学数据科学培训计划(BDS-TP)以 培训下一代科学家，以应对与馆藏相关的巨大挑战， 整合、分析、解读生物医学大数据。这一培训计划将加强 新成立的RCMI健康差距研究中心的生物医学研究基础设施 佐治亚州大学。特别强调健康差异数据集的使用和管理，这些数据集代表 国家少数民族健康和健康差距研究所阐述的影响程度和领域 (NIMHD)研究框架，BDS-TP的上述目标将通过两个途径实现 具体目标包括：1)促进生物医学数据科学家与BDS-TP之间的合作 参与者和RCHDR调查人员。方法：确定并邀请具有以下专业知识的知名科学家 BDS的特定领域，担任特定BSD-TP培训模块的领导者/讲师，并促进 多学科合作；以及2)建设JSU和其他密西西比州HBCU的能力- 历史上的黑人学院和大学(密西西比州阿尔科恩州立大学 大学和杜加鲁学院)生物医学数据科学。方法：组织培训培训员 培训STEM(科学、技术、工程和技术)教职员工和博士后训练营 研究人员如何将尖端数据科学技术应用于不同的生物医学数据类型，如何 使用Galaxy和计算资源(如Jupyter Hub)，并使用R、Python和其他数据进行编码 科学工具，如R-Studio、Galaxy或CarpEntry；组织专注于特定生物医学的Codeathon 使用各种类型的数据集的主题。这些具体目标的实现将大大增强 项目参与者的数据科学技能，从数据收集、管理和管理的基本最佳实践 对机器学习和人工智能等关键复杂计算技术的分析 促进少数群体健康和健康差距的科学研究。

PROJECT SUMMARY Jackson State University (JSU) proposes to develop a Biomedical Data Science Training Program (BDS-TP) to train the next generation of scientists to address the monumental challenge associated with the curation, integration, analysis, and interpretation of biomedical big data. This training program will strengthen the biomedical research infrastructure of the newly established RCMI Center for Health Disparities Research at JSU. With a special emphasis on the use and management of health disparities datasets representing multiple levels and domains of influence articulated in the National Institute of Minority Health and Health Disparities (NIMHD) Research Framework, the above-stated goal of the BDS-TP will be accomplished through two specific aims that include: 1) Forster Collaborations between biomedical data scientists and BDS-TP participants and RCHDR investigators. Approach: Identify and invite renowned scientists with expertise in specific areas of BDS to serve as leaders/instructors of specific BSD-TP training modules, and facilitate multidisciplinary collaborations; and 2) Build the capacity of JSU and other Mississippi’s HBCU- Historically Black Colleges and Universities (Alcorn State University, Mississippi Valley State University, and Tugaloo College) in biomedical data science. Approach: Organize Train-the-Trainer bootcamps to train the STEM (science, technology, engineering and technology) faculty and post-doctoral research fellows on how to apply cutting-edge data science techniques to different biomedical data types, how to use Galaxy and computational resources like Jupyter Hub, and coding with R, Python, and other data science tools such R-Studio, Galaxy, or Carpentries; and organize Codeathons focused on specific biomedical topics using various types of datasets. The achievements of these specific aims will significantly enhance the data science skills of program participants, ranging from basic best practices in data collection, curation and analysis to complex computational techniques like machine learning and artificial intelligence that are critical for advancing the science of minority health and health disparities.