Strengthening the Biomedical Data Science Training Program at Jackson State University

项目摘要 杰克逊州立大学（JSU）建议制定生物医学数据科学培训计划（BDS-TP）， 培养下一代科学家应对与策展相关的巨大挑战， 整合、分析和解读生物医学大数据。该培训计划将加强 新成立的RCMI健康差异研究中心的生物医学研究基础设施， JSU。特别强调使用和管理代表多种健康差异的数据集， 国家少数民族健康和健康差异研究所阐述的影响水平和领域 （NIMHD）研究框架，BDS-TP的上述目标将通过两个 具体目标包括：1）生物医学数据科学家和BDS-TP之间的福斯特合作 参与者和RCHDR研究人员。方法：确定并邀请在以下方面具有专长的知名科学家： 企业发展服务的具体领域，作为具体的企业发展服务-贸易点培训模块的领导者/教员，并促进 多学科合作;和2）建立JSU和其他密西西比的HBCU的能力- 历史上的黑人学院和大学（奥尔康州立大学，密西西比河谷州 大学和Tugaloo学院）的生物医学数据科学。方法：组织培训员培训 训练营，以培养STEM（科学，技术，工程和技术）教师和博士后 研究人员如何将尖端的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Google Hub，并使用R，Python和其他数据进行编码 科学工具，如R-Studio，Galaxy或Carpentries;并组织专注于特定生物医学的Codeathons 使用各种类型数据集的主题。这些具体目标的实现将大大加强 计划参与者的数据科学技能，从数据收集，策展和 分析到复杂的计算技术，如机器学习和人工智能， 促进少数民族健康和健康差异的科学。

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 achievement 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.