Enhancing Diversity through IHSAN (Interdisciplinary Health disparities and data Science trAiNing)

通过 IHSAN（跨学科健康差异和数据科学培训）增强多样性

• 批准号: 10452040
• 负责人: Elizabeth O. Ofili
• 金额: $ 35.5万
• 依托单位: MOREHOUSE SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-08 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10452040
• 关键词: Address; Award; Behavioral; Biomedical Research; Biometry; Collaborations; Communities; Coupled; Data Analyses; Data Coordinating Center; Data Science; Data Set; Development; Diagnosis; Discipline; Economic Factors; Educational workshop; Environmental Risk Factor; Evaluation; Financial Support; Fostering; Funding; Gap Junctions; Goals; Hispanics; Incentives; Indigenous; Informatics; Infrastructure; Institution; Intervention; Knowledge; Lead; Learning; Link; Machine Learning; Mentors; Methods; Minority-Serving Institution; Modeling; Needs Assessment; Pathway interactions; Positioning Attribute; Reproducibility; Research; Research Infrastructure; Research Personnel; Research Project Grants; Resource Sharing; Resources; Respondent; Scientist; Talents; Training; Underrepresented Populations; United States National Institutes of Health; Universities; Visualization; biomedical data science; cloud platform; community engagement; data access; disparity reduction; faculty mentor; health data; health disparity; health equity promotion; improved; innovation; inter-institutional; investigator training; learning progression; minority health; next generation; programs; project-based learning; repository; skills; social factors; student participation; student training; technological innovation; tool; underserved community

PROJECT SUMMARY/ABSTRACT To address the gap in expertise in data science in some RCMI U54 Centers, we propose developing a coordinated RCMI Data Science Initiative with two goals: (1) Upskilling RCMI investigators in data science, including tools, methods, and best practices with a focus on reproducible data analysis and effective knowledge mobilization around health disparities and (2) Incentivizing collaborations at the nexus of data science and health disparities between investigators within and outside of the RCMI consortium. Our specific aims are: 1. To enhance the data science capacity of RCMI investigators across the RCMI consortium by collaboratively developing a project-based curriculum and organizing workshops introducing data science fundamentals and tools as well as NIH-funded resources and datasets relevant to minority health disparities. Investigators will learn how to access data from the All of Us research workbench, link it to community-level datasets available on Google’s Cloud Platform, create visualizations, build and evaluate machine learning models, and upload replication files into a repository using R, SQL, and Git. 2. To stimulate data science collaborations across U54 centers as well as between U54 and non-RCMI institutions by organizing Data Science and Health Disparities Demo Days and launching Enrichment awards to support development of collaborative research projects. Our goal is to provide financial support to develop promising research collaborations into proposals for pilot funding. Our long term goal is to leverage existing partnerships with the Atlanta University Center (AUC) Data Science Initiative, the Georgia CTSA, and the National Research Mentoring Network (NRMN) to build a well-coordinated infrastructure to support collaboration between RCMI and non RCMI investigators, and train the next generation of diverse investigators leveraging advances in data science and machine learning to address minority health and health disparities in the 21st century.

项目总结/摘要 为了解决一些RCMI U 54中心在数据科学专业知识方面的差距，我们建议 制定协调的RCMI数据科学计划，目标有两个：（1）提高RCMI的技能 数据科学研究人员，包括工具，方法和最佳实践，重点是 围绕健康差距进行可重复的数据分析和有效的知识动员;（2） 鼓励在数据科学和健康差距之间的联系中进行合作 RCMI联盟内外的调查人员。我们的具体目标是： 1.提高整个RCMI的RCMI调查人员的数据科学能力 通过合作开发基于项目的课程和组织讲习班， 介绍数据科学基础知识和工具以及NIH资助的资源和数据集 与少数民族的健康差距有关。研究人员将学习如何从所有的 美国研究工作台，将其链接到谷歌云上提供的社区级数据集 平台，创建可视化，构建和评估机器学习模型，并上传 使用R、SQL和Git将复制文件复制到存储库中。 2.促进U 54中心之间以及U 54之间的数据科学合作 和非RCMI机构通过组织数据科学和健康差异演示日， 启动“丰富”奖，以支持合作研究项目的发展。我们 目标是提供财政支持，将有前途的研究合作发展成提案 试点资金。 我们的长期目标是利用与亚特兰大大学中心（AUC）的现有合作伙伴关系 数据科学倡议、格鲁吉亚CTSA和国家研究指导网络 （NRMN）建立一个协调良好的基础设施，以支持RCMI和 非RCMI调查员，并培训下一代多样化的调查员， 数据科学和机器学习的进步，以解决少数群体的健康和健康差距 在21世纪。