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.

项目概要/摘要 为了解决一些 RCMI U54 中​​心在数据科学专业知识方面的差距，我们建议 制定协调一致的 RCMI 数据科学计划，有两个目标：(1) 提高 RCMI 技能 数据科学领域的研究者，包括工具、方法和最佳实践，重点关注 围绕健康差异进行可重复的数据分析和有效的知识调动，以及 (2) 激励数据科学与健康差异之间的合作 RCMI 联盟内外的调查人员。我们的具体目标是： 1. 提高整个 RCMI 范围内 RCMI 研究人员的数据科学能力 联盟通过合作开发基于项目的课程并组织研讨会 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 与少数族裔的健康差异有关。研究人员将学习如何访问所有的数据 美国研究工作台，将其链接到 Google 云上可用的社区级数据集 平台，创建可视化，构建和评估机器学习模型，然后上传 使用 R、SQL 和 Git 将文件复制到存储库中。 2. 促进 U54 中​​心之间以及 U54 之间的数据科学合作 和非 RCMI 机构组织数据科学和健康差异演示日以及 推出丰富奖以支持合作研究项目的发展。我们的 目标是提供财政支持，将有前景的研究合作转化为提案 以获得试点资金。 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 数据科学计划、佐治亚州 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，并利用这些优势培训下一代多元化调查员 数据科学和机器学习的进步可解决少数族裔的健康和健康差异 在21世纪。