Training Biomedical Research Teams for Rigor and Reproducibility in Data Science

培训生物医学研究团队以确保数据科学的严谨性和可重复性

• 批准号: 10723223
• 负责人: Juan B Gutierrez
• 金额: $ 45.62万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10723223
• 关键词: Address; Applications Grants; Awareness; Biomedical Research; Collaborations; Communities; Complement; Data; Data Analyses; Data Collection; Data Science; Data Sources; E-learning; Educational Curriculum; Educational process of instructing; Ensure; Ethical Issues; Face; Faculty; Friends; Fruit; Funding Agency; Goals; Human Resources; Individual; Institution; Instruction; Investigation; Journals; Judgment; Learning; Life Cycle Stages; Mentors; Meta-Analysis; Metadata; Methodology; Minority-Serving Institution; Outcomes Research; Persons; Phase; Play; Population; Process; Professional Organizations; Reporting; Reproducibility; Research; Research Personnel; Research Project Grants; Resources; Role; Shapes; Societies; Stress; Teacher Professional Development; Technical Expertise; Thinking; Time; Trainers Training; Training; Training Activity; Training Programs; Underrepresented Populations; Universities; Work; adaptive learning; bilingualism; biomedical data science; career; complex data; computerized data processing; data management; data sharing; design; educational atmosphere; flexibility; health record; heterogenous data; improved; innovation; insight; instructor; lectures; minority investigator; online course; predictive modeling; programs; recruit; research study; skill acquisition; skills; sound; tool

Abstract: We will develop a training program to shape the thinking, impart skills and tools for rigor and reproducibility in biomedical data science, and ensure the application of such skills and tools in a wide range of biomedical research through a learning phase (bootcamp with collaborative learning) and an implementation phase (mentoring). In addition, we will enable our trainees to teach their newly acquired skills at their institutions. Our short-term goal is to shape the thinking of biomedical researchers from diverse backgrounds and equip them with skills and tools to improve the rigor and reproducibility of their research. Our long-term goal is to have a long-lasting impact on rigor and reproducibility through the transfer of skills from our trainees to their trainees, improve research outcomes and its benefits to the society, and to strengthen a diverse biomedical data science workforce. Research projects with long data manipulation pipelines face rigor and reproducibility challenges throughout their lifecycle. Despite the efforts of the research community to promote rigor and reproducibility, there lacks systematic training for researchers to build the technical know-how to achieve this in practice. Our program will focus on six topics: 1) Ethical issues in biomedical data science. 2) Data management, representation, data sharing with confidentiality considerations, and metadata. 3) Rigorous statistical design. 4) Design and reporting of predictive modeling. 5) Reproducible workflow. 6) Meta-analysis. Our program will support diversity at four levels. Scientifically, we train researchers who use diverse types of data (from -omics data all the way to population data) to address research questions at various scales. Professionally, we will train faculty and technical personnel at any career stage. Demographically, we will ensure that researchers from underrepresented groups have a strong presence in our program, through intense recruitment effort and by building a friendly learning environment. Institutionally, we will train researchers from major research universities as well as from institutions with limited resources, and we will especially welcome researchers from Minority-Serving Institutions. Our training program will focus on teams of faculty (project PIs) and technical personnel. They both play critical roles to ensure rigor and reproducibility, but may approach this from different perspectives. Training them together will allow them to benefit from each other’s scientific expertise and technical skills and address rigor and reproducibility in a collaborative manner. We will use a combination of training components (lectures, small group intensive sessions and team projects) through an online adaptive learning tool to effectively accommodate the highly variable scientific and technical backgrounds of our teams of trainees.

摘要：我们将制定一项培训计划，以塑造思维，传授严谨和 生物医学数据科学的可重复性，并确保这种技能和工具在广泛的 通过学习阶段(协作学习的训练营)和实施进行生物医学研究 阶段(指导)。此外，我们将使我们的受训人员能够在他们的 机构。我们的短期目标是塑造来自不同背景的生物医学研究人员的思维 并为他们配备技能和工具，以提高他们研究的严谨性和重复性。我们的长期合作 我们的目标是通过传授学员的技能，对精确度和重复性产生持久的影响 对学员来说，提高研究成果及其对社会的效益，并加强多元化 生物医学数据科学工作者。 具有长数据操作管道的研究项目在整个过程中都面临着严谨和可重复性的挑战 它们的生命周期。尽管研究界努力促进严谨性和可重复性，但缺乏 对研究人员进行系统培训，以建立在实践中实现这一目标的技术诀窍。我们的节目 将集中讨论六个主题：1)生物医学数据科学中的伦理问题。2)数据管理、表示、数据 以保密考虑进行共享，以及元数据。3)严谨的统计设计。4)设计和 预测建模的报告。5)可重现的工作流。6)Meta分析。 我们的计划将在四个层面上支持多样性。科学上，我们培训使用不同类型的 数据(从组学数据一直到人口数据)，以解决各种规模的研究问题。 在专业方面，我们将在职业生涯的任何阶段培训教师和技术人员。在人口统计方面，我们将 确保来自代表性不足群体的研究人员在我们的项目中有强大的存在，通过密集的 通过招聘工作和营造友好的学习环境。在制度上，我们将培训研究人员 来自主要研究型大学以及资源有限的机构，我们将特别 欢迎来自少数群体服务机构的研究人员。 我们的培训计划将侧重于教师团队(项目PI)和技术人员。他们两个都玩 关键作用，以确保严密性和可重复性，但可以从不同的角度处理这一问题。培训 它们的结合将使它们能够从彼此的科学专长和技术技能中受益，并解决 严密性和可重复性，以协作的方式。我们将使用培训组件的组合 (讲座、小组密集会议和团队项目)通过在线适应性学习工具 有效地适应我们受训团队高度多变的科学和技术背景。