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，以塑造严格的思维，技能和工具 生物医学数据科学的可重复性，并确保在广泛的多种技能和工具中应用 通过学习阶段（带协作学习的训练营）和实施的生物医学研究 阶段（指导）。此外，我们将使我们的学员能够在他们的 我们的短期目标是塑造潜水员背景的生物医学研究人员的思想 并为他们配备技能和工具，以提高其研究的严格性和可重复性。我们的长期 目标是通过从我们的培训中转移技能对严谨性和可重复性产生持久的影响 对他们的培训，改善研究成果及其对社会的利益，并加强潜水员 生物医学数据科学劳动力。 长期数据操纵管道的研究项目面临严格和可重复性的挑战 他们的生命周期。尽管研究界努力促进严格和可重复性，但缺乏 为研究人员建立技术知识的系统培训，以实践实现这一目标。我们的计划 将重点关注六个主题：1）生物医学数据科学中的道德问题。 2）数据管理，表示，数据 有信心的考虑和元数据分享。 3）严格的统计设计。 4）设计和 预测建模的报告。 5）可重现的工作流程。 6）荟萃分析。 我们的计划将支持四个级别的多样性。从科学上讲，我们培训了使用潜水员类型的研究人员 数据（从-omics数据一直到人口数据），以在各种规模上解决研究问题。 从职业上讲，我们将在任何职业阶段培训教师和技术人员。从人口统计学上，我们会的 确保来自代表性不足的群体的研究人员通过激烈 招聘工作和建立友好的学习环境。从制度上，我们将培训研究人员 来自主要的研究大学以及资源有限的机构，尤其是 欢迎来自少数派服务机构的研究人员。 我们的培训计划将重点关注教职员工（PIS PIS）和技术人员。他们俩都在玩 确保严格性和可重复性的关键作用，但可以从不同的角度来实现这一目标。训练 他们在一起将使他们从彼此的科学专业知识和技术技能和地址中受益 以协作方式进行严格和可重复性。我们将结合培训组件 （讲座，小组密集型会议和团队项目）通过在线自适应学习工具 有效地适应我们培训团队的高度可变科学和技术背景。