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：我们将制定一个培训计划，以塑造思维、传授严格和有效的技能和工具。 生物医学数据科学的可重复性，并确保这些技能和工具在广泛的领域中的应用 通过学习阶段（协作学习的训练营）和实施的生物医学研究 此外，我们将使我们的学员能够在他们的岗位上教授他们新获得的技能。 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，以提高他们长期研究的严谨性和可重复性。 目标是通过学员传授技能，对严谨性和可重复性产生长期影响 向受训者提供帮助，提高研究成果及其对社会的效益，并加强多元化 生物医学数据科学劳动力。 具有长数据处理流程的研究项目始终面临着严格性和可重复性的挑战 尽管研究界努力提高严谨性和可重复性，但仍存在不足。 为研究人员提供系统培训，以掌握在实践中实现这一目标的技术知识。 将重点关注六个主题：1）生物医学数据科学的伦理问题2）数据管理、表示、数据。 考虑保密性和元数据的共享 3) 严格的统计设计 4) 设计和。 5) 可重复的工作流程。 6) 荟萃分析。 我们的计划将在四个层面上支持多样性，我们培训使用不同类型的研究人员。 数据（从组学数据一直到人口数据）来解决各种规模的研究问题。 在专业上，我们将培训任何职业阶段的教师和技术人员。 确保来自代表性不足群体的研究人员在我们的项目中拥有强大的影响力，通过激烈的 我们将在制度上培养研究人员。 来自主要研究型大学以及资源有限的机构，我们将特别 欢迎来自少数民族服务机构的研究人员。 我们的培训计划将重点关注教师团队（项目 PI）和技术人员。 确保严格性和可重复性的关键角色，但可以从不同的角度进行培训。 他们在一起将使他们能够从彼此的科学专业知识和技术技能中受益，并解决 我们将以协作的方式结合使用培训组件。 （讲座、小组强化课程和团队项目）通过在线自适应学习工具 有效适应我们受训人员团队高度可变的科学和技术背景。