Training Biomedical Research Teams for Rigor and Reproducibility in Data Science

翻译后摘要：我们将制定一个培训计划，以塑造思维，传授技能和工具的严谨性， 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应用， 生物医学研究通过学习阶段（与协作学习的训练营）和实施 阶段（指导）。此外，我们将使我们的学员能够在他们的学校教授他们新掌握的技能。 机构职能体系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 并为他们提供技能和工具，以提高他们研究的严谨性和可重复性。我们的长期 我们的目标是通过培训生的技能转移，对严谨性和可重复性产生持久的影响 提高研究成果及其对社会的益处，并加强多样化的 生物医学数据科学的劳动力。 具有长数据操作管道的研究项目在整个过程中面临严格性和可重复性的挑战 他们的生命周期。尽管研究界努力促进严谨性和可重复性， 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知识。我们的节目 将重点讨论六个主题：1）生物医学数据科学中的伦理问题。2)数据管理、表示、数据 有保密考虑的共享和元数据。3)严格的统计设计。4)设计与 预测建模的报告。5)可复制的工作流程。6)荟萃分析. 我们的计划将在四个层面上支持多样性。在科学方面，我们培训研究人员，他们使用各种类型的 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。 在职业生涯中，我们将在任何职业阶段培训教师和技术人员。从人口统计学上讲，我们将 确保来自代表性不足的群体的研究人员在我们的计划中有很强的存在，通过激烈的 招聘工作和建立友好的学习环境。在制度上，我们将培养研究人员 来自主要的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。 我们的培训计划将侧重于教师团队（项目PI）和技术人员。他们都玩 关键作用，以确保严谨性和可重复性，但可能会从不同的角度来处理这一点。培训 他们一起将使他们受益于彼此的科学专业知识和技术技能， 严谨性和可重复性。我们将使用一个组合的培训组件 （讲座，小组密集会议和团队项目）通过在线自适应学习工具， 有效地适应我们的学员团队的高度可变的科学和技术背景。

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.