Improving reproducibility of recording and pre-processing experimental biomedical data

提高记录和预处理实验生物医学数据的再现性

• 批准号: 9886251
• 负责人: Brooke Anderson
• 金额: $ 9.9万
• 依托单位: COLORADO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9886251
• 关键词: Address; American; Attention; Biology; Biomedical Research; Book Chapters; Books; Collection; Colorado; Communicable Diseases; Complement; Computer software; Computing Methodologies; Data; Data Analyses; Data Collection; Ecosystem; Educational Materials; Educational workshop; Ensure; Evaluation; Exercise; Experimental Designs; Faculty; Feedback; Focus Groups; Funding; Goals; Guidelines; Health; Human; Immunology; Instruction; Knowledge; Laboratories; Laboratory Research; Language; Licensing; Measurement; Methods; Microbiology; Modification; Participant; Persons; Process; Program Evaluation; Publishing; R programming language; Reproducibility; Research; Research Activity; Research Personnel; Research Project Grants; Resources; Sampling; Science; Science, Technology, Engineering and Mathematics; Scientist; Societies; Software Tools; Surveys; Systems Analysis; Techniques; Technology; Testing; Text; Training; Training Activity; Uncertainty; United States; United States National Institutes of Health; Universities; Vendor; Video Recording; base; biomedical scientist; data structure; drug development; electronic book; experimental study; flexibility; follow-up; improved; innovation; lectures; literate; open source; programs; structured data; symposium; tool; translational medicine; undergraduate student; vaccine development; web site

Project Abstract: An essential aspect of both translational medicine and human health-based team science is the integration of biomedical research results that are generated by multiple laboratories using widely varying experimental systems and data analysis methods. Beyond the uncertainties of experimental design and measurement, there are several critical points in the subsequent research workflow where lack of rigor and transparency may compromise the reproducibility of these laboratory results. In this proposal, we focus on data recording and data pre-processing as key steps of research workflows, and we propose to develop training modules that provide general principles, software tools, and exercises to broadly enhance data reproducibility of these steps in biomedical research. We aim to ensure these training modules are clear, relevant, and useful to laboratory-based researchers, whose attention is rather to their experimental technique and collection of accurate data, and who may have little or no background in the use of general purpose software tools. To ensure this, we will feature in these training modules examples from recent and ongoing NIH-funded microbi- ology and immunology research programs devoted to drug and vaccine development for infectious diseases at Colorado State University. There will be two instructional sequences of modules, “Improving the Repro- ducibility of Experimental Data Recording”, with eleven training modules, and “Improving the Reproducibility of Experimental Data Pre-Processing”, with nine training modules. The R programming language, and an ecosystem of related reproducibility tools, will form the technical basis for implementation of the modules in these sequences, while modules on principles and examples will be accessible to biomedical researchers regardless of programming knowledge. These training modules will be collectively published as an open on- line book using the bookdown technology, leveraging literate programming. Each module will form a chapter of this book, and will feature an embedded YouTube video of 10–25 minutes, with accompanying text in the book to provide trainees with a more detailed written reference they can refer to after completing the video module. Each module's chapter will conclude with practical exercises or open discussion questions to com- plement the material taught in the video. To ensure this material is completely free and open to researchers in the United States, we will publish this online book, and its embedded videos and additional content, under a Creative Commons license.

项目摘要： 转化医学和基于人类健康的团队科学的一个重要方面是整合 生物医学研究结果是由多个实验室使用广泛不同的实验 系统和数据分析方法。除了实验设计和测量的不确定性之外， 在随后的研究工作中，有几个关键点缺乏严谨性和透明度， 可能会损害这些实验室结果的再现性。在本提案中，我们重点关注数据记录 和数据预处理作为研究工作的关键步骤，我们建议开发培训模块， 提供了一般原则、软件工具和练习，以广泛提高这些数据的可重复性。 生物医学研究的步骤。我们的目标是确保这些培训模块是明确的，相关的，有用的， 以实验室为基础的研究人员，他们的注意力是他们的实验技术和收集， 准确的数据，以及可能很少或没有通用软件工具使用背景的人。到 为了确保这一点，我们将在这些培训模块中介绍最近和正在进行的NIH资助的微生物研究的例子， 致力于传染病药物和疫苗开发的生物学和免疫学研究项目 在科罗拉多州立大学。将有两个模块的教学序列，“提高再现- 实验数据记录教学”，包括11个培训模块，以及“提高再现性 实验数据预处理”，有九个培训模块。R编程语言，以及 相关再现性工具的生态系统，将构成模块实施的技术基础， 这些序列，而原理和实例模块将提供给生物医学研究人员 不管编程知识。这些培训模块将作为一个开放的出版物集中在- 线书使用bookdown技术，利用识字编程。每个模块将组成一个章节 这本书，并将采用嵌入式YouTube视频的10-25分钟，伴随着文字在 一本书，为学员提供更详细的书面参考资料，供他们在完成录像后参考 module.每个模块的章节都将以实际练习或开放式讨论问题结束，以供参考。 补充视频中教授的材料。确保这些材料完全免费并向研究人员开放 在美国，我们将发布这本在线书籍，以及它的嵌入式视频和其他内容， 知识共享许可证。