R3EASONING

研究实践中的错误往往源于应用 科学推理的基本原理。在生物医学科学中，这样的错误是严重的。 促成了越来越多的文章撤回，因此也加剧了 公众对科学事业的不信任。特别是在全球大流行的时候，这些 这种倾向可能对科学和社会有害。大数据在多个领域的作用 科学在不断地发展，因此，我们需要更多的实践者，他们不仅能 解决纸上的统计问题集，但能够将这些技能转化为研究 练习一下。这项提案的目标是制作和初步评估教育材料 这有助于缓解这种情况。在一项初步研究中，我们将生产“R3easing” 模块，建立在良好科学的三个R的基础上的指导性案例研究方法 实践：严谨、可重复性和责任感。该模块展示了常见的 在专家访谈的帮助下，数据科学领域的错误。经验丰富的从业者 来自JHSPH分子微生物学和免疫学、流行病学和 生物统计学，以及约翰霍普金斯大学韦尔奇医学图书馆的数据管理专家， 将提供他们从概念上从科学推理的陷阱中学到的洞察力 他们在科学领域的职业生涯。学生将这些概念应用到他们的学科背景中，制定 提出改进建议，并相互批评对方的理由。 R3排序模块被设计为一个关于基本错误的全在线、分阶段的案例研究方法 数据科学实践中的分析。由于单元细分为专题单元， 整个R3季节模块或部分可以灵活地集成到各种数据科学中 课程，视课程空间而定。R3季节模块将在一个 招生人数多，研究生水平，JHSPH统计推理在线课程。这门课 为各种生物医学和公共卫生科学领域的研究生提供服务。此设置 为课程参与者提供了一个独特的机会，以拓宽他们的研究技能，交流， 并跨越学科界限进行协作。 R3调期模块，经修订和初步验证后将免费提供， 可供教育工作者和研究从业者在其发展的几个层次上使用 其他机构测试理解和实践技能的差异是否可衡量。 本文提出的工作成果可以作为未来、长期和更大规模工作的基础。 跨机构和学习者群体的后续研究。

Errors in research practice frequently stem from insufficient capabilities in applying the fundamentals of scientific reasoning. In the biomedical sciences, such mistakes are significant contributors to the increasing numbers of article retractions and hence also exacerbate the public’s mistrust in the scientific enterprise. Particularly in times of a global pandemic, these tendencies can be detrimental for science and society. The role of big data in many fields of science is continuously on the rise, hence, we need more practitioners who are not only capable to of solving statistical problem sets on paper but able to transfer those skills into research practice. The goal of this proposal is to produce and initially evaluate educational materials that can help mitigate this situation. In a pilot study, we will produce the “R3easoning” module, a guided case study approach that builds on the three R’s of good scientific practice: Rigor, Reproducibility and Responsibility. The module showcases common errors in the data science fields with the help of expert interviews. Experienced practitioners from the JHSPH departments of Molecular Microbiology and Immunology, Epidemiology and Biostatistics, as well as data management experts from the Johns Hopkins Welch Medical Library, will provide insights into what they learned conceptually from pitfalls in scientific reasoning during their careers in science. Students apply these concepts to their disciplinary context, formulate recommendations for improvement, and critique each other’s rationales. The R3easoning module is designed as an all-online, staged case study approach on basic error analysis in data science practice. Due to the module’s subdivision into thematic units, either the entire R3easoning module or portions can be flexibly integrated into a variety of data science programs, depending on curricular space. The R3easoning module will be piloted and tested in a large enrollment, graduate level, online course on statistical reasoning at the JHSPH. The course serves graduate students across a variety of biomedical and public health sciences. This setting provides a unique chance for course participants to broaden their research skills, communicate, and collaborate across disciplinary boundaries. The R3easoning module, which will be made freely available after revision and initial validation, could be used by educators and research practitioners at several levels of their development at other institutions to test whether differences in understanding and practice skills are measurable. Results from the work proposed here could serve as a basis for future, long-term and larger-scale follow-up studies across institutions and learner populations.