R3EASONING

R3推理

• 批准号: 10453658
• 负责人: Gundula Bosch
• 金额: $ 8.71万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10453658
• 关键词: Address; Applied Research; Basic Science; Big Data; Biological Sciences; Biometry; Case Study; Coin; Communicable Diseases; Communication; Community Health; Comprehension; Concept Inventory; Critiques; Data; Data Science; Development; Education; Educational Materials; Educational process of instructing; Educational workshop; Effectiveness; Enrollment; Epidemiology; Evaluation; Evaluation Studies; Exercise; Experimental Designs; Feedback; Focus Groups; Follow-Up Studies; Future; Generations; Goals; Health; Health Sciences; Immunology; Institution; Interview; Learning Module; Measurable; Medical Libraries; Microbiology; Molecular; Names; Oral; Paper; Participant; Peer Review; Performance; Pilot Projects; Population; Problem Sets; Production; Public Health; Public Health Schools; Recommendation; Reproducibility; Research; Role; Science; Scientist; Statistical Computing; Statistical Methods; Students; Suggestion; Surveys; Testing; Time; Training; Validation; Work; base; career; data acquisition; data management; design; experience; flexibility; global health; graduate student; impression; improved; innovation; insight; learning outcome; meetings; online course; pandemic disease; pilot test; programs; research in practice; research to practice; science and society; science education; skills; statistics; stem; teacher

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.

研究实践中的错误往往源于应用能力不足 在生物医学科学中，此类错误是重大的。 导致文章撤回数量不断增加，从而也加剧了 公众对科学事业的不信任，尤其是在全球大流行时期。 大数据在许多领域的作用可能会给科学和社会带来困扰。 科学在不断发展，因此，我们需要更多的实践者，他们不仅有能力 能够在纸上解决统计问题，但能够将这些技能转化为研究 该提案的目标是制作和评估初步教育材料。 在试点研究中，我们将制作“R3easoning”，以帮助缓解这种情况。 模块，一种基于良好科学的三个 R 的指导性案例研究方法 实践：严谨性、可重复性和责任感 该模块展示了共同点。 在经验丰富的从业者的帮助下纠正数据科学领域的错误。 来自 JHSPH 分子微生物学和免疫学、流行病学和 生物统计学以及来自约翰霍普金斯韦尔奇医学图书馆的数据管理专家， 将深入了解他们在科学推理过程中从概念上学到的东西 学生将这些概念应用到他们的学科背景中，制定他们的科学生涯。 提出改进建议，并互相批评对方的理由。 R3easoning 模块被设计为针对基本错误的全在线、分阶段案例研究方法 由于该模块细分为主题单元，因此 整个R3easoning模块或部分可以灵活地集成到各种数据科学中 R3easoning 模块将在一个项目中进行试点和测试。 JHSPH 的大量招生、研究生水平统计推理在线课程。 研究生服务于各种生物医学和公共卫生科学领域。 为课程参与者提供了一个独特的机会来拓展他们的研究技能、交流、 并跨学科界限进行合作。 R3easoning 模块将在修订和初步验证后免费提供， 教育工作者和研究实践者可以在其发展的多个层面上使用 其他机构来测试理解和实践技能的差异是否可以衡量。 这里提出的工作结果可以作为未来、长期和更大规模的基础 跨机构和学习者群体的后续研究。