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R3EASONING

R3推理

基本信息

批准号：
10665589
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
项目类别：
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/10665589
关键词：
Applied Research Basic Science Big Data Biological Sciences Biometry Case Study Coin Collaborations Communicable Diseases Communication Community Health Comprehension Concept Inventory Critiques Data Data Science Development Education Educational Materials Educational Models 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 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 Traction Training Validation Work career data acquisition data management design experience flexibility global health graduate student impression improved innovation insight learning outcome meetings online course pandemic disease programs research in practice research to practice science and society science education skills statistics stem teacher

项目摘要

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，科学推理的基础在生物医学科学中，文章撤回数量增加的贡献者，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时期，这种倾向可能对科学和社会有害。大数据在许多领域的作用科学在不断发展，因此，我们需要更多的从业者，在纸上解决统计问题集，但能够将这些技能转化为研究实践本提案的目标是制作和初步评估教育材料可以帮助缓解这种情况。在试点研究中，我们将制作“R3推理” 模块，一个指导性的案例研究方法，建立在良好的科学研究的三个R的基础上，实践：严谨性、再现性和责任感。该模块展示了常见在专家访谈的帮助下，分析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，以及约翰霍普金斯韦尔奇医学图书馆的数据管理专家，将提供他们从科学推理的陷阱中从概念上学到的东西的见解，他们的科学事业学生将这些概念应用到他们的学科背景中，提出改进建议，并批评对方的理由。 R3推理模块设计为一个全在线、分阶段的基本错误案例研究方法数据科学实践分析。由于模块细分为主题单元，整个推理模块或部分可以灵活地集成到各种数据科学中课程，取决于课程空间。R3easoning模块将在大招生，研究生水平，在JHSPH统计推理在线课程。过程为各种生物医学和公共卫生科学的研究生提供服务。此设置为课程参与者提供了一个独特的机会，以扩大他们的研究技能，沟通，跨学科合作 R3推理模块将在修订和初步验证后免费提供，可供教育工作者和研究工作者在其发展的几个层次上使用，其他机构来测试理解和实践技能的差异是否是可衡量的。本文提出的工作结果可以作为未来长期和更大规模的基础。对各机构和学习者群体进行后续研究。