Short Courses on the Conduct of Reproducible Aging Research with Big Data

利用大数据进行可重复衰老研究的短期课程

基本信息

批准号：
10501970
负责人：
JUNE ML CHAN
金额：
$ 15.01万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-15 至 2027-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10501970
关键词：
Active Learning Address Administrator Aging Applications Grants Attitude Behavioral Behavioral Research Benchmarking Big Data Biometry California Code Cognition Communities Complex Critiques Data Data Analyses Data Collection Data Set Data Sources Discipline Documentation Ecosystem Educational Curriculum Elderly Elements Epidemiology Evaluation Face Fostering Foundations Health Hybrids Hygiene Interdisciplinary Study Learning Learning Module Measures Mental Depression Methodology Nature Participant Persons Physicians Postdoctoral Fellow Preparation Professional Role Protocols documentation Publications Publishing Quality Control Reporting Reproducibility Research Research Design Research Personnel Research Training San Francisco Science Scientific Advances and Accomplishments Scientist Sea Series Social Sciences Technical Expertise Techniques Trainers Training Training Training Activity Training Programs Universities Work aging population behavioral/social science career complex data computing resources course module data quality disability dissemination strategy hands-on learning health disparity healthy aging journal article learning strategy population health programs racism response satisfaction self-directed learning skills social social epidemiology social integration social science research symposium tool

项目摘要

ABSTRACT A sea-change in attitudes towards reproducible research in social science and epidemiology has occurred over the past 30 years. Reproducibility has moved from a footnote to center-stage and is now recognized as an essential component of scientific rigor. The concepts of reproducible science relate not only to the capacity to reproduce the work of a specific study when using the same data, but to the larger ecosystem in which research is planned, fielded, critiqued, and interpreted. Systemic biases and error-prone research pipelines both compromise reproducibility and are now recognized to hinder scientific progress. The nature of research has also evolved, with increasing emphasis on data analysis, growing access to extensive computational power, and large, complex data sets. Behavioral and social science research on healthy aging faces special concerns for reproducibility and these concepts should be integral to training on aging research. The University of California, San Francisco Training in Reproducible Research on Aging for Social Science and Epidemiology (UCSF-TRASE) program will develop (AIM 1) intensive short courses on reproducible research perspectives and skills. We propose four brief (3-day), intensive training modules. Each module combines didactics and experiential learning, with a substantive focus on health disparities and aging, and methodologic focus on causal inference. Module 1 introduces concepts of reproducibility for research on population health and aging. This module is appropriate for researchers and consumers of scientific research and will provide critical evaluation skills relevant for reviewing journal articles and grant applications, interpreting published findings, and leading research Module 1 assumes a basic scientific research background but will be accessible to, for example, practicing physicians, science journalists, administrators, as well as graduate and post-doctoral trainees. Module 2 provides skills for implementing reproducible analyses, such as pre-registration; statistical coding hygiene and evaluation; transparent reporting; and documentation, including for collaborative projects. Module 3 addresses fielding primary data collection to foster reproducibility, considering study design, statistical power, protocol documentation, data quality control. Module 4 provides training on integrating evidence to enhance reproducibility of scientific advances, e.g., meta-research, evidence triangulation approaches. Each training module can stand alone or be combined for a more comprehensive skill set. We emphasize hands-on skills building to learn best practices in the context of contemporary problems. The modules build on the outstanding foundation in the existing UCSF training programs, using many activities already demonstrated to succeed in our other training programs, and curating for the intensive short-course format to provide participants, across career stages, with both conceptual and technical skills to enhance reproducibility. We propose (AIM 2) to roll out the modules with ongoing evaluation and refinement and (AIM 3) use multiple dissemination strategies to maximize the impact of the curriculum.

抽象的对社会科学和流行病学可重复研究的态度发生了巨大变化过去30年。可重复性已从脚注转移到中心舞台，现在被认为是科学严谨性的重要组成部分。可重复科学的概念不仅涉及重复性科学的能力使用相同的数据重现特定研究的工作，但在更大的生态系统中研究是经过计划、落实、批评和解释的。系统性偏差和容易出错的研究渠道两者都会损害可重复性，并且现在被认为会阻碍科学进步。研究的性质随着对数据分析的日益重视、对广泛计算的访问不断增加，它也在不断发展强大的能力以及大型、复杂的数据集。健康老龄化的行为和社会科学研究面临特殊对可重复性的关注和这些概念应成为衰老研究培训的一部分。加州大学旧金山分校社会老龄化可重复研究培训科学与流行病学 (UCSF-TRASE) 计划将开发 (AIM 1) 强化短期课程可重复的研究观点和技能。我们提出四个简短（为期 3 天）的强化培训模块。每个模块结合了教学法和体验式学习，重点关注健康差异和老龄化，方法论侧重于因果推理。模块 1 介绍了研究可重复性的概念人口健康和老龄化。该模块适合科学研究的研究人员和消费者并将提供与审查期刊文章和资助申请相关的关键评估技能，解释已发表的研究结果和领先的研究模块 1 假设有基本的科学研究背景但可供执业医师、科学记者、行政人员以及研究生和博士后实习生。模块 2 提供实施可重复分析的技能，例如预先登记；统计编码卫生和评估；透明的报告；和文档，包括用于合作项目。模块 3 涉及现场主要数据收集以促进可重复性，考虑研究设计、统计功效、方案文件、数据质量控制。模块 4 提供整合证据以提高科学进步的可重复性的培训，例如元研究、证据三角测量方法。每个培训模块可以单独使用，也可以组合起来以实现更多培训综合技能。我们强调实践技能的培养，以学习最佳实践当代问题。这些模块建立在 UCSF 现有培训的出色基础之上计划，利用许多已经在我们其他培训计划中取得成功的活动，并策划强化短期课程形式为跨职业阶段的参与者提供概念和技术技能，以提高可重复性。我们建议（AIM 2）推出持续评估的模块和细化（AIM 3）使用多种传播策略来最大限度地发挥课程的影响。