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：
10681414
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/10681414
关键词：
Acceleration 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 Proliferating 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 and 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再现研究的态度的态度发生了变化过去30年。可重复性已从脚注转移到中心阶段，现在被认为是科学严谨的基本组成部分。可再现科学的概念不仅与使用相同的数据时，重现特定研究的工作，但要在更大的生态系统中研究的计划，进行了，批评和解释。系统性偏见和容易出错的研究管道两者都妥协可重复性，现在被认为会阻碍科学进步。研究的本质随着对数据分析的越来越重视，也不断进化功率和大型复杂数据集。关于健康衰老的行为和社会科学研究特殊对可重复性和这些概念的关注应该是老化研究培训不可或缺的一部分。加利福尼亚大学旧金山分校的社会衰老可再现研究培训科学与流行病学（UCSF-TRASE）计划将开发（AIM 1）密集的简短课程可再现的研究观点和技能。我们提出了四个简短（3天）的强化培训模块。每个模块将教学用学和体验学习结合在一起，并实质上关注健康差异和衰老，和方法论的重点是因果推断。模块1介绍了研究的可重复性概念人口健康和衰老。该模块适合科学研究的研究人员和消费者并将提供有关审查期刊文章和赠款申请的关键评估技能，解释已发表的发现，领先的研究模块1假定基础科学研究背景但是，例如，医师，科学记者，管理员以及研究生和博士后学员。模块2提供了实施可重复分析的技能，例如预注册；统计编码卫生和评估；透明报告；和文档，包括用于协作项目。模块3地址针对主数据收集以促进可重复性，考虑研究设计，统计能力，协议文档，数据质量控制。模块4提供对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剖分方法。每个培训模块可以独立或合并更多全面的技能。我们强调动手技能建设以在当代问题。这些模块建立在现有UCSF培训的杰出基础上计划，使用已经证明在我们的其他培训计划中取得成功的许多活动，并策划为了使密集的短道格式为参与者提供跨职业阶段的参与者以及概念和提高可重复性的技术技能。我们建议（目标2）通过持续的评估推出模块和改进和（目标3）使用多种传播策略来最大程度地提高课程的影响。