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

• 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
• 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

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.

摘要 在社会科学和流行病学中，对可重复研究的态度发生了翻天覆地的变化。 过去30年生殖已经从一个脚注转移到中心舞台，现在被认为是一个 科学严谨性的重要组成部分。可再生科学的概念不仅涉及到 当使用相同的数据时，复制特定研究的工作，但要复制到更大的生态系统中， 对研究进行计划、实地考察、评论和解释。系统性偏见和容易出错的研究渠道 两者都损害了可重复性，并且现在被认为阻碍了科学进步。研究的性质 随着对数据分析的日益重视，越来越多的人可以获得广泛的计算能力， 功率和大型复杂数据集。健康老龄化的行为和社会科学研究面临着特殊的挑战， 这些概念应该是衰老研究培训的组成部分。 加州大学旧金山分校弗朗西斯科社会老龄化可再生研究培训 科学和流行病学（UCSF-TRASE）计划将开发（AIM 1）强化短期课程， 可复制的研究视角和技能。我们提出了四个简短的（3天），密集的培训模块。每个 模块结合了教学法和经验学习，实质性的重点是健康差距和老龄化， 和方法论上的重点是因果推理。模块1介绍了研究的再现性概念， 人口健康和老龄化。本模块适用于科研人员和科研消费者 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假设基础科研背景 但是，例如，执业医生、科学记者、管理人员以及 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 预登记;统计编码卫生和评估;透明的报告;和文件，包括 合作项目。模块3讨论了现场原始数据收集以促进再现性， 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 关于综合证据以提高科学进步的可重复性的培训，例如，元研究， 证据三角法。每个培训模块可以单独使用，也可以组合使用， 综合技能我们强调实践技能的建设，以学习最佳实践的背景下， 当代问题。这些模块建立在现有UCSF培训的优秀基础上 计划，使用许多活动已经证明在我们的其他培训计划取得成功，并策划 为密集的短期课程形式，为参与者，跨越职业生涯阶段， 提高再现性的技术技能。我们建议（AIM 2）推出模块，并进行持续评估 目标3）使用多种传播策略，以最大限度地发挥课程的影响。