How to be FAIR: A Self-study Program for Integrating FAIR Principles into Best Data Management Practices

如何做到公平：将公平原则融入最佳数据管理实践的自学计划

• 批准号: 10409793
• 负责人: Kathryn Ann Kaiser
• 金额: $ 10.78万
• 依托单位: UNIVERSITY OF ALABAMA AT BIRMINGHAM
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10409793
• 关键词: Affect; Alabama; Ancillary Study; Archives; Area; Basic Science; Behavior Therapy; Biomedical Research; COVID-19; COVID-19 pandemic; Cardiovascular Diseases; Clinical; Clinical Research; Clinical Trials; Communication; Computer software; Consult; Country; Data; Data Science; Data Set; Data Sources; Development; Development Plans; Discipline; E-learning; Education; Educational Curriculum; FAIR principles; Face; Foundations; Health; Human; Individual; Instruction; Intervention; Journals; Lead; Life Cycle Stages; Medicine; Metadata; Methods; Mission; National Institute of General Medical Sciences; New England; Output; Performance; Pharmacotherapy; Plague; Practice Management; Preparation; Process; Program Evaluation; Protocols documentation; Reading; Records; Reproducibility; Research; Research Design; Research Personnel; Resources; Science; Scientist; Self Assessment; Self-Examination; Series; Software Validation; Training; Training Activity; Training Programs; United States National Institutes of Health; Universities; Work; career; cost; data cleaning; data management; data reuse; experience; experimental study; formative assessment; improved; interoperability; iterative design; laboratory experience; learning materials; mortality; next generation; novel; preservation; programs; response; senior faculty; syntax; web portal

The advancement of human health, from basic science to human health interventions, is dependent on the rigor, reproducibility, and transparency (RRT) of scientific research. Reasons for the lack of RRT include incomplete communication of scientific protocols, unidentified differences in scientific protocols, undisclosed or uncontrolled confounding factors, poorly designed studies, and unintentional misapplication of statistical approaches. In addition to these reasons, lack of clear data management practices and the metadata documenting those practices vastly exacerbates the underlying issue of inadequate rigor, reproducibility, and especially transparency by leaving a vast component of the scientific process completely undocumented. In the absence of strong data management and metadata recorded during the data life cycle, the final data from an experiment may be completely irreproducible. In the absence of strong data management and metadata recorded during the data life cycle, an independent set of researchers’ ability to reuse appropriately and confidently the final data becomes nonexistent. We therefore propose creating a set of training modules focused on the FAIR (Findable, Accessible, Interoperable, Reusable) data principles to educate researchers at all career levels about these issues in the research data life cycle that can impact RRT. We specifically propose 10 complimentary modules that present fundamental data management practices and explain how to implement FAIR data principles in those practices with specific examples. Though complimentary, each module can be taken individually allowing researchers to self-study at their desired pace. We will evaluate each module for content validity, face validity, and educational value by consulting with statistical experts, experienced lab/clinical researchers, and early career investigators. Finally, we will reinforce modules with additional online instructional content, including tutorial reading lists and self-assessment quizzes. Our team will widely disseminate the instructional materials leveraging our experience and resources creating and sharing online educational content, and we commit to maintain the materials in an openly available web portal at no cost to end users. By further expanding and explaining topics supporting principles of RRT, we contribute broadly to the mission of the NIH by illustrating and promoting the highest level of scientific integrity and rigor in the conduct of science. We specifically contribute to the mission of the NIGMS by "training the next generation of scientists, in enhancing the diversity of the scientific workforce, and in developing research capacities throughout the country."

从基础科学到人类健康干预措施的人类健康的发展取决于 科学研究的严格，可重复性和透明度（RRT）。缺乏RRT的原因包括 科学协议的不完整沟通，科学协议上未知的差异，毫无公开或 不受控制的混杂因素，设计较差的研究以及对统计的无意化误用 方法。除了这些原因，缺乏明确的数据管理实践和元数据 记录这些实践极大地加剧了严格，可重复性不足的根本问题 尤其是通过将科学过程的巨大组成部分留下完全没有证件来透明。在 在数据生命周期中记录的没有强大的数据管理和元数据，最终数据来自 实验可能完全不可培养。在没有强大的数据管理和元数据的情况下 在数据生命周期期间记录，一组独立的研究人员重复使用的能力和 自信的最终数据不存在。因此，我们建议创建一组培训模块 专注于公平（可访问，可访问，可互操作，可重复使用的）数据原则，以教育研究人员 在研究数据生命周期中，所有有关这些问题的职业水平可能会影响RRT。我们具体 提案10的免费模块，这些模块呈现基本的数据管理实践，并解释了如何 在这些实践中实施公平的数据原则，并具有特定的示例。虽然免费，但每个 可以单独采用模块，使研究人员可以按照所需的速度进行自学。我们将评估 每个模块的内容有效性，面部有效性和教育价值通过咨询统计专家， 经验丰富的实验室/临床研究人员和早期职业研究人员。最后，我们将使用 其他在线教学内容，包括教程阅读列表和自我评估测验。我们的团队 将广泛传播利用我们的经验和资源创造的教学材料和 共享在线教育内容，我们致力于在公开可用的Web门户中维护材料 最终用户无需费用。通过进一步扩展和解释RRT的支持原则的主题，我们做出了贡献 通过说明和促进最高水平的科学完整性和严谨性，从而广泛地实现了NIH的使命。 科学的行为。我们通过“训练下一代 科学家，增强科学劳动力的多样性以及发展研究能力 全国。”