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

人类健康的进步，从基础科学到人类健康干预，取决于 科学研究的严谨性、重复性和透明度(RRT)。缺乏RRT的原因包括 科学方案沟通不完整、科学方案差异不明、未披露或 不受控制的混杂因素，设计不佳的研究，以及对统计学的无意误用 接近了。除了这些原因外，缺乏明确的数据管理做法和元数据 记录这些实践极大地加剧了不充分的严谨性、可重复性和 尤其是通过将科学过程的很大一部分完全留在没有文件记录的情况下实现透明度。在 缺乏强大的数据管理和在数据生命周期期间记录的元数据，最终数据来自 实验可能是完全不可重现的。在缺乏强大的数据管理和元数据的情况下 在数据生命周期中记录的，一组独立的研究人员适当重用和 可以肯定的是，最终数据变得不存在。因此，我们提议创建一套培训模块 重点关注公平(可查找、可访问、可互操作、可重复使用)数据原则，以教育研究人员 在研究数据生命周期中，所有职业水平都会影响RRT。我们特别指出 推荐10个补充模块，介绍基本的数据管理实践，并解释如何 在这些实践中实施公平数据原则，并提供具体的例子。虽然是免费的，但每个人 模块可以单独学习，允许研究人员以他们想要的速度自学。我们将评估 每个模块的内容效度、表面效度和教育价值通过咨询统计专家， 有经验的实验室/临床研究人员，以及职业生涯早期的研究人员。最后，我们将使用以下内容加强模块 其他在线教学内容，包括教程阅读清单和自我评估测验。我们的团队 将广泛传播教学材料，利用我们的经验和资源创建和 共享在线教育内容，我们承诺在一个开放的门户网站上维护这些材料 而不会给最终用户带来任何成本。通过进一步扩展和解释支持RRT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来广泛地履行国家卫生研究院的使命 科学的行为。我们特别为NIGMS的使命做出了贡献，我们通过培训下一代 在加强科学队伍的多样性和发展研究能力方面 在全国范围内都是如此。

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."