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措施，都取决于 科学研究的严谨性、可重复性和透明度（RRT）。缺乏RRT的原因包括 科学方案的沟通不完整，科学方案中未识别的差异，未披露或 不受控制的混杂因素，设计不良的研究，以及统计学的无意误用 接近。除了这些原因之外，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缺乏严谨性、可重复性和 尤其是透明度，因为科学过程的很大一部分完全没有记录。在 由于在数据生命周期中缺乏强有力的数据管理和元数据记录， 实验可能是完全不可复制的。在缺乏强有力的数据管理和元数据的情况下， 在数据生命周期中记录，一组独立的研究人员适当重用的能力， 最后的数据肯定是不存在的。因此，我们建议创建一套培训模块， 专注于公平（可查找，可扩展，可互操作，可重用）数据原则，以教育研究人员 在研究数据生命周期中，所有职业级别的人都可以了解这些可能影响RRT的问题。我们特别 提出10个补充模块，介绍基本的数据管理实践，并解释如何 在这些实践中用具体的例子来实施FAIR数据原则。虽然是免费的，但每个 模块可以单独进行，使研究人员能够以自己想要的速度自学。我们将评估 每个模块的内容有效性，表面有效性和教育价值，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，我们将加强模块， 额外的在线教学内容，包括辅导阅读列表和自我评估测验。我们的团队 将广泛传播教学材料，利用我们的经验和资源， 共享在线教育内容，我们承诺在公开的门户网站上维护这些材料 对最终用户来说是免费的。通过进一步扩展和解释支持RRT原则的主题， 通过说明和促进最高水平的科学完整性和严谨性， 科学的行为我们通过“培训下一代”为NIGMS的使命做出了具体贡献 科学工作者的多样性和发展研究能力 在全国"

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."