Facultad de Ciencias Biomédicas y de la Salud, Universidad Europea de Madrid, Villaviciosa de Odón, Spain.
Internal Medicine Department, Hospital Universitario Infanta Leonor, Madrid, Spain.
BMC Med Res Methodol. 2024 Nov 16;24(1):284. doi: 10.1186/s12874-024-02404-1.
Data literacy, the ability to understand and effectively communicate with data, is crucial for researchers to interpret and validate data. However, low reproducibility in biomedical research is nowadays a significant issue, with major implications for scientific progress and the reliability of findings. Recognizing this, funding bodies such as the European Commission emphasize the importance of regular data management practices to enhance reproducibility. Establishing a standardized framework for statistical methods and data analysis is essential to minimize biases and inaccuracies. The FAIR principles (Findable, Accessible, Interoperable, Reusable) aim to enhance data interoperability and reusability, promoting transparent and ethical data practices. The study presented here aimed to train postgraduate students at the Universidad Europea de Madrid in data literacy skills and FAIR principles, assessing their application in master thesis projects. A total of 46 participants, including students and mentors, were involved in the study during the 2022-2023 academic year. Students were trained to prioritize FAIR data sources and implement Data Management Plans (DMPs) during their master's thesis. An 11-item questionnaire was developed to evaluate the FAIRness of research data, showing strong internal consistency. The study found that integrating FAIR principles into educational curricula is crucial for enhancing research reproducibility and transparency. This approach equips future researchers with essential skills for navigating a data-driven scientific environment and contributes to advancing scientific knowledge.
数据素养，即理解和有效沟通数据的能力，对于研究人员解释和验证数据至关重要。然而，如今生物医学研究的可重复性低是一个重大问题，对科学进展和研究结果的可靠性都有重大影响。认识到这一点，欧洲委员会等资助机构强调定期进行数据管理实践的重要性，以提高可重复性。建立标准化的统计方法和数据分析框架对于最小化偏差和不准确至关重要。FAIR 原则（可发现、可访问、可互操作、可重用）旨在提高数据的互操作性和可重用性，促进透明和道德的数据实践。本研究旨在培训马德里欧洲大学的研究生掌握数据素养技能和 FAIR 原则，并评估它们在硕士论文项目中的应用。在 2022-2023 学年期间，共有 46 名参与者，包括学生和导师，参与了这项研究。学生们接受了培训，以便在硕士论文中优先考虑 FAIR 数据源并实施数据管理计划 (DMP)。开发了一个包含 11 个项目的问卷来评估研究数据的 FAIRness，显示出很强的内部一致性。研究发现，将 FAIR 原则纳入教育课程对于提高研究的可重复性和透明度至关重要。这种方法使未来的研究人员具备在数据驱动的科学环境中导航的必要技能，有助于推进科学知识。
