Miller Gregor, Spiegel Elmar
Core Facility Statistical Consulting, Helmholtz Zentrum München, 85764, Neuherberg, Germany.
Sci Data. 2025 Jan 17;12(1):95. doi: 10.1038/s41597-024-04312-x.
Ensuring the integrity of research data is crucial for the accuracy and reproducibility of any data-based scientific study. This can only be achieved by establishing and implementing strict rules for the handling of research data. Essential steps for achieving high-quality data involve planning what data to gather, collecting it in the correct manner, and processing it in a robust and reproducible way. Despite its importance, a comprehensive framework detailing how to achieve data quality is currently unavailable. To address this gap, our study proposes guidelines designed to establish a reliable approach to data handling. They provide clear and practical instructions for the complete research process, including an overall data collection strategy, variable definitions, and data processing recommendations. In addition to raising awareness about potential pitfalls and establishing standardization in research data usage, the proposed guidelines serve as a reference for researchers to provide a consistent standard of data quality. Furthermore, they improve the robustness and reliability of the scientific landscape by emphasising the critical role of data quality in research.
确保研究数据的完整性对于任何基于数据的科学研究的准确性和可重复性至关重要。这只能通过建立和实施严格的研究数据处理规则来实现。获得高质量数据的基本步骤包括规划要收集的数据、以正确的方式收集数据以及以稳健且可重复的方式处理数据。尽管其很重要,但目前尚无详细说明如何实现数据质量的全面框架。为了弥补这一差距,我们的研究提出了旨在建立可靠数据处理方法的指南。它们为整个研究过程提供了清晰实用的指导,包括总体数据收集策略、变量定义和数据处理建议。除了提高对潜在陷阱的认识并在研究数据使用中建立标准化之外,拟议的指南还为研究人员提供了一个参考,以提供一致的数据质量标准。此外,它们通过强调数据质量在研究中的关键作用,提高了科学领域的稳健性和可靠性。