【摘要】 科学数据支持了科技论文研究过程、结果和结论，科学数据的质量势必影响到科技论文的质量，进而影响到科技期刊的内容质量。但是，目前稿件评审流程中，并没有将关联数据质量评审纳入论文稿件质量评审的必需环节。本文基于《全球变化科学研究数据出版系统》（中英文）与地学期刊合作出版论文关联原创数据的质量评审实践，从论文中的数据和术语、模型算法、图表3个方面，进行了关联数据质量评审提升科技论文质量的多维度分析。实践表明，关联数据质量评审可以有效提升科技论文质量，体现在：1）提升论文中数据的科学性和准确性；2）使论文术语表述准确而严谨；3）完善论文模型和算法，实现研究过程的可重复；4）使论文的图表能够正确表达。本研究建议将关联数据质量的评审纳入科技论文稿件评审的必需环节。更多还原

【Abstract】 Scientific data supports the research process,results,and conclusions of the scientific papers,and its quality inevitably affects the quality of both the papers and the journals.However,in the current process of sci-tech journal publishing,data quality review has not yet been incorporated as a mandatory step in the peer review of scientific papers.Based on the practice of data quality peer review during the collaboration between the Global Change Research Data Publishing & Repository and geoscience journals,we conducted a multidimensional analysis of how associated data quality review can improve the quality of scientific papers.We propose that data quality peer review is an indispensable part of paper review,enhancing paper quality in four aspects:terminology,data,methods,figures,and tables.Specifically,data quality peer review improves the scientific accuracy and reliability of the data,ensures precise and rigorous terminology,refines models and algorithms to support reproducibility,and guarantees the correct presentation of figures and tables.We recommend that data quality peer review be adopted as a necessary component of the peer review process for scientific papers.更多还原