摘要
[目的/意义]FAIR原则在科技期刊的应用有助于增强期刊论文支撑数据的可发现、交互、共享与重用。评估FAIR实施情况有利于其推广实施,以期为我国期刊论文的支撑数据共享与重用提供有益参考。[方法/过程]本文在国外FAIR原则评估模型的基础上,综合考虑各模型的优势和指标设计特点,结合《数据分析与知识发现》期刊论文相关的科学数据特征,构建FAIR原则指标评价体系,基于该体系从4个维度分析调研结果,最后对中文期刊论文的支撑数据FAIR应用提出合理化建议与优化策略。[结果/结论]FAIR原则在期刊论文支撑数据的应用仍需进一步完善,科研人员的数据共享意识及对于FAIR原则的认知度远远不够,建议从宏观和微观两个层面推广FAIR原则及其实施,推动数据更加开放和可重用。
[Purpose/Significance]The application of FAIR principle in scientific and technological journals helps to enhance the discoverability,interaction,sharing,and reuse of journal paper supporting data.Evaluating the implementation of FAIR is beneficial for its promotion and implementation,with the aim of providing useful references for supporting data sharing and reuse in Chinese journal papers.[Method/Process]On the basis of foreign FAIR principle evaluation models,the paper comprehensively considered the advantages of each model and the design characteristics of indicators,and combined the characteristics of supporting data related to papers in“Data Analysis and Knowledge Discovery”to build a FAIR principle index evaluation system.Based on this system,the paper analyzed the survey results from four dimensions.Finally,the paper proposed rationalization suggestions and optimization strategies for the application of FAIR to the support data of Chinese journal papers.[Result/Conclusion]The application of FAIR principles in supporting data for journal papers still needs further improvement,and researchers'awareness of data sharing and understanding of FAIR principles are far from sufficient.The paper proposes to promote FAIR principles and implementation from both macro and micro levels to promote more open and reusable data.
作者
刘桂锋
王清炫
韩牧哲
Liu Guifeng;Wang Qingxuan;Han Muzhe(Scientific Information Institute,Jiangsu University,Zhenjiang 212013,China;Library,Jiangsu University,Zhenjiang 212013,China)
出处
《现代情报》
CSSCI
北大核心
2024年第2期17-29,共13页
Journal of Modern Information
基金
国家社会科学基金一般项目“科学数据融合模式设计与体系建构研究”(项目编号:21BTQ080)。
关键词
FAIR原则
期刊论文
支撑数据
数据管理
数据科学
应用评估
案例分析
FAIR principles
journal papers
supporting data
data management
data science
application evaluation
case analysis
