随着开放科学理念的不断深入，科学数据的应用领域不断扩大，科学数据开放共享的益处逐渐显现，科学数据政策的重要引导作用也逐渐受到关注。近年来，我国政府更加重视科学数据共享，先后颁布多个数据共享政策，明确了各级行政单位、科研单位在科学数据管理、开放共享等方面的职责要求，对科学数据的汇交、保存、重用过程做出了明确规范。但与英国等科学数据领先发展国家相比，现阶段我国科学数据政策仍存在部分主体参与度不高、协同效果不好等问题，我国科学数据开放共享政策发展仍有许多提升空间。

本研究使用文献调研法、内容分析法等方法，面向中英两国政府部门、科研资助机构、高校和科研机构、数据知识库、学术出版商五类主体收集科学数据政策，并通过两国政策门户网站收集科技政策和知识产权政策，分别选取对应的检索渠道，设定相应的筛选规则，构建出本研究的政策文本库。针对政策文本，本研究使用LDA模型对两国科学数据政策中的协同内容主题随时间的发展情况进行分析，划分得出两国各自科学数据政策中协同内容发展的三个阶段。此外，本研究还挖掘了两国科学数据政策、科技政策、知识产权政策的主题特征，从科学数据FAIR原则四个目标的角度出发，探究各主体科学数据政策协同情况，探索科学数据政策与其他类型政策之间的协同关系，对比得出两国各主体、各类型政策的协同机制差异。

通过比较分析，本研究发现中英两国科学数据政策中协同内容主题在发展初期均面向学科层面，不同之处在于现阶段我国科学数据政策中协同内容更关注主体职责的明确、数据处理的规范性和科学数据重用，英国科学数据政策中协同内容更关注对科研人员的引导、科学数据技术支持、各类型机构的合作。与英国科学数据政策协同机制相比，我国科学数据政策协同机制现有的不足之处主要包括科研资助机构参与协同程度不够、数据知识库与其他主体的合作较少、高校科研机构与学术出版商的协同合作程度不深、科学数据共享的技术支持不够强等。建议未来我国科研资助机构完善政策内容，数据知识库增加与其他主体的合作，高校和科研机构加强和学术出版商的协同合作并完善科学数据共享的技术支持，进而优化我国科学数据政策协同机制，推动我国科学数据FAIR原则的实现。

As the concept of open science continues to deepen, the application fields of scientific data continue to expand, the benefits of open sharing of scientific data gradually become apparent, and the important guiding role of scientific data policy has gradually attracted attention. In recent years, the Chinese government has paid more attention to scientific data sharing and has promulgated a number of data sharing policies, clarifying the responsibilities and requirements of administrative units and scientific research units at all levels in scientific data management and open sharing, and clearly regulating the archiving, preservation and reuse of scientific data. However, compared with countries such as the UK that are leading the development of scientific data, China's scientific data policy still has problems nowadays such as low participation of some subjects and poor synergy effects. There is still much room for improvement in the development of China's open and shared scientific data policy.

This study uses literature research, content analysis and other methods to collect scientific data policies from five types of entities: government departments, scientific research funding agencies, universities and scientific research institutions, data repositories, and academic publishers in China and the United Kingdom. Science and technology policies and intellectual property policies were collected through the policy portals of the two countries, corresponding search channels were selected, and corresponding filtering rules were set to construct a policy text library for this study. Focusing on the policy text, this study uses the LDA model to analyze the development of synergy content themes in the scientific data policies of the two countries over time, and divides the development of synergy content in the scientific data policies of the two countries into three stages. In addition, this study also explores the thematic characteristics of scientific data policies, technology policies, and intellectual property policies in the two countries. Starting from the perspective of the four objectives of the FAIR principle of scientific data, it explores the synergy of scientific data policies among various entities, explores the synergistic relationship between scientific data policies and other types of policies, and compares the differences in the synergy mechanisms of various entities and types of policies in the two countries.

Through comparative analysis, this study found that the themes of synergistic content in scientific data policies between China and the UK were both oriented towards disciplinary levels in the early stages of development. The difference lies in the fact that synergistic content in China's scientific data policy focuses more on clarifying the main responsibilities, standardizing data processing, and reusing scientific data nowadays, while synergistic content in the UK's scientific data policy focuses more on guiding researchers, supporting scientific data technology, and collaborating with various types of institutions. Compared with the scientific data policy synergy mechanism in the UK, the shortcomings of China's scientific data policy synergy mechanism mainly include insufficient participation of research funding institutions in collaboration, less cooperation between data knowledge bases and other entities, insufficient collaboration between university research institutions and academic publishers, and insufficient technical support for scientific data sharing nowadays. Suggestions for future research funding institutions in China to improve policy content, increase cooperation with other entities in data knowledge bases, strengthen collaboration between universities and research institutions and academic publishers, and improve technical support for scientific data sharing, in order to optimize the synergy mechanism of scientific data policies in China and promote the implementation of the FAIR principle for scientific data.