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论文编号：	14806
作者编号：	1120180968
上传时间：	2024/6/11 23:22:53
中文题目：	开放科学环境下社会科学数据共享模式研究
英文题目：	Research on Social Science Data Sharing Model in Open Scientific Environment
指导老师：	柯平
中文关键字：	开放科学；社会科学；科学数据；数据共享；共享模式
英文关键字：	Open Science; Social Science; Scientific Data; Data Sharing; Sharing
中文摘要：	进入21世纪以来，开放科学理念在全球范围内快速渗透并深刻塑造科研生态环境，成为全球共识并驱动科研模式转型。国际组织和各国政府积极推行开放科学政策，如我国于2021年发起成立开放科学国际创新联盟并发布“开放科学实践北京倡议”，并在党的二十大报告中明确指出建设全球领先的开放创新体系，并将其纳入《中华人民共和国科学技术进步法》的战略发展目标。同年，联合国教科文组织第41届大会通过《开放科学建议书》，进一步强化了开放科学作为全球科研新规范的地位。开放科学秉持的自由、开放、合作、共享原则，不仅革新了科学研究与发现的方法论，而且深刻重构了全球科研架构，对科技创新、经济发展乃至全球治理产生了深远的影响。随着第四科研范式“数据密集型科学”和第五科研范式“人工智能驱动科学”的演进，特别是在社会科学领域，大数据与数据科学的勃兴促进了跨学科融合，突破了传统学科边界，推动社会科学步入全面、系统的研究新阶段，同时也对社会科学数据共享提出了更高要求和全新挑战，包括但不限于数据共享范围的扩大、服务模式的革新和应用标准的确立。 尽管数据共享的重要性广受认同，但在具体实践中，社会科学数据共享仍然面临诸多难题，例如定性数据价值未得到有效挖掘、数据孤岛现象严重、数据标准化程度不足、隐私保护机制不健全以及权益归属不明晰等。鉴于当前社会科学研究对高质量数据共享的迫切需求以及现存共享困境，本研究旨在深度剖析社会科学数据共享的核心机制及其内在运行逻辑，重点关注开放科学环境下数据共享的必要条件、参与主体、影响因素、共享途径和过程机制，并尝试构建一套适应开放科学环境的社会科学数据共享模式。通过这些努力，本研究旨在为我国乃至全球社会科学数据共享的理论建构与实践策略提供有力支持和前瞻性指导。 具体而言，本研究的主要研究工作与结论包括： 第一，通过系统梳理社会环境、制度环境、技术环境和理念环境，详细阐述了开放科学背景下社会科学数据共享的共享前提—社会科学数据共享意愿与需求、共享基础—社会科学数据、共享保障—政策资金与技术保障、共享支撑—基础设施建设、共享关键—平台运营与评价激励以及共享手段—共享过程控制等。同时，对共享主体——包括数据生产者、所有者、管理者、服务者和使用者等多元角色，以及各主体间复杂的利益权责关系进行了深度解析，明确了他们在数据共享过程中的差异化功能定位。 第二，研究从动机、机会和能力三个层面识别并剖析了影响社会科学数据共享的关键因素，发现社会科学数据共享动机层面的预期性能与职业收益、努力与感知努力、关系与存储信任影响，机会层面的组织、社会、职业风险与共享便利条件影响，能力层面的认知经验与技能、个人伦理与价值观影响，并深入剖析了不同影响因素对社会科学数据共享过程的具体作用机理。 第三，研究系统梳理了开放科学环境下社会科学数据共享的多种方式，不仅探讨了国际、国内、机构和个体等不同协作的数据共享，分析了政府、学术界和市场等不同主导共享，论述了完全、部分和受限等不同层级共享，原始数据、处理数据、分析结果等不同阶段共享，公益、利益和交换等不同动机驱动等数据共享途径，以及数据平台、存储库和出版物等不同路径共享。 第四，基于生命周期理论，本研究构建了社会科学数据共享的全过程模型，将整个共享过程划分为前期准备（包括规划、生成、收集、处理阶段）、共享实施（包含直接共享、提交审核、存储管理、同行评审、出版发布等环节）以及后期重用（涉及访问、引用、反馈和评估等步骤）阶段，系统搭建了社会科学数据共享的全链条流程。 第五，综合上述研究成果，本研究构建了开放科学环境下社会科学数据共享模式，揭示了开放科学全方位影响和推动社会科学数据共享的动态过程，强调了共享条件的重要性及其在拓展共享广度与深度中的关键作用，阐明了社会科学领域研究者在开放科学环境中承担的多重角色及其作为共享者的中心地位，以及如何围绕社会科学数据的多元内容，将数据共享融入科学研究的各个阶段和关键环节之中。 第六，基于实证数据分析，本研究指出了开放科学环境下社会科学数据共享面临的意愿与认知障碍、技能缺失、制度与设施不足等问题，并结合影响因素和已构建的共享模式，从文化氛围营造、能力提升、制度保障强化、设施完善、机制改进和战略协同等多个层面，提出了有针对性的促进策略，为我国乃至全球范围内开放科学环境下社会科学数据共享的实践与政策制定提供理论支撑与实践启示。
英文摘要：	Since the beginning of the 21st century, the concept of open science has rapidly penetrated and profoundly shaped the global research ecosystem, becoming a global consensus and driving the transformation of research models. International organizations and governments worldwide have actively implemented open science policies. For example, China launched the International Innovation Alliance for Open Science in 2021 and issued the "Beijing Initiative for the Practice of Open Science," explicitly stating in the report of the 20th National Congress of the Communist Party of China the construction of a globally leading open innovation system, incorporating it into the strategic development goals of the "Science and Technology Progress Law of the People's Republic of China." In the same year, UNESCO's 41st General Conference passed the "Recommendation on Open Science," further strengthening the status of open science as a new global norm for scientific research. Open science, adhering to the principles of freedom, openness, cooperation, and sharing, not only innovated the methodologies of scientific research and discovery but also profoundly restructured the global scientific research framework, impacting technological innovation, economic development, and even global governance. With the evolution of the fourth scientific paradigm of "data-intensive science" and the fifth paradigm of "artificial intelligence-driven science," especially in the social sciences, the rise of big data and data science has promoted interdisciplinary integration, breaking traditional disciplinary boundaries and pushing social science into a comprehensive, systematic new stage of research. This also poses higher demands and new challenges for social science data sharing, including but not limited to expanding the scope of data sharing, innovating service models, and establishing application standards. Despite widespread recognition of the importance of data sharing, there are still many challenges in its practical implementation in the social sciences, such as the underutilization of qualitative data, severe data silo issues, insufficient data standardization, inadequate privacy protection mechanisms, and unclear data ownership. Given the urgent need for high-quality data sharing in current social science research and the existing challenges, this study aims to deeply analyze the core mechanisms and intrinsic logic of social science data sharing, focusing on the necessary conditions, participants, influencing factors, sharing methods, and process mechanisms under the open science environment, and attempts to construct a social science data sharing model adapted to the open science environment. Through these efforts, this study aims to provide robust support and forward-looking guidance for the theoretical construction and practical strategies of social science data sharing in China and globally. Specifically, the main research work and conclusions of this study include: First, by systematically combing through the social, institutional, technological, and conceptual environments, the study elaborately explains the prerequisites for social science data sharing under the open science background—including the willingness and demand for data sharing, the foundation of social science data, the guarantee of policy funding and technology, the support of infrastructure construction, the key of platform operation and evaluation incentives, and the means of process control. It also deeply analyzes the diverse roles of data producers, owners, managers, service providers, and users, as well as the complex interest and responsibility relationships among them, clarifying their differentiated functional positioning in the data sharing process. Second, the study identifies and analyzes key factors influencing social science data sharing from the perspectives of motivation, opportunity, and capability, finding that anticipated performance and professional gains, effort and perceived effort, relationships and trust in storage affect the motivation level; organizational, social, professional risks, and conditions for sharing convenience influence the opportunity level; cognitive experience and skills, personal ethics, and values impact the capability level. It also delves into the specific mechanisms through which different factors affect the social science data sharing process. Third, the study systematically combs through various methods of social science data sharing in the open science environment, not only discussing international, domestic, institutional, and individual data sharing collaboration but also analyzing government-led, academic-led, and market-led sharing, discussing full, partial, and restricted sharing levels, sharing of raw data, processed data, and analysis results, and different motives such as public interest, profit, and exchange-driven sharing, as well as different paths like data platforms, repositories, and publications. Fourth, based on lifecycle theory, this study constructs a full-process model of social science data sharing, dividing the entire sharing process into preliminary preparation (including planning, generating, collecting, processing stages), implementation of sharing (involving direct sharing, submission for review, storage management, peer review, publishing, etc.), and post-sharing reuse (involving access, citation, feedback, and evaluation steps), systematically building a complete chain of social science data sharing processes. Fifth, synthesizing the research findings, this study constructs a social science data sharing model in the open science environment, revealing the dynamic process by which open science comprehensively influences and promotes social science data sharing. It emphasizes the importance of sharing conditions and their key role in expanding the breadth and depth of sharing, clarifies the multiple roles of social science researchers in the open science environment and their central position as sharers, and how to integrate data sharing into various stages and key aspects of scientific research around the diverse content of social science data. Sixth, based on empirical data analysis, this study identifies challenges faced by social science data sharing in an open science context, such as willingness and perception barriers, skills shortages, and insufficient institutions and facilities. It also outlines targeted promotion strategies from multiple aspects, including cultivating a culture, enhancing capabilities, strengthening institutional guarantees, improving facilities, refining mechanisms, and strategic collaboration, based on the influencing factors and the sharing models already established. These strategies provide theoretical support and practical insights for the practice and policy-making of social science data sharing in open science environments, both in China and globally.
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