Abstract:

[Purpose/Significance] The release of the Trusted Data Space Development Action Plan (2024-2028) marks the strategic shift of data governance in China, and the trusted data space is positioned as the key infrastructure for the safe circulation and value realization of data elements. For university libraries, this national strategic deployment is not only an important opportunity to expand the service boundary, but also an inevitable requirement for their transformation from traditional document-based institutions to data-driven knowledge service institutions. At present, the research on trusted data space mostly focuses on the field of public libraries or pan-library, and research on university libraries with unique resource endowments, service traditions and institutional constraints is still scarce. This study aims to address a research gap by systematically exploring the integration of university libraries into trusted data space. It seeks to provide theoretical guidance and practical solutions for their transformation and development in the data age. [Method/Process] This study is guided by the action plan and adopts a multi-stage research approach. First, the trusted data space is systematically analyzed in four dimensions, core features, technical architecture, business activities, and governance mechanisms. Secondly, based on the public information on the official website of the library, the policy documents of the institutional repository and the publicly published literature, five representative university libraries in China and two international cases were selected for comparative analysis, and a four-dimensional analysis framework covering data resource construction, technology platform deployment, data governance mechanism and knowledge service innovation was constructed to evaluate their existing data capabilities. Thirdly, combined with the identified shortcomings, the transformation logic framework was constructed from the four dimensions of function, service, governance and value. Finally, the design includes three stages of start-up, construction and expansion, and refines seven practical paths: strategic guidance, data center governance, technology base construction, talent cultivation, service innovation, ecological coordination and value balance. [Results/Conclusions] The study found that university libraries have natural advantages in integrating into the trusted data space, including large-scale data resource aggregation capabilities, independent technology platform upgrade capabilities, scenario-based expansion of knowledge service capabilities, and institutional credibility. However, systematic ability still has four shortcomings. First, ownership of the data is unclear as it involves multiple right holders, such as authors, sponsors, libraries and database providers. Second, the standard connection is insufficient, and traditional metadata standards such as MARC and DC are difficult to adapt to data space standards such as DCAT and Schema.org. Third, the security mechanism is weak, and there is a lack of data classification, dynamic access control and the whole process audit. Fourth, the service model is limited, and the service is still mainly within the campus, lacking cross-domain expansion. In view of the above problems, this study proposed that university libraries should undergo a fundamental transformation in four areas. 1) A shift in focus from collecting resources to becoming a data hub. 2) A shift in services, from provding information to co-creating knowledge. 3) A shift in governance, from self-management to ecological synergy. 4) In terms of value orientation, realizing the dynamic balance between openness and security. The seven practical paths and the three-stage roadmap can provide operational implementation guidance for university libraries to break through the traditional service boundary, activate the potential of data elements, and deeply participate in the national data infrastructure and innovative ecological construction. Future research can empirically test and optimize the proposed framework through action research or pilot projects, and further explore the quantitative evaluation mechanism and value return model of data contribution.

Key words: the trusted data space, high-quality datasets, university library, transformation logic, practical pathway