In the digital economy era, the efficient, secure, and compliant circulation of cross-border data flow has become a key issue for the coordination of global industrial chains and the deepening of regional cooperation. It is a driving force for the high-quality development of the global digital economy. Currently, cross-border data flow is confronted with multiple challenges, including the interweaving of driving forces and contradictions, inadequate adaptation between mechanisms and technologies, and poor connection between compliance requirements and practical implementation. There is an urgent need to formulate systematic solutions from both theoretical and practical perspectives. To this end, this journal has invited five experts from universities and enterprises to organize a roundtable discussion on the complete logical chain of "the underlying logic, mechanism construction, trend prediction, compliance governance, and scenario-based implementation of cross-border data flow". The key viewpoints are as follows: 1) Dynamic Mechanism and Governance Logic of Cross-border Data Flow: Cross-border data flow is jointly driven by three major forces: economic interests, technological innovation, and international cooperation. Meanwhile, it faces core contradictions including the trade-off between sovereign security and flow efficiency, fragmentation of rules and institutional coordination, and technological balance and the digital divide. It is necessary to establish a governance philosophy of "dynamic balance" and build a multilateral co-governance system through three types of tools-algorithm-based supervision, technology empowerment, and institutional experimentation-to promote the shift from "fragmented rule-based games" to "systematic coordination". 2) Construction of a Collaborative Mechanism for Cross-border Data Flow: The mechanism for cross-border data flow needs to break through the limitations of a single dimension and form a multi-dimensional collaborative system integrating "policy, technology, and industry". At the policy level, regulatory sandbox pilots, standard mutual recognition, and compliance infrastructure sharing are adopted to address regulatory barriers. At the technical level, scenario-specific needs are met based on a maturity gradient, and the integrated innovation of "technology + management" is promoted. At the industry level, the self-regulatory role of professional fields such as library and information science (LIS) is leveraged to compensate for the rigidity of policies and build a closed-loop governance structure. 3) Trend Evolution and Risk Resilience of Cross-border Data Flow: In the next 3 to 5 years, cross-border data flow will exhibit characteristics of structural growth and domain differentiation. Smart manufacturing and digital trade will drive growth on a large scale, while smart healthcare and modern agriculture will emerge as core sectors. It is imperative to address bottlenecks in infrastructure upgrading and the impact of "black swan" events, establish a risk resilience system from technical, governance and strategic dimensions, and promote service model innovation in LIS as well as advance layout in the agricultural sector. 4) Compliance Governance and China's Path for Cross-border Data Flow: China has established a hierarchical and classified governance framework centered on three fundamental laws, and explored practical paths through institutional innovations such as the negative list system in free trade pilot zones. To tackle challenges including discrepancies in legal compliance requirements, technical barriers, and the complexity of regulatory coordination, it is necessary to strengthen legal synergy and rule mutual recognition, advance infrastructure construction and technological innovation, and improve the compliance service support system, thereby forming a China-specific path that balances security and controllability with high efficiency and convenience. 5) Practice of Cross-border Data Circulation and Credit Product Mutual Recognition: Cross-border data circulation lays a core foundation for the cross-border mutual recognition of credit products, which holds significant strategic value for promoting the facilitation of international trade and supporting the international development of enterprises. Currently, it faces challenges such as data security compliance, standard discrepancies, and high technical costs. To advance the implementation of cross-border mutual recognition of credit products, efforts should be made to improve the legal and regulatory framework and standard system, strengthen the construction of technical infrastructure, deepen international cooperation and mutual recognition mechanisms, and cultivate international credit service institutions.

[Purpose/Significance] From the perspective of value chain collaboration, a trusted data space system adapted to the characteristics of smart library scenarios is constructed, aiming to solve the systematic problems such as fragmented cross-domain integration, a lack of trusted guarantee, and inefficient value transformation in current library data governance. The study will contribute to improving the theoretical framework for governing library data. It also provides practical guidance on balancing the contradiction between data circulation and security, as well as on releasing the operational value of data elements. This helps smart libraries to strengthen their core functions in terms of public cultural service provision and knowledge empowerment. [Method/Process] Adopting a public value approach, we analyzed the coupling logic and value dimension of technical collaboration, rights and responsibilities, and scenario adaptation in the value chain links, as well as the hierarchical improvement laws of the data, knowledge, service and ecosystem layers. This was based on clarifying the four core elements of the trusted data space of smart libraries: data, subject, technology and system. We also examined the characteristics of trusted collaboration and value progression. The collaborative optimization process was examined in conjunctionwas with the links between the various stages of the data lifecycle. The path of expansion for the cross-chain ecosystem was constructed through collaboration between libraries, industry links, and social empowerment. We ensure a high degree of compatibility with the scene requirements of smart libraries. [Results/Conclusions] The trusted data space system of smart libraries consolidates the foundation of data trustworthiness through technological integration, activates the efficiency of the value network through the collaboration of subjects, consolidates the basis of operation guarantee through institutional norms, and extends the coverage boundary of services through value transformation, thus forming a governance pattern of four-dimensional interaction among technology, subjects, systems, and values. Based on this, four collaborative strategies, namely ecological niche reconstruction, capability leap, dynamic risk governance and value closed loop, are proposed. These strategies ultimately facilitate a systematic transition from the aggregation of data resources to the co-creation of ecological value. In the future, the element configuration and collaborative mechanism of the trusted data space can be optimized in combination with the service positioning of different libraries. The goal can be achieved through pilot construction, which will allow us to collect practical data, verify the system's feasibility and effectiveness, and explore the integrated application path of AI large models and trusted data spaces.

[Purpose/Significance] The digital characteristic collections of libraries are facing significant challenges in terms of data circulation and value, which greatly limits their potential utility. To address these issues, this study proposes to establish a trusted data space specifically designed for the digital special collections of libraries. The main objective is to reduce the costs related to trust and promote the full utilization of its multi-dimensional value in areas such as cultural heritage protection, academic research, industrial innovation, and social education. By creating a secure and interoperable environment for data sharing, the plan aims to transform the way digital special collections are managed, accessed and utilized, thereby enhancing their contribution to broader social goals. [Method/Process] This study centers on the trusted data space to explore the cross-domain circulation and value release mechanism of digital specials. It aims to build a dedicated and trusted data space for libraries, break down data barriers, and activate multi-dimensional value. The investigation follows a structured approach centered on requirements analysis, framework construction and strategy formulation. This research is based on the concept and technical foundation of the trusted data space, taking into account the unique attributes and sharing requirements of digital special collections. A comprehensive theoretical framework has been developed and centers around three core capability streams: resource interaction, trusted governance, and value co-creation. These flows are supported by a five-layer architecture model: infrastructure, data interaction, data elementization, intelligent services, and value realization. To illustrate the practical application of this framework, typical usage scenarios were analyzed to demonstrate how special collected data can be transformed from raw resources into valuable assets, and the characteristics and key tasks of specific stages were examined in detail. In addition, a multi-faceted implementation strategy has been proposed to address real-world challenges, including stakeholder reluctance, technological heterogeneity, and conflicts in rights management. These strategies emphasize the development of intelligent resources, the integration of multi-modal and heterogeneous technologies, policy incentive mechanisms, and the establishment of a sound data element market. [Results/Conclusions] The trusted data space proposed in this paper provides a systematic and effective solution for the trusted circulation and efficient utilization of cultural data. It transforms digital characteristic collections into open and reusable assets, thereby significantly enhancing the quality and scope of public cultural services. This development is in line with and supports the national strategic goals of building a "cultural power" and a "Digital China". Looking ahead, future research should prioritize the shift from theoretical conceptualization to practical implementation. This includes integrating technical solutions with actual service workflows and clarifying the unique role of libraries in the broader data ecosystem. To ensure long-term success and influence, key challenges such as sustainable business models and scientific and reasonable evaluation mechanisms must be addressed.

[Purpose/Significance] Rural cultural memory is an important component of social memory. It represents a collection of cultural memories related to villages, village histories, and village customs within specific rural spatial-temporal contexts. In the context of digital-intelligence development, the digital-intelligent transmission of rural cultural memory can promote the protection, revitalization, and utilization of rural cultural resources. This study focuses on how intelligent data can empower the digital-intelligent inheritance of rural cultural memory. It reviews construction projects in the fields of rural memory initiatives and cultural heritage, and proposes paths for leveraging intelligent data to facilitate the digital-intelligent inheritance of rural cultural memory from the perspectives of resource, technology, and service. [Method/Process] The research classifies rural memory and rural digital memory, summarizes the smart data studies in the field of culture heritage, investigates and analyzes the current status of representative rural cultural projects and cultural heritage construction projects from the perspectives of resources, technologies and services. At the resource level, multimodal and high-value rural cultural resources and their associated data are aggregated, with wide-ranging sources and diverse data formats. At the technology level, technical support is provided to achieve the integration and correlation of multimodal data. At the service level, the intelligent platform offers multi-scenario services, such as data acquisition, data correlation analysis, and data crowdsourcing. The practical experience of intelligent cultural heritage projects, along with the concept of intelligent cultural heritage data, provides methodological insights and reference paths for the resource construction, technology application, and service implementation in the digital-intelligent inheritance of rural cultural memory. [Results/Conclusions] Smart data provide new concepts of resource integration, new technology application and intelligent service for the inheritance of rural cultural memory. Existing cultural heritage intelligent projects provide approaches for the digital-intelligent inheritance of rural cultural memory. Finally, this study proposes paths for smart data empowering digital-intelligent inheritance of cultural memory from the perspectives of data resource construction, technological innovation, and service philosophy. At the resource level, multiple stakeholders are coordinated to integrate high-quality data resources. At the technology level, efforts should focus on phased objectives and technology aggregation to unlock the value of rural cultural memory. At the service level, the construction of an intelligent service space for rural cultural memory is recommended to address diverse needs. In the future, the digital-intelligent inheritance of rural cultural memory should align with the characteristics of rural cultural resources to construct interoperable smart data models. This will enable the high-level integration and interconnection of digital rural cultural resources. It will foster a model in which digital intelligent technologies and the utilization of rural cultural resources integrate and reinforce each other mutually.

[Purpose/Significance] As an emerging technology, the use of artificial intelligence-generated content (AIGC) tools is comprehensively influenced by factors such as individuals, tasks, and tools themselves. From an educational perspective, one effective way to influence user behavior is to improve the outcomes of graduate students' use of AIGC tools. This study aims to reveal the key dimensions and influencing factors of AIGC use by analyzing graduate students' spontaneous behaviors when using AIGC tools. It further seeks to improve the application efficiency of AIGC in graduate students' learning and scientific research, and promote deeper integration between tools and academic activities. [Method/Process] The research follows the logic of "from the spontaneous behavior of users to the active guidance of educators", mainly adopting the semi-structured interview method to collect data, and the thematic analysis method to analyze data. Semi-structured interviews were conducted with 25 graduate students from Chinese universities or scientific research institutions. The interviewees included 14 master's students and 11 doctoral students, covering three disciplinary categories: natural sciences (11 students), social sciences (10 students), and humanities (4 students). According to thematic analysis, the interview data were coded, and theoretical saturation was tested. On this basis, a theoretical model of the outcome and its influencing factors of graduate students' use of AIGC tools was constructed, and targeted suggestions were put forward from the perspective of information literacy education. [Results/Conclusions] The use outcome of graduate students' AIGC tool use includes three dimensions: task completion, subjective satisfaction, and process harvest. Its influencing factors involve four aspects: task & situation, personal characteristics, behavioral process, and tool characteristics. 1) task & situation: The use outcome is affected by the matching degree between task demands and application scenarios; 2) personal characteristics: The use outcome is influenced by graduate students' own basic abilities, subjective attitudes, and tool operation skills; 3) behavioral process: The use outcome is significantly impacted by the input of instructions to tools and the provided content; 4) tool characteristics: The use outcome is notably affected by tools' technical functions and operational limitations. Regarding AIGC tool-related education, it is suggested that information literacy educators emphasize the application scenarios of tools, improve the comprehensive ability of graduate students, carry out diversified teaching and training, and pay attention to the dynamics of tool and technology. This study still has some limitations. For instance, it has only identified the dimensions and influencing factors of graduate students' AIGC tool use outcome. Future research will further explore the causal pathways involved in the model through empirical studies.

[Purpose/Significance] The formulation of evidence-based science and technology policy critically relies on the timely and accurate provision of relevant, high-quality evidence. However, current evidence recommendation practices often suffer from significant limitations in both accuracy and efficiency, hindering the scientific rigor and intelligent application of evidence within the policy-making process. These shortcomings hinder policymakers' ability to leverage the most pertinent research and data, potentially leading to suboptimal decisions. Addressing this critical gap, this research proposes a novel knowledge graph-based evidence recommendation method. The primary objective is to substantially enhance the scientific foundation and intelligent capabilities of evidence utilization during policy formulation. This method aims to empower policymakers by providing more reliable, contextually relevant, and efficiently retrieved data support. Ultimately this will foster more robust, transparent, and demonstrably effective science and technology policies grounded in comprehensive research insights. [Method/Process] To achieve these objectives, this study systematically constructs a domain-specific knowledge graph meticulously centered on the intricate citation relationships between policy documents and academic research papers. This graph serves as the foundational semantic network representing entities (policies, articles, topics, authors, institute etc.) and their multifaceted interconnections. Most importantly, we introduce and adapt the Knowledge Graph Attention Network (KGAT) algorithm n an innovative way. Leveraging KGAT's sophisticated graph attention mechanisms, our model effectively captures and learns complex, high-order semantic relationships between policy requirements (represented as queries or specific nodes) and potential evidence sources (research paper nodes). This deep relational understanding enables nuanced evidence relevance scoring and personalized recommendation. To rigorously validate the proposed method's practical efficacy and performance, we conducted an extensive empirical study within the specific domain of agricultural science and technology policy. Furthermore, to demonstrate real-world applicability and provide a tangible tool for policymakers, we designed and implemented a fully functional Evidence Intelligent Recommendation System (EIRS). This system seamlessly integrates the core KG-based recommendation engine and incorporates advanced intelligent analysis capabilities. Significantly, EIRS supports an end-to-end workflow initiated by natural language policy questions posed by users, enabling intuitive interaction and precise, demand-driven evidence retrieval and recommendation. [Results/Conclusions] Experimental results, conducted on real-world datasets within the agricultural science and technology policy domain, demonstrate the superior performance of the proposed KGAT-based recommendation method. It consistently outperforms several state-of-the-art baseline algorithms across multiple key evaluation metrics, including precision, recall, normalized discounted cumulative gain (NDCG), and mean reciprocal rank (MRR). This quantitatively confirms its significantly stronger recommendation capability. In addition to quantitative metrics, the model inherently offers enhanced explainability due to the transparent nature of the knowledge graph structure and the attention weights learned by KGAT, allowing for insights into why specific evidence is recommended, based on its semantic connections to the policy query. Concurrently, the implemented EIRS has proven to be highly effective in practice. It efficiently identifies and recommends evidence resources exhibiting a strong match with complex policy requirements expressed in natural language. The system's successful deployment underscores its potential to tangibly augment the scientific underpinning of science and technology policy development. By effectively bridging the gap between vast research knowledge and specific policy needs through intelligent, accurate, and explainable recommendations, this research provides a novel, practical pathway towards realizing truly intelligent and rigorously evidence-based policy formulation processes. The methodology and system prototype offer a valuable and adaptable framework for various policy domains beyond the presented case study.