关键词: 数据要素化, 科学数据共享, 政策分析框架, SOR理论

Abstract:

[Purpose/Significance] Against the backdrop of data becoming a key factor of production, the sharing and utilization of scientific data face significant challenges, including "market failure" and a fragmented policy landscape. Existing academic efforts often analyze policies from an isolated perspective. These efforts lack a holistic framework to understand how policies interact with multiple stakeholders to create value from data. This study aims to address this gap by constructing an integrated analytical framework for scientific data sharing policies. Its primary significance lies in providing a systematic tool to deconstruct policy architecture, dynamically reveal the internal transmission mechanism from policy intervention to value realization, and offer evidence-based insights for optimizing top-level design. This contributes to building a more efficient data governance ecosystem, ultimately enhancing the allocation efficiency of scientific resources and national innovation capacity. [Methods/Process] The research employs a mixed-method approach combining theoretical construction and empirical text analysis. Firstly, through a synthesis of literature on policy instruments, stakeholder theory, and data factorisation, a three-dimensional analytical framework encompassing "Policy Instruments, Stakeholders, and Factorisation Stages" was constructed. To animate this static structure, the Stimulus-Organism-Response (SOR) model was introduced as an overarching theoretical lens, formulating a "policy stimulus (S) → stakeholder perception/organism (O) → factorisation response (R)" dynamic mechanism. Secondly, to empirically apply and validate the framework, representative policy documents, including the national "Measures for the Management of Scientific Data" and selected local implementation rules, were chosen as cases. Using qualitative data analysis software NVivo 12, 174 relevant policy clauses were extracted. A rigorous coding process based on the three-dimensional framework was conducted independently by two researchers to ensure reliability. The inter-coder consistency was measured with Cohen's Kappa coefficient, yielding a result of 0.82, which indicates almost perfect agreement. Discrepancies were resolved through discussion and expert consultation. Finally, statistical analysis was performed on the coded data to quantify the distribution of policy attention and identify characteristic patterns. [Results/Conclusions] The study yields three sets of core findings. First, it conceptualizes the factorisation of scientific data as a three-stage transition: "Digitization" (transforming raw information into structured data), "Valorization" (enhancing data into valuable assets through processing), and "Sharization" (releasing multiplied value through circulation and reuse). Second, the quantitative analysis reveals a distinct imbalance in current policy attention allocation. Regarding policy instruments, emphasis is heavily skewed towards "Planning & Organization" (35.63%) and "Sharing & Reuse" (21.84%), while the crucial intermediate stage of "Storage & Publication" is under-supported (10.34%). Concerning stakeholders, "Sharers" (e.g., researchers) are the central focus (43.10%), whereas "Intermediators" (e.g., data centers) are relatively marginalized (23.56%). In terms of factorisation goals, policies overwhelmingly prioritize the final "Sharization" stage (71.84%), overlooking the foundational "Digitization" and "Valorization" stages. Third, the research identifies several synergistic and effective policy pathways, such as "Mandatory Submission + Standard Constraints" and "Data Processing + Talent Incentives". Based on these conclusions, the study proposes that future policy optimization should focus on rebalancing attention towards intermediate processes and intermediary actors, strengthening whole-lifecycle governance, and enhancing the synergy of policy tools. Exploring innovative governance models like data trusts is also recommended to foster a sustainable data-sharing ecosystem. A main limitation of this study is its reliance on textual analysis; future research could employ surveys or interviews to empirically validate the SOR mechanism by measuring stakeholders' actual perceptions and behavioral responses, and test the framework's applicability in other specific data domains.

Key words: data factorisation, scientific data sharing, policy analysis framework, SOR theory