[目的/意义] 信任在开放科学数据重用中扮演着重要的驱动作用，从信息加工理论视角探索开放科学存储库数据重用信任评估要素，可以对国内陆续建设的开放数据存储库管理以及提升数据的重用率起到借鉴和推进作用。[方法/过程] 基于信息加工理论的精细加工可能性模型（ELM）构建开放科学数据重用信任评估模型，以目前科研数据聚集速度和重用最为活跃的kaggle平台的实际数据进行剖析和验证，并选择线性核函数的支持向量回归（SVR）分析和比较不同变量对开放科学数据重用信任的影响。[结果/结论] 来源特征信息和内容特征信息整体上与数据集下载量和数据集引用量存在显著正相关关系，但具体因素对两者的影响存在差异。基于分析结果，提出提升数据集质量、加强数据透明度和可追溯性、鼓励用户参与和评价、推动数据共享和交流以及建立信任评估体系等措施。
[Purpose/Significance] Trust plays an important driving role in open science data reuse. Exploring the trust evaluation elements for open science data reuse from the perspective of information processing theory can provide reference and promotion for the management of open data repositories and the improvement of data reuse rate. [Method/Process] A trust evaluation model for open science data reuse was constructed based on the information processing theory of elaboration likelihood model (ELM). The actual data from the Kaggle platform was analyzed and verified. And support vector regression (SVR) with linear kernel function was selected to analyze and compare the influence of different variables on the trust of open science data reuse. [Result/Conclusion] Source and content information show a significant positive correlation with dataset download and citation volume, but the influence of specific factors on these two differs. Based on the analysis, several measures are proposed, such as improving dataset quality, strengthening data transparency and traceability, encouraging user participation and evaluation, promoting data sharing and communication, and establishing a trust evaluation system.
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