[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.
Yu Huiping
,
Guo Shaokun
,
Li Jun
. Research on Trust Evaluation Elements for Open Science Data Reuse Based on ELM Model[J]. Library and Information Service, 2025
, 69(20)
: 110
-119
.
DOI: 10.13266/j.issn.0252-3116.2025.20.009
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