Subjects: Library Science，Information Science >> Libraries submitted time 2024-04-18
Abstract： Purpose/Significance FAIR principle is a data management principle that guarantees the identification, traceability, sharing and reuse of scientific data, and it is of great significance for the improvement of the academic repositories quality by evaluating the application of the FAIR principle in the open data repositories for academic libraries in China. Methods/Process This study constructs a FAIR assessment framework applicable to China’s open data repositories of academic libraries based on the existing FAIR principle framework through network research, and evaluates and analyzes China’s academic libraries’ open data repositories in terms of the four dimensions, namely, finable, accessible, interoperable, and reusable. Results/Conclusions Based on the above research, this study puts forward suggestions for improving the construction of academic libraries’ open data repositories from the aspects of clarifying the repositories’ own positioning, improving data quality, interoperability of platform data, standardizing data standards, and transforming data effectiveness.
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Subjects: Library Science，Information Science >> Library Science submitted time 2023-10-08 Cooperative journals: 《知识管理论坛》
Abstract： [Purpose/Significance] Combing the relevant influencing factors of the citation frequency of a single paper and the research status of the prediction of the citation frequency, this paper provides a comprehensive and systematic cognitive framework from the perspective of the involvement of scientific researchers and scientific research institutions in such research. [Method/Process] Using the literature research method, through the systematic combing of the existing literature, this paper summarized the relevant contents and characteristics of the influencing factors, research objects and research methods of citation prediction, compared and analyzed different methods by means of list, and summarized the common problems and some innovative solutions of the existing research. [Result/Conclusion] In the process of systematic combing and summarizing, it is found that the causal relationship between influencing factors and prediction results is not clear, the research sample data is lack of diversity, the relationship between the applicability of research results and prediction cycle is not clear, and the interpretability of model evaluation is weak. Therefore, we should improve the follow-up research quality from the aspects of solving the preconditions of the problem, selecting targeted samples, improving the extraction methods of influencing factors, and using mathematical thinking mode for modeling.
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