Subjects: Library Science，Information Science >> Library Science submitted time 2023-10-08 Cooperative journals: 《知识管理论坛》
Abstract： [Purpose/significance] This paper aims to provide an effective control approach and method for the data quality control in the scientific research project cycle of universities. [Method/process] It built a data quality and quality control architecture system around the scientific research project cycle and the data quality control cycle, and implemented data quality control from the perspectives of cognition, management, and process under this system, and introduced quality gap models and companies. Control methods such as architecture model and process analysis analyzed the data quality control mechanism in the scientific research project cycle of universities. [Result/conclusion] A scientific research data quality control architecture system and a data quality control model suitable for the scientific research project cycle were established, which provided theoretical support for the quality control of scientific research data in universities.
Subjects: Library Science，Information Science >> Information Science submitted time 2023-04-01 Cooperative journals: 《图书情报工作》
Abstract： [Purpose/significance] Scientific research communities are important knowledge groups in contemporary science. Studying the evolutionary characteristics of scientific research communities is of great significance for exploring the law of field development and promoting knowledge innovation.[Method/process] This article took the field of Library and Information Science (LIS) as an example. From the perspective of evolutionary event detection, this paper used the Leiden algorithm to detect scientific research communities, and constructed their evolution paths and evolution trees. On this basis, this paper identified the evolution events of scientific research communities, and revealed the evolution modes and evolution characteristics of scientific research communities from three aspects:the overall analysis of the evolution, the evolution paths and the characteristics of the evolution trees, and the statistical characteristics of group evolution events.[Result/conclusion] The research shows that the scale of scientific research communities is developing vigorously, and the evolution trees of scientific research communities present two evolution modes. Most of the evolutionary events of growth type occurred in large communities with a relatively high volume of posts, while both ‘form’ and ‘dissovle’ evolution events occurred in small communities with a relatively high volume of posts. The average community size of evolutionary events such as ‘merge’, ‘partial merge’, ‘split’, and ‘shrink’ is small, and the volume of publications is low. These characteristics further prove that the cooperation and exchanges between scientific research communities tend to be frequent, and the evolution of scientific research communities has become increasingly complex.
Subjects: Library Science，Information Science >> Information Science submitted time 2023-04-01 Cooperative journals: 《图书情报工作》
Abstract： [Purpose/significance] The behavior of academic literature downloading is an essential step in the process of academic retrieval. Predicting download behavior of academic literature is conducive to the in-depth understanding of the retrieval behavior of researchers, and provides a basis for optimizing retrieval results of academic resource retrieval platforms and restructuring ranking, to improve the retrieval function and service quality of retrieval system.[Method/process] This paper constructed a multi-dimensional feature system of researchers' academic literature download behavior, and proposed two sub-classifiers based on query relevance and user behavior respectively relying on machine learning algorithms. A weighted strategy was adopted to construct a hybrid model of download behavior prediction of academic literature.[Result/conclusion] The experiment results show that the Random Forest algorithm achieves the best performance in both classifiers. Compared to the model trained with only query relevance features, the accuracy of the hybrid model is increased by 2.3%, and the F1 value is increased by 1.3%. The sub-classifiers based on user behavior have higher weights in the hybrid model. "downloads" "whether professional/advanced search is used"and "published time" make a significant contribution to the academic literature download prediction task.
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