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首页> 外文会议>IIAI International Conference on Advanced Applied Informatics >Deciphering the Department-Discipline Relationships within a University through Bibliometric Analysis of Publications Aided with Multivariate Techniques
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Deciphering the Department-Discipline Relationships within a University through Bibliometric Analysis of Publications Aided with Multivariate Techniques

机译：通过对多变量技术辅助的出版物分析来解读大学内的部门纪律关系

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摘要

This study explores a practical approach to decipher the department-discipline relationships between the organizational research units dedicated to natural science, technology, engineering & medical (STEM) fields and 22 disciplinary categories used in Essential Science Indicators database (ESI 22 fields), for a Japanese national university as seen in a set of peer-reviewed journal publications (articles & reviews) indexed in the Web of Science (WoS) Core Collection database for a 5-years period. The procedure involved several steps such as (i) identification of publications of each organizational research unit through disambiguation of the affiliation data; (ii) assigning each publication to the corresponding ESI field based on journal title; (iii) aggregating bibliometric information of all publications for each research unit and discipline, and (iv) performing multivariate analysis, e.g., clustering and correspondence analysis, to extract proximity relationships and internal structures that enable regrouping the obtained data and visualizing them using two-dimensional plots and bar diagrams. This approach may be easily adapted for analysis using other available disciplinary (subject areas or categories) schemes. Moreover, such analysis can be further extended to lower hierarchical levels, such as research divisions or research teams comprising a complex multidisciplinary department. The proposed affiliation-based analysis is useful for initial understanding the disciplinary contribution of the university departments to overall research output, e.g., for analysis of ranking based on performance for past 5-6 years tracing past history. It can be easily adapted to the bottom-up research performance analysis (based on current researchers) required for research administration or research strategy formulation based on the research output of the immediate past.
机译：本研究探讨了译成了致力于自然科学，技术，工程和医疗（Stem）领域的组织研究单位与必要科学指标数据库（ESI 22田地）的22个学科类别之间的分解部门 - 学科关系的实用方法。日本国立大学如一套同行评审的期刊出版物（文章和评论）索引，在科学网上（WOS）核心收集数据库中为5年。该程序涉及若干步骤，例如（i）通过歧义附属数据的歧义来确定每个组织研究单位的出版物; （ii）将每个发布分配给基于期刊标题的相应ESI字段; （iii）对每个研究单位和纪律的所有出版物的所有出版物的伯格计数信息，（iv）进行多变量分析，例如聚类和对应分析，以提取能够重新组合获得的数据并使用两个 - 的接近关系和内部结构来提取邻近关系和内部结构尺寸图和条形图。使用其他可用的学科（主题区域或类别）方案，可以容易地适用这种方法。此外，这种分析可以进一步扩展到较低的分层水平，例如包括复杂多学科部门的研究划分或研究团队。拟议的基于联盟的分析可用于初步了解大学部门对整体研究产出的纪律贡献，例如，根据追溯到历史过去5 - 6年的绩效的绩效分析。它可以很容易地适应基于立即过去的研究产出的研究管理或研究策略制定所需的自下而上的研究性能分析（基于当前的研究人员）。

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