| Abstrakt: |
Purpose: Linked data was developed and introduced as the best practice for publishing and linking structured data on the Web. In studies related to the scientific collaboration network, which is defined by co-authorship relationships, Social Network Analysis (SNA) is applied. Identifying influential researchers in co-authorship networks across different scientific fields can be considered one of the goals of scientometric studies. The purpose of the current research is to determine the productivity and efficiency of researchers in the field of linked data. Additionally, it aims to identify and analyze the most influential researchers in linked data using the Scholarly Influence Model. Methodology: The current research is an applied study and has been conducted using common techniques in Scientometrics, specifically coauthorship and network analysis methods. To obtain the primary data, the keyword "Linked data" was searched in the Web of Science Database, which contains 4612 records from 1983 to 2019. The data was saved in plaintext format and then processed by Bibexcel software. Based on coauthorship, the number of unique researchers was determined to be 48,643. The names of the researchers were transferred from Bibexcel to Excel software and sorted alphabetically. Then, they were edited, modified, and unified into preferred names. In the following, Bradford's Law was applied to determine the sample size with a cutoff of 38 in order to facilitate easier analysis in the co-authorship network. The sample size was determined to be 174 researchers. BibExcel has been used to calculate the productivity (number of articles), efficiency (number of citations), and H-index of the researchers. After creating a co-authorship matrix of researchers in BibExcel, it was converted into a correlation matrix using UCINET in order to calculate the degree, betweenness, and closeness centrality. In addition, the g-index and hc-index of 174 researchers were manually calculated using Excel software. Next, the relationship between productivity and efficiency, as well as the impact of social and environmental influences on ideational influence, were investigated using Lisrel Software. Findings: The findings showed that Bizer C. and Berners-Lee T. are considered to be the most influential researchers in the field of linked data, with the highest productivity and efficiency. In terms of coauthorship, "Auer S" and "Klyne G" have the highest degree centrality. In terms of closeness centrality, "Fellegi I" and "Zhang Y" have the highest scores, while "Fellegi I" and "Rubin D" have the highest scores in betweenness centrality. Regarding the hypotheses, there is a significant relationship between the productivity and efficiency of researchers in the field of linked data. Also, the findings showed that higher productivity is associated with higher betweenness and degree centrality. However, there is no significant relationship between closeness centrality score. Specifically, "Bizer C", "Berners-Lee T", "Hogan A", and "Auer S" have the highest scores in the indicators of social and ideational influences. Furthermore, it was found that social influence has a positive effect on venue and ideational influence, meaning that researchers with higher social influence also have higher venue and ideational influence. In addition, social influence has a positive effect on ideational influence, meaning that researchers with higher social influence also possess higher ideational influence. Conclusion: The favorable status of researchers in terms of productivity and efficiency, as well as their high scores in the indicators of degree and betweenness centrality in the co-authorship network, can indicate their significant scientific influence in this field. This finding confirms the positive and significant impact of triple relationships in the Scholarly Influence Model. Generally, the results can provide a deeper understanding of the quantitative and qualitative status of scientific publications and leading researchers in this field. Using a combination of productivity and efficiency indicators, along with the components of the Scholarly Influence Model, can help identify top researchers in each scientific field. [ABSTRACT FROM AUTHOR] |
