Social Science Publications in Seven Indonesian University (2008-2018)
Description
The presented bibliometric dataset relates to a research article entitled “University Reform and the development of social sciences in Indonesia” published by the International Journal of Educational Development [1]. The dataset is substantiated by informed peer reviews as a way to conduct a longitudinal analysis of the contents of social science publications stored in the Scopus database. These publications were produced by academics at the faculties of social and political sciences at the seven state-owned universities in Indonesia over ten years from the beginning of Indonesian university reform in 2008 until July 2018. The dataset is the only data on social sciences currently available in Indonesia. The data collection involved listing the names of academics from faculty websites and searching their publications in the Scopus database. The authors act as informed peer reviewers to identify the publications as mainstream and non mainstream, their themes, research collaboration, policy and academic contributions. The dataset informs the state of the art of social sciences not only at university level but also at national level. Informed peer reviews are needed to supplement bibliometric data to assess the quality of social scientific works.
Files
Steps to reproduce
Bibliometric methods substantiated by informed peer review were used to conduct a longitudinal analysis of the contents of scientific publications stored in the Scopus database. These publications include journal articles, conference proceedings, books and book chapters produced by academics at the faculties of social and political sciences at the seven state-owned universities in Indonesia over the ten years from the beginning of Indonesian university reform in 2008 until July 2018. The data collection procedure was as follows: first, the researcher collected the names of lecturers at the faculties of social and political sciences at each of the universities, then input these names into the Scopus author search function. The researcher then clicked on the title of each academic work/conference proceeding attributed to the names, and read the abstract. The abstracts were read to confirm whether the names of the lecturers listed matched the faculty and university of origin. Third, the author filtered out works produced outside the time scope of the research of 2008 to July 2018. Fourth, the variables selected as initial data, i.e. author, document title, document type, year, link, source title, volume, issue and pages, were exported to CSV files, and later converted to Excel files. The data were differentiated by university and sorted by year (2008-July 2018). Fifth, more variables were added to the per university raw data in the Excel files, namely collaboration, theme, main concept, concept application and policy contribution. The data for each university was then merged into one file for data cleaning using IBM SPSS Statistics V22.0. Quantitative data processing and analysis were then performed by producing univariate, bivariate and multivariate tables and graphs of the variables categorized based on the abstract of each publication. The authors, acting as informed peer reviewers, performed the categorization of theory extension, policy contribution and themes.