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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

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Making social sciences more scientific: Literature review by structured data.

Vuong Quan-Hoang^1,2, Le Anh-Vinh³, La Viet-Phuong^1,2,4

¹Center for Interdisciplinary Social Research, Phenikaa University, Hanoi 10000, Vietnam.

|April 8, 2020

Summary

This study introduces a new data-driven method for literature reviews using the SSHPA (Social Sciences and Humanities Peer Awards) database. The SDA (SSHPA Data Analysis) tool enhances efficiency and analytical depth in scientific research synthesis.

Keywords:

Database Literature review NAFOSTED, National Foundation for Science & Technology Development SDA, SSHPA Data Analysis SSHPA, Social Sciences and Humanities Peer Awards Structured data Vietnam

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Area of Science:

Social Sciences
Humanities
Bibliometrics
Data Science

Background:

Traditional literature reviews can be time-consuming and lack analytical depth.
Existing databases may not be optimized for comprehensive review synthesis.
Vietnamese social scientists' productivity data is available through the SSHPA open database.

Purpose of the Study:

To propose a novel, data-driven methodology for conducting literature reviews.
To enhance the efficiency and analytical capabilities of the literature review process.
To leverage structured data from the SSHPA database for improved research synthesis.

Main Methods:

Creation of a specialized database, SDA (SSHPA Data Analysis), from the SSHPA database.
Structuring and assigning topic and methodological attributes to over 2200 scientific articles.
Utilizing computational algorithms combined with expert knowledge for data analysis.

Main Results:

SDA enables efficient scanning and analysis of a large corpus of scientific articles.
The system allows for the querying of comprehensive data tables, graphs, and diagrams.
The methodology offers a significant improvement over traditional literature review approaches.

Conclusions:

The SDA system provides an efficient and analytical method for literature reviews.
This approach enhances the synthesis of scientific productivity data.
The proposed method has the potential to transform how literature reviews are conducted in the Social Sciences and Humanities.