ChatGPT and Gemini-Supported Bibliometric Analysis Training (Part II)

Dear Members of Ibn Haldun University, 

As the Coordination of Innovative Learning and Teaching-Artificial Intelligence, we are continuing our bibliometric analysis training series, which we organize to enhance academic productivity.

In Part 2 of the training, which serves as a continuation of the first session, we will focus on overcoming the technical challenges most frequently encountered by researchers during the literature review process and on building comprehensive datasets. In this session, participants will be shown, through hands-on applications, how large-scale data obtained from different academic databases can be managed and integrated into a single analysis pool.

Training Scope and Learning Outcomes

In this training, participants will learn the following advanced data processing techniques:

• Working with Large-Scale Datasets (WoS):
  Overcoming the “500 records per download” limitation in the Web of Science (WoS) database and seamlessly merging partially downloaded files in the R/Biblioshiny environment without data loss.
• Scopus Database Management:
  Downloading bibliometric data from the Scopus database in the correct format and preparing it for analysis.
• Database Integration (WoS + Scopus):
  To ensure comprehensive literature coverage, cleaning and merging datasets with different structures obtained from WoS and Scopus into a single Master Data file (merge operations).

This technical training aims to help you gain control over a broader literature pool in your academic studies. We look forward to your participation.