Mining Indian Scholar’s Information: Data Overview, Benefits and Challenges
Kiran Sharma1, Ziya Uddin1★★ Corresponding author
- 1School of Engineering and Technology, BML Munjal University, Sidhhrawali, Gurugram, Haryana, INDIA.
CORRESPONDENCE
Ziya Uddin
School of Engineering and Technology, BML Munjal University, Sidhhrawali, Gurugram, Haryana, INDIA.
Received: 06-11-2024; Revised: 24-01-2025; Accepted: 12-04-2025.
Volume 4, Issue 1 · pp. 96–102 · PUBLISHED · DOI: 10.5530/jcitation.20250140
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ABSTRACT
Understanding the growth and impact of science in any nation is crucial and scholarly publishing databases play a significant role in this endeavour. Currently, the most popular scholarly databases include Scopus, Web of Science and Dimensions, which are subscription-based. Additionally, there are some open-access databases available, but their accessibility is limited. India also has its own scholarly information dataset called VIDWAN, managed by INFLIBNET, which encompasses the research profiles of scholars. This database is designed to recommend peers based on expertise, facilitating collaboration within the country. However, its limitation lies in the fact that users can only view the information and cannot access the data directly. To overcome this limitation, the paper scraped data from the VIDWAN portal and organized it into a structured format. The dataset contains information on scholars’ profiles such as name, gender, position, affiliation, publication details sourced from Scopus and experiences such as PhD handles and research funding. The paper describes the coverage of data from various aspects, including funding details. Typically, such information is gathered from multiple sources to analyze individual performance. Therefore, this Indian scholar dataset serves as a valuable asset for assessing individual performance. Finally, it highlights the limitations encountered and proposes possible solutions to overcome them.
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Sharma, K., & Uddin, Z. (). Mining Indian Scholar’s Information: Data Overview, Benefits and Challenges. Journal of Data Science, Informetrics, and Citation Studies, 4(1), 96–102. https://doi.org/10.5530/jcitation.20250140
