Academic Scholar Profiles and Citation Analytics

Selecting the right mentor for advanced study is a pivotal decision that can significantly alter a student's career trajectory. This collection of academic profiles and citation analytics was developed by a data analyst to evaluate potential target professors for international study opportunities. By examining specific research interests, citation counts, and publication histories, the data provides an objective framework for comparing scholars. It transforms subjective academic reputations into actionable metrics, allowing prospective students and researchers to identify the most influential figures in their chosen fields of interest.

The information is delivered in a compact CSV file titled scholars.csv, with a file size of 454 bytes. The record set consists of 13 unique entries across 3 columns. All data points demonstrate 100% validity with no mismatched or missing information. The resource holds a maximum usability score of 10.00 and is expected to receive annual updates to maintain the accuracy of the scholar profiles.

This resource is ideal for prospective international students who need to shortlist faculty members based on research alignment and academic impact. It is well-suited for university admissions offices looking to benchmark faculty performance or for librarians tracking institutional research output. Additionally, academic consultants can use the data to create visualisations of networking clusters and citation trends among specific research groups.

The scope of the data is focused on a specific group of 13 target scholars relevant to the creator's academic interests. While the geographic location of the universities is not explicitly stated in the primary identifiers, the demographic focus is on professors and academic researchers within the higher education sector. The data provides a current snapshot of these individuals' professional standings and digital identifiers.

CC0: Public Domain

Graduate students can leverage these records to conduct due diligence on potential advisors before submitting university applications. Academic researchers may utilise the citation metrics to identify key collaborators or competitors within their niche. Furthermore, data science students can use the clean, structured identifiers to practice web scraping and automated data enrichment by linking the Google IDs to live citation databases.

Original Data Source:Academic Scholar Profiles and Citation Analytics