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In today's academic environment, social media platforms, ResearchGate in particular, play an essential role in facilitating collaboration and communication among researchers. Even with the wide usage of this site, there remains a significant gap in the availability of structured datasets that focus on researchers and their academic outputs. This lack of accessible data obstructs comprehensive analysis and evaluation of research trends and impact. This study seeks to address this gap by employing web scraping techniques to construct a dataset derived from ResearchGate, a leading platform for academic professionals. The introduced dataset consists of eight key features, including metrics related to publications, citations, and areas of research specialization. The availability of such a dataset not only provides a valuable resource for future research but also enables scholars to analyze research performance, identify collaboration opportunities, and uncover trends in academic productivity. Beyond dataset construction, this paper also details the exploratory data analysis (EDA) conducted to derive insights from the collected data. The K-means clustering algorithm was applied to categorize researchers according to their publication and citation patterns, offering a clearer understanding of academic achievements. The main contribution of this work is the establishment of a researcher’s dataset that can be used in future studies to analyze and examine the efforts and trends of researchers in their scientific work.