Dataset for the Modeling and Bibliometric Analysis of Business plan for Entrepreneurship

This dataset was developed to provide comprehensive insights into the scholarly evolution of research on business plans in the context of entrepreneurship. It integrates two major components: bibliometric mapping covering the period 1984–2024 and projection modeling for the period 2025–2034. The data were retrieved from Scopus and filtered using explicit inclusion criteria, including relevance to entrepreneurial business planning, coverage of a full year, peer-reviewed articles, and accessibility for analysis. In total, the dataset encompasses 1,011 peer-reviewed documents, produced by 2,151 authors across 628 publication sources.For bibliometric analysis, metadata are provided in CSV format containing extensive bibliographical fields such as author(s), document title, year, DOI, source title, affiliations, abstract, keywords, citation count, funding details, and references. The analysis and visualization were carried out using R Biblioshiny for thematic mapping and trend topics, and Microsoft Excel for main information and annual publication production. For modeling, Python was applied to generate projection analyses of annual scientific production using polynomial regression. The modeling outputs include both PNG images and CSV files of annual publication production and projection. Additional visualizations include a Research Flowchart (PNG), Thematic Map (PNG), and Trend Topic (PNG).The dataset provides significant value for researchers, practitioners, and policymakers by presenting structured bibliometric evidence alongside predictive modeling of future research trajectories in entrepreneurial business planning. By making the dataset openly available, it supports transparency, replicability, and future exploration of entrepreneurship research dynamics.