Abstract
Effective navigation and strategic foresight are imperative in the dynamic realm of academic repositories. This study aims to comprehensively understand global scholarly communication dynamics by analyzing repository distribution and forecasting future trends using predictive modeling methodologies. Employing a mixed-methods approach, this research blends quantitative analysis and predictive modeling techniques. It encompasses phases such as distribution analysis of repositories across continents, examination of repository counts across countries using GeoPandas and Matplotlib, optimization of Long short-term memory (LSTM) networks, application of Random Forest Regressor models, and time series analysis using Autoregressive Integrated Moving Average (ARIMA) models. The study reveals profound disparities in repository counts across continents, reflecting variations in research infrastructure. Visualizations vividly depict geographical patterns and disparities in repository distribution. Predictive modeling techniques offer insights into future trends in repository development, empowering decision-makers with strategic insights. While this study provides valuable insights, limitations exist, such as the scope limited to OpenDOAR repositories and the need for further refinement in predictive modeling techniques; future research should explore additional factors influencing repository development.
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