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Thanks to reporting requirements, technical reports for publicly traded mining companies are available online through company websites and online repositories. With advances in data storage and analytical tools, there is now an opportunity to automate the extraction and comparison of technical information from these publicly available reports. These technical reports contain detailed data on mine site conditions, geology, resource estimation, mining methods, material handling, underground ventilation, processing, cost models, and more. This study applies a Python-based data scraping approach to systematically collect and analyze mine design data across a broad sample of reports. By organizing this information by commodity, mining method, and project context, the analysis enables cross-project comparisons and benchmarking. This study evaluated production rates, pit designs, ventilation parameters, environmental impact, socio-economic impact, and water resources assessments, and more. The resulting dataset provides a valuable reference framework for those developing new feasibility studies or evaluating project viability.
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