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Dear Researchers and Interested Parties, It is with great enthusiasm that we share our page on Zenodo, where we provide detailed maps (30 m resolution) of soil carbon stocks in Rondônia, Brazil. These maps were generated using machine learning techniques, using the Random Forest model implemented in the caret package. This initiative aims to provide a deeper and more accurate understanding of the spatial distribution of carbon in the soil, contributing significantly to environmental studies and climate change mitigation strategies in the region. Available resources: High Resolution Maps: We provide detailed maps of the estimates and uncertainties of soil carbon stocks at different depths (0-5; 5-15; 15-30; 30-60 and 60-100 cm). The maps include mean values (Mg ha-1), quantiles (Mg ha-1) and coefficients of variation (%), all in "tif" format, with a spatial resolution of 30 m and SAD 1969 Lambert South America projection system (EPSG :102015). The entire process was conducted in open source (R Language). The codes and database used can be found in the GitHub repository, and more information about the methodology is available in the following publication: Moquedace, C. M., Baldi, C. G. O., Siqueira, R. G., Cardoso I. M., Souza, E. F. M., Fontes, R. L. F., Francelino, M. R., Gomes, L. C., Fernandes-Filho, E. I. High-resolution mapping of soil carbon stocks in the Western Amazon. Geoderma Regional, v. 36, p. e00773, 2024. DOI: 10.1016/j.geodrs.2024.e00773 Availability objectives: Promote scientific collaborations: We encourage researchers, scientists, and organizations to explore and use this data to enrich their own research and projects related to soil carbon and climate change. Enhance environmental understanding: By providing open access to these maps, we aim to contribute to a deeper understanding of environmental processes in Rondônia, Brazil and, by extension, enable the implementation of sustainable strategies. Stimulating innovation: We believe that sharing this data will stimulate innovation in modeling and spatial analysis methods, driving advances in the prediction of soil carbon stocks, especially in the Amazon. Thank you in advance for your interest and collaboration. Together, we can advance knowledge and the search for sustainable solutions to important environmental challenges.