*** Carbon storage and distribution in terrestrial ecosystems of Canada *** Authors: C. Sothe,1* A. Gonsamo,1 J. Arabian,2 W. A. Kurz,3 S. A. Finkelstein,4 J. Snider2 1School of Earth, Environment & Society, McMaster University, Hamilton, Ontario, Canada. 2World Wildlife Fund Canada, Toronto, Ontario, Canada. 3Canadian Forest Service, Natural Resources Canada, Victoria, British Columbia, Canada. 4Department of Earth Sciences, University of Toronto, Toronto, Ontario, Canada. Corresponding author: Camile Sothe (sothec@mcmaster.ca) ***General Introduction*** This dataset contains maps with the spatial distribuion of carbon stock in plants of Canada and associated uncertainties. It is being made public to act as supplementary data for the publication 'Large soil carbon storage in terrestrial ecosystems of Canada', currently under review. The maps were produced in the Remote Sensing Lab, McMaster University, between January and December 2020. This research project was made possible by a grant from the World Wildlife Fund (WWF)- Canada ***Purpose of the project*** This project aimed to produce the first wall-to-wall estimate of carbon stocks in plants and soils of Canada at 250 m spatial resolution using multisource satellite, climate and topographic data and a machine-learning algorithm. ***Methods*** This dataset contains the map with total carbon stored in plants of forested areas in Canada (AGB, BGB and dead plants) and carbon stock uncertainty. To estimate the carbon stored in plants of forest areas, we used 47,967 ground measurements of AGB and 58 covariates mainly composed of optical data, terrain parameters, structural parameters (e.g., SAR data, clump index, canopy heights – generated from satellite LiDAR- included in the other dataset), soil type map and radiation flux data. We used a random forest model for spatial prediction of AGB in forest areas while 1st and 3rd quantiles of RF quantile regression were used to build the uncertainty map. After generating the AGB map, the root biomass of forest areas was computed by its relationship to AGB according to forest type. The dead plant materials were computed by a linear regression between live and dead AGB defined with ground measurements. Ultimately, the AGB as well as dead plant materials and BGB were multiplied by 0.5 to provide the carbon maps. ***Description of the data*** -250m spatial resolution -WGS-84 projection -Area= 3.4 million km² -Units= kg/m² -Years= 2015-2019 -includes carbon stored in above and belowground biomass and dead plant materials