{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["12(1)"],"submitter":["Lv Z"],"pubmed_abstract":["Under the strategic objectives of carbon peaking and carbon neutrality, energy transition driven by new quality productive forces has emerged as a central theme in China's energy development. Among these, the intelligent sorting and analysis of raw coal using deep learning constitute a pivotal technical process. However, the progress of intelligent coal preparation in China has been constrained by the absence of accurate and large-scale data. To address this gap, this study introduces DsCGF, a large-scale, open-source raw coal image dataset. Over the past five years, extensive raw coal image samples were systematically collected and meticulously annotated from three representative mining regions in China, resulting in a dataset comprising over 270,000 visible-light images. These images are annotated at multiple levels, targeting three primary categories: coal, gangue, and foreign objects, and are designed for three core computer vision tasks: image classification, object detection, and instance segmentation. Comprehensive evaluation results indicate that the DsCGF can effectively support further research into the intelligent sorting of raw coal."],"journal":["Scientific data"],"pagination":["403"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11890867"],"repository":["biostudies-literature"],"pubmed_title":["A large-scale open image dataset for deep learning-enabled intelligent sorting and analyzing of raw coal."],"pmcid":["PMC11890867"],"pubmed_authors":["Lv Z","Sun M","Sha T","Fan Y","Cui Y","Lv H","Wang W","Tu Y","Wu Y","Xu Z"],"additional_accession":[]},"is_claimable":false,"name":"A large-scale open image dataset for deep learning-enabled intelligent sorting and analyzing of raw coal.","description":"Under the strategic objectives of carbon peaking and carbon neutrality, energy transition driven by new quality productive forces has emerged as a central theme in China's energy development. Among these, the intelligent sorting and analysis of raw coal using deep learning constitute a pivotal technical process. However, the progress of intelligent coal preparation in China has been constrained by the absence of accurate and large-scale data. To address this gap, this study introduces DsCGF, a large-scale, open-source raw coal image dataset. Over the past five years, extensive raw coal image samples were systematically collected and meticulously annotated from three representative mining regions in China, resulting in a dataset comprising over 270,000 visible-light images. These images are annotated at multiple levels, targeting three primary categories: coal, gangue, and foreign objects, and are designed for three core computer vision tasks: image classification, object detection, and instance segmentation. Comprehensive evaluation results indicate that the DsCGF can effectively support further research into the intelligent sorting of raw coal.","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Mar","modification":"2025-04-04T08:22:27.854Z","creation":"2025-04-04T08:22:27.854Z"},"accession":"S-EPMC11890867","cross_references":{"pubmed":["40057526"],"doi":["10.1038/s41597-025-04719-0"]}}