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    <responseDate>2026-10-14T20:17:15Z</responseDate>
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    <identifier>10.57760/sciencedb.32271</identifier>
    <datestamp>2026-03-27T08:59:57Z</datestamp>
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  <dc:date>2026-03-27</dc:date>
  <dc:title>EMCFsys</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.32271</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>Electron microscope cell images foundation dataset with more than 4 million data.EMCF Dataset, the first large-scale, multi-domain foundation dataset specifically designed for deeplearning model pretraining. Its raw data were aggregated from two primary channels: millions images acquired in our laboratory and a vast collection of 2D and 3D images compiled from 6,825 public repositories, studies, and publications. Recognizing the inherent heterogeneity in magnification, resolution, and quality across these sources, we implemented a systematic curation pipeline to filter, clean, and standardize the data. This process yielded the final EMCF Dataset, comprising 4,002,802 high-quality electron micrographs. The dataset is characterized by its immense diversity, spanning 14 distinct EM technologies, including Transmission (TEM), Scanning (SEM), Focused Ion Beam SEM (FIB-SEM), and cryo-Electron Tomography (cryo-ET) and so on (Fig. 1c). Biologically, it covers six major kingdoms: Animalia, Plantae, Fungi, Protista, Bacteria, and Viruses.</dc:description>
  <dc:subject> Scanning Electron Microscope (SEM); Foundation model; EM</dc:subject>
  <dc:creator>Zeyu Yu</dc:creator>
  <dc:creator>Xuping Feng</dc:creator>
  <dc:creator>Yong He</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by-nc-sa/4.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:publisher>Science Data Bank</dc:publisher>
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