Cryogenic-sample Electron Microscopy (cryo-EM) has become a fundamental technique in structural biology, yet the assessment of post-processing methods remains challenging due to the lack of standardized and reproducible resources. Here, we present a curated dataset—carefully divided into non-overlapping training, validation, and test subsets—that includes half-maps, average maps, fitted atomic models, and post-processed volumes generated with diverse approaches, including Deep Learning methods such as CryoTEN, EMReady, EMReady2, and DeepEMhancer. Precomputed quality metrics derived from Phenix tools, and Q-scores are also provided, enabling quantitative evaluation of map–model agreement as well as global and local structural quality across different post-processing methods. This standardized resource enables independent and reproducible comparison of cryo-EM post-processing methods by the community. In addition, we provide a unified workflow and the code used for metric computation, allowing reproducible and consistent evaluation and facilitating the assessment of new methods. Overall, this dataset and evaluation framework provide a standardized reference for comparative studies and support the development of new cryo-EM map enhancement strategies.