EMReady2: Post-Processed Cryo-EM Maps for Resolution Enhancement

Cryo-EM test set maps post-processed using the EMReady2 deep learning framework for resolution-dependent and heterogeneity-aware enhancement. Curated by Florea Toma and Andreea V. in 2026, the data originates from research published by Cao et al. in late 2025. The collection focuses on improving structural biology map quality through specialized neural network processing.

Use Cases

• Benchmarking map enhancement using EMReady2 post-processed outputs
• Evaluating resolution-dependent deep learning models in structural biology
• Analyzing heterogeneity-aware processing effects on cryo-EM map metrics

Strengths

• Employs resolution-dependent deep learning via EMReady2
• Incorporates heterogeneity-aware processing for structural biology
• Derived from peer-reviewed research (bioRxiv 2025)

Limitations

• Unknown record count
• Metadata does not define specific file formats or voxel dimensions
• Restricted to the outputs of a single post-processing method

Provenance

Source

H. Cao et al., bioRxiv (2025) and Dataverse

Collection Method

Deep learning post-processing of existing cryo-EM test sets

Freshness

Last updated March 2026; based on a September 2025 bioRxiv publication.