The latest documentation for CryoBench is available at our homepage and also at our manual.

For any feedback, questions, or bugs, please file a Github issue, start a Github discussion, or email.

To run the metrics, you have to install cryodrgn. cryodrgn may be installed via pip, and we recommend installing cryodrgn in a clean conda environment.

# Create and activate conda environment
(base) $ conda create --name cryodrgn python=3.9
(cryodrgn) $ conda activate cryodrgn

# install cryodrgn
(cryodrgn) $ pip install cryodrgn

More installation instructions are found in the documentation.

Datasets are available for download at Zenodo.

1. Conf-het (IgG-1D, IgG-RL): https://zenodo.org/records/11629428.
2. Comp-het (Ribosembly, Tomotwin-100): https://zenodo.org/records/12528292.
3. Spike-MD: https://zenodo.org/records/14941494.

Look at the repo cryosim.

Look at the repo metrics/fsc

Look at the repo metrics/visualization

Please submit any bug reports, feature requests, or general usage feedback as a github issue or discussion.

Jeon, Minkyu, et al. "CryoBench: Diverse and challenging datasets for the heterogeneity problem in cryo-EM." NeurIPS 2024 Spotlight. paper.

@inproceedings{jeon2024cryobench,
 author = {Jeon, Minkyu and Raghu, Rishwanth and Astore, Miro and Woollard, Geoffrey and Feathers, Ryan and Kaz, Alkin and Hanson, Sonya M. and Cossio, Pilar and Zhong, Ellen D.},
 booktitle = {Advances in Neural Information Processing Systems},
 title = {CryoBench: Diverse and challenging datasets for the heterogeneity problem in cryo-EM},
 year = {2024}
}