# Attribution and modification notice

## Original material

**DeepDeWedge Tutorial Data**
Creator: Simon Wiedemann
DOI: <https://doi.org/10.6084/m9.figshare.25043435.v1>
Figshare file id: `45582309`
Archive member: `tutorial_data/fitted_model.ckpt`
License: Creative Commons Attribution 4.0 International

The method is described by Simon Wiedemann and Reinhard Heckel, *A deep
learning method for simultaneous denoising and missing wedge reconstruction in
cryogenic electron tomography*, Nature Communications 15, 8255 (2024),
<https://doi.org/10.1038/s41467-024-51438-y>.

Pinned upstream code: <https://github.com/MLI-lab/DeepDeWedge/tree/072075692a44a8f17394214369e6e762abe52bc3>
(BSD-2-Clause).

## Changes in this package

On 2026-09-04 Scitomo freshly converted only the authoritative checkpoint
`official/fitted_model.ckpt`, after byte-size and SHA-256 verification, through
the exact pinned upstream source and current generic FORMAT 2 exporter. The
54 U-Net state tensors were explicitly mapped into canonical Network state.
The two fitted affine quantities were preserved as external DeepDeWedge
inference-profile state; they are not Network state. No old Hugging Face
Safetensors or format-1 package artifact was conversion input.

No endorsement by the cited authors, the Machine Learning and Information
Processing Laboratory, Figshare, or the rights holders is implied.