| # Attribution and modification notice |
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|
| ## Original material |
|
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| **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 |
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| 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>. |
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| Pinned upstream code: <https://github.com/MLI-lab/DeepDeWedge/tree/072075692a44a8f17394214369e6e762abe52bc3> |
| (BSD-2-Clause). |
|
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| ## Changes in this package |
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|
| 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. |
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| No endorsement by the cited authors, the Machine Learning and Information |
| Processing Laboratory, Figshare, or the rights holders is implied. |
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