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Fix model-weight distribution so the repo is clone-and-run #1

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Fix model-weight distribution so the repo is clone-and-run #1

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22 changes: 12 additions & 10 deletions .gitignore
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Original file line number Diff line number Diff line change
@@ -1,5 +1,7 @@
# Large binary data (EEG .mat files, model weights — download via BDSP)
# All directories below mirror s3://bdsp-opendata-credentialed/iiic-freq3/data/.
# The gitignored weights below mirror s3://bdsp-opendata-credentialed/grond/data/.
# After cloning, sync them with:
# aws s3 sync s3://bdsp-opendata-credentialed/grond/data/ data/
data/eeg/
data/dl_cache/
data/cet_cache/
Expand Down Expand Up @@ -104,12 +106,12 @@ Thumbs.db
# Logs
*.log
*.zip

# Reproducibility data bank — rebuild via code/data_management/build_grond_h5_bank.py or download from S3 release
data/grond_data.h5

# Recovery audit logs (regenerable + can be large)
results/c1_repro/*.log

# Per-rater personalized email drafts (regenerated as needed)
paper_materials/independent_expert_tasks/email_to_*.md
# Reproducibility data bank — rebuild via code/data_management/build_grond_h5_bank.py or download from S3 release
data/grond_data.h5
# Recovery audit logs (regenerable + can be large)
results/c1_repro/*.log
# Per-rater personalized email drafts (regenerated as needed)
paper_materials/independent_expert_tasks/email_to_*.md
14 changes: 13 additions & 1 deletion README.md
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Expand Up @@ -85,13 +85,25 @@ aws s3 cp s3://bdsp-opendata-credentialed/grond/grond_data.h5 data/grond_data.h5

This requires AWS credentials with access to the `bdsp-opendata-credentialed` bucket. To request access, visit the [Brain Data Science Platform (BDSP)](https://bdsp.io).

Pre-trained model weights live in this git repository under `data/hemi_cache/`, `data/pd_channel_cache/`, `data/cet_cache/`, and `data/models/`. Once you have `grond_data.h5` + the git repo, you can reproduce every figure and every number in the manuscript with `python paper_materials/generate_all_figures.py`.
Pre-trained model weights are distributed via S3 (they are gitignored — too large for git). After cloning, sync them into `data/`:

```bash
# Inference model weights a fresh clone is missing (~12 MB)
aws s3 sync s3://bdsp-opendata-credentialed/grond/data/ data/
```

This adds `data/pd_channel_cache/` (ChannelPD-Net), `data/cet_cache/` (CET-UNet), `data/dl_cache/` (ridge frequency model), and `data/models/` (BIPD GBT). The HemiCET timing weights (`data/hemi_cache/hemi_cet_v2/`) and `data/unified_model_cache/` already ship in the git repo. Once you have `grond_data.h5` + the git repo + the synced weights, you can reproduce every figure and every number in the manuscript with `python paper_materials/generate_all_figures.py`.

### S3 Data Contents

```
s3://bdsp-opendata-credentialed/grond/
├── grond_data.h5 (1.69 GB) — the entire dataset
├── data/ (~12 MB) — gitignored inference weights
│ ├── pd_channel_cache/ ChannelPD-Net (cnn_attn folds)
│ ├── cet_cache/ CET-UNet evidence folds
│ ├── dl_cache/ ridge frequency model
│ └── models/ BIPD GBT classifier
└── independent_expert_tasks/ (470 MB) — labeling viewers
├── lpd_task.html
├── gpd_task.html
Expand Down