DEPLOY.md

Deploying the upgraded Hugging Face dataset card

This folder contains drop-in replacements for the metadata files in the monteirot/wellbench dataset repo:

File	Replaces
README.md	The current 376-byte README that only says "Benchmark data: …"
.gitattributes	The current .gitattributes (recommended LFS rules)

Your existing croissant.json (31.9 kB) and the data folders (synthetic_datasets/, CTGAN_synthetic_data/) are not modified.

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How to upload

Option A — Web UI (one file at a time)

1. Open https://huggingface.co/datasets/monteirot/wellbench/tree/main.
2. Click the existing README.md → Edit (pencil icon) → paste the new contents of README.md → Commit changes.
3. Repeat for .gitattributes.

Option B — huggingface-cli (recommended for both files at once)

pip install -U huggingface_hub
huggingface-cli login

cd huggingface/    # this folder
huggingface-cli upload monteirot/wellbench README.md       --repo-type dataset
huggingface-cli upload monteirot/wellbench .gitattributes  --repo-type dataset

Option C — Git over HTTPS (full repo flow)

git lfs install
git clone https://huggingface.co/datasets/monteirot/wellbench
cd wellbench

cp ../huggingface/README.md       README.md
cp ../huggingface/.gitattributes  .gitattributes

git add README.md .gitattributes
git commit -m "Upgrade dataset card and LFS rules"
git push

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After uploading — verify in this order

1. Card YAML parses cleanly. Refresh https://huggingface.co/datasets/monteirot/wellbench. The left rail should now show:

   • Tasks: Tabular Regression · Time Series Forecasting
   • Modalities: Tabular
   • Tags: synthetic-data · synthetic-tabular · well-logs · petrophysics · pore-pressure · ctgan · physics-based · benchmark · neurips · …
   • License: MIT
   • Size: 100K < n < 1M

2. All six configs appear. The Dataset Viewer top-bar should show the selector with: physics_zone_1 (default) · physics_zone_2 · physics_zone_3 · ctgan_zone_1 · ctgan_zone_2 · ctgan_zone_3. Each should expose its three named splits.

3. The Viewer renders rows. Pick physics_zone_1 / missa_keswal_01 — you should see numerical columns DEPTH, GR, DT, RHOB, RT, HP, OB, DT_NCT, PPP. If column types look wrong, the auto-Parquet conversion needs a re-run; click Refresh dataset in the Settings.

4. load_dataset round-trips. From a fresh Python:

   from datasets import load_dataset
   ds = load_dataset("monteirot/wellbench", "physics_zone_1")
   print(ds)
   df = ds["missa_keswal_01"].to_pandas()
   assert {"DEPTH","GR","DT","RHOB","RT","HP","OB","DT_NCT","PPP"} <= set(df.columns)
   

5. Croissant validates. The Hub auto-generates a Croissant record from the README YAML at the /croissant endpoint. Confirm it's well-formed:

   pip install "mlcroissant[parquet]"
   mlcroissant validate \
     --jsonld https://huggingface.co/api/datasets/monteirot/wellbench/croissant
   # exit 0 → ready for the NeurIPS D&B submission requirement
   

   Optional: run the same validator on the manually authored croissant.json you already host:

   curl -L https://huggingface.co/datasets/monteirot/wellbench/resolve/main/croissant.json \
     -o /tmp/croissant.json
   mlcroissant validate --jsonld /tmp/croissant.json
   

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Croissant: the two files explained

There are now effectively two Croissant records for this dataset, and that's fine:

Source	Generated from	Use it for
…/api/datasets/monteirot/wellbench/croissant	README YAML (auto)	Discovery, NeurIPS D&B compliance check
…/resolve/main/croissant.json	Hand-authored	The mlc.Dataset("croissant.json") example in your code, custom record sets

Recommended: keep both. The auto-generated one is regenerated on every push and always reflects the current YAML; the hand-authored one lets you expose richer record sets (like your physics-samples example) that the auto-generator doesn't infer from filenames alone.

If the two ever drift in licence, citation, or column types, fix the hand-authored file — reviewers will compare them.

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Optional next steps

These are not in this folder, but are worth doing before NeurIPS submission:

1. Add a Croissant-RAI block to croissant.json — dataLifecycle, dataCollection, dataPreprocessing, dataLimitations, dataBiases, personalSensitiveInformation. Use https://croissant-editor.com/.

2. Convert CSVs to Parquet for the Viewer to run faster and column types to be preserved without inference. Hugging Face will auto-convert on the refs/convert/parquet branch, but committing Parquet directly gives you control over schemas and row-group sizes.

3. Mint a Zenodo DOI by tagging a release of the GitHub repo and enabling the Zenodo–GitHub integration. Add the DOI to both the GitHub README and the Citation block of this dataset card.

4. Cross-link: ensure the Hugging Face dataset card and the GitHub README both link bidirectionally and that the BibTeX entries match.