DEPLOY.md

# Deploying the upgraded Hugging Face dataset card

This folder contains drop-in replacements for the metadata files in the
[`monteirot/wellbench`](https://huggingface.co/datasets/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.

---

## 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)

```bash
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)

```bash
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
```

---

## 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&nbsp;<&nbsp;n&nbsp;<&nbsp;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:

   ```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:

   ```bash
   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:

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

---

## 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.

---

## 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.