diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..33cfcb6072f814eb267e1139b10ee37235b51938
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,5 @@
+.omx/
+.venv/
+__pycache__/
+*.pyc
+.cache/
diff --git a/README.md b/README.md
index 32897cd3e640101ba184f8c4ccd896981de3804a..2d647a8e06e19493dfd8f3e0ebe76b095a62287f 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,197 @@
 ---
 license: mit
+pretty_name: Baseline NLOS Histogram Dataset
+language:
+- zxx
+tags:
+- lidar
+- nlos
+- time-of-flight
+- histograms
+- synthetic-data
+size_categories:
+- 10K<n<100K
+configs:
+- config_name: synthetic
+  default: true
+  data_files:
+  - split: train
+    path: metadata/synthetic_train.csv
+  - split: validation
+    path: metadata/synthetic_validation.csv
+  - split: test
+    path: metadata/synthetic_test.csv
+- config_name: experimental
+  data_files:
+  - split: unassigned
+    path: metadata/experimental.csv
 ---
+
+# Baseline NLOS histogram dataset
+
+38,000 synthetic and 180 real experimental histogram samples for research on
+binary classification in non-line-of-sight (NLOS) sensing. Each HDF5 file contains
+one `32 × 32 × 64` histogram. Model weights are not included.
+
+## Contents
+
+| Source | Negative (0) | Positive (1) | Total |
+|---|---:|---:|---:|
+| Synthetic | 19,000 | 19,000 | 38,000 |
+| Experimental | 90 | 90 | 180 |
+
+Synthetic samples include `clean` (1,000 per class) and `noise_1` through
+`noise_36` (500 per class per condition). The condition names are identifiers,
+not calibrated noise magnitudes. Experimental samples retain their original
+acquisition groups in nine balanced folders.
+
+```text
+data/
+  synthetic_data/
+    tmf8829_32x32_negative/{clean,noise_1,...,noise_36}/histogram/*.h5
+    tmf8829_32x32_positive/{clean,noise_1,...,noise_36}/histogram/*.h5
+  experiment_data/
+    fold_1/ ... fold_9/
+    manifest.csv
+metadata/
+  synthetic_train.csv
+  synthetic_validation.csv
+  synthetic_test.csv
+  experimental.csv
+  validation_summary.json
+manifest.csv
+SHA256SUMS
+examples/load_sample.py
+scripts/validate_dataset.py
+requirements.txt
+```
+
+All manifest paths are relative to the **repository root**, including paths in
+`data/experiment_data/manifest.csv`. The root manifest indexes every sample;
+metadata CSVs select subsets of those same records. The Hugging Face viewer
+configurations expose these metadata rows, not decoded histogram tensors.
+
+## Labels and schema
+
+`label=0` means the source dataset's **negative** class; `label=1` means its
+**positive** class. Labels are inherited from the source filenames and manifest.
+The exact physical criterion defining target presence/absence is not documented
+in the packaged source metadata and requires author confirmation.
+
+| Field | Meaning |
+|---|---|
+| HDF5 key | `stamped_histogram` |
+| Shape | `(32, 32, 64)` |
+| Axes | Two spatial pixel axes, followed by histogram bin index |
+| Synthetic dtype | `int64` |
+| Experimental dtype | `uint32` |
+| Values | Stored histogram counts; no normalization applied during packaging |
+
+Spatial orientation, bin width, timing offset, and physical range units have not
+been verified. Do not infer them from the tensor shape. All experimental files
+have root attributes `image_height=32`, `image_width=32`, `bin_number=64`,
+`range_min=0.0`, and `range_max=1890.0`; the range units are unspecified.
+
+Manifest columns: `sample_id` (unique path-derived identifier), `path`, `label`,
+`label_name`, `source`, `fold`, `condition`, `split`, `h5_key`, `height`, `width`,
+`bins`, `dtype`, and `sha256`. Synthetic `fold` fields are empty. Experimental
+`condition` matches the fold identifier.
+
+## Splits and experimental groups
+
+The synthetic split assignments are preserved from the original training run:
+26,600 train, 5,700 validation, and 5,700 test samples (70/15/15). The source
+`val` name is exported as `validation`. No new random split was generated.
+The source configuration specifies seed 42.
+
+| Folder | Original group | Positive | Negative |
+|---|---|---:|---:|
+| fold_1 | 1_experiment | 10 | 10 |
+| fold_2 | 2_experiment | 10 | 10 |
+| fold_3 | 3_experiment | 10 | 10 |
+| fold_4 | 4_experiment | 10 | 10 |
+| fold_5 | 5_experiment | 10 | 10 |
+| fold_6 | addition_test/left | 10 | 10 |
+| fold_7 | addition_test/right | 10 | 10 |
+| fold_8 | addition_test/long | 10 | 10 |
+| fold_9 | addition_test/short | 10 | 10 |
+
+Experimental samples have `split=unassigned`: these folders preserve acquisition
+groups and do not themselves specify a cross-validation protocol. For evaluation,
+state whether real data are held out entirely or used in group-based cross-validation.
+Keep each acquisition group together when constructing a new experimental split.
+The historical use of these samples for model selection has not been audited.
+
+The supplied synthetic split is a historical baseline, not a guarantee of
+independence across related clean/noisy scenes. Scene-family identifiers are not
+included, so scene-level leakage cannot be ruled out. Fit preprocessing statistics
+on training data only and report the split and preprocessing used.
+
+## Loading a sample
+
+```bash
+python -m pip install -r requirements.txt
+python examples/load_sample.py --source experimental --fold fold_1
+python examples/load_sample.py --source synthetic --split train
+```
+
+After publication, replace `PeizhaoEdinburgh/Occlusion_Based_NLOS` with the repository ID:
+
+```bash
+python examples/load_sample.py --repo-id PeizhaoEdinburgh/Occlusion_Based_NLOS --source experimental --fold fold_1
+```
+
+Use `--revision COMMIT_HASH` to pin the remote version. The example downloads the
+manifest and one sample, verifies its SHA-256 hash and schema, and loads it with
+`h5py`. It returns the stored values without training-specific preprocessing.
+For direct access:
+
+```python
+import h5py
+
+with h5py.File("data/experiment_data/fold_1/1_positive_histogram_capture_1.h5", "r") as f:
+    histogram = f["stamped_histogram"][()]  # shape (32, 32, 64)
+```
+
+## Provenance and limitations
+
+The source collection is identified as `LameDar2/tmf8829_32x32` and the baseline
+run as
+`20260726_212202_tmf8829_36noise_seed_lhs_plus30_36_global_log_count_coarse_multiscale_h128_d4_aug_standard_hist_coarse_multiscale_mlp`.
+The synthetic directory names identify the TMF8829 sensor setting. Complete
+simulation parameters, sensor calibration, acquisition dates, physical setup
+measurements, and noise-condition definitions are not supplied here.
+
+The experimental files were moved into the fold structure without changing their
+contents. Two spelling errors in filenames (`positve`, `negtive`) were corrected.
+Original experiment group names are recorded above. Machine-specific source paths
+are excluded from the portable manifests.
+
+This dataset is intended for research. Balanced class counts do not establish
+real-world prevalence or generalization to other sensors and environments.
+No paper citation or author attribution has been supplied yet.
+
+## Integrity
+
+```bash
+python scripts/validate_dataset.py
+# Alternatively, verify file bytes only from the repository root:
+sha256sum --check SHA256SUMS
+```
+
+The validator checks all 38,180 sample hashes and HDF5 schemas, complete manifest
+coverage, split consistency, and the nine balanced experimental groups.
+
+## License and publication status
+
+The repository specifies the MIT license. Author confirmation is still needed
+for attribution, label semantics, physical units, and acquisition/generation details.
+
+Dataset repository: [PeizhaoEdinburgh/Occlusion_Based_NLOS](https://huggingface.co/datasets/PeizhaoEdinburgh/Occlusion_Based_NLOS).
+Local runtime and environment directories such as `.omx/` and `.venv/` are excluded
+from the published package.
+
+The metadata layout follows Hugging Face's
+[manual configuration documentation](https://huggingface.co/docs/hub/datasets-manual-configuration).
+Remote loading uses the documented
+[Hub download API](https://huggingface.co/docs/huggingface_hub/guides/download).
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