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README.md

@@ -1,4 +1,12 @@
 ---
+license: cc-by-sa-4.0
+task_categories:
+- graph-ml
+pretty_name: 2D quasistatic non-linear structural mechanics with finite elasticity
+  and topology variations
+tags:
+- physics learning
+- geometry learning
 dataset_info:
   features:
   - name: Base_2_2/Zone
@@ -57,3 +65,109 @@ configs:
   - split: test
     path: data/test-*
 ---
+<p align='center'>
+<img src='https://i.ibb.co/JR7bWLNn/Logo-2-D-Multiscale-Hyperelasticity-2-consolas-100.png' alt='https://i.ibb.co/JR7bWLNn/Logo-2-D-Multiscale-Hyperelasticity-2-consolas-100.png' width='1000'/>
+<img src='https://i.ibb.co/zHFQFnPR/2-D-Multiscale-Hyperelasticity.png' alt='https://i.ibb.co/zHFQFnPR/2-D-Multiscale-Hyperelasticity.png' width='1000'/>
+</p>
+
+```yaml
+owner: Safran
+license: cc-by-sa-4.0
+data_production:
+  type: simulation
+  physics: 2D quasistatic non-linear structural mechanics, finite elasticity (large
+    strains), P1 elements, compressible hyperelastic material
+  simulator: fenics
+num_samples:
+  train: 764
+  test: 376
+storage_backend: hf_datasets
+
+```
+This dataset was generated with [`plaid`](https://plaid-lib.readthedocs.io/), we refer to this documentation for additional details on how to extract data from `plaid_sample` objects.
+
+The simplest way to use this dataset is to first download it:
+```python
+from plaid.storage import download_from_hub
+
+repo_id = "channel/dataset"
+local_folder = "downloaded_dataset"
+
+download_from_hub(repo_id, local_folder)
+```
+
+Then, to iterate over the dataset and instantiate samples:
+```python
+from plaid.storage import init_from_disk
+
+local_folder = "downloaded_dataset"
+split_name = "train"
+
+datasetdict, converterdict = init_from_disk(local_folder)
+
+dataset = datasetdict[split]
+converter = converterdict[split]
+
+for i in range(len(dataset)):
+    plaid_sample = converter.to_plaid(dataset, i)
+```
+
+It is possible to stream the data directly:
+```python
+from plaid.storage import init_streaming_from_hub
+
+repo_id = "channel/dataset"
+
+datasetdict, converterdict = init_streaming_from_hub(repo_id)
+
+dataset = datasetdict[split]
+converter = converterdict[split]
+
+for sample_raw in dataset:
+    plaid_sample = converter.sample_to_plaid(sample_raw)
+```
+
+Sample features can then be retrieved as follows:
+```python
+from plaid.storage import load_problem_definitions_from_disk
+local_folder = "downloaded_dataset"
+pb_defs = load_problem_definitions_from_disk(local_folder)
+
+# or
+from plaid.storage import load_problem_definitions_from_hub
+repo_id = "channel/dataset"
+pb_defs = load_problem_definitions_from_hub(repo_id)
+
+
+pb_def = next(iter(pb_defs.values()))
+
+plaid_sample = ... # use a method from above to instantiate a plaid sample
+
+for t in plaid_sample.get_all_time_values():
+    for path in pb_def.input_features:
+        feature = plaid_sample.get_feature_by_path(path=path, time=t)
+        ...
+    for path in pb_def.output_features:
+        feature = plaid_sample.get_feature_by_path(path=path, time=t)
+        ...
+```
+
+For those familiar with HF's `datasets` library, raw data can be retrieved without using the `plaid` library:
+```python
+from datasets import load_dataset
+
+repo_id = "channel/dataset"
+
+datasetdict = load_dataset(repo_id)
+
+for split_name, dataset in datasetdict.items():
+    for raw_sample in dataset:
+        for feat_name in dataset.column_names:
+            feature = raw_sample[feat_name]
+```
+Notice that raw data refers to the variable features only, with a specific encoding for time variable features.
+
+### Dataset Sources
+
+- **Papers:**
+   - [arxiv](https://arxiv.org/abs/2505.02974)