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README.md

@@ -1,23 +1,81 @@
 ---
-dataset_info:
-  features:
-  - name: Composition
-    dtype: string
-  - name: Initial Temperature (degC)
-    dtype: int64
-  - name: Final Temperature (degC)
-    dtype: int64
-  - name: TEC (x10^-6 K^-1)
-    dtype: float64
-  splits:
-  - name: train
-    num_bytes: 6880
-    num_examples: 137
-  download_size: 4724
-  dataset_size: 6880
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+license: other
+task_categories:
+  - tabular-regression
+  - tabular-classification
+tags:
+  - materials-science
+  - chemistry
+  - foundry-ml
+  - scientific-data
+size_categories:
+  - 1K<n<10K
 ---
+
+# Machine-learning prediction of thermal expansion coefficient for perovskite oxides with experimental validation
+
+Dataset containing 137 perovskite thermal expansion coefficients from experiment
+
+## Dataset Information
+
+- **Source**: [Foundry-ML](https://github.com/MLMI2-CSSI/foundry)
+- **DOI**: [10.18126/avvr-p174](https://doi.org/10.18126/avvr-p174)
+- **Year**: 2023
+- **Authors**: McGuinness, Kevin P., Oliynyk, Anton O., Lee, Sangjoon, Molero-Sanchez, Beatriz, Addo, Paul Kwesi
+- **Data Type**: tabular
+
+### Fields
+
+| Field | Role | Description | Units |
+|-------|------|-------------|-------|
+| Composition | input | Material composition with sites |  |
+| Initial Temperature (degC) | input | Initial temperature | degC |
+| Final Temperature (degC) | input | Final temperature | degC |
+| TEC (x10^-6 K^-1) | target | Thermal expansion coefficient | K^-1 |
+
+
+### Splits
+
+- **train**: train
+
+
+## Usage
+
+### With Foundry-ML (recommended for materials science workflows)
+
+```python
+from foundry import Foundry
+
+f = Foundry()
+dataset = f.get_dataset("10.18126/avvr-p174")
+X, y = dataset.get_as_dict()['train']
+```
+
+### With HuggingFace Datasets
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("Dataset_perovskite_tec")
+```
+
+## Citation
+
+```bibtex
+@misc{https://doi.org/10.18126/avvr-p174
+doi = {10.18126/avvr-p174}
+url = {https://doi.org/10.18126/avvr-p174}
+author = {McGuinness, Kevin P. and Oliynyk, Anton O. and Lee, Sangjoon and Molero-Sanchez, Beatriz and Addo, Paul Kwesi}
+title = {Machine-learning prediction of thermal expansion coefficient for perovskite oxides with experimental validation}
+keywords = {machine learning, foundry}
+publisher = {Materials Data Facility}
+year = {root=2023}}
+```
+
+## License
+
+other
+
+---
+
+*This dataset was exported from [Foundry-ML](https://github.com/MLMI2-CSSI/foundry), a platform for materials science datasets.*