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README.md

@@ -1,19 +1,79 @@
 ---
-dataset_info:
-  features:
-  - name: name
-    dtype: string
-  - name: Tc
-    dtype: float64
-  splits:
-  - name: train
-    num_bytes: 456874
-    num_examples: 16414
-  download_size: 317285
-  dataset_size: 456874
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+license: cc-by-4.0
+task_categories:
+  - tabular-regression
+  - tabular-classification
+tags:
+  - materials-science
+  - chemistry
+  - foundry-ml
+  - scientific-data
+size_categories:
+  - 1K<n<10K
 ---
+
+# Machine learning modeling of superconducting critical temperature
+
+Dataset containing experimentally measured superconducting critical temperatures for 16414 materials
+
+## Dataset Information
+
+- **Source**: [Foundry-ML](https://github.com/MLMI2-CSSI/foundry)
+- **DOI**: [10.18126/xlfr-hjrn](https://doi.org/10.18126/xlfr-hjrn)
+- **Year**: 2022
+- **Authors**: Stanev, Valentin, Oses, Corey, Kusne, A. Gilad, Rodriguez, Efrain, Paglione, Johnpierre, Curtarolo, Stefano, Takeuchi, Ichiro
+- **Data Type**: tabular
+
+### Fields
+
+| Field | Role | Description | Units |
+|-------|------|-------------|-------|
+| name | input | Material composition |  |
+| Tc | target | Experimental superconducting critical temperature | K |
+
+
+### 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/xlfr-hjrn")
+X, y = dataset.get_as_dict()['train']
+```
+
+### With HuggingFace Datasets
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("superconductivity_v1.1")
+```
+
+## Citation
+
+```bibtex
+@misc{https://doi.org/10.18126/xlfr-hjrn
+doi = {10.18126/xlfr-hjrn}
+url = {https://doi.org/10.18126/xlfr-hjrn}
+author = {Stanev, Valentin and Oses, Corey and Kusne, A. Gilad and Rodriguez, Efrain and Paglione, Johnpierre and Curtarolo, Stefano and Takeuchi, Ichiro}
+title = {Machine learning modeling of superconducting critical temperature}
+keywords = {machine learning, foundry}
+publisher = {Materials Data Facility}
+year = {root=2022}}
+```
+
+## License
+
+CC-BY 4.0
+
+---
+
+*This dataset was exported from [Foundry-ML](https://github.com/MLMI2-CSSI/foundry), a platform for materials science datasets.*