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README.md

@@ -1,21 +1,80 @@
 ---
-dataset_info:
-  features:
-  - name: Impurity
-    dtype: string
-  - name: Host
-    dtype: string
-  - name: Effective_charge_regression
-    dtype: float64
-  splits:
-  - name: train
-    num_bytes: 978
-    num_examples: 49
-  download_size: 2326
-  dataset_size: 978
-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
 ---
+
+# Exploring effective charge in electromigration using machine learning
+
+Dataset containing effective charge values for 49 metal host-impurity pairs
+
+## Dataset Information
+
+- **Source**: [Foundry-ML](https://github.com/MLMI2-CSSI/foundry)
+- **DOI**: [10.18126/abxi-r7eb](https://doi.org/10.18126/abxi-r7eb)
+- **Year**: 2022
+- **Authors**: Liu, Yu-chen, Afflerbach, Ben, Jacobs, Ryan, Lin, Shih-kang, Morgan, Dane
+- **Data Type**: tabular
+
+### Fields
+
+| Field | Role | Description | Units |
+|-------|------|-------------|-------|
+| Impurity | input | Impurity element |  |
+| Host | input | Host element |  |
+| Effective_charge_regression | target | Alloy effective charge values (target) |  |
+
+
+### 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/abxi-r7eb")
+X, y = dataset.get_as_dict()['train']
+```
+
+### With HuggingFace Datasets
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("electromigration_v1.1")
+```
+
+## Citation
+
+```bibtex
+@misc{https://doi.org/10.18126/abxi-r7eb
+doi = {10.18126/abxi-r7eb}
+url = {https://doi.org/10.18126/abxi-r7eb}
+author = {Liu, Yu-chen and Afflerbach, Ben and Jacobs, Ryan and Lin, Shih-kang and Morgan, Dane}
+title = {Exploring effective charge in electromigration using machine learning}
+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.*