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README.md

@@ -1,27 +1,83 @@
 ---
-dataset_info:
-  features:
-  - name: Passage
-    dtype: string
-  - name: DOI
-    dtype: string
-  - name: Material
-    dtype: string
-  - name: Rc
-    dtype: float64
-  - name: Unit
-    dtype: string
-  - name: log(Rc)
-    dtype: float64
-  splits:
-  - name: train
-    num_bytes: 157452
-    num_examples: 297
-  download_size: 63338
-  dataset_size: 157452
-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
 ---
+
+# Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineering
+
+Dataset containing LLM-derived experimental critical cooling rates of 297 metallic glasses
+
+## Dataset Information
+
+- **Source**: [Foundry-ML](https://github.com/MLMI2-CSSI/foundry)
+- **DOI**: [10.18126/ndyp-yv32](https://doi.org/10.18126/ndyp-yv32)
+- **Year**: 2024
+- **Authors**: Polak, Maciej P., Morgan, Dane
+- **Data Type**: tabular
+
+### Fields
+
+| Field | Role | Description | Units |
+|-------|------|-------------|-------|
+| Passage | input | Text passage LLM found to get data |  |
+| DOI | input | Original data reference |  |
+| Material | input | Material composition |  |
+| Rc | input | Critical cooling rate | K/s |
+| Unit | input | Critical cooling rate units |  |
+| log(Rc) | target | Critical cooling rate (log scale) | K/s |
+
+
+### 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/ndyp-yv32")
+X, y = dataset.get_as_dict()['train']
+```
+
+### With HuggingFace Datasets
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("Dataset_metallicglass_Rc_LLM")
+```
+
+## Citation
+
+```bibtex
+@misc{https://doi.org/10.18126/ndyp-yv32
+doi = {10.18126/ndyp-yv32}
+url = {https://doi.org/10.18126/ndyp-yv32}
+author = {Polak, Maciej P. and Morgan, Dane}
+title = {Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineering}
+keywords = {machine learning, foundry}
+publisher = {Materials Data Facility}
+year = {root=2024}}
+```
+
+## License
+
+other
+
+---
+
+*This dataset was exported from [Foundry-ML](https://github.com/MLMI2-CSSI/foundry), a platform for materials science datasets.*