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README.md

@@ -1,31 +1,85 @@
 ---
-dataset_info:
-  features:
-  - name: Cement (component 1)(kg in a m^3 mixture)
-    dtype: float64
-  - name: Blast Furnace Slag (component 2)(kg in a m^3 mixture)
-    dtype: float64
-  - name: Fly Ash (component 3)(kg in a m^3 mixture)
-    dtype: float64
-  - name: Water  (component 4)(kg in a m^3 mixture)
-    dtype: float64
-  - name: Superplasticizer (component 5)(kg in a m^3 mixture)
-    dtype: float64
-  - name: Coarse Aggregate  (component 6)(kg in a m^3 mixture)
-    dtype: float64
-  - name: Age (day)
-    dtype: int64
-  - name: 'Concrete compressive strength(MPa, megapascals) '
-    dtype: float64
-  splits:
-  - name: train
-    num_bytes: 65920
-    num_examples: 1030
-  download_size: 24558
-  dataset_size: 65920
-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 in concrete science: applications, challenges, and best practices
+
+Dataset containing concrete compressive strength for 1030 materials
+
+## Dataset Information
+
+- **Source**: [Foundry-ML](https://github.com/MLMI2-CSSI/foundry)
+- **DOI**: [10.18126/8k1f-mx77](https://doi.org/10.18126/8k1f-mx77)
+- **Year**: 2022
+- **Authors**: Li, Zhanzhao, Yoon, Jinyoung, Zhang, Rui, Rajabipour, Farshad, Srubar III, Wil V., Dabo, Ismaila, Radlińska, Aleksandra
+- **Data Type**: tabular
+
+### Fields
+
+| Field | Role | Description | Units |
+|-------|------|-------------|-------|
+| Cement (component 1)(kg in a m^3 mixture) | input | Amount of cement | kg/m^3 |
+| Blast Furnace Slag (component 2)(kg in a m^3 mixture) | input | Amount of blast furnace slag | kg/m^3 |
+| Fly Ash (component 3)(kg in a m^3 mixture) | input | Amount of fly ash | kg/m^3 |
+| Water  (component 4)(kg in a m^3 mixture) | input | Amount of water | kg/m^3 |
+| Superplasticizer (component 5)(kg in a m^3 mixture) | input | Amount of superplasticizer | kg/m^3 |
+| Coarse Aggregate  (component 6)(kg in a m^3 mixture) | input | Amount of coarse aggregate | kg/m^3 |
+| Age (day) | input | Age of concrete | days |
+| Concrete compressive strength(MPa, megapascals)  | target | Concrete compressive strength | MPa |
+
+
+### Splits
+
+- **train**: train
+
+
+## Usage
+
+### With Foundry-ML (recommended for materials science workflows)
+
+```python
+from foundry import Foundry
+
+f = Foundry()
+dataset = f.get_dataset("10.18126/8k1f-mx77")
+X, y = dataset.get_as_dict()['train']
+```
+
+### With HuggingFace Datasets
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("Dataset_concrete_compressive_strength")
+```
+
+## Citation
+
+```bibtex
+@misc{https://doi.org/10.18126/8k1f-mx77
+doi = {10.18126/8k1f-mx77}
+url = {https://doi.org/10.18126/8k1f-mx77}
+author = {Li, Zhanzhao and Yoon, Jinyoung and Zhang, Rui and Rajabipour, Farshad and Srubar III, Wil V. and Dabo, Ismaila and Radlińska, Aleksandra}
+title = {Machine learning in concrete science: applications, challenges, and best practices}
+keywords = {machine learning, foundry}
+publisher = {Materials Data Facility}
+year = {root=2022}}
+```
+
+## License
+
+other
+
+---
+
+*This dataset was exported from [Foundry-ML](https://github.com/MLMI2-CSSI/foundry), a platform for materials science datasets.*