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README.md

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 ---
-dataset_info:
-  features:
-  - name: ID
-    dtype: int64
-  - name: composition
-    dtype: string
-  - name: source
-    dtype: string
-  - name: temperature
-    dtype: float64
-  - name: target
-    dtype: float64
-  - name: family
-    dtype: string
-  - name: ChemicalFamily
-    dtype: string
-  - name: structure
-    dtype: int64
-  - name: pretty_formula
-    dtype: string
-  - name: classification_target
-    dtype: int64
-  - name: loco
-    dtype: int64
-  - name: formula
-    dtype: string
-  - name: log_target
-    dtype: float64
-  splits:
-  - name: train
-    num_bytes: 73792
-    num_examples: 372
-  download_size: 35650
-  dataset_size: 73792
-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
 ---
+
+# A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning
+
+Dataset containing experimental solid state Li electrolyte conductivities for 372 materials
+
+## Dataset Information
+
+- **Source**: [Foundry-ML](https://github.com/MLMI2-CSSI/foundry)
+- **DOI**: [10.18126/k9tn-3965](https://doi.org/10.18126/k9tn-3965)
+- **Year**: 2023
+- **Authors**: Hargreaves, Cameron J., Gaultois, Michael W., Daniels, Luke M., Watts, Emma J., Kurlin, Vitaliy A., Moran, Michael, Dang, Yun, Morris, Rhun, Morscher, Alexandra, Thompson, Kate, Wright, Matthew A., Prasad, Beluvalli-Eshwarappa, Blanc, Frédéric, Collins, Chris M., Crawford, Catriona A., Duff, Benjamin B., Evans, Jae, Gamon, Jacinthe, Han, Guopeng, Leube, Bernhard T., Niu, Hongjun, Perez, Arnaud J., Robinson, Aris, Rogan, Oliver, Sharp, Paul M., Shoko, Elvis, Sonni, Manel, Thomas, William J., Vasylenko, Andrij, Wang, Lu, Rosseinsky, Matthew J., Dyer, Matthew S.
+- **Data Type**: tabular
+
+### Fields
+
+| Field | Role | Description | Units |
+|-------|------|-------------|-------|
+| ID | input | Entry ID |  |
+| composition | input | Material composition |  |
+| source | input | Original data source |  |
+| temperature | input | Temperature of measured conductivity | degC |
+| target | input | Li conductivity (log scale) | S/cm |
+| family | input | Material structure family |  |
+| ChemicalFamily | input | Material chemical family |  |
+| structure | input | Structure group encoding |  |
+| pretty_formula | input | Material composition reduced form |  |
+| classification_target | input | Designation of low/high conductivity |  |
+| loco | input | Cluster number |  |
+| formula | input | Material composition reduced form |  |
+| log_target | target | Li conductivity (log scale) | S/cm |
+
+
+### 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/k9tn-3965")
+X, y = dataset.get_as_dict()['train']
+```
+
+### With HuggingFace Datasets
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("Dataset_Li_conductivity")
+```
+
+## Citation
+
+```bibtex
+@misc{https://doi.org/10.18126/k9tn-3965
+doi = {10.18126/k9tn-3965}
+url = {https://doi.org/10.18126/k9tn-3965}
+author = {Hargreaves, Cameron J. and Gaultois, Michael W. and Daniels, Luke M. and Watts, Emma J. and Kurlin, Vitaliy A. and Moran, Michael and Dang, Yun and Morris, Rhun and Morscher, Alexandra and Thompson, Kate and Wright, Matthew A. and Prasad, Beluvalli-Eshwarappa and Blanc, Frédéric and Collins, Chris M. and Crawford, Catriona A. and Duff, Benjamin B. and Evans, Jae and Gamon, Jacinthe and Han, Guopeng and Leube, Bernhard T. and Niu, Hongjun and Perez, Arnaud J. and Robinson, Aris and Rogan, Oliver and Sharp, Paul M. and Shoko, Elvis and Sonni, Manel and Thomas, William J. and Vasylenko, Andrij and Wang, Lu and Rosseinsky, Matthew J. and Dyer, Matthew S.}
+title = {A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning}
+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.*