Merge branch 'main' of https://huggingface.co/datasets/rmauder/IsItABarrel

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+intermediate/*

README.md

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----
-license: agpl-3.0
----
+---
+language: en
+license: agpl-3.0
+#size_categories: 
+pretty_name: 'IsItABarrel Database'
+tags:
+  - biology
+  - chemistry
+dataset_summary: a curated database of approximately two million bacterial transmembrane beta barrels (TMBBs)
+#dataset_description: 
+#acknowledgements:
+repo: rmauder/IsItABarrel
+citation_bibtex:
+  -@article{Montezano2023,
+  title = {General features of transmembrane beta barrels from a large database},
+  volume = {120},
+  ISSN = {1091-6490},
+  url = {http://dx.doi.org/10.1073/pnas.2220762120},
+  DOI = {10.1073/pnas.2220762120},
+  number = {29},
+  journal = {Proceedings of the National Academy of Sciences},
+  publisher = {Proceedings of the National Academy of Sciences},
+  author = {Montezano,  Daniel and Bernstein,  Rebecca and Copeland,  Matthew M. and Slusky,  Joanna S. G.},
+  year = {2023},
+  month = jul}
+citation_apa: 
+  -Montezano, D., Bernstein, R., Copeland, M. M., & Slusky, J. S. G. (2023). General features of transmembrane beta barrels from a large database. Proceedings of the National Academy of Sciences of the United States of America, 120(29), e2220762120. doi:10.1073/pnas.2220762120
+
+---
+# IsItABarrel
+a searchable curated database of approximately two million bacterial transmembrane beta barrels (TMBBs)
+
+## QuickStart Usage
+
+### Install HuggingFace Datasets package
+
+Each subset can be loaded into python using the HuggingFace [datasets] (https://huggingface.co/docs/datasets/index) library.  First, from the command line install the 'datsets' library
+ $ pip install datasets
+
+Optionally set the cache directory, e.g.
+ $ HF\_HOME=${HOME}/.cache/huggingface/
+ $ export HF\_HOME
+
+then, from within python load the datasets library
+ \>\>\> import datasets
+
+### Load model datasets
+
+To load one of the IsItABarrel model datasets, use 'datasets.load_datase(...)':
+
+ \>\>\> dataset\_tag \= "\<DATASET TAG\>"
+ \>\>\> dataset \= datasets.load\_dataset(
+  path \= "\<HF PATH TO DATASET\>",
+  name \= f"{dataset\_tag}",
+  data\_dir \= f"{dataset\_tag}")\['train'\]
+
+and the dataset is loaded as a 'datasets.arrow_dataset.Dataset'
+
+ \>\>\> dataset
+ \<RESULT OF LOADING DATASET MODEL\>
+
+Which is a column oriented format that can be accessed directly, converted in to a 'pandas.DataFrame', or 'parquet' format, e.g.
+
+ \>\>\> dataset.data.column('\<COLUMN NAME IN DATASET\>')
+ \>\>\> dataset.to\_pandas()
+ \>\>\> dataset.to\_parquet("dataset.parquet")
+
+### <BREIF EXAMPLE OF HOW TO USE DIFFERENT PARTS OF THE DATASET>
+
+## Dataset Details
+
+### Dataset Description <DETAILED DESCRIPTION OF DATASET>
+
+- **Acknowledgements:** <ACKNOWLEDGEMENTS>
+
+- **License:** agpl-3.0
+
+### Dataset Sources - **Repository:** <https://isitabarrel.ku.edu/search> - **Paper:** <citation>
+
+## Uses <DESCRIPTION OF INTENDED USE OF DATASET>
+
+### Out-of-Scope Use <DESCRIPTION OF OUT OF SCOPE USES OF DATASET>
+
+### Source Data <DESCRIPTION OF SOURCE DATA>
+
+## Citation <BIBTEX REFERENCE FOR DATASET>
+
+## Dataset Card Authors