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@@ -237,9 +237,17 @@ with (snapshot / "metadata.csv").open(newline="") as handle:
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  print(out_dir)
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  ```
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- ## Notes
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- - This is a raw mirror, not a cleaned or reformatted training set.
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- - The alignment tree is preserved under its original relative paths inside the tar shards.
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- - Tar shards are uncompressed. The goal is predictable extraction and straightforward random access to individual members.
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- - Check the upstream OpenProteinSet/OpenFold data terms and cite the original resources as appropriate for your use case.
 
 
 
 
 
 
 
 
 
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  print(out_dir)
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  ```
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+ ## Citation
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+ ```
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+ @inproceedings{ahdritz2023openproteinset,
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+ title = {OpenProteinSet: Training Data for Structural Biology at Scale},
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+ author = {Ahdritz, Gustaf and Bouatta, Nazim and Kadyan, Sachin and Jarosch, Lukas and Berenberg, Daniel and Fisk, Ian and Watkins, Andrew M. and Ra, Stephen and Bonneau, Richard and AlQuraishi, Mohammed},
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+ booktitle = {Advances in Neural Information Processing Systems 36: Datasets and Benchmarks Track},
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+ year = {2023},
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+ url = {https://openreview.net/forum?id=gO0kS0eE0F},
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+ eprint = {2308.05326},
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+ archivePrefix = {arXiv},
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+ primaryClass = {q-bio.BM}
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+ }
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+ ```