Update README and LICENSE

LICENSE.md

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+Rosetta Software
+================
+
+0. Preamble
+--------------
+
+_This preamble is explanatory in nature, and is not part of the license agreement._
+
+a. While the Rosetta source code is published on GitHub, it is not "Open Source" (according to the OSI definition). Use of Rosetta is governed by the license agreement -- either the one below or a separate commercial agreement obtained through University of Washington CoMotion) 
+b. Use for commercial purposes is not permitted under the following license. To obtain a commercial/for-profit license (or for questions about whether your use is considered commercial), please contact University of Washington CoMotion (license@uw.edu). If you and your organization have a commercial license or an Institution Participation Agreement for Rosetta from UW CoMotion, the terms of that license supersede the ones below. 
+c. All forks of the Rosetta code must maintain the current licensing restrictions. 
+d. Modifications and improvements to the Rosetta code are encouraged. Please see CONTRIBUTING.md for more details.
+
+Rosetta Software Non-Commercial License Agreement
+--------------------------
+
+The Rosetta software ("Software") has been developed by the contributing researchers and institutions of the Rosetta Commons ("Developers") and made available through the University of Washington ("UW") for noncommercial, non-profit use. For more information about the Rosetta Commons, please see www.rosettacommons.org. If you wish to use the Software for any commercial purposes, including fee-based service projects, you will need to execute a separate licensing agreement with the UW and pay a fee. In that case, please contact: license@uw.edu.
+
+The Software was developed through support of a variety of funding sources, including the National Institutes of Health, Human Frontier Science Program Grant, National Science Foundation, Office of Naval Research, Packard Foundation, the Damon Runyon Cancer Research Foundation, Jane Coffin Childs Foundation, Los Alamos National Lab, Alfred P. Sloan Foundation, European Research Council, Israel Science Foundation, Bill & Melinda Gates Foundation and the Howard Hughes Medical Institute (HHMI).
+
+“Non-Commercial User” means 1) employees of not-for-profit research institutions, government laboratories, and universities conducting research excluding (a) commercial service; or (b) contract research or services for a for profit company where the intellectual property resulting from such research or service is owned by the for-profit company , and 2) individuals excluding (a) any use by, for, or on behalf of an entity organized for profit; or (b) any use intended for or directed toward commercial advantage or monetary compensation.
+
+“Software” includes both source and executable copies of the Rosetta software as distributed by the Rosetta Commons.
+
+UW and the Developers grant Non-Commercial Users the rights to perform, display, reproduce and modify the Software solely for internal Non-Commercial purposes, on the following conditions:
+
+1. You shall not distribute the Software or any modifications to the Software. As an exception, you may fork official Rosetta Commons repositories and make modifications to the source code in such forks on the same version control platform on which those official repositories are hosted.
+
+2. You retain in Software and any modifications to Software, all copyright, trademark, or other notices pertaining to Software as provided by UW and Developers.
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+3. If you institute litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Software or any part of the Software constitutes direct or contributory patent infringement, then any license granted to you herein shall terminate as of the date such litigation is filed.
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+4. You acknowledge that the Developers, UW and its licensees may develop modifications to Software that may be substantially similar to your modifications of Software, and that the Developers, UW and its licensees shall not be constrained in any way by you in Developer's, UW's or its licensees' use or management of such modifications. You acknowledge the right of the Developers and UW to prepare and publish modifications to Software that may be substantially similar or functionally equivalent to your modifications and improvements, and if you obtain patent protection for any modification or improvement to Software you agree not to allege or enjoin infringement of your patent by the Developers, UW or by any of UW's licensees obtaining modifications or improvements to Software from the UW or the Developers.
+
+5. You agree to acknowledge the contribution of the Developers and the Software make to your research, and cite appropriate references about the Software in your publications (see CITING_ROSETTA.md).
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+6. Any risk associated with using the Software is with you and/or your institution. Software is experimental in nature and is made available as a research courtesy "AS IS," without obligation by UW or Developers to provide accompanying services or support.
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+

README.md

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-A 'curated' dataset from proteinMPNN training data
+---
+language: en
+license: other
+license_name: rosetta-license-1.0
+license_link: LICENSE.md
+size_categories: 10k<n<100k
+pretty_name: 'Curated ProteinMPNN training dataset'
+tags:
+  - chemistry
+  - biology
+dataset_summary: 'The multi-chain training data for ProteinMPNN'
+dataset_description:
+acknowledgements: 'We kindly acknowledge the ProteinMPNN team, RosettaCommons, and the following institutions: University of California, Los Angeles; University of Maryland; University of Oregon; University of Michigan; University of Pennsylvania; and the Wistar Institute'
+repo: https://github.com/dauparas/ProteinMPNN
+citation_bibtex: '@article{Dauparas2022,
+    title = {Robust deep learning–based protein sequence design using ProteinMPNN},
+    volume = {378},
+    ISSN = {1095-9203},
+    url = {http://dx.doi.org/10.1126/science.add2187},
+    DOI = {10.1126/science.add2187},
+    number = {6615},
+    journal = {Science},
+    publisher = {American Association for the Advancement of Science (AAAS)},
+    author = {Dauparas,  J. and Anishchenko,  I. and Bennett,  N. and Bai,  H. and Ragotte,  R. J. and Milles,  L. F. and Wicky,  B. I. M. and Courbet,  A. and de Haas,  R. J. and Bethel,  N. and Leung,  P. J. Y. and Huddy,  T. F. and Pellock,  S. and Tischer,  D. and Chan,  F. and Koepnick,  B. and Nguyen,  H. and Kang,  A. and Sankaran,  B. and Bera,  A. K. and King,  N. P. and Baker,  D.},
+    year = {2022},
+    month = oct,
+    pages = {49–56}
+    }'
+citation_apa: Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., … Baker, D. (2022). Robust deep learning-based protein sequence design using ProteinMPNN. Science (New York, N.Y.), 378(6615), 49–56. doi:10.1126/science.add2187
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: "data.csv"
+  - split: test
+    path: "holdout.csv"
+  - : validation
+    path: "holdout.csv"
+---
+
+# Curated ProteinMPNN training dataset
+
+The multi-chain training data for ProteinMPNN
+
+## 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 `datasets` 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 `group_mpnn` model datasets, use `datasets.load_dataset(...)`:
+
+    >>> dataset_tag = "train"
+    >>> dataset_models = datasets.load_dataset(
+      path = "leebecca/group_mpnn",
+      name = f"{dataset_tag}_models",
+      data_dir = f"{dataset_tag}")['train']
+
+and the dataset is loaded as a `datasets.arrow_dataset.Dataset`
+
+    >>> dataset_models
+    Dataset({
+        features: ['id', 'pdb', 'Filter_Stage2_aBefore', 'Filter_Stage2_bQuarter', 'Filter_Stage2_cHalf', 'Filter_Stage2_dEnd', 'clashes_bb', 'clashes_total', 'score', 'silent_score', 'time'],
+        num_rows: 211069
+    })
+
+which is a column oriented format that can be accessed directly, converted in to a `pandas.DataFrame`, or `parquet` format, e.g.
+
+    >>> dataset_models.data.column('pdb')
+    >>> dataset_models.to_pandas()
+    >>> dataset_models.to_parquet("dataset.parquet")
+
+## Dataset Details
+
+### Dataset Description
+This dataset contains metadata and per-chain sequence and tensorized coorindates for the multi-chain training data for ProteinMPNN
+
+- **Acknowledgements:**
+  We kindly acknowledge the ProteinMPNN team, RosettaCommons, and the following institutions: University of California, Los Angeles; University of Maryland; University of Oregon; University of Michigan; University of Pennsylvania; and the Wistar Institute.
+
+- **License:** rosetta-license-1.0
+
+### Dataset Sources
+- **Repository:** https://github.com/dauparas/ProteinMPNN/tree/main/training
+- **Paper:**
+  Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., … Baker, D. (2022). Robust deep learning-based protein sequence design using ProteinMPNN. Science (New York, N.Y.), 378(6615), 49–56. doi:10.1126/science.add2187
+
+## Uses
+Exploration of sequence-structure relationships, not limited to inverse folding models.
+
+### Out-of-Scope Use
+This dataset has been curated with restraints imposed by the ProteinMPNN team. Thus caution much be used when using
+it as training data for protein structure prediction.
+
+### Source Data
+A set of 19,700 high-resolution single-chain structures from the Protein Data Bank (PDB) were split into train, validation, and test sets (80/10/10) based on the CATH protein classification database. This set contains protein assemblies in the PDB (as of 2 August 2021) determined by x-ray crystallography or cryo–electron microscopy (cryo-EM) to better than 3.5-Å resolution and with fewer than 10,000 residues.
+
+## Citation
+
+    @article{Dauparas2022,
+    title = {Robust deep learning–based protein sequence design using ProteinMPNN},
+    volume = {378},
+    ISSN = {1095-9203},
+    url = {http://dx.doi.org/10.1126/science.add2187},
+    DOI = {10.1126/science.add2187},
+    number = {6615},
+    journal = {Science},
+    publisher = {American Association for the Advancement of Science (AAAS)},
+    author = {Dauparas,  J. and Anishchenko,  I. and Bennett,  N. and Bai,  H. and Ragotte,  R. J. and Milles,  L. F. and Wicky,  B. I. M. and Courbet,  A. and de Haas,  R. J. and Bethel,  N. and Leung,  P. J. Y. and Huddy,  T. F. and Pellock,  S. and Tischer,  D. and Chan,  F. and Koepnick,  B. and Nguyen,  H. and Kang,  A. and Sankaran,  B. and Bera,  A. K. and King,  N. P. and Baker,  D.},
+    year = {2022},
+    month = oct,
+    pages = {49–56}
+    }
+
+## Dataset Card Authors
+Miranda Simpson (miranda13nicoles@gmail.com), Becca Lee (beccalee5@g.ucla.edu), Nathaniel Felbinger (nfelbing@umd.edu), Pratyush Dhal (pdhal@umich.edu), Colby Agostino (colby.agostino@pennmedicine.upenn.edu)