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+# DataBack: Dataset of CNF Formulas and Backbone Variable Phases
+
+## What is DataBack
+`DataBack` is a dataset that consists of SAT CNF formulas, each labeled with the phases of its backbone variables. Within `DataBack`, there are two distinct subsets: the pre-training set, named `DataBack-PT`, and the fine-tuning set, named `DataBack-FT`. The state-of-the-art backbone extractor, `CadiBack`, has been employed to obtain the backbone labels. Due to the increased complexity of the fine-tuning formulas, we have allocated a timeout of 1,000 seconds for the pre-training formulas and 5,000 seconds for the fine-tuning ones.
+
+The `DataBack` dataset has been employed to both pre-train and fine-tune our `NeuroBack` model, which has demonstrated significant improvements in SAT solving efficiency. For an in-depth exploration of `DataBack`, please refer to [our `NeuroBack` paper](https://arxiv.org/pdf/2110.14053.pdf).
+
+## Authors
+Wenxi Wang, Yang Hu, Mohit Tiwari, Sarfraz Khurshid, Ken McMillan, Risto Miikkulainen
+
+## Publication
+If you use `DataBack` in your research, please kindly cite [our paper](https://arxiv.org/pdf/2110.14053.pdf):
+```bib
+@article{wang2023neuroback,
+  author       = {Wang, Wenxi and
+                  Hu, Yang and
+                  Tiwari, Mohit and
+                  Khurshid, Sarfraz and
+                  McMillan, Kenneth L. and
+                  Miikkulainen, Risto},
+  title        = {NeuroBack: Improving CDCL SAT Solving using Graph Neural Networks},
+  journal={arXiv preprint arXiv:2110.14053},
+  year={2021}
+}
+```
+## Directory Structure
+```
+|- original             # Original CNFs and their backbone variable phases
+|    |- cnf_pt.tar.gz   # CNFs for model pre-training
+|    |- bb_pt.tar.gz    # Backbone phases for pre-training CNFs
+|    |- cnf_ft.tar.gz   # CNFs for model fine-tuning
+|    |- bb_ft.tar.gz    # Backbone phases for fine-tuning CNFs
+|
+|- dual                      # Dual CNFs and their backbone variable phases
+|    |- dual_cnf_pt.tar.gz   # Dual CNFs for model pre-training
+|    |- dual_bb_pt.tar.gz    # Backbone phases for dual pre-training CNFs
+|    |- dual_cnf_ft.tar.gz   # Dual CNFs for model fine-tuning
+|    |- dual_bb_ft.tar.gz    # Backbone phases for dual fine-tuning CNFs
+```
+
+## File Naming Convention
+In the original directory, each CNF tar file contains compressed CNF files named: `[cnf_name].cnf.[compression_format]`, where `[compression_format]` could be bz2, lzma, xz, gz, etc. Correspondingly, each backbone tar file comprises compressed backbone files named: `[cnf_name].cnf.backbone.xz`. It is important to note that a compressed CNF file will always share its `[cnf_name]` with its associated compressed backbone file.
+
+In the dual directory, the naming convention remains consistent, but with an added `d_` prefix for each compressed CNF or backbone file to indicate it pertains to a dual CNF formula.
+
+## Contact
+Wenxi Wang ([email protected]), Yang Hu ([email protected])