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README.md

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+# SynPAT: Synthetic Physical Theories with Data
+
+This dataset contains systems of dimensionally consistent equations generated across various configurations of:
+
+- Number of variables: 6 to 9
+- Number of derivatives: 2 to 4
+- Number of equations: 4 to 6
+
+Each configuration includes three generated systems with:
+
+- Symbolic equations (`system.txt`)
+- Numerical data (`system.dat`)
+- Replacements (`replacement_*.txt`)
+- Consequences (`consequence.txt`, `consequence.dat`)
+- Noisy variants for each noise level (`*_0.001.dat`, ..., `*_0.1.dat`)
+
+## Purpose
+
+Designed to benchmark symbolic regression models on machine-assisted physical discovery tasks.
+
+## File structure
+
+The dataset is packaged as a single `dataset.zip` archive. Inside are directories for each configuration (`vars_X_derivs_Y_eqns_Z/`) containing 3 systems each.
+
+## Citation
+
+> Srivastava et al., **SynPAT: A System for Generating Synthetic Physical Theories with Data**, NeurIPS 2025 (submitted)
+
+## License
+
+[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
+
+## Related resources
+
+- 📘 [Project GitHub](https://github.com/jlenchner/theorizer)
+- 📄 [Croissant metadata](https://w3id.org/croissant)