Update README.md

README.md

@@ -59,6 +59,38 @@ We provide some basic baselines for this task. Benchmarking details can be found
 
 The \\(\pm\\) signs indicate 95% confidence intervals from random weight initialization and training.
 
+- **Curated by:** Herman Chau
+- **Funded by:** Pacific Northwest National Laboratory
+- **Language(s) (NLP):** NA
+- **License:** CC-by-2.0
+
+### Dataset Sources
+
+The dataset was generated using [SageMath](https://www.sagemath.org/). Data generation scripts can be found [here](https://github.com/pnnl/ML4AlgComb/tree/master/mheight_function).
+
+- **Repository:** [ACD Repo](https://github.com/pnnl/ML4AlgComb/tree/master/mheight_function)
+  
+## Citation
+
+**BibTeX:**
+
+
+    @article{chau2025machine,
+        title={Machine learning meets algebraic combinatorics: A suite of datasets capturing research-level conjecturing ability in pure mathematics},
+        author={Chau, Herman and Jenne, Helen and Brown, Davis and He, Jesse and Raugas, Mark and Billey, Sara and Kvinge, Henry},
+        journal={arXiv preprint arXiv:2503.06366},
+        year={2025}
+    }
+
+
+**APA:**
+
+Chau, H., Jenne, H., Brown, D., He, J., Raugas, M., Billey, S., & Kvinge, H. (2025). Machine learning meets algebraic combinatorics: A suite of datasets capturing research-level conjecturing ability in pure mathematics. arXiv preprint arXiv:2503.06366.
+
+## Dataset Card Contact
+
+Henry Kvinge, [email protected]
+
 ## References
 
 [1] Gaetz, Christian, and Yibo Gao. "On the minimal power of \\(q\\) in a Kazhdan-Lusztig polynomial." arXiv preprint arXiv:2303.13695 (2023).