readme

Author: dskoda

README.md

@@ -10,6 +10,8 @@ Benjamin Yu, Vincenzo Lordi, Daniel Schwalbe-Koda. "Maximizing Efficiency of Dat
 - All the raw data for plotting the notebooks can be downloaded using the `download.sh` script.
 - The Jupyter Notebooks in `nbs` contain all the code required to reproduce the analysis and the plots shown in the manuscript.
 
+The algorithms are implemented under the [QUESTS package](https://github.com/dskoda/quests).
+
 ## Installing and running
 
 To reproduce the results from the manuscript, first create a new Python environment using your preferred virtual environment (e.g., `venv` or `conda`).
@@ -85,7 +87,7 @@ The tarfile contains files of the following formats:
 
 ### Citing
 
-If you use the algorithms/benchmarks for compressing datasets in this work, please cite the following preprint:
+If you use the algorithms/benchmarks for compressing datasets in this work, please cite the following papers:
 
 ```bibtex
 @article{yu2025compression,
@@ -94,11 +96,7 @@ If you use the algorithms/benchmarks for compressing datasets in this work, plea
     year = {2025},
     journal = {arXiv},
 }
-```
-
-If you use QUESTS or its data/examples in a publication, please cite the following paper:
 
-```bibtex
 @article{schwalbekoda2025information,
     title = {Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory},
     author = {Schwalbe-Koda, Daniel and Hamel, Sebastien and Sadigh, Babak and Zhou, Fei and Lordi, Vincenzo},
@@ -111,6 +109,8 @@ If you use QUESTS or its data/examples in a publication, please cite the followi
 }
 ```
 
+The code used to analyze and compress the dataset is available under the [QUESTS](https://github.com/dskoda/quests) package.
+
 ## License
 
 This repository is distributed under the following license: MIT