Update dataset license to noncommercial

README.md

@@ -1,5 +1,5 @@
 ---
-license: cc-by-4.0
+license: cc-by-nc-4.0
 task_categories:
 - image-classification
 language:
@@ -37,6 +37,12 @@ This dataset has also been used in Active Prompt Tuning (APT) experiments. Image
 
 We thank Dr. Jan Cendelín and Dr. Yaroslav Kolinko at the Faculty of Medicine in Pilsen, Charles University, Czech Republic, for collecting and providing the tissue images and associated metadata. We are also grateful for their support in making this dataset publicly available so that the broader biomedical imaging and machine learning communities can use it.
 
+## License and attribution
+
+This dataset is released under the [Creative Commons Attribution-NonCommercial 4.0 International License](https://creativecommons.org/licenses/by-nc/4.0/). You may share and adapt the dataset for non-commercial purposes with appropriate attribution. Commercial use requires separate written permission from the rights holders.
+
+If you use the dataset, fold manifests, seed prompts, or prompt bank in research, cite the associated APT paper listed below and retain attribution to the dataset contributors.
+
 ## Image layout
 
 ```text
@@ -104,3 +110,15 @@ The APT repository also includes `setup_lurcher_data.py`, which downloads the im
 ## Intended use
 
 Use these fixed folds when comparing against the APT paper or repository results. Do not regenerate splits for direct comparison to reported fold-level numbers.
+
+## Citation
+
+```bibtex
+@inproceedings{kandiyana2026apt,
+  title={A Human-in-the-Loop Framework for Efficient Prompt Selection in Microscopy Vision-Language Models},
+  author={Kandiyana, Abhiram and Mali, Ankur and Hall, Lawrence O. and Mouton, Peter R. and Goldgof, Dmitry},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
+  pages={6229-6238},
+  year={2026}
+}
+```