Update dataset card with paper link and license metadata

This PR improves the dataset card for `ICDOPS-QA-2024` by:

* Adding the official paper link, "[Unlocking Public Catalogues: Instruction-Tuning LLMs for ICD Coding of German Tumor Diagnoses](https://huggingface.co/papers/2510.13624)", prominently at the top of the card and updating the "Citable publication" section. This ensures proper attribution and makes the associated research easily discoverable.
* Including a BibTeX entry for the paper in the "Citation and further information" section, facilitating academic referencing.
* Adding the `license: other` tag to the YAML metadata. This aligns with Hugging Face best practices for datasets with custom licensing terms, which are fully detailed in the Markdown content.

README.md

@@ -1,4 +1,12 @@
 ---
+language:
+- de
+size_categories:
+- 100K<n<1M
+task_categories:
+- question-answering
+- text-classification
+pretty_name: ICDOPS-QA-2024
 dataset_info:
   features:
   - name: question
@@ -16,23 +24,18 @@ configs:
   data_files:
   - split: train
     path: data/train-*
-task_categories:
-- question-answering
-- text-classification
-language:
-- de
 tags:
 - medical
 - icd-10
 - icd-o-3
 - ops
-size_categories:
-- 100K<n<1M
-pretty_name: ICDOPS-QA-2024
+license: other
 ---
 
 # Dataset Card for ICDOPS-QA-2024
 
+Paper: [Unlocking Public Catalogues: Instruction-Tuning LLMs for ICD Coding of German Tumor Diagnoses](https://huggingface.co/papers/2510.13624)
+
 ## Content
 This dataset contains 518,116 question-answer pairs in German medical language with a focus on **oncological diagnoses**, covering the following **medical coding systems**, including:
 - **ICD-10-GM** (International Statistical Classification of Diseases, 10th revision, German Modification) via the [alphabetical index (Alpha-ID)](https://www.bfarm.de/EN/Code-systems/Classifications/ICD/ICD-10-GM/Alphabetical-index/_node.html)
@@ -42,14 +45,14 @@ This dataset contains 518,116 question-answer pairs in German medical language w
 The dataset uses the 2024 versions of the coding systems.
 
 ## Loading the dataset
-```{python}
+```python
 import datasets
 datasets.load_dataset("stefan-m-lenz/ICDOPS-QA-2024", split="train")
 ```
 
 ## Dataset Structure
 ### Schema
-```{json}
+```json
 {
     'question': string
     'answer': string
@@ -126,4 +129,12 @@ The following models have been fine-tuned using the dataset:
 ## Citation and further information
 This dataset and the corresponding trained models were developed by Dr. Stefan Lenz and Lakisha Ortiz Rosario in the Medical Informatics Unit of Dr. Torsten Panholzer, at the Institute of Medical Biometry, Epidemiology and Informatics (IMBEI) of the University Medical Center of the Johannes Gutenberg University Mainz. The work was conducted in cooperation with Georg Vollmar at the University Cancer Center Mainz, under the leadership of Prof. Dr. Thomas Kindler.
 
-Citable publication: TBA
+Citable publication: [Unlocking Public Catalogues: Instruction-Tuning LLMs for ICD Coding of German Tumor Diagnoses](https://huggingface.co/papers/2510.13624)
+```bibtex
+@article{lenz2024unlocking,
+  title={Unlocking Public Catalogues: Instruction-Tuning LLMs for ICD Coding of German Tumor Diagnoses},
+  author={Lenz, Stefan M and Ortiz Rosario, Lakisha and Vollmar, Georg and Panholzer, Torsten and Kindler, Thomas},
+  journal={arXiv preprint arXiv:2510.13624},
+  year={2024}
+}
+```