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Update dataset card with paper link and license metadata (#2)

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- Update dataset card with paper link and license metadata (16777d04a5a497adf3e4dcc68b106daf6ccf9984)


Co-authored-by: Niels Rogge <[email protected]>

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  1. README.md +22 -11
README.md CHANGED
@@ -1,4 +1,12 @@
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  ---
 
 
 
 
 
 
 
 
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  dataset_info:
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  features:
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  - name: question
@@ -16,23 +24,18 @@ configs:
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  data_files:
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  - split: train
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  path: data/train-*
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- task_categories:
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- - question-answering
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- - text-classification
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- language:
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- - de
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  tags:
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  - medical
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  - icd-10
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  - icd-o-3
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  - ops
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- size_categories:
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- - 100K<n<1M
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- pretty_name: ICDOPS-QA-2024
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  ---
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  # Dataset Card for ICDOPS-QA-2024
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  ## Content
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  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:
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  - **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
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  The dataset uses the 2024 versions of the coding systems.
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  ## Loading the dataset
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- ```{python}
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  import datasets
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  datasets.load_dataset("stefan-m-lenz/ICDOPS-QA-2024", split="train")
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  ```
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  ## Dataset Structure
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  ### Schema
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- ```{json}
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  {
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  'question': string
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  'answer': string
@@ -126,4 +129,12 @@ The following models have been fine-tuned using the dataset:
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  ## Citation and further information
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  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.
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- Citable publication: TBA
 
 
 
 
 
 
 
 
 
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  ---
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+ language:
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+ - de
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+ size_categories:
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+ - 100K<n<1M
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+ task_categories:
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+ - question-answering
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+ - text-classification
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+ pretty_name: ICDOPS-QA-2024
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  dataset_info:
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  features:
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  - name: question
 
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  data_files:
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  - split: train
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  path: data/train-*
 
 
 
 
 
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  tags:
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  - medical
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  - icd-10
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  - icd-o-3
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  - ops
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+ license: other
 
 
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  ---
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  # Dataset Card for ICDOPS-QA-2024
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+ Paper: [Unlocking Public Catalogues: Instruction-Tuning LLMs for ICD Coding of German Tumor Diagnoses](https://huggingface.co/papers/2510.13624)
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+
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  ## Content
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  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:
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  - **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)
 
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  The dataset uses the 2024 versions of the coding systems.
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  ## Loading the dataset
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+ ```python
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  import datasets
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  datasets.load_dataset("stefan-m-lenz/ICDOPS-QA-2024", split="train")
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  ```
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  ## Dataset Structure
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  ### Schema
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+ ```json
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  {
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  'question': string
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  'answer': string
 
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  ## Citation and further information
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  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.
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+ Citable publication: [Unlocking Public Catalogues: Instruction-Tuning LLMs for ICD Coding of German Tumor Diagnoses](https://huggingface.co/papers/2510.13624)
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+ ```bibtex
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+ @article{lenz2024unlocking,
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+ title={Unlocking Public Catalogues: Instruction-Tuning LLMs for ICD Coding of German Tumor Diagnoses},
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+ author={Lenz, Stefan M and Ortiz Rosario, Lakisha and Vollmar, Georg and Panholzer, Torsten and Kindler, Thomas},
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+ journal={arXiv preprint arXiv:2510.13624},
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+ year={2024}
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+ }
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+ ```