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README.md

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+---
+language:
+- en
+license: mit
+pretty_name: Cancer Abstract Dataset
+size_categories:
+- 1K\<n\<10K
+tags:
+- biomedical
+- oncology
+- cancer
+- text-classification
+- nlp
+- graph-neural-networks
+- document-classification
+task_categories:
+- text-classification
+---
+
+# Cancer Abstract Dataset
+
+## Dataset Summary
+
+The **Cancer Abstract Dataset** is a curated collection of biomedical
+research abstracts categorized by cancer type. It was developed to
+support research in document classification, low-resource biomedical
+NLP, and graph-based deep learning approaches.
+
+This dataset was introduced in the following publication:
+
+> Hossain, E., Nuzhat, T., Masum, S., et al.\
+> **R-GAT: cancer document classification leveraging graph-based
+> residual network for scenarios with limited data.**\
+> *Scientific Reports*, 16, 6582 (2026).\
+> https://doi.org/10.1038/s41598-026-39894-6
+
+------------------------------------------------------------------------
+
+## Dataset Description
+
+This dataset contains categorized research abstracts related to major
+cancer types. It is suitable for:
+
+-   Biomedical text classification
+-   Topic modeling
+-   Low-resource learning experiments
+-   Graph-based NLP methods
+-   Transformer-based fine-tuning
+-   Benchmarking uncertainty-aware LLMs
+
+------------------------------------------------------------------------
+
+## Dataset Structure
+
+### Total Samples
+
+**1,874 abstracts**
+
+### Format
+
+CSV (Comma-Separated Values)
+
+### Fields
+
+  Field        Description
+  ------------ -----------------------------
+  `Abstract`   Full research abstract text
+  `Category`   Cancer type label
+
+### Categories
+
+-   `Lung_Cancer`
+-   `Thyroid_Cancer`
+-   `Colon_Cancer`
+-   `Generic`
+
+------------------------------------------------------------------------
+
+## Example Usage
+
+``` python
+from datasets import load_dataset
+
+dataset = load_dataset("EliasHossain/CancerAbstracts")
+
+print(dataset["train"][0])
+```
+
+------------------------------------------------------------------------
+
+## Intended Use
+
+The dataset is intended for:
+
+-   Supervised text classification
+-   Graph neural network research
+-   Transformer-based fine-tuning
+-   Biomedical NLP benchmarking
+-   Limited-data learning evaluation
+
+This dataset is **not intended for clinical decision-making**.
+
+------------------------------------------------------------------------
+
+## Data Collection and Processing
+
+Abstracts were curated and categorized for research purposes in
+oncology-related document classification experiments. Standard
+preprocessing steps were applied to ensure formatting consistency.
+
+No personally identifiable information (PII) or protected health
+information (PHI) is included.
+
+------------------------------------------------------------------------
+
+## Citation
+
+If you use this dataset, please cite:
+
+``` bibtex
+@article{hossain2026rgat,
+  title={R-GAT: cancer document classification leveraging graph-based residual network for scenarios with limited data},
+  author={Hossain, Elias and Nuzhat, Tasfia and Masum, S. and others},
+  journal={Scientific Reports},
+  volume={16},
+  pages={6582},
+  year={2026},
+  doi={10.1038/s41598-026-39894-6}
+}
+```
+
+------------------------------------------------------------------------
+
+## Contributors
+
+-   **Elias Hossain**\
+    Mississippi State University, USA
+
+-   **Tasfia Nuzhat**\
+    Chittagong Independent University, Bangladesh
+
+------------------------------------------------------------------------
+
+## License
+
+MIT License