---
tags:
- biology
- chemistry
size_categories:
- 10K<n<100K
dataset_info:
  features:
  - name: pmid
    dtype: string
  - name: section
    dtype: string
  - name: text
    dtype: string
  - name: annotations
    sequence:
      sequence: int64
  splits:
  - name: train
    num_bytes: 120073099
    num_examples: 17619
  download_size: 61504820
  dataset_size: 120073099
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---
# Alzheimer's Disease Tagged Articles Dataset

This dataset contains a collection of scientific articles related to **Alzheimer's disease**, annotated with biomedical entities (such as diseases, genes, and species) using NCBI’s **PubTator** tools.

## Data Sources and Annotation Tools

- Entity annotations were generated using NCBI's standard models and API calls to the **PubTator3 API**:
  - **TaggerOne** and **GNORM** for `gene_species_tagged_articles.json`
  - **TaggerOne** alone for `disease_tagged_articles.json`
  - **PubTator3 API** for 'converted_from_bioc.json'

These tools are widely used in biomedical NLP for tagging mentions of diseases, genes, and species within PubMed articles.

## Dataset Structure

The dataset is a flattened version of the original JSON files, where each record represents a tagged article section. The structure includes:

- `pmid`: PubMed ID of the article
- `section`: Type of section (e.g., `title`, `abstract`)
- `text`: Lowercased content of the section
- `annotations`: List of `[start, end]` character offsets identifying entity mentions

Example:
```json
{
  "pmid": "34379990",
  "section": "abstract",
  "text": "clinical progression of tauopathies may result...",
  "annotations": [[0, 26], [45, 53], ...]
}
````

## Preprocessing

The dataset has been simplified:

* Original nested structures were flattened
* Tuples were converted to JSON-compliant lists
* Only `title` and `abstract` sections are currently included

## Future Work

* Extend tagging beyond titles and abstracts to include full-text sections (e.g., introduction, methods, results)
* Add entity labels (e.g., `Disease`, `Gene`, `Species`) in a future version

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("AbrehamT/tagged_articles")
```

## License

This dataset is released under the **MIT License**.

## Acknowledgments

This dataset relies on NCBI’s **PubTator**, **GNORM**, and **TaggerOne** models. Credit goes to the developers of these tools and the researchers whose articles form the dataset foundation.