---
dataset_name: EMS-Knowledge
pretty_name: EMS Knowledge Base (Flattened Sections)
language:
  - en
license: mit
tags:
  - emergency-medical-services
  - ems
  - medical
  - knowledge-base
  - retrieval
  - question-answering
size_categories:
  - 10K<n<100K
task_categories:
  - question-answering
  - text-retrieval
---

# EMS-Knowledge

**EMS-Knowledge** is a lightweight, retrieval-friendly dataset distilled from
Emergency Medical Services (EMS) reference materials.  
Each row contains a short section of text paired with its **file name**, **topic**, and **section title**,
making it easy to build search / RAG pipelines, create study aids, or ground QA systems. See more on our [project page](https://uva-dsa.github.io/EMSQA/).


This repository includes:
1. A **tabular split** (single split named `train`) with the following columns:
   - `file` — source filename (e.g., `anatomy.json`)
   - `source` — folder label (here: `knowledge`)
   - `topic` — coarse topic derived from file stem (e.g., `anatomy`, `trauma`)
   - `section` — section/heading within the file
   - `text` — the normalized section text

2. The original JSON files under **`raw/`** for direct use:
   - `airway, respiratory, ventilation.json`
   - `anatomy.json`
   - `assessment.json`
   - `cardiovascular.json`
   - `ems_operations.json`
   - `medical.json`
   - `others.json`
   - `pediatrics.json`
   - `pharmacology.json`
   - `terminology.json`
   - `trauma.json`

> 💡 The JSON files are heterogeneous collections of (section → text) entries.
> The table view flattens these into uniform rows for easier indexing and filtering.

---

## Dataset Structure

**Split**: `train` (tabular)

**Columns**
- `file` (string)
- `source` (string)
- `topic` (string)
- `section` (string)
- `text` (string)

**Cardinality**: depends on the number of sections parsed from the JSONs.  
(Each JSON entry becomes one row.)

---

## Citation

If you use EMS-MCQA in your work, please cite:

```bibtex
@misc{ge2025expertguidedpromptingretrievalaugmentedgeneration,
  title         = {Expert-Guided Prompting and Retrieval-Augmented Generation for Emergency Medical Service Question Answering},
  author        = {Xueren Ge and Sahil Murtaza and Anthony Cortez and Homa Alemzadeh},
  year          = {2025},
  eprint        = {2511.10900},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2511.10900},
}
```

---

## Quick Start

```python
from datasets import load_dataset

# Load table (single split named "train")
ds = load_dataset("Xueren/EMS-Knowledge", split="train")
print(ds)
print(ds[0])

# Filter by topic
anatomy = ds.filter(lambda x: x["topic"] == "anatomy")

# Simple keyword search over section text
needle = "spinal cord"
hits = ds.filter(lambda x: needle.lower() in x["text"].lower())
print(len(hits), "matches for", needle)

# Group sections by file/topic (example)
by_file = ds.to_pandas().groupby("file").size().sort_values(ascending=False)
print(by_file.head())