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.

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:

@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

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())