README.md

metadata

license: mit
language:
  - en
tags:
  - science
  - accelerator-physics
  - particle-accelerator
pretty_name: Accel-IR
size_categories:
  - 1K<n<10K
task_categories:
  - text-retrieval
  - question-answering
configs:
  - config_name: expert_core
    data_files:
      - split: test
        path: Accel_IR_expert_core.csv
  - config_name: augmented
    data_files:
      - split: test
        path: Accel_IR_augmented.csv

Accel-IR Benchmark: A Gold Standard for Particle Accelerator Physics

This repository contains the Accel-IR Benchmark, a domain-specific Information Retrieval (IR) dataset for particle accelerator physics. It was developed as part of the Master's Thesis "From Dataset to Optimization: A Benchmarking Framework for Information Retrieval in the Particle Accelerator Domain" by Qing Dai (University of Zurich, 2025), in collaboration with the Paul Scherrer Institute (PSI).

Dataset Configurations

This benchmark is available in two configurations. You can load specific versions based on your evaluation needs:

Configuration	Rows	Description	Use Case
expert_core	390	Purely expert-annotated pairs. Labeled by 7 domain experts (PhDs/Researchers) from PSI.	Precise evaluation against human ground truth.
augmented	1,357	The expert_core + curated hard negatives. The negatives were generated using a expert-validated automatic annotation pipeline.	Realistic IR evaluation with many more negatives than positives.

Dataset Structure

Data Fields

Each row in the dataset represents a query-document pair with the following columns:

• Source: The referenced paper or an IPAC publication, source of the chunks.
• Question: The domain-specific scientific question.
• Answer: Answer to the question.
• Question_type: The category of the question, simulating diverse information needs:
  • Fact: Specific details or parameters.
  • Definition: Explanations of concepts/terms.
  • Reasoning: Logic behind phenomena or mechanisms.
  • Summary: Key points or conclusions.
• Referenced_file(s): Referenced papers for the questions, provided by experts.
• chunk_text: The text passage retrieved from domain-expert-referenced papers or IPAC conference papers.
• expert_annotation (Core only): The raw relevance score given by domain experts on a 5-point Likert scale:
  • 1: Irrelevant
  • 2: Partially Irrelevant
  • 3: Hard to Decide (Excluded from Core)
  • 4: Partially Relevant
  • 5: Relevant
• specific to paper: Indicates if the question is "Context-Dependent" (answerable only by the referenced paper) or "General" (answerable by broader domain knowledge).
• Label: The binary ground truth used for evaluation metrics (nDCG/MAP).
  • 1 (Relevant): Derived from expert scores 4 & 5.
  • 0 (Irrelevant): Derived from expert scores 1 & 2, or pipeline hard negatives.

Creation Methodology

1. Expert Core:

   • Created by 7 domain experts from the Electron Beam Instrumentation Group at PSI.
   • Experts reviewed query-chunk pairs and annotated them on a 1-5 scale using a custom interface.
   • Pairs labeled as "3 - Not Sure" were removed to ensure no ambiguity.

2. Augmentation (Hard Negatives):

   • To simulate realistic retrieval scenarios where negatives far outnumber positives, the core dataset was augmented.
   • Hard Negatives were generated using an expert-validated automatic annotation pipeline.

Usage

You can load the datasets using the Hugging Face datasets library.

Load the Expert Core (390 pairs)

from datasets import load_dataset

# Load the pure expert-annotated subset
ds_core = load_dataset("qdai/Accel-IR", "expert_core", split="test")

print(ds_core[0])

Citation

If you use this dataset, please cite:

Qing Dai, "From Dataset to Optimization: A Benchmarking Framework for Information Retrieval in the Particle Accelerator Domain", Master's Thesis, University of Zurich, 2025.