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OneStop: A 360-Participant English Eye Tracking Dataset with Different Reading Regimes.

Yevgeni Berzak1, Jonathan Malmaud2, Omer Shubi3

  • 1Technion - Israel Institute of Technology, Faculty of Data and Decision Sciences, Haifa, Israel. berzak@technion.ac.il.

Scientific Data
|December 2, 2025
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Summary
This summary is machine-generated.

The OneStop Eye Movements corpus offers extensive reading data for 360 native English speakers, enabling new insights into reading comprehension and human language processing.

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Area of Science:

  • Cognitive Science
  • Computational Linguistics
  • Educational Technology

Background:

  • Eye tracking is crucial for understanding reading processes.
  • Existing datasets lack sufficient scale and diversity for comprehensive analysis.
  • Bridging eye tracking data with Natural Language Processing (NLP) and Artificial Intelligence (AI) remains a challenge.

Purpose of the Study:

  • Introduce OneStop Eye Movements, a large-scale corpus of English L1 reading.
  • Provide unprecedented data volume (152 hours, 2.6 million tokens) for reading research.
  • Facilitate integration of eye tracking data into NLP, AI, Human Computer Interaction (HCI), and educational applications.

Main Methods:

  • Collected 152 hours of eye movement recordings from 360 native English speakers.
  • Utilized piloted reading comprehension materials with 486 questions and text annotations.
  • Included diverse reading regimes: ordinary, information seeking, repeated, and simplified reading.

Main Results:

  • Created the largest public English L1 eye tracking dataset for reading.
  • The corpus contains 2.6 million word tokens of eye movement data.
  • Materials support behavioral analyses of reading comprehension.

Conclusions:

  • The OneStop corpus enables novel research in reading and human language processing.
  • It facilitates the use of eye tracking data in NLP, AI, HCI, and education.
  • This resource advances the study of reading behavior and cognitive processes.