Eye-Tracking-while-Reading: A Living Survey of Datasets with Open Library Support

2026-02-23

AI Summary

This work addresses the fragmentation and lack of standardized formats across eye-tracking reading datasets, which hinder interoperability and reuse. To overcome these challenges, the study systematically catalogs existing public datasets and introduces a dynamic review platform—the first of its kind—covering 45 distinct features, implemented with web-based interactive visualizations. Furthermore, all datasets are integrated into the Python library pymovements, offering a unified, standardized API alongside comprehensive metadata. This effort substantially enhances dataset discoverability and usability, advancing eye-tracking reading research toward FAIR (Findable, Accessible, Interoperable, Reusable) principles. By facilitating cross-disciplinary sharing and reproducibility, the project establishes a robust data foundation for both cognitive science and artificial intelligence applications.

Technology Category

• Computer Vision: Motion & Tracking
• Humans and AI: Interaction Techniques and Devices
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Eye-tracking-while-reading corpora are a valuable resource for many different disciplines and use cases. Use cases range from studying the cognitive processes underlying reading to machine-learning-based applications, such as gaze-based assessments of reading comprehension. The past decades have seen an increase in the number and size of eye-tracking-while-reading datasets as well as increasing diversity with regard to the stimulus languages covered, the linguistic background of the participants, or accompanying psychometric or demographic data. The spread of data across different disciplines and the lack of data sharing standards across the communities lead to many existing datasets that cannot be easily reused due to a lack of interoperability. In this work, we aim at creating more transparency and clarity with regards to existing datasets and their features across different disciplines by i) presenting an extensive overview of existing datasets, ii) simplifying the sharing of newly created datasets by publishing a living overview online, https://dili-lab.github.io/datasets.html, presenting over 45 features for each dataset, and iii) integrating all publicly available datasets into the Python package pymovements which offers an eye-tracking datasets library. By doing so, we aim to strengthen the FAIR principles in eye-tracking-while-reading research and promote good scientific practices, such as reproducing and replicating studies.

Problem

• eye-tracking-while-reading
• datasets
• interoperability
• data sharing
• FAIR principles

Innovation

• eye-tracking-while-reading
• FAIR data
• living survey
• pymovements
• dataset interoperability