Eye-Tracking-while-Reading: A Living Survey of Datasets with Open Library Support
Deborah N. Jakobi, David R. Reich, Paul Prasse, Jana M. Hofmann, Lena S. Bolliger, Lena A. J\"ager

TL;DR
This paper provides a comprehensive, up-to-date overview of eye-tracking-while-reading datasets, promoting data sharing, interoperability, and reproducibility through an online living repository and integration into a Python library.
Contribution
It introduces an extensive, curated overview of datasets, a living online repository, and integration into a Python package to enhance data accessibility and standardization.
Findings
Over 45 dataset features documented
Integration into pymovements library
Promotion of FAIR data principles
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…
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Taxonomy
TopicsGaze Tracking and Assistive Technology · Neurobiology of Language and Bilingualism · Text Readability and Simplification
