Preprocessing Eye-Tracking Data for Reading: A Community Survey on Practices, Challenges, and Needs

Preprocessing eye-tracking data is a critical yet challenging step in eye-tracking research, particularly in reading studies. Current practices are characterized by limited standardization, a strong reliance on proprietary or inflexible tools, and heterogeneous documentation routines, which hinder reproducibility, comparability, and data reuse. To systematically assess community practices and needs, we conducted a large-scale survey (N = 108) with a focus on reading research. The survey examined preprocessing workflows, tool usage, perceived challenges, data sharing and documentation strategies, and expectations regarding preprocessing tools and outputs. The findings indicate a strong demand for transparent, interoperable, and well-documented preprocessing workflows, as well as practical guidance such as tutorials and user support. Participants emphasized the importance of standardized terminology, flexible automation, and reusable output structures. These results provide an empirical basis for tool development, methodological guidelines, and infrastructure initiatives aimed at improving transparency, standardization, and long-term reusability in eye-tracking research.