Reporting Eye-Tracking Data Quality: Towards a New Standard
This addresses the issue for researchers in eye-tracking and related fields by enabling more diverse use cases and cross-dataset comparisons, though it is incremental as it builds on existing data-sharing practices.
The paper tackles the problem of limited re-usability of eye-tracking datasets by advocating for sharing raw data with quality reports instead of filtered datasets, resulting in the development of automated data quality reporting standards and metrics integrated into the open-source Python package pymovements.
Eye-tracking datasets are often shared in the format used by their creators for their original analyses, usually resulting in the exclusion of data considered irrelevant to the primary purpose. In order to increase re-usability of existing eye-tracking datasets for more diverse and initially not considered use cases, this work advocates a new approach of sharing eye-tracking data. Instead of publishing filtered and pre-processed datasets, the eye-tracking data at all pre-processing stages should be published together with data quality reports. In order to transparently report data quality and enable cross-dataset comparisons, we develop data quality reporting standards and metrics that can be automatically applied to a dataset, and integrate them into the open-source Python package pymovements (https://github.com/aeye-lab/pymovements).