Reporting Eye-Tracking Data Quality: Towards a New Standard
Deborah N. Jakobi Daniel G. Krakowczyk Lena A. Jäger
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).