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Empowering applied researchers to choose the best-fitting detector for their research needs is the primary contribution of this paper. We developed a framework to systematically assess and compare the effectiveness of 13 state-of-the-art algorithms through a unified application interface. Hence, we more than double the number of algorithms that are currently usable within a single software package and allow researchers to identify the best-suited algorithm for a given scientific setup. Our framework validation on retrospective data underscores its suitability for algorithm selection. Through a detailed and reproducible step-by-step workflow, we hope to contribute towards significantly improved data quality in scientific experiments.<\/jats:p>","DOI":"10.3390\/s24092688","type":"journal-article","created":{"date-parts":[[2024,4,24]],"date-time":"2024-04-24T03:56:31Z","timestamp":1713930991000},"page":"2688","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Improving Eye-Tracking Data Quality: A Framework for Reproducible Evaluation of Detection Algorithms"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9301-8872","authenticated-orcid":false,"given":"Christopher","family":"Gundler","sequence":"first","affiliation":[{"name":"Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6368-2117","authenticated-orcid":false,"given":"Matthias","family":"Temmen","sequence":"additional","affiliation":[{"name":"EyeTrax GmbH & Co. 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