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To address this challenge, we attempt to elaborate an approach for an improved detection of outliers. We discuss an approach for outlier quantification for bathymetric data. As a\u00a0use case, we selected ocean science (multibeam) data to calculate the outlierness for each data point. The benefit of outlier quantification is a\u00a0more accurate estimation of which outliers should be removed or further analyzed. To shed light on the subject, this paper is structured as follows: first, a\u00a0summary of related works on outlier detection is provided. The usefulness for a\u00a0structured approach of outlier quantification is then discussed using multibeam data. This is followed by a\u00a0presentation of the challenges for a\u00a0suitable solution, and the paper concludes with a\u00a0summary.<\/jats:p>","DOI":"10.1007\/s00287-022-01469-w","type":"journal-article","created":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T14:06:08Z","timestamp":1659449168000},"page":"218-222","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Outlier quantification for multibeam data"],"prefix":"10.1007","volume":"45","author":[{"given":"Tobias","family":"Ziolkowski","sequence":"first","affiliation":[]},{"given":"Agnes","family":"Koschmider","sequence":"additional","affiliation":[]},{"given":"Peer","family":"Kr\u00f6ger","sequence":"additional","affiliation":[]},{"given":"Colin","family":"Devey","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,2]]},"reference":[{"key":"1469_CR1","volume-title":"Outliers in statistical data","author":"V Barnett","year":"1994","unstructured":"Barnett\u00a0V, Lewis\u00a0T (1994) Outliers in statistical data, 3rd\u00a0edn. 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