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Training artificial intelligence-based systems becomes possible in this domain, e.g., to follow the European recommendation to screen negative smears to reduce false negative cases. The first step for such a process is segmenting the cells. A large and manually segmented dataset is required for this task, which can be used to train deep learning-based solutions. We describe a corresponding dataset with accurate manual segmentations for the enclosed cells. Altogether, the APACS23 (\n                    <jats:bold>A<\/jats:bold>\n                    nnotated\n                    <jats:bold>PA<\/jats:bold>\n                    p smear images for\n                    <jats:bold>C<\/jats:bold>\n                    ell\n                    <jats:bold>S<\/jats:bold>\n                    egmentation 20\n                    <jats:bold>23<\/jats:bold>\n                    ) dataset contains about 37\u00a0000 manually segmented cells and is separated into dedicated training and test parts, which could be used for an official benchmark of scientific investigations or a grand challenge.\n                  <\/jats:p>","DOI":"10.1038\/s41597-024-03566-9","type":"journal-article","created":{"date-parts":[[2024,7,6]],"date-time":"2024-07-06T11:02:04Z","timestamp":1720263724000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Pixel-wise segmentation of cells in digitized Pap smear images"],"prefix":"10.1038","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4405-2040","authenticated-orcid":false,"given":"Balazs","family":"Harangi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gergo","family":"Bogacsovics","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1958-5144","authenticated-orcid":false,"given":"Janos","family":"Toth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilona","family":"Kovacs","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erzsebet","family":"Dani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andras","family":"Hajdu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,6]]},"reference":[{"key":"3566_CR1","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung, H. et al. 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