{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T23:09:51Z","timestamp":1793660991643,"version":"4.5.1"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T00:00:00Z","timestamp":1782432000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T00:00:00Z","timestamp":1782432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["BR 6259\/2-2"],"award-info":[{"award-number":["BR 6259\/2-2"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Forschung, Technologie und Raumfahrt","doi-asserted-by":"publisher","award":["LuFo-VI 20W2102C"],"award-info":[{"award-number":["LuFo-VI 20W2102C"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Data"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>We present a curated dataset of planar displacement fields from eight fatigue crack growth experiments obtained via full-field digital image correlation (DIC). The dataset covers multiple aerospace-grade aluminium alloys, specimen geometries, material orientations, and load configurations, providing a diverse experimental basis for data-driven fracture mechanics research. Crack tip locations are consistently annotated using an iterative correction procedure applied to all measurements, and fracture mechanical descriptors like stress-intensity factors are provided as additional labels. The dataset comprises 8,794 unique experimentally observed displacement fields and a total of 70,352 supervised samples generated through standardized interpolation and augmentation. DIC data is provided as uniformly interpolated displacement grids at three standardized resolutions (28\u00a0\u00d7\u00a028, 64\u00a0\u00d7\u00a064, and 128\u00a0\u00d7\u00a0128 pixels), each available in three dataset sizes to support scalable use cases ranging from educational applications to high-capacity model development. Accompanying metadata and a Python interface facilitate filtering, loading, and integration into reproducible machine learning and fracture mechanics workflows.<\/jats:p>","DOI":"10.1038\/s41597-026-07722-1","type":"journal-article","created":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T11:36:30Z","timestamp":1782473790000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Annotated digital image correlation displacement fields from fatigue crack growth experiments on aluminium alloys"],"prefix":"10.1038","volume":"13","author":[{"given":"David","family":"Melching","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ferdinand","family":"D\u00f6mling","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florian","family":"Paysan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erik","family":"Schultheis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric","family":"Dietrich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric","family":"Breitbarth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,26]]},"reference":[{"key":"7722_CR1","doi-asserted-by":"publisher","first-page":"103240","DOI":"10.1016\/j.advengsoft.2022.103240","volume":"173","author":"H Bolandi","year":"2022","unstructured":"Bolandi, H., Li, X., Salem, T., Boddeti, V. 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Fatigue & Fracture of Engineering Materials & Structures 44, 1336\u20131348, https:\/\/doi.org\/10.1111\/ffe.13433 (2021).","journal-title":"Fatigue & Fracture of Engineering Materials & Structures"},{"key":"7722_CR6","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-13275-1","volume":"12","author":"D Melching","year":"2022","unstructured":"Melching, D., Strohmann, T., Requena, G. & Breitbarth, E. Explainable machine learning for precise fatigue crack tip detection. Scientific Reports 12, 9513, https:\/\/doi.org\/10.1038\/s41598-022-13275-1 (2022).","journal-title":"Scientific Reports"},{"key":"7722_CR7","doi-asserted-by":"publisher","first-page":"108432","DOI":"10.1016\/j.ijfatigue.2024.108432","volume":"187","author":"D Melching","year":"2024","unstructured":"Melching, D., Paysan, F., Strohmann, T. & Breitbarth, E. An iterative crack tip correction algorithm discovered by physical deep symbolic regression. 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