نبذة مختصرة : Publisher Copyright: © The Author(s) 2024. This work was supported by the national project MArIA - Plataforma Integrada de desenvolvimento de modelos de Inteligência artificial para o mar, with grant number POCI-05-5762-FSE-000400. The research conducted by Ricardo Moura was funded by national funds through the Fundação para a Ciência e a Tecnologia (FCT), I.P., Center for Mathematics and Applications (NOVA Math) under the scope of the projects UIDB/00297/2020 (https://doi.org/10.54499/UIDB/00297/2020) and UIDP/00297/2020 (https://doi.org/10.54499/ UIDP/00297/2020). The research carried out by Victor Lobo and Miguel de Castro Neto was supported by national funds through FCT under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS ; As a coastal state, Portugal must ensure active surveillance over its maritime area, ensuring its proper control and inspection. One of the most critical inspection activities is the fishery inspection. To protect biodiversity, we must ensure that all the ships comply with the existing safety regulations and respect the current fishing quotas. This georeferenced dataset describes the fisheries inspections done in Portuguese waters between 2015 and 2023. Since we are dealing with occurrences that may have originated some legal process to the ship’s owner, we have ensured data anonymization by pre-processing the dataset to maintain its accuracy while guaranteeing no unique identifiers exist. All the pre-processing performed to ensure data consistency and accuracy is described in detail to allow a quick analysis and implementation of new algorithms. The data containing the results of these inspections can be easily analyzed to implement data mining algorithms that can efficiently retrieve more knowledge and, e.g., suggest new areas of actuation or new strategies. ; publishersversion ; epub_ahead_of_print
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