Centro de Matemática e Aplicações (NovaMath), Universidade Nova de Lisboa, 2829-516, Caparica, Portugal.
Portuguese Navy Research Center (CINAV), Portuguese Naval Academy (Escola Naval), Almada, 2810-001, Portugal.
Sci Data. 2024 Apr 10;11(1):362. doi: 10.1038/s41597-024-03088-4.
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.
作为一个沿海国家,葡萄牙必须对其海域进行积极监测,确保对其进行适当的控制和检查。其中最关键的检查活动之一是渔业检查。为了保护生物多样性,我们必须确保所有船只都符合现有的安全规定,并遵守当前的捕鱼配额。该地理参考数据集描述了 2015 年至 2023 年期间在葡萄牙水域进行的渔业检查。由于我们正在处理可能导致船东启动某些法律程序的事件,因此我们通过对数据集进行预处理来确保数据匿名化,在保证不存在唯一标识符的同时保持数据的准确性。为了确保数据的一致性和准确性,对所有执行的预处理都进行了详细描述,以便快速分析和实施新的算法。这些检查的结果所包含的数据可以进行轻松地分析,以实施数据挖掘算法,从而有效地检索更多的知识,并例如,建议采取新的行动领域或新的策略。