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Fisheries Inspection in Portuguese Waters from 2015 to 2023.
Moura, Ricardo; Pessanha Santos, Nuno; Vala, Alexandra; Mendes, Leonor; Simões, Paula; de Castro Neto, Miguel; Lobo, Victor.
Afiliação
  • Moura R; Centro de Matemática e Aplicações (NovaMath), Universidade Nova de Lisboa, 2829-516, Caparica, Portugal. rp.moura@fct.unl.pt.
  • Pessanha Santos N; Portuguese Navy Research Center (CINAV), Portuguese Naval Academy (Escola Naval), Almada, 2810-001, Portugal. rp.moura@fct.unl.pt.
  • Vala A; Department of Mathematics, ISEG - School of Economics and Management, Universidade de Lisboa, Lisboa, Portugal. rp.moura@fct.unl.pt.
  • Mendes L; Portuguese Navy Research Center (CINAV), Portuguese Naval Academy (Escola Naval), Almada, 2810-001, Portugal.
  • Simões P; Portuguese Military Research Center (CINAMIL), Portuguese Military Academy (Academia Militar), Lisbon, 1169-203, Portugal.
  • de Castro Neto M; Institute for Systems and Robotics (ISR), Instituto Superior Técnico (IST), Lisbon, 1049-001, Portugal.
  • Lobo V; Portuguese Navy Research Center (CINAV), Portuguese Naval Academy (Escola Naval), Almada, 2810-001, Portugal.
Sci Data ; 11(1): 362, 2024 Apr 10.
Article em En | MEDLINE | ID: mdl-38600185
ABSTRACT
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.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sci Data Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Portugal

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sci Data Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Portugal
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