In an era where maritime security is paramount, the Georeferenced dataset of maritime piracy in the Gulf of Guinea from 2010 to 2021¹ emerges as a critical resource. This meticulously compiled collection provides an exhaustive analysis of maritime piracy incidents in the Gulf of Guinea from 2010 to 2021. It offers raw data and a combination of geographical precision with meteorological insights.

This data paper, authored by Ricardo Moura, Nuno Pessanha Santos, Victor Lobo, André Rocha, and Miguel de Castro Neto, was published in Springer Nature's Scientific Data journal in 2023. The dataset² presented is a multifaceted collection of data about maritime piracy in the Gulf of Guinea, comprising 595 reported pirate attacks from 2010 to 2021. It encompasses various variables, meticulously curated to provide a comprehensive picture of each piracy incident. These variables include detailed geographical information on piracy incidents, meteorological conditions such as wind speed, wave height, and rainfall levels, and specifics about the piracy attacks, like the type of weaponry used and the classification of each attack. Additionally, the dataset includes ship-related information such as the ship's status (sailing, anchored, or docked) and type, and it rigorously addresses missing data elements like distance to the coast, state flag, and the number of criminals involved.

The dataset also features an interactive map³ that showcases the locations of all piracy incidents within the designated period, enabling users to visually engage with the data. Users can access detailed information about each incident, including the date, classification of the attack, and type of weaponry used, by clicking on individual markers.

The dataset's comprehensive approach not only aids in addressing the immediate concerns of maritime piracy but also serves as a model for gathering and analyzing data in this area. Its integration of geographical, meteorological, and incident-specific data provides a template for future maritime security research and data collection efforts, offering a blueprint for effectively collating and utilizing data to understand and mitigate complex security threats.

The dataset is also part of the Mar-IA⁴ project, which focuses on maritime Artificial Intelligence (AI). This platform is dedicated to data governance and value creation from data sources, leveraging data science and AI. The project aims to develop a national governance model for data and enhance value through data science and AI, supported by the collective intelligence of stakeholders in the maritime field.
We wanted to provide a resource for understanding maritime piracy in the Gulf of Guinea. Its methodological rigor, comprehensive coverage, and practical implications make it a valuable tool for researchers, policymakers, and maritime security professionals. This dataset not only enhances our current understanding of maritime piracy but also sets a new standard for data collection and analysis in the field, offering insights that are vital for developing effective strategies in maritime security.

¹ Moura, R., Pessanha Santos, N., Rocha, A. et al. Georeferenced dataset of maritime piracy in the Gulf of Guinea from 2010 to 2021. Sci Data 10, 876 (2023). https://doi.org/10.1038/s41597-023-02706-x

² Moura, R., Pessanha Santos, N., Rocha, A., Lobo, V. & Neto, MdC. Georeferenced dataset of maritime piracy in the gulf of guinea from 2010 to 2021, https://doi.org/10.6084/m9.figshare.c.6829425.v1 (2023).

³ https://piracygulfguinea.tiiny.site/

⁴ For more information and to access the dataset, you can visit the Mar-IA project website: https://mar-ia.pt/