Marine science and technology are rapidly evolving fields that increasingly rely on advanced data analysis to understand complex ocean dynamics. The growing availability of diverse datasets, ranging from satellite remote sensing and acoustic surveys to in situ observations by autonomous vehicles, has created unprecedented opportunities for discovery. However, these datasets are often high-dimensional, heterogeneous, and difficult to interpret using conventional methods. Data science and Artificial Intelligence (AI) provide powerful approaches to extract meaningful patterns, improve predictive models, and support sustainable management of marine resources. By integrating modern computational techniques, researchers can advance the understanding of ocean dynamics and phenomena, support conservation efforts, and enhance maritime operations.
Despite notable advances in data science regarding ocean dynamics and phenomena, many obstacles still exist in converting raw data into useful knowledge. Traditional methods are limited in managing the complexity, scale, and variability of multimodal ocean datasets. This Research Topic aims to address these challenges by encouraging innovation at the intersection of data science, Artificial Intelligence (AI), and marine applications. The objective is to develop methods that improve data integration, automate feature extraction, and boost the reliability of predictive and decision-support systems. Through AI-powered modelling, anomaly detection, ocean phenomena modelling, and ecosystem monitoring, this initiative strives to advance scientific discovery while meeting societal needs such as sustainable fisheries, climate resilience, and maritime security. By connecting marine science with computational intelligence, this Research Topic seeks to offer scalable, data-driven solutions that empower researchers and policymakers alike.
This Research Topic welcomes manuscripts focused on the application of data science and Artificial Intelligence (AI) in the maritime field. Submissions may explore machine learning, deep learning, or statistical models used for ocean observations and forecasting. They can also cover data integration from various sources and scales, tools for ecosystem assessment, conservation, and resource management, AI techniques for prediction, anomaly detection, and environmental monitoring, as well as innovative data pipelines or frameworks for marine research. Emphasis should be on methodological innovation, validation, and practical significance. Interdisciplinary studies combining computational sciences with oceanography, ecology, or marine engineering are highly encouraged. The sharing of data to enable other researchers and applications to develop new applications and validate the findings is also highly encouraged.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Community Case Study
Data Report
Editorial
FAIR² Data
General Commentary
Hypothesis and Theory
Methods
Mini Review
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
