DOWNLOADING, PROCESSING, AND VISUALIZING REAL MARINE DATASETS: TURNING OCEANOGRAPHIC DATA INTO MEANINGFUL SCIENTIFIC STORIES
Keywords:
Marine Data, Oceanography, Data Visualization, Copernicus Marine Service, NASA OceanColor, NOAA ERDDAP, Python, Sea Surface Temperature, Chlorophyll-a, Scientific StorytellingAbstract
The rapid growth of open-access marine data repositories has transformed oceanographic research, education, and environmental monitoring. However, transforming large volumes of raw satellite and ocean model data into interpretable scientific knowledge remains a significant challenge. This paper presents a practical workflow for downloading, processing, and visualizing real marine datasets obtained from major international marine data infrastructures, including the European Space Agency (ESA) Copernicus Marine Service, NASA OceanColor, and NOAA ERDDAP. The workflow demonstrates how artificial intelligence-assisted programming, combined with Python scientific libraries, can accelerate the transition from natural-language research questions to reproducible analytical workflows. Real examples based on Sea Surface Temperature (SST) and chlorophyll-a observations are presented, including case studies from the Black Sea, Burgas Bay, the Baltic Sea region near Riga, and educational examples associated with European universities. The study illustrates how carefully designed visualizations transform complex marine datasets into meaningful scientific stories that support environmental understanding and decision-making.
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References
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Copyright (c) 2026 Димитър Минчев
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Articles published in "Computer Science and Communications" Magazine are licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
