A High-Quality Ocean Observation Profile Datasets Construction Scheme Based on Multi-Source Data Cleaning and Fusion

doi:10.11871/jfdc.issn.2096-742X.2026.03.007

Abstract

Abstract:

[Background] With the development of ocean observation technologies, various marine equipment and programs have emerged, propelling research in marine science into a “data-intensive”stage characterized by big data. [Objective] To integrate heterogeneous ocean observation data from diverse sources into a comprehensive and unified dataset, thereby enhancing holistic scientific capabilities in addressing marine research questions, this paper proposes a scheme for standardizing, annotating, and cleaning multi-source heterogeneous in situ ocean observation profile data to construct a high-quality ocean observation profile dataset. [Methods] Specifically, the scheme involves acquiring multi-source in-situ ocean observation profile data and corresponding metadata from several ocean data centers/agencies; applying a unique identifier derived from the raw data and descriptors to sequentially execute greylist filtering, multi-version filtering, and high-frequency observations filtering based on spatiotemporal characteristics, yielding refined ocean observation profile data; standardizing the processed data, followed by quality control and bias correction to construct a high-quality profile dataset. [Conclusions] This scheme promotes the application of multi-source heterogeneous profile data, improving data consistency, accuracy, and usability.

Key words: ocean big data, data clean, ocean observation, datasets

YUAN Huifeng,ZHU Yujing,PAN Yuying,ZHANG Rongwang,JIN Zhong. A High-Quality Ocean Observation Profile Datasets Construction Scheme Based on Multi-Source Data Cleaning and Fusion[J]. Frontiers of Data and Computing, 2026, 8(3): 68-80.

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