A regional ocean database for the Coastal China Sea.

IF 6.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-09-23 DOI:10.1038/s41597-025-05840-w

Cece Wang, Bei Su, Jun Sun, Xiaoke Hu, Jihua Liu

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引用次数: 0

Abstract

Access to high-quality marine geophysical and biogeochemical in-situ data poses a challenge for model evaluation and parameter calibration of the Coastal China Sea (CCS). We describe a new regional ocean database for CCS (RODCCS) with original data from six repositories. The database covers the region of 116-135°E in longitude and 20-42°N in latitude, which embraces the Bohai Sea, the Yellow Sea, the East China Sea and a part of the Sea of Japan. About 3.9 million data points are collected and sorted according to variable types, including temperature, salinity, dissolved oxygen, silicate, nitrate, nitrite, ammonium, phosphate, Chlorophyll a, dissolved inorganic carbon, dissolved organic carbon, and particulate organic carbon. These data are quality-controlled (QCed) with six QC checks and stored in a Network Common Data Format (NetCDF) file. RODCCS includes twelve NetCDF files, each with a unified structure. The database is easily accessed and of high quality after QC checks, making it suitable for a wide range of marine modelling as well as field research for the CCS.

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中国沿海区域海洋数据库。

获取高质量的海洋地球物理和生物地球化学原位数据对中国沿海（CCS）的模型评估和参数校准提出了挑战。我们描述了一个新的区域海洋CCS数据库（RODCCS），该数据库使用了来自六个存储库的原始数据。该数据库覆盖经度116-135°E，纬度20-42°N的区域，包括渤海、黄海、东海和日本海的一部分。根据变量类型，包括温度、盐度、溶解氧、硅酸盐、硝酸盐、亚硝酸盐、铵态盐、磷酸盐、叶绿素a、溶解无机碳、溶解有机碳、颗粒有机碳等，收集并整理了约390万个数据点。这些数据通过六次QC检查进行质量控制，并存储在网络通用数据格式（NetCDF）文件中。RODCCS包括12个NetCDF文件，每个文件具有统一的结构。该数据库易于访问，并且经过质量控制检查后质量高，使其适合于广泛的海洋建模以及CCS的实地研究。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.