A dataset of prokaryotic diversity in the surface layer of the China Seas.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Yanting Liu, Jinxin Xu, Lu Liu, Xiaomeng Wang, Jiaxin Chen, Yunxuan Li, Jiandong Zhang, Chunshan Li, Sijun Huang, Kai Tang, Qiang Zheng
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引用次数: 0

Abstract

Coastal and nearshore zones, severing as a connection between the land and the open ocean, are some of the most productive and complex ecosystems, where prokaryotes are abundant and highly diverse. However, the systematic study of the diversity of prokaryotes on a large-scale range in coastal and nearshore zones is limited due to scattered sampling sites, various sampling collection methods, and different data processing methods across various studies. Here, we provide a dataset of 16S rRNA gene sequences obtained from the surface water samples across the China Seas, including the Bohai Sea, the Yellow Sea, the East China Sea, and the South China Sea. The dataset comprises 1,194 samples collected through field sampling and literature search. A total of 30,308 operational taxonomic units clustered at 97% sequence identity were obtained. Sixty-five bacterial and nine archaeal phyla were identified. This dataset offers a basic understanding of prokaryotic diversity in the China Seas, also provides a foundation for in-depth investigations into prokaryotic distribution across different regions and their interactions in various environments.

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来源期刊
Scientific Data
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.
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