A comprehensive grain-size database of surface sediments from the Taklamakan Desert
Abstract This study compiles the most comprehensive open-access surface sediment grain-size database (n = 596 samples) spanning the entire Taklamakan Desert, obtained through systematic field sampling and laser diffraction analysis. It provides essential data for understanding the desert formation,...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04936-7 |
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| author | Huiliang Li Xin Gao Yongcheng Zhao Jie Zhou Zihao Hu Zhuo Chen Zuowei Yang Shengyu Li |
| author_facet | Huiliang Li Xin Gao Yongcheng Zhao Jie Zhou Zihao Hu Zhuo Chen Zuowei Yang Shengyu Li |
| author_sort | Huiliang Li |
| collection | DOAJ |
| description | Abstract This study compiles the most comprehensive open-access surface sediment grain-size database (n = 596 samples) spanning the entire Taklamakan Desert, obtained through systematic field sampling and laser diffraction analysis. It provides essential data for understanding the desert formation, evolution, sand sources, and the restoration of aeolian environments. By analyzing key sediment parameters (mean grain size, sorting, skewness, kurtosis) and particle compositions, the dataset reveals sediment transport dynamics and depositional processes critical for understanding desert formation, sand provenance, and aeolian environmental reconstruction. The quantitative characterization of sediment texture and sorting mechanisms provides foundational data for investigating regional dust emissions, wind erosion patterns, and sediment transport capacities. While the primary focus is on the Taklamakan Desert, the methodology and dataset apply to other arid regions, making it a valuable resource for comparative desert studies. It is an indispensable tool for researchers investigating desert landscapes and addressing environmental challenges related to desertification and aeolian processes. |
| format | Article |
| id | doaj-art-a54365d6efa34f8593ccce13bb0ab25e |
| institution | DOAJ |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-a54365d6efa34f8593ccce13bb0ab25e2025-08-20T03:10:07ZengNature PortfolioScientific Data2052-44632025-04-0112111010.1038/s41597-025-04936-7A comprehensive grain-size database of surface sediments from the Taklamakan DesertHuiliang Li0Xin Gao1Yongcheng Zhao2Jie Zhou3Zihao Hu4Zhuo Chen5Zuowei Yang6Shengyu Li7College of Ecology and Environment, Xinjiang UniversityState key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of SciencesState key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of SciencesState key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of SciencesState key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of SciencesState key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of SciencesState key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of SciencesState key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of SciencesAbstract This study compiles the most comprehensive open-access surface sediment grain-size database (n = 596 samples) spanning the entire Taklamakan Desert, obtained through systematic field sampling and laser diffraction analysis. It provides essential data for understanding the desert formation, evolution, sand sources, and the restoration of aeolian environments. By analyzing key sediment parameters (mean grain size, sorting, skewness, kurtosis) and particle compositions, the dataset reveals sediment transport dynamics and depositional processes critical for understanding desert formation, sand provenance, and aeolian environmental reconstruction. The quantitative characterization of sediment texture and sorting mechanisms provides foundational data for investigating regional dust emissions, wind erosion patterns, and sediment transport capacities. While the primary focus is on the Taklamakan Desert, the methodology and dataset apply to other arid regions, making it a valuable resource for comparative desert studies. It is an indispensable tool for researchers investigating desert landscapes and addressing environmental challenges related to desertification and aeolian processes.https://doi.org/10.1038/s41597-025-04936-7 |
| spellingShingle | Huiliang Li Xin Gao Yongcheng Zhao Jie Zhou Zihao Hu Zhuo Chen Zuowei Yang Shengyu Li A comprehensive grain-size database of surface sediments from the Taklamakan Desert Scientific Data |
| title | A comprehensive grain-size database of surface sediments from the Taklamakan Desert |
| title_full | A comprehensive grain-size database of surface sediments from the Taklamakan Desert |
| title_fullStr | A comprehensive grain-size database of surface sediments from the Taklamakan Desert |
| title_full_unstemmed | A comprehensive grain-size database of surface sediments from the Taklamakan Desert |
| title_short | A comprehensive grain-size database of surface sediments from the Taklamakan Desert |
| title_sort | comprehensive grain size database of surface sediments from the taklamakan desert |
| url | https://doi.org/10.1038/s41597-025-04936-7 |
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