Huiliang Li, Xin Gao, Yongcheng Zhao, Jie Zhou, Zihao Hu, Zhuo Chen, Zuowei Yang, Shengyu Li
{"title":"塔克拉玛干沙漠表层沉积物粒度综合数据库。","authors":"Huiliang Li, Xin Gao, Yongcheng Zhao, Jie Zhou, Zihao Hu, Zhuo Chen, Zuowei Yang, Shengyu Li","doi":"10.1038/s41597-025-04936-7","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"585"},"PeriodicalIF":5.8000,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A comprehensive grain-size database of surface sediments from the Taklamakan Desert.\",\"authors\":\"Huiliang Li, Xin Gao, Yongcheng Zhao, Jie Zhou, Zihao Hu, Zhuo Chen, Zuowei Yang, Shengyu Li\",\"doi\":\"10.1038/s41597-025-04936-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":21597,\"journal\":{\"name\":\"Scientific Data\",\"volume\":\"12 1\",\"pages\":\"585\"},\"PeriodicalIF\":5.8000,\"publicationDate\":\"2025-04-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scientific Data\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41597-025-04936-7\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-025-04936-7","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
A comprehensive grain-size database of surface sediments from the Taklamakan Desert.
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.
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.