This data is derived from the Supplementary Tables of the paper: Chen, F. H., Welker, F., Shen, C. C., Bailey, S. E., Bergmann, I., Davis, S., Xia, H., Wang, H., Fischer, R., Freidline, S. E., Yu, T. L., Skinner, M. M., Stelzer, S., Dong, G. R., Fu, Q. M., Dong, G. H., Wang, J., Zhang, D. J., & Hublin, J. J. (2019). A late Middle Pleistocene Denisovan mandible from the Tibetan Plateau. Nature, 569, 409-412. This research is another breakthrough made by academician Fahu Chen and his team over the years research of human activities and environmental adaptation on the Tibetan Plateau. The research team analyzed the newly discovered hominid mandible fossils in Xiahe County, Gansu Province, China, and identified it belongs to Denisovan of the Tibetan Plateau, which suggested to call Xiahe Denisovan. The team conducted a multidisciplinary analysis of the fossil, including chronology, physique morphology, molecular archaeology, living environment and human adaptation. It is the first Denisovan fossil found outside the Denisova Cave in the Altai Mountains and the earliest evidence of human activity on the Tibetan Plateau (160 kyr BP). This study provides key evidence for further study of Denisovans' physical characteristics and distribution in East Asia, it also provides evidence of a deep evolutionary history of these archaic hominins within the challenging environment of the Tibetan Plateau. This data contains 6 tables, table name and contents are as follows: t1: Distances in mm between meshes generated from CT versus photoscans (PS). t2: Measurements of the Xiahe mandible after reconstruction. t3: Comparative Dental metrics. t4: Comparative crown morphology. t5: Uniprot accession numbers for protein sequences of extant primates used in the phylogenetic analyses. t6: Specimen names and numbers.
This data set includes a monthly composite of 30 m × 30 m surface vegetation coverage products in the Qilian Mountain Area in 2019. In this paper, the maximum value composition (MVC) method is used to synthesize monthly NDVI products and calculate FVC by using the reflectance data of Landsat 8 and sentinel 2 red and near infrared channels. The data is monthly synthesized by Google Earth engine cloud platform, and the index is calculated by the model. The missing pixels are interpolated with good quality, which can be used in environmental change monitoring and other fields.
The data includes the runoff components of the main stream and four tributaries in the source area of the Yellow River. In 2014-2016, spring, summer and winter, based on the measurement of radon and tritium isotopic contents of river water samples from several permafrost regions in the source area of the Yellow River, and according to the mass conservation model and isotope balance model of river water flow, the runoff component analysis of river flow was carried out, and the proportion of groundwater supply and underground ice melt water in river runoff was preliminarily divided. The quality of the data calculated by the model is good, and the relative error is less than 20%. The data can provide help for the parameter calibration of future hydrological model and the simulation of hydrological runoff process.
How the Tibetan people adapt to the extreme environment of the plateau is not clear at present. Metabolism, as an important phenotype, plays an important role in maintaining the normal biological function of individuals. Previous studies have shown that some small metabolic molecules can adapt to the extreme environment by regulating the biological processes such as energy metabolism and oxidative stress. In view of this, this project is to find the relationship between the human metabolism and the extreme environmental adaptation by studying the unique metabolic characteristics of Tibetan population compared with the plain population, and then study the plateau adaptation mechanism of Tibetan population from the perspective of metabolism. This data is the metabolome data generated during the implementation of this project. The current data includes the metabolome data of 30 people in the plain. The combined analysis of this data and the subsequent metabolome data can be used to study the metabolism characteristics of the Tibetan people at high altitude in the low oxygen environment.
1) Data content: this data is the placenta umbilical cord endothelial cells (HUVEC) transcriptome data of high altitude Tibetan and lowland Han population generated during the implementation of the project, including the RNA-seq data of 3 high altitude Tibetan HUVEC and 3 lowland Han placenta HUVEC. Each RNA-seq data is 6G sequencing depth, which can be used to study the effect of high altitude Tibetan population and lowland Han population for gene expression patterns at hypoxic environment. 2) Data source and processing method: own data, the pair end 150bp sequencing method using Illumina x-ten sequencing platform. 3) Data quality: 6G data depth, q30 > 90%. 4) Results and prospects of data application: the data will be used to validate the gene expression pattern of high altitude hypoxia adaptation gene to hypoxia environment at the cell level.
The average altitude of the Tibetan Plateau is more than 4000 meters. The harsh environment such as high cold and low oxygen poses a huge challenge to human survival. However, since the late Paleolithic period, Tibetan people in the plateau have reached the Plateau, and in the Neolithic period, people began to permanently settled on the high-altitude areas on a large scale. The history of population migration in this process has become the focus of different fields. In order to analyze the genetic structure of Tibetan population from the perspective of the whole genome and trace back the history of human settlement on the plateau, we obtained the whole genome variation data of 20 Tibetan individuals. The SNP typing of 20 samples was carried out by DNA array method, and about 700000 loci (including nuclear genome, mitochondrial DNA and Y chromosome) of each sample were obtained. Based on the above data, relevant biological information analysis (mainly including chip site quality control analysis, Y chromosome and mitochondrial DNA haplotype analysis) was carried out. This data is helpful to analyze the genetic structure of Tibetan population from the perspective of nuclear genome, Y chromosome and mitochondrial DNA. By comparing with the data of people around the plateau, we can trace the migration and settlement history of the plateau population comprehensively.