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A late Middle Pleistocene Denisovan mandible from the Tibetan Plateau

本数据集来源于论文: 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. 该成果是陈发虎院士带领其团队多年来在青藏高原开展过去人类活动和环境适应研究获得的又一突破性进展。研究团队分析了甘肃夏河县新发现的古人类下颌骨化石,可以确定其为青藏高原的丹尼索瓦人,建议命名为夏河丹尼索瓦人,简称夏河人。研究团队针对该化石开展了年代学、体质形态学、分子考古学、生存环境、人类适应等多学科综合分析。结果发现,该化石目前是除阿尔泰山地区丹尼索瓦洞以外发现的首例丹尼索瓦人化石,也是青藏高原发现的最早人类活动证据(距今16万年前)。该研究为进一步探讨丹尼索瓦人的体质形态特征及其在东亚地区的分布、青藏高原早期人类活动历史及其对高海拔环境适应等问题提供了关键证据。 数据提取自论文中Supplementary Tables。 数据集包含6个数据表,数据表名称和内容分别为: t1: Distances in mm between meshes generated from CT versus photoscans (PS)(扫描图与CT形成的网格间以毫米为单位的距离); t2: Measurements of the Xiahe mandible after reconstruction(对夏河人下颌重建后的测量); t3: Comparative Dental metrics(牙科指标比较); t4: Comparative crown morphology(牙冠形态比较); t5: Uniprot accession numbers for protein sequences of extant primates used in the phylogenetic analyses(用在系统发育分析中的现存灵长类动物蛋白质序列在蛋白质仓库中的唯一标识号); t6: Specimen names and numbers(样本的名称和编号)。

Key Datasets More

A late Middle Pleistocene Denisovan mandible from the Tibetan Plateau

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.


Asian monsoon experiment on the Tibetan Plateau (GAME/Tibet) dataset for global energy water cycle (1997-1998)

The GAME/Tibet project conducted a short-term pre-intensive observing period (PIOP) at the Amdo station in the summer of 1997. From May to September 1998, five consecutive IOPs were scheduled, with approximately one month per IOP. More than 80 scientific workers from China, Japan and South Korea went to the Tibetan Plateau in batches and carried out arduous and fruitful work. The observation tests and plans were successfully completed. After the completion of the IOP in September, 1998, five automatic weather stations (AWS), one Portable Atmospheric Mosonet (PAM), one boundary layer tower and integrated radiation observatory (Amdo) and nine soil temperature and moisture observation stations have been continuously observed to date and have obtained extremely valuable information for 8 years and 6 months consecutively (starting from June 1997). The experimental area is located in Nagqu, in northern Tibet, and has an area of 150 km × 200 km (Fig. 1), and observation points are also established in D66, Tuotuohe and the Tanggula Mountain Pass (D105) along the Qinghai-Tibet Highway. The following observation stations (sites) are set up on different underlying surfaces including plateau meadows, plateau lakes, and desert steppe. (1) Two multidisciplinary (atmosphere and soil) observation stations, Amdo and NaquFx, have multicomponent radiation observation systems, gradient observation towers, turbulent flux direct measurement systems, soil temperature and moisture gradient observations, radiosonde, ground soil moisture observation networks and multiangle spectrometer observations used as ground truth values for satellite data, etc. (2) There are six automatic weather stations (D66, Tuotuohe, D105, D110, Nagqu and MS3608), each of which has observations of wind, temperature, humidity, pressure, radiation, surface temperature, soil temperature and moisture, precipitation, etc. (3) PAM stations (Portable Automated Meso - net) located approximately 80 km north and south of Nagqu (MS3478 and MS3637) have major projects similar to the two integrated observation stations (Amdo and NaquFx) above and to the wind, temperature and humidity turbulence observations. (4) There are nine soil temperature and moisture observation sites (D66, Tuotuohe, D110, WADD, NODA, Amdo, MS3478, MS3478 and MS3637), each of which has soil temperature measurements of 6 layers and soil moisture measurement of 9 layers. (5) A 3D Doppler Radar Station is located in the south of Nagqu, and there are seven encrypted precipitation gauges in the adjacent (within approximately 100 km) area. The radiation observation system mainly studies the plateau cloud and precipitation system and serves as a ground true value station for the TRMM satellite. The GAME-Tibet project seeks to gain insight into the land-atmosphere interaction on the Tibetan Plateau and its impact on the Asian monsoon system through enhanced observational experiments and long-term monitoring at different spatial scales. After the end of 2000, the GAME/Tibet project joined the “Coordinated Enhanced Observing Period (CEOP)” jointly organized by two international plans, GEWEX (Global Energy and Water Cycle Experiment) and CL IVAR (Climate Change and Forecast). The Asia-Australia Monsoon Project (CAMP) on the Tibetan Plateau of the Global Coordinated Enhanced Observation Program (CEOP) has been started. The data set contains POP data for 1997 and IOP data for 1998. Ⅰ. The POP data of 1997 contain the following. 1. Precipitation Gauge Network (PGN) 2. Radiosonde Observation at Naqu 3. Analysis of Stable Isotope for Water Cycle Studies 4. Doppler radar observation 5. Large-Scale Hydrological Cycle in Tibet (Link to Numaguchi's home page) 6. Portable Automated Mesonet (PAM) [Japanese] 7. Ground Truth Data Collection (GTDC) for Satellite Remote Sensing 8. Tanggula AWS (D105 station in Tibet) 9. Syamboche AWS (GEN/GAME AWS in Nepal) Ⅱ. The IOP data of 1998 contain the following. 1. Anduo (1) PBL Tower, 2) Radiation, 3) Turbulence SMTMS 2. D66 (1) AWS (2) SMTMS (3) GTDC (4) Precipitation 3. Toutouhe (1) AWS (2) SMTMS (3 )GTDC 4. D110 (1) AWS (2) SMTMS (3) GTDC (4) SMTMS 5. MS3608 (1) AWS (2) SMTMS (3) Precipitation 6. D105 (1) Precipitation (2) GTDC 7. MS3478(NPAM) (1) PAM (2) Precipitation 8. MS3637 (1) PAM (2) SMTMS (3) Precipitation 9. NODAA (1) SMTMS (2) Precipitation 10. WADD (1) SMTMS (2) Precipitation (3) Barometricmd 11. AQB (1) Precipitation 12. Dienpa (RS2) (1) Precipitation 13. Zuri (1) Precipitation (2) Barometricmd 14. Juze (1) Precipitation 15. Naqu hydrological station (1) Precipitation 16. MSofNaqu (1) Barometricmd 16. Naquradarsite (1)Radar system (2) Precipitation 17. Syangboche [Nepal] (1) AWS 18. Shiqu-anhe (1) AWS (2) GTDC 19. Seqin-Xiang (1) Barometricmd 20. NODA (1)Barometricmd (2) Precipitation (3) SMTMS 21. NaquHY (1) Barometricmd (2) Precipitation 22. NaquFx(BJ) (1) GTDC(2) PBLmd (3) Precipitation 23. MS3543 (1) Precipitation 24. MNofAmdo (1) Barometricmd 25. Mardi (1) Runoff 26. Gaize (1) AWS (2) GTDC (3) Sonde A CD of the data GAME-Tibet POP/IOP dataset cd (vol. 1) GAME-Tibet POP/IOP dataset cd (vol. 2)


Long-term sequence dataset of China snow depth (1979-2018)

This data set is an upgraded version of the “Long-term Sequence Data Set of China Snow Depth". The source data of the dataset differ from those of the previous version. Because AMSR-E stopped running in 2011, snow depth from 2008 to 2018 is extracted using the brightness temperature of the SSMI/S sensor. This dataset provides daily data of snow depth distribution in China from January 1, 1979, to December 31, 2018, with a spatial resolution of 0.25 degrees. The original data used to invert the snow depth dataset are the daily passive microwave brightness temperature data (EASE-Grid) from SMMR (1979-1987), SSM/I (1987-2007) and SSMI/S (2008-2018) processed by the National Snow and Ice Data Center (NSIDC). Because the three sensors are mounted on different platforms, there is a certain system inconsistency in the obtained data. The time consistency of the brightness temperature data is improved by cross-calibrating the brightness temperatures of different sensors. The snow depth inversion is then performed using the algorithm specifically modified for China by Dr. Tao Che based on the Chang algorithm. For the specific inversion method, please refer to the data specification, “Long-term Sequence Data Set of China Snow Depth (1979-2018) Introduction. doc". The data set is a latitude and longitude projection, with one file each day, the naming convention of which is year + day; for example, 1990001 represents the first day of 1990, and 1990207 represents the 207th day of 1990. For a detailed data description, please refer to the data file.


The second glacier inventory dataset of China (version 1.0) (2006-2011)

China's second glacier inventory uses the high-resolution Landsat TM/ETM+ remote sensing satellite data as the main glacier boundary data source and extracts the data source with the latest global digital elevation model, SRTM V4, as the glacier attribute, using the current international ratio threshold segmentation method to extract the glacier boundary in bare ice areas. The ice ridge extraction algorithm is developed to extract the glacier ice ridge, and it is used for the segmentation of a single glacier. At the same time, the international general algorithm is used to calculate the glacier attributes, so that the vector data and attribute data that contain the glacier information of the main glacier regions in west China are obtained. Compared with some field GPS field measurement data and higher resolution remote sensing images (such as from QuickBird and WorldView), the glacial vector data in the second glacier inventory data set of China have higher positioning accuracy and can meet the requirements for glacial data in national land, water conservancy, transportation, environment and other fields. Glacier inventory attributes: Glc_Name, Drng_Code, FCGI_ID, GLIMS_ID, Mtn_Name, Pref_Name, Glc_Long, Glc_Lati, Glc_Area, Abs_Accu, Rel_Accu, Deb_Area, Deb_A_Accu, Deb_R_Accu, Glc_Vol_A, Glc_Vol_B, Max_Elev, Min_Elev, Mean_Elev, MA_Elev, Mean_Slp, Mean_Asp, Prm_Image, Aux_Image, Rep_Date, Elev_Src, Elev_Date, Compiler, Verifier. For a detailed data description, please refer to the second glacier inventory data description.


Long-term surface soil freeze-thaw states dataset of China using the dual-index algorithm (1978-2015)

This dataset uses daily temperature data from SMMR (1978-1987), SSM/I (1987-2009) and SSMIS (2009-2015). It is generated by the dual-index (TB, 37v, SG) freeze-thaw discrimination algorithm. The classification results include the frozen surface, the thawed surface, the deserts and water bodies. The data coverage is the main part of China’s mainland, with a spatial resolution of 25.067525 km via the EASE-Grid projection method, and it is stored in ASCIIGRID format. All the ASCII files in this data set can be opened directly with a text program such as Notepad. Except for the head file, the body content is numerically characterized by the freeze/thaw status of the surface soil: 1 for frozen, 2 for thawed, 3 for desert, and 4 for precipitation. If you want to use the icon for display, we recommend using the ArcView + 3D or Spatial Analyst extension module for reading; in the process of reading, a grid format file will be generated, and the displayed grid file is the graphical expression of the ASCII file. The read method comprises the following. [1] Add the 3D or Spatial Analyst extension module to the ArcView software and then create a new View. [2] Activate View, click File menu, and select the Import Data Source option. When the Import Data Source selection box pops up, select ASCII Raster in the Select import file type box. When the dialog box for selecting the source ASCII file automatically pops up, click to find any ASCII file in the data set, and then press OK. [3] Type the name of the Grid file in the Output Grid dialog box (it is recommended that a meaningful file name is used for later viewing) and click the path to store the Grid file, press OK again, and then press Yes (to select integer data) and Yes (to put the generated grid file into the current view). The generated files can be edited according to the Grid file standard. This completes the process of displaying an ASCII file into a Grid file. [4] In the batch processing, the ASCIGRID command of ARCINFO can be used to write AML files, and then use the Run command to complete the process in the Grid module: Usage: ASCIIGRID <in_ascii_file> <out_grid> {INT | FLOAT}. The production of this data is supported by the following Natural Science Foundation Projects: Environmental and Ecological Science Data Center of West China (90502010), Land Data Assimilation System of West China (90202014) and Active and Passive Microwave Radiation Transmission Simulation and Radiation Scattering Characteristics of the Frozen Soil (41071226).


A monthly air temperature and precipitation gridded dataset on 0.025° spatial resolution in China during(1951-2011)

Gridded climatic datasets with fine spatial resolution can potentially be used to depict the climatic characteristics across the complex topography of China. In this study we collected records of monthly temperature at 1153 stations and precipitation at 1202 stations in China and neighboring countries to construct a monthly climate dataset in China with a 0.025° resolution (~2.5 km). The dataset, named LZU0025, was designed by Lanzhou University and used a partial thin plate smoothing method embedded in the ANUSPLIN software. The accuracy of LZU0025 was evaluated based on three aspects: (1) Diagnostic statistics from the surface fitting model during 1951–2011. The results indicate a low mean square root of generalized cross validation (RTGCV) for the monthly air temperature surface (1.06 °C) and monthly precipitation surface (1.97 mm1/2). (2) Error statistics of comparisons between interpolated monthly LZU0025 with the withholding of climatic data from 265 stations during 1951–2011. The results show that the predicted values closely tracked the real true values with values of mean absolute error (MAE) of 0.59 °C and 70.5 mm, and standard deviation of the mean error (STD) of 1.27 °C and 122.6 mm. In addition, the monthly STDs exhibited a consistent pattern of variation with RTGCV. (3) Comparison with other datasets. This was done in two ways. The first was via comparison of standard deviation, mean and time trend derived from all datasets to a reference dataset released by the China Meteorological Administration (CMA), using Taylor diagrams. The second was to compare LZU0025 with the station dataset in the Tibetan Plateau. Taylor diagrams show that the standard deviation, mean and time trend derived from LZU had a higher correlation with that produced by the CMA, and the centered normalized root-mean-square difference for this index derived from LZU and CMA was lower. LZU0025 had high correlation with the Coordinated Energy and Water Cycle Observation Project (CEOP) - Asian Monsoon Project, (CAMP) Tibet surface meteorology station dataset for air temperature, despite a non-significant correlation for precipitation at a few stations. Based on this comprehensive analysis, we conclude that LZU0025 is a reliable dataset. LZU0025, which has a fine resolution, can be used to identify a greater number of climate types, such as tundra and subpolar continental, along the Himalayan Mountain. We anticipate that LZU0025 can be used for the monitoring of regional climate change and precision agriculture modulation under global climate change.








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