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资源与生态数据

资源与生态数据集。

地图服务

地理空间地图服务。

Latest datasets More

Land Cover Dataset at Qilian Mountain Area from 1985 to 2019 (V2.0)

本数据集包括祁连山区域1985-2019年的30m土地覆盖分类产品。该产品首先利用Landsat-8/OLI构造2015年时间序列数据,针对各类地物随时间变化呈现的NDVI时间序列曲线不同,对不同地物特征进行知识归纳,设定规则提取不同地物,得到2015年的土地覆盖分类图。分类系统参考了IGBP分类系统和FROM_LC分类系统,共分为耕地、林地、草地、灌丛、湿地、水体、不透水面、裸地、冰川和积雪共10大类。由Google Earth高清影像和实地调研数据进行精度评价,得出2015年土地覆盖分类产品的总体精度高达92.19%。以2015年的土地覆盖分类产品为基础,基于Google Earth Engine平台的Landsat系列数据和强大地数据处理能力,利用变化检测的思想和方法,生产出1985-2019年的土地覆盖分类产品。对分类产品进行比较,得出基于Google Earth Engine平台生产的土地覆盖分类产品与基于时间序列方法得到的分类产品具有很好的一致性。总之,祁连山核心区的土地覆盖数据集具有较高的总体精度,且基于Google Earth Engine平台样本训练的方法能够在时间和空间上对现有的分类产品进行扩展,能够在长时间序列上反映更多的土地覆盖类型变化信息。其中,1985-2015年的土地利用产品为5年1期,2015-2019年的土地利用产品为1年1期。

2020-09-07

Genome Assembly of Alpine Species Salix Brachista on the Tibetan Plateau (2019)

本数据集来源于论文:Chen, J.*#, Huang, Y.*#, Brachi, B.*#, Yun, Q.*#, Zhang, W., Lu, W., Li, H., Li, W., Sun, X., Wang, G., He, J., Zhou, Z., Chen, K., Ji, Y., Shi, M., Sun, W., Yang, Y.*, Zhang, R.#, Abbott, R. J.*, & Sun, H.* (2019). Genome-wide analysis of Cushion willow provides insights into alpine plant divergence in a biodiversity hotspot. Nature Communications, 10(1), 5230. doi:10.1038/s41467-019-13128-y. 本数据集包含青藏高原高山植物小垫柳Fasta格式的基因组组装文件,包括核苷酸(DNA)、核糖核酸(RNA)、蛋白质编码序列(Protein)序列数据,以及gff格式的基因组组装注释文件。 组装等级:染色体级别 基因组覆盖程度:全基因组 参考基因组:是 组装方法:SMARTdenovo 1.0; CANU 1.3 测序方法及测序深度: PacBio, 125×; Illumina Hiseq X Ten, 43×; Oxford Nanopore Technologies, 74× 基因组组装统计: 基因组大小(bp):339,587,529 GC含量:34.15% 染色体数量:19 细胞器基因组数量:2 基因组组装序列数量:30 最大组装序列长度(bp):39,688,537 最小组装序列长度(bp):57,080 平均组装序列长度(bp):11,319,584 基因组组装序列N50(bp):17,922,059 基因组组装序列N90(bp):13,388,179 全基因组组装注释: Protein:30,209 tRNA:784 rRNA:118 ncRNA:671 详细的注释信息请参见附件。 本数据集中也包含文章中Supplementary Information中的表格数据,数据列表参见附件。 基因组项目号为:GWHAAAA00000000(https://bigd.big.ac.cn/gwh/Assembly/663/show)。

2020-08-20
Key Datasets More

Agriculture facilitated permanent human occupation of the Tibetan Plateau after 3,600 BP

This dataset is collected from the Supplementary Materials part of the paper "Chen, F.H., Dong, G.H., Zhang, D.J., Liu, X.Y., Jia, X., An, C.B., Ma, M.M., Xie, Y.W., Barton, L., Ren, X.Y., Zhao, Z.J., & Wu, X.H. (2015). Agriculture facilitated permanent human occupation of the Tibetan Plateau after 3600 BP. Science, 347, 248–250.". In this paper, researchers analyzed animal bones, plant remains and other artefacts from 53 sites across the northeastern Tibetan plateau and found that humans began to relocate to the elevations above 4000 masl after the emergence of Barley. According to the study, the prehistoric human expansion into the higher, colder altitudes of the Tibetan plateau took place as the continental temperatures had themselves become colder after 3,600 calendar years before the present, thus, the key impetus of the expansion was agricultural innovation rather than climate change. This dataset contains 4 tables, table names and content are as follows: Data list: The data name list of the rest tables; t1: Calibrated radiocarbon dates and domesticated plant and animal remains from sites investigated on the NETP; t2: Radiocarbon dates of the Paleolithic sites on the Tibetan Plateau; t3: OSL dates of the Paleolithic sites on the Tibetan Plateau. See attachments for data details: Supplementary Materials.pdf, Agriculture Facilitated Permanent Human Occupation of the Tibetan Plateau after 3,600 BP.pdf.

2020-06-19

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.

2020-07-15

Long-term series of daily snow depth dataset in China (1979-2019)

This data set is an upgraded version of the “Long-term series of daily snow depth dataset in China". This dataset provides daily data of snow depth distribution in China from January 1, 1979, to December 31, 2019, with a spatial resolution of 0.25 degrees. The original data used to derive 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 (2008-2019) which were archived in the National Snow and Ice Data Center (NSIDC). Because the brightness temperatures come from different sensors, there is a certain system inconsistency among them. Therefore, before the derivation of snow depth, the inter-sensor calibration were performed to improve the temporal consistency of the brightness temperature data. Based on the calibrated brightness temperatures, the modified Chang algorithm developed by Dr. Tao Che, was used to retrieve daily snow depth. The algorithm details were introduced in the data specification document- “Long-term Sequence Data Set of China Snow Depth (1979-2019) Introduction. doc". The projection of the data set is latitude and longitude. The data of each day was stored in a file, and 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 specification document.

2020-03-19

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).

2020-01-09

数据集(个)

总数据(GB)

36,252

总注册用户(人)

8,925,093

元数据浏览量(次)

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