This dataset is the population index, which includes the dataset of Qinghai Province and Tibet Autonomous Region. It can be used for the coupling coordination relationship between urbanization and eco-environment in Qinghai-Tibet Plateau. The time span in Tibet Autonomous Region is 1995-2016. Permanent residents is based on the population census and the annual population change sampling survey. In addition to the total permanent population, the data were also calculated by gender and urban and rural areas. The time span is from 1952 to 2015 in Qinghai Province, and the indices are resident population, birth, death and natural increase. All data is from the statistical yearbook.
DU Yunyan
The dataset records 1960-2017 years of rural population statistics in 65 along the Belt and Road.Data sources: (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme. The data set contains 3 tables: (1))Rural population;(2)Rural population (% of total population;(3)Rural population growth (annual %).
XU Xinliang
The dataset records 1960-2017 years of urban population statistics in 65 along the Belt and Road.Data sources: (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme. The data set contains 3 tables: (1))Urban population;(2)Urban population (% of total population;(3)Urban population growth (annual %).
XU Xinliang
The socio-economic development data set of Qilian mountain basin includes the socio-economic development indicators of 5 prefecture level cities and 14 districts and counties in 1949-2015, such as industrial structure, population scale, labor force, employment, etc. They are the data subsets of social and economic development of prefecture level cities in Qilian mountain basin and county level cities in Qilian mountain basin. The data comes from Gansu statistical yearbook, Gansu Development Yearbook, Qinghai statistical yearbook, Qinghai national economic and social development statistical bulletin, national agricultural product cost and income data compilation, Xining statistical yearbook. As the data source is the provincial and Municipal Statistical Yearbook published publicly, the data has not been cross verified, and the data consistency test and accuracy verification need to be carried out in the process of data analysis and application. The data set is a macro data set reflecting the social and economic development of Qilian mountain basin, with full coverage and long time series. It can provide basic information for the changes of social and economic development of Qilian mountain basin.
WU Feng
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.
LI Gonghua
Taking 2005 as the base year, the future population scenario prediction adopted the Logistic model of population, and it not only can better describe the change pattern of population and biomass but is also widely applied in the economic field. The urbanization rate was predicted using the urbanization Logistic model. Based on the existing urbanization horizontal sequence value, the prediction model was established by acquiring the parameters in the parametric equation applying nonlinear regression. The urban population was calculated by multiplying the predicted population by the urbanization rate. The Logistic model was used to predict the future gross national product of each county (or city), and then, according to the economic development level of each county (or city) in each period (in terms of real GDP per capita),the corresponding industrial structure scenarios in each period were set, and each industry’s output value was predicted. The trend of changes in industrial structure in China and the research area lagged behind the growth of GDP, and, therefore, it was adjusted according to the need of the future industrial structure scenarios of the research area.
ZHONG Fanglei, YANG Linsheng
By applying Supply-demand Balance Analysis, the water resource supply and demand of the whole river basin and each county or district were calculated, based on which the vulnerability of the water resources system of the basin was evaluated. The IPAT equation was used to set a future water resource demand scenario, setting variables such as future population growth rate, economic growth rate, and unit GDP water consumption to establish the scenario. By taking 2005 as the base year and using assorted forecasting data of population size and economic scale, the future water demand scenarios of various counties and cities from 2010 to 2050 were forecast. By applying the basic structure of the HBV conceptual hydrological model of the Swedish Hydrometeorological Institute, a model of the variation tendency of the basin under climate change was designed. The glacial melting scenario was used as the model input to construct the runoff scenario under climate change. According to the national regulations of the water resources allocation of the basin, a water distribution plan was set up to calculate the water supply comprehensively. Considering of the supply and demand situation, the water resource system vulnerability was evaluated by the water shortage rate. By calculating the (grain production) land pressure index of the major counties and cities in the basin, the balance of supply and demand of land resources under the climate change, glacial melt and population growth scenarios was analyzed, and the vulnerability of the agricultural system was evaluated. The Miami formula and HANPP model were used to calculate the human appropriation of net primary biomass and primary biomass in the major counties and cities for the future, and the vulnerability of ecosystems from the perspective of supply and demand balance was assessed.
YANG Linsheng, ZHONG Fanglei
Taking 2000 as the base year, the future population scenario prediction adopted the Logistic model of population, and it not only can better describe the change pattern of population and biomass but also is widely applied in the economic field. The urbanization rate was predicted using the urbanization Logistic model. Based on the existing urbanization horizontal sequence value, the prediction model was established by acquiring the parameters in the parametric equation applying nonlinear regression. The urban population was calculated by multiplying the predicted population by the urbanization rate. The Logistic model was used to predict the future gross national product of each county (or city), and then, according to the economic development level of each county (or city) in each period (in terms of real GDP per capita), the corresponding industrial structure scenarios in each period were set, and the output value of each industry was predicted. The trend of industrial structure changing in China and the research area lagged behind the growth of GDP, so it was adjusted according to the need of the future industrial structure scenarios of the research area.
ZHONG Fanglei
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.
QI Xuebin
The data set records one belt, one road, 65 years' 1960-2017 years population age composition, including the age group population and its proportion in the total population. Data sources: (1) United Nations Population Division, world population prospects: 2017, 2018 revision; (2) census reports and other statistical publications of the National Bureau of statistics; (3) Eurostat: population statistics; (4) United Nations Statistics Division, population and vital statistics reports (different years); (5) United States Census Bureau: international database; (6) Pacific Community Secretariat: statistical and demographic programme. One belt, one road, the future population development, and the future development of social economy. The data set contains six data tables: the total population aged 0-14, the proportion aged 0-14, the total population aged 15-64, the proportion aged 15-64 women, the total population aged 65 and above, and the proportion aged 65 and above
XU Xinliang
The data set records one belt, one road, 1960-2017 countries' population data along 65 countries. The total population is the sum of population groups living in a certain time and a certain area. Population density is the number of people per unit land area. Population growth rate is the rate of population growth caused by natural and migration changes in a certain period of time. Total population, population density and population growth rate are the most basic indicators in population statistics. They are of great significance for understanding national conditions and national strength, formulating population plans and economic and social development plans, and carrying out population scientific research. Data sources: (1) United Nations Population Division, world population prospects: 2017, 2018 revision; (2) census reports and other statistical publications of the National Bureau of statistics; (3) Eurostat: population statistics; (4) United Nations Statistics Division, population and vital statistics reports (different years); (5) United States Census Bureau: international database; (6) Pacific Community Secretariat: statistical and demographic programme. The data set contains three data tables: total population, population density, population growth rate,
XU Xinliang
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.
KONG Qingpeng
The data recorded one belt, one road, 65 countries, 1990-2017 years of labor force. The labor force includes people aged 15 and over who provide labor for the production of goods and services in a specific period of time. It includes those who are currently employed and those who are unemployed but seeking work, as well as first-time job seekers. Data source: according to the data of the ILO, the ILO and the world bank population estimates. Labor data retrieved in September 2018. The data set reflects one belt, one road, the state of labor resources in the countries along the route, and is also an important part of the basic national conditions, and is also one of the important bases for formulating economic and social development strategies.
XU Xinliang
In 2000, the population grid data of Heihe River Basin was generated based on 1:100000 land use data and population statistics data of each county in 2000. Using principal component analysis and factor analysis, four factors are extracted from 11 regionalization indexes, and the Heihe River Basin is divided into four population distribution characteristic regions by using factor scores for hierarchical clustering. The linear regression model between rural residential land, cultivated land area and rural population is established based on the population statistical data of each county in 2000. The total population of each district and county is controlled. The population coefficient is adjusted according to the principle of different population distribution characteristics. The cultivated land population distribution coefficient is modified in the middle green continent, and the grassland population distribution is increased in the upstream mountainous area and the downstream desert oasis area Coefficient. The spatial distribution of urban population density in river basin is simulated by using the exponential model. Based on the above methods, the population spatial distribution results of 25m grid in Heihe River Basin and the data of 1km grid on scale are finally obtained. At the township level, the accuracy of the results of population spatialization is verified, and compared with the population data of Heihe River Basin estimated by the existing databases (GPW 1995, UNEP / grid1995, landscan 2002 and cn2000pop). The results show that the methods and models used in this study can obtain more accurate spatial distribution data of population in the basin.
WANG Xuemei, MA Mingguo
The data includes 30 items of data in four categories: basic information, comprehensive economy, agriculture and industry, education, health and social security in Qinghai Province and Tibet Autonomous Region. It covers the basic data reflecting human activities, such as population, employees, industrial output value, agricultural machinery power, facility agriculture, etc. of the main county administrative units of the Qinghai Tibet Plateau. The data are sorted out according to the statistical yearbook data of China's counties from 2001 to 2018. For the convenience of application, the data of Qinghai and Tibet are independently tabulated and included in the data of each year. The data can be used to analyze human activities and social and economic development in the county, as well as agricultural and rural development and change process.
The data set includes: population and GDP data of the arctic (1990-2015) and county-level population and GDP data of the third pole region (gansu, qinghai and Tibet) (1970-2016). Socio-economic statistical attributes include: population (ten thousand), GDP (ten thousand yuan), total industrial and agricultural output (ten thousand yuan), total agricultural output (ten thousand yuan), and total industrial output (ten thousand yuan). The arctic population data are mainly derived from the world populationProspects: 2017 revision by the Department of economic and social affairs, which divides the total population by region and country. The data of the third pole mainly refer to the statistical yearbook of gansu province, qinghai province and Tibet autonomous region.County records of gansu, qinghai and Tibet autonomous regions.
Department of Economic and Social Affairs, National Bureau of Statistics, Qinghai Provincial Bureau of Statistics
The data set contains series data of populations of major cities and counties on the Tibetan Plateau from 1970 to 2006. It is used to study social and economic changes on the Tibetan Plateau. The table has six fields. Field 1: Year Interpretation: Year of the data Field 2: Province Interpretation: The province from which the data were obtained Field 3: City/Prefecture Interpretation: The city or prefecture from which the data were obtained Field 4: County Interpretation: The name of the county Field 5: Population (10,000) Interpretation: Population Field 6: Data Sources Interpretation: Source of Data Extraction The data comes from the statistical yearbook and county annals of Tibet Autonomous Region, Qinghai, Sichuan, Gansu, Yunnan and Xinjiang. Some are listed as follows: [1] Gansu Yearbook Editorial Committee. Gansu Yearbook [J]. Beijing: China Statistics Press, 1984, 1988-2009 [2] Statistical Bureau of Yunnan Province. Yunnan Statistical Yearbook [J]. Beijing: China Statistics Press, 1988-2009 [3] Statistical Bureau of Sichuan Province, Sichuan Survey Team. Sichuan Statistical Yearbook [J]. Beijing: China Statistics Press, 1987-1991, 1996-2009 [4] Statistical Bureau of Xinjiang Uighur Autonomous Region . Xinjiang Statistical Yearbook [J]. Beijing: China Statistics Press, 1989-1996, 1998-2009 [5] Statistical Bureau of Tibetan Autonomous Region. Tibet Statistical Yearbook [J]. Beijing: China Statistics Press, 1986-2009 [6] Statistical Bureau of Qinghai Province. Qinghai Statistical Yearbook [J]. Beijing: China Statistics Press, 1986-1994, 1996-2008. [7] County Annals Editorial Committee of Huzhu Tu Autonomous County. County Annals of Huzhu Tu Autonomous County [J]. Qinghai: Qinghai People's Publishing House, 1993 [8] Haiyan County Annals Editorial Committee. Haiyan County Annals[J]. Gansu: Gansu Cultural Publishing House, 1994 [9] Menyuan County Annals Editorial Committee. Menyuan County Annals[J]. Gansu: Gansu People's Publishing House, 1993 [10] Guinan County Annals Editorial Committee. Guinan County Annals [J]. Shanxi: Shanxi People's Publishing House, 1996 [11] Guide County Annals Editorial Committee. Guide County Annals[J]. Shanxi: Shanxi People's Publishing House, 1995 [12] Jianzha County Annals Editorial Committee. Jianzha County Annals [J]. Gansu: Gansu People's Publishing House, 2003 [13] Dari County Annals Editorial Committee. Dari County Annals [J]. Shanxi: Shanxi People's Publishing House, 1993 [14] Golmud City Annals Editorial Committee. Golmud City Annals [J]. Beijing: Fangzhi Publishing House, 2005 [15] Delingha City Annals Editorial Committee. Delingha City Annals [J]. Beijing: Fangzhi Publishing House, 2004 [16] Tianjun County Annals Editorial Committee. Tianjun County Annals [J]. Gansu: Gansu Cultural Publishing House, 1995 [17] Naidong County Annals Editorial Committee. Naidong County Annals [J]. Beijing: China Tibetology Press, 2006 [18] Gulang County Annals Editorial Committee. Gulang County Annals [J]. Gansu: Gansu People's Publishing House, 1996 [19] County Annals Editorial Committee of Akesai Kazak Autonomous County. County Annals of Akesai Kazakh Autonomous County [J]. Gansu: Gansu People's Publishing House, 1993 [20] Minxian County Annals Editorial Committee. Minxian County Annals [J]. Gansu: Gansu People's Publishing House, 1995 [21] Dangchang County Annals Editorial Committee. Dangchang County Annals [J]. Gansu: Gansu Cultural Publishing House, 1995 [22] Dangchang County Annals Editorial Committee. Dangchang County Annals(Sequel) (1985-2005) [J]. Gansu: Gansu Cultural Publishing House, 2006 [23] Wenxian County Annals Editorial Committee. Wenxian County Annals[J]. Gansu: Gansu Cultural Publishing House, 1997 [24] Kangle County Annals Editorial Committee. Kangle County Annals [J]. Shanghai: Sanlian Bookstore. 1995 [25] County Annals Editorial Committee of Jishishan (Baoan, Dongxiang, Sala) Autonomous County. County Annals of Jishishan (Baoan, Dongxiang, Sala) Autonomous County[J], Gansu: Gansu Cultural Publishing House, 1998 [26] Luqu County Annals Editorial Committee. Luqu County Annals [J]. Gansu: Gansu People's Publishing House, 2006 [27] Zhouqu County Annals Editorial Committee. Zhouqu County Annals [J]. Shanghai: Sanlian Bookstore. 1996 [28] Xiahe County Annals Editorial Committee. Xiahe County Annals [J]. Gansu: Gansu Cultural Publishing House, 1999 [29] Zhuoni County Annals Editorial Committee. Zhuoni County Annals [J]. Gansu: Gansu Nationality Publishing House, 1994 [30] Diebu County Annals Editorial Committee. Diebu County Annals [J]. Gansu: Lanzhou University Press, 1998 [31] Pengxian County Annals Editorial Committee. Pengxian County Annals [J]. Sichuan: Sichuan People's Publishing House, 1989 [32] Guanxian County Annals Editorial Committee. Guanxian County Annals [J]. Sichuan: Sichuan People's Publishing House, 1991 [33] Wenjiang County Annals Editorial Committee. Wenjiang County Annals [J]. Sichuan: Sichuan People's Publishing House, 1990 [34] Shifang County Annals Editorial Committee. Shifang County Annals [J]. Sichuan: Sichuan University Press, 1988 [35] Tianquan County Annals Editorial Committee. Tianquan County Annals [J]. Sichuan: Sichuan Science and Technology Press, 1997 [36] Shimian County Annals Editorial Committee. Shimian County Annals [J]. Sichuan: Sichuan Cishu Publishing House, 1999 [37] Lushan County Annals Editorial Committee. Lushan County Annals [J]. Sichuan: Fangzhi Publishing House, 2000 [38] Hongyuan County Annals Editorial Committee. Hongyuan County Annals [J]. Sichuan: Sichuan People's Publishing House, 1996 [39] Wenchuan County Annals Editorial Committee. Wenchuan County Annals [J]. Sichuan: Bayu Shushe, 2007 [40] Derong County Annals Editorial Committee. Derong County Annals [J]. Sichuan: Sichuan University, 2000 [41] Baiyu County Annals Editorial Committee. Baiyu County Annals [J]. Sichuan: Sichuan University Press, 1996 [42] Batang County Annals Editorial Committee. Batang County Annals [J]. Sichuan: Sichuan Nationality Publishing House, 1993 [43] Jiulong County Annals Editorial Committee. Jiulong County Annals(Sequel) (1986-2000) [J]. Sichuan: Sichuan Science and Technology Press, 2007 [44] County Annals Editorial Committee of Derung-Nu Autonomous County Gongshan. County Annals of Derung-Nu Autonomous County Gongshan [J]. Beijing: Nationality Publishing House, 2006 [45] Lushui County Annals Editorial Committee. Lushui County Annals [J]. Yunnan: Yunnan People's Publishing House, 1995 [46] Deqin County Annals Editorial Committee. Deqin County Annals [J]. Yunnan: Yunnan Nationality Publishing House, 1997 [47] Yutian County Annals Editorial Committee. Yutian County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 2006 [48] Cele County Annals Editorial Committee. Cele County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 2005 [49] Hetian County Annals Editorial Committee. Hetian County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 2006 [50] Qiemo County Local Chronicles Editorial Committee. Qiemo County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 1996 [51] Shache County Annals Editorial Committee. Shache County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 1996 [52] Yecheng County Annals Editorial Committee. Yecheng County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 1999 [53] Akto County Local Chronicles Editorial Committee. Akto County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 1996 [54] Wuqia County Local Chronicles Editorial Committee. Wuqia County Annals [J]. Xinjiang: Xinjiang People's Publishing House, 1995
National Bureau of Statistics
Taking 2005 as the base year, the future population scenario was predicted by adopting the Logistic model of population. It not only can better describe the change pattern of population and biomass but is also widely applied in the economic field. The urbanization rate was predicted by using the urbanization Logistic model. Based on the existing urbanization horizontal sequence value, the prediction model was established by acquiring the parameters in the parametric equation by nonlinear regression. The urban population was calculated by multiplying the predicted population by the urbanization rate. The data adopted the non-agricultural population. The Logistic model was used to predict the future gross national product of each county (or city), and then, according to the economic development level of each county (or city) in each period (in terms of GDP per capita),the corresponding industrial structure scenarios in each period were set, and the output value of each industry was predicted. The trend of changes in industrial structure in China and the research area lagged behind the growth of GDP and was therefore adjusted according to the need of the future industrial structure scenarios of the research area.
ZHONG Fanglei
Taking 2005 as the base year, the future population scenario was predicted by adopting the logistic model of population. This model not only effectively describes the pattern of changes in population and biomass but is also widely applied in the field of economics. The urbanization rate was predicted using the urbanization logistic model. Based on the observed horizontal pattern of urbanization, a predictive model was established by determining the parameters in the parametric equation by applying nonlinear regression. The urban population was calculated by multiplying the predicted population by the urbanization rate. The data represent the non-agricultural population. The logistic model was used to predict the future gross domestic product of each county (or city), and then the economic development level of each county (or city) in each period (in terms of GDP per capita). The corresponding industrial structure scenarios in each period were set, and the output value of each industry was predicted. The trend of industrial structure changes in China and the research area lagged behind the growth in GDP, so the changes were adjusted according to the need for future industrial structure scenarios in the research area.
YANG Linsheng, ZHONG Fanglei
This data set contains demographic structure and quantity statistics of Qinghai from 1952 to 2016. The data were derived from the Qinghai Society and Economics Statistical Yearbook and Qinghai Statistical Yearbook. The accuracy of the data is consistent with that of the statistical yearbooks. Table 1: The table of rural grassroots organizations contains 10 fields. Field 1: Year of the data Field 2: Number of townships and towns Field 3: Number of townships Field 4: Number of towns Field 5: Number of villagers’ committees Field 6: Number of households in rural areas, unit: 10,000 Field 7: Population of rural areas, unit: 10,000 Field 8: Number of workers in rural areas, unit: 10,000 Field 9: Number of male workers, unit: 10,000 Field 10: Number of female workers, unit: 10,000 Table 2: The table of demographic statistics in Qinghai contains 8 fields. Field 1: Year of the data Field 2: Total population Field 3: Male population, unit: 10,000 Field 4: Female population, unit: 10,000 Field 5: Urban population, unit: 10,000 Field 6: Rural population, unit: 10,000 Field 7: Agricultural population, unit: 10,000 Field 8: Non-agricultural population, unit: 10,000 Table 3: The table describing the structure of rural workers contains 9 fields. Field 1: Year of the data Field 2: Number of workers in agricultural, forestry, animal husbandry and fishery sectors, unit: 10,000 Field 3: Number of workers in industry, unit: 10,000 Field 4: Number of workers in the construction sector, unit: 10,000 Field 5: Number of workers in the transportation, storage industry and post trade sectors, unit: 10,000 Field 6: Number of workers in the information industry, unit: 10,000 Field 7: Number of workers in commerce, unit: 10,000 Field 8: Number of workers in the accommodation and catering industry, unit: 10,000 Field 9: Number of workers in other industries Table 4: The table of demographic statistics for each county contains 9 fields. Field 1: Districts and counties Field 2: Year Field 3: Year-end total household number Field 4: Rural household number Field 5: Year-end total population Field 6: Rural total population Field 7: Year-end workers Field 8: Rural workers Field 9: Agriculture, forestry, animal husbandry and fishery
Qinghai Provincial Bureau of Statistics
Contact Support
Northwest Institute of Eco-Environment and Resources, CAS
0931-4967287
poles@itpcas.ac.cn
Links
National Tibetan Plateau Data CenterFollow Us
A Big Earth Data Platform for Three Poles © 2018-2020 No.05000491 | All Rights Reserved
｜ 
No.11010502040845
Tech Support: westdc.cn
