Data content: Industrial added value of national economy (monthly) (2010-2021) Data source and processing method: obtain the original data of the third pole (China) industrial economy in 2010-2021 from the official website of the World Bank and Sina.com, and obtain the industrial economy data set in 2010-2021 (China) through data sorting, screening and cleaning. The data starts from 2010 to 2021 in Microsoft Excel (xls) format. Data quality description: excellent Data application achievements and prospects: provide effective reference as social, industrial and economic data
FU Wenxue
Data content: foreign economy and trade_ Total import and export of goods (1991-2021) Data source and processing method: The original data of foreign trade and investment of the third pole (China region) from 2015 to 2021 were obtained from the official website of the World Bank and Sina.com, and the data set of foreign trade and investment of the third pole (China region) from 1991 to 2021 was obtained through data sorting, screening and cleaning. The data started from 1991 to 2021 in Microsoft Excel (xls) format. Data quality description: excellent Data application achievements and prospects: provide effective reference as socio-economic data
FU Wenxue
Data content: annual statistics of gross domestic product (GDP) (1991-2021), domestic assets and liabilities data (2011-2020) and domestic input and output data (2012-2018) Data source and processing method: The original macroeconomic data of the third pole (China) from 2015 to 2021 were obtained from the official website of the World Bank and Sina.com, and the macroeconomic data set of the third pole (China) from 1991 to 2021 was obtained through data sorting, screening and cleaning. The data was stored in Microsoft Excel (xls) format. Data quality description: excellent Data application achievements and prospects: provide effective reference as socio-economic data
FU Wenxue
Data content: price index_ Consumer Price Index (CPI) (2009-2022) Data source and processing method: The original data of the third pole (China) price index economy from 2015 to 2022 were obtained from the official website of the World Bank and Sina.com, and the economic data set of the third pole (China) price index from 2009 to 2022 was obtained through data collation, screening and cleaning. The data started from 2009 to 2022 in Microsoft Excel (xls) format. Data quality description: excellent Data application achievements and prospects: provide effective reference as socio-economic data
FU Wenxue
Data content: money supply (2012-2021) and assets and liabilities of financial institutions (2007-2020) Data source and processing method: The original data of the third pole (China) banks and currencies from 2015 to 2021 were obtained from the official website of the World Bank and Sina.com, and the data set of the third pole (China) banks and currencies from 2012 to 2021 was obtained through data sorting, screening and cleaning. The data started from 2012 to 2021 in Microsoft Excel (xls) format. Data quality description: excellent Data application achievements and prospects: provide effective reference as socio-economic data
FU Wenxue
This data set is the daily vorticity related flux observation data of Naqu flux station (31.64 ° N 92.01 ° E, 4598 m a.s.l.), including ecosystem net ecosystem productivity (NEP), total primary productivity (GPP) and ecosystem respiration (ER) data. The main steps of data pre-processing include wild point removal (± 3 σ）、 Coordinate axis rotation (3D wind rotation), Webb Pearman Leuning correction, outlier elimination, carbon flux interpolation and decomposition, etc. Missing data are interpolated through the nonlinear empirical formula between CO2 flux value (Fc) and environmental factors.
ZHANG Yangjian
This dataset is the daily vorticity related flux observation data of Naqu flux station (31.64 ° N 92.01 ° E, 4598 m a.s.l.), including net ecosystem productivity (NEP), total primary productivity (GPP), ecosystem respiration (ER), evapotranspiration, latent heat, sensible heat, air temperature, relative humidity, wind speed, soil temperature, soil moisture and other data. The main steps of data pre-processing include wild point removal (± 3 σ）、 Coordinate axis rotation (3D wind rotation), Webb Pearman Leuning correction, outlier elimination, carbon flux interpolation and decomposition, etc. Missing data are interpolated through the nonlinear empirical formula between CO2 flux value (Fc) and environmental factors.
ZHANG Yangjian
This data set contains sequence data of the number variation of livestock in the major cities and counties of the Tibetan Plateau from 1970 to 2006. It is used to study the social and economic changes of the Tibetan Plateau. The table has ten 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: Large livestock (10,000) Interpretation: The number of large livestock such as cattle, horses, mules, donkeys, and camels. Field 6: Cattle herd (10,000) Interpretation: Number of cattle Field 7: Equine animals(10,000) Interpretation: The number of equine animals such as horses, mules and donkeys. Field 8: Horses (10,000) Interpretation: The number of horses Field 9: Sheep (10,000) Interpretation: The number of sheep Field 10: Data Sources Interpretation: Source of Data The data come from the statistical yearbook and county annals. 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 Statics of China
Data content: national economy_ Industrial value added (monthly) (2010-2019) Data source and processing method: the original industrial economic data of China (including the third pole) from the official website of the world bank and sina.com from 2010 to 2019 are obtained through data sorting, screening and cleaning. The data start time is from 2010 to 2019 in Microsoft Excel (xlsx) format.
FU Wenxue
Data content: Foreign Economic and trade_ Total import and export of goods (1952-2019) and foreign economic and trade_ Total import and export by trade (1981-2019) Data sources and processing methods: the original data of China's foreign trade and investment from 2015 to 2019 (including the third pole) were obtained from the official website of the world bank and sina.com, and the foreign trade and investment data set of China (including the third pole) from 1952 to 2019 was obtained through data sorting, screening and cleaning. The data start time is from 1952 to 2019 in Microsoft Excel (xlsx) format.
FU Wenxue
Data content: annual GDP statistics (1990-2019), quarterly cumulative GDP statistics (1990-2019) and GDP (2010-2019) Data sources and processing methods: the original macroeconomic data of China (including the third pole) from the official website of the world bank and sina.com from 1990 to 2019 are obtained through data sorting, screening and cleaning. The data are stored in Microsoft Excel (xlsx) format.
FU Wenxue
This data set contains information on natural disasters in Qinghai over nearly 50 years, including the times, places and the consequences of natural disasters such as droughts, floods, hail, continuous rain, snow disasters, cold waves and strong temperature drops, low temperature freezing injuries, gales and sandstorms, pest plagues, rats, and geological disasters. Qinghai Province is located in the northeastern part of the Tibetan Plateau and has a total area of 720,000 square kilometers. Numerous rivers, glaciers and lakes lie in the province. Because two mother rivers of the Chinese nation, the Yangtze River and the Yellow River, and the famous international river—the Lancang River—originated here, it is known as the "Chinese Water Tower"; there are 335,000 square meters of available grasslands in the province, and the natural pasture area ranks fourth in the country after those of Inner Mongolia, Tibet and Xinjiang. There are various types of grasslands, abundant grassland resources, and 113 families, 564 genera and 2100 species of vascular plants, which grow and develop under the unique climatic condition of the Tibetan Plateau and strongly represent the characteristics of the plateau ecological environment. As the main part of the Tibetan Plateau, Qinghai Province is one of the centers of the formation and evolution of biological species in China. It is also a sensitive area and fragile zone for the study of climate and ecological environment in the international field of sciences and technology. The terrain and land-forms in Qinghai are complex, with interlaced mountains, valleys and basins, widely distributed snow and glaciers, the Gobi and other deserts and grassland. Complex terrain conditions, high altitudes and harsh climatic conditions make Qinghai a province with frequent meteorological disasters. The main meteorological disasters include droughts, floods, hail, continuous rain, snow disasters, cold waves and strong temperature drops, low temperature freezing injuries, gales and sandstorms. The data are extracted from the Qinghai Volume of Chinese Meteorological Disaster Dictionary, with manual entry, summarizing and proofreading.
Qinghai Provincial Bureau of Statistics
The development resilience of social employment in the countries along the Belt and Road reflects the level of resilience of social employment in the countries along the Belt and Road, and the higher the value of the data, the stronger the development resilience of social employment in the countries along the Belt and Road. The data product of social employment development resilience is prepared by referring to the World Bank statistical database, using the year-by-year data of the ratio of total unemployment to total labour force in the countries along the Belt and Road from 2000 to 2019, and based on sensitivity and adaptability analysis by considering the year-by-year changes of each indicator. A comprehensive diagnostic was carried out to generate a resilience product for the development of social employment. "The data set on the resilience of social employment development in the countries along the Belt and Road is an important reference for analysing and comparing the resilience of the current population growth in each country.
XU Xinliang
Macroeconomics refers to the entire national economy or the national economy as a whole, as well as its economic activities and operational status. "The data set of macroeconomic development resilience of countries along the Belt and Road reflects the level of macroeconomic development resilience of the countries along the Belt and Road, and the higher the data value, the stronger the macroeconomic development resilience of the countries along the Belt and Road. The macroeconomic development resilience dataset is prepared with reference to the World Bank's statistical database, using year-on-year changes in four indicators: GDP per capita, gross fixed capital formation as a percentage of GDP, inflation as measured by the GDP deflator, and total savings as a percentage of GDP for countries along the "Belt and Road" from 2000 to 2019. The macroeconomic development resilience product was prepared through a comprehensive diagnosis based on sensitivity and adaptability analysis, taking into account the year-on-year changes of each indicator. "The resilience dataset of macroeconomic development of countries along the Belt and Road is an important reference for analysing and comparing the resilience of macroeconomic development of various countries.
XU Xinliang
The resilience of education in Belt and Road countries reflects the level of resilience of education in the countries along the Belt and Road, and the higher the value, the stronger the resilience of education in the countries along the Belt and Road. The data on the resilience of educational conditions are prepared by referring to the World Bank's statistical database, using year-on-year data on four indicators - literacy rate, education expenditure, secondary school enrolment rate and tertiary enrolment rate - for countries along the Belt and Road from 2000 to 2019, and taking into account the year-on-year changes in each indicator. Based on the sensitivity and adaptability analysis, a comprehensive diagnosis was carried out to generate a resilience product for the development of education conditions. "The data set on the resilience of educational conditions in countries along the Belt and Road is an important reference for analysing and comparing the current resilience of educational conditions in each country.
XU Xinliang
"The Belt and Road countries' external trade system resilience dataset comprehensively reflects the level of resilience of each country's external trade system, and the higher the value of the data, the stronger the resilience of the external trade system of the countries along the Belt and Road. The World Bank's statistical database was used for the preparation of the external trade system resilience data, and the annual data of three indicators, namely the ratio of trade volume to gross national product (GDP), the annual growth rate of exports of goods and services, and the annual growth rate of imports of goods and services of countries along the Belt and Road, were used from 2000 to 2019. On the basis of the year-on-year changes in each indicator, a comprehensive diagnosis based on sensitivity and adaptability analysis was carried out to generate a resilience product for the foreign trade system. Please refer to the documentation for the methodology of preparing the data set. "The resilience dataset of the foreign trade system of countries along the Belt and Road is an important reference for analysing and comparing the current resilience of the foreign trade system of each country.
XU Xinliang
The resilience of health care development in countries along the Belt and Road reflects the level of resilience of health care development in the countries along the Belt and Road, and the higher the value of the data, the stronger the resilience of health care development in the countries along the Belt and Road. The World Bank statistical database was used for the preparation of the health resilience data. Based on the year-on-year data of these four indicators, and taking into account the year-on-year changes of each indicator, the product of resilience in the development of healthcare conditions was prepared through comprehensive diagnosis based on sensitivity and adaptability analysis. "The Resilience in Health Care Development dataset for countries along the Belt and Road is an important reference for analysing and comparing the current resilience in health care development in each country.
XU Xinliang
"The resilience dataset reflects the level of resilience of industrial and service development in the countries along the Belt and Road, and the higher the value, the stronger the resilience of industrial and service development in the countries along the Belt and Road. The resilience of industrial and service sector development data products are prepared with reference to the World Bank's statistical database, using the year-on-year changes of two indicators, namely the value added of industry as a percentage of GDP and the value added of service sector as a percentage of GDP, for countries along the Belt and Road from 2000 to 2019, and on the basis of considering the year-on-year changes of each indicator. Based on the sensitivity and adaptability analysis, a comprehensive diagnostic was prepared to generate products on the resilience of industrial and service sector development. "The resilience dataset of industrial and service sector development in countries along the Belt and Road is an important reference for analysing and comparing the current resilience of industrial and service sector development in each country.
XU Xinliang
This data set contains the results of the calculation of Net Primary Productivity (NPP) on the Tibetan Plateau based on ecological models and remote sensing data from 1982 to 2006. Ecosystem NPP of the Tibetan Plateau was generated based on the remote sensing Advanced Very High Resolution Radiometer (AVHRR) data and the Carnegie-Ames-Stanford Approach (CASA) model(1982-2006), the soil carbon content was generated based on the second soil census data, and the biomass carbon data were generated based on the High Resolution Biosphere Model (HRBM) model. Forest ecosystem NPP of the Tibetan Plateau (1982-2006): npp_forest82.e00，npp_forest83.e00，npp_forest84.e00，npp_forest85.e00，npp_forest86.e00， npp_forest87.e00，npp_forest88.e00，npp_forest89.e00，npp_forest90.e00，npp_forest91.e00， npp_forest92.e00，npp_forest93.e00，npp_forest94.e00，npp_forest95.e00，npp_forest96.e00， npp_forest97.e00，npp_forest98.e00，npp_forest99.e00，npp_forest00.e00，npp_forest01.e00， npp_forest02.e00，npp_forest03.e00，npp_forest04.e00，npp_forest05.e00，npp_forest06.e00 Grassland ecosystem NPP of the Tibetan Plateau(1982-2006)： npp_grass82.e00，npp_grass83.e00，npp_grass84.e00，npp_grass85.e00，npp_grass86.e00， npp_grass87.e00，npp_grass88.e00，npp_grass89.e00，npp_grass90.e00，npp_grass91.e00， npp_grass92.e00，npp_grass93.e00，npp_grass94.e00，npp_grass95.e00，npp_grass96.e00， npp_grass97.e00，npp_grass98.e00，npp_grass99.e00，npp_grass00.e00，npp_grass01.e00，npp_grass02.e00，npp_grass03.e00，npp_grass04.e00，npp_grass05.e00，npp_grass06.e00. Biomass carbon and soil carbon of the Tibetan Plateau： Biomass.e00，Socd.e00. The soil carbon content data （Socd） are generated based on data of the second soil census of China and Soil Map of China (1:1,000,000) by soil subclass interpolation. The NPP data are generated from the CASA model and AVHRR data simulation: Potter CS, Randerson JT, Field CB et al. Terrestrial ecosystem production: a process model based on global satellite and surface data. Global Biogeochemical Cycles, 1993, 7: 811–841. The biomass carbon data are generated via HRBM model simulation： McGuire AD, Sitch S, et al. Carbon balance of the terrestrial biosphere in the twentieth century: Analyses of CO2, climate and land use effects with four process-based ecosystem models. Global Biogeochem. Cycles, 2001, 15 (1), 183-206. The raw data are mainly remote sensing data and field observation data with high accuracy; the verification and adjustment of the measured data in the field during the production were undertaken to maintain the error of the simulation results and the field measured data within the acceptable range as much as possible; the verification results of the NPP data and the field measured data show that the error remains within 15%. The spatial resolution is 0.05°×0.05° (longitude×latitude).
ZHOU Caiping
The data set is based on the NPP simulated by 16 dynamic global vegetation models (TRENDY v8) under S2 Scenario (CO2+Climate) and represents the net primary productivity of the ecosystem. Data was derived from Le Quéré et al. (2019). The range of source data is global, and the Qinghai Tibet plateau region is selected in this data set. Original data is interpolated into 0.5*0.5 degree by the nearest neighbor method in space, and the original monthly scale is maintained in time. The data set is the standard model output data, which is often used to evaluate the temporal and spatial patterns of gross primary productivity, and compared with other remote sensing observations, flux observations and other data.
STEPHEN Sitch
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