This dataset records the full text of the Qinghai Provincial Financial Budget Implementation Bulletin from 2002 to 2017. The data were compiled from the Qinghai Provincial Statistical Yearbook issued by the Qinghai Provincial Bureau of Statistics. The dataset contains 16 documents, including: the report on the implementation of Qinghai's 2002 National Economic and Social Development Plan and the draft plan for 2003. docx, the report on the implementation of Qinghai's 2002 financial budget and the draft financial budget for 2003. docx, the report on the implementation of Qinghai's 2003 financial budget and the draft financial budget for 2004. docx, the report on the implementation of Qinghai's 2006 financial budget and the draft financial budget for 2007. docx, Report on the implementation of Qinghai's 2007 financial budget and the 2008 financial budget draft Docx, the report on the implementation of Qinghai's financial budget in 2008 and the draft financial budget in 2009. docx, the report on the implementation of Qinghai's financial budget in 2016 and the draft financial budget in 2017. docx, the report on the implementation of Qinghai's financial budget in 2017 and the draft financial budget in 2018. docx, etc.
Qinghai Provincial Bureau of Statistics
The data set includes the implied water resources and land resource flows among 11 cities and counties in the Heihe River basin, including Ganzhou, Sunan, Minle, Linze, Gaotai, Shandan, Suzhou, Jinta, Jiayuguan and Ejina. Table 1 includes the transfer volume of virtual water resources and virtual land resources among multiple regions. Table 2 includes the virtual water resources export volume of each regional sub sector and the virtual water resources import volume of each regional sub sector. Table 3 includes the export volume of virtual land resources of each regional sub sector and the import volume of virtual land resources of each regional sub sector. Based on the input-output tables of 11 cities and counties in the Heihe River Basin, we investigate the consumption, loss and flow of water and land resources in each economic sector, construct a coupled water-land resource accounting statement, and calculate the virtual water resources and virtual land resources flow by sector in each region based on the input-output analysis method. The water consumption and land use data of each region and sector are obtained from official statistical yearbook data.
CHEN Bin
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 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 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 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
Provide detailed spatial distribution of GDP data in China from 1990 to 2015 year by year. The data is 1km grid data, and the unit is 10000 yuan / square kilometer. This grid data comprehensively considers multiple factors for weight allocation to realize the spatialization of GDP, which is convenient for data sharing and spatial statistical analysis. The data comes from the resource and environment science data registration and publication system. The year-on-year data is obtained by linear interpolation of the original data, and is saved in GeoTIFF file format. The methods and standards of data over the years are consistent, the coverage is complete, and the collection and processing process is traceable and reliable.
WANG Can , WANG Jiachen
"One belt, one road" along the lines of risk rating, credit risk rating and Moodie's national sovereignty rating reflects the structure of sovereign risk in every country. The rating of Moodie's national sovereignty is from the highest Aaa to the lowest C level, and there are twenty-one levels. Data source: organized by the author. Data quality is good. The rating level is divided into two parts, including investment level and speculation level. AAA level is the highest, which is the sovereign rating of excellent level. It means the highest credit quality and the lowest credit risk. The interest payment has sufficient guarantee and the principal is safe. The factors that guarantee the repayment of principal and interest are predictable even if they change. The distribution position is stable. C is the lowest rating, indicating that it cannot be used for real investment.
SONG Tao
Based on the non survey method, referring to the provincial input-output table and county-level statistical data of the Qilian Mountain region, the project compiled the input-output table of the Qilian Mountain Region in 2017. This table provides a data basis for analyzing the production and consumption of regional economy and the virtual water resources contained in its products or services. The input-output table uses the input-output tables of Qinghai Province, Inner Mongolia Autonomous Region and Gansu Province in 2017, analyzes the industrial production, residents' consumption and interregional trade information of districts and counties included in the Qilian Mountains, and constructs the input-output table of the Qilian Mountains. The input-output table is the characterization of the regional macroeconomic structure and the level of regional products or services.
WU Feng
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
Vulnerability of disaster bearing body is the degree of damage that human social and economic activities may suffer under the disturbance or pressure of natural disasters under a certain social and economic background, that is, the nature that disaster bearing body is vulnerable to damage and loss in the face of natural disasters. Based on the actual scientific research and expert guidance, this data constructs the vulnerability assessment indicators of disaster bearing bodies from the three aspects of exposure, sensitivity and adaptability, and uses the revised serv vulnerability model to calculate the Himalayan surrounding areas (domestic part) and the Asian water tower area. In order to systematically analyze the vulnerability of disaster bearing bodies in the study area, this data selects indicators from six aspects: population, economy, traffic lines, ecological environment, livestock and buildings, and constructs an indicator system of 6 first-class indicators, 18 second-class indicators and 29 third-class indicators. After the obtained vulnerability assessment results of population, economy, traffic lines, ecological environment, livestock and buildings are normalized, the vulnerability assessment maps of Himalayan surrounding areas (domestic part) and Asian water tower area are obtained by vector superposition.
ZHOU Qiang, CHEN Yingming , LIU Fenggui, CHEN Ruishan , CHEN Qiong, XIA Xingsheng , NIU Baicheng , DUAN Yufang
The data is 1:250000 socio-economic data of Sichuan Tibet line and surrounding areas, including GDP, population and other data. Population and GDP are one of the important indicators of social and economic development, regional planning and resource and environmental protection. Administrative regions are usually taken as the basic statistical unit. The spatialization of population and GDP replaces the traditional administrative statistics unit with spatial statistics unit, which brings great convenience for data sharing and spatial statistical analysis among multiple fields. The data comes from the kilometer grid data set of China's population and GDP spatial distribution of resource and environmental science and data center. The data set of China's population and GDP spatial distribution kilometer grid of resource and environmental science and data center is cut according to the scope of Sichuan Tibet railway and surrounding areas. The data is in grid format and accurate to every square kilometer. It is applicable to the Sichuan Tibet line and surrounding areas. Population and GDP are one of the important indicators of social and economic development, regional planning and resource and environmental protection.
WANG Zhonggen
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
This data set records the statistical data of per capita GDP and growth rate and ranking (2010-2018) of all regions in China, and the data are divided by year. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set contains eight data tables, each of which has the same structure. For example, the data table of 2017-2018 has four fields: Field 1: Region Field 2: quantity Field 3: Rank Field 4: growth rate
Qinghai Provincial Bureau of Statistics
The distribution data of Central Asia desert oil and gas fields are in the form of vector data in ". SHP". Including the distribution of oil and gas fields and major urban settlements in the five Central Asian countries. The data is extracted and cut from modis-mcd12q product. The spatial resolution of the product is 500 m, and the time resolution is 1 year. IGBP global vegetation classification scheme is adopted as the classification standard. The scheme is divided into 17 land cover types, among which the urban data uses the construction and urban land in the scheme. The data can provide data support for the assessment and prevention of sandstorm disasters in Central Asia desert oil and gas fields and green town.
GAO Xin
Grassland actual net primary production (NPPa) was calculated by CASA model. CASA model was calculated with the combination of satellite-observed NDVI and climate (e.g. temperature, precipitation and radiation) as the driving factors, and other factors, such as land-use change and human harvest from plant material, were reflected by the changes of NDVI. CASA NPP was determined by two variables, absorbed photosynthetically active radiation’ (APAR) and the light-use efficiency (LUE). Grassland potential net primary production (NPPp) was calculated by TEM model. TEM is one of process-based ecosystem model, which was driven by spatially referenced information on vegetation type, climate, elevation, soils, and water availability to calculate the monthly carbon and nitrogen fluxes and pool sizes of terrestrial ecosystems. TEM can be only applied in mature and undisturbed ecosystem without take the effects of land use into consideration due to it was used to make equilibrium predications. Grassland potential aboveground biomass (AGBp) was estimated by random forest (RF) algorithm, using 345 AGB observation data in fenced grasslands and their corresponding climate data, soil data, and topographical data.
NIU Ben, ZHANG Xianzhou
It is summarized that the agricultural and socio-economic status of the five Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan and Turkmenistan) in 2016. This data comes from the statistical yearbook of five Central Asian countries, including six elements: total population, cultivated land area, grain production area, GDP, proportion of agricultural GDP to total GDP, proportion of industrial GDP to total GDP, and forest area. Detailed statistics of the six socio-economic elements of the five Central Asian countries. It can be seen from the statistics that there are different emphases among the six elements of the five Central Asian countries. This data provides basic data for the project, facilitates the subsequent analysis of the ecological and social situation in Central Asia, and provides data support for the project data analysis.
LIU Tie
The Belt and Road financial system resilience dataset reflects the level of resilience of the financial system of each country, and the higher the value of the data, the stronger the resilience of the financial system of the countries along the Belt and Road. The financial system resilience includes money market development resilience, banking system resilience, and stock market resilience. The data products are prepared with reference to the World Bank's statistical database, using broad money growth (annual percentage), real interest rates, net domestic credit (current local currency) as a percentage of GDP, and net domestic credit (current local currency) for countries along the Belt and Road from 2000 to 2019. The financial system resilience product is prepared through a comprehensive diagnosis based on sensitivity and adaptability analysis, taking into account year-on-year changes in each indicator, using year-on-year data on six indicators: bank liquidity reserves as a percentage of bank assets, bank capital as a percentage of assets, and total stock transactions as a percentage of GDP. The financial system resilience dataset for countries along the "Belt and Road" is an important reference for the analysis and comparison of the current financial system resilience of each country.
XU Xinliang
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
The GDP per capita growth resilience dataset for countries along the Belt and Road is a comprehensive reflection of the level of GDP per capita growth resilience of each country. The GDP per capita growth resilience dataset was prepared with reference to the World Bank's statistical database, using year-on-year data on GDP per capita (constant 2010 US dollars) for countries along the Belt and Road from 2000 to 2019, and based on sensitivity and adaptability analysis, taking into account the year-on-year changes of each indicator. Through a comprehensive diagnostic, a product on GDP per capita growth resilience was prepared. "The GDP per capita growth resilience dataset for countries along the Belt and Road is an important reference for analysing and comparing the current GDP per capita growth resilience of 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 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
"The resilience of the domestic economic systems of the countries along the Belt and Road reflects the level of resilience of the domestic economic systems of each country, and the higher the value of the data, the stronger the resilience of the domestic economic systems of the countries along the Belt and Road. The resilience of domestic economic systems includes macroeconomic development resilience, industrial and service sector development resilience, and the data products are prepared with reference to the World Bank statistical database, using GDP per capita, gross fixed capital formation as a percentage of GDP, inflation as measured by GDP deflator, and gross savings as measured by GDP deflator for countries along the Belt and Road from 2000 to 2019. The resilience products of the domestic economic system are prepared through a comprehensive diagnosis based on sensitivity and adaptability analysis, taking into account the year-on-year changes of each indicator, using year-on-year data of six indicators: GDP per capita, gross fixed capital formation as a percentage of GDP, gross savings as a percentage of GDP, industrial value added as a percentage of GDP, and service value added as a percentage of GDP. "The resilience dataset of the domestic economic systems of the countries along the Belt and Road is an important reference for analysing and comparing the resilience of the domestic economic systems of various countries.
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 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
Taking the ISI Web of Science database as the data source, in which TS=(tibet* or himalaya* or qomolangma or "mt everest" or qinghai or karakorum or karakoram or kunlun* or qilian* or hengduan* or muztagata or tanggula or hengduan* or tianshan Or qiangtang* or "yarlung zangbo" or qaidam or pamir* or gangdise or gangdese) as the search term, the literature of Tibetan Plateau research before October,2016, was searched and collected.
GUO Xuejun
The data set recorded the statistical data of medical institutions, beds and personnel in Qinghai province from 1952 to 2020. The data were divided according to the main years. The data came from The Health Commission of Qinghai Province, and the data of medical institutions were adjusted by the provincial health department according to the uniform caliber. Since 2019, village clinics have been included in the list of medical and health institutions as required by the National Health Commission. The data are collected from qinghai Statistical Yearbook released by Qinghai Provincial Bureau of Statistics. The dataset contains 34 data tables, which are: Number of Health institutions, Beds and Personnel in main years (1952-1998). XLS, number of Health institutions, beds and Personnel in Main years (1952-1999). XLS, Number of health institutions, beds and Personnel in Main years (1952-2000). Number of Health institutions, Beds and Personnel in main years (1952-2001). XLS, Number of Health Institutions, Beds and Personnel in Main years (1952-2002). XLS, Number of Health Institutions, Beds and Personnel in Main years (2009-2020). Number of health institutions, beds and personnel (1952-2018). XLS, etc. The data table structure is the same. For example, the data table from 1952 to 2018 has 4 fields: Field 1: Medical facility Field 2: Number of beds Field 3: Number of patients Field 4: Other
Qinghai Provincial Bureau of Statistics
The data set recorded the basic situation of ordinary middle schools in Qinghai Province from 1952 to 2020. The data were classified by year and regional indicators such as Xining city, Haidong Prefecture, Haibei Prefecture, Huangnan Prefecture, Hainan Prefecture, Goluo Prefecture, Yushu Prefecture and Haixi Prefecture. The data are collected from qinghai Statistical Yearbook released by Qinghai Provincial Bureau of Statistics. The dataset contains 26 data tables, which are: Basic information of Ordinary middle schools 1952-2013 XLS Basic information of Ordinary middle schools 1952-2014 XLS Basic information of Ordinary secondary schools 1952-2015. XLS Main years General secondary school basic information 1952-2016. XLS Basic information of General Secondary schools 1952-2017. XLS Main years Basic information of ordinary middle schools 1952-2018. XLS, etc., similar data table structure. For example, the basic information of ordinary middle schools from 1952 to 2018 data table has 4 fields: Field 1: Number of graduates Field 2: Enrollment Field 3: Number of students enrolled Field 4: Number of staff
Qinghai Provincial Bureau of Statistics
The data set records the statistical data of total wages of employees in all units in Qinghai Province by registration type from 2010 to 2020. The data are divided by major years. The data are collected from qinghai Statistical Yearbook released by Qinghai Provincial Bureau of Statistics. The dataset contains 9 data tables, which are: Total wage of employed Persons in all units by type of registration 2010-2011. XLS, Total wage of employed Persons in all Units by type of registration 2010-2012. XLS, Total wage of Employed Persons in All Units by type of registration 2010-2013 XLS, Total wage of Employed Persons in All Units by type of registration 2010-2020 XLS, Total wage of Employed Persons in All Units by type of registration 2010-2014 XLS, Total wage of employed Persons in all units by type of registration 2010-2015 XLS total wage of employed Persons in all Units by type of registration 2010-2016 XLS Total wage of employed Persons in all Units by type of registration 2010-2017 XLS, Total wages of employees in all units by type of registration 2010-2018. XLS. The data table structure is the same. For example, the 2010-2011 data table has 5 fields: Field 1: Item Field 2: State-owned units Field 3: Town collective units Field 4: Private sector Field 5: Other units
Qinghai Provincial Bureau of Statistics
The data set recorded the basic situation of primary schools in Qinghai province from 1952 to 2020. The data were divided by year and regional indicators in Xining city, Haidong Prefecture, Haibei Prefecture, Huangnan Prefecture, Hainan Prefecture, Guoluo Prefecture, Yushu Prefecture and Haixi Prefecture. The data are collected from qinghai Statistical Yearbook released by Qinghai Provincial Bureau of Statistics. The dataset contains 22 data tables, all of which have the same structure. For example, the data table from 1952 to 1998 has seven fields: Field 1: Year Field 2: Number of graduates Field 3: Enrollment Field 4: Number of students enrolled Field 5: Number of staff Field 6: Full-time teacher Field 7: Number of students per full-time teacher
Qinghai Provincial Bureau of Statistics
The data set records the basic statistics of the non-public economy in Qinghai province from 2000 to 2020, and the data is divided by economic type and industry category. The data are collected from qinghai Statistical Yearbook released by Qinghai Provincial Bureau of Statistics. The dataset contains 15 data tables, which are as follows: Index of Economic Benefit of Non-Public Industrial Enterprises (2007) 2008. XLS, Basic Conditions of Non-public Economy 2000-2002. XLS, Basic Conditions of Non-Public Economy 2000-2003. Basic Statistics on the Non-Public Sector of the Economy 2006-2010 XLS, Basic Statistics on the Non-Public Sector 2007-2011 XLS, Basic Statistics on the Non-Public Sector of the Economy 2008-2011 XLS, Basic Statistics on the Non-Public Sector of the Economy 2008-2011 XLS, Basic Statistics on the Non-public Sector of the Economy 2007-2010 XLS, Basic Statistics on the Non-public Sector of the Economy 2008-2011 XLS, Basic Statistics on the Non-public sector of the Economy 2008-2011 XLS, Basic Statistics on the Non-public sector of the Economy 2008-2011 XLS, Basic Statistics on non-public Sector of the Economy 2010-2012 XLS, Basic Statistics on Non-public Sector of the Economy 2013-2015 XLS, Basic Statistics on Non-public Sector of the Economy 2014-2016 XLS, Basic Statistics on Non-public Sector of the Economy 2015-2017 XLS, Basic Situation of Non-public Economy in Qinghai Province (2019). XLS, Basic Situation of Non-public Economy in Qinghai Province (2020). XLS. The data table structure is the same. For example, the data table from 2015 to 2017 has 7 fields: Field 1: indicator Field 2: Item Field 3: Number of households Field 4: Practitioner Field 5: Registered capital Field 6: Sales revenue or operating revenue Field 7: Total output
Qinghai Provincial Bureau of Statistics
This dataset records the statistical data of comprehensive energy balance sheet of Qinghai Province from 2000 to 2020. The data are divided by energy available for local consumption, input (-) output (+) of processing and conversion, loss and terminal consumption. The data are collected from qinghai Statistical Yearbook released by Qinghai Provincial Bureau of Statistics. The dataset contains 14 data tables, all of which have the same structure. For example, there are 30 fields in the data table from 2012 to 2017, among which the main fields are: Field 1: Item Field 2:2012 Field 3:2013 Field 4:2014 Field 5:2015 Field 6:2016 Field 7:2017
Qinghai Provincial Bureau of Statistics
The data set records the statistical data of retail and catering chain operation in Qinghai province from 2009 to 2020, and the data is divided by registration type, industry group, business type, etc. The data are collected from qinghai Statistical Yearbook released by Qinghai Provincial Bureau of Statistics. The dataset contains 12 data tables, which are: Retail and catering chain operation consolidated table 2009. XLS Retail and catering chain operation comprehensive table 2010. XLS Retail chain operation comprehensive table 2011. XLS Retail chain operation comprehensive table 2012. XLS Retail chain operation comprehensive table 2013. XLS Retail chain operation comprehensive table 2014. XLS Retail chain operation comprehensive table 2015. XLS Retail chain operation comprehensive table 2016. XLS Retail chain operation comprehensive table 2017. XLS Retail chain operation comprehensive table 2018. XLS Retail chain operation comprehensive table 2019. XLS Retail chain operation comprehensive table 2020. XLS The data table structure is the same. For example, the comprehensive table of retail and restaurant chain operation in 2010 has 6 fields: Field 1: indicator Field 2: Total number of chain stores Field 3: Number of stores Field 4: Number of employees at year-end Field 5: Total purchase amount of goods in chain stores Field 6: Chain store merchandise sales
Qinghai Provincial Bureau of Statistics
The data set recorded the statistical data of maternity insurance participants and fund collection in Qinghai province from 1999 to 2020. The data were divided by insurance participants, women, the number of times enjoying maternity insurance benefits in the current period, by treatment category, the number of births in the current period, the number of family planning operations, fund revenue and expenditure, insurance participants and other indicators. The data are collected from qinghai Statistical Yearbook released by Qinghai Provincial Bureau of Statistics. The dataset contains 22 data tables, which are: XLS, Maternity Insurance Personnel and Fund Collection (2001). XLS, Maternity Insurance Personnel and Fund Collection (2002). XLS, Maternity Insurance personnel and Fund Collection (2002). XLS, Maternity Insurance Personnel and Fund Collection (2005). XLS, Maternity Insurance Personnel and Fund Collection (2006). XLS, Maternity Insurance personnel and Fund Collection (2005). Number of Participants in Maternity Insurance and Fund Collection (2007). XLS, Number of Participants in Maternity Insurance in Qinghai Province (2014-2019). XLS, Number of Participants in Maternity Insurance in Qinghai Province (2015-2020). Maternity insurance participants and fund collection (2018). XLS, et al. The data table structure is the same. For example, the data table from 2015 to 2020 has 7 fields: Field 1: indicator Field 2:2015 Field 3:2016 Field 4:2017 Field 5:2018 Field 6:2019 Field 7:2020
Qinghai Provincial Bureau of Statistics
This data set records the statistical data of the composition of non-public economic added value in Qinghai Province from 2000 to 2020. The data are divided according to economic type, industry category and industrial structure. The data is compiled from the statistical yearbook of Qinghai Province issued by Qinghai Provincial Bureau of statistics. The data set contains 14 data tables, namely: the composition of non-public economic added value from 2000 to 2002. XLS, non-public economy Composition of added value 2000-2003 Xls, composition of added value of non-public economy from 2009 to 2011 Xls, composition project of non-public economic added value, 2006-2009 Xls, value added component of non - public economy, 2006 Xls, value added component of non-public economy, 2007-2010 Xls, value added component of non - public economy, 2007 Xls, non-public economic value added component project, 2008 Xls, composition project of non-public economic added value, 2010-2012 Xls, composition project of non-public economic added value, 2013-2015 Xls, composition project of non-public economic added value, 2014-2016 Xls, non-public economic value added component project, 2015-2017 Xls, composition project of non-public economic added value in Qinghai Province (2018-2019) Xls, composition project of non-public economic added value in Qinghai Province (2019-2020) xls。 The data table structure is different. For example, the data table for 2000-2002 has seven fields: Field 1: indicator Field 2: total output Field 3: add value Field 4: remuneration of workers Field 5: depreciation of fixed assets Field 6: net production tax Field 7: operating surplus
Qinghai Provincial Bureau of Statistics
The data set recorded the statistics of investment of real estate development enterprises (units) by use in Qinghai Province from 1997 to 2020, divided by year and regions such as Xining, Haidong, Haibei prefecture, Hainan Prefecture, And Huangnan Prefecture. The data are collected from qinghai Statistical Yearbook released by Qinghai Provincial Bureau of Statistics. The dataset contains 15 data tables, which are: Investment of Real Estate Development Enterprises (units) by Use 2000-2009 XLS, Investment of Real Estate Development Enterprises (units) by Use 2000-2010 XLS, Investment of Real Estate Development Enterprises (units) by Use 2000-2011 XLS, Investment amount of Real Estate Development Enterprises (Units) by Use 2000-2012 XLS, Investment Amount of Real Estate Development Enterprises (units) by Use 2000-2013 XLS, Investment Amount completed by Real Estate Development Enterprises (units) by Use 1997-2005 XLS, Amount of Investment Completed by Real Estate Development enterprises (Units) by Use 1997-2006 XLS, Amount of Investment Completed by Real Estate Development Enterprises (units) by Use 1997-2007 XLS, Amount of completed investment of Real estate development enterprises (units) by Use 1997-2008 XLS, Amount of Investment of Real estate development enterprises (units) by Use in Qinghai Province (2001-2020) XLS, etc. The data table structure is the same. For example, the data table from 2000 to 2009 has 6 fields: Field 1: Year region Field 2: Investment completed for the current year Field 3: Residence Field 4: Office Field 5: Commercial premises Field 6: Other
Qinghai Provincial Bureau of Statistics
The data set recorded the statistical data of the main indexes of agricultural economic benefits in Qinghai province in the main years from 1952 to 2020. The data were divided by years. The per capita output value was calculated at the 1990 constant price before 2004, and the current price in 2005. The data are collected from qinghai Statistical Yearbook released by Qinghai Provincial Bureau of Statistics. The dataset contains 22 data tables, all of which have the same structure. For example, the 2018 table has 4 fields: Field 1: Year Field 2: Food production per capita Field 3: Oil production per capita Field 4: Gross agricultural output per capita
Qinghai Provincial Bureau of Statistics
The dataset recorded the statistical data of fixed asset investment in projects of more than 5 million yuan in Qinghai Province from 1996 to 2020, divided by year and regions such as Xining city, Haidong Prefecture, Haibei Prefecture, Huangnan Prefecture, Hainan Prefecture, Guoluo Prefecture, Yushu Prefecture and Haixi Prefecture. The data are collected from qinghai Statistical Yearbook released by Qinghai Provincial Bureau of Statistics. The dataset contains six data tables, which are: Investment in urban fixed assets of 5 million Yuan and above (2011). XLS, investment in fixed assets of 5 million Yuan and above in the whole society (2011). XLS, investment in fixed assets of projects of 5 million Yuan and above (1996-2018). 5. XLS, Fixed asset investment Growth rate of Projects over 5 million Yuan in Qinghai Province (1996-2020) The data table structure is the same. For example, the 2011 table has 6 fields: Field 1: New project started Field 2: All completed and put into production projects Field 3: Project completion and commissioning rate Field 4: Fixed asset investment amount Field 5: Added fixed assets Field 6: Delivery utilization rate
Qinghai Provincial Bureau of Statistics
The data set records the statistical data of the total output index of Qinghai Province in major years from 1952 to 2019. The data is divided by year, where the index is: the previous year is 100. The data are collected from qinghai Statistical Yearbook released by Qinghai Provincial Bureau of Statistics. The dataset contains 37 data tables, all of which have the same structure. For example, the 2018 table has seven fields: Field 1: Total output Field 2: Primary industry Field 3: Secondary industry Field 4: Tertiary industry Field 5: Agriculture, forestry, animal husbandry and fishery Field 6: Industry Field 7: Construction industry
Qinghai Provincial Bureau of Statistics
This data set records the statistics of the basic situation of lawyers, notaries and mediation work in Qinghai Province from 1998 to 2020. Data according to the lawyer, lawyer (a), a worker as perennial legal adviser (people), and the unit (point), defense and agency (piece) of criminal procedure, civil case litigation agent (a), administrative case litigation agent (a), non-litigation legal affairs (pieces), write legal documents (pieces), notarization work, handling of foreign-related notarization (a), Notary office (number), notary personnel (person), notary (person), handling domestic notarization (documents), handling foreign notarization (documents), handling notarization (documents) related to Hong Kong, Macao and Taiwan, receiving visitors (person-times), handling letters (documents), entrusting (documents) from foreign notary offices, and income from notary fees (ten thousand yuan). The data are collected from qinghai Statistical Yearbook released by Qinghai Provincial Bureau of Statistics. The dataset contains 23 data tables, which are: Basic information on lawyer, notarization and mediation work 2006-2007. XLS Basic information on lawyer, notarization and mediation 1998. XLS Basic information on lawyer, notarization and mediation 1999. XLS Basic information on lawyer, notarization and mediation work 2000-2001. XLS Basic information on lawyer, notarization and mediation work 2000. XLS Basic information of lawyer, notarization and mediation work 2001-2002. XLS Basic information of lawyer, notarization and mediation work 2002-2003. XLS Basic information on lawyer, notarization and mediation work 2004-2005. XLS Basic information on lawyer, notarization and mediation work 2004.xls Basic information on lawyer, notarization and mediation work 2005-2006. XLS Basic information of lawyer, notarization and mediation work 2006-2008. XLS Basic information on lawyer, notarization and mediation work 2007-2009. XLS Basic information on lawyer, notarization and mediation work 2008-2010. XLS Basic Information on lawyer, notarization and mediation work 2009-2011. XLS Basic Statistics on lawyer, notarization and mediation work 2011-2012 XLS Basic statistics on lawyer, notarization and mediation work 2012-2013. XLS Basic Statistics on lawyer and notarial work 2013-2014 XLS Basic Statistics on lawyer and notarial work 2014-2015 XLS Basic Statistics on lawyer and notarial work 2015-2016. XLS Basic Statistics on lawyers and notarization work 2016-2017. XLS Basic Statistics on lawyers and notarization work 2017-2018. XLS Basic Statistics on lawyers and notarization work 2018-2019. XLS Basic Statistics on lawyer and notarial work 2019-2020. XLS The data table structure is similar. For example, the 2003 data table of various professional and technical personnel by industry has three fields: Field 1: Item Field 2:2006 Field 3:2007
Qinghai Provincial Bureau of Statistics
The data records statistical data on main indicators of local industrial enterprises above designated size in Qinghai province from 2008 to 2020. The data is divided by state-owned holding enterprises, registration types and industrial sectors. The data are collected from qinghai Statistical Yearbook released by Qinghai Provincial Bureau of Statistics. The dataset consists of 11 data tables with the same structure for each year. For example, the 2017 table has 11 fields. Field 1: Item Field 2: Item Field 3: Number of enterprise units Field 4: Loss-making enterprise Field 5: Total industrial output value Field 6: Total assets Field 7: Total current assets Field 8: Total fixed assets Field 9: Original price of fixed assets Field 10: Accumulated depreciation Field 11: Current year depreciation
Qinghai Provincial Bureau of Statistics
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