The data set records one belt, one road, 65 countries, 1961-2009 years of agricultural machinery (tractor) quantity and other relevant data. Agricultural machinery refers to the number of wheeled and tracked tractors (excluding horticultural tractors) used in agriculture at the end of a specified calendar year or the first quarter of the following year. Data source: Food and Agriculture Organization, electronic files and web site. Agricultural machinery reduces labor intensity, reduces hard labor, alleviates labor shortage, improves productivity and timeliness of agricultural activities, improves effective utilization of resources, increases market access, and helps reduce climate related hazards. In the future, agricultural machinery will play a greater role in ensuring the environmental sustainability of agriculture. The data set contains two data tables: Agricultural Machinery (tractors per 100 square kilometers of arable land), agricultural machinery (number of tractors).
By archaeological investigation and excavation in Tibetan Plateau and Hexi corridor, we discovered more than 40 Neolithic and Bronze Age sites, including Zongri, Sanjiaocheng, Huoshiliang, Ganggangwa, Yigediwonan, Shaguoliang, Guandi, Maolinshan, Dongjicuona, Nuomuhong, Qugong, Liding and so on. In this dataset, there are some basic informations about these sites, such as location, longitude, latitude, altitude, material culture and so on. On this Basis, we identified animal remains, plant fossil, selected some samples for radiocarbon dating, optically stimulated luminescence dating, stable carbon, nitrogen isotopes, polle, fungal sporen and environmental proxies. This dataset provide important basic data for understanding when and how prehistoric human lived in the Tibetan Plateau during the Neolithic and Bronze Age.
DONG Guanghui YANG Xiaoyan Lü Hongliang LIU Xiangjun HOU Guangliang
By archaeological investigation and excavation in Tibetan Plateau, we discovered 14 Paleolithic sites, including 151, Baishiya karst cave，Meilongdapu cave, Hejishihuotang and Tangda. In this dataset, there are some basic informations about these sites, such as location, longitude, latitude, altitude, material culture and so on. On this Basis, we identified and analysed stone artifacts, animal remains and plant fossil, and obtained a batch of dating data of uranium series, radiocarbon and optically stimulated luminescence. This dataset provide important basic data for understanding when and how prehistoric human lived in the Tibetan Plateau during the Paleolithic.
CHEN Fahu ZHANG Dongju LIU Xiangjun HOU Guangliang
Gridded population with 100m spaital resolution of the 34 key areas along One Belt One Road in 2010, which indicates that the population count (Unit: person) per pixel (i.e., grid). This data is derived from geodata institute of Southampton University, UK. The prejection transform and extraction processes were done to generate the gridded population with 100m spaital resolution of the 8 key areas along One Belt One Road in 2010. The original gridded popution is spatially downscaled from census data and multisource data by the random forest method. Accurate population data at finer scale are fundamental for a broad range of applications by governments, nongovernmental organizations, and companies, including the urban planing, election, risk estimation, disaster rescue, disease control, and poverty reduction.
GE Yong LI Qiangzi DONG Wen
The degree of opening to the outside world refers to the degree of opening to the outside world of a country or region's economy, which is embodied in the degree of opening to the outside world of the market, usually including the amount of import and export, the use of foreign capital, the level of tariff, the convenience of customs clearance, free trade agreements, market access, capital exchange, intellectual property protection, etc. The data are one belt, one road, 64 countries, including the net inflow of foreign direct investment (US $100 million), total import (US $100 million) and total export volume (US $100 million). Data sources include the world bank, the United Nations Conference on Trade and development, and the WTO. The 64 countries along the line include 16 in West Asia and North Africa, 16 in central and Eastern Europe, 5 other CIS countries, 8 in South Asia, 11 in Southeast Asia, including Myanmar, Vietnam and Thailand, and 5 in Mongolia, Russia and Central Asia.
Gross domestic product (GDP) is a monetary measure of the market value of all the final goods and services produced in a period of time, which has been used to determine the economic performance of a whole country or region. We have collected the published GDP data. Collect the public GDP data. On the basic of 1-kilometer scale global GDP grid data in 2010 released by the United Nations, the total GDP of the node area was obtained. The lighting data and land use data of node areas are took as auxiliary data, after data preprocessing, data interpolation and multiple regression analysis, establish the relationship between GDP and the hundred meter scale multiple data, and then, the GDP data of 34 key node areas are obtained.
GE Yong LI Qiangzi DONG Wen
"Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.This dataset includes demographic data of 22 countries from 1960 to 2018, including Sri Lanka, Bangladesh, Pakistan, India, Maldives, etc. Data fields include: country, year, population ratio, male ratio, female ratio, population density (km). Source: ( 1 ) United Nations Population Division. World Population Prospects: 2019 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. Periodicity: Annual Statistical Concept and Methodology: Population estimates are usually based on national population censuses. Estimates for the years before and after the census are interpolations or extrapolations based on demographic models. Errors and undercounting occur even in high-income countries. In developing countries errors may be substantial because of limits in the transport, communications, and other resources required to conduct and analyze a full census. The quality and reliability of official demographic data are also affected by public trust in the government, government commitment to full and accurate enumeration, confidentiality and protection against misuse of census data, and census agencies' independence from political influence. Moreover, comparability of population indicators is limited by differences in the concepts, definitions, collection procedures, and estimation methods used by national statistical agencies and other organizations that collect the data. The currentness of a census and the availability of complementary data from surveys or registration systems are objective ways to judge demographic data quality. Some European countries' registration systems offer complete information on population in the absence of a census. The United Nations Statistics Division monitors the completeness of vital registration systems. Some developing countries have made progress over the last 60 years, but others still have deficiencies in civil registration systems. International migration is the only other factor besides birth and death rates that directly determines a country's population growth. Estimating migration is difficult. At any time many people are located outside their home country as tourists, workers, or refugees or for other reasons. Standards for the duration and purpose of international moves that qualify as migration vary, and estimates require information on flows into and out of countries that is difficult to collect. Population projections, starting from a base year are projected forward using assumptions of mortality, fertility, and migration by age and sex through 2050, based on the UN Population Division's World Population Prospects database medium variant."
"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.
The data include the coastal ports and airport distribution in the Belt and Road region. The data are from the Natural Earth global port and airport data. The data are cut according to the standard map of the 65 countries along the Belt and road, and further corrected, then the distribution of the ports and airports in the area along the B&R is obtained. This data is mainly one to analyze the B&R area's important spatial layout and main characteristics of the transportation facilities, and to get other attributes data of port and airport in the following research, including the throughput of different port cargo types, the incoming and outgoing throughput, the number of docks and berths, the number of passengers on the airport, the data of the flights and routes of ports and airports, we can get further understanding of the spatial differentiation of the distribution of ports and airports in the B&R region.
The matching and zoning of water and land resources in Central Asian countries under the background of climate change can provide support for the development of water and land resources and agricultural production in Central Asian countries, and is of great significance to the social stability of the core region of the Silk Road Economic Belt. Based on the collected meteorological, water resources, land use and remote sensing data, the present situation of water and land resources development and utilization in Central Asia is analyzed. Based on the evaluating the characteristics and shortages of agricultural soil and water resources, using the DPSIR model and the theory of supply and demand, we constructed the index structure framework (SDCSL) of the study area, and the principal component analysis and cluster analysis are used to divide the regional of water and land resources utilization. Finally, we discussed the measures and ways to achieve the effective matching of agricultural water and land resources in different regions, so as to provide scientific reference and theoretical basis for the effective matching and sustainable development of regional agricultural water and land resources.
ZHOU Hongfei YAO Haijiao LI Li Food and Agriculture Organization of the United Nations（FAO）
The data set includes the spatial distribution of grass yield in the Qinghai-Tibetan Plateau in 1980, 1990, 2000, 2010, and 2017. The gross primary productivity (GPP) of grassland in the Qinghai-Tibetan Plateau was simulated based on the ecological hydrological dynamic model VIP (vegetation interface process) with independent intellectual property of Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences. The net primary productivity (NPP) was estimated by empirical coefficient, and converted it into dry matter, and then the hay yield was estimated by root-shoot ratio. The spatial resolution is 1km. The data set will provide the basis for grassland resource management, development, utilization and the formulation of the strategy of "grass for livestock".
In order to study the relationship between the spread of cyanine and human activities, we will resequence the cyanine varieties from the Qinghai Tibet Plateau and its surrounding areas, as well as Pakistan, India, Nepal, Germany, Japan and other places. At the same time, we will cluster the gene families, and make statistics of unique, shared genes and gene families. In addition, we will also carry out the analysis of gene family expansion and contraction, and system development Tree construction, genome-wide replication events and other analysis. The aim is to analyze the molecular basis of adaptation of traditional species of cyanine to the plateau under the dual pressures of human activities and regional climate. Therefore, this study is helpful to reveal the adaptive mechanism of cyanine to adapt to the plateau ecological environment and the influence of artificial domestication and human selection on its genetic differentiation in the process of evolution.
Through the bioinformatics analysis after Hi-C sequencing, most of the sequences in the preliminary assembled genome sketch can be located on the chromosome, and the sequence and direction of these sequences on the chromosome can be determined, which lays an important foundation for obtaining high-quality sequence map. Therefore, by using this technology, the research team can divide the sequence in the sketch of the genome sequence of Aralia racemosa into groups with the same chromosome number as the species, and determine the order and orientation of all sequences in each group. After that, we can combine the data of reference genome, EST sequence, related species and genetic map of Aralia racemosa The accuracy of grouping and the order and direction between sequences were evaluated.
In order to analyze how and when it entered the Qinghai Tibet Plateau and explore the relationship between its spread and domestication in the Qinghai Tibet Plateau and the plateau settlement of early human activities and the ancient Silk Exchange, in June 2018, the research team used three generations of genome sequencing technology to sequence the whole genome and de novo assembly of F1 generation of its self bred varieties in Nangqian County, Qinghai Province, and got a genome size of 40 9.69 MB, contig N50 is 1.21 MB. This result can provide genetic basis for studying the relationship between plant diffusion and human activities. At the same time, this study is helpful to reveal the influence of artificial domestication and human selection on the genetic differentiation of cyanine, and the adaptive mechanism of cyanine to adapt to the plateau ecological environment.
Gridded population with 100m spaital resolution of the 34 key areas along One Belt One Road in 2015, which indicates that the population count per pixel (i.e., grid). This data is derived from geodata institute of Southampton University, UK. The prejection transform and extraction processes were done to generate the gridded population with 100m spaital resolution of the 8 key areas along One Belt One Road in 2015. The original gridded popution is spatially downscaled from census data and multisource data by the random forest method. Accurate population data at finer scale are fundamental for a broad range of applications by governments, nongovernmental organizations, and companies, including the urban planing, election, risk estimation, disaster rescue, disease control, and poverty reduction.
GE Yong LING Feng
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.
The data set is obtained by UAV aerial photography during five field visits to the Qinghai Tibet Plateau in 2018-2019. The data size is 77.6 GB, including more than 11600 aerial photos. The aerial film was shot in five times, from July 19, 2018 to July 26, 2018, September 9, 2018 to September 16, 2018, April 24, 2019 to May 10, 2019, July 6, 2019 to July 20, 2019, September 1, 2019 to September 7, 2019. The shooting location mainly includes the roads and surrounding areas between major cities in Lhasa, shigaze, Naqu, Shannan, Linzhi, Changdu, Diqing, Ganzi, ABA, Gannan and Golog. The aerial photos clearly reflect the local land use / cover type, vegetation distribution, grassland degradation, vegetation coverage, river and lake distribution and other information. The aerial photos have longitude and latitude and altitude information, which can provide better verification information for the remote sensing interpretation of land use / cover, and also can be used for the estimation of vegetation coverage, and for the study of land use in the study area Good reference information is provided.
LV Changhe LIU Yaqun
Referring to the temperature-humidity index formula proposed by J.E. Oliver in 1973, the temperature-humidity index of thethe Green Silk Road Countries(GSRCs) is calculated based on the annual average temperature and relative humidity. The climate suitability assessment of human settlements of the GSRCs is carried out on the basis of the temperature-humidity index. the climate suitability of human settlements in different areas of GSRCs can be divided into five categories: Non-suitable area,Critically suitable area, Low suitable area, Moderately suitable area and High suitable area, based on the distribution characteristics of temperature-humidity index and its correlation with population distribution, according to the regional characteristics and differences of temperature and relative humidity, and referring to the physiological climate evaluation standard of temperature-humidity index.
FENG Zhiming LIN Yumei
The data defines LC classes using a set of classifiers. The system was designed as a hierarchical classification, which allows adjusting the thematic detail of the legend to the amount of information available to describe each LC class, whilst following a standardized classification approach. As the CCI-LC maps are designed to be globally consistent, their legend is determined by the level of information that is available and that makes sense at the scale of the entire world. The “level 1” legend – also called “global” legend – presented in Table 3-1 meets this requirement. This legend counts 22 classes and each class is associated with a ten values code (i.e. class codes of 10, 20, 30, etc.). The CCI-LC maps are also described by a more detailed legend, called “level 2” or “regional”. This level 2 legend makes use of more accurate and regional information – where available – to define more LCCS classifiers and so to reach a higher level of detail in the legend. This regional legend has therefore more classes which are listed in Appendix 1. The regional classes are associated with nonten values (i.e. class codes such as 11, 12, etc.). They are not present all over the world since they were not properly discriminated at the global scale.
The data set of agricultural activity intensity of the Qinghai Tibet Plateau is based on the County-Level Agricultural statistical data, including the annual cultivated land area, agricultural, forestry, animal husbandry and fishery labor force, total power of agricultural machinery, rural power consumption, effective irrigation area, pesticide use, fertilizer use, total output of grain crops, and total output value of agricultural, forestry, animal husbandry and fishery. The agricultural input index and output index are taken as the first level indicators, and the unit cultivated land area is constructed The intensity index system of agricultural activity is composed of 10 indexes, such as total power of agricultural machinery, fertilizer application amount per unit cultivated area and labor productivity. Entropy method was used to determine the weight of each index, and the input-output index of county-level agriculture in the Qinghai Tibet Plateau was obtained by AHP. The basic data comes from the statistical data released by the National Bureau of statistics, and the original data has been approved and corrected, with high reliability. The input-output index, input-output index and input-output index of county level in the Qinghai Tibet Plateau from 1980s to 2015 included in the data set reflect the spatiotemporal variation characteristics of the intensity of agricultural production activities in the Qinghai Tibet Plateau to a certain extent, and provide data support and theoretical reference for the local agricultural development.