The whole mitochondrial genomes of 68 Tibetan samples were sequenced by high-throughput second-generation sequencing. The average depth of sequencing was 1000 ×, ensuring that the mitochondrial genome of each sample was completely covered (100%). Based on the phylogenetic analysis, we control the quality of these data to ensure that there is no sample pollution and other quality problems. According to the phylogenetic tree, each individual was allocated into haplogroups. The results showed that in Lhasa Tibetan population, M9a1c1b1a was the highest (19.12%), followed by G2 (13.23%), M13a (11.76%), C4a (7.35%), D4 (7.35%), A11a1a (5.88%), M9a1b (5.88%), and F1c, F1g, B4, F1d, M62b, F1a, F1b, G1, M11, M8a, U7a, Z3a. These haplogroups have different originations, including Paleolithic components (M13a, M62b, M9a1b, etc.), northern China millet farmers’ components (M9a1c1b1a and A11a1a), components distributed mainly in southern East Asia (F1a, etc.), northern East Asian haplogroups (C4a, D4, etc.). It is worth noting that the maternal component of Lhasa Tibetans is mainly composed of millet agricultural population in northern China, indicating the important impact of genetic input of millet agricultural population in northern China on the genetic structure of the population in this area. Taken together, the maternal genetic structure of Lhasa Tibetan population exhibits time stratification, which may represent the genetic imprint of different population entering the region in different periods.
KONG Qingpeng
The Grassland Degradation Assessment Dataset in agricultural and pastoral areas of the Qinghai-Tibet Plateau (QTP) is a data set based on the 500m Global Land Degradation Assessment Data (2015), which is evaluated according to the degree of grassland degradation or improvement. In this dataset, the grassland degradation of the QTP was divided into two evaluation systems. At the first level, the grassland degradation assessment was divided into 3 types, including no change type, improvement type and degradation type. At the second level, the grassland degradation assessment on the QTP was divided into 9 types, among which the type with no change was class 1, represented by 0. There were 4 types of improvement: slight improvement (3), relatively significant improvement (6), significant improvement (9) and extremely significant improvement (12). The degradation types can be divided into 4 categories: slight degradation (-3), relatively obvious degradation (-6), obvious degradation (-9) and extremely obvious degradation (-12). This dataset covers all grassland areas on the QTP with a spatial resolution of 500m and a time of 2015. The projection coordinate system is D_Krasovsky_1940_Albers. The data are stored in TIFF format, named “grassdegrad”, and the data volume is 94.76 MB. The data were saved in compressed file format, named “500 m grid data of grassland degradation assessment in agricultural and pastoral areas of the Qinghai-Tibet Plateau in 2015”. The file volume is 2.54 MB. The data can be opened by ArcGIS, QGIS, ENVI, and ERDAS software, which can provide reference for grassland ecosystem management and restoration on the QTP.
LIU Shiliang, SUN Yongxiu, LIU Yixuan
It is not clear how the Tibetan people adapt to the extreme environment on the plateau. As an important phenotype, metabolism plays an important role in maintaining the normal biological function of individuals. Previous studies have shown that some small metabolic molecules can adapt to the extreme environment by regulating energy metabolism, oxidative stress and other biological processes. In view of this, the project is expected to find the relationship between human metabolism and extreme environmental adaptation by studying the unique metabolic characteristics of Tibetan people compared with plain people, and then study the plateau adaptation mechanism of Tibetan people from the perspective of metabolism. This data is the metabolomic data generated during the implementation of the project, and the current data includes the metabolomic data of 30 people in the plain. The combined analysis of these data and the subsequent metabolomic data can be used to study the metabolic characteristics of Tibetan people in the plateau hypoxia environment. This data set is the update and continuation of metabolomic data v1.0 of modern Chinese population.
LI Gonghua
As the “third pole” of the world, the Qinghai-Tibet Plateau (QTP) is extremely ecologically sensitive and fragile while facing increasing human activities and overgrazing. In this study, eight types of spatial data were firstly selected, including grazing intensity, Night-Time Light, population density, Gross Domestic Product (GDP) density, the ratio of cultivated land, the slope of the Normalized Difference Vegetation Index (NDVI), distance to road, and distance to town. Then, the entropy weight method was applied to determine the weight of each factor. Finally, the five-year interval human activity intensity data in 1990, 1995, 2000, 2005, 2010 and 2015 were made in the agricultural and pastoral areas of QTP through the spatial overlap method. By preparing the historical spatial datasets of human activity intensity, our study will help to explore the influence of human disturbance on the alpine ecosystems on the QTP and provide effective support for decision-making of government aiming at regional ecosystem management and sustainable development.
LIU Shiliang, SUN Yongxiu, LIU Yixuan, LI Mingqi
The natural resources dataset of the Qinghai-Tibetan Plateau covers 215 counties in this area. The observation intervals are 5 years from 2000-2015. The indicators are rainfall, temperature, humidity, population, and land area. The data sources are meteorological station data, regional statistical yearbook, etc., which are expressed by Excel. This data provides a reference for understanding the natural background conditions on the county scale in the Qinghai Tibet Plateau.
FENG Xiaoming
The natural resources dataset of the Qinghai-Tibetan Plateau covers 215 counties in this area. The observation intervals are 5 years from 2000-2015. The indicators are rainfall, temperature, humidity, population, and land area. The data sources are meteorological station data, regional statistical yearbook, etc., which are expressed by Excel. This data provides a reference for understanding the natural background conditions on the county scale in the Qinghai Tibet Plateau.
FENG Xiaoming
This data set records the division of Qinghai Province from 2000 to 208 according to urban and rural areas, as well as economic types and quantitative statistics. The data are collected from the statistical yearbook: Qinghai statistical yearbook, and the accuracy is the same as the statistical yearbook extracted from the data. The data set contains three data tables, which are 2005-2006, 2006-2007 and 2007-2008 year-end employment statistics by urban and rural areas. The data table structure is the same. Each data table has five fields, such as the number of employed persons at the end of the year by urban and rural areas in 2005-2006: Field 1: towns in 2005 Field 2: 2005 rural Field 3: towns in 2006 Field 4: villages in 2006 Field 5: 2005 total Field 6: 2006 total
ZHAO Hu
This data is the population grid data of ten meter scale in 2019. Each grid expresses the total number of population within this range (unit: person). The source data of this data comes from Myanmar's 2019 1km population data set in the world pop data center（ https://www.worldpop.org/geodata/summary?id=40443 ）, the obtained source data are processed by projection transformation and clipping to obtain the population distribution in Yangon, and then the data are downscaled, The spatial distribution data set of refined population (10m) in Yangon deep water port area is obtained. Regular ten meter scale population grid data are obtained by spatial scale conversion and downscaling. Each grid population is calculated by random forest method according to the population of each administrative unit and multi-source auxiliary data. Population data can be used in many fields, including urban planning, elections, risk assessment, disaster relief, disease prevention and control, poverty reduction and poverty alleviation, etc;
GE Yong, LI Qiangzi, LI Yi
The population grid data of 100 meter scale in 2010, each grid expresses the total number of population within the range (unit: person). The data is from the Institute of earth data, University of Southampton, UK. The data is processed by projection transformation and clipping to get the population distribution in Yangon area. Then the data is downscaled to get the refined population spatial distribution data set in Yangon deepwater port area. This data is based on the census data of administrative units, and the regular 100 meter scale population grid data is obtained through spatial scale conversion. Each grid population is calculated by using random forest method according to the population of each administrative unit and multi-source auxiliary data. Population data can be used in many fields, including urban planning, elections, risk assessment, disaster relief, disease prevention and control, poverty alleviation and so on;
GE Yong, LI Qiangzi, LI Yi
The refined population spatial distribution data set of Hambantota port area is generated by reanalysis based on hrsl data of Sri Lanka. Hrsl data provides an estimate of the population distribution in 2015 at a resolution of 1 arcsec (about 30 meters). The latest census information and built-up area information based on satellite images are used in hrsl data. This data set is based on hrsl data. Firstly, the boundary of buildings is extracted from the 0.5m resolution remote sensing image by computer vision technology, and the building types (high-rise buildings, medium and low rise buildings, bungalows, etc.) are determined by combining with manual visual interpretation and field sampling. The population distribution area mask is constructed in the building area, and the 10 meter grid is used as the analysis unit to calculate the population distribution in the unit According to the proportion of different building types, the proportion of main land use types, building density, distance from road and other related indicators, the average density of building type consistent area is calculated from hrsl data, and the corresponding population density of each building is obtained by machine learning method. Then, the population data in the area is allocated to the corresponding unit by proportional allocation method, and the 10 meter resolution is obtained Population distribution products. The data is distributed in the form of GeoTIFF files. Population GeoTIFF represents population estimates (in person) and provides detailed estimates for population, infrastructure and Sustainability Studies in the humanitarian field.
The data set of socio-economic vulnerability parameters in the agricultural and pastoral areas of the Qinghai Tibet Plateau mainly contains the socio-economic vulnerability parameter data at county level. The data time range is from 2000 to 2015, involving 112 counties and districts in Qinghai Province and Tibet Autonomous Region. The main parameters include population density, the proportion of unit employees in the total population, the proportion of rural employees in the total population, the proportion of agricultural, forestry, animal husbandry and fishery employees in rural employees, per capita GDP, per capita savings balance of residents, per capita cultivated land area, per capita grain output, and people Average oil production, livestock stock per unit area, per capita meat production, the proportion of primary and secondary school students in the total population, and the number of hospital beds per 10000 people. The entropy weight method is used to calculate the weight of each index, and ArcGIS is used to spatialize, and finally the county scale socio-economic vulnerability parameter data is obtained. The original data is from the statistical yearbook of Qinghai Province and Tibet Autonomous Region. The data are expressed by shape file and excel file. This data set will provide reference for socio-economic vulnerability assessment and selection of typical agricultural and pastoral areas.
ZHAN Jinyan, TENG Yanmin, LIU Shiliang
This data is the distribution data of the prehistoric era sites on the Qinghai-Tibet Plateau and surrounding areas, which is derived from the Supplementary Maps of the paper: Chen, F.H., Dong, G.H., Zhang, D.J., Liu, X.Y., Jia, X., An, C.B., Ma, M.M., Xie, Y.W., Barton, L., Ren, X.Y., Zhao, Z.J., & Wu, X.H. (2015). Agriculture facilitated permanent human occupation of the Tibetan Plateau after 3600 BP. SCIENCE, 347, 248-250. The Qinghai-Tibet Plateau, with an average altitude of more than 4000m, is the highestand largest plateau all around the world, and also is one of the most unsuitable areas for human life with long-term on the earth. The remains at the archaeological site are direct evidences left behind the ancient human activities. The original data of this data is digitized from the results of the Qinghai-Tibet Plateau high-textual census and archaeological survey (Qinghai Volume and Tibet Volume of the Chinese Cultural Relics Atlas). The map was digitized mainly based on the distribution maps of the sites, and the latitude and longitude coordinates and altitude were obtained. a total of 6,950 sites, most of which are distributed in the northern part of the plateau. The age range of the site is between 7000BP and 2300BP. This data set is of reference value for the research on the process and power of human diffusion to the Tibetan Plateau in the prehistoric era and other studies related to human activities in the Tibetan Plateau and the prehistoric era.
DONG Guanghui , LIU Fengwen
This data set includes a monthly composite of 30 m × 30 m surface vegetation coverage products in the Qilian Mountain Area in 2019. In this paper, the maximum value composition (MVC) method is used to synthesize monthly NDVI products and calculate FVC by using the reflectance data of Landsat 8 and sentinel 2 red and near infrared channels. The data is monthly synthesized by Google Earth engine cloud platform, and the index is calculated by the model. The missing pixels are interpolated with good quality, which can be used in environmental change monitoring and other fields.
QI Xuebin
The data includes the runoff components of the main stream and four tributaries in the source area of the Yellow River. In 2014-2016, spring, summer and winter, based on the measurement of radon and tritium isotopic contents of river water samples from several permafrost regions in the source area of the Yellow River, and according to the mass conservation model and isotope balance model of river water flow, the runoff component analysis of river flow was carried out, and the proportion of groundwater supply and underground ice melt water in river runoff was preliminarily divided. The quality of the data calculated by the model is good, and the relative error is less than 20%. The data can provide help for the parameter calibration of future hydrological model and the simulation of hydrological runoff process.
QI Xuebin
1) Data content: this data is the placenta umbilical cord endothelial cells (HUVEC) transcriptome data of high altitude Tibetan and lowland Han population generated during the implementation of the project, including the RNA-seq data of 3 high altitude Tibetan HUVEC and 3 lowland Han placenta HUVEC. Each RNA-seq data is 6G sequencing depth, which can be used to study the effect of high altitude Tibetan population and lowland Han population for gene expression patterns at hypoxic environment. 2) Data source and processing method: own data, the pair end 150bp sequencing method using Illumina x-ten sequencing platform. 3) Data quality: 6G data depth, q30 > 90%. 4) Results and prospects of data application: the data will be used to validate the gene expression pattern of high altitude hypoxia adaptation gene to hypoxia environment at the cell level.
QI Xuebin
The average altitude of the Tibetan Plateau is more than 4000 meters. The harsh environment such as high cold and low oxygen poses a huge challenge to human survival. However, since the late Paleolithic period, Tibetan people in the plateau have reached the Plateau, and in the Neolithic period, people began to permanently settled on the high-altitude areas on a large scale. The history of population migration in this process has become the focus of different fields. In order to analyze the genetic structure of Tibetan population from the perspective of the whole genome and trace back the history of human settlement on the plateau, we obtained the whole genome variation data of 20 Tibetan individuals. The SNP typing of 20 samples was carried out by DNA array method, and about 700000 loci (including nuclear genome, mitochondrial DNA and Y chromosome) of each sample were obtained. Based on the above data, relevant biological information analysis (mainly including chip site quality control analysis, Y chromosome and mitochondrial DNA haplotype analysis) was carried out. This data is helpful to analyze the genetic structure of Tibetan population from the perspective of nuclear genome, Y chromosome and mitochondrial DNA. By comparing with the data of people around the plateau, we can trace the migration and settlement history of the plateau population comprehensively.
KONG Qingpeng
How the Tibetan people adapt to the extreme environment of the plateau is not clear at present. Metabolism, as an important phenotype, plays an important role in maintaining the normal biological function of individuals. Previous studies have shown that some small metabolic molecules can adapt to the extreme environment by regulating the biological processes such as energy metabolism and oxidative stress. In view of this, this project is to find the relationship between the human metabolism and the extreme environmental adaptation by studying the unique metabolic characteristics of Tibetan population compared with the plain population, and then study the plateau adaptation mechanism of Tibetan population from the perspective of metabolism. This data is the metabolome data generated during the implementation of this project. The current data includes the metabolome data of 30 people in the plain. The combined analysis of this data and the subsequent metabolome data can be used to study the metabolism characteristics of the Tibetan people at high altitude in the low oxygen environment.
LI Gonghua
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
"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."
DONG Wen
"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
Contact Support
Northwest Institute of Eco-Environment and Resources, CAS
0931-4967287
poles@itpcas.ac.cn
Links
National Tibetan Plateau Data CenterFollow Us
A Big Earth Data Platform for Three Poles © 2018-2020 No.05000491 | All Rights Reserved
｜ 
No.11010502040845
Tech Support: westdc.cn
