The western and northeastern Yunnan is located in the southeast of the Qinghai Tibet Plateau. Previous genetic studies have shown that there are substantial genetic imprints of late Paleolithic human in this region, and these ancient genetic imprints are likely to spread further to the Qinghai Tibet Plateau. Therefore, the genetic study of the population in this area is helpful to clarify the migration history of early human settlement in the Qinghai Tibet Plateau. In this study, we studied the genetics of Dai people in different areas of Yunnan Province. The mitochondrial DNA hypervariable regions of 264 Dai individuals were sequenced by Sanger sequencing. Based on phylogenetic analysis, we control the quality of these data to ensure that there is no sample contamination and other quality problems. According to the revised Cambridge Reference Sequence, the variants were recorded. According to the phylogenetic tree of mitochondrial DNA in the world population (PhyloTree.org), each sample was allocated into certain haplogrop. Based on the published mtDNA data of Dai people in other areas, the maternal genetic structure and formation mechanism of Dai population were systematically studied. The results showed that there was a close genetic relationship among the Dai populations in different regions, and the haplogroups (F1a, M7B and B5a) shared by these populations could be traced back to southern China, suggesting that the Dai population might have originated in southern China and migrated southward to the mainland and Southeast Asia in the Iron or Bronze age. The genetic differentiation of the Dai population in different regions is consistent with the phenomenon that their language and culture have some differences, which indicates that the Dai people and the surrounding populations in the southward migration.
KONG Qingpeng
The spatial distribution data set of disaster prevention and mitigation facilities in hambantota and Colombo (2016-2018) is obtained by extracting classification information from high-resolution remote sensing images. Based on the fusion of 1-2m remote sensing image data, combined with POI data, the distribution information of hospital, fire protection and refuge facilities were extracted respectively. On this basis, the relevant layers and poi layers of OSM were superimposed with the extracted results and images. Through visual inspection, errors were found and the extracted results were corrected. Finally, hambantuota was formed Vector layer data of disaster prevention and mitigation related facilities in the node and Colombo area.
DONG Wen
On the basis of the global tropical cyclone track dataset, the global disaster events and losses dataset, the global tide level observation dataset and DEM data, coastline distribution data, land cover information, population and other related data of Hambantota, indicators related to the disaster danger of storm surge in each unit are extracted and calculated using ten meters grid as evaluation unit. Based on statistical method, the tide level of every 20 years, 50 years and 100 years is estimated. The comprehensive index of storm surge disaster danger is constructed, and the danger index of storm surge is obtained by using the weighted method, which can be used to evaluate the danger level of storm surge in each assessment unit. The data set includes 20-year, 50-year and 100-year hazard assessment results of the port area of Hambantota.
DONG Wen
The spatial distribution data set of infrastructures such as traffic and water system in the areas of hambantota and Colombo (2016-2018) is obtained by extracting classification information from high-resolution remote sensing images. Based on the 1-2m remote sensing image data, the distribution information of road, water, coastline, and coastal facilities are extracted respectively. On this basis, the road, and other layers of OSM are superimposed with the extracted results and images. Through visual inspection, errors are found and the extracted results are corrected. Finally, the hambantota node area dataset is formed road, water system, coastline, and coastal facilities distribution layer of the region. This data set contains the data information of two key node regions of hambantota and Colombo.
DONG Wen
The road data of 34 key areas along the Belt and Road is first collected from the Internet and then re-processed. Road data can be obtained from the OpenStreetMap open source wiki map. OpenStreetMap is a project designed to create and provide free geographic data (such as street maps) to anyone. First, we download the road data with the country where the key area along the One Belt One Road is located, then clip and extract according to the area, and then calculate the road length in each unit to obtain. Based on OpenStreetMap, it is finally integrated into a road length infrastructure element data product. The road length data can provide important basic data for the development of socio-economic infrastructure and transportation in key area and regions along the Belt and Road.
GE Yong LING Feng
Economic data( Per capita GDP, GDP growth rate, Primary, secondary and tertiary industries to GDP, Gini index, Engel coefficient) of 34 key areas along the One Belt One Road are downscaled from coarse data. First, we collect the statistics of economic data( Per capita GDP, GDP growth rate, Primary, secondary and tertiary industries to GDP, Gini index, Engel coefficient) at the national or provincial scales, and use GIS spatial analysis methods to analyze the relationship between economic data and covariables (e.g.,night lighting NPP-VIIRS, road network density). Then, spatial regression analysis method is used to model relationship between the economic data and covariables, and economic data( Per capita GDP, GDP growth rate, Primary, secondary and tertiary industries to GDP, Gini index, Engel coefficient) at county level were downscaled and predicted. Based on statistical data and spatial analysis, the data of economic adult is finally integrated. The economic data( Per capita GDP, GDP growth rate, Primary, secondary and tertiary industries to GDP, Gini index, Engel coefficient) can provide important basic data for the development of social and economic research on key areas and regions along the Belt and Road.
GE Yong LING Feng
The urbanization rate data of 34 key areas along the One Belt One Road are downscaled from coarse data. First, we collect the urbanization rate statistical data at the national or provincial scales, and use GIS spatial analysis methods to analyze the relationship between urbanization rate and covariables (e.g.,night lighting NPP-VIIRS). The spatial regression analysis method is used to model relationship between the urbanization rate data and covariables, and then the county-level urbanization rate data were downscaled and predicted. Based on statistical data and spatial analysis, it is finally integrated into urbanization rate data. The data can provide important basic data for the development of social and economic research on key area and regions along the Belt and Road.
GE Yong LING Feng
The airport data of the 34 key areas along One Belt One Road were first collected from the Internet and then re-processed. First, Using several key words about airport, web pages were then collected by Google and Baidu search engine. We analyze the information on the webpage and check the statistics and characteristics of the airport.The core information such as the location, name, type, size and country of each airport in the 34 key node areas is extracted. Based on statistical data and web information, it is finally integrated into a data product of airport infrastructure elements. This data can provide important basic data for the development of socio-economic infrastructure, transportation and other research on key area and regions of the Belt and Road.
GE Yong LING Feng
The data involved three periods of geodetic glacier mass storage change of three Rongbuk glaciers and its debris-covered ice in the Rongbuk Catchment from 1974-2016 (unit: m w.e. a-1). It is stored in the ESRI vector polygon format. The data sets are composed of three periods of glacier surface elevation difference between 1974-2000，2000-2016 and 1974-2006, i.e. DHPRISM2006-DEM1974（DH2006-1974）、DHSRTM2000-DEM1974（DH2000-1974）、DHASTER2016-SRTM2000（DH2016-2000）. DH2006-1974 was surface elevation change between ALOS/PRISMDEM(PRISM2006) and DEM1974, i.e. the DEM1974 was subtracted from PRISM2006, DH2006-1974 =PRISM2006 – DEM1974. The PRISM2006 was generated from stereo pairs of ALOS/PRISM on 4 Dec. 2006. The earlier historical DEM (DEM1974, spatial resolution 25m) was derived from 1:50,000 topographic maps in October 1974(DEM1974,spatial resolution 25m). The uncertainty in the ice free areas of DHPRISM2006-DEM1974 was ±0.24 m a-1. DHSRTM2000-DEM1974（DH2000-1974）was surface elevation change between SRTM DEM(SRTM2000) and DEM1974. The uncertainty in the ice free areas of DHSRTM2000-DEM1974 was ±0.13 m a-1. DHASTER2016-SRTM2000（DH2016-2000）was the surface elevation change between ASTER DEM2016 and SRTM DEM(SRTM2000). The uncertainty in the ice free areas of DHASTER2016-SRTM2000 was ±0.08 m a-1. Glacier-averaged annual mass balance change (m w.e.a-1) was averaged annually for each glacier, which was calculated by DH2006-1974/DH2000-1974/DH2016-2000, glacier coverage area and ice density of 850 ± 60 kg m−3. The attribute data includes Glacier area by Shape_Area (m2), EC2000-1974/EC2016-2000/EC2006-1974, i.e. Glacier-averaged surface elevation change in each period(m a-1), MB2000-1974/ MB2016-2000/MB2006-1974, i.e. Glacier-averaged annual mass balance in each period (m w.e.a-1), and MC2000-1974/ MC2016-2000/MC2006-1974,Glacier-averaged annual mass change in each period(m3 w.e.a-1), Uncerty_EC is the maximum uncertainty of glacier surface elevation change（m a-1）、Uncerty_MB, is the maximum uncertainty of glacier mass balance（m w.e. a-1），Uncerty_MC, is the maximum uncertainty of glacier mass change（m3w.e. a-1）。 MinUnty_EC，is the minimum uncertainty of glacier surface elevation change，MinUnty_MB，is the minimum uncertainty of glacier mass balance（m w.e. a-1），MinUnty_MC is the minimum uncertainty of glacier mass change（m3 w.e. a-1.The data sets could be used for glacier change, hydrological and climate change studies in the Himalayas and High Mountain Asia.
YE Qinghua
We obtained the whole genome variation data of 30 Tibetan individuals. The SNP typing of 30 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. First, after extracting genomic DNA, DNA amplification, enzymatic fragmentation, precipitation and re suspension were carried out. After the sample was incubated overnight and hybridized with beadchip, the DNA was annealed to obtain a site-specific 50 mer probe, covalently coupled with an Infinium bead type. Then Infinium XT was used to extend the enzyme base to give the allele specificity, and then fluorescent staining was carried out. The fluorescence intensity of the beads was detected by iSCAN system, and the Illumina software automatically performed the analysis and genotype recognition. Finally, the SNP typing results 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
On the basis of the global tropical cyclone track dataset, the global disaster events and losses dataset, the global tide level observation dataset and DEM data, coastline distribution data, land cover information, population and other related data of the Belt and Road, indicators related to the vulnerability of storm surge in each unit are extracted and calculated using 100 meter grid as evaluation unit, such as population density, land cover type, etc. The comprehensive index of storm surge vulnerability is constructed, and the vulnerability index of storm surge is obtained by using the weighted method. Finally, the storm surge vulnerability index is normalized to 0-1, which can be used to evaluate the vulnerability level of storm surge in each assessment unit.
DONG Wen
On the basis of the global tropical cyclone track dataset, the global disaster events and losses dataset, the global tide level observation dataset and DEM data, coastline distribution data, land cover information, population and other related data of the Belt and Road, indicators related to the disaster risk and vulnerability of storm surge in each unit are extracted and calculated using10 meter grid as evaluation unit, such as historical intensity of tide level frequency of storm historic arrival, historical loss, population density, land cover type, etc. The comprehensive index of storm surge disaster risk is constructed, and the risk index of storm surge is obtained by using the weighted method. Finally, the storm surge risk index is normalized to 0-1, which can be used to evaluate the risk level of storm surge in each assessment unit. The data set includes 20-year, 50-year, and 100-year corresponding risks.
DONG Wen
1. Data overview: This data set is the daily scale groundwater level data of Qilian station from November 1, 2011 to December 31, 2011. In October 2011, two groundwater monitoring wells were arranged in hulugou small watershed. Well 1 is located beside the general control hydrological section of hulugou watershed, with a depth of 12.8m and an aperture of 12cm. Well 2 is located in the east of the Delta, about 100m away from the river, with a depth of 14.7m and an aperture of 12cm. 2. Data content: U20hobo water level sensor is arranged in the groundwater well, which is mainly used to monitor the change of groundwater level and temperature in hulugou small watershed. The data content is the temperature and atmospheric pressure inside the hole, and the data is the daily scale data. 3. Space time scope: Geographic coordinates of well 1: longitude: longitude: 99 ° 53 ′ E; latitude: 38 ° 16 ′ n; altitude: 2974m (near the hydrological section at the outlet of the basin). Geographic coordinates of well 2: longitude: 99 ° 52 ′ E; latitude: 38 ° 15 ′ n; altitude: 3204.1m (east side of the East Branch of the delta).
HAN Chuntan CHEN Rensheng SONG Yaoxuan LIU Junfeng YANG Yong QING Wenwu LIU Zhangwen
1) Data content: multi-model ensemble mean wind speed at 200 hPa and 850 hPa during the Last Glacial Maximum, mid-Holocene and pre-industrial period (reflecting high and low level westerlies), 850 hPa meridional and zonal winds (reflecting the East Asian monsoon circulation) and zonal mass streamfunction (reflecting Walker circulation); 2) Data sources: monthly data simulated by multiple climate models from the second and third stages of the international Paleoclimate Modelling Intercomparison Project; processing methods: multi-model equal weight arithmetic mean, monthly climate average; 3) Data application: used for the study of paleoclimate change and dynamic mechanism.
TIAN Zhiping WANG Na
The Xiadong section locates at the Xiadong village region in Tsochen County, Tibet. The Permian strata in this region includes Largar, Angjie and Xiala formations. The Xiadong Section locates at the north of the Xiadong Village. The section is composed of entirely carbonates with abundant fusulines, smaller foraminifers and corals. The column exhibit the occurrences of fusulines and smaller foraminifers and their biostratigraphy. According to the fusulines, the age of the Xiala Formation at this section is middle Permian age. The fusulines can be subdivided into two assemblages, respectively Chenella changanchiaoensis-Neoschwagerina cheni in the lower and Nankinella-Chusenella assemblage in the upper. The foraminifers are divided into four assemblages, respectively Lasiodiscus tenuis-Palaeotextularia angusta elongata assemblage, Agathammina pusilla-A.vachardi assemblage, Hemigordiopsis-Midiella assemblage and Pachyphloi-Nodosinelloides assemblage.
ZHANG Yichun
25 members consisting of researchers from Nanjing Institute of Geology and Palaeontology, CAS and Nanjing University, reporters from Beijing News, technicians from China Unicom, drivers and kitchener undertook the investigation on the Palaeozoic strata and faunas from various regions in northern Tibet from 30 August to 3 October. The expedition areas include areas in northern Selingco, Rejuechaka and Rongma region in northern Nyima County, Wenbu area in southern Nyima County. The objective of the expedition includes: (1) the origin of the Permian limestone blocks within the Bangong-Nujiang suture zones; (2) the Permian-Triassic strata, faunas and floras in the Rejuechaka region, northern Tibet; (3) the Ordovician cephalopods in the Rongma area, Nyima County; (4) the Permian sequence and faunas in the Wenbu area, southern Nyima County. This album contains the full record of the investigation and geological phenomenon. The links in the album can directly link to the video in internet.
ZHANG Yichun
25 members consisting of researchers from Nanjing Institute of Geology and Palaeontology, CAS and Nanjing University, reporters from Beijing News, technicians from China Unicom, drivers and kitchener undertook the investigation on the Palaeozoic strata and faunas from various regions in northern Tibet from 5 September to 2 October. The expedition areas include areas in northern Selingco, Rejuechaka and Rongma region in northern Nyima County, Wenbu area in southern Nyima County. In northern Selingco region, the expedition focused on the faunas from the exotic limestone blocks within the Bangong-Nujiang suture zone. In the Rejuechaka region, the expedition attention was paid on the Permian-Triassic successions, sea-level changes, and Permian and Triassic faunas and floras. In the Rongma area, the Ordovician cephalopod and Permian microfossils within the Longmu Co-Shuanghu suture zone was investigated. In the Wenbu area, the research attention was paid on the stratigraphic transition from the ice-houce to green-house conditions during the early Permian time. This document record the full information about the field investigation.
ZHANG Yichun
The data set is based on the reflectance of MODIS channels and the observation data of SIF to establish the neural network model, so as to obtain the SIF data with high spatial and temporal resolution, which is often used as a reference for primary productivity. The data is from Zhang et al. (2018), and the specific algorithm is shown in the article. The source data range is global, and the Qinghai Tibet plateau region is selected in this data set. This data integrates the original 4-day time scale data into the monthly data. The processing method is to take the maximum value of the month, so as to achieve the effect of removing noise as much as possible. This data set is often used to evaluate the temporal and spatial patterns of vegetation greenness and primary productivity, which has practical significance and theoretical value.
ZHANG Yao
Land surface temperature (LST) is a key variable for high temperature and drought monitoring and climate and ecological environment research. Due to the sparse distribution of ground observation stations, thermal infrared remote sensing technology has become an important means of quickly obtaining ground temperature over large areas. However, there are many missing and low-quality values in satellite-based LST data because clouds cover more than 60% of the global surface every day. This article presents a unique LST dataset with a monthly temporal resolution for China from 2003 to 2017 that makes full use of the advantages of MODIS data and meteorological station data to overcome the defects of cloud influence via a reconstruction model. We specifically describe the reconstruction model, which uses a combination of MODIS daily data, monthly data and meteorological station data to reconstruct the LST in areas with cloud coverage and for grid cells with elevated LST error, and the data performance is then further improved by establishing a regression analysis model. The validation indicates that the new LST dataset is highly consistent with in situ observations. For the six natural subregions with different climatic conditions in China, verification using ground observation data shows that the root mean square error (RMSE) ranges from 1.24 to 1.58 K, the mean absolute error (MAE) varies from 1.23 to 1.37 K and the Pearson coefficient (R2) ranges from 0.93 to 0.99. The new dataset adequately captures the spatiotemporal variations in LST at annual, seasonal and monthly scales. From 2003 to 2017, the overall annual mean LST in China showed a weak increase. Moreover, the positive trend was remarkably unevenly distributed across China. The most significant warming occurred in the central and western areas of the Inner Mongolia Plateau in the Northwest Region, and the average annual temperature change is greater than 0.1K (R>0:71, P<0:05), and a strong negative trend was observed in some parts of the Northeast Region and South China Region. Seasonally, there was significant warming in western China in winter, which was most pronounced in December. The reconstructed dataset exhibits significant improvements and can be used for the spatiotemporal evaluation of LST in high-temperature and drought-monitoring studies. More detail please refer to Zhao et al (2020). doi.org/10.5281/zenodo.3528024
MAO Kebiao
The Land Surface Temperature in China dataset contains land surface temperature data for China (about 9.6 million square kilometers of land) during the period of 2003-2017, in Celsius, in monthly temporal and 5600 m spatial resolution. It is produced by combing MODIS daily data(MOD11C1 and MYD11C1), monthly data(MOD11C3 and MYD11C3) and meteorological station data to reconstruct real LST under cloud coverage in monthly LST images, and then a regression analysis model is constructed to further improve accuracy in six natural subregions with different climatic conditions.
MAO Kebiao
