(1) All data are measured at the station where each scientific research route is completed each time. (2) The sample number represents the team members and data contributors participating in the scientific examination; Different numbers represent different people. (3) Systolic blood pressure, diastolic blood pressure and pulse were measured by OMRON arm electronic sphygmomanometer; The data of blood oxygen saturation (SpO2) and heart rate were measured by fish jump finger clip oximeter; All hormones were determined by Shanghai ELISA kit. (4) There are two groups of blood pressure, pulse, oxygen saturation and heart rate data in each batch; One group (in the evening) is measured after arriving at a new destination, and the other group (in the morning) is measured before arriving at a new accommodation every day for breakfast; Hormone data were measured after blood collection in some accommodation points and taken back to the laboratory for treatment. (5) When the human body enters the high altitude hypoxic environment, heart rate, oxygen saturation and blood pressure are very sensitive response indicators. Blood pressure, heart rate and oxygen saturation are important indicators to reflect the degree of hypoxia. In particular, continuous detection of subjects can show the change process of hypoxic stress and adjustment. (6) From the perspective of physiology, it is analyzed that after people face hypoxic stress, the body increases or decreases the hormone level to maintain normal life activities, so as to achieve an adaptive protective mechanism, which provides a theoretical basis for the development of hypoxic drugs in the future; The choice of traveling on the plateau has a profound impact on the development of the plateau, which is not only conducive to the development of the plateau, but also has a profound impact on the development of the plateau. Health index data of some scientific research team members on the Qinghai Tibet Plateau (2019-2021)
LI Yaxiong
1) In mountainous areas, due to the complex topographic and geological background conditions, landslides are very easy to occur triggered by external factors such as rainfall, snow melting, earthquake and human engineering activities, resulting in the loss of life and property and the destruction of the natural environment. In order to meet the safety of project site construction, the rationality of land use planning and the urgent needs of disaster mitigation, it is necessary to carry out regional landslide sensitivity evaluation. When many different evaluation results are obtained by using a variety of different methods, how to effectively combine these results to obtain the optimal prediction is a technical problem that is still not difficult to solve at present. It is still very lack in determining the optimal strategy and operation execution of the optimal method for landslide sensitivity evaluation in a certain area. 2) Using the traditional classical multivariate classification technology, through the evaluation of model results and error quantification, the optimal evaluation model is combined to quickly realize the high-quality evaluation of regional landslide sensitivity. The source code is written based on the R language software platform. The user needs to prepare a local folder separately to read and store the software operation results. The user needs to remember the folder storage path and make corresponding settings in the software source code. 3) The source code designs two different modes to display the operation results of the model. The analysis results are output in the standard format of text and graphic format and the geospatial mode that needs spatial data and is displayed in the standard geographic format. 4) it is suitable for all people interested in landslide risk assessment. The software can be used efficiently by experienced researchers in Colleges and universities, and can also be used by government personnel and public welfare organizations in the field of land and environmental planning and management to obtain landslide sensitivity classification results conveniently, quickly, correctly and reliably. It can serve regional land use planning, disaster risk assessment and management, disaster emergency response under extreme induced events (earthquake or rainfall, etc.), and has great practical guiding significance for the selection of landslide monitoring equipment and the reasonable and effective layout and operation of early warning network. It can be popularized and applied in areas with serious landslide development
YANG Zhongkang
People in the plain often have altitude reaction after entering Tibet. In order to deeply analyze the change pattern of genomic expression profile in the process of altitude response and altitude acclimation. Based on the mRNA transcriptome sequencing method, we have obtained the transcriptome sequencing data of 46 individuals before entering Tibet (collection place: Chongqing). We first collected the peripheral blood samples of 46 plain Han individuals, treated the peripheral blood with red blood cell lysate (Tiangen), centrifuged at 4000 rpm for 10 min, separated and extracted white blood cells, and extracted the total RNA of each sample by Trizol method. Then 46 libraries were constructed by poly (a) capture method. Poly (a) + mRNA library was isolated from 1 g total RNA of each sample with oligo (DT) beads. The construction of RNA SEQ library was carried out according to the preparation scheme of truseq RNA library. The 46 RNA libraries were double ended sequenced using novaseq platform. The sequencing results were 150 BP reads fastq files, and the data volume of each sample exceeded 6.0 GBP. The transcriptome data of plain people before entering Tibet can be used as the baseline data after entering Tibet. By comparing and analyzing the transcriptome data of plain people before and after entering Tibet, screening the significantly differentially expressed genes before and after entering Tibet, and annotating the biological functions of differentially expressed genes, we can deeply analyze the gene expression change mode and function regulation network mechanism in the process of altitude reaction and altitude acclimatization.
KONG Qingpeng
This data set includes the social, economic, resource and other relevant index data of Gansu, Qinghai, Sichuan, Tibet, Xinjiang and Yunnan in the Qinghai Tibet Plateau from 2000 to 2015. The data are derived from Gansu statistical yearbook, Qinghai statistical yearbook, Sichuan statistical yearbook, Xizang statistical yearbook, Xinjiang statistical yearbook, Yunnan statistical Yearbook China county (city) socio economic statistical yearbook And China economic network, guotai'an, etc. The statistical scale is county-level unit scale, including 26 county-level units such as Yumen City, Aksai Kazak Autonomous Region and Subei Mongolian Autonomous County in Gansu Province, 41 county-level units such as Delingha City, Ulan county and Tianjun County in Qinghai Province, 46 counties such as Shiqu County, Ruoergai County and ABA County in Sichuan Province, and 78 counties such as Ritu County, Gaize county and bango County in Tibet, 14 counties including Wuqia County, aktao county and Shache County in Xinjiang Province, and 9 counties including Deqin County, Zhongdian county and Fugong County in Yunnan Province; Variables include County GDP, added value of primary industry, added value of secondary industry, added value of tertiary industry, total industrial output value of Industrial Enterprises above Designated Size, total retail sales of social consumer goods, balance of residents' savings deposits, grain output, total sown area of crops, number of students in ordinary middle schools and land area. The data set can be used to evaluate the social, economic and resource status of the Qinghai Tibet Plateau.
CHEN Yizhong
Under the funding of the first project (Development of Multi-scale Observation and Data Products of Key Cryosphere Parameters) of the National Key Research and Development Program of China-"The Observation and Inversion of Key Parameters of Cryosphere and Polar Environmental Changes", the research group of Zhang, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, developed the snow depth downscaling product in the Qinghai-Tibet Plateau. The snow depth downscaling data set for the Tibetan Plateau is derived from the fusion of snow cover probability dataset and Long-term snow depth dataset in China. The sub-pixel spatio-temporal downscaling algorithm is developed to downscale the original 0.25° snow depth dataset, and the 0.05° daily snow depth product is obtained. By comparing the accuracy evaluation of the snow depth product before and after downscaling, it is found that the root mean square error of the snow depth downscaling product is 0.61 cm less than the original product. The details of the product information of the Downscaling of Snow Depth Dataset for the Tibetan Plateau (2000-2018) are as follows. The projection is longitude and latitude, the spatial resolution is 0.05° (about 5km), and the time is from September 1, 2000 to September 1, 2018. It is a TIF format file. The naming rule is SD_yyyyddd.tif, where yyyy represents year and DDD represents Julian day (001-365). Snow depth (SD), unit: centimeter (cm). The spatial resolution is 0.05°. The time resolution is day by day.
YAN Dajiang, MA Ning, MA Ning, ZHANG Yinsheng
This data set contains the selection criteria and database of international fragile ecosystem national parks. Typical countries such as the United States, Canada, Australia, New Zealand, Norway, Sweden, South Africa and Tanzania are selected as representatives Table 1 includes: selection criteria for different levels, including 4 indicators for the first level, 16 indicators for the second level, and 72 indicators for the third level; Table 2 includes the list of national parks in typical countries such as the United States, Canada, Australia, New Zealand, Norway, Sweden, South Africa, Tanzania and other typical countries, and the selected indicators include the country, the name of the National Park, the protected time and supervision time, area, description, IUCN management type, governance type, management organization and international standards.
PEI Huijuan
The Central Asia climate data set comes from three recently developed global reanalysis data sets. Merra uses the latest version of the global earth observation system (GEOS-5), which can absorb satellite radiation and conventional observations. Era interim is a global land surface reanalysis data set, which is generated at T255 (80km) horizontal resolution. The product update is about one month delayed from real-time. CFSR is one of the latest global reanalysis climate data sets, which has been widely used in climate change research. These datasets have different spatial resolutions and cover 1979-2014. Atmospheric reanalysis data is driven by a variety of data, combined with observation data under strict quality control, using data assimilation techniques and numerical prediction models to obtain historical observation data. In recent years, the new generation of historical reanalysis data is suitable for studying the climate pattern of Central Asia with high spatial heterogeneity of precipitation and complex topography due to its high spatial resolution. More accuracy verification and applicability of this data can be referred to: Hu Z , Zhang C , Hu Q , et al. Temperature Changes in Central Asia from 1979 to 2011 Based on Multiple Datasets*[J]. Journal of Climate, 2014, 27(3):1143-1167.
The data set analyzes the spatial and temporal distribution, impact and loss of typical global flood disasters from 2018 to 2019. In 2018, there were 109 flood disasters in the world, with a death toll of 1995. The total number of people affected was 12.62 million. The direct economic loss was about 4.5 billion US dollars, which was at a low level in the past 30 years. The number of global flood incidents in 2018 was higher in the first half of the year than in the second half of the year, and the frequency of occurrence was higher from May to July. Therefore, based on three typical disaster events such as the hurricane flood in Florence in the United States in 2018, the flooding of the Niger River in Nigeria in 2018, and the Shouguang flood in Shandong Province in 2018, the disaster background, hazard factors, and disaster situation were analyzed. .
JIANG Zijie, JIANG Weiguo, WU Jianjun, ZHOU Hongmin
Based on the international trade data from UN comtrade crude oil resources (2709), after sorting, extracting, compiling and spatializing, flow map was made on arcgis 10.2 software platform. From the perspective of central Asia's oil trade relations, priority should be given to ensuring the oil exports of European countries as the main direction of central Asia's oil exports. Before 2006, the exports to Europe accounted for more than 90% of central Asia's exports.From the perspective of export volume and trade relations, since the disintegration of the Soviet union, central Asia has been seeking to diversify its exports and establish broader trade relations, with the number of exporting countries increasing from 3 in 1993 to 18 in 2016.Before 1995, central Asia exported only a small amount of oil, less than 1 million tons. From 1996 to 2013, oil exports increased rapidly and reached a peak.
YANG Yu, HE Ze
Basic Geographic Data Set of Resources and Environment in Central and Western Asia Region, includes six parts: administrative divisions map, topographic and geomorphological map, river system maps, precipitation map, temperature map and potential evapotranspiration map. The precipitation and temperature datasets are interpolated based on the ground observations, while the potential evapotranspiration dataset is calculated based on the Penman-Monteith equation. The precipitation, temperature and potential evapotranspiration datasets are resampled from the original 0.5° CRU dataset by using the linear interpolation method in ArcGIS software. This dataset is made based a large number of gauge observations with good quality control and homogeneity check. The results of the related studies (Deng and Chen, 2017; Li et al., 2017; Li et al., 2016) suggested that this dataset is applicable and satisfactory for the climatological studies. The data produced by the key laboratory of remote sensing and GIS, Xinjiang institute of ecology and geography, Chinese Academy of Sciences. Data production Supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDA20030101.
XU Wenqiang
This dataset includes the concentrations and spatial pattern of mercury (Hg) in the foliage of the local tree species over the easteran and the southern Tibetan Plateau. Fifty-three leaf samples were collected, and cold vapor atomic fluorescence spectrophotometry (CVAFS) was used to analyse the Hg contents. The limit of detection (LOD) for this method is 1.8 ng/g. The standard reference material, foliage GB GSW-11, which is supplied by National Institute of Metrology P.R.China, was also analyzed for assessing the accuracy of this method, and the recoveries of this method were 94.6%±9.7%. This dataset will provide the informations of foliage absoprtion to Hg over the Tibetan Plateau.
WANG Xiaoping
Geladaindong ice core records could provide a unique opportunity for studying climatic and environmental changes in the central TP. Based on a 147 m deep ice core drilled by the Sino-US Cooperation Expedition in 2005 at Mt. Geladaindong, we analyzed oxygen and major ion by using MAT253 isotope mass spectrometer and Ion Chromatograph. Multiparametric dating approach is adopted to establish an accurate chronology. Glaciochemical records were reconstructed to reveal the annual climatic and environmental changes during the period of 1477~1982 AD.
KANG Shichang
This data originates from the National Geographic Information Resources Catalogue Service System, which was provided free to the public by the National Basic Geographic Information Center in November 2017. We have spliced and cut the source of the three rivers as a whole, so as to facilitate the use of the study of the source area of the three rivers. The data trend is 2015. This data set includes 1:250,000 natural place names (AANP) in Sanjiangyuan area, including traffic element names, memorial sites and historic sites, mountain names, water system names, marine geographical names, natural geographical names, etc. Natural Place Name Data (AANP) Attribute Item Names and Definitions: Attribute Item Description Fill in Example NAME Name Ramsay Laboniwa PINYIN Chinese Pinyin Lamusailabaoniwa CLASS Toponymic Classification Code HB
National Catalogue Service for Geographic Information
This data was originated from the National Geographic Information Resources Catalogue Service System, which was provided free to the public by the National Basic Geographic Information Center in November 2017. We have spliced and cut the source of the three rivers as a whole, so as to facilitate the use of the study of the source area of the three rivers. The data trend is 2017. This data set is AGNP data of 1:1 million residential place names in Sanjiangyuan area, including administrative place names at all levels and urban and rural residential place names. Names and Definitions of Attribute Items of Residential Place Name Data (AGNP): Attribute Item Description Fill in Example CLASS Geographical Name Classification Code AK NAME Name Quanqu Village PINYIN Chinese Pinyin Quanqucun GNID Place Name Code 632524000000 XZNAME Township Name Ziketan Township
National Catalogue Service for Geographic Information
This data comes from the National Geographic Information Resources Catalogue Service System, which was provided free to the public by the National Basic Geographic Information Center in November 2017. We have spliced and cut the source of the three rivers as a whole, so as to facilitate the use of the study of the source area of the three rivers. The data trend is 2017. This data set is composed of 1:1 million natural place names (AANP) in Sanjiangyuan area, including traffic element names, memorial sites and historic sites, mountain names, river system names, marine geographical names, natural geographical names, etc. Natural Place Name Data (AANP) Attribute Item Names and Definitions: Attribute Item Description Fill in Example CLASS Toponymic Classification Code NAME in Chinese words PINYIN in Chinese Pinyin
National Catalogue Service for Geographic Information
This data comes from the National Geographic Information Resources Catalogue Service System, which was provided free to the public by the National Basic Geographic Information Center in November 2017. We have spliced and cut the source of the three rivers as a whole, so as to facilitate the use of the study of the source area of the three rivers. The data trend is 2015. This data set includes 1:250,000 residential place names (AANP) in Sanjiangyuan area, including administrative place names at all levels and urban and rural residential place names. Names and Definitions of Attribute Items of Residential Place Name Data (AANP): Attribute Item Description Fill in Example NAME Name Quanqu Village PINYIN Chinese Pinyin Quanqucun CLASS Geographical Name Classification Code AK GNID Place Name Code 632524000000 XZNAME Township Name Ziketan Township
National Catalogue Service for Geographic Information
This data set includes the information of 21 conventional meteorological observation stations in Heihe River Basin and its surrounding areas, of which Wutonggou and Quixote stations have been cancelled in the 1980s, and other stations have operated since the establishment of the station. Station name, longitude and latitude 1. Mazong mountain 97.1097 41.5104 2. Yumen town 97.5530 39.8364 3. Wutonggou 98.3248 40.4697 4. Jiuquan 98.4975 39.7036 5. Jinta 98.9058 39.9988 6. Dingxin 99.5117 40.3080 7. Gaotai 99.7907 39.3623 8. Linze 100.165 39.1385 9. Sunan 99.6178 38.8399 10. Yeniugou 99.5830 38.4167 11. Tole 98.0147 39.0327 12. Ejina Banner 101.088 41.9351 13. Guaizi Lake 102.283 41.3662 14. Zhangye 100.460 38.9124 15. Shandan 101.083 38.7746 16. Folk music 100.826 38.4376 17. Alxa Right Banner 101.429 39.1407 18. Yongchang 101.578 38.1771 19. Qilian 100.238 38.1929 20. Gangcha 100.111 37.2478 21. Menyuan 101.379 37.2513 22. Gekkot 99.7063 41.9183 23. Jiayuguan 98.2241 39.7975
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