(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
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
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
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