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Based on the observation monthly climatic data collected from 2766 weather observation stations on global during the period from 1981 to 2010, and the climatic scenarios data of SSP1_2.6、SSP1_4.5 and SSP1_8.5 scenarios released by CMIP6, the mean annual biotemperature, average total annual precipitation and potential evapotranspiration ratio on spatial resolution of 0.1º× 0.1º were respectively obtained by operating a high accuracy and speed method of surfacing modeling (HASM) (Yue, 2010, Yue et al., 2016) during all the four periods from 2020 to 2050 per decade. The method for surface modelling of land cover scenarios (SMLCS) has been developed to simulate the scenarios of land cover in Eurasia （Fan et al., 2019, 2020, 2021）. Finally, the scenario dataset of land cover under scenario SSP1_2.6、SSP1_4.5 and SSP1_8.5 were simulated by the SMLCS method from 2020 to 2050. 采用1981-2010年全球2766个气象观测站的观测月气候数据，以及CMIP6发布的SSP1_2.6、SSP1_4.5和SSP1_8.5情景的气候情景数据。通过运行高精度面建模方法（HASM）(Yue, 2010, Yue et al., 2016)，分别获得2020-2050年间每10年的空间分辨率为0.1º×0.1º的平均生物温度数据、多年平均年降水和潜在蒸散比率数据。采用自主研发的土地覆被情景曲面建模（SMLCS）方法（Fan et al., 2019, 2020, 2021），实现了SSP1_2.6、SSP1_4.5和SSP1_8.5情景的2020-2050年间每10年的全球土地覆被变化情景模拟。

This dataset contains the grid data of the first leaf date (FLD) and first flower date (FFD) of six woody plants in Europe (34°57′N-72°3′N，25°3′W-40°3′E) from 1951 to 2021, with a spatial resolution of 0.1° and a temporal resolution of 1 day. The quality evaluation of the grid phenology data shows that the average error of FLD and FFD is 7.9 and 7.6 days respectively, which has high simulation accuracy.Method: Based on the in-situ phenology observations from the Pan European Phenology Project (PEP725) in the past 70 years, this dataset employed three phenology models (Unichill, Unified and Temporal-Spatial Coupling) to predict and upscale the phenology data on the continental scale, and developed a grid phenology dataset of woody plants in Europe.Dataset composition: The dataset contains the gridded phenology data of six woody plants in Europe from 1951 to 2021, including the spring FLD (BBCH11.zip) and the spring FFD (BBCH60.zip). The annual data of each species is stored as a Geotiff file with 651 row × 371 column. The data is named according to "year (YYYY) + species genus (Genus) + phenophase (_xx)". For example, "2021Aesculus_11. tif" is the grid data file of the FLD of European Aesculus in 2021. The unit of phenology data is Julian day of year (DOY), which represents the actual number of days from the date of phenology occurrence to January 1 of the current year. The valid value is 1-366, and the invalid filling value is 999. The spatial reference system of the data is EPSG:4326 (WGS84). This dataset contains the grid data of the first leaf date (FLD) and first flower date (FFD) of six woody plants in Europe (34°57′N-72°3′N，25°3′W-40°3′E) from 1951 to 2021, with a spatial resolution of 0.1° and a temporal resolution of 1 day. The quality evaluation of the grid phenology data shows that the average error of FLD and FFD is 7.9 and 7.6 days respectively, which has high simulation accuracy.Method: Based on the in-situ phenology observations from the Pan European Phenology Project (PEP725) in the past 70 years, this dataset employed three phenology models (Unichill, Unified and Temporal-Spatial Coupling) to predict and upscale the phenology data on the continental scale, and developed a grid phenology dataset of woody plants in Europe.Dataset composition: The dataset contains the gridded phenology data of six woody plants in Europe from 1951 to 2021, including the spring FLD (BBCH11.zip) and the spring FFD (BBCH60.zip). The annual data of each species is stored as a Geotiff file with 651 row × 371 column. The data is named according to "year (YYYY) + species genus (Genus) + phenophase (_xx)". For example, "2021Aesculus_11. tif" is the grid data file of the FLD of European Aesculus in 2021. The unit of phenology data is Julian day of year (DOY), which represents the actual number of days from the date of phenology occurrence to January 1 of the current year. The valid value is 1-366, and the invalid filling value is 999. The spatial reference system of the data is EPSG:4326 (WGS84).

As the most complex component in the transmission system, the operating state of the wind turbine gearbox has a tremendous impact on the monitoring of the health status and operation control of the wind turbine equipment. Abnormalities in wind turbines that lead to downtime not only result in a loss of electrical energy, but also a significant increase in maintenance costs. Therefore, with the wind turbine gearbox as the main object of study, the following studies were carried out: For microscopic local conditions in gearbox gear systems, a method for obtaining modal data using finite element simulation analysis of single tooth faults is proposed. Using a combination of deep auto-encoder structures and BP structures for secondary training strategies, a linear and non-linear performance evaluation method is proposed, which takes into account the relationship between performance and efficiency. Hyper-parameter configuration in deep transfer structures is often arbitrary, so a hierarchical transfer network structure hyper-parameter searching method is proposed to address the gearbox planetary system fault classification problem. The algorithm is validated using the classical LeNet-5 reconfiguration transfer application on a modal dataset of the planetary system. Finally, a stability validation and results analysis of the algorithm performance is carried out. A compressed sensing-based sparse signal decomposition method is proposed, and the structure of the transfer network is redesigned to achieve deep migration learning from rolling bearing faults to gear faults. A new network architecture was designed using a plug-and-play attention module. Pre-training models were designed and produced for fault data to improve the accuracy and recognition speed of fault diagnosis model classification. Finally, the effects of the same number of samples in the source and target domains and different distributions of sample features on the performance of the transfer learning method and the effects of hyper-parameters on the final performance of the network structure are verified.

As the main body of terrestrial ecosystems, forests account for over 65% of the fixed carbon content each year, and forest biomass accounts for about 90% of the total biomass of terrestrial ecosystems. They play an important role in regulating global carbon balance and slowing down the rise of greenhouse gas concentrations. Tianshan spruce, as an important forest resource in Xinjiang, has significant ecological and economic value. Constructing its biomass spatiotemporal dataset can provide basic data for the assessment of regional carbon sequestration potential, and provide scientific basis for the protection and sustainable management of Tianshan spruce forests. The dataset includes text data and image data, among which Excel text data collects the structural data of the dense area of Xinjiang Tianshan spruce in 2002, 2007, 2012, and 2017 for each sample plot; Grid image data includes terrain data such as altitude, remote sensing data such as normalized vegetation index, meteorological data such as annual average precipitation, annual average runoff depth, annual average easterly wind speed, and biomass distribution map of spruce dense areas. The comprehensiveness of these data is crucial for revealing the growth trends and changes of spruce in the Tianshan Mountains. It not only has important scientific value and practical application potential in the fields of ecological protection and climate change research in the Tianshan Mountains of Xinjiang, but also provides valuable data resources for researchers in the area of ecosystem management and related fields.  As the main body of terrestrial ecosystems, forests account for over 65% of the fixed carbon content each year, and forest biomass accounts for about 90% of the total biomass of terrestrial ecosystems. They play an important role in regulating global carbon balance and slowing down the rise of greenhouse gas concentrations. Tianshan spruce, as an important forest resource in Xinjiang, has significant ecological and economic value. Constructing its biomass spatiotemporal dataset can provide basic data for the assessment of regional carbon sequestration potential, and provide scientific basis for the protection and sustainable management of Tianshan spruce forests. The dataset includes text data and image data, among which Excel text data collects the structural data of the dense area of Xinjiang Tianshan spruce in 2002, 2007, 2012, and 2017 for each sample plot; Grid image data includes terrain data such as altitude, remote sensing data such as normalized vegetation index, meteorological data such as annual average precipitation, annual average runoff depth, annual average easterly wind speed, and biomass distribution map of spruce dense areas. The comprehensiveness of these data is crucial for revealing the growth trends and changes of spruce in the Tianshan Mountains. It not only has important scientific value and practical application potential in the fields of ecological protection and climate change research in the Tianshan Mountains of Xinjiang, but also provides valuable data resources for researchers in the area of ecosystem management and related fields. 

This dataset is based on the raw data from 2015 to 2023 collected from the standard surface meteorological observation field and micrometeorology observation tower in Baotianman Station. Through data processing and quality control, data products at half-hour scale was formed. The indicators in the dataset include atmospheric elements (air temperature and humidity, wind speed and direction, photosynthetically active radiation, net radiation, total radiation, atmosphere pressure, rainfall) and soil elements (3 layers of soil temperature, 3 layers of soil water content, soil heat flux).  This dataset is based on the raw data from 2015 to 2023 collected from the standard surface meteorological observation field and micrometeorology observation tower in Baotianman Station. Through data processing and quality control, data products at half-hour scale was formed. The indicators in the dataset include atmospheric elements (air temperature and humidity, wind speed and direction, photosynthetically active radiation, net radiation, total radiation, atmosphere pressure, rainfall) and soil elements (3 layers of soil temperature, 3 layers of soil water content, soil heat flux). 

Janzen-Connell (JC) effects, hypothesized to be mostly driven by negative plant-soil feedbacks (PSFs), are considered to be the key mechanism that regulates tropical forest plant diversity and coexistence. However, intraspecific variation in JC effects may weaken this mechanism, with the strength of PSFs being a potentially key variable process. We conducted a manipulated experiment with seedlings from two populations of Pometia pinnata (Sapindaceae), a tropical tree species in southwest China. We aimed to measure the intraspecific difference in PSF magnitude caused by inoculating the soil from different P. pinnata source populations and growing seedlings under differing light intensity and water availability treatments, and at varying plant densities. We found negative PSFs for both populations with the inoculum soil originating from the same sites, but PSFs differed significantly with the inoculum soil from different sites. PSF strength responded differently to biotic and abiotic drivers; PSF strength was weaker in low moisture and high light treatments than in high moisture and low light treatments. Our study documents intraspecific variation in JC effects: specifically, P. pinnata have less defences to their natively-sourced soil, but are more defensive to the soil feedbacks from soil sourced from other populations. Our results imply that drought and light intensity tended to weaken JC effects, which may result in loss of species diversity with climate change. Janzen-Connell (JC) effects, hypothesized to be mostly driven by negative plant-soil feedbacks (PSFs), are considered to be the key mechanism that regulates tropical forest plant diversity and coexistence. However, intraspecific variation in JC effects may weaken this mechanism, with the strength of PSFs being a potentially key variable process. We conducted a manipulated experiment with seedlings from two populations of Pometia pinnata (Sapindaceae), a tropical tree species in southwest China. We aimed to measure the intraspecific difference in PSF magnitude caused by inoculating the soil from different P. pinnata source populations and growing seedlings under differing light intensity and water availability treatments, and at varying plant densities. We found negative PSFs for both populations with the inoculum soil originating from the same sites, but PSFs differed significantly with the inoculum soil from different sites. PSF strength responded differently to biotic and abiotic drivers; PSF strength was weaker in low moisture and high light treatments than in high moisture and low light treatments. Our study documents intraspecific variation in JC effects: specifically, P. pinnata have less defences to their natively-sourced soil, but are more defensive to the soil feedbacks from soil sourced from other populations. Our results imply that drought and light intensity tended to weaken JC effects, which may result in loss of species diversity with climate change.

Vegetation phenology is one of the sensitive indicators reflecting global climate change and vegetation growth. Inner Mongolia is an important ecological security barrier in the north of China, and a key area for resource development, environmental protection and ecological security in China. Studying its vegetation phenological changes can know its vegetation growth status, which is of great significance for understanding the characteristics of climate change and extreme climate events in the region. Based on the normalized differential vegetation index (NDVI) data product in MOD13Q1 product, this study use Google Earth Engine platform to process MODIS-NDVI raw data for format conversion, projection conversion and clipping, and exports NDVI long time series data from 2000 to 2021, and dynamic threshold method was used to obtain Inner Mongolia vegetation phenology data set from 2001 to 2020. The dataset includes remote sensing monitoring data of the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS) in Inner Mongolia from 2001 to 2019. And the spatial resolution is 250 m. It provides data support for understanding the temporal and spatial variation of vegetation phenology in Inner Mongolia and its response to climate change. Vegetation phenology is one of the sensitive indicators reflecting global climate change and vegetation growth. Inner Mongolia is an important ecological security barrier in the north of China, and a key area for resource development, environmental protection and ecological security in China. Studying its vegetation phenological changes can know its vegetation growth status, which is of great significance for understanding the characteristics of climate change and extreme climate events in the region. Based on the normalized differential vegetation index (NDVI) data product in MOD13Q1 product, this study use Google Earth Engine platform to process MODIS-NDVI raw data for format conversion, projection conversion and clipping, and exports NDVI long time series data from 2000 to 2021, and dynamic threshold method was used to obtain Inner Mongolia vegetation phenology data set from 2001 to 2020. The dataset includes remote sensing monitoring data of the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS) in Inner Mongolia from 2001 to 2019. And the spatial resolution is 250 m. It provides data support for understanding the temporal and spatial variation of vegetation phenology in Inner Mongolia and its response to climate change.

The Great Wall Station and Zhongshan Station are Chinese permanent scientific research stations in Antarctica, located on the King George Island and in the Larsemann Hills. These two stations are representative observation stations for the study of climate change in the coastal areas of the West Antarctica and the East Antarctica respectively. After the completion of the two stations, surface meteorological observation is carried out according to the specifications of the China Meteorological Administration. This dataset collected the surface meteorological observation data of the Great Wall Station and Zhongshan Station from 1985 to 2022. The data has been quality controlled according to the "Specifications for surface meteorological observation". Nowadays long-term continuous surface meteorological observation data has been obtained including surface air temperature, relative humidity, air pressure, wind speed, wind direction and cloud amount. This dataset can be used for the research of weather processes, climate change and numerical weather forecast in the Antarctic. The Great Wall Station and Zhongshan Station are Chinese permanent scientific research stations in Antarctica, located on the King George Island and in the Larsemann Hills. These two stations are representative observation stations for the study of climate change in the coastal areas of the West Antarctica and the East Antarctica respectively. After the completion of the two stations, surface meteorological observation is carried out according to the specifications of the China Meteorological Administration. This dataset collected the surface meteorological observation data of the Great Wall Station and Zhongshan Station from 1985 to 2022. The data has been quality controlled according to the "Specifications for surface meteorological observation". Nowadays long-term continuous surface meteorological observation data has been obtained including surface air temperature, relative humidity, air pressure, wind speed, wind direction and cloud amount. This dataset can be used for the research of weather processes, climate change and numerical weather forecast in the Antarctic.

Meteorological data is an foundational data for field scientific observation and research. Surface meteorological observation is an important component of meteorological observation. Due to its adherence to unified surface meteorological observation standards, it has better comparability in space, which is also its distinguishing feature from microclimate observation. Henan Xiaolangdi Forest Ecosystem National Observation and Research Station (referred to as Xiaolangdi Station) is located in the transitional zone of the second and third steps of China's landforms in the southern Taihang Mountains. It is a key ecological area of the Yellow River and belongs to a warm temperate sub humid monsoon climate with rich biodiversity. This dataset is based on the raw data collected from the surface meteorological observation field of Xiaolangdi Station. It is the daily meteorological data product from 2018 to 2020 after data processing and quality control. It covers the daily data of temperature, maximum temperature, minimum temperature, relative humidity, wind speed, maximum wind speed, net radiation, direct radiation, air pressure, precipitation, 0-80cm multi-layer soil temperature (total of 17 observation elements). This dataset can provide background information for climate change research and ecological civilization construction, and to provide data support for maintaining ecological security and promoting high-quality development in the Yellow River Basin. Meteorological data is an foundational data for field scientific observation and research. Surface meteorological observation is an important component of meteorological observation. Due to its adherence to unified surface meteorological observation standards, it has better comparability in space, which is also its distinguishing feature from microclimate observation. Henan Xiaolangdi Forest Ecosystem National Observation and Research Station (referred to as Xiaolangdi Station) is located in the transitional zone of the second and third steps of China's landforms in the southern Taihang Mountains. It is a key ecological area of the Yellow River and belongs to a warm temperate sub humid monsoon climate with rich biodiversity. This dataset is based on the raw data collected from the surface meteorological observation field of Xiaolangdi Station. It is the daily meteorological data product from 2018 to 2020 after data processing and quality control. It covers the daily data of temperature, maximum temperature, minimum temperature, relative humidity, wind speed, maximum wind speed, net radiation, direct radiation, air pressure, precipitation, 0-80cm multi-layer soil temperature (total of 17 observation elements). This dataset can provide background information for climate change research and ecological civilization construction, and to provide data support for maintaining ecological security and promoting high-quality development in the Yellow River Basin.