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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。

Comparison of the water budget for the typical cropland and pear orchard ecosystems in the North China Plain Comparison of the water budget for the typical cropland and pear orchard ecosystems in the North China Plain

Spatio-temporal variations in extreme drought in China during 1961–2015 Spatio-temporal variations in extreme drought in China during 1961–2015

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).

Wind turbine roll decay curves Wind turbine roll decay curves

This article presents the data of the published paper: Threshold photoelectron spectroscopy of the HO2 radical (J. Chem. Phys. 153, 124306 (2020)) 本论文展示了已发表论文的数据：Threshold photoelectron spectroscopy of the HO2 radical (J. Chem. Phys. 153, 124306 (2020))

In this dataset, a near-infrared laser heterodyne spectrometer developed by the laboratory is used to investigate the inversion of greenhouse gas column concentration and approximately evaluate the system measurement errors based on the optimal estimation algorithm. Firstly, the spectral database and the calculation results from the reference forward model are compared with the ground-based FTIR results, thereby selecting the detection window, the corresponding laser and detector. Secondly, the optimal estimation concentration inversion algorithm based on the reference forward model is established, and the LevenbergMarquardt (LM) iterative method is adopted to realize the inversion of the concentration and vertical distribution profile of atmospheric CO2 column in the whole layer, and the long-term observation comparative experiment is carried out to verify the feasibility of this algorithm. Finally, by simulating the selected detection window spectrum in different white noise, the approximate corresponding relationship between the system signal-noise-ratio (SNR) and CO2 column concentration measuring error is eventually obtained. 利用实验室研制的近红外激光外差光谱仪，开展了基于最优估计算法的温室气体柱浓度反演和系统测量误差的近似评估等相关工作。首先， 通过光谱数据库、参考正向模型计算结果与傅里叶变换红外光谱技术探测结果筛选出了探测窗口， 并以此为依据选择了相应的激光器和探测器; 其次, 建立了基于参考正向模型最优估计浓度反演算法，采用 Levenberg-Marquardt (LM) 迭代方法， 实现了整层大气 CO2 柱浓度及垂直分布廓线的反演， 并开展了长期观测对比实验, 验证了反演算法的可行性, 通过模拟所选探测窗口波段在不同白噪声条件下的正向大气透过率谱, 获得了系统 SNR 与柱浓度测量误差之间的近似对应关系。

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.

This article presents the data of the published paper: Dissociation of High-Lying Electronic States of NO2+ in the 15.5−20 eV Region (J. Phys. Chem. A 2021, 125, 1517-1525) This article presents the data of the published paper: Dissociation of High-Lying Electronic States of NO2+ in the 15.5−20 eV Region (J. Phys. Chem. A 2021, 125, 1517-1525)

The dataset mainly includes crystal structures of the five mentioned prototype compounds, energy above convex hull (Ehull) data, bandgap data, density of states data, absorption spectra data, and SLME data. The dataset consists of 8 main folders. Folder 1 contains crystal structure data for the five prototypes used in constructing the database ("data of crystal structure, .vesta/.vasp"; VESTA), as shown in Figure 1 of the article. Folder 2 contains thermodynamic stability calculation data for the materials ("data of Ehull, .csv"). Folder 3 contains PBE band gap calculation data for the materials ("data of PBEbandgap, .csv"). Folder 4 contains data for the band gap difference calculations ("data of deltaEg, .csv"). Folder 2, 3, 4 provides the plotting data for Figure 3 in the article. Folder 5 contains data for the calculation of the spectral limit maximum efficiency (SLME) ("data of SLME, .csv"), which corresponds to the data in Figure 4(a) and Figure 4(c) of the article. Folder 6 contains phonon dispersion spectrum data ("data of Phonon dispersion, .dat"), corresponding to the plotting data for Figure 4(b) and supplementary figures S1, S2, and S3 in the article. Folder 7 contains the absorption spectra data for the four materials with SLME exceeding 31% and the absorption spectra data for CZTS and CZTSe ("data of absorption spectra, .dat"), corresponding to the plotting data for Figure 4(d) in the article. Folder 8 contains the band structure and carrier effective mass data for the four materials with SLME exceeding 31% ("data of best4, .dat"), corresponding to the plotting data for Figure 5 and supplementary Figure S2 in the article. The dataset mainly includes crystal structures of the five mentioned prototype compounds, energy above convex hull (Ehull) data, bandgap data, density of states data, absorption spectra data, and SLME data. The dataset consists of 8 main folders. Folder 1 contains crystal structure data for the five prototypes used in constructing the database ("data of crystal structure, .vesta/.vasp"; VESTA), as shown in Figure 1 of the article. Folder 2 contains thermodynamic stability calculation data for the materials ("data of Ehull, .csv"). Folder 3 contains PBE band gap calculation data for the materials ("data of PBEbandgap, .csv"). Folder 4 contains data for the band gap difference calculations ("data of deltaEg, .csv"). Folder 2, 3, 4 provides the plotting data for Figure 3 in the article. Folder 5 contains data for the calculation of the spectral limit maximum efficiency (SLME) ("data of SLME, .csv"), which corresponds to the data in Figure 4(a) and Figure 4(c) of the article. Folder 6 contains phonon dispersion spectrum data ("data of Phonon dispersion, .dat"), corresponding to the plotting data for Figure 4(b) and supplementary figures S1, S2, and S3 in the article. Folder 7 contains the absorption spectra data for the four materials with SLME exceeding 31% and the absorption spectra data for CZTS and CZTSe ("data of absorption spectra, .dat"), corresponding to the plotting data for Figure 4(d) in the article. Folder 8 contains the band structure and carrier effective mass data for the four materials with SLME exceeding 31% ("data of best4, .dat"), corresponding to the plotting data for Figure 5 and supplementary Figure S2 in the article.