cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8271952
title: ELITE land surface temperature: seamless 1km LST over China (2007)

description: The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (https://elite.bnu.edu.cn). This dataset is the ELITE seamless 1km LST over China landmass (2002-2020). Firstly, a look-up-table-based empirical retrieval algorithm is developed for retrieving microwave LST from AMSR-E/AMSR2 observations. Then, AMSR-E/AMSR2 LST is downscaled using the geographically weighted regression to obtain 1km LST. Finally, the multi-scale kalman filter is used to fuse MODIS LST and AMSR-E/AMSR2 LST to generate a 1km seamless LST data set. The ground valuation results show that the root mean square error (RMSE) of the 1km seamless LST is about 3K. In addition, the spatial distribution of the 1km seamless LST is consistent with MODIS LST and CLDAS LST. This is the seamless LST dataset in 2007. Please <em><strong>click here</strong></em> to download the ELITE LST product in 2006 and <em><strong>click here</strong></em> to download the ELITE LST product in 2008. <strong>Dataset Characteristics:</strong> Spatial Coverage: China Temporal Coverage: 2007 Spatial Resolution: 1 KM Temporal Resolution: 2 times per day Data Format: hdf Scale: 0.02 <strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong> Xu, S., & Cheng, J. (2021). A new land surface temperature fusion strategy based on cumulative distribution function matching and multiresolution Kalman filtering. Remote Sensing of Environment, 254, 112256 Zhang, Q., Wang, N., Cheng, J., & Xu, S. (2020). A Stepwise Downscaling Method for Generating High-Resolution Land Surface Temperature From AMSR-E Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5669-5681 Zhang, Q., & Cheng, J. (2020). An Empirical Algorithm for Retrieving Land Surface Temperature From AMSR-E Data Considering the Comprehensive Effects of Environmental Variables. Earth and Space Science, 7, e2019EA001006. https://doi.org/10.1029/2019EA001006 If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).
DOI: 10.57841/casdc.0001983
title: 食品级滑石粉对复合铝基润滑脂摩擦学性能的影响
DOI: 10.5281/zenodo.8273134
title: ELITE land surface temperature: seamless 1km LST over China (2008)

description: The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (https://elite.bnu.edu.cn). This dataset is the ELITE seamless 1km LST over China landmass (2002-2020). Firstly, a look-up-table-based empirical retrieval algorithm is developed for retrieving microwave LST from AMSR-E/AMSR2 observations. Then, AMSR-E/AMSR2 LST is downscaled using the geographically weighted regression to obtain 1km LST. Finally, the multi-scale kalman filter is used to fuse MODIS LST and AMSR-E/AMSR2 LST to generate a 1km seamless LST data set. The ground valuation results show that the root mean square error (RMSE) of the 1km seamless LST is about 3K. In addition, the spatial distribution of the 1km seamless LST is consistent with MODIS LST and CLDAS LST. This is the seamless LST dataset in 2008. Please <em><strong>click here</strong></em> to download the ELITE LST product in 2007 and <em><strong>click here</strong></em> to download the ELITE LST product in 2009. <strong>Dataset Characteristics:</strong> Spatial Coverage: China Temporal Coverage: 2008 Spatial Resolution: 1 KM Temporal Resolution: 2 times per day Data Format: hdf Scale: 0.02 <strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong> Xu, S., & Cheng, J. (2021). A new land surface temperature fusion strategy based on cumulative distribution function matching and multiresolution Kalman filtering. Remote Sensing of Environment, 254, 112256 Zhang, Q., Wang, N., Cheng, J., & Xu, S. (2020). A Stepwise Downscaling Method for Generating High-Resolution Land Surface Temperature From AMSR-E Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5669-5681 Zhang, Q., & Cheng, J. (2020). An Empirical Algorithm for Retrieving Land Surface Temperature From AMSR-E Data Considering the Comprehensive Effects of Environmental Variables. Earth and Space Science, 7, e2019EA001006. https://doi.org/10.1029/2019EA001006 If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).
DOI: 10.5281/zenodo.8273133
title: ELITE land surface temperature: seamless 1km LST over China (2008)

description: The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (https://elite.bnu.edu.cn). This dataset is the ELITE seamless 1km LST over China landmass (2002-2020). Firstly, a look-up-table-based empirical retrieval algorithm is developed for retrieving microwave LST from AMSR-E/AMSR2 observations. Then, AMSR-E/AMSR2 LST is downscaled using the geographically weighted regression to obtain 1km LST. Finally, the multi-scale kalman filter is used to fuse MODIS LST and AMSR-E/AMSR2 LST to generate a 1km seamless LST data set. The ground valuation results show that the root mean square error (RMSE) of the 1km seamless LST is about 3K. In addition, the spatial distribution of the 1km seamless LST is consistent with MODIS LST and CLDAS LST. This is the seamless LST dataset in 2008. Please <em><strong>click here</strong></em> to download the ELITE LST product in 2007 and <em><strong>click here</strong></em> to download the ELITE LST product in 2009. <strong>Dataset Characteristics:</strong> Spatial Coverage: China Temporal Coverage: 2008 Spatial Resolution: 1 KM Temporal Resolution: 2 times per day Data Format: hdf Scale: 0.02 <strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong> Xu, S., & Cheng, J. (2021). A new land surface temperature fusion strategy based on cumulative distribution function matching and multiresolution Kalman filtering. Remote Sensing of Environment, 254, 112256 Zhang, Q., Wang, N., Cheng, J., & Xu, S. (2020). A Stepwise Downscaling Method for Generating High-Resolution Land Surface Temperature From AMSR-E Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5669-5681 Zhang, Q., & Cheng, J. (2020). An Empirical Algorithm for Retrieving Land Surface Temperature From AMSR-E Data Considering the Comprehensive Effects of Environmental Variables. Earth and Space Science, 7, e2019EA001006. https://doi.org/10.1029/2019EA001006 If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).
DOI: 10.5281/zenodo.8273152
title: ELITE land surface temperature: seamless 1km LST over China (2009)

description: The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (https://elite.bnu.edu.cn). This dataset is the ELITE seamless 1km LST over China landmass (2002-2020). Firstly, a look-up-table-based empirical retrieval algorithm is developed for retrieving microwave LST from AMSR-E/AMSR2 observations. Then, AMSR-E/AMSR2 LST is downscaled using the geographically weighted regression to obtain 1km LST. Finally, the multi-scale kalman filter is used to fuse MODIS LST and AMSR-E/AMSR2 LST to generate a 1km seamless LST data set. The ground valuation results show that the root mean square error (RMSE) of the 1km seamless LST is about 3K. In addition, the spatial distribution of the 1km seamless LST is consistent with MODIS LST and CLDAS LST. This is the seamless LST dataset in 2009. Please <em><strong>click here</strong></em> to download the ELITE LST product in 2008 and <em><strong>click here</strong></em> to download the ELITE LST product in 2010. <strong>Dataset Characteristics:</strong> Spatial Coverage: China Temporal Coverage: 2009 Spatial Resolution: 1 KM Temporal Resolution: 2 times per day Data Format: hdf Scale: 0.02 <strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong> Xu, S., & Cheng, J. (2021). A new land surface temperature fusion strategy based on cumulative distribution function matching and multiresolution Kalman filtering. Remote Sensing of Environment, 254, 112256 Zhang, Q., Wang, N., Cheng, J., & Xu, S. (2020). A Stepwise Downscaling Method for Generating High-Resolution Land Surface Temperature From AMSR-E Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5669-5681 Zhang, Q., & Cheng, J. (2020). An Empirical Algorithm for Retrieving Land Surface Temperature From AMSR-E Data Considering the Comprehensive Effects of Environmental Variables. Earth and Space Science, 7, e2019EA001006. https://doi.org/10.1029/2019EA001006 If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).
DOI: 10.5281/zenodo.8273151
title: ELITE land surface temperature: seamless 1km LST over China (2009)

description: The <strong>E</strong>ssential therma<strong>L</strong> <strong>I</strong>nfrared remo<strong>T</strong>e s<strong>E</strong>nsing (<strong>ELITE</strong>) product suite currently has four types of products, including land surface temperature (LST: clear-sky and all-sky), emissivity (NBE: narrowband emissivity; BBE: broadband emissivity; and spectral emissivity), the component of surface radiation and energy budget (SLUR: surface longwave upwelling radiation; SLDR: surface longwave downward radiation SLDR; SLNR: surface longwave net radiation), and the component of Earth’s radiation budget (OLR; outgoing longwave radiation; RSR: reflected solar radiation). The spatial-temporal resolutions of the ELITE products are mainly determined by the employed satellite data sources. For more information about ELITE products, please refer to the website (https://elite.bnu.edu.cn). This dataset is the ELITE seamless 1km LST over China landmass (2002-2020). Firstly, a look-up-table-based empirical retrieval algorithm is developed for retrieving microwave LST from AMSR-E/AMSR2 observations. Then, AMSR-E/AMSR2 LST is downscaled using the geographically weighted regression to obtain 1km LST. Finally, the multi-scale kalman filter is used to fuse MODIS LST and AMSR-E/AMSR2 LST to generate a 1km seamless LST data set. The ground valuation results show that the root mean square error (RMSE) of the 1km seamless LST is about 3K. In addition, the spatial distribution of the 1km seamless LST is consistent with MODIS LST and CLDAS LST. This is the seamless LST dataset in 2009. Please <em><strong>click here</strong></em> to download the ELITE LST product in 2008 and <em><strong>click here</strong></em> to download the ELITE LST product in 2010. <strong>Dataset Characteristics:</strong> Spatial Coverage: China Temporal Coverage: 2009 Spatial Resolution: 1 KM Temporal Resolution: 2 times per day Data Format: hdf Scale: 0.02 <strong>Citation </strong>(Please cite these papers when using the data)<strong>:</strong> Xu, S., & Cheng, J. (2021). A new land surface temperature fusion strategy based on cumulative distribution function matching and multiresolution Kalman filtering. Remote Sensing of Environment, 254, 112256 Zhang, Q., Wang, N., Cheng, J., & Xu, S. (2020). A Stepwise Downscaling Method for Generating High-Resolution Land Surface Temperature From AMSR-E Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5669-5681 Zhang, Q., & Cheng, J. (2020). An Empirical Algorithm for Retrieving Land Surface Temperature From AMSR-E Data Considering the Comprehensive Effects of Environmental Variables. Earth and Space Science, 7, e2019EA001006. https://doi.org/10.1029/2019EA001006 If you have any questions, please contact Prof. Jie Cheng (eliteqrs@126.com).
DOI: 10.5281/zenodo.8286923
title: Dataset - Zhang and Tian (2023)

description: BDstation-data composes of the data at stations KMI and XAN including the teleseismic receiver functions, Pn-wave receiver functions, and the raw waveform data. DNAdata composes of the data of a dense seismic modal array in North China including the raw data, the teleseismic receiver functions and the Pn-wave receiver functions with different Gaussian filter values. All files are SAC binary format.
DOI: 10.5281/zenodo.8286922
title: Dataset - Zhang and Tian (2023)

description: BDstation-data composes of the data at stations KMI and XAN including the teleseismic receiver functions, Pn-wave receiver functions, and the raw waveform data. DNAdata composes of the data of a dense seismic modal array in North China including the raw data, the teleseismic receiver functions and the Pn-wave receiver functions with different Gaussian filter values. All files are SAC binary format.
DOI: 10.5517/ccdc.csd.cc2dqng8
title: CCDC 2226310: Experimental Crystal Structure Determination

description: Related Article: Zoe Y. Marr, Rajani Thapa Magar, Bertrand Fournier, Jason B. Benedict, Jeffrey J. Rack|2023|Chemical Science|14|7279|doi:10.1039/D3SC01526B
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fg5lp
title: CCDC 2247952: Experimental Crystal Structure Determination

description: Related Article: Stephan Reichl, Felix Riedlberger, Martin Piesch, Gábor Balázs, Michael Seidl, Manfred Scheer|2023|Chemical Science|14|7285|doi:10.1039/D3SC01488F
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fg5mq
title: CCDC 2247953: Experimental Crystal Structure Determination

description: Related Article: Stephan Reichl, Felix Riedlberger, Martin Piesch, Gábor Balázs, Michael Seidl, Manfred Scheer|2023|Chemical Science|14|7285|doi:10.1039/D3SC01488F
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fg5nr
title: CCDC 2247954: Experimental Crystal Structure Determination

description: Related Article: Stephan Reichl, Felix Riedlberger, Martin Piesch, Gábor Balázs, Michael Seidl, Manfred Scheer|2023|Chemical Science|14|7285|doi:10.1039/D3SC01488F
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fg5qt
title: CCDC 2247956: Experimental Crystal Structure Determination

description: Related Article: Stephan Reichl, Felix Riedlberger, Martin Piesch, Gábor Balázs, Michael Seidl, Manfred Scheer|2023|Chemical Science|14|7285|doi:10.1039/D3SC01488F
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fjmd0
title: CCDC 2250302: Experimental Crystal Structure Determination

description: Related Article: Jing Sun, Xueyan Liu, Qiu Sun, Ying Han, Chao-Guo Yan|2023|J.Org.Chem.|88|11562|doi:10.1021/acs.joc.3c00887
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2f3ftt
title: CCDC 2237636: Experimental Crystal Structure Determination

description: Related Article: Melike Şevik, Sinem Merve Sezdi, Enes Kavak, Tuğba Alp Arici, Mürsel Arici|2023|Cryst.Growth Des.|23|5163|doi:10.1021/acs.cgd.3c00400
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fnrxs
title: CCDC 2254286: Experimental Crystal Structure Determination

description: Related Article: Alexey A. Artemjev, Alexey S. Kubasov, Maxim L. Kuznetsov, Mariya V. Grudova, Victor N. Khrustalev, Andreii S. Kritchenkov, Alexander G. Tskhovrebov|2023|CrystEngComm|25|3691|doi:10.1039/D3CE00385J
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fnryt
title: CCDC 2254287: Experimental Crystal Structure Determination

description: Related Article: Alexey A. Artemjev, Alexey S. Kubasov, Maxim L. Kuznetsov, Mariya V. Grudova, Victor N. Khrustalev, Andreii S. Kritchenkov, Alexander G. Tskhovrebov|2023|CrystEngComm|25|3691|doi:10.1039/D3CE00385J
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fnrzv
title: CCDC 2254288: Experimental Crystal Structure Determination

description: Related Article: Alexey A. Artemjev, Alexey S. Kubasov, Maxim L. Kuznetsov, Mariya V. Grudova, Victor N. Khrustalev, Andreii S. Kritchenkov, Alexander G. Tskhovrebov|2023|CrystEngComm|25|3691|doi:10.1039/D3CE00385J
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fw1gv
title: CCDC 2260317: Experimental Crystal Structure Determination

description: Related Article: Jianbo Zhao, Min Wu, Jiamin Luo, Lei Shi, Hao Li|2023|Org.Biomol.Chem.|21|6675|doi:10.1039/D3OB01006F
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2fxrz3
title: CCDC 2261976: Experimental Crystal Structure Determination

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description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2g065w
title: CCDC 2264307: Experimental Crystal Structure Determination

description: Related Article: Franz A. Mautner, Roland C. Fischer, Ana Torvisco, Kai Nakashima, Makoto Handa, Masahiro Mikuriya, Nahed M. H. Salem, Gabriel J. Overby, Madison R. Maier, Febee R. Louka, Salah S. Massoud|2023|New J.Chem.|47|15638|doi:10.1039/D3NJ02380J
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.