cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.2909/c1c46cb2-b02b-4013-aae5-a54a8c018b1e
title: Start-of-season Date 2017-present (raster 10 m), Europe, yearly, Sept. 2021

description: The Start-of-Season Date (SOSD), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The Start-of-Season Date (SOSD) marks the date when the vegetation growing season starts in the time profile of the Plant Phenology Index (PPI). The start-of-season occurs, by definition, when the PPI value reaches 25% of the season amplitude during the green-up period. The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories, which is a filtered time series with regular 10-day time step. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The Start-of-Season Date is one of the 13 parameters. The full list is available in the table 3 of the Product User Manual https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-seasonal-trajectories/ A complementary quality indicator (QFLAG) provides a confidence level, that is described in table 4 of the same manual. The SOSD dataset is made available as raster files with 10 x 10m resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2017 onwards. It is updated in the first quarter of each year.
DOI: 10.57841/casdc.0001982
title: 载荷和转速对铜合金材料摩擦磨损性能的影响
DOI: 10.2909/a04554dd-74be-4ce7-8dfc-5effca405eaa
title: Slope of the Green-down Period 2017-present (raster 10 m), Europe, yearly, Sept. 2021

description: The slope of the green-down or senescent period (Right Slope, RSLOPE), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The slope of the green-down or senescent period (RSLOPE) expresses the rate of change in the values of the Plant Phenology Index (PPI) at the day when the vegetation growing season ends. The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories, which is a filtered time series with regular 10-day time step. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The green-down period slope is one of the 13 parameters. The full list is available in the table 3 of the Product User Manual https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-seasonal-trajectories/ A complementary quality indicator (QFLAG) provides a confidence level, that is described in table 4 of the same manual. The RSLOPE dataset is made available as raster files with 10 x 10m resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2017 onwards. It is updated in the first quarter of each year.
DOI: 10.5281/zenodo.8271722
title: ELITE land surface temperature: seamless 1km LST over China (2003)

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 2003. Please <em><strong>click here</strong></em> to download the ELITE LST product in 2002 and <em><strong>click here</strong></em> to download the ELITE LST product in 2004. <strong>Dataset Characteristics:</strong> Spatial Coverage: China Temporal Coverage: 2003 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 (Jie_Cheng@bnu.edu.cn).
DOI: 10.5281/zenodo.8271721
title: ELITE land surface temperature: seamless 1km LST over China (2003)

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 2003. Please <em><strong>click here</strong></em> to download the ELITE LST product in 2002 and <em><strong>click here</strong></em> to download the ELITE LST product in 2004. <strong>Dataset Characteristics:</strong> Spatial Coverage: China Temporal Coverage: 2003 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 (Jie_Cheng@bnu.edu.cn).
DOI: 10.2909/ecba54a6-bdc3-429e-8474-d8dcd0f20971
title: Slope of the Green-up Period 2017-present (raster 10 m), Europe, yearly, Sept. 2021

description: The Slope of the green-up period (Left Slope, LSLOPE), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European High Resolution Vegetation Phenology and Productivity (HR-VPP) component of the Copernicus Land Monitoring Service (CLMS). The slope of the green-up period (LSLOPE) expresses the rate of change in the values of the Plant Phenology Index (PPI) at the day when the vegetation growing season starts. The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories, which is a filtered time series with regular 10-day time step. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The green-up period slope is one of the 13 parameters. The full list is available in the table 3 of the Product User Manual https://land.copernicus.eu/user-corner/technical-library/product-user-manual-of-seasonal-trajectories/ A complementary quality indicator (QFLAG) provides a confidence level, that is described in table 4 of the same manual. The LSLOPE dataset is made available as raster files with 10 x 10m resolution, in UTM/WGS84 projection corresponding to the Sentinel-2 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2017 onwards. It is updated in the first quarter of each year.
DOI: 10.5281/zenodo.8274961
title: ELITE land surface temperature: seamless 1km LST over China (2016)

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 2016. Please <em><strong>click here</strong></em> to download the ELITE LST product in 2015 and <em><strong>click here</strong></em> to download the ELITE LST product in 2017. <strong>Dataset Characteristics:</strong> Spatial Coverage: China Temporal Coverage: 2016 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.8274960
title: ELITE land surface temperature: seamless 1km LST over China (2016)

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 2016. Please <em><strong>click here</strong></em> to download the ELITE LST product in 2015 and <em><strong>click here</strong></em> to download the ELITE LST product in 2017. <strong>Dataset Characteristics:</strong> Spatial Coverage: China Temporal Coverage: 2016 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: 10.2909/1aeea29f-cea2-4494-af80-789a038a87b8
title: European Ground Motion Service: Basic 2015-2021 (vector), Europe, yearly, Feb. 2023

description: The European Ground Motion Service (EGMS) is a component of the Copernicus Land Monitoring Service. EGMS provides consistent, regular, standardised, harmonised and reliable information regarding natural and anthropogenic ground motion phenomena over the Copernicus Participating States and across national borders, with millimetre accuracy. This set of metadata describes the first product level of EGMS: Basic. The EGMS Basic - provides InSAR displacement data provided in the satellite Line-of-Sight (LOS), with annotated geo-localisation and quality measures per measurement point. This product is generated from the interferometric analysis of Sentinel-1 radar images at full resolution. It contains line of sight velocity maps in ascending and descending orbits with annotated geolocalisation and quality parameters per measurement point. The Basic product is referred to a local reference point; therefore, ground motion measurements are meaningful only within a small subset of the full product. It is not possible to compare deformation from adjacent areas belonging to different processing units of the same level. EGMS Basic is visualised as a vector map of measurement points colour-coded by average line-of-sight velocity and distributed to users in comma-separated values format. Each point is associated with a time series of displacement, i.e. a plot with values of displacement per acquisition of the satellite. The product is generated for both ascending and descending orbits. The processing of the dataset has taken place in the period from August 2022 to January 2023.
DOI: 10.5281/zenodo.8249976
title: Data for the manuscript "Bridged Nucleic Acid ASOs over Locked Nucleic Acid ASOs and their impact on the structure and stability of ASO/RNA duplexes"

description: Data for the manuscript "Bridged Nucleic Acid ASOs over Locked Nucleic Acid ASOs and their impact on the structure and stability of ASO/RNA duplexes". The dataset contains: <strong>Table 1: </strong>Co-ordinates of optimized structures of LNA; N-MeO-amino-BNA; 2′,4′-BNA<sup>NC</sup>[NH]; 2′,4′-BNA<sup>NC</sup>[NMe] and N-Me-aminooxy-BNA monomer nucleotides without the phosphate backbone each containing nucleobases Adenine (A), Guanine (G), 5-Methylcytosine (C) and Thymine (T) calculated at the M06-2X/6-311G(d,p) level of theory. <strong>Table 2: </strong>Co-ordinates of optimized structures of LNA; N-MeO-amino-BNA; 2′,4′-BNA<sup>NC</sup>[NH]; 2′,4′-BNA<sup>NC</sup>[NMe]; N-Me-aminooxy-BNA monomer nucleotides and PS-LNA; PS-N-MeO-amino-BNA; PS-2′,4′-BNA<sup>NC</sup>[NH]; PS-2′,4′-BNA<sup>NC</sup>[NMe]; PS-N-Me-aminooxy-BNA monomer nucleotides (R & S conformers) each containing nucleobases Adenine (A), Guanine (G), 5-Methylcytosine (C) and Thymine (T) calculated at the M06-2X/6-311G(d,p) level of theory. <strong>Table 3: </strong>Frcmod files of LNA; N-MeO-amino-BNA; 2′,4′-BNA<sup>NC</sup>[NH]; 2′,4′-BNA<sup>NC</sup>[NMe]; N-Me-aminooxy-BNA monomer nucleotides and PS-LNA; PS-N-MeO-amino-BNA; PS-2′,4′-BNA<sup>NC</sup>[NH]; PS-2′,4′-BNA<sup>NC</sup>[NMe]; PS-N-Me-aminooxy-BNA monomer nucleotides (R & S conformers) each containing nucleobases Adenine (A), Guanine (G), 5-Methylcytosine (C) and Thymine (T) optimised at the M06-2X/6-311G(d,p) level of theory. <strong>Table 4:</strong> Library files of LNA; N-MeO-amino-BNA; 2′,4′-BNA<sup>NC</sup>[NH]; 2′,4′-BNA<sup>NC</sup>[NMe]; N-Me-aminooxy-BNA monomer nucleotides and PS-LNA; PS-N-MeO-amino-BNA; PS-2′,4′-BNA<sup>NC</sup>[NH]; PS-2′,4′-BNA<sup>NC</sup>[NMe]; PS-N-Me-aminooxy-BNA monomer nucleotides (R & S conformers) each containing nucleobases Adenine (A), Guanine (G), 5-Methylcytosine (C) and Thymine (T) optimised at the M06-2X/6-311G(d,p) level of theory.
DOI: 10.5281/zenodo.8249975
title: Data for the manuscript "Bridged Nucleic Acid ASOs over Locked Nucleic Acid ASOs and their impact on the structure and stability of ASO/RNA duplexes"

description: The dataset contains: DFT and MD Data for the manuscript "Bridged Nucleic Acid ASOs over Locked Nucleic Acid ASOs and their impact on the structure and stability of ASO/RNA duplexes".
DOI: 10.5281/zenodo.8296719
title: Data for the manuscript "Bridged Nucleic Acid ASOs over Locked Nucleic Acid ASOs and their impact on the structure and stability of ASO/RNA duplexes"

description: The dataset contains: DFT and MD Data for the manuscript "Bridged Nucleic Acid ASOs over Locked Nucleic Acid ASOs and their impact on the structure and stability of ASO/RNA duplexes".
DOI: 10.5281/zenodo.8296862
title: Data for the manuscript "Bridged Nucleic Acid ASOs over Locked Nucleic Acid ASOs and their impact on the structure and stability of ASO/RNA duplexes"

description: The dataset contains: DFT and MD Data for the manuscript "Bridged Nucleic Acid ASOs over Locked Nucleic Acid ASOs and their impact on the structure and stability of ASO/RNA duplexes".
DOI: 10.2909/bef2507a-13cf-44c0-a809-0a1566f27631
title: European Ground Motion Service: Calibrated 2015-2021 (vector), Europe, yearly, Feb. 2023

description: The European Ground Motion Service (EGMS) is a component of the Copernicus Land Monitoring Service. EGMS provides consistent, regular, standardised, harmonised and reliable information regarding natural and anthropogenic ground motion phenomena over the Copernicus Participating States and across national borders, with millimetre accuracy. This set of metadata describes the second product level of EGMS: Calibrated. This product is considered the main EGMS product as it serves the needs of most users. It contains the same type of information as the Basic product (https://sdi.eea.europa.eu/catalogue/srv/eng/catalog.search#/metadata/1aeea29f-cea2-4494-af80-789a038a87b8), but the measurement points are referenced to a model derived from global navigation satellite system data. Thus, the measurements are not relative anymore and are considered as absolute. The calibrated product makes it possible to compare ground motion measurements from adjacent areas belonging to different products of the same level. EGMS Calibrated is visualised as a vector map of measurement points, colour-coded by average velocity, and distributed to users in comma-separated values format. Each point is associated with a time series of displacement, i.e. a plot with values of displacement per acquisition of the satellite. The product is generated for both ascending and descending orbits.