cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24049401
title: Preterm infants first breastfeeding

description: Data on preterm infants first breastfeeding attempt - timing and performance.Not for commercial use.
DOI: 10.6084/m9.figshare.24049401.v1
title: Preterm infants first breastfeeding

description: Data on preterm infants first breastfeeding attempt - timing and performance.Not for commercial use.
DOI: 10.2909/201ee90c-1971-4bdc-855e-9c9bcbc2c647
title: Season Amplitude 2017-present (raster 10 m), Europe, yearly, Sept. 2021

description: The Season Amplitude (AMPL), 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 Season Amplitude (AMPL) is the difference between the maximum and minimum Plant Phenology Index (PPI) values reached during the season. 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 Amplitude 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 AMPL 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: 10.5517/ccdc.csd.cc2fns0x
title: CCDC 2254289: 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.cc2g62wn
title: CCDC 2269971: Experimental Crystal Structure Determination

description: Related Article: Andy Pelter, Simon J. Coles, Michael B. Hursthouse|2004|University of Southampton, Crystal Structure Report Archive||893|
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: 10.5258/ecrystals/30
title: 1,3,8,10-Tetrakis-(4-fluoro-phenyl)-1,3,8,10-tetrahydro-4,5,6,7-tetrathia-1,3,8,10-tetraaza-dicyclopenta[a,c]cyclooctene-2,9-dithione
DOI: 10.25505/fiz.icsd.cc217n54
title: CSD 1884185: Experimental Crystal Structure Determination

description: Related Article: Francisco Colmenero, Jakub Plášil, Jiří Sejkora|2019|Dalton Trans.|48|16722|doi:10.1039/C9DT03256H
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc21520b
title: CSD 1881700: Experimental Crystal Structure Determination

description: Related Article: I.A. Trussov, L.L. Male, M.L. Sanjuan, A. Orera, P.R. Slater|2019|J.Solid State Chem.|272|157|doi:10.1016/j.jssc.2019.02.014
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc221cn5
title: CSD 1907946: Experimental Crystal Structure Determination

description: Related Article: Y.-Y. Guo, A.S. Gibbs, J.M. Perez-Mato, P. Lightfoot|2019|IUCrJ|6|438|doi:10.1107/S2052252519003804
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc1y4gtc
title: CSD 1791763: Experimental Crystal Structure Determination

description: Related Article: H.G. von Schnering, R. Kroener, M. Baitinger, K. Peters, R. Nesper, Yu. Grin'|2000|Z.Kristallogr.-New Cryst.Struct.|215|205|doi:10.1515/ncrs-2000-0207
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.2909/5ae0f2a2-7ad8-4f7c-878d-f1b09d78d7a1
title: Seasonal Productivity 2017-present (raster 10 m), Europe, yearly, Sept. 2021

description: The Seasonal Productivity (SPROD), 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 Seasonal Productivity (SPROD), or small integral, is the growing season integral that is computed as the sum of all daily Plant Phenology Index (PPI) values between the dates of the season start (SOSD) and end (EOSD), minus their base level value. 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 Seasonal Productivity 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 SPROD 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.8271665
title: ELITE land surface temperature: seamless 1km LST over China (2002)

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 2002. Please <em><strong>click here</strong></em> to download the ELITE LST product in 2003. <strong>Dataset Characteristics:</strong> Spatial Coverage: China Temporal Coverage: 2002 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.8271664
title: ELITE land surface temperature: seamless 1km LST over China (2002)

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 2002. Please <em><strong>click here</strong></em> to download the ELITE LST product in 2003. <strong>Dataset Characteristics:</strong> Spatial Coverage: China Temporal Coverage: 2002 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: 10.2909/977e4bb8-407f-48ec-b4c4-403bca5a6a3b
title: Total Productivity 2017-present (raster 10 m), Europe, yearly, Sept. 2021

description: The Total Productivity (TPROD), 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 Total Productivity (TPROD), or large integral, is the growing season integral computed as the sum of all daily Plant Phenology Index values between the dates of the season start (SOSD) and end (EOSD). 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 Total Productivity 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 TPROD 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: 10.2909/e2f5fae4-7efc-440b-b04f-4f6ee1c48e69
title: Season Maximum Date 2017-present (raster 10 m), Europe, yearly, Sept. 2021

description: The Season Maximum Date (MAXD), 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 Season Maximum Date (MAXD) is the date in the vegetation growing season when the maximum Plant Phenology Index (PPI) value is reached. 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 Season Maximum 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 MAXD 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: 10.6084/m9.figshare.24049449
title: Appendix to BEH 3831

description: Statistical tests perfomred in the course of the study.
DOI: 10.6084/m9.figshare.24049449.v1
title: Appendix to BEH 3831

description: Statistical tests perfomred in the course of the study.
DOI: 10.5281/zenodo.8208513
title: Measuring Magnetic 1/f Noise in Superconducting Microstructures and the Fluctuation-Dissipation Theorem - Data

description: Figures and corresponding data associated with the manuscript 'Measuring Magnetic 1/f Noise in Superconducting Microstructures and the Fluctuation-Dissipation Theorem' by Herbst et al.
description: The research leading to these results has received funding from the European Union's Horizon 2020 Research and Innovation Programme, under Grant Agreement no 824109 (European Microkelvin Platform). This work has been performed with support of the research project SuperLSI (reference number: 13N16255) which is funded by the German ministry of education and research (BMBF) within the research program `Enabling Technologies für die Quantentechnologien'.
DOI: 10.5281/zenodo.7774387
title: Measuring Magnetic 1/f Noise in Superconducting Microstructures and the Fluctuation-Dissipation Theorem - Data

description: Figures and corresponding data associated with the manuscript 'Measuring Magnetic 1/f Noise in Superconducting Microstructures and the Fluctuation-Dissipation Theorem' by Herbst et al.
description: The research leading to these results has received funding from the European Union's Horizon 2020 Research and Innovation Programme, under Grant Agreement no 824109 (European Microkelvin Platform). This work has been performed with support of the research project SuperLSI (reference number: 13N16255) which is funded by the German ministry of education and research (BMBF) within the research program `Enabling Technologies für die Quantentechnologien'.
DOI: 10.2909/774f56fc-e0e3-4918-aaea-c181bab0c2a3
title: Season Maximum Value 2017-present (raster 10 m), Europe, yearly, Sept. 2021

description: The Season Maximum Value (MAXV), 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 Season Maximum Value (MAXV) provides the maximum (peak) value that the Plant Phenology Index (PPI) reaches during the vegetation growing season. 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 Season Maximum Value 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 MAXV 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: 10.23728/fmi-b2share.003652e36b1b4faba14283de53c50b61
title: Satellite-retrieved AOD, CO, HCHO and NO2 background over the PEEX domain

description: Satellite-retrieved monthly AOD, CO, HCHO and NO2 background over the PEEX domain for May-August, obtained from the period 2002-2022. For manuscript submitted to Big Earth Data
DOI: 10.2909/b11b3dcb-28dc-4f10-bd54-be0d50eb326a
title: Start-of-season Value 2017-present (raster 10 m), Europe, yearly, Sept. 2021

description: The Start-of-Season Value (SOSV), 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 Value (SOSV) provides the value of the Plant Phenology Index (PPI) at the start of the vegetation growing season. 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 Value 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 SOSV 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 and for two seasons in each year from 2017 onwards. It is updated in the first quarter of each year.