cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7890143
title: MUSES Leaf Area Index (LAI) Monthly Global 1km SIN Grid in 2004

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global LAI product at 1 km spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2004. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2003</strong></em>, <em>and <strong>click here</strong> to download the MUSES LAI product <strong>in 2005</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2004 Spatial Resolution: 1 km Temporal Resolution: 1 month Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7890142
title: MUSES Leaf Area Index (LAI) Monthly Global 1km SIN Grid in 2004

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global LAI product at 1 km spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2004. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2003</strong></em>, <em>and <strong>click here</strong> to download the MUSES LAI product <strong>in 2005</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2004 Spatial Resolution: 1 km Temporal Resolution: 1 month Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.6084/m9.figshare.22741374
title: Bootstrap inference for panel data quantile regression*

description: This paper develops bootstrap methods for practical statistical inference in panel data quantile regression models with fixed effects. We consider random-weighted bootstrap resampling and formally establish its validity for asymptotic inference. The bootstrap algorithm is simple to implement in practice by using a weighted quantile regression estimation for fixed effects panel data. We provide results under conditions that allow for temporal dependence of observations within individuals, thus encompassing a large class of possible empirical applications. Monte Carlo simulations provide numerical evidence the proposed bootstrap methods have correct finite sample properties. Finally, we provide an empirical illustration using the environmental Kuznets curve.
DOI: 10.34740/kaggle/dsv/5592456
title: 2021 World Population (updated daily)
DOI: 10.5281/zenodo.7853095
title: PrimeWater Virtual Lab: Experiment C3

description: <strong>Input and output data for Experiment C3 of PrimeWater's Virtual Lab.</strong> Experiment Name: <strong>Quantile-based modeling of phytoplankton dynamics using Random Forests</strong>
DOI: 10.5281/zenodo.7853094
title: PrimeWater Virtual Lab: Experiment C3

description: <strong>Input and output data for Experiment C3 of PrimeWater's Virtual Lab.</strong> Experiment Name: <strong>Quantile-based modeling of phytoplankton dynamics using Random Forests</strong>
DOI: 10.5281/zenodo.7890952
title: Beamtime ES-1145

description: This dataset contains all the raw XRD data of beamtime ES-1145 at ESRF. A logbook describing samples, experimental conditions, scan numbers, etc. is included.
DOI: 10.5281/zenodo.6985444
title: Data underpinning "Disorder-induced spin-charge separation in the 1-D Hubbard model"

description: Many-body localisation is believed to be generically unstable in quantum systems with continuous non-Abelian symmetries, even in the presence of strong disorder. Breaking these symmetries can stabilise the localised phase, leading to the emergence of an extensive number of quasi-locally conserved quantities known as local integrals of motion, or l-bits. Using a sophisticated non-perturbative technique based on continuous unitary transforms, we investigate the one-dimensional Hubbard model subject to both spin and charge disorder, compute the associated l-bits and demonstrate that the disorder gives rise to a novel form of spin-charge separation. We examine the role of symmetries in delocalising the spin and charge degrees of freedom, and show that while symmetries generally lead to delocalisation through multi-particle resonant processes, certain subsets of states appear stable.
description: A README file is available in the .zip archive which explains the subfolders and file structure within. See https://github.com/sjt48/PyFlow for an up-to-date version of the code used to generate this dataset, including version history and any bug fixes.
DOI: 10.5281/zenodo.6985445
title: Data underpinning "Disorder-induced spin-charge separation in the 1-D Hubbard model"

description: Many-body localisation is believed to be generically unstable in quantum systems with continuous non-Abelian symmetries, even in the presence of strong disorder. Breaking these symmetries can stabilise the localised phase, leading to the emergence of an extensive number of quasi-locally conserved quantities known as local integrals of motion, or l-bits. Using a sophisticated non-perturbative technique based on continuous unitary transforms, we investigate the one-dimensional Hubbard model subject to both spin and charge disorder, compute the associated l-bits and demonstrate that the disorder gives rise to a novel form of spin-charge separation. We examine the role of symmetries in delocalising the spin and charge degrees of freedom, and show that while symmetries generally lead to delocalisation through multi-particle resonant processes, certain subsets of states appear stable.
description: A README file is available in the .zip archive which explains the subfolders and file structure within. See https://github.com/sjt48/PyFlow for an up-to-date version of the code used to generate this dataset, including version history and any bug fixes.
DOI: 10.5281/zenodo.7825945
title: Data underpinning "Disorder-induced spin-charge separation in the 1-D Hubbard model"

description: Many-body localisation is believed to be generically unstable in quantum systems with continuous non-Abelian symmetries, even in the presence of strong disorder. Breaking these symmetries can stabilise the localised phase, leading to the emergence of an extensive number of quasi-locally conserved quantities known as local integrals of motion, or l-bits. Using a sophisticated non-perturbative technique based on continuous unitary transforms, we investigate the one-dimensional Hubbard model subject to both spin and charge disorder, compute the associated l-bits and demonstrate that the disorder gives rise to a novel form of spin-charge separation. We examine the role of symmetries in delocalising the spin and charge degrees of freedom, and show that while symmetries generally lead to delocalisation through multi-particle resonant processes, certain subsets of states appear stable.
description: A README file is available in the .zip archive which explains the subfolders and file structure within. See https://github.com/sjt48/PyFlow for an up-to-date version of the code used to generate this dataset, including version history and any bug fixes.
DOI: 10.5281/zenodo.7701862
title: Galaxy Training Material for Mass spectrometry: GC-MS data processing (with XCMS, RAMClustR, RIAssigner, and matchms)

description: This dataset contains the training data for the <strong>Mass spectrometry: GC-MS data processing (with XCMS, RAMClustR, RIAssigner, and matchms)</strong> GTN tutorial. It includes 3 GC-[EI+]-HRMS files from seminal plasma samples, the RECETOX Metabolome HR-[EI+]-MS library collected from mostly endogoenous compounds from MetaSci Human Metabolite Library, reference alkanes, and sample metadata table.
DOI: 10.5281/zenodo.7701861
title: Galaxy Training Material for Mass spectrometry: GC-MS data processing (with XCMS, RAMClustR, RIAssigner, and matchms)

description: This dataset contains the training data for the <strong>Mass spectrometry: GC-MS data processing (with XCMS, RAMClustR, RIAssigner, and matchms)</strong> GTN tutorial. It includes 3 GC-[EI+]-HRMS files from seminal plasma samples, the RECETOX Metabolome HR-[EI+]-MS library collected from mostly endogoenous compounds from MetaSci Human Metabolite Library, reference alkanes, sample metadata table, and preprocessed XCMS object.
DOI: 10.5281/zenodo.7890956
title: Galaxy Training Material for Mass spectrometry: GC-MS data processing (with XCMS, RAMClustR, RIAssigner, and matchms)

description: This dataset contains the training data for the <strong>Mass spectrometry: GC-MS data processing (with XCMS, RAMClustR, RIAssigner, and matchms)</strong> GTN tutorial. It includes 3 GC-[EI+]-HRMS files from seminal plasma samples, the RECETOX Metabolome HR-[EI+]-MS library collected from mostly endogoenous compounds from MetaSci Human Metabolite Library, reference alkanes, sample metadata table, and preprocessed XCMS object.
DOI: 10.5281/zenodo.7780171
title: PrimeWater Virtual Lab: Experiment C1

description: <strong>Input and output data for Experiment C1 of PrimeWater's Virtual Lab.</strong> Experiment Name: Assessing the relevance of hydrometeorological variables in data-driven forecasting of phytoplankton dynamics in surface waters
DOI: 10.5281/zenodo.7780172
title: PrimeWater Virtual Lab: Experiment C1

description: <strong>Input and output data for Experiment C1 of PrimeWater's Virtual Lab.</strong> Experiment Name: Assessing the relevance of hydrometeorological variables in data-driven forecasting of phytoplankton dynamics in surface waters
DOI: 10.5281/zenodo.7780519
title: PrimeWater Virtual Lab: Experiment C1

description: <strong>Input and output data for Experiment C1 of PrimeWater's Virtual Lab.</strong> Experiment Name: Assessing the relevance of hydrometeorological variables in data-driven forecasting of phytoplankton dynamics in surface waters
DOI: 10.5517/ccdc.csd.cc2bgrsc
title: CCDC 2159143: Experimental Crystal Structure Determination

description: Related Article: Moorthy Mathivanan, Balamurugan Tharmalingam, Ottoor Anitha, Jan Grzegorz Małecki, Balasubramanian Murugesapandian|2023|J.Mol.Liq.|382|121845|doi:10.1016/j.molliq.2023.121845
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.cc2c8wk3
title: CCDC 2183285: Experimental Crystal Structure Determination

description: Related Article: Xuelin Yue|2022|CSD Communication|||
description: Related Article: Xuelin Yue, Yijie Gao, Junwei Huang, Yadong Feng, Xiuling Cui|2023|Org.Lett.|25|2923|doi:10.1021/acs.orglett.3c01032
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.cc2czg72
title: CCDC 2204014: Experimental Crystal Structure Determination

description: Related Article: Liesa Eickhoff, Pascal Kramer, Jonas Bresien, Dirk Michalik, Alexander Villinger, Axel Schulz|2023|Inorg.Chem.|62|6768|doi:10.1021/acs.inorgchem.3c00481
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.cc2czg83
title: CCDC 2204015: Experimental Crystal Structure Determination

description: Related Article: Liesa Eickhoff, Pascal Kramer, Jonas Bresien, Dirk Michalik, Alexander Villinger, Axel Schulz|2023|Inorg.Chem.|62|6768|doi:10.1021/acs.inorgchem.3c00481
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.cc2czg94
title: CCDC 2204016: Experimental Crystal Structure Determination

description: Related Article: Liesa Eickhoff, Pascal Kramer, Jonas Bresien, Dirk Michalik, Alexander Villinger, Axel Schulz|2023|Inorg.Chem.|62|6768|doi:10.1021/acs.inorgchem.3c00481
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.cc2czgb5
title: CCDC 2204017: Experimental Crystal Structure Determination

description: Related Article: Liesa Eickhoff, Pascal Kramer, Jonas Bresien, Dirk Michalik, Alexander Villinger, Axel Schulz|2023|Inorg.Chem.|62|6768|doi:10.1021/acs.inorgchem.3c00481
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.cc2ddvth
title: CCDC 2216897: Experimental Crystal Structure Determination

description: Related Article: Vindhya Hegde, Sreekala C. O, Naveen V. Kulkarni, Jomon Mathew|2023|J.Mol.Struct.|1286|135508|doi:10.1016/j.molstruc.2023.135508
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.cc2dgzxs
title: CCDC 2218946: Experimental Crystal Structure Determination

description: Related Article: Sugiarto, Jun Shinogi, Masahiro Sadakane|2023|Inorg.Chem.|62|6759|doi:10.1021/acs.inorgchem.3c00479
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.cc2dgzyt
title: CCDC 2218947: Experimental Crystal Structure Determination

description: Related Article: Sugiarto, Jun Shinogi, Masahiro Sadakane|2023|Inorg.Chem.|62|6759|doi:10.1021/acs.inorgchem.3c00479
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.cc2dgzzv
title: CCDC 2218948: Experimental Crystal Structure Determination

description: Related Article: Sugiarto, Jun Shinogi, Masahiro Sadakane|2023|Inorg.Chem.|62|6759|doi:10.1021/acs.inorgchem.3c00479
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.cc2dh00y
title: CCDC 2218949: Experimental Crystal Structure Determination

description: Related Article: Sugiarto, Jun Shinogi, Masahiro Sadakane|2023|Inorg.Chem.|62|6759|doi:10.1021/acs.inorgchem.3c00479
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.cc2dh8w2
title: CCDC 2219224: Experimental Crystal Structure Determination

description: Related Article: Deborah A Lima, Airton G. Bispo-Jr, Diogo A. Galico, Sergio F. N. Coelho, João H. Araujo Neto, Javier A. Ellena, Lanousse Petiote, Italo O. Mazali, Fernando A. Sigoli|2023|Inorg.Chem.|62|6808|doi:10.1021/acs.inorgchem.3c00555
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.cc2dh8x3
title: CCDC 2219225: Experimental Crystal Structure Determination

description: Related Article: Deborah A Lima, Airton G. Bispo-Jr, Diogo A. Galico, Sergio F. N. Coelho, João H. Araujo Neto, Javier A. Ellena, Lanousse Petiote, Italo O. Mazali, Fernando A. Sigoli|2023|Inorg.Chem.|62|6808|doi:10.1021/acs.inorgchem.3c00555
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.cc11jlz4
title: CCDC 969276: Experimental Crystal Structure Determination

description: Related Article: Dirk Schray, Dennis Westerbeck, Jonas Braun, Yanhua Lan, Silvia Gómez-Coca, Wolfgang Wernsdorfer, Eliseo Ruiz, Christopher E. Anson, Jürgen Schnack, Annie K. Powell|2023|Inorg.Chem.|62|6642|doi:10.1021/acs.inorgchem.3c00042
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.