cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7890995
title: MUSES Leaf Area Index (LAI) Monthly Global 1km SIN Grid in 2002

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 2002. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2001</strong></em>, <em>and <strong>click here</strong> to download the MUSES LAI product <strong>in 2003</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2002 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: 10.25582/data.2022-05.2508528/1930380
title: Data for EMSL Project 51787 from May 2022
DOI: 10.25582/data.2022-05.2509108/1930506
title: Data for EMSL Project 51738 from May 2022
DOI: 10.25582/data.2022-05.2508526/1931757
title: Data for EMSL Project 51787 from May 2022
DOI: 10.25582/data.2022-05.2508585/1931760
title: Data for EMSL Project 51787 from May 2022
DOI: 10.25582/data.2022-05.2508527/1931764
title: Data for EMSL Project 51787 from May 2022
DOI: 10.25582/data.2022-05.2508617/1932084
title: Data for EMSL Project 51613 from May 2022
DOI: 10.25582/data.2022-05.2508508/1932458
title: Data for EMSL Project 51787 from May 2022
DOI: 10.25582/data.2022-05.2508507/1932462
title: Data for EMSL Project 51787 from May 2022
DOI: 10.25582/data.2022-05.2508586/1932463
title: Data for EMSL Project 51787 from May 2022
DOI: 10.25582/data.2022-05.2508550/1932471
title: Data for EMSL Project 51787 from May 2022
DOI: 10.25582/data.2022-05.2508506/1932472
title: Data for EMSL Project 51787 from May 2022
DOI: 10.25582/data.2022-05.2510674/1932965
title: Data for EMSL Project 51613 from May 2022
DOI: 10.25582/data.2022-05.2508589/1933775
title: Data for EMSL Project 51787 from May 2022
DOI: 10.25582/data.2022-05.2508553/1933783
title: Data for EMSL Project 51787 from May 2022
DOI: 10.25582/data.2022-05.2508802/1942324
title: Data for EMSL Project 51130 from May 2022
DOI: 10.5281/zenodo.7892048
title: MUSES Leaf Area Index (LAI) Monthly Global 1km SIN Grid in 2001

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 2001. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2000</strong></em>, <em>and <strong>click here</strong> to download the MUSES LAI product <strong>in 2002</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2001 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.7892047
title: MUSES Leaf Area Index (LAI) Monthly Global 1km SIN Grid in 2001

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 2001. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2000</strong></em>, <em>and <strong>click here</strong> to download the MUSES LAI product <strong>in 2002</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2001 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: 10.5439/1972055
title: mfrsr (00)

description: The visible multifilter rotating shadowband radiometer (MFRSR) is a passive instrument that measures global and diffuse components of solar irradiance at six narrowband channels and one open, or broadband, channel. Direct irradiance is not a primary measurement, but is calculated using diffuse and global measurements. To collect one data record, the MFRSR takes measurements at four different shadowband positions. The first measurement is taken with the shadowband in the nadir (home) position. The next three measurements are, in order, the first side-band, sun-blocked, and second side-band. The side-band measurements are used to correct for the portion of the sky obscured by the shadowband. The nominal wavelengths of the narrowband channels are 415, 500, 615, 673, 870, and 940 nm. From such measurements, the atmosphere’s aerosol optical depth at each wavelength can be inferred. In turn, these optical depths may be used to derive information about the column abundances of ozone and water vapor, as well as aerosol and other atmospheric constituents.
DOI: 10.5517/ccdc.csd.cc2f8xm8
title: CCDC 2242900: Experimental Crystal Structure Determination

description: Related Article: Eric S. Yang, Emma Combey, Jose M. Goicoechea|2023|Chemical Science|14|4627|doi:10.1039/D3SC01126G
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.cc2f8xn9
title: CCDC 2242901: Experimental Crystal Structure Determination

description: Related Article: Eric S. Yang, Emma Combey, Jose M. Goicoechea|2023|Chemical Science|14|4627|doi:10.1039/D3SC01126G
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.cc2f8xpb
title: CCDC 2242902: Experimental Crystal Structure Determination

description: Related Article: Eric S. Yang, Emma Combey, Jose M. Goicoechea|2023|Chemical Science|14|4627|doi:10.1039/D3SC01126G
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.cc2f8xqc
title: CCDC 2242903: Experimental Crystal Structure Determination

description: Related Article: Eric S. Yang, Emma Combey, Jose M. Goicoechea|2023|Chemical Science|14|4627|doi:10.1039/D3SC01126G
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.cc2f8xsf
title: CCDC 2242905: Experimental Crystal Structure Determination

description: Related Article: Eric S. Yang, Emma Combey, Jose M. Goicoechea|2023|Chemical Science|14|4627|doi:10.1039/D3SC01126G
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.cc2f8xtg
title: CCDC 2242906: Experimental Crystal Structure Determination

description: Related Article: Eric S. Yang, Emma Combey, Jose M. Goicoechea|2023|Chemical Science|14|4627|doi:10.1039/D3SC01126G
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.cc2f8xzm
title: CCDC 2242911: Experimental Crystal Structure Determination

description: Related Article: Eric S. Yang, Emma Combey, Jose M. Goicoechea|2023|Chemical Science|14|4627|doi:10.1039/D3SC01126G
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.cc2fbb59
title: CCDC 2244250: Experimental Crystal Structure Determination

description: Related Article: Paul J. Fischer, Charley B. Roe, Jasmine N. Stephenson, Rachel J. Dunscomb, Camille L. Carthy, Chip Nataro, Victor G. Young|2023|Dalton Trans.|52|5606|doi:10.1039/D3DT00735A
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.cc2fbb6b
title: CCDC 2244251: Experimental Crystal Structure Determination

description: Related Article: Paul J. Fischer, Charley B. Roe, Jasmine N. Stephenson, Rachel J. Dunscomb, Camille L. Carthy, Chip Nataro, Victor G. Young|2023|Dalton Trans.|52|5606|doi:10.1039/D3DT00735A
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.cc2fbb8d
title: CCDC 2244253: Experimental Crystal Structure Determination

description: Related Article: Paul J. Fischer, Charley B. Roe, Jasmine N. Stephenson, Rachel J. Dunscomb, Camille L. Carthy, Chip Nataro, Victor G. Young|2023|Dalton Trans.|52|5606|doi:10.1039/D3DT00735A
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.cc2fbb9f
title: CCDC 2244254: Experimental Crystal Structure Determination

description: Related Article: Paul J. Fischer, Charley B. Roe, Jasmine N. Stephenson, Rachel J. Dunscomb, Camille L. Carthy, Chip Nataro, Victor G. Young|2023|Dalton Trans.|52|5606|doi:10.1039/D3DT00735A
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.cc2fbbbg
title: CCDC 2244255: Experimental Crystal Structure Determination

description: Related Article: Paul J. Fischer, Charley B. Roe, Jasmine N. Stephenson, Rachel J. Dunscomb, Camille L. Carthy, Chip Nataro, Victor G. Young|2023|Dalton Trans.|52|5606|doi:10.1039/D3DT00735A
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.cc2fbbfk
title: CCDC 2244258: Experimental Crystal Structure Determination

description: Related Article: Paul J. Fischer, Charley B. Roe, Jasmine N. Stephenson, Rachel J. Dunscomb, Camille L. Carthy, Chip Nataro, Victor G. Young|2023|Dalton Trans.|52|5606|doi:10.1039/D3DT00735A
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.cc2fbbgl
title: CCDC 2244259: Experimental Crystal Structure Determination

description: Related Article: Paul J. Fischer, Charley B. Roe, Jasmine N. Stephenson, Rachel J. Dunscomb, Camille L. Carthy, Chip Nataro, Victor G. Young|2023|Dalton Trans.|52|5606|doi:10.1039/D3DT00735A
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.cc2fbhcn
title: CCDC 2244411: Experimental Crystal Structure Determination

description: Related Article: Eilidh G. Young, Phillip S. Grant, Daniel P. Furkert, Margaret A. Brimble|2023|Org.Lett.|25|2895|doi:10.1021/acs.orglett.3c00902
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: 10.6084/m9.figshare.20391117.v2
title: Global fractional urban changes at 1km under diverse SSP-RCP scenarios throughout 2100

description: We developed a gridded global proportional urban extent dataset (2015-2100, 5-year interval) at 1km spatial resolution under various future development pathways. Unlike the commonly used binary (0-non-urban, 1-urban) urban products, this dataset provides the fractional information (i.e., ISA from 0 to 1) within each 1km pixel. Here, each .zip file represents one of eight scenarios, containing 17 GEOTIFF files (2015-2100, 5-year interval), which can be opened by ArcMap or QGIS. Questions about this dataset can be emailed to Prof. Xuecao Li (xuecaoli@cau.edu.cn)
DOI: 10.25901/zdq8-8j47
title: Gingin Flux Data Release 2023_v1
DOI: 10.6084/m9.figshare.22757078
title: The field gravity and magnetic data of the ore concentration area in the middle and lower reaches of the Yangtze River.rar

description: Geophysical surveys were conducted around the study area, mainly consisting of 1:20,0000 gravity and 1:5,0000 high-precision aeromagnetic surveys. The field gravity and magnetic data are comprehensive reflections of the subsurface and the surrounding geological bodies. To obtain the gravity and magnetic responses of subsurface geological bodies, the measured gravity and aeromagnetic data need to be corrected, the gravity data need to be corrected for mixed zero drift, latitude, intermediate layer, and topography, and the aeromagnetic data need to be corrected for integrated effects, normal field, daily variation, and compensation and equalization to obtain the Bouguer gravity anomaly and magnetic anomaly
DOI: 10.25919/nj3t-fv52
title: Parkes observations for project PX500 semester 2023APRS_03

description: This proposal relates to time purchased by the Chinese Academy of Sciences for Parkes follow-up confirmation and timing of FAST discoveries. As the observing time has been purchased this proposal (and the related PX501) does not require TAC grading. The primary reason for submitting a proposal is to ensure an outreach statement and that the proposal is entered into OPAL for the data archive to access. The Chinese team have two time requests. The time scheduled for the first, relating to this proposal, will be allocated in blocks of 24 hours and the data will become available in the data archive with an 18 month embargo period. The second type of time will be scheduled in shorter blocks and the data archived with a 10 year embargo.
DOI: 10.21227/v1vj-4q77
title: Froth flotation fault conditions data set

description: The data set was collected from a flotation plant in China and was mainly used to identify the fault conditions in the flotation process.
DOI: 10.25345/c57p8tp9v
title: MassIVE MSV000091851 - GNPS_TBC_Tanxiaoyan_Heqingxiu_20220504_Ms_Data
title: Component Analysis of Different Extracted Parts of TBC
DOI: 10.3886/e112877v1-138020
title: Replication data for: CoVaR

description: CoVaR, defined as the change in the value at risk of the financial system conditional on an institution being under distress relative to its median state. Our estimates show that characteristics such as leverage, size, maturity mismatch, and asset price booms significantly predict CoVaR. We also provide out-of-sample forecasts of a countercyclical, forward-looking measure of systemic risk, and show that the 2006:IV value of this measure would have predicted more than one-third of realized CoVaR during the 2007-2009 financial crisis.