cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25505/fiz.icsd.cc2cp89n
title: CSD 2195181: Experimental Crystal Structure Determination

description: Related Article: Elena V. Khramenkova, Harshini Venkatraman, Victor Soethout, Evgeny A. Pidko|2022|Phys.Chem.Chem.Phys.(PCCP)|24|27047|doi:10.1039/D2CP03603G
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.cc2cp8bp
title: CSD 2195182: Experimental Crystal Structure Determination

description: Related Article: Elena V. Khramenkova, Harshini Venkatraman, Victor Soethout, Evgeny A. Pidko|2022|Phys.Chem.Chem.Phys.(PCCP)|24|27047|doi:10.1039/D2CP03603G
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.cc2cp8cq
title: CSD 2195183: Experimental Crystal Structure Determination

description: Related Article: Elena V. Khramenkova, Harshini Venkatraman, Victor Soethout, Evgeny A. Pidko|2022|Phys.Chem.Chem.Phys.(PCCP)|24|27047|doi:10.1039/D2CP03603G
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.cc2cp8dr
title: CSD 2195184: Experimental Crystal Structure Determination

description: Related Article: Elena V. Khramenkova, Harshini Venkatraman, Victor Soethout, Evgeny A. Pidko|2022|Phys.Chem.Chem.Phys.(PCCP)|24|27047|doi:10.1039/D2CP03603G
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.cc2cp8fs
title: CSD 2195185: Experimental Crystal Structure Determination

description: Related Article: Elena V. Khramenkova, Harshini Venkatraman, Victor Soethout, Evgeny A. Pidko|2022|Phys.Chem.Chem.Phys.(PCCP)|24|27047|doi:10.1039/D2CP03603G
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.cc2cp8gt
title: CSD 2195186: Experimental Crystal Structure Determination

description: Related Article: Elena V. Khramenkova, Harshini Venkatraman, Victor Soethout, Evgeny A. Pidko|2022|Phys.Chem.Chem.Phys.(PCCP)|24|27047|doi:10.1039/D2CP03603G
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.cc2cp8hv
title: CSD 2195187: Experimental Crystal Structure Determination

description: Related Article: Elena V. Khramenkova, Harshini Venkatraman, Victor Soethout, Evgeny A. Pidko|2022|Phys.Chem.Chem.Phys.(PCCP)|24|27047|doi:10.1039/D2CP03603G
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.cc2cp8jw
title: CSD 2195188: Experimental Crystal Structure Determination

description: Related Article: Elena V. Khramenkova, Harshini Venkatraman, Victor Soethout, Evgeny A. Pidko|2022|Phys.Chem.Chem.Phys.(PCCP)|24|27047|doi:10.1039/D2CP03603G
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: 10.5517/ccdc.csd.cc2cq3fn
title: CCDC 2195991: Experimental Crystal Structure Determination

description: Related Article: Jun Luo, Bao-Shu Liu, Yun-Chang Zhang, Bing-Kai Wang, Bei-Bei Guo, Lan She, Tian-Heng Chen|2023|J.Mol.Struct.|1274|134460|doi:10.1016/j.molstruc.2022.134460
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: 10.5281/zenodo.7334898
title: SciExpeM - 10.5281/zenodo.7334898

description: Han, Xinlu; Wang, Zhihua; Costa, Mário; Sun, Zhiwei; He, Yong; Cen, Kefa - Combustion And Flame, 2019, (206), 214-226
DOI: 10.5281/zenodo.7334897
title: SciExpeM - 10.5281/zenodo.7334898

description: Han, Xinlu; Wang, Zhihua; Costa, Mário; Sun, Zhiwei; He, Yong; Cen, Kefa - Combustion And Flame, 2019, (206), 214-226
DOI: 10.5281/zenodo.7334912
title: SciExpeM - 10.5281/zenodo.7334912

description: Lhuillier, Charles, 0000-0001-8592-1973; Brequigny, Pierre; Lamoureux, Nathalie; Contino, Francesco, 0000-0002-8341-4350; Mounaïm-Rousselle, Christine - Fuel, 2020, (263), 116653
DOI: 10.5281/zenodo.7334911
title: SciExpeM - 10.5281/zenodo.7334912

description: Lhuillier, Charles, 0000-0001-8592-1973; Brequigny, Pierre; Lamoureux, Nathalie; Contino, Francesco, 0000-0002-8341-4350; Mounaïm-Rousselle, Christine - Fuel, 2020, (263), 116653
DOI: 10.5281/zenodo.3676056
title: Mid to late Pleistocene IODP Expedition 354 Bengal Fan 8⁰ North transect age models, sedimentation rate stack, magnetic susceptibility stack, and XRF data

description: Mid to late Pleistocene age models for the International Ocean Discovery Program (IODP) Expedition 354 8⁰ North drilling transect. Stacked records of sedimentation rates and magnetic susceptibility. U-channel XRF scans of calcareous clay sediments at Site U1452. <strong>Abstract:</strong> We investigate chronology and age uncertainty for the middle to upper Pleistocene lower Bengal Fan using a novel age-depth modeling approach that factors litho-, magneto-, bio-, cyclo-, and seismic stratigraphic constraints, based on results from the International Ocean Discovery Program Expedition 354 Bengal Fan and analysis of the GeoB97-020/027 seismic line. The initial chronostratigraphic framework is established using regionally extensive hemipelagic sediment units and only age-depth models of fan deposits that respect the superposition of channel-levee systems between sites are accepted. In doing so, we reconstruct signals of regional sediment accumulation rate and lithogenic sediment input through the perspective of a two-dimensional ~320 km transect at 8⁰ N that are consistent with more distal and more ambiguous regional records. This chronology allows us to discuss the depositional history of the middle to upper Pleistocene lower Bengal Fan within the context of sea level, climate, and tectonic controls. We hypothesize, based on the timing of accumulation rate changes, that progradation and intensification of the Bengal Fan’s channel-levee system at 8⁰ N was largely driven by increases in sea level amplitude during this time. However, it is also possible this progradation was influenced by changes in Pleistocene climate and increased Himalayan erosion rates, driving greater sediment flux to the fan.
description: {"references": ["Reilly, B., Bergmann, F., Weber, M., Stoner, J., Selkin, P., Meynadier, L., Schwenk, T., Spiess, V., France-Lanord, C., (2020) Mid to Late Pleistocene evolution of the Bengal Fan: Integrating core and seismic observations for chronostratigraphic modeling of the IODP Expedition 354 8\u2070 North transect, Geochemistry, Geophysics, Geosystems."]}
DOI: 10.5281/zenodo.3676057
title: Mid to late Pleistocene IODP Expedition 354 Bengal Fan 8⁰ North transect age models, sedimentation rate stack, magnetic susceptibility stack, and XRF data

description: Mid to late Pleistocene age models for the International Ocean Discovery Program (IODP) Expedition 354 8⁰ North drilling transect. Stacked records of sedimentation rates and magnetic susceptibility. U-channel XRF scans of calcareous clay sediments at Site U1452. <strong>Abstract:</strong> We investigate chronology and age uncertainty for the middle to upper Pleistocene lower Bengal Fan using a novel age-depth modeling approach that factors litho-, magneto-, bio-, cyclo-, and seismic stratigraphic constraints, based on results from the International Ocean Discovery Program Expedition 354 Bengal Fan and analysis of the GeoB97-020/027 seismic line. The initial chronostratigraphic framework is established using regionally extensive hemipelagic sediment units and only age-depth models of fan deposits that respect the superposition of channel-levee systems between sites are accepted. In doing so, we reconstruct signals of regional sediment accumulation rate and lithogenic sediment input through the perspective of a two-dimensional ~320 km transect at 8⁰ N that are consistent with more distal and more ambiguous regional records. This chronology allows us to discuss the depositional history of the middle to upper Pleistocene lower Bengal Fan within the context of sea level, climate, and tectonic controls. We hypothesize, based on the timing of accumulation rate changes, that progradation and intensification of the Bengal Fan’s channel-levee system at 8⁰ N was largely driven by increases in sea level amplitude during this time. However, it is also possible this progradation was influenced by changes in Pleistocene climate and increased Himalayan erosion rates, driving greater sediment flux to the fan.
description: {"references": ["Reilly, B., Bergmann, F., Weber, M., Stoner, J., Selkin, P., Meynadier, L., Schwenk, T., Spiess, V., France-Lanord, C., (2020) Mid to Late Pleistocene evolution of the Bengal Fan: Integrating core and seismic observations for chronostratigraphic modeling of the IODP Expedition 354 8\u2070 North transect, Geochemistry, Geophysics, Geosystems."]}
DOI: 10.5281/zenodo.7334935
title: SciExpeM - 10.5281/zenodo.7334935

description: Lhuillier, Charles, 0000-0001-8592-1973; Brequigny, Pierre; Lamoureux, Nathalie; Contino, Francesco, 0000-0002-8341-4350; Mounaïm-Rousselle, Christine - Fuel, 2020, (263), 116653
DOI: 10.5281/zenodo.7334934
title: SciExpeM - 10.5281/zenodo.7334935

description: Lhuillier, Charles, 0000-0001-8592-1973; Brequigny, Pierre; Lamoureux, Nathalie; Contino, Francesco, 0000-0002-8341-4350; Mounaïm-Rousselle, Christine - Fuel, 2020, (263), 116653
DOI: 10.25380/iastate.21259206
title: Data For: Diversifying and Perennializing Plants in Agroecosystems Alters Retention of New C and N from Crop

description: These data are soil, CO2 efflux, dissolved organic carbon leaching, and various other measures from a mesocosm experiment performed in long-term (12-years) crop diversity experiment near Hickory Corners, MI, United States.Briefly, we tracked dual-labelled (13C and 15N), isotopically enriched wheat (<em>Triticum aestivum</em>) residue <em>in situ</em> for two years as it decomposed in three agroecosystems: maize-soybean rotation (CS), maize-soybean-wheat plus red clover and cereal rye cover crops (CSW2), and spring fallow management with regeneration of natural grassland species (7-10 species; SF). We measured losses of wheat residue (Cwheat and Nwheat) in leached soil solution and greenhouse gas fluxes, as well as how much was recovered in microbial biomass and bulk soil at 5-cm increments down to 20 cm. COLLECTION INFORMATION: <strong>Time period(s):</strong> 2011 to 2013 <strong>Location(s):</strong> Hickory Corners, MI, United States <strong>Long-term Experiment:</strong> Cropping Biodiversity Gradient Experiment <strong>Further Site Information: </strong>https://lter.kbs.msu.edu/research/long-term-experiments/biodiversity-gradient/
DOI: 10.5281/zenodo.7334939
title: SciExpeM - 10.5281/zenodo.7334939

description: Lhuillier, Charles, 0000-0001-8592-1973; Brequigny, Pierre; Lamoureux, Nathalie; Contino, Francesco, 0000-0002-8341-4350; Mounaïm-Rousselle, Christine - Fuel, 2020, (263), 116653
DOI: 10.5281/zenodo.7334938
title: SciExpeM - 10.5281/zenodo.7334939

description: Lhuillier, Charles, 0000-0001-8592-1973; Brequigny, Pierre; Lamoureux, Nathalie; Contino, Francesco, 0000-0002-8341-4350; Mounaïm-Rousselle, Christine - Fuel, 2020, (263), 116653
DOI: 10.5281/zenodo.7334941
title: SciExpeM - 10.5281/zenodo.7334941

description: Lhuillier, Charles, 0000-0001-8592-1973; Brequigny, Pierre; Lamoureux, Nathalie; Contino, Francesco, 0000-0002-8341-4350; Mounaïm-Rousselle, Christine - Fuel, 2020, (263), 116653
DOI: 10.5281/zenodo.7334940
title: SciExpeM - 10.5281/zenodo.7334941

description: Lhuillier, Charles, 0000-0001-8592-1973; Brequigny, Pierre; Lamoureux, Nathalie; Contino, Francesco, 0000-0002-8341-4350; Mounaïm-Rousselle, Christine - Fuel, 2020, (263), 116653
DOI: 10.5281/zenodo.7334945
title: SciExpeM - 10.5281/zenodo.7334945

description: Lhuillier, Charles, 0000-0001-8592-1973; Brequigny, Pierre; Lamoureux, Nathalie; Contino, Francesco, 0000-0002-8341-4350; Mounaïm-Rousselle, Christine - Fuel, 2020, (263), 116653
DOI: 10.5281/zenodo.7334944
title: SciExpeM - 10.5281/zenodo.7334945

description: Lhuillier, Charles, 0000-0001-8592-1973; Brequigny, Pierre; Lamoureux, Nathalie; Contino, Francesco, 0000-0002-8341-4350; Mounaïm-Rousselle, Christine - Fuel, 2020, (263), 116653
DOI: 10.5281/zenodo.7334953
title: SciExpeM - 10.5281/zenodo.7334953

description: Lhuillier, Charles, 0000-0001-8592-1973; Brequigny, Pierre; Lamoureux, Nathalie; Contino, Francesco, 0000-0002-8341-4350; Mounaïm-Rousselle, Christine - Fuel, 2020, (263), 116653
DOI: 10.5281/zenodo.7334952
title: SciExpeM - 10.5281/zenodo.7334953

description: Lhuillier, Charles, 0000-0001-8592-1973; Brequigny, Pierre; Lamoureux, Nathalie; Contino, Francesco, 0000-0002-8341-4350; Mounaïm-Rousselle, Christine - Fuel, 2020, (263), 116653
DOI: 10.5281/zenodo.7334962
title: SciExpeM - 10.5281/zenodo.7334962

description: Lhuillier, Charles, 0000-0001-8592-1973; Brequigny, Pierre; Lamoureux, Nathalie; Contino, Francesco, 0000-0002-8341-4350; Mounaïm-Rousselle, Christine - Fuel, 2020, (263), 116653
DOI: 10.5281/zenodo.7334961
title: SciExpeM - 10.5281/zenodo.7334962

description: Lhuillier, Charles, 0000-0001-8592-1973; Brequigny, Pierre; Lamoureux, Nathalie; Contino, Francesco, 0000-0002-8341-4350; Mounaïm-Rousselle, Christine - Fuel, 2020, (263), 116653
DOI: 10.5281/zenodo.7334964
title: SciExpeM - 10.5281/zenodo.7334964

description: Lhuillier, Charles, 0000-0001-8592-1973; Brequigny, Pierre; Lamoureux, Nathalie; Contino, Francesco, 0000-0002-8341-4350; Mounaïm-Rousselle, Christine - Fuel, 2020, (263), 116653
DOI: 10.5281/zenodo.7334963
title: SciExpeM - 10.5281/zenodo.7334964

description: Lhuillier, Charles, 0000-0001-8592-1973; Brequigny, Pierre; Lamoureux, Nathalie; Contino, Francesco, 0000-0002-8341-4350; Mounaïm-Rousselle, Christine - Fuel, 2020, (263), 116653
DOI: 10.6084/m9.figshare.21388395
title: Fast Variational Bayes Methods for Multinomial Probit Models

description: The multinomial probit model is often used to analyze choice behavior. However, estimation with existing Markov chain Monte Carlo (MCMC) methods is computationally costly, which limits its applicability to large choice datasets. This article proposes a variational Bayes method that is accurate and fast, even when a large number of choice alternatives and observations are considered. Variational methods usually require an analytical expression for the unnormalized posterior density and an adequate choice of variational family. Both are challenging to specify in a multinomial probit, which has a posterior that requires identifying restrictions and is augmented with a large set of latent utilities. We employ a spherical transformation on the covariance matrix of the latent utilities to construct an unnormalized augmented posterior that identifies the parameters, and use the conditional posterior of the latent utilities as part of the variational family. The proposed method is faster than MCMC, and can be made scalable to both a large number of choice alternatives and a large number of observations. The accuracy and scalability of our method is illustrated in numerical experiments and real purchase data with one million observations.
DOI: 10.6084/m9.figshare.21388395.v2
title: Fast Variational Bayes Methods for Multinomial Probit Models

description: The multinomial probit model is often used to analyze choice behavior. However, estimation with existing Markov chain Monte Carlo (MCMC) methods is computationally costly, which limits its applicability to large choice datasets. This article proposes a variational Bayes method that is accurate and fast, even when a large number of choice alternatives and observations are considered. Variational methods usually require an analytical expression for the unnormalized posterior density and an adequate choice of variational family. Both are challenging to specify in a multinomial probit, which has a posterior that requires identifying restrictions and is augmented with a large set of latent utilities. We employ a spherical transformation on the covariance matrix of the latent utilities to construct an unnormalized augmented posterior that identifies the parameters, and use the conditional posterior of the latent utilities as part of the variational family. The proposed method is faster than MCMC, and can be made scalable to both a large number of choice alternatives and a large number of observations. The accuracy and scalability of our method is illustrated in numerical experiments and real purchase data with one million observations.
DOI: 10.6084/m9.figshare.21388395.v1
title: Fast variational Bayes methods for multinomial probit models

description: The multinomial probit model is often used to analyze choice behaviour. However, estimation with existing Markov chain Monte Carlo (MCMC) methods is computationally costly, which limits its applicability to large choice data sets. This paper proposes a variational Bayes method that is accurate and fast, even when a large number of choice alternatives and observations are considered. Variational methods usually require an analytical expression for the unnormalized posterior density and an adequate choice of variational family. Both are challenging to specify in a multinomial probit, which has a posterior that requires identifying restrictions and is augmented with a large set of latent utilities. We employ a spherical transformation on the covariance matrix of the latent utilities to construct an unnormalized augmented posterior that identifies the parameters, and use the conditional posterior of the latent utilities as part of the variational family. The proposed method is faster than MCMC, and can be made scalable to both a large number of choice alternatives and a large number of observations. The accuracy and scalability of our method is illustrated in numerical experiments and real purchase data with one million observations.
DOI: 10.5281/zenodo.7334970
title: SciExpeM - 10.5281/zenodo.7334970

description: Lhuillier, Charles, 0000-0001-8592-1973; Brequigny, Pierre; Lamoureux, Nathalie; Contino, Francesco, 0000-0002-8341-4350; Mounaïm-Rousselle, Christine - Fuel, 2020, (263), 116653
DOI: 10.5281/zenodo.7334969
title: SciExpeM - 10.5281/zenodo.7334970

description: Lhuillier, Charles, 0000-0001-8592-1973; Brequigny, Pierre; Lamoureux, Nathalie; Contino, Francesco, 0000-0002-8341-4350; Mounaïm-Rousselle, Christine - Fuel, 2020, (263), 116653
DOI: 10.5281/zenodo.7334976
title: SciExpeM - 10.5281/zenodo.7334976

description: Lhuillier, Charles, 0000-0001-8592-1973; Brequigny, Pierre; Lamoureux, Nathalie; Contino, Francesco, 0000-0002-8341-4350; Mounaïm-Rousselle, Christine - Fuel, 2020, (263), 116653
DOI: 10.5281/zenodo.7334975
title: SciExpeM - 10.5281/zenodo.7334976

description: Lhuillier, Charles, 0000-0001-8592-1973; Brequigny, Pierre; Lamoureux, Nathalie; Contino, Francesco, 0000-0002-8341-4350; Mounaïm-Rousselle, Christine - Fuel, 2020, (263), 116653
DOI: 10.57995/q981-9m02
title: Golf Census 2022

description: Golf Census 2022 provides a dataset of how people who participate in the sport of golf are supported regarding sports injuries, mental health, safeguarding, career transitions, education, and equality, diversity and inclusion.

The dataset includes responses from 2333 people collected in 2022 across 51 countries, including amateur golfers, professional golfers, elite amateur golfer, golf coaches, golf caddies, golf teaching professionals, golf architects, golf equipment manufacturers, golf greenskeepers, and golf volunteers. The data were collected via an online survey.
DOI: 10.18112/openneuro.ds002761.v1.1.2
title: memoryreplay