cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7306875
title: model output of Cumulus convection over land in cloud-resolving simulations with a coupled ray tracer

description: <strong>768_*</strong>: Output of cloud resolving simulations of a diurnal cycle (Augst 15th, 2016) with shallow cumulus clouds over Cabauw, the Netherlands. All simulations are performed with MicroHH, with radiative transfer computed either with a two-stream approximation or with ray tracing. 2s_het: main simulation with two-stream solver 2s_hom_sw: two-stream solver, horizontally averaged surface solar radiative fluxes 2s_hom_hrsw: two-stream solver, horizontally averaged heating rates rt_het: main simulation with ray tracing, 256 samples per pixel rt_hom_sw: ray tracing, horizontally averaged surface solar radiative fluxes, 256 samples per pixel rt_hom_hrsw: ray tracing, horizontally averaged heating rates, 256 samples per pixel rt_het_128: ray tracing, 128 samples per pixel rt_het:_64 ray tracing, 64 samples per pixel rt_het:_32 ray tracing, 32 samples per pixel
description: Funded by the Dutch Research Council (NWO), grant numbers VI.Vidi.192.068 and 184.034.015
DOI: 10.6084/m9.figshare.21462918
title: Support information

description: Response voltage and data processing of electronic nose sensor
DOI: 10.6084/m9.figshare.21462918.v2
title: Table S1.xlsx

description: support imformation
DOI: 10.6084/m9.figshare.21462918.v3
title: Support information

description: Response voltage and data processing of electronic nose sensor
DOI: 10.6084/m9.figshare.21524883
title: File S1_high risk species information

description: File S1 in Chapter 1
DOI: 10.6084/m9.figshare.21524883.v1
title: File S1_high risk species information

description: File S1 in Chapter 1
DOI: 10.5517/ccdc.csd.ccz6srp
title: CCDC 900263: Experimental Crystal Structure Determination

description: Related Article: S.ANITHA, J.KARTHIKEYAN, A.NITYANANDA SHETTY|2022|CSD Communication|||
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.cc2211z5
title: CCDC 1907646: Experimental Crystal Structure Determination

description: Related Article: Shen Shen|2022|CSD Communication|||
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.cc234vz3
title: CCDC 1941095: Experimental Crystal Structure Determination

description: Related Article: Krishnamoorthy Bellie Sundaram|2022|CSD Communication|||
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.cc251r33
title: CCDC 1997674: Experimental Crystal Structure Determination

description: Related Article: xiaozheng su|2022|CSD Communication|||
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.cc2cbfy3
title: CCDC 2184785: Experimental Crystal Structure Determination

description: Related Article: Haoran Hu, Bitao Jiang, Bo Zhang, Rong Wang, Chong Zhang, Chengguo Sun, Bingcheng Hu|2022|New J.Chem.|46|18316|doi:10.1039/D2NJ03526J
description: Related Article: Hu Haoran|2022|CSD Communication|||
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.cc2cbjr0
title: CCDC 2184872: Experimental Crystal Structure Determination

description: Related Article: Manuel Fernandez|2022|CSD Communication|||
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.cc2cd0jb
title: CCDC 2186291: Experimental Crystal Structure Determination

description: Related Article: zhong zheng|2022|CSD Communication|||
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.cc2cd0rk
title: CCDC 2186298: Experimental Crystal Structure Determination

description: Related Article: Ranga Rao Gangavarapu|2022|CSD Communication|||
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.cc2ch445
title: CCDC 2189286: Experimental Crystal Structure Determination

description: Related Article: Zheng Meijia|2022|CSD Communication|||
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.cc2chbv2
title: CCDC 2189494: Experimental Crystal Structure Determination

description: Related Article: Xi Wang|2022|CSD Communication|||
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.cc2chn6r
title: CCDC 2189784: Experimental Crystal Structure Determination

description: Related Article: Aixi Luo Xiaoming Zhu|2022|CSD Communication|||
description: Related Article: Xiai Luo, Zhongtao Yang, Jia Zheng, Gang Liang, Hui Luo, Weiguang Yang|2022|Org.Lett.|24|7300|doi:10.1021/acs.orglett.2c02705
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.cc2chq1n
title: CCDC 2189841: Experimental Crystal Structure Determination

description: Related Article: Jiawei Lin|2022|CSD Communication|||
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.cc2chqk5
title: CCDC 2189857: Experimental Crystal Structure Determination

description: Related Article: Ruming Jiang, Nan Zheng and Zujin Zhao|2022|CSD Communication|||
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.cc2chw5y
title: CCDC 2190000: Experimental Crystal Structure Determination

description: Related Article: Berta Covelo|2022|CSD Communication|||
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.cc2cy53m
title: CCDC 2202770: Experimental Crystal Structure Determination

description: Related Article: Zhong-Yuan Li, Jin-Ping Zhang, Yun-Yi Ying, Dong Yan, Lijuan Jiao, Erhong Hao|2022|Org.Lett.|24|7888|doi:10.1021/acs.orglett.2c02853
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: 10.4121/21511107.v1
title: Dataset used for a study on the financial feasibility of offshore mussel farming using the SMART approach

description: This is a dataset that was used for a study on the financial feasibility of offshore mussel farming using the SMART farm approach. We approached numerous entities (both governmental and business) to generate this data. This data demonstrates that offshore mussel farming in the Dutch North Sea using the SMART approach can be meaningfully profitable.
DOI: 10.4121/21511107
title: Dataset used for a study on the financial feasibility of offshore mussel farming using the SMART approach

description: This is a dataset that was used for a study on the financial feasibility of offshore mussel farming using the SMART farm approach. We approached numerous entities (both governmental and business) to generate this data. This data demonstrates that offshore mussel farming in the Dutch North Sea using the SMART approach can be meaningfully profitable.
DOI: 10.4121/21510030.v1
title: Dataset of Cone Penetration Tests (CPTs) to study soil heterogeneity

description: The research involves site investigations at three locations along the drainage canal “Achterwaterschap” in the western part of the Alblasserwaard region in the Netherlands. This area is part of the administration of Waterschap Rivierenland (WSRL). The site investigations are performed at the dike posts AC 075, AC 090 and AC 251. The in situ tests are performed in the inner toe of the dike. The research has been carried out for Rijkswaterstaat WVL. Rijkswaterstaat is part of the Dutch Ministry of Infrastructure and Water Management and responsible for the design, construction, management and maintenance of the main infrastructure facilities in the Netherlands. Rijkswaterstaat has given consent for making this dataset publicly available. The dataset includes a total of 60 CPTs at close distance, spreaded over three different sites. The sites are located at 1.5 km distance (AC075 and AC090) and 15 km distance (AC251). The dataset is described in the following reports. Please refer to these reports, and refer to the DOI of this dataset when using. [Deltares, 2019] Van Duinen, T.A. (2019) Variability of the cone penetration resistance. Deltares report 11202225-005-GEO-0013. https://puc.overheid.nl/doc/PUC_660096_31 [Van Duinen, 2019] Van Duinen, T.A. (2019) Variability and accuracy of shear strength measurements in soft soils. Master Thesis Utrecht University. https://studenttheses.uu.nl/handle/20.500.12932/32852
DOI: 10.4121/21510030
title: Dataset of Cone Penetration Tests (CPTs) to study soil heterogeneity

description: The research involves site investigations at three locations along the drainage canal “Achterwaterschap” in the western part of the Alblasserwaard region in the Netherlands. This area is part of the administration of Waterschap Rivierenland (WSRL). The site investigations are performed at the dike posts AC 075, AC 090 and AC 251. The in situ tests are performed in the inner toe of the dike. The research has been carried out for Rijkswaterstaat WVL. Rijkswaterstaat is part of the Dutch Ministry of Infrastructure and Water Management and responsible for the design, construction, management and maintenance of the main infrastructure facilities in the Netherlands. Rijkswaterstaat has given consent for making this dataset publicly available. The dataset includes a total of 60 CPTs at close distance, spreaded over three different sites. The sites are located at 1.5 km distance (AC075 and AC090) and 15 km distance (AC251). The dataset is described in the following reports. Please refer to these reports, and refer to the DOI of this dataset when using. [Deltares, 2019] Van Duinen, T.A. (2019) Variability of the cone penetration resistance. Deltares report 11202225-005-GEO-0013. https://puc.overheid.nl/doc/PUC_660096_31 [Van Duinen, 2019] Van Duinen, T.A. (2019) Variability and accuracy of shear strength measurements in soft soils. Master Thesis Utrecht University. https://studenttheses.uu.nl/handle/20.500.12932/32852
DOI: 10.4121/16778794.v1
title: Data underlying the publication: Regional barystatic sea-level trends and uncertainties 1993-2016

description: Regional barystatic sea-level change trend and uncertainties, from 2003-2016 and 1993-2016. <br><b>Attention: This dataset is linked to a <i>submitted</i> manuscript, and has not undergo peer review yet!</b><br>Barystatic sea-level change (also known as ocean mass change) is driven by the exchange of freshwater between the land and the ocean, such as melting of continental ice from glaciers and ice sheets, and variations in land water storage. <br>Here, we use a range of estimates for the individual freshwater sources, which are used to compute regional patterns (fingerprints) of barystatic sea-level change.<br>We then compute the trend (rate of sea-level change), and quantify three types of uncertainties of these regional barystatic sea-level change fields:1. Intrinsic uncertainty: related to the observational error;2. Temporal uncertainty: related to the temporal variability in the time series;3. Spatial-structural: related to the location/distribution of the mass change sources; <br>The methods used to obtain this dataset, as well as the results, are presented in the manuscript <i>"Trends and Uncertainties of Regional Barystatic Sea-level Change in the Satellite Altimetry Era", </i>submitted to the journal <i>Earth System Dynamics </i><i><br></i>Code for exploring this dataset can be found on: https://github.com/carocamargo/barystatic_SL <br>
DOI: 10.4121/16778794.v2
title: Data underlying the publication: Trends and Uncertainties of Mass-driven Sea-level Change in the Satellite Altimetry Era (1993-2016)

description: Regional mass-drive (barystatic) sea-level change trend and uncertainties, from 2003-2016 and 1993-2016.<br> Barystatic sea-level change (also known as ocean mass change) is driven by the exchange of freshwater between the land and the ocean, such as melting of continental ice from glaciers and ice sheets, and variations in land water storage. <br> Here, we use a range of estimates for the individual freshwater sources, which are used to compute regional patterns (fingerprints) of barystatic sea-level change. <br> We then compute the trend (rate of sea-level change), and quantify three types of uncertainties of these regional barystatic sea-level change fields: 1. Intrinsic uncertainty: related to the observational error; 2. Temporal uncertainty: related to the temporal variability in the time series; 3. Spatial-structural: related to the location/distribution of the mass change sources; <br> The methods used to obtain this dataset, as well as the results, are presented in the manuscript <em>"Trends and Uncertainties of Mass-driven Sea-level Change in the Satellite Altimetry Era", published</em> in <em>Earth System Dynamics (</em>https://doi.org/10.5194/esd-2021-80<em>)</em> <br> <br> Code for exploring this dataset can be found on: https://github.com/carocamargo/barystatic_SL <br>
DOI: 10.4121/16778794.v3
title: Data underlying the publication: Trends and Uncertainties of Mass-driven Sea-level Change in the Satellite Altimetry Era (1993-2016)

description: Regional mass-drive (barystatic) sea-level change trend and uncertainties, from 2003-2016 and 1993-2016.<br> Barystatic sea-level change (also known as ocean mass change) is driven by the exchange of freshwater between the land and the ocean, such as melting of continental ice from glaciers and ice sheets, and variations in land water storage. <br> Here, we use a range of estimates for the individual freshwater sources, which are used to compute regional patterns (fingerprints) of barystatic sea-level change. <br> We then compute the trend (rate of sea-level change), and quantify three types of uncertainties of these regional barystatic sea-level change fields: 1. Intrinsic uncertainty: related to the observational error; 2. Temporal uncertainty: related to the temporal variability in the time series; 3. Spatial-structural: related to the location/distribution of the mass change sources; <br> The analysis and results published are based on relative sea-level fingerprints. We also provide the absolute fingerprints. <br> The methods used to obtain this dataset, as well as the results, are presented in the manuscript <em>"Trends and Uncertainties of Mass-driven Sea-level Change in the Satellite Altimetry Era", published</em> in <em>Earth System Dynamics (</em>https://doi.org/10.5194/esd-2021-80<em>)</em> <br> <br> Code for exploring this dataset can be found on: https://github.com/carocamargo/barystatic_SL <br>
DOI: 10.4121/16778794.v4
title: Data underlying the publication: Trends and Uncertainties of Mass-driven Sea-level Change in the Satellite Altimetry Era (1993-2016)

description: Regional mass-drive (barystatic) sea-level change trend and uncertainties, from 2003-2016 and 1993-2016.<br> Barystatic sea-level change (also known as ocean mass change) is driven by the exchange of freshwater between the land and the ocean, such as melting of continental ice from glaciers and ice sheets, and variations in land water storage. <br> Here, we use a range of estimates for the individual freshwater sources, which are used to compute regional patterns (fingerprints) of barystatic sea-level change. <br> We then compute the trend (rate of sea-level change), and quantify three types of uncertainties of these regional barystatic sea-level change fields: 1. Intrinsic uncertainty: related to the observational error; 2. Temporal uncertainty: related to the temporal variability in the time series; 3. Spatial-structural: related to the location/distribution of the mass change sources; <br> The analysis and results published are based on relative sea-level fingerprints. We also provide the absolute fingerprints. <br> The methods used to obtain this dataset, as well as the results, are presented in the manuscript <em>"Trends and Uncertainties of Mass-driven Sea-level Change in the Satellite Altimetry Era", published</em> in <em>Earth System Dynamics (</em>https://doi.org/10.5194/esd-2021-80<em>)</em> <br> <br> Code for exploring this dataset can be found on: https://github.com/carocamargo/barystatic_SL <br>
DOI: 10.4121/16778794
title: Data underlying the publication: Trends and Uncertainties of Mass-driven Sea-level Change in the Satellite Altimetry Era (1993-2016)

description: Regional mass-drive (barystatic) sea-level change trend and uncertainties, from 2003-2016 and 1993-2016.<br> Barystatic sea-level change (also known as ocean mass change) is driven by the exchange of freshwater between the land and the ocean, such as melting of continental ice from glaciers and ice sheets, and variations in land water storage. <br> Here, we use a range of estimates for the individual freshwater sources, which are used to compute regional patterns (fingerprints) of barystatic sea-level change. <br> We then compute the trend (rate of sea-level change), and quantify three types of uncertainties of these regional barystatic sea-level change fields: 1. Intrinsic uncertainty: related to the observational error; 2. Temporal uncertainty: related to the temporal variability in the time series; 3. Spatial-structural: related to the location/distribution of the mass change sources; <br> The analysis and results published are based on relative sea-level fingerprints. We also provide the absolute fingerprints. <br> The methods used to obtain this dataset, as well as the results, are presented in the manuscript <em>"Trends and Uncertainties of Mass-driven Sea-level Change in the Satellite Altimetry Era", published</em> in <em>Earth System Dynamics (</em>https://doi.org/10.5194/esd-2021-80<em>)</em> <br> <br> Code for exploring this dataset can be found on: https://github.com/carocamargo/barystatic_SL <br>
DOI: 10.4121/19650846.v1
title: Artificial Social Agent Questionnaire Instrument

description: We present the ASA Questionnaire, an instrument for evaluating human interaction with an artificial social agent (ASA), resulted from multi-year efforts involving more than 100 Intelligent Virtual Agent (IVA) researchers worldwide within the OSF work-group of Artificial Social Agent Evaluation Instrument (https://osf.io/6duf7/). It has 19 measurement constructs constituted by 90 items, which capture more than 80% of the constructs identified in empirical studies published in the IVA conference 2013-2018. In addition, a representative item of each construct or dimension was selected to create a 24-item short version of the ASA questionnaire. Whereas the long version is suitable for a comprehensive evaluation of human-ASA interaction, the short version allows quick analysis and description of the interaction with the ASA. To support reporting ASA questionnaire results, we also put forward a ASA chart. The chart provides a quick overview of agent profile.