cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc28sxvv
title: CCDC 2109328: Experimental Crystal Structure Determination

description: Related Article: Guoping Li, Liang-Wen Feng, Subhrangsu Mukherjee, Leighton O. Jones, Robert M. Jacobberger, Wei Huang, Ryan M. Young, Robert M. Pankow, Weigang Zhu, Norman Lu, Kevin L. Kohlstedt, Vinod K. Sangwan, Michael R. Wasielewski, Mark C. Hersam, George C. Schatz, Dean M. DeLongchamp, Antonio Facchetti, Tobin J. Marks|2022|Energy Environ.Sci.|15|645|doi:10.1039/D1EE03225A
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.cc29012g
title: CCDC 2115194: Experimental Crystal Structure Determination

description: Related Article: Jin-Yue Zeng, Xiao-Shuang Wang, Bo-Ru Xie, Qian-Ru Li, Xian-Zheng Zhang|2022|J.Am.Chem.Soc.|144|1218|doi:10.1021/jacs.1c10110
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.cc2blzlj
title: CCDC 2163198: Experimental Crystal Structure Determination

description: Related Article: M. Li, W. Tang, J. Gong|2023|IUCrJ|10|38|doi:10.1107/S2052252522011034
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.cc2bvk0s
title: CCDC 2170465: Experimental Crystal Structure Determination

description: Related Article: Kuntal Pal|2022|CSD Communication|||
description: Related Article: Jayanta Bag, Surajit Das, Souvik Mukherjee, Prasanta Ghosh, Kuntal Pal|2023|New J.Chem.|47|4321|doi:10.1039/D2NJ03355K
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.cc2bvk1t
title: CCDC 2170466: Experimental Crystal Structure Determination

description: Related Article: Kuntal Pal|2022|CSD Communication|||
description: Related Article: Jayanta Bag, Surajit Das, Souvik Mukherjee, Prasanta Ghosh, Kuntal Pal|2023|New J.Chem.|47|4321|doi:10.1039/D2NJ03355K
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.cc2dnjn8
title: CCDC 2224270: Experimental Crystal Structure Determination

description: Related Article: Jan Alfuth, Katarzyna Kazimierczuk, Tadeusz Połoński, Teresa Olszewska|2023|CrystEngComm|25|1403|doi:10.1039/D2CE01710E
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.cc2dnjp9
title: CCDC 2224271: Experimental Crystal Structure Determination

description: Related Article: Jan Alfuth, Katarzyna Kazimierczuk, Tadeusz Połoński, Teresa Olszewska|2023|CrystEngComm|25|1403|doi:10.1039/D2CE01710E
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.cc2dpgyj
title: CCDC 2225178: Experimental Crystal Structure Determination

description: Related Article: Gyandshwar Kumar Rao, Anirban Dutta, Nisha Yadav, Preeti Oswal, Arun Kumar, Ajai K. Singh|2023|New J.Chem.|47|4218|doi:10.1039/D2NJ05357H
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.cc2dpgzk
title: CCDC 2225179: Experimental Crystal Structure Determination

description: Related Article: Gyandshwar Kumar Rao, Anirban Dutta, Nisha Yadav, Preeti Oswal, Arun Kumar, Ajai K. Singh|2023|New J.Chem.|47|4218|doi:10.1039/D2NJ05357H
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.cc2dpl90
title: CCDC 2225282: Experimental Crystal Structure Determination

description: Related Article: D. Sai Reddy, Ivan M. Novitskiy, Andrei G. Kutateladze|2023|Org.Lett.|25|1131|doi:10.1021/acs.orglett.3c00092
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.cc2dplb1
title: CCDC 2225283: Experimental Crystal Structure Determination

description: Related Article: D. Sai Reddy, Ivan M. Novitskiy, Andrei G. Kutateladze|2023|Org.Lett.|25|1131|doi:10.1021/acs.orglett.3c00092
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.cc2dplc2
title: CCDC 2225284: Experimental Crystal Structure Determination

description: Related Article: D. Sai Reddy, Ivan M. Novitskiy, Andrei G. Kutateladze|2023|Org.Lett.|25|1131|doi:10.1021/acs.orglett.3c00092
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.cc2dnjl6
title: CCDC 2224268: Experimental Crystal Structure Determination

description: Related Article: Jan Alfuth, Katarzyna Kazimierczuk, Tadeusz Połoński, Teresa Olszewska|2023|CrystEngComm|25|1403|doi:10.1039/D2CE01710E
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.22182673
title: Matrix profiles

description: A few matrix profiles computed with SCAMP
DOI: 10.6084/m9.figshare.22182673.v1
title: Matrix profiles

description: A few matrix profiles computed with SCAMP
DOI: 10.6084/m9.figshare.22182562
title: SMD Simulations of GC-analogue Sequence

description: Steered Molecular Dynamics (SMD) simulations of the following system in explicit water: H-NS; dsDNA with nucleotide sequences 5’-GCGGCGCGCCGC-3’, and neutralized with 50 mM NaCl performed with GROMACS, version 2020.4 patched with PLUMED version 2.6.3. <br> All dry_*.pdb and dry_*.xtc contain the topology and SMD trajectories. The COLVAR_* files contain the work and bias output. And the run.mdp and plumed.dat contain the input settings containing the pulling coordinate and simulations settings. The SMD runs are 100 ns long and the force constant is 500 kJ/mol.
DOI: 10.6084/m9.figshare.21304482
title: SMD Simulations of High Affinity Sequence

description: Steered Molecular Dynamics (SMD) simulations of the following system in explicit water: H-NS; dsDNA with nucleotide sequences 5’-GCAATATATTGC-3, and neutralized with 50 mM NaCl performed with GROMACS, version 2020.4 patched with PLUMED version 2.6.3. <br> All dry_*.pdb and dry_*.xtc contain the topology and SMD trajectories. The COLVAR_* files contain the work and bias output. And the run.mdp and plumed.dat contain the input settings containing the pulling coordinate and simulations settings. The SMD runs are 100 ns long and the force constant is 500 kJ/mol.
DOI: 10.6084/m9.figshare.21304482.v1
title: SMD Simulations of High Affinity Sequence

description: Steered Molecular Dynamics (SMD) simulations of the following system in explicit water: H-NS; dsDNA with nucleotide sequences 5’-GCAATATATTGC-3, and neutralized with 50 mM NaCl performed with GROMACS, version 2020.4 patched with PLUMED version 2.6.3. <br> All dry_*.pdb and dry_*.xtc contain the topology and SMD trajectories. The COLVAR_* files contain the work and bias output. And the run.mdp and plumed.dat contain the input settings containing the pulling coordinate and simulations settings. The SMD runs are 100 ns long and the force constant is 500 kJ/mol.
DOI: 10.5281/zenodo.7681095
title: Multiparametric dataset

description: Dataset used for comparision between single and multiparametric regression in luminescence thermometry
DOI: 10.5281/zenodo.7681094
title: Multiparametric dataset

description: Dataset used for comparision between single and multiparametric regression in luminescence thermometry
DOI: 10.21954/ou.rd.22147955
title: Background of Bias: Subtle changes in line up backgrounds increase the own race bias

description: In police photo lineups there can sometimes be small variations in shades and hues of the background images due to the faces being filmed under different lighting and cameras. Own race bias refers to a situation where people are better at remembering the faces of those who are the same race as them and find it more difficult to recognise faces from a different race. In this paper we investigated the influence of small colour variations in backgrounds for the recognition of Black and White faces. Across 3 experiments we found when small changes were introduced into the backgrounds of the images this increased false identifications for previously unseen Black faces, but not White faces. This finding suggests that the police need to ensure that the backgrounds of the photo lineups they use are all uniform to reduce mistaken identifications of innocent suspects. <br> A dataset collected through Gorilla for face recognition data for Experiments 2 and 3 from the paper have been uploaded in Excel. The data consists of percentage of responses from the face recognition tasks, see the Excel doc for data labels. <br> Please cite Havard et al (2023) A Background of Bias: Subtle changes in line up backgrounds increase the own race bias, Journal of Police and Criminal Psychology, in press, if using this data.
DOI: 10.21954/ou.rd.22147955.v1
title: Background of Bias: Subtle changes in line up backgrounds increase the own race bias

description: In police photo lineups there can sometimes be small variations in shades and hues of the background images due to the faces being filmed under different lighting and cameras. Own race bias refers to a situation where people are better at remembering the faces of those who are the same race as them and find it more difficult to recognise faces from a different race. In this paper we investigated the influence of small colour variations in backgrounds for the recognition of Black and White faces. Across 3 experiments we found when small changes were introduced into the backgrounds of the images this increased false identifications for previously unseen Black faces, but not White faces. This finding suggests that the police need to ensure that the backgrounds of the photo lineups they use are all uniform to reduce mistaken identifications of innocent suspects. <br> A dataset collected through Gorilla for face recognition data for Experiments 2 and 3 from the paper have been uploaded in Excel. The data consists of percentage of responses from the face recognition tasks, see the Excel doc for data labels. <br> Please cite Havard et al (2023) A Background of Bias: Subtle changes in line up backgrounds increase the own race bias, Journal of Police and Criminal Psychology, in press, if using this data.
DOI: 10.6084/m9.figshare.22159244
title: Data: Invertebrate herbivory damage of lowland plant species decreases after an experimental shift to higher altitudes

description: This dataset contains data used in the following manuscript: Hrubá K., Hucková D., & Klečka J. (2023) Invertebrate herbivory damage of lowland plant species decreases after an experimental shift to higher altitudes. <br> The dataset contains data on plant traits and herbivory damage measured on individual plants in a field experiment.
DOI: 10.6084/m9.figshare.22159244.v1
title: Data: Invertebrate herbivory damage of lowland plant species decreases after an experimental shift to higher altitudes

description: This dataset contains data used in the following manuscript: Hrubá K., Hucková D., & Klečka J. (2023) Invertebrate herbivory damage of lowland plant species decreases after an experimental shift to higher altitudes. <br> The dataset contains data on plant traits and herbivory damage measured on individual plants in a field experiment.
DOI: 10.34740/kaggle/dsv/5074390
title: 2021 World Population (updated daily)
DOI: 10.5281/zenodo.7681081
title: manuscript - X Supplementary materials

description: Supplementary materials for MDPI
DOI: 10.5281/zenodo.7681080
title: manuscript - X Supplementary materials

description: Supplementary materials for MDPI
DOI: 10.5281/zenodo.7681097
title: Numerical results data of 'Impact of Injection Pressure and Polyaxial Stress on Hydraulic Fracture Propagation and Permeability Evolution in Greywacke: Insights from Discrete Element Models of a Laboratory Test'

description: Numerical results data of '<strong>Impact of Injection Pressure and Polyaxial Stress on Hydraulic Fracture Propagation and Permeability Evolution in Greywacke: Insights from Discrete Element Models of a Laboratory Test</strong>'
DOI: 10.5281/zenodo.7681096
title: Numerical results data of 'Impact of Injection Pressure and Polyaxial Stress on Hydraulic Fracture Propagation and Permeability Evolution in Greywacke: Insights from Discrete Element Models of a Laboratory Test'

description: Numerical results data of '<strong>Impact of Injection Pressure and Polyaxial Stress on Hydraulic Fracture Propagation and Permeability Evolution in Greywacke: Insights from Discrete Element Models of a Laboratory Test</strong>'
DOI: 10.6084/m9.figshare.21746957
title: GCF-1.0

description: This is the GCF_1.0 version.
DOI: 10.6084/m9.figshare.21746957.v3
title: GCF-1.0

description: This is the GCF_1.0 version.
DOI: 10.6084/m9.figshare.21746957.v2
title: GCF-1.0

description: This is the GCF_1.0 version.
DOI: 10.5281/zenodo.7681145
title: Intra-annual-tree-ring-stable-carbon-and-oxygen-isotope-of-Chinese-pine-in-Helan-Mountains

description: The data are intra-annual series of tree-ring stable carbon (δ<sup>13</sup>Ccor) and oxygen (δ<sup>18</sup>O) isotopes from Chinese pine (<em>Pinus tabuliformis</em>) in Helan Mountains
DOI: 10.5281/zenodo.7681144
title: Intra-annual-tree-ring-stable-carbon-and-oxygen-isotope-of-Chinese-pine-in-Helan-Mountains

description: The data are intra-annual series of tree-ring stable carbon (δ<sup>13</sup>Ccor) and oxygen (δ<sup>18</sup>O) isotopes from Chinese pine (<em>Pinus tabuliformis</em>) in Helan Mountains
DOI: 10.5281/zenodo.7180973
title: CLASSIG

description: This package contains the data and models from my thesis <em>"Computational Methods for Investigating Syntactic Change: Automatic Identification of Extraposition in Modern and Historical German"</em>. The work originated from the research project <em>"Information Management as a Factor for Syntactic Variation in the History of German"</em> (SFB 1102, Project C6). The folder <strong>classig-data.zip</strong> includes the annotated data sets, language models, and detailed evaluation results. The folder <strong>classig-models.zip</strong> contains the trained chunker and parser models. Data and models can be used with the CLASSIG pipeline to reproduce the results of my thesis and create new annotations. Additional documentation for data (https://github.com/rubcompling/classig-data) and models (https://github.com/rubcompling/classig-pipeline) is provided at GitHub and in my thesis.
DOI: 10.5281/zenodo.7180972
title: CLASSIG

description: This package contains the data and models from my thesis <em>"Computational Methods for Investigating Syntactic Change: Automatic Identification of Extraposition in Modern and Historical German"</em>. The work originated from the research project <em>"Information Management as a Factor for Syntactic Variation in the History of German"</em> (SFB 1102, Project C6). The folder <strong>classig-data.zip</strong> includes the annotated data sets, language models, and detailed evaluation results. The folder <strong>classig-models.zip</strong> contains the trained chunker and parser models. Data and models can be used with the CLASSIG pipeline to reproduce the results of my thesis and create new annotations. Additional documentation for data (https://github.com/rubcompling/classig-data) and models (https://github.com/rubcompling/classig-pipeline) is provided at GitHub and in my thesis.
DOI: 10.5281/zenodo.7680828
title: LiMeS-lab: An integrated laboratory for the development of Liquid-Metal Shield technologies for fusion reactors

description: The liquid metal shield laboratory (LiMeS-Lab) will provide the infrastructure to develop, test, and compare liquid metal divertor designs for future fusion reactors. The main research topics of LiMeS-lab will be liquid metal interactions with the substrate material of the divertor, the continuous circulation and capillary refilling of the liquid metal during intense plasma heat loading and the retention of plasma particles in the liquid metal. To facilitate the research, four new devices are in development at the Dutch Institute for Fundamental Energy Research and the Eindhoven University of Technology: LiMeS-AM: a custom metal 3D printer based on powder bed fusion; LiMeS-Wetting, a plasma device to study the wetting of liquid metals on various substrates with different surface treatments; LiMeS-PSI, a linear plasma generator specifically adapted to operate continuous liquid metal loops. Special diagnostic protection will also be implemented to perform measurements in long duration shots without being affected by the liquid metal vapor; LiMeS-TDS, a thermal desorption spectroscopy system to characterize deuterium retention in a metal vapor environment. Each of these devices has specific challenges due to the presence and deposition of metal vapors that need to be addressed in order to function. In this paper, an overview of LiMeS-Lab will be given and the conceptual designs of the last three devices will be presented.
DOI: 10.5281/zenodo.7680827
title: LiMeS-lab: An integrated laboratory for the development of Liquid-Metal Shield technologies for fusion reactors

description: The liquid metal shield laboratory (LiMeS-Lab) will provide the infrastructure to develop, test, and compare liquid metal divertor designs for future fusion reactors. The main research topics of LiMeS-lab will be liquid metal interactions with the substrate material of the divertor, the continuous circulation and capillary refilling of the liquid metal during intense plasma heat loading and the retention of plasma particles in the liquid metal. To facilitate the research, four new devices are in development at the Dutch Institute for Fundamental Energy Research and the Eindhoven University of Technology: LiMeS-AM: a custom metal 3D printer based on powder bed fusion; LiMeS-Wetting, a plasma device to study the wetting of liquid metals on various substrates with different surface treatments; LiMeS-PSI, a linear plasma generator specifically adapted to operate continuous liquid metal loops. Special diagnostic protection will also be implemented to perform measurements in long duration shots without being affected by the liquid metal vapor; LiMeS-TDS, a thermal desorption spectroscopy system to characterize deuterium retention in a metal vapor environment. Each of these devices has specific challenges due to the presence and deposition of metal vapors that need to be addressed in order to function. In this paper, an overview of LiMeS-Lab will be given and the conceptual designs of the last three devices will be presented.
DOI: 10.34740/kaggle/dsv/5074532
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5281/zenodo.7681170
title: Training datasets of size 1K, 3K, and 9K for four utility function variants

description: The datasets include 1K, 3K, and 9K training data points that are used for training utility-change prediction models in systems with ground truth from four different mathematical complexities: Linear utility function Saturating utility function Discontinuous utility function Combined utility function ( combining the above three variants)
DOI: 10.5281/zenodo.7681169
title: Training datasets of size 1K, 3K, and 9K for four utility function variants

description: The datasets include 1K, 3K, and 9K training data points that are used for training utility-change prediction models in systems with ground truth from four different mathematical complexities: Linear utility function Saturating utility function Discontinuous utility function Combined utility function ( combining the above three variants)