cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc21cp15
title: CCDC 1888056: Experimental Crystal Structure Determination

description: Related Article: E. Goreshnik|2019|J.Fluorine Chem.|226|109352|doi:10.1016/j.jfluchem.2019.109352
DOI: 10.5517/ccdc.csd.cc21h7sl
title: CCDC 1891489: Experimental Crystal Structure Determination

description: Related Article: Jun-Liang Dong, Fei Xie, Jia-Qiang Du, Hai-Ming Lan, Ru-Xia Yang, Duo-Zhi Wang|2019|J.Solid State Chem.|279|120917|doi:10.1016/j.jssc.2019.120917
DOI: 10.5517/ccdc.csd.cc21h7tm
title: CCDC 1891490: Experimental Crystal Structure Determination

description: Related Article: Jun-Liang Dong, Fei Xie, Jia-Qiang Du, Hai-Ming Lan, Ru-Xia Yang, Duo-Zhi Wang|2019|J.Solid State Chem.|279|120917|doi:10.1016/j.jssc.2019.120917
DOI: 10.5517/ccdc.csd.cc21h7vn
title: CCDC 1891491: Experimental Crystal Structure Determination

description: Related Article: Jun-Liang Dong, Fei Xie, Jia-Qiang Du, Hai-Ming Lan, Ru-Xia Yang, Duo-Zhi Wang|2019|J.Solid State Chem.|279|120917|doi:10.1016/j.jssc.2019.120917
DOI: 10.5517/ccdc.csd.cc21h7wp
title: CCDC 1891492: Experimental Crystal Structure Determination

description: Related Article: Jun-Liang Dong, Fei Xie, Jia-Qiang Du, Hai-Ming Lan, Ru-Xia Yang, Duo-Zhi Wang|2019|J.Solid State Chem.|279|120917|doi:10.1016/j.jssc.2019.120917
DOI: 10.5517/ccdc.csd.cc21h7xq
title: CCDC 1891493: Experimental Crystal Structure Determination

description: Related Article: Jun-Liang Dong, Fei Xie, Jia-Qiang Du, Hai-Ming Lan, Ru-Xia Yang, Duo-Zhi Wang|2019|J.Solid State Chem.|279|120917|doi:10.1016/j.jssc.2019.120917
DOI: 10.5517/ccdc.csd.cc21mrm2
title: CCDC 1895824: Experimental Crystal Structure Determination

description: Related Article: Min Yu, Jian Li, Xiao-Feng Wang, Fan Liu, Guang-Xiang Liu|2019|J.Solid State Chem.|278|120873|doi:10.1016/j.jssc.2019.07.034
DOI: 10.5517/ccdc.csd.cc21mrn3
title: CCDC 1895825: Experimental Crystal Structure Determination

description: Related Article: Min Yu, Jian Li, Xiao-Feng Wang, Fan Liu, Guang-Xiang Liu|2019|J.Solid State Chem.|278|120873|doi:10.1016/j.jssc.2019.07.034
DOI: 10.5517/ccdc.csd.cc21mrp4
title: CCDC 1895826: Experimental Crystal Structure Determination

description: Related Article: Min Yu, Jian Li, Xiao-Feng Wang, Fan Liu, Guang-Xiang Liu|2019|J.Solid State Chem.|278|120873|doi:10.1016/j.jssc.2019.07.034
DOI: 10.5517/ccdc.csd.cc21mrq5
title: CCDC 1895827: Experimental Crystal Structure Determination

description: Related Article: Min Yu, Jian Li, Xiao-Feng Wang, Fan Liu, Guang-Xiang Liu|2019|J.Solid State Chem.|278|120873|doi:10.1016/j.jssc.2019.07.034
DOI: 10.5517/ccdc.csd.cc21qk9n
title: CCDC 1898511: Experimental Crystal Structure Determination

description: Related Article: Zhao-Hao Li, Li-Ping Xue, Qiu-Pei Qin, Yi-Jing Zhao|2019|J.Solid State Chem.|278|120908|doi:10.1016/j.jssc.2019.120908
DOI: 10.5517/ccdc.csd.cc217c6w
title: CCDC 1883907: Experimental Crystal Structure Determination

description: Related Article: Jinxia Liang, Jie Zhang, Jinfang Liang, Lijun Zhai, Xiaoyan Niu, Tuoping Hu|2019|J.Solid State Chem.|279|120909|doi:10.1016/j.jssc.2019.120909
DOI: 10.5517/ccdc.csd.cc21v0jg
title: CCDC 1901835: Experimental Crystal Structure Determination

description: Related Article: Tomohiko Hamaguchi, Keisuke Kai, Isao Ando, Ken Kawano, Kosei Yamauchi, Ken Sakai|2020|Inorg.Chim.Acta|505|119498|doi:10.1016/j.ica.2020.119498
DOI: 10.5517/ccdc.csd.cc21y65d
title: CCDC 1904893: Experimental Crystal Structure Determination

description: Related Article: Li-Xin You, Yang Guo, Shi-Yu Xie, Shu-Ju Wang, Gang Xiong, Ileana Dragutan, Valerian Dragutan, Fu Ding, Ya-Guang Sun|2019|J.Solid State Chem.|278|120900|doi:10.1016/j.jssc.2019.120900
DOI: 10.5517/ccdc.csd.cc21y67g
title: CCDC 1904895: Experimental Crystal Structure Determination

description: Related Article: Li-Xin You, Yang Guo, Shi-Yu Xie, Shu-Ju Wang, Gang Xiong, Ileana Dragutan, Valerian Dragutan, Fu Ding, Ya-Guang Sun|2019|J.Solid State Chem.|278|120900|doi:10.1016/j.jssc.2019.120900
DOI: 10.5517/ccdc.csd.cc21y68h
title: CCDC 1904896: Experimental Crystal Structure Determination

description: Related Article: Li-Xin You, Yang Guo, Shi-Yu Xie, Shu-Ju Wang, Gang Xiong, Ileana Dragutan, Valerian Dragutan, Fu Ding, Ya-Guang Sun|2019|J.Solid State Chem.|278|120900|doi:10.1016/j.jssc.2019.120900
DOI: 10.5519/qd.yl6qfx3o
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (5 records)
DOI: 10.14469/hpc/9986
title: NMR Data

description: NMR Data for Compounds 1a-2i
DOI: 10.5281/zenodo.5851409
title: Avizo reconstruction of skull of Siamogale melilutra dataset

description: <em><strong>Siamogale melilutra</strong></em> is an extinct species of giant otter from the late Miocene from Yunnan province, China. Ranking among the largest fossil otters, <em>Siamogale</em> represents a feeding ecomorphology with no living analog. Its giant size and high mandibular strength confer shell-crushing capability matched only by other extinct molluscivores, such as the marine bear <em>Kolponomos</em>.<sup>[1] </sup>The skull reveals a combination of otter-like and badger-like cranial and dental characteristics. The new species belongs to the Lutrinae because of its possession of a large infraorbital canal and ventral expansion of the mastoid process, among other traits.<sup>[2]</sup> <em>Siamogale melilutra</em> was about 1.9 m (6.25 ft) in overall length and weighed at least 40 kg (88 pounds).<sup>[3]</sup> The remains of the skull were found in China and were re-created with a special program called the CT scan which is able to reconstruct the skeleton without being damaged. (From https://en.wikipedia.org/wiki/Siamogale_melilutra). This dataset was generated by Avizo 2020.2. It represents our reconstruction of the skull of Siamogale melilutra. The original CT files are available on MorphoSource.org, media 000413248, DOI 10.17602/M2/M413248. A mesh PLY file of the reconstructed skull generated from this Avizo dataset is also available at MorphoSource.org, media 000413543, DoI 10.17602/M2/M413543.
description: For description of this specimen see: http://dx.doi.org/10.1080/14772019.2016.1267666 fo
description: {"references": ["A new otter of giant size, Siamogale melilutra sp. nov. (Lutrinae: Mustelidae: Carnivora), from the latest Miocene Shuitangba site in northeastern Yunnan, south-western China, and a totalevidence phylogeny of lutrines"]}
DOI: 10.5281/zenodo.5851408
title: Avizo reconstruction of skull of Siamogale melilutra dataset

description: <em><strong>Siamogale melilutra</strong></em> is an extinct species of giant otter from the late Miocene from Yunnan province, China. Ranking among the largest fossil otters, <em>Siamogale</em> represents a feeding ecomorphology with no living analog. Its giant size and high mandibular strength confer shell-crushing capability matched only by other extinct molluscivores, such as the marine bear <em>Kolponomos</em>.<sup>[1] </sup>The skull reveals a combination of otter-like and badger-like cranial and dental characteristics. The new species belongs to the Lutrinae because of its possession of a large infraorbital canal and ventral expansion of the mastoid process, among other traits.<sup>[2]</sup> <em>Siamogale melilutra</em> was about 1.9 m (6.25 ft) in overall length and weighed at least 40 kg (88 pounds).<sup>[3]</sup> The remains of the skull were found in China and were re-created with a special program called the CT scan which is able to reconstruct the skeleton without being damaged. (From https://en.wikipedia.org/wiki/Siamogale_melilutra). This dataset was generated by Avizo 2020.2. It represents our reconstruction of the skull of Siamogale melilutra. The original CT files are available on MorphoSource.org, media 000413248, DOI 10.17602/M2/M413248. A mesh PLY file of the reconstructed skull generated from this Avizo dataset is also available at MorphoSource.org, media 000413543, DoI 10.17602/M2/M413543.
description: For description of this specimen see: http://dx.doi.org/10.1080/14772019.2016.1267666 fo
description: {"references": ["A new otter of giant size, Siamogale melilutra sp. nov. (Lutrinae: Mustelidae: Carnivora), from the latest Miocene Shuitangba site in northeastern Yunnan, south-western China, and a totalevidence phylogeny of lutrines"]}
DOI: 10.34740/kaggle/dsv/3166800
title: Mahanagar Yatayat Dataset
DOI: 10.5281/zenodo.5997092
title: Molecular Dynamics simulations of shear droplets

description: This dataset contains the results of non-equilibrium Molecular Dynamic simulations of 2-dimensional SPC/E water nanodroplets confined between silica-like walls and under shear flow conditions, performed using Gromacs. The main purposes of these simulations are: a) to study the motion of three-phases contact lines over high-friction surfaces, b) to study the critical transition leading to droplet breakage and c) to test the modelling and prediction capabilities of continuous fluid dynamics simulation methods. The investigation of the points above is carried out in an article recently submitted to the Journal of Fluid Mechanics. The preprint can be found on arXiv (see references); please refer to the preprint for a detailed description of the molecular simulations and of the tested CFD methods. The publication of this dataset not only grants the reproducibility of the results discussed in the article, but also serves as collection of benchmarks for the fellow researchers willing to test improved and/or alternative models to describe the motion of contact lines.
DOI: 10.5281/zenodo.5951222
title: Wallops SuperDARN data in netCDF format (2020-Feb)

description: 2020-Feb Wallops SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.5951221
title: Wallops SuperDARN data in netCDF format (2020-Feb)

description: 2020-Feb Wallops SuperDARN radar data in netCDF format. These files were produced using versions 2.5 and 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6036425
title: DataOnFigures

description: Dataset for publication "Combining life cycle sustainability assessment with agent-based modeling and techno-economic analysis to support an integrated assessment of chemical recycling contribution to UN Sustainable Development Goals" in the Journal of Industrial Ecology
DOI: 10.17034/57c858bd-52ab-46c7-ac95-594e1fb66542
title: Dataset for "Atropselective synthesis of N-aryl pyridones via dynamic kinetic resolution enabled by noncovalent interactions"

description: The dynamic kinetic resolution of C–N atropisomeric pyridones was achieved via asymmetric phase-transfer catalysis, exploiting a rotational barrier-lowering hydrogen bond in the starting materials. X-ray and NMR experiments revealed the presence of a barrier-raising ground state CH⋯π interaction in the product, supported by DFT calculations. A co-crystal of the quinidine-derived phase-transfer catalyst and substrate reveals key substrate–catalyst non-covalent interactions. File Contents: 1) Excel worksheet containing extracted energies 2) Associated Gaussian com and log files for geometry optimisation and single point energy (ecp2) calculations Files have been split into separate folders for each R group.
DOI: 10.5281/zenodo.6036467
title: Data for the publication "Assessing the potential for simplification in global climate model cloud microphysics"

description: This repository contains the data for the paper: Authors: Ulrike Proske, Sylvaine Ferrachat, David Neubauer, Martin Staab, and Ulrike Lohmann<br> Titel: Assessing the potential for simplification in global climate model cloud microphysics<br> Date: 2022 Note that the scripts can be found in the accompanying package (https://doi.org/10.5281/zenodo.5506588)
DOI: 10.6084/m9.figshare.19154030
title: ExperimentDatenerhebung.sav

description: Dataset: dog-assisted interventions in higher education
DOI: 10.6084/m9.figshare.19154030.v1
title: ExperimentDatenerhebung.sav

description: Dataset: dog-assisted interventions in higher education
DOI: 10.6084/m9.figshare.7780637
title: Design, Identification, and Sensitivity Analysis for Patient Preference Trials

description: Social and medical scientists are often concerned that the external validity of experimental results may be compromised because of heterogeneous treatment effects. If a treatment has different effects on those who would choose to take it and those who would not, the average treatment effect estimated in a standard randomized controlled trial (RCT) may give a misleading picture of its impact outside of the study sample. Patient preference trials (PPTs), where participants’ preferences over treatment options are incorporated in the study design, provide a possible solution. In this paper, we provide a systematic analysis of PPTs based on the potential outcomes framework of causal inference. We propose a general design for PPTs with multi-valued treatments, where participants state their preferred treatments and are then randomized into either a standard RCT or a self-selection condition. We derive nonparametric sharp bounds on the average causal effects among each choice-based subpopulation of participants under the proposed design. We also propose a sensitivity analysis for the violation of the key ignorability assumption sufficient for identifying the target causal quantity. The proposed design and methodology are illustrated with an original study of partisan news media and its behavioral impact. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.7780637.v4
title: Design, Identification, and Sensitivity Analysis for Patient Preference Trials

description: Social and medical scientists are often concerned that the external validity of experimental results may be compromised because of heterogeneous treatment effects. If a treatment has different effects on those who would choose to take it and those who would not, the average treatment effect estimated in a standard randomized controlled trial (RCT) may give a misleading picture of its impact outside of the study sample. Patient preference trials (PPTs), where participants’ preferences over treatment options are incorporated in the study design, provide a possible solution. In this paper, we provide a systematic analysis of PPTs based on the potential outcomes framework of causal inference. We propose a general design for PPTs with multi-valued treatments, where participants state their preferred treatments and are then randomized into either a standard RCT or a self-selection condition. We derive nonparametric sharp bounds on the average causal effects among each choice-based subpopulation of participants under the proposed design. We also propose a sensitivity analysis for the violation of the key ignorability assumption sufficient for identifying the target causal quantity. The proposed design and methodology are illustrated with an original study of partisan news media and its behavioral impact. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.7780637.v3
title: Design, Identification, and Sensitivity Analysis for Patient Preference Trials

description: Social and medical scientists are often concerned that the external validity of experimental results may be compromised because of heterogeneous treatment effects. If a treatment has different effects on those who would choose to take it and those who would not, the average treatment effect estimated in a standard randomized controlled trial (RCT) may give a misleading picture of its impact outside of the study sample. Patient preference trials (PPTs), where participants’ preferences over treatment options are incorporated in the study design, provide a possible solution. In this paper, we provide a systematic analysis of PPTs based on the potential outcomes framework of causal inference. We propose a general design for PPTs with multi-valued treatments, where participants state their preferred treatments and are then randomized into either a standard RCT or a self-selection condition. We derive nonparametric sharp bounds on the average causal effects among each choice-based subpopulation of participants under the proposed design. We also propose a sensitivity analysis for the violation of the key ignorability assumption sufficient for identifying the target causal quantity. The proposed design and methodology are illustrated with an original study of partisan news media and its behavioral impact. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.7756823
title: Bayesian Probabilistic Numerical Methods in Time-Dependent State Estimation for Industrial Hydrocyclone Equipment

description: The use of high-power industrial equipment, such as large-scale mixing equipment or a hydrocyclone for separation of particles in liquid suspension, demands careful monitoring to ensure correct operation. The fundamental task of state-estimation for the liquid suspension can be posed as a time-evolving inverse problem and solved with Bayesian statistical methods. In this article, we extend Bayesian methods to incorporate statistical models for the error that is incurred in the numerical solution of the physical governing equations. This enables full uncertainty quantification within a principled computation-precision trade-off, in contrast to the over-confident inferences that are obtained when all sources of numerical error are ignored. The method is cast within a sequential Monte Carlo framework and an optimized implementation is provided in Python.
DOI: 10.6084/m9.figshare.7433192
title: Optimal Sparse Singular Value Decomposition for High-Dimensional High-Order Data

description: In this article, we consider the sparse tensor singular value decomposition, which aims for dimension reduction on high-dimensional high-order data with certain sparsity structure. A method named sparse tensor alternating thresholding for singular value decomposition (STAT-SVD) is proposed. The proposed procedure features a novel double projection & thresholding scheme, which provides a sharp criterion for thresholding in each iteration. Compared with regular tensor SVD model, STAT-SVD permits more robust estimation under weaker assumptions. Both the upper and lower bounds for estimation accuracy are developed. The proposed procedure is shown to be minimax rate-optimal in a general class of situations. Simulation studies show that STAT-SVD performs well under a variety of configurations. We also illustrate the merits of the proposed procedure on a longitudinal tensor dataset on European country mortality rates. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.7756823.v2
title: Bayesian Probabilistic Numerical Methods in Time-Dependent State Estimation for Industrial Hydrocyclone Equipment

description: The use of high-power industrial equipment, such as large-scale mixing equipment or a hydrocyclone for separation of particles in liquid suspension, demands careful monitoring to ensure correct operation. The fundamental task of state-estimation for the liquid suspension can be posed as a time-evolving inverse problem and solved with Bayesian statistical methods. In this article, we extend Bayesian methods to incorporate statistical models for the error that is incurred in the numerical solution of the physical governing equations. This enables full uncertainty quantification within a principled computation-precision trade-off, in contrast to the over-confident inferences that are obtained when all sources of numerical error are ignored. The method is cast within a sequential Monte Carlo framework and an optimized implementation is provided in Python.
DOI: 10.6084/m9.figshare.7063802
title: Efficient Signal Inclusion With Genomic Applications

description: This article addresses the challenge of efficiently capturing a high proportion of true signals for subsequent data analyses when sample sizes are relatively limited with respect to data dimension. We propose the signal missing rate (SMR) as a new measure for false-negative control to account for the variability of false-negative proportion. Novel data-adaptive procedures are developed to control SMR without incurring many unnecessary false positives under dependence. We justify the efficiency and adaptivity of the proposed methods via theory and simulation. The proposed methods are applied to GWAS on human height to effectively remove irrelevant single nucleotide polymorphisms (SNPs) while retaining a high proportion of relevant SNPs for subsequent polygenic analysis. Supplementary materials for this article are available online.