cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc25lz4x
title: CCDC 2014229: Experimental Crystal Structure Determination

description: Related Article: S.O. Kolade, J.U. Izunobi, A.T. Gordon, E.C. Hosten, I.A. Olasupo, A.S. Ogunlaja, O.T. Asekun, O.B. Familoni|2022|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|78|730|doi:10.1107/S2053229622010130
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.cc26gdtx
title: CCDC 2039639: Experimental Crystal Structure Determination

description: Related Article: S.O. Kolade, J.U. Izunobi, A.T. Gordon, E.C. Hosten, I.A. Olasupo, A.S. Ogunlaja, O.T. Asekun, O.B. Familoni|2022|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|78|730|doi:10.1107/S2053229622010130
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.cc25lz5y
title: CCDC 2014230: Experimental Crystal Structure Determination

description: Related Article: S.O. Kolade, J.U. Izunobi, A.T. Gordon, E.C. Hosten, I.A. Olasupo, A.S. Ogunlaja, O.T. Asekun, O.B. Familoni|2022|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|78|730|doi:10.1107/S2053229622010130
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.cc25lz6z
title: CCDC 2014231: Experimental Crystal Structure Determination

description: Related Article: S.O. Kolade, J.U. Izunobi, A.T. Gordon, E.C. Hosten, I.A. Olasupo, A.S. Ogunlaja, O.T. Asekun, O.B. Familoni|2022|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|78|730|doi:10.1107/S2053229622010130
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.cc25lz70
title: CCDC 2014232: Experimental Crystal Structure Determination

description: Related Article: S.O. Kolade, J.U. Izunobi, A.T. Gordon, E.C. Hosten, I.A. Olasupo, A.S. Ogunlaja, O.T. Asekun, O.B. Familoni|2022|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|78|730|doi:10.1107/S2053229622010130
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.cc26gdsw
title: CCDC 2039638: Experimental Crystal Structure Determination

description: Related Article: S.O. Kolade, J.U. Izunobi, A.T. Gordon, E.C. Hosten, I.A. Olasupo, A.S. Ogunlaja, O.T. Asekun, O.B. Familoni|2022|Acta Crystallogr.,Sect.C:Cryst.Struct.Chem.|78|730|doi:10.1107/S2053229622010130
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.cc2cx0t4
title: CCDC 2201676: Experimental Crystal Structure Determination

description: Related Article: Parthasarathy Gayathri, Sasikala Ravi, K. Akshaya, Subramanian Karthikeyan, Mehboobali Pannipara, Abdullah G. Al-Sehemi, Dohyun Moon, Savarimuthu Philip Anthony|2022|CrystEngComm|24|8126|doi:10.1039/D2CE01210C
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.cc2cx0v5
title: CCDC 2201677: Experimental Crystal Structure Determination

description: Related Article: Parthasarathy Gayathri, Sasikala Ravi, K. Akshaya, Subramanian Karthikeyan, Mehboobali Pannipara, Abdullah G. Al-Sehemi, Dohyun Moon, Savarimuthu Philip Anthony|2022|CrystEngComm|24|8126|doi:10.1039/D2CE01210C
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.cc2cx0w6
title: CCDC 2201678: Experimental Crystal Structure Determination

description: Related Article: Parthasarathy Gayathri, Sasikala Ravi, K. Akshaya, Subramanian Karthikeyan, Mehboobali Pannipara, Abdullah G. Al-Sehemi, Dohyun Moon, Savarimuthu Philip Anthony|2022|CrystEngComm|24|8126|doi:10.1039/D2CE01210C
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.cc2cx0x7
title: CCDC 2201679: Experimental Crystal Structure Determination

description: Related Article: Parthasarathy Gayathri, Sasikala Ravi, K. Akshaya, Subramanian Karthikeyan, Mehboobali Pannipara, Abdullah G. Al-Sehemi, Dohyun Moon, Savarimuthu Philip Anthony|2022|CrystEngComm|24|8126|doi:10.1039/D2CE01210C
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.cc2cx0y8
title: CCDC 2201680: Experimental Crystal Structure Determination

description: Related Article: Parthasarathy Gayathri, Sasikala Ravi, K. Akshaya, Subramanian Karthikeyan, Mehboobali Pannipara, Abdullah G. Al-Sehemi, Dohyun Moon, Savarimuthu Philip Anthony|2022|CrystEngComm|24|8126|doi:10.1039/D2CE01210C
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.cc2cx0z9
title: CCDC 2201681: Experimental Crystal Structure Determination

description: Related Article: Parthasarathy Gayathri, Sasikala Ravi, K. Akshaya, Subramanian Karthikeyan, Mehboobali Pannipara, Abdullah G. Al-Sehemi, Dohyun Moon, Savarimuthu Philip Anthony|2022|CrystEngComm|24|8126|doi:10.1039/D2CE01210C
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.cc2cmvrm
title: CCDC 2193831: Experimental Crystal Structure Determination

description: Related Article: Juan Sun, Min Chen, Ying Li, Xingran Liu, Xinyang Fu, Lijun He, Chuanming Jin|2022|CrystEngComm|24|8160|doi:10.1039/D2CE01344D
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.cc2cmvsn
title: CCDC 2193832: Experimental Crystal Structure Determination

description: Related Article: Juan Sun, Min Chen, Ying Li, Xingran Liu, Xinyang Fu, Lijun He, Chuanming Jin|2022|CrystEngComm|24|8160|doi:10.1039/D2CE01344D
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.cc2cmvtp
title: CCDC 2193833: Experimental Crystal Structure Determination

description: Related Article: Juan Sun, Min Chen, Ying Li, Xingran Liu, Xinyang Fu, Lijun He, Chuanming Jin|2022|CrystEngComm|24|8160|doi:10.1039/D2CE01344D
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.cc2cz6m6
title: CCDC 2203778: Experimental Crystal Structure Determination

description: Related Article: Masahide Tominaga, Yuki Kawanishi, Tadashi Hyodo, Masatoshi Kawahata, Kentaro Yamaguchi|2022|Tetrahedron|128|133122|doi:10.1016/j.tet.2022.133122
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.7384212
title: Waveform data for centroid moment tensor solutions presented in publication "Bayesian seismic source inversion with a 3-D Earth model of the Japanese islands"

description: This dataset contains waveform data for centroid moment tensor solutions inferred using Hamiltonian Monte Carlo sampling algorithm and a 3-D Earth model of the Japanese islands. Specifically, it includes processed observed waveforms from the Full Range Seismograph Network of Japan (F-Net, http://www.fnet.bosai.go.jp) and synthetic waveforms for the maximum-likelihood solutions as well as Global Centroid Moment Tensor (GCMT) solutions for all study events inverted at different periods. Detailed description of the dataset is included in the README file.
DOI: 10.6084/m9.figshare.21651362.v1
title: second_zip

description: dataset for second evaluation
DOI: 10.6084/m9.figshare.21405104.v2
title: A Reproducing Kernel Hilbert Space Framework for Functional Classification

description: The intrinsic infinite-dimensional nature of functional data creates a bottleneck in the application of traditional classifiers to functional settings. These classifiers are generally either unable to generalize to infinite dimensions or have poor performance due to the curse of dimensionality. To address this concern, we propose building a distance-weighted discrimination (DWD) classifier on scores obtained by projecting data onto one specific direction. We choose this direction by minimizing, over a reproducing kernel Hilbert space, an empirical risk function containing the DWD classifier loss function. Our proposed classifier avoids overfitting and enjoys the appealing properties of DWD classifiers. We further extend this framework to accommodate functional data classification problems where scalar covariates are involved. In contrast to previous work, we establish a nonasymptotic estimation error bound on the relative misclassification rate. Through simulation studies and a real-world application, we demonstrate that the proposed classifier performs favorably relative to other commonly used functional classifiers in terms of prediction accuracy in finite-sample settings. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.21505054
title: Gibbs Priors for Bayesian Nonparametric Variable Selection with Weak Learners

description: We consider the problem of high-dimensional Bayesian nonparametric variable selection using an aggregation of so-called “weak learners.” The most popular variant of this is the Bayesian additive regression trees (BART) model, which is the natural Bayesian analog to boosting decision trees. In this article, we use Gibbs distributions on random partitions to induce sparsity in ensembles of weak learners. Looking at BART as a special case, we show that the class of Gibbs priors includes two recently proposed models—the Dirichlet additive regression trees (DART) model and the spike-and-forest model—as extremal cases, and we show that certain Gibbs priors are capable of achieving the benefits of both the DART and spike-and-forest models while avoiding some of their key drawbacks. We then show the promising performance of Gibbs priors for other classes of weak learners, such as tensor products of spline basis functions. A Pólya Urn scheme is developed for efficient computations. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.21505054.v1
title: Gibbs Priors for Bayesian Nonparametric Variable Selection with Weak Learners

description: We consider the problem of high-dimensional Bayesian nonparametric variable selection using an aggregation of so-called “weak learners.” The most popular variant of this is the Bayesian additive regression trees (BART) model, which is the natural Bayesian analog to boosting decision trees. In this article, we use Gibbs distributions on random partitions to induce sparsity in ensembles of weak learners. Looking at BART as a special case, we show that the class of Gibbs priors includes two recently proposed models—the Dirichlet additive regression trees (DART) model and the spike-and-forest model—as extremal cases, and we show that certain Gibbs priors are capable of achieving the benefits of both the DART and spike-and-forest models while avoiding some of their key drawbacks. We then show the promising performance of Gibbs priors for other classes of weak learners, such as tensor products of spline basis functions. A Pólya Urn scheme is developed for efficient computations. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.21405104
title: A Reproducing Kernel Hilbert Space Framework for Functional Classification

description: The intrinsic infinite-dimensional nature of functional data creates a bottleneck in the application of traditional classifiers to functional settings. These classifiers are generally either unable to generalize to infinite dimensions or have poor performance due to the curse of dimensionality. To address this concern, we propose building a distance-weighted discrimination (DWD) classifier on scores obtained by projecting data onto one specific direction. We choose this direction by minimizing, over a reproducing kernel Hilbert space, an empirical risk function containing the DWD classifier loss function. Our proposed classifier avoids overfitting and enjoys the appealing properties of DWD classifiers. We further extend this framework to accommodate functional data classification problems where scalar covariates are involved. In contrast to previous work, we establish a nonasymptotic estimation error bound on the relative misclassification rate. Through simulation studies and a real-world application, we demonstrate that the proposed classifier performs favorably relative to other commonly used functional classifiers in terms of prediction accuracy in finite-sample settings. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.21405104.v1
title: A reproducing kernel Hilbert space framework for functional classification

description: The intrinsic infinite-dimensional nature of functional data creates a bottleneck in the application of traditional classifiers to functional settings. These classifiers are generally either unable to generalize to infinite dimensions or have poor performance due to the curse of dimensionality. To address this concern, we propose building a distance-weighted discrimination (DWD) classifier on scores obtained by projecting data onto one specific direction. We choose this direction by minimizing, over a reproducing kernel Hilbert space, an empirical risk function containing the DWD classifier loss function. Our proposed classifier avoids overfitting and enjoys the appealing properties of DWD classifiers. We further extend this framework to accommodate functional data classification problems where scalar covariates are involved. In contrast to previous work, we establish a non-asymptotic estimation error bound on the relative misclassification rate. Through simulation studies and a real-world application, we demonstrate that the proposed classifier performs favorably relative to other commonly-used functional classifiers in terms of prediction accuracy in finite-sample settings.
DOI: 10.26093/cds/vizier.19070018
title: Red Supergiants in M31 and M33. I. The complete sample

description: VizieR online Data Catalogue associated with article published in journal Astronomical Journal (AAS) with title 'Red Supergiants in M31 and M33. I. The Complete Sample.' (bibcode: 2021ApJ...907...18R)
DOI: 10.5281/zenodo.7384201
title: Mating system and speciation I: accumulation of genetic incompatibilities in allopatry

description: All datasets used to create the graphics of Marie-Orleach et al, <em>PLoS Genetics</em>, 2023
DOI: 10.5281/zenodo.7384200
title: Mating system and speciation I: accumulation of genetic incompatibilities in allopatry

description: All datasets used to create the graphics of Marie-Orleach et al, <em>PLoS Genetics</em>, 2023
DOI: 10.6084/m9.figshare.21651419
title: third_zip

description: third zip collection of fuzzing
DOI: 10.6084/m9.figshare.21651419.v1
title: third_zip

description: third zip collection of fuzzing
DOI: 10.3886/e119451v2-130970
title: National Neighborhood Data Archive (NaNDA): Socioeconomic Status and Demographic Characteristics of Census Tracts, United States, 2008-2017

description: This dataset contains measures of socioeconomic and demographic characteristics by US census tract for the years 2008-2017. Example measures include population density; population distribution by race, ethnicity, age, and income; and proportion of population living below the poverty level, receiving public assistance, and female-headed families. The dataset also contains a set of index variables to represent neighborhood disadvantage and affluence.<br><br>A curated version of this data is available through ICPSR at http://dx.doi.org/10.3886/ICPSR38528.v1.<br>
description: All census tracts in the United States, including Puerto Rico. Smallest Geographic Unit: census tract
description: This dataset complements a similar version (National Neighborhood Data Archive [NaNDA]: Neighborhood Socioeconomic and Demographic Characteristics of Census Tracts, United States, 2000-2010, https://www.openicpsr.org/openicpsr/project/111107/) which is based on the same theoretical findings and covers the period 2000-2010. Users wanting to combine this dataset with measures from 2000-2010 should be aware of key differences between how measures are calculated across the two datasets. For more information, see the "Usage Note" section of the documentation.
DOI: 10.6084/m9.figshare.21651467
title: Data for Mitchell et al. Journal of Wildlife Management

description: Data used in Mitchell et al. Journal of Wildlife Management analysis of factors affecting wetland occupancy by duck broods.
DOI: 10.6084/m9.figshare.21651467.v1
title: Data for Mitchell et al. Journal of Wildlife Management

description: Data used in Mitchell et al. Journal of Wildlife Management analysis of factors affecting wetland occupancy by duck broods.
DOI: 10.5281/zenodo.7380447
title: JGR_DATASETS_2022JC019050

description: First baroclinic Rossby radius of deformation computed from hydrography from the World Ocean Atlas 2018.
DOI: 10.5281/zenodo.7380446
title: JGR_DATASETS_2022JC019050

description: First baroclinic Rossby radius of deformation computed from hydrography from the World Ocean Atlas 2018.
DOI: 10.5281/zenodo.7384271
title: Six hundred years of reconstructed atmospheric river activity on the US west coast

description: These are the reconstruction data from the article: Six hundred years of reconstructed atmospheric river activity along the US West Coast (under review at the Journal of Geophysical Research: Atmosphere). The earliest start year of the reconstruction are mentioned in the files (either 1916 or 1400) and the corresponding models are mentioned as PCR (Regression) or NN (Neural Network).