cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc23jmmx
title: CCDC 1952399: Experimental Crystal Structure Determination

description: Related Article: Lauren L. K. Taylor, Iñigo J. Vitorica-Yrezabal, Ivana Borilović, Floriana Tuna, Imogen A. Riddell|2021|Chem.Commun.|57|11252|doi:10.1039/D1CC04413C
DOI: 10.5517/ccdc.csd.cc23jmny
title: CCDC 1952400: Experimental Crystal Structure Determination

description: Related Article: Lauren L. K. Taylor, Iñigo J. Vitorica-Yrezabal, Ivana Borilović, Floriana Tuna, Imogen A. Riddell|2021|Chem.Commun.|57|11252|doi:10.1039/D1CC04413C
DOI: 10.5517/ccdc.csd.cc23jmpz
title: CCDC 1952401: Experimental Crystal Structure Determination

description: Related Article: Lauren L. K. Taylor, Iñigo J. Vitorica-Yrezabal, Ivana Borilović, Floriana Tuna, Imogen A. Riddell|2021|Chem.Commun.|57|11252|doi:10.1039/D1CC04413C
DOI: 10.5517/ccdc.csd.cc23jmq0
title: CCDC 1952402: Experimental Crystal Structure Determination

description: Related Article: Lauren L. K. Taylor, Iñigo J. Vitorica-Yrezabal, Ivana Borilović, Floriana Tuna, Imogen A. Riddell|2021|Chem.Commun.|57|11252|doi:10.1039/D1CC04413C
DOI: 10.5517/ccdc.csd.cc243d9z
title: CCDC 1969470: Experimental Crystal Structure Determination

description: Related Article: Maria-Gabriela Alexandru, Diana Visinescu, Beatrice Cula, Sergiu Shova, Renato Rabelo, Nicolás Moliner, Francesc Lloret, Joan Cano, Miguel Julve|2021|Dalton Trans.|50|14640|doi:10.1039/D1DT02512K
DOI: 10.5517/ccdc.csd.cc243db0
title: CCDC 1969471: Experimental Crystal Structure Determination

description: Related Article: Maria-Gabriela Alexandru, Diana Visinescu, Beatrice Cula, Sergiu Shova, Renato Rabelo, Nicolás Moliner, Francesc Lloret, Joan Cano, Miguel Julve|2021|Dalton Trans.|50|14640|doi:10.1039/D1DT02512K
DOI: 10.5517/ccdc.csd.cc243dc1
title: CCDC 1969472: Experimental Crystal Structure Determination

description: Related Article: Maria-Gabriela Alexandru, Diana Visinescu, Beatrice Cula, Sergiu Shova, Renato Rabelo, Nicolás Moliner, Francesc Lloret, Joan Cano, Miguel Julve|2021|Dalton Trans.|50|14640|doi:10.1039/D1DT02512K
DOI: 10.5517/ccdc.csd.cc243dd2
title: CCDC 1969473: Experimental Crystal Structure Determination

description: Related Article: Maria-Gabriela Alexandru, Diana Visinescu, Beatrice Cula, Sergiu Shova, Renato Rabelo, Nicolás Moliner, Francesc Lloret, Joan Cano, Miguel Julve|2021|Dalton Trans.|50|14640|doi:10.1039/D1DT02512K
DOI: 10.5517/ccdc.csd.cc243dg4
title: CCDC 1969475: Experimental Crystal Structure Determination

description: Related Article: Maria-Gabriela Alexandru, Diana Visinescu, Beatrice Cula, Sergiu Shova, Renato Rabelo, Nicolás Moliner, Francesc Lloret, Joan Cano, Miguel Julve|2021|Dalton Trans.|50|14640|doi:10.1039/D1DT02512K
DOI: 10.5517/ccdc.csd.cc243dh5
title: CCDC 1969476: Experimental Crystal Structure Determination

description: Related Article: Maria-Gabriela Alexandru, Diana Visinescu, Beatrice Cula, Sergiu Shova, Renato Rabelo, Nicolás Moliner, Francesc Lloret, Joan Cano, Miguel Julve|2021|Dalton Trans.|50|14640|doi:10.1039/D1DT02512K
DOI: 10.5517/ccdc.csd.cc243dj6
title: CCDC 1969477: Experimental Crystal Structure Determination

description: Related Article: Maria-Gabriela Alexandru, Diana Visinescu, Beatrice Cula, Sergiu Shova, Renato Rabelo, Nicolás Moliner, Francesc Lloret, Joan Cano, Miguel Julve|2021|Dalton Trans.|50|14640|doi:10.1039/D1DT02512K
DOI: 10.5517/ccdc.csd.cc25hmkw
title: CCDC 2011018: Experimental Crystal Structure Determination

description: Related Article: Sukhen Bala, Sohel Akhtar, Jun-Liang Liu, Guo-Zhang Huang, Si-Guo Wu, Avik De, Krishna Sundar Das, Sayan Saha, Ming-Liang Tong, Raju Mondal|2021|Chem.Commun.|57|11177|doi:10.1039/D1CC02990H
DOI: 10.5517/ccdc.csd.cc25hmlx
title: CCDC 2011019: Experimental Crystal Structure Determination

description: Related Article: Sukhen Bala, Sohel Akhtar, Jun-Liang Liu, Guo-Zhang Huang, Si-Guo Wu, Avik De, Krishna Sundar Das, Sayan Saha, Ming-Liang Tong, Raju Mondal|2021|Chem.Commun.|57|11177|doi:10.1039/D1CC02990H
DOI: 10.5517/ccdc.csd.cc25hmmy
title: CCDC 2011020: Experimental Crystal Structure Determination

description: Related Article: Sukhen Bala, Sohel Akhtar, Jun-Liang Liu, Guo-Zhang Huang, Si-Guo Wu, Avik De, Krishna Sundar Das, Sayan Saha, Ming-Liang Tong, Raju Mondal|2021|Chem.Commun.|57|11177|doi:10.1039/D1CC02990H
DOI: 10.5517/ccdc.csd.cc25v8cp
title: CCDC 2021242: Experimental Crystal Structure Determination

description: Related Article: Kazuki Nakaya, Shintaro Takahashi, Akihiko Ishii, Kajjana Boonpalit, Panida Surawatanawong, Norio Nakata|2021|Dalton Trans.|50|14810|doi:10.1039/D1DT01856F
DOI: 10.5517/ccdc.csd.cc26ccrq
title: CCDC 2036723: Experimental Crystal Structure Determination

description: Related Article: Xu Dong, Li-Ping Xu, Yi Yang, Yunxia Liu, Xin Li, Qing Liu, Liang Zheng, Fagang Wang, Hui Liu|2021|Org.Chem.Front.|8|6009|doi:10.1039/D1QO00942G
DOI: 10.5517/ccdc.csd.cc26ybdx
title: CCDC 2053979: Experimental Crystal Structure Determination

description: Related Article: Li-Ping Chen, Jiang-Fei Chen, Yu-Jiao Zhang, Xing-Yi He, Ya-Fei Han, Yu-Ting Xiao, Gui-Fen Lv, Xin Lu, Fan Teng, Qing Sun, Jin-Heng Li|2021|Org.Chem.Front.|8|6067|doi:10.1039/D1QO01147B
DOI: 10.5517/ccdc.csd.cc26ybgz
title: CCDC 2053981: Experimental Crystal Structure Determination

description: Related Article: Li-Ping Chen, Jiang-Fei Chen, Yu-Jiao Zhang, Xing-Yi He, Ya-Fei Han, Yu-Ting Xiao, Gui-Fen Lv, Xin Lu, Fan Teng, Qing Sun, Jin-Heng Li|2021|Org.Chem.Front.|8|6067|doi:10.1039/D1QO01147B
DOI: 10.5517/ccdc.csd.cc26ybh0
title: CCDC 2053982: Experimental Crystal Structure Determination

description: Related Article: Li-Ping Chen, Jiang-Fei Chen, Yu-Jiao Zhang, Xing-Yi He, Ya-Fei Han, Yu-Ting Xiao, Gui-Fen Lv, Xin Lu, Fan Teng, Qing Sun, Jin-Heng Li|2021|Org.Chem.Front.|8|6067|doi:10.1039/D1QO01147B
DOI: 10.5517/ccdc.csd.cc26zhf4
title: CCDC 2055096: Experimental Crystal Structure Determination

description: Related Article: Yu Mao, Yeqing Liu, Lei Yu, Shengyang Ni, Yi Wang, Yi Pan|2021|Org.Chem.Front.|8|5968|doi:10.1039/D1QO00932J
DOI: 10.5258/soton/d1782
title: Data for: The response of Common carp (Cyprinus carpio) to insonified bubble curtains.

description: Data for article "The response of Common carp (Cyprinus carpio) to insonified bubble curtains" in The Journal of The Acoustical Society of America. All data collected for experiments 1 to 3, includes: - Annotated datasets - Readme files - R code and labview virtual instruments used - .txt files with results outputs from R Studio Note: Experiments are listed in chronological order rather than the order listed in the paper (i.e. Exp 1 = Exp 1 in the paper, Exp 2 = Exp 3 in the paper, and Exp 3 = Exp 2 in the paper)
DOI: 10.15482/usda.adc/1523462
title: Drosophila takahashii genome assembly Dtak02082011
DOI: 10.15482/usda.adc/1523463
title: Drosophila takahashii genome annotations v0.5.3 for genome assembly Dbip02082011
DOI: 10.5519/qd.h3w5w6vx
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5257/iea/wes/2021
title: World Energy Statistics (2021 Edition)

description: World Energy Statistics provides comprehensive world energy statistics on all energy sources - coal, gas, oil, electricity, renewables and waste. It covers energy supply and consumption for 150 countries and regions, including all OECD countries, over 100 other key energy-producing and -consuming countries, as well as world totals and various regional aggregates. The dataset includes detailed tables by country in original units, and summary time-series on production, trade, and final consumption by sector.
DOI: 10.2905/ed8e8e11-62c3-4895-a7b9-5ef851f112ed
title: GHS_STAT_DUC R2019A - GHS Degree of Urbanisation Classification (2015, 2000, 1990, 1975), R2019A

description: This dataset contains the Global Degree of Urbanisation Classification of adminstrative units delineated by the Global ADMinistrative layer version 3.6 (GADM36) according to the application stage 2 of the Degree of Urbanisation. The classification is based on the Global Human Settlement Layer (GHSL) global datasets GHS R2019: the settlement classification GHS-SMOD (GHS_SMOD_POP_GLOBE_R2019A), population grid GHS-POP (GHS_POP_MT_GLOBE_R2019A), and built-up area GHS-BUILT (GHS_BUILT_LDSMT_GLOBE_R2018A) layers for target years 1975, 1990, 2000 and 2015. This dataset contains a package composed by one excel table, a summary table gathering global summary statistics and by countries and territories in GADM3.6, and 24 CSV files, one per GADM level (from 0 to 5) and epoch (1975-1990-2000-2015), with all administrative units classified according to the Degree of Urbanisation
DOI: 10.15482/usda.adc/1523464
title: Drosophila elegans genome annotations v0.5.3 for genome assembly Dele02082011
DOI: 10.6084/m9.figshare.16571151.v3
title: Zn-atz-oba gas sorption data Exp. and Sim.

description: Zn-atz-oba gas sorption data
DOI: 10.6084/m9.figshare.16571151
title: Zn-atz-oba gas sorption data Exp. and Sim.

description: Zn-atz-oba gas sorption data
DOI: 10.6084/m9.figshare.16571151.v2
title: Zn-atz-oba gas sorption data Exp. and Sim.

description: Zn-atz-oba gas sorption data
DOI: 10.5281/zenodo.5588118
title: Dataset for the publication entitled: "Assessment of lithium ion battery ageing by combined impedance spectroscopy, functional microscopy and finite element modelling""

description: Related to the publication: https://doi.org/10.1016/j.jpowsour.2021.230459 Datasets for the following Figures: Figure 2. Figure 3. Figure 5. Figure 7.
description: Related ot publication: https://doi.org/10.1016/j.jpowsour.2021.230459
DOI: 10.5281/zenodo.5599904
title: Arius leptaspis RB0493 ARBOR SPAdes preassembly

description: .
DOI: 10.5281/zenodo.5599903
title: Arius leptaspis RB0493 ARBOR SPAdes preassembly

description: .
DOI: 10.6084/m9.figshare.13483345
title: Assurance for Sample Size Determination in Reliability Demonstration Testing

description: Manufacturers are required to demonstrate that products meet reliability targets. A way to achieve this is with reliability demonstration tests (RDTs), where a number of products are put on test and the test is passed or failed according to a decision rule based on the observed outcomes. There are various methods for determining the sample size for RDTs, typically based on the power of a hypothesis test following the RDT or risk criteria. Bayesian risk criteria approaches combine the choice of sample size with the analysis of the test data while relying on the specification of acceptable and rejectable reliability levels. In this article, we offer an alternative approach to sample size determination based on the idea of assurance. This approach chooses the sample size to provide a specified probability that the RDT will result in a successful outcome. It separates the design and analysis of the RDT, allowing different priors for the producer and consumer. We develop the assurance approach for sample size calculations in RDTs for binomial and Weibull likelihoods and propose appropriate prior distributions for the design and analysis of the test. In each case, we illustrate the approach with an example based on real data.
DOI: 10.6084/m9.figshare.13493342.v1
title: Modality-Constrained Density Estimation via Deformable Templates

description: Estimation of a probability density function (pdf) from its samples, while satisfying certain shape constraints, is an important problem that lacks coverage in the literature. This article introduces a novel geometric, deformable template constrained density estimator (dtcode) for estimating pdfs constrained to have a given number of modes. Our approach explores the space of thus-constrained pdfs using the set of shape-preserving transformations: an arbitrary template from the given shape class is transformed via a shape-preserving transformation to obtain the final optimal estimate. The search for this optimal transformation, under the maximum-likelihood criterion, is performed by mapping transformations to the tangent space of a Hilbert sphere, where they are effectively linearized, and can be expressed using an orthogonal basis. This framework is first applied to (univariate) unconditional densities and then extended to conditional densities. We provide asymptotic convergence rates for dtcode, and an application of the framework to the speed distributions for different traffic flows on Californian highways.
DOI: 10.6084/m9.figshare.13493342
title: Modality-Constrained Density Estimation via Deformable Templates

description: Estimation of a probability density function (pdf) from its samples, while satisfying certain shape constraints, is an important problem that lacks coverage in the literature. This article introduces a novel geometric, deformable template constrained density estimator (dtcode) for estimating pdfs constrained to have a given number of modes. Our approach explores the space of thus-constrained pdfs using the set of shape-preserving transformations: an arbitrary template from the given shape class is transformed via a shape-preserving transformation to obtain the final optimal estimate. The search for this optimal transformation, under the maximum-likelihood criterion, is performed by mapping transformations to the tangent space of a Hilbert sphere, where they are effectively linearized, and can be expressed using an orthogonal basis. This framework is first applied to (univariate) unconditional densities and then extended to conditional densities. We provide asymptotic convergence rates for dtcode, and an application of the framework to the speed distributions for different traffic flows on Californian highways.
DOI: 10.6084/m9.figshare.13483345.v3
title: Assurance for Sample Size Determination in Reliability Demonstration Testing

description: Manufacturers are required to demonstrate that products meet reliability targets. A way to achieve this is with reliability demonstration tests (RDTs), where a number of products are put on test and the test is passed or failed according to a decision rule based on the observed outcomes. There are various methods for determining the sample size for RDTs, typically based on the power of a hypothesis test following the RDT or risk criteria. Bayesian risk criteria approaches combine the choice of sample size with the analysis of the test data while relying on the specification of acceptable and rejectable reliability levels. In this article, we offer an alternative approach to sample size determination based on the idea of assurance. This approach chooses the sample size to provide a specified probability that the RDT will result in a successful outcome. It separates the design and analysis of the RDT, allowing different priors for the producer and consumer. We develop the assurance approach for sample size calculations in RDTs for binomial and Weibull likelihoods and propose appropriate prior distributions for the design and analysis of the test. In each case, we illustrate the approach with an example based on real data.
DOI: 10.6084/m9.figshare.13483345.v2
title: Assurance for Sample Size Determination in Reliability Demonstration Testing

description: Manufacturers are required to demonstrate that products meet reliability targets. A way to achieve this is with reliability demonstration tests (RDTs), where a number of products are put on test and the test is passed or failed according to a decision rule based on the observed outcomes. There are various methods for determining the sample size for RDTs, typically based on the power of a hypothesis test following the RDT or risk criteria. Bayesian risk criteria approaches combine the choice of sample size with the analysis of the test data while relying on the specification of acceptable and rejectable reliability levels. In this article, we offer an alternative approach to sample size determination based on the idea of assurance. This approach chooses the sample size to provide a specified probability that the RDT will result in a successful outcome. It separates the design and analysis of the RDT, allowing different priors for the producer and consumer. We develop the assurance approach for sample size calculations in RDTs for binomial and Weibull likelihoods and propose appropriate prior distributions for the design and analysis of the test. In each case, we illustrate the approach with an example based on real data.
DOI: 10.6084/m9.figshare.13143288.v1
title: Template Priors in Bayesian Curve Registration

description: In experiments where observations on each experimental unit are functional in nature, it is often the case that, in addition to variability along the horizontal axis (height or amplitude variability), there are also lateral displacements/deformations in curves (referred to as phase variability). Unlike the former, the latter form of variability is often treated as a nuisance parameter when making inferences. Therefore, it is common in functional data analysis to reduce this variability by aligning curves through a process called curve registration. Often, expert knowledge regarding the location and time that certain curve features occur is available to guide the curve realignment. We propose a Bayesian model that permits incorporating this knowledge when registering curves using a Gaussian process prior formulation. This novel approach capitalizes on the interpolation property of predictive distributions from Gaussian processes while still preserving the flexibility found in modern registration techniques. We detail computational strategies and illustrate the utility of the method through a simulation study and an analysis of knee-power biomechanics. Supplementary materials for the article are available online.
DOI: 10.15482/usda.adc/1523465
title: Drosophila kikkawai genome assembly Dkik02082011
DOI: 10.15482/usda.adc/1523466
title: Drosophila kikkawai genome annotations v0.5.3 for genome assembly Dkik02082011
DOI: 10.5519/qd.azf5x3sv
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4932458 records)
DOI: 10.15468/wqgf3t
title: Early Land Plants Today: Index of Liverworts & Hornworts 2013 - 2014

description: This dataset contains the digitized treatments in Plazi based on the original journal article Söderström, Lars, Hagborg, Anders, Konrat, Matt Von (2016): Early Land Plants Today: Index of Liverworts & Hornworts 2013 - 2014. Phytotaxa 269 (3): 133-185, DOI: http://dx.doi.org/10.11646/phytotaxa.269.3.1, URL: http://dx.doi.org/10.11646/phytotaxa.269.3.1
DOI: 10.5281/zenodo.5600068
title: Open data for publication Reconstruction of Ice Surface upon Acetone Adsorption: An in-situ ATR-IR Modulation Excitation Spectroscopy Study

description: Original data for publication "Reconstruction of Ice Surface upon Acetone Adsorption: An in-situ ATR-IR Modulation Excitation Spectroscopy Study". Original data used for the Figures.
DOI: 10.5281/zenodo.5600067
title: Open data for publication Reconstruction of Ice Surface upon Acetone Adsorption: An in-situ ATR-IR Modulation Excitation Spectroscopy Study

description: Original data for publication "Reconstruction of Ice Surface upon Acetone Adsorption: An in-situ ATR-IR Modulation Excitation Spectroscopy Study". Original data used for the Figures.
DOI: 10.6084/m9.figshare.13286479
title: A Random Fourier Feature Method for Emulating Computer Models With Gradient Information

description: Computer models with gradient information are increasingly used in engineering and science. The gradient-enhanced Gaussian process emulator can be used for emulating such models. Because the size of the covariance matrix increases proportionally with the dimension of inputs and the sample size, it is computationally challenging to fit such an emulator for large datasets. We propose a random Fourier feature method to mitigate this difficulty. The key idea of the proposed method is to employ random Fourier features to obtain an easily computable, low-dimensional feature representation for shift-invariant kernels involving gradients. The effectiveness of the proposed method is illustrated by several examples.
DOI: 10.6084/m9.figshare.13061590
title: Joint Models for Event Prediction From Time Series and Survival Data

description: We present a nonparametric prognostic framework for individualized event prediction based on joint modeling of both time series and time-to-event data. Our approach exploits a multivariate Gaussian convolution process (MGCP) to model the evolution of time series signals and a Cox model to map time-to-event data with time series data modeled through the MGCP. Taking advantage of the unique structure imposed by convolved processes, we provide a variational inference framework to simultaneously estimate parameters in the joint MGCP-Cox model. This significantly reduces computational complexity and safeguards against model overfitting. Experiments on synthetic and real world data show that the proposed framework outperforms state-of-the art approaches built on two-stage inference and strong parametric assumptions. Technical details are available in the supplementary materials.
DOI: 10.6084/m9.figshare.13061590.v2
title: Joint Models for Event Prediction From Time Series and Survival Data

description: We present a nonparametric prognostic framework for individualized event prediction based on joint modeling of both time series and time-to-event data. Our approach exploits a multivariate Gaussian convolution process (MGCP) to model the evolution of time series signals and a Cox model to map time-to-event data with time series data modeled through the MGCP. Taking advantage of the unique structure imposed by convolved processes, we provide a variational inference framework to simultaneously estimate parameters in the joint MGCP-Cox model. This significantly reduces computational complexity and safeguards against model overfitting. Experiments on synthetic and real world data show that the proposed framework outperforms state-of-the art approaches built on two-stage inference and strong parametric assumptions. Technical details are available in the supplementary materials.
DOI: 10.6084/m9.figshare.13061590.v1
title: Joint Models for Event Prediction from Time Series and Survival Data

description: We present a non-parametric prognostic framework for individualized event prediction based on joint modeling of both time series and time-to-event data. Our approach exploits a multivariate Gaussian convolution process (MGCP) to model the evolution of time series signals and a Cox model to map time-to-event data with time series data modeled through the MGCP. Taking advantage of the unique structure imposed by convolved processes, we provide a variational inference framework to simultaneously estimate parameters in the joint MGCP-Cox model. This significantly reduces computational complexity and safeguards against model overfitting. Experiments on synthetic and real world data show that the proposed framework outperforms state-of-the art approaches built on two-stage inference and strong parametric assumptions. Technical details are available in the supplementary materials.
DOI: 10.6084/m9.figshare.13286479.v4
title: A Random Fourier Feature Method for Emulating Computer Models With Gradient Information

description: Computer models with gradient information are increasingly used in engineering and science. The gradient-enhanced Gaussian process emulator can be used for emulating such models. Because the size of the covariance matrix increases proportionally with the dimension of inputs and the sample size, it is computationally challenging to fit such an emulator for large datasets. We propose a random Fourier feature method to mitigate this difficulty. The key idea of the proposed method is to employ random Fourier features to obtain an easily computable, low-dimensional feature representation for shift-invariant kernels involving gradients. The effectiveness of the proposed method is illustrated by several examples.