cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccb1cyn
title: CCDC 299241: Experimental Crystal Structure Determination

description: Related Article: G.S.Nichol, W.Clegg|2006|Cryst.Growth Des.|6|451|doi:10.1021/cg0503806
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.29334721
title: How to Estimate Intraclass Correlation Coefficients for Interrater Reliability from Planned Incomplete Data

description: The interrater reliability (IRR) of observational data is often estimated by means of intraclass correlation coefficients (ICCs), which are flexible IRR estimators that are based on the variance decomposition of scores obtained by observations. ICCs are typically estimated using mean squares from an ANOVA model, the computation of which is not straightforward for incomplete data. However, many studies in behavioral research use planned missing observational designs, in which the raters partially vary across subjects. Planned missing designs result in incomplete data. Therefore, we simulated planned incomplete data and compared the computational accuracy (bias of point estimates, bias of variability estimates, root mean squared error, and coverage rates) and computational feasibility (convergence rates and estimation time) of three recently proposed estimation methods for ICCs: Markov chain Monte Carlo estimation of Bayesian hierarchical linear models, maximum likelihood estimation of random-effects models, and maximum likelihood estimation of common-factor models. Maximum likelihood estimation of random-effects models with Monte-Carlo confidence intervals is preferred based on all criteria. This article is accompanied by R code, which enables researchers to apply these estimation methods. A demonstration of the R code to a real-data set from an educational context is provided.
DOI: 10.5517/ccb1dc3
title: CCDC 299254: Experimental Crystal Structure Determination

description: Related Article: Jing-Ping Wang, Jun-Wei Zhao, Xian-Ying Duan, Jing-Yang Niu|2006|Cryst.Growth Des.|6|507|doi:10.1021/cg050433j
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/ccb1dd4
title: CCDC 299255: Experimental Crystal Structure Determination

description: Related Article: Jing-Ping Wang, Jun-Wei Zhao, Xian-Ying Duan, Jing-Yang Niu|2006|Cryst.Growth Des.|6|507|doi:10.1021/cg050433j
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.29334721.v1
title: How to Estimate Intraclass Correlation Coefficients for Interrater Reliability from Planned Incomplete Data

description: The interrater reliability (IRR) of observational data is often estimated by means of intraclass correlation coefficients (ICCs), which are flexible IRR estimators that are based on the variance decomposition of scores obtained by observations. ICCs are typically estimated using mean squares from an ANOVA model, the computation of which is not straightforward for incomplete data. However, many studies in behavioral research use planned missing observational designs, in which the raters partially vary across subjects. Planned missing designs result in incomplete data. Therefore, we simulated planned incomplete data and compared the computational accuracy (bias of point estimates, bias of variability estimates, root mean squared error, and coverage rates) and computational feasibility (convergence rates and estimation time) of three recently proposed estimation methods for ICCs: Markov chain Monte Carlo estimation of Bayesian hierarchical linear models, maximum likelihood estimation of random-effects models, and maximum likelihood estimation of common-factor models. Maximum likelihood estimation of random-effects models with Monte-Carlo confidence intervals is preferred based on all criteria. This article is accompanied by R code, which enables researchers to apply these estimation methods. A demonstration of the R code to a real-data set from an educational context is provided.
DOI: 10.6084/m9.figshare.29334721.v2
title: How to Estimate Intraclass Correlation Coefficients for Interrater Reliability from Planned Incomplete Data

description: The interrater reliability (IRR) of observational data is often estimated by means of intraclass correlation coefficients (ICCs), which are flexible IRR estimators that are based on the variance decomposition of scores obtained by observations. ICCs are typically estimated using mean squares from an ANOVA model, the computation of which is not straightforward for incomplete data. However, many studies in behavioral research use planned missing observational designs, in which the raters partially vary across subjects. Planned missing designs result in incomplete data. Therefore, we simulated planned incomplete data and compared the computational accuracy (bias of point estimates, bias of variability estimates, root mean squared error, and coverage rates) and computational feasibility (convergence rates and estimation time) of three recently proposed estimation methods for ICCs: Markov chain Monte Carlo estimation of Bayesian hierarchical linear models, maximum likelihood estimation of random-effects models, and maximum likelihood estimation of common-factor models. Maximum likelihood estimation of random-effects models with Monte-Carlo confidence intervals is preferred based on all criteria. This article is accompanied by R code, which enables researchers to apply these estimation methods. A demonstration of the R code to a real-data set from an educational context is provided.
DOI: 10.6084/m9.figshare.29820450
title: How to Get MAD: Generating Uniformly Sampled Correlation Matrices with a Fixed Mean Absolute Discrepancy

description: This article describes a simple and fast algorithm for generating uniformly sampled correlation matrices (<b><i>R</i></b>) with a fixed mean absolute discrepancy (MAD) relative to a target (population) Rpop. The algorithm can be profitably used in many settings including model robustness studies and stress testing of investment portfolios, or in dynamic model-fit analyses to generate <b><i>R</i></b> matrices with a known degree of model-approximation error (as operationalized by the MAD). Using results from higher-dimensional geometry, I show that Rn×n matrices with a fixed MAD lie in the intersection of two sets that represent: (a) an elliptope and (b) the surface of a cross-polytope. When <i>n</i> = 3, these sets can be visualized as an elliptical tetrahedron and the surface of an octahedron. An online supplement includes R code for implementing the algorithm and for reproducing all of the results in the article.
DOI: 10.6084/m9.figshare.29820450.v1
title: How to Get MAD: Generating Uniformly Sampled Correlation Matrices with a Fixed Mean Absolute Discrepancy

description: This article describes a simple and fast algorithm for generating uniformly sampled correlation matrices (<b><i>R</i></b>) with a fixed mean absolute discrepancy (MAD) relative to a target (population) Rpop. The algorithm can be profitably used in many settings including model robustness studies and stress testing of investment portfolios, or in dynamic model-fit analyses to generate <b><i>R</i></b> matrices with a known degree of model-approximation error (as operationalized by the MAD). Using results from higher-dimensional geometry, I show that Rn×n matrices with a fixed MAD lie in the intersection of two sets that represent: (a) an elliptope and (b) the surface of a cross-polytope. When <i>n</i> = 3, these sets can be visualized as an elliptical tetrahedron and the surface of an octahedron. An online supplement includes R code for implementing the algorithm and for reproducing all of the results in the article.
DOI: 10.5517/ccb1d2t
title: CCDC 299245: Experimental Crystal Structure Determination

description: Related Article: C.B.Aakeroy, I.Hussain, J.Desper|2006|Cryst.Growth Des.|6|474|doi:10.1021/cg050391z
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.29223783
title: Ruling out Latent Time-Varying Confounders in Two-Variable Multi-Wave Studies

description: There has been considerable interest in estimating causal cross-lagged effects in two-variable, multi-wave designs. However, there does not currently exist a strategy for ruling out unmeasured time-varying covariates that may act as confounders. In this paper, we propose a new strategy for testing whether an unmeasured time-varying covariate explains all covariation between the two “causal” variables in the data. That model, called the <i>Latent Time-Varying Covariate</i> (LTVC) model, can be tested with observations for two variables assessed across three or more measurement waves. If the LTVC model fits well, then a time˗varying covariate can explain the covariance structure, which undermines the plausibility of causal cross-lagged effects. Although the LTVC model tends to be underpowered when causal cross-lagged effects are small, if testable stationarity constraints on the LTVC model are imposed, adequate power can be achieved. We illustrate the LTVC approach with three examples from the literature. Additionally, we introduce the LTVC-CLPM model, which is identified given strong stationarity constraints. Also considered are multivariate and multi-factor models, the inclusion of measured time-invariant covariates in model, measurement of the stability of the LTVC, and the lag-lead model. These methods allow researchers to probe the assumption that an unmeasured time˗varying confounder is the source of all the <i>X-Y</i> covariation. Our methods help researchers to rule out certain forms of confounding in two-variable, multi-wave designs.
DOI: 10.6084/m9.figshare.29223783.v1
title: Ruling out Latent Time-Varying Confounders in Two-Variable Multi-Wave Studies

description: There has been considerable interest in estimating causal cross-lagged effects in two-variable, multi-wave designs. However, there does not currently exist a strategy for ruling out unmeasured time-varying covariates that may act as confounders. In this paper, we propose a new strategy for testing whether an unmeasured time-varying covariate explains all covariation between the two “causal” variables in the data. That model, called the <i>Latent Time-Varying Covariate</i> (LTVC) model, can be tested with observations for two variables assessed across three or more measurement waves. If the LTVC model fits well, then a time˗varying covariate can explain the covariance structure, which undermines the plausibility of causal cross-lagged effects. Although the LTVC model tends to be underpowered when causal cross-lagged effects are small, if testable stationarity constraints on the LTVC model are imposed, adequate power can be achieved. We illustrate the LTVC approach with three examples from the literature. Additionally, we introduce the LTVC-CLPM model, which is identified given strong stationarity constraints. Also considered are multivariate and multi-factor models, the inclusion of measured time-invariant covariates in model, measurement of the stability of the LTVC, and the lag-lead model. These methods allow researchers to probe the assumption that an unmeasured time˗varying confounder is the source of all the <i>X-Y</i> covariation. Our methods help researchers to rule out certain forms of confounding in two-variable, multi-wave designs.
DOI: 10.6084/m9.figshare.29457959
title: Empirical Bayes Priors for MCMC Estimation of the Multivariate Social Relations Model

description: The social relations model (SRM) is a linear random-effects model applied to examine dyadic round-robin data within social networks. Such data have a unique multilevel structure in that dyads are cross-classified within individuals who may be nested within different social networks. The SRM decomposes perceptual or behavioral measures into multiple components: case-level random effects (in-coming and out-going effects) and dyad-level residuals (relationship effects), the associations among which are often of substantive interest. Multivariate SRM analyses are increasingly common, requiring more sophisticated estimation algorithms. This article evaluates Markov chain Monte Carlo (MCMC) estimation of multivariate-SRM parameters, compares MCMC to maximum-likelihood estimation, and introduces two methods to reduce bias in MCMC point estimates using empirical-Bayes priors. Four simulation studies are presented, two of which reveal dependency of small-group results on priors by manipulating location and precision hyperparameters, respectively. The third simulation study explores the impact of sampling more small groups on prior sensitivity. The fourth simulation study explores how Bayesian model averaging might compensate for underestimated variance due to empirical-Bayes priors. Finally, recommendations for future research are made and extensions of the SRM are discussed.
DOI: 10.6084/m9.figshare.29457959.v1
title: Empirical Bayes Priors for MCMC Estimation of the Multivariate Social Relations Model

description: The social relations model (SRM) is a linear random-effects model applied to examine dyadic round-robin data within social networks. Such data have a unique multilevel structure in that dyads are cross-classified within individuals who may be nested within different social networks. The SRM decomposes perceptual or behavioral measures into multiple components: case-level random effects (in-coming and out-going effects) and dyad-level residuals (relationship effects), the associations among which are often of substantive interest. Multivariate SRM analyses are increasingly common, requiring more sophisticated estimation algorithms. This article evaluates Markov chain Monte Carlo (MCMC) estimation of multivariate-SRM parameters, compares MCMC to maximum-likelihood estimation, and introduces two methods to reduce bias in MCMC point estimates using empirical-Bayes priors. Four simulation studies are presented, two of which reveal dependency of small-group results on priors by manipulating location and precision hyperparameters, respectively. The third simulation study explores the impact of sampling more small groups on prior sensitivity. The fourth simulation study explores how Bayesian model averaging might compensate for underestimated variance due to empirical-Bayes priors. Finally, recommendations for future research are made and extensions of the SRM are discussed.
DOI: 10.15468/dl.2sf7u5
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Rheiformes)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((-40 0,-30 0,-30 10,-40 10,-40 0))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025359-251009101135966/datasets/export for details.
DOI: 10.5517/ccb1d3v
title: CCDC 299246: Experimental Crystal Structure Determination

description: Related Article: C.B.Aakeroy, I.Hussain, J.Desper|2006|Cryst.Growth Des.|6|474|doi:10.1021/cg050391z
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/ccb1d4w
title: CCDC 299247: Experimental Crystal Structure Determination

description: Related Article: C.B.Aakeroy, I.Hussain, J.Desper|2006|Cryst.Growth Des.|6|474|doi:10.1021/cg050391z
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/ccb1d5x
title: CCDC 299248: Experimental Crystal Structure Determination

description: Related Article: C.B.Aakeroy, I.Hussain, J.Desper|2006|Cryst.Growth Des.|6|474|doi:10.1021/cg050391z
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: 10.15468/dl.dra3gf
title: Occurrence Download

description: A dataset containing 11863 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Bucerotiformes)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((30 -40,40 -40,40 -30,30 -30,30 -40))"
 ]
}

 The dataset includes 11863 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025357-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/ccb1d6y
title: CCDC 299249: Experimental Crystal Structure Determination

description: Related Article: C.B.Aakeroy, I.Hussain, J.Desper|2006|Cryst.Growth Des.|6|474|doi:10.1021/cg050391z
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/ccb1d7z
title: CCDC 299250: Experimental Crystal Structure Determination

description: Related Article: C.B.Aakeroy, I.Hussain, J.Desper|2006|Cryst.Growth Des.|6|474|doi:10.1021/cg050391z
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/ccb1d80
title: CCDC 299251: Experimental Crystal Structure Determination

description: Related Article: C.B.Aakeroy, I.Hussain, J.Desper|2006|Cryst.Growth Des.|6|474|doi:10.1021/cg050391z
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/ccb1d91
title: CCDC 299252: Experimental Crystal Structure Determination

description: Related Article: C.B.Aakeroy, I.Hussain, J.Desper|2006|Cryst.Growth Des.|6|474|doi:10.1021/cg050391z
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: 10.15468/dl.ed33dj
title: Occurrence Download

description: A dataset containing 128 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eurhadina loewii",
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "HasCoordinate is true"
 ]
}

 The dataset includes 128 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025358-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/ccb1gsl
title: CCDC 299329: Experimental Crystal Structure Determination

description: Related Article: N.J.Burke, A.D.Burrows, M.F.Mahon, J.E.Warren|2006|Cryst.Growth Des.|6|546|doi:10.1021/cg050499e
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/ccb1gtm
title: CCDC 299330: Experimental Crystal Structure Determination

description: Related Article: N.J.Burke, A.D.Burrows, M.F.Mahon, J.E.Warren|2006|Cryst.Growth Des.|6|546|doi:10.1021/cg050499e
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/ccb1gvn
title: CCDC 299331: Experimental Crystal Structure Determination

description: Related Article: N.J.Burke, A.D.Burrows, M.F.Mahon, J.E.Warren|2006|Cryst.Growth Des.|6|546|doi:10.1021/cg050499e
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/ccb1gwp
title: CCDC 299332: Experimental Crystal Structure Determination

description: Related Article: N.J.Burke, A.D.Burrows, M.F.Mahon, J.E.Warren|2006|Cryst.Growth Des.|6|546|doi:10.1021/cg050499e
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.