cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13482276
title: Evaluation of re-sampling methods on performance of machine learning models to predict landslide susceptibility

description: This study tests the applicability of three resampling methods (i.e. bootstrapping, random-subsampling and cross-validation) for enhancing the performance of eight machine-learning models: boosted regression trees, flexible discriminant analysis, random forests, mixture discriminate analysis, multivariate adaptive regression splines, classification and regression trees, support vector machines and generalized linear models, compared to the use of the original data. The results of models were evaluated using correlation (COR), area under curve (AUC), true skill statistic (TSS), receiver-operating characteristic and the probability of detection (POD). The evaluation showed that the bootstrapping technique improved the performance of all models. The Bootstrapping-random forest (with COR = 0.75, AUC = 0.92, TSS = 0.80 and POD = 0.98) proved to be the best model for landslide prediction. Among the 18 contributing factors, distance from fault, curvature and precipitation were the most influential in all 32 models .HighlightsHazard prediction of landslide by the 8 machine-learning (ML) models.Multiple morphometric, climatic, geologic, vegetation and human factors were used.Tests the applicability of three resampling methods.The performance of the ML models and coupling models were assessed. Hazard prediction of landslide by the 8 machine-learning (ML) models. Multiple morphometric, climatic, geologic, vegetation and human factors were used. Tests the applicability of three resampling methods. The performance of the ML models and coupling models were assessed.
DOI: 10.6084/m9.figshare.13482276.v1
title: Evaluation of re-sampling methods on performance of machine learning models to predict landslide susceptibility

description: This study tests the applicability of three resampling methods (i.e. bootstrapping, random-subsampling and cross-validation) for enhancing the performance of eight machine-learning models: boosted regression trees, flexible discriminant analysis, random forests, mixture discriminate analysis, multivariate adaptive regression splines, classification and regression trees, support vector machines and generalized linear models, compared to the use of the original data. The results of models were evaluated using correlation (COR), area under curve (AUC), true skill statistic (TSS), receiver-operating characteristic and the probability of detection (POD). The evaluation showed that the bootstrapping technique improved the performance of all models. The Bootstrapping-random forest (with COR = 0.75, AUC = 0.92, TSS = 0.80 and POD = 0.98) proved to be the best model for landslide prediction. Among the 18 contributing factors, distance from fault, curvature and precipitation were the most influential in all 32 models .HighlightsHazard prediction of landslide by the 8 machine-learning (ML) models.Multiple morphometric, climatic, geologic, vegetation and human factors were used.Tests the applicability of three resampling methods.The performance of the ML models and coupling models were assessed. Hazard prediction of landslide by the 8 machine-learning (ML) models. Multiple morphometric, climatic, geologic, vegetation and human factors were used. Tests the applicability of three resampling methods. The performance of the ML models and coupling models were assessed.
DOI: 10.17029/01021f33-5351-4721-ba3b-948395d37bed
title: Railway Work, Life & Death - Railway Inspectorate, 1900-1939

description: c.21,000 British & Irish railway staff accident reports, covering 1900-1939 (including the initial run of data, 1911-15, recorded elsewhere in PURE). Produced in collaboration with volunteers at the National Railway Museum. Part of the 'Railway Work, Life & Death' project.
DOI: 10.17632/jhmg2n63g2.1
title: Self-reported Assessment of the Socio-economic Impact of Anticancer Chemotherapy-related Neurotoxicity

description: Original (Italian) and English translation of the questionnaires used to perform the study
DOI: 10.17632/jhmg2n63g2.2
title: Self-reported Assessment of the Socio-economic Impact of Anticancer Chemotherapy-related Neurotoxicity

description: Original (Italian) and English translation of the questionnaires used to perform the study.
Original dataset used to perform the analysis (in Italian)
DOI: 10.17029/a3d42a09-97c2-48b1-95d9-8b6dc3884a54
title: Dataset for "Extension and Validation of A Novel Destination Brand Equity Model"

description: SPSS data set.
DOI: 10.6084/m9.figshare.21842130
title: A polygenic basis for birth weight in a wild population of red deer (Cervus elaphus).

description: Individual phenotypic data on birth weight in red deer (<em>Cervus elaphus</em>), and associated genomic data. See ‘Data_description.txt’ for more details. Please contact the authors if you plan to use this data.
DOI: 10.5281/zenodo.7516467
title: IMMERSE H2020 PROJECT - WP8 - downstream service UNIBO

description: The archive contains the following datasets: - Daily mean Temperature of CMEMS reanalysis product (IBI_REANALYSIS_PHYS_005_002 product) with 1/12° horizontal resolution [form 13 to 19 November 2002] in the Galicia Coastal area. - Hourly mean Temperature from a downscaling experiment with NEMO4.2 with horizontal resolution of 1/48° [form 13 to 19 November 2002] in the Galicia Coastal area. - Daily gap-free (L4) Satellite sea surface temperatures (SST) dataset at (0.0417° x 0.0417°) horizontal resolution representative of night SST values (00:00 UTC).
DOI: 10.5281/zenodo.7516466
title: IMMERSE H2020 PROJECT - WP8 - downstream service UNIBO

description: The archive contains the following datasets: - Daily mean Temperature of CMEMS reanalysis product (IBI_REANALYSIS_PHYS_005_002 product) with 1/12° horizontal resolution [form 13 to 19 November 2002] in the Galicia Coastal area. - Hourly mean Temperature from a downscaling experiment with NEMO4.2 with horizontal resolution of 1/48° [form 13 to 19 November 2002] in the Galicia Coastal area. - Daily gap-free (L4) Satellite sea surface temperatures (SST) dataset at (0.0417° x 0.0417°) horizontal resolution representative of night SST values (00:00 UTC).
DOI: 10.6084/m9.figshare.21757691
title: GENOMIC DIVERSITY GRADIENTS AND FUNCTIONAL DIFFERENTIATION PUT NORTHEAST PACIFIC RIBBON KELP LINEAGES IN THE SPECIATION GREY ZONE

description: The transition from reproductively isolated populations to species is not well understood. Genotyping entire genomes holds promise to enhance insights into the process of speciation and the evolutionary relationships among related taxa. Gulf of Alaska ribbon kelp was once recognized as four species before they were folded into <em>Alaria marginata</em> on the basis of DNA barcode markers, though several lineages have continued to be recognized. Here, we used whole genome sequencing datasets to test the hypothesis that these lineages represent incipient species. Whole genomes of 69 individuals from five genetically distinctive lineages in the Gulf of Alaska (USA) and Salish Sea (Canada) were analyzed, along with 63 genomes from three other species of <em>Alaria</em>. Our analysis of &gt;3.4 million Single Nucleotide Polymorphisms reaffirm that organellar and nuclear phylogenetic signals are incongruent in <em>Alaria</em>, producing different topologies among five organellar and six nuclear <em>A. marginata</em> lineages. Lineages also display reproductive isolation, evidenced by a lack of recent admixture across genomes. Genetic distances between <em>A. marginata</em> lineages exceed levels expected of population level divergence, but fall short of distances between species of <em>Alaria</em>. Moreover, we provide evidence of functional genomic differences between the <em>A. marginata</em> lineages, exceeding differences expected between populations, but falling short of larger differences among species. Our results place <em>A. marginata</em> lineages in an evolutionary grey zone, where lineages display substantial differentiation, but not to the level expected of <em>Alaria</em> species. This information shifts taxonomic conversations towards a genome-scale framework that provides a more comprehensive picture of divergence, connectivity, and functional innovation for defining lineages. <br> The datasets presented here pertain to values of exon coverage for the various species of Alaria and lineages of A. marginata. These include gene name/product, uniprot IDs, and GO terms. Tables are for Alaria esculenta populations, Alaria species, and A. marginata lineages.
DOI: 10.6084/m9.figshare.21757691.v3
title: GENOMIC DIVERSITY GRADIENTS AND FUNCTIONAL DIFFERENTIATION PUT NORTHEAST PACIFIC RIBBON KELP LINEAGES IN THE SPECIATION GREY ZONE

description: The transition from reproductively isolated populations to species is not well understood. Genotyping entire genomes holds promise to enhance insights into the process of speciation and the evolutionary relationships among related taxa. Gulf of Alaska ribbon kelp was once recognized as four species before they were folded into <em>Alaria marginata</em> on the basis of DNA barcode markers, though several lineages have continued to be recognized. Here, we used whole genome sequencing datasets to test the hypothesis that these lineages represent incipient species. Whole genomes of 69 individuals from five genetically distinctive lineages in the Gulf of Alaska (USA) and Salish Sea (Canada) were analyzed, along with 63 genomes from three other species of <em>Alaria</em>. Our analysis of &gt;3.4 million Single Nucleotide Polymorphisms reaffirm that organellar and nuclear phylogenetic signals are incongruent in <em>Alaria</em>, producing different topologies among five organellar and six nuclear <em>A. marginata</em> lineages. Lineages also display reproductive isolation, evidenced by a lack of recent admixture across genomes. Genetic distances between <em>A. marginata</em> lineages exceed levels expected of population level divergence, but fall short of distances between species of <em>Alaria</em>. Moreover, we provide evidence of functional genomic differences between the <em>A. marginata</em> lineages, exceeding differences expected between populations, but falling short of larger differences among species. Our results place <em>A. marginata</em> lineages in an evolutionary grey zone, where lineages display substantial differentiation, but not to the level expected of <em>Alaria</em> species. This information shifts taxonomic conversations towards a genome-scale framework that provides a more comprehensive picture of divergence, connectivity, and functional innovation for defining lineages. <br> The datasets presented here pertain to values of exon coverage for the various species of Alaria and lineages of A. marginata. These include gene name/product, uniprot IDs, and GO terms. Tables are for Alaria esculenta populations, Alaria species, and A. marginata lineages.
DOI: 10.6084/m9.figshare.21757691.v2
title: GENOMIC DIVERSITY GRADIENTS AND FUNCTIONAL DIFFERENTIATION PUT NORTHEAST PACIFIC RIBBON KELP LINEAGES IN THE SPECIATION GREY ZONE

description: The transition from reproductively isolated populations to species is not well understood. Genotyping entire genomes holds promise to enhance insights into the process of speciation and the evolutionary relationships among related taxa. Gulf of Alaska ribbon kelp was once recognized as four species before they were folded into <em>Alaria marginata</em> on the basis of DNA barcode markers, though several lineages have continued to be recognized. Here, we used whole genome sequencing datasets to test the hypothesis that these lineages represent incipient species. Whole genomes of 69 individuals from five genetically distinctive lineages in the Gulf of Alaska (USA) and Salish Sea (Canada) were analyzed, along with 63 genomes from three other species of <em>Alaria</em>. Our analysis of &gt;3.4 million Single Nucleotide Polymorphisms reaffirm that organellar and nuclear phylogenetic signals are incongruent in <em>Alaria</em>, producing different topologies among five organellar and six nuclear <em>A. marginata</em> lineages. Lineages also display reproductive isolation, evidenced by a lack of recent admixture across genomes. Genetic distances between <em>A. marginata</em> lineages exceed levels expected of population level divergence, but fall short of distances between species of <em>Alaria</em>. Moreover, we provide evidence of functional genomic differences between the <em>A. marginata</em> lineages, exceeding differences expected between populations, but falling short of larger differences among species. Our results place <em>A. marginata</em> lineages in an evolutionary grey zone, where lineages display substantial differentiation, but not to the level expected of <em>Alaria</em> species. This information shifts taxonomic conversations towards a genome-scale framework that provides a more comprehensive picture of divergence, connectivity, and functional innovation for defining lineages. <br> The datasets presented here pertain to values of exon coverage for the various species of Alaria and lineages of A. marginata. These include gene name/product, uniprot IDs, and GO terms. Tables are for Alaria esculenta populations, Alaria species, and A. marginata lineages.
DOI: 10.5281/zenodo.7516489
title: Najwyższy Sąd Wojskowy w Warszawie 1919-1962

description: Zdeponowane dane zawierają dwa teksty pokonferencyjne„The military origins of penal liability for causing a road accident in Polish penal law, according to the Penal Code of the Polish Army of 1944” oraz „Supreme Military Court in Poland in 1919-1939 - legal bases, organization and jurisprudence” wraz z bibliografią i aktami prawnymi, które stanowią rezultat kwerendy w ramach projektu Miniatura 5 Nr 2021/05/X/HS5/01048, finansowanego przez Narodowe Centrum Nauki.
DOI: 10.5281/zenodo.7516488
title: Najwyższy Sąd Wojskowy w Warszawie 1919-1962

description: Zdeponowane dane zawierają dwa teksty pokonferencyjne„The military origins of penal liability for causing a road accident in Polish penal law, according to the Penal Code of the Polish Army of 1944” oraz „Supreme Military Court in Poland in 1919-1939 - legal bases, organization and jurisprudence” wraz z bibliografią i aktami prawnymi, które stanowią rezultat kwerendy w ramach projektu Miniatura 5 Nr 2021/05/X/HS5/01048, finansowanego przez Narodowe Centrum Nauki.
DOI: 10.6084/m9.figshare.21644453
title: Quasi-Monte Carlo Methods for Binary Event Models with Complex Family Data

description: The generalized linear mixed model for binary outcomes with the probit link function is used in many fields but has a computationally challenging likelihood when there are many random effects. We extend a previously used importance sampler, making it much faster in the context of estimating heritability and related effects from family data by adding a gradient and a Hessian approximation and making a faster implementation. Additionally, a graph-based method is suggested to simplify the likelihood when there are thousands of individuals in each family. Simulation studies show that the resulting method is orders of magnitude faster, has a negligible efficiency loss, and confidence intervals with nominal coverage. We also analyze data from a large study of obsessive-compulsive disorder based on Swedish multi-generational data. In this analysis, the proposed method yielded similar results to a previous analysis, but was much faster. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.21644453.v1
title: Quasi-Monte Carlo Methods for Binary Event Models with Complex Family Data

description: The generalized linear mixed model for binary outcomes with the probit link function is used in many fields but has a computationally challenging likelihood when there are many random effects. We extend a previously used importance sampler, making it much faster in the context of estimating heritability and related effects from family data by adding a gradient and a Hessian approximation and making a faster implementation. Additionally, a graph-based method is suggested to simplify the likelihood when there are thousands of individuals in each family. Simulation studies show that the resulting method is orders of magnitude faster, has a negligible efficiency loss, and confidence intervals with nominal coverage. We also analyze data from a large study of obsessive-compulsive disorder based on Swedish multi-generational data. In this analysis, the proposed method yielded similar results to a previous analysis, but was much faster. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.21657766
title: Graphical and Computational Tools to Guide Parameter Choice for the Cluster Weighted Robust Model

description: The Cluster Weighted Robust Model (CWRM) is a recently introduced methodology to robustly estimate mixtures of regressions with random covariates. The CWRM allows users to flexibly perform regression clustering, safeguarding it against data contamination and spurious solutions. Nonetheless, the resulting solution depends on the chosen number of components in the mixture, the percentage of impartial trimming, the degree of heteroscedasticity of the errors around the regression lines and of the clusters in the explanatory variables. Therefore, an appropriate model selection is crucially required. Such a complex modeling task may generate several “legitimate” solutions: each one derived from a distinct hyperparameters specification. The present article introduces a two step-monitoring procedure to help users effectively explore such a vast model space. The first phase uncovers the most appropriate percentages of trimming, whilst the second phase explores the whole set of solutions, conditioning on the outcome derived from the previous step. The final output singles out a set of “top” solutions, whose optimality, stability and validity is assessed. Novel graphical and computational tools—specifically tailored for the CWRM framework—will help the user make an educated choice among the optimal solutions. Three examples on real datasets showcase our proposal in action. Supplementary files for this article are available online.
DOI: 10.6084/m9.figshare.21657766.v2
title: Graphical and Computational Tools to Guide Parameter Choice for the Cluster Weighted Robust Model

description: The Cluster Weighted Robust Model (CWRM) is a recently introduced methodology to robustly estimate mixtures of regressions with random covariates. The CWRM allows users to flexibly perform regression clustering, safeguarding it against data contamination and spurious solutions. Nonetheless, the resulting solution depends on the chosen number of components in the mixture, the percentage of impartial trimming, the degree of heteroscedasticity of the errors around the regression lines and of the clusters in the explanatory variables. Therefore, an appropriate model selection is crucially required. Such a complex modeling task may generate several “legitimate” solutions: each one derived from a distinct hyperparameters specification. The present article introduces a two step-monitoring procedure to help users effectively explore such a vast model space. The first phase uncovers the most appropriate percentages of trimming, whilst the second phase explores the whole set of solutions, conditioning on the outcome derived from the previous step. The final output singles out a set of “top” solutions, whose optimality, stability and validity is assessed. Novel graphical and computational tools—specifically tailored for the CWRM framework—will help the user make an educated choice among the optimal solutions. Three examples on real datasets showcase our proposal in action. Supplementary files for this article are available online.
DOI: 10.6084/m9.figshare.21657766.v1
title: Graphical and computational tools to guide parameter choice for the cluster weighted robust model

description: The Cluster Weighted Robust Model (CWRM) is a recently introduced methodology to robustly estimate mixtures of regressions with random covariates. The CWRM allows users to flexibly perform regression clustering, safeguarding it against data contamination and spurious solutions. Nonetheless, the resulting solution depends on the chosen number of components in the mixture, the percentage of impartial trimming, the degree of heteroscedasticity of the errors around the regression lines and of the clusters in the explanatory variables. Therefore an appropriate model selection is crucially required. Such a complex modeling task may generate several “legitimate” solutions: each one derived from a distinct hyper-parameters specification. The present paper introduces a two step-monitoring procedure to help users effectively explore such a vast model space. The first phase uncovers the most appropriate percentages of trimming, whilst the second phase explores the whole set of solutions, conditioning on the outcome derived from the previous step. The final output singles out a set of “top” solutions, whose optimality, stability and validity is assessed. Novel graphical and computational tools - specifically tailored for the CWRM framework - will help the user make an educated choice among the optimal solutions. Three examples on real datasets showcase our proposal in action. Supplementary files for this article are available online.
DOI: 10.5281/zenodo.7516449
title: Dataset for accumulation of pTau231 at the post synaptic density in Alzheimer's disease

description: Dataset included from Journal of Alzheimer's disease publication "Accumulation of pTau231 at postsynaptic density in Alzheimer's Disease. This includes validation of synaptosomal fraction richness in Figure 5, tTau and pTau231 Simoa Quanterix synaptosomal fraction data for Supplementary figure 2, entorhinal cortex PSD95 P4 adjusted total tau and pTau231 values from Simoa Quanterix for generation of model shown in figure 4, quantitative AMPA immunohistochemical staining data used for generating figure 2, NanoString protein panel data used to generate heatmap in figure 7, quantitative immunofluorescence staining data for pTau214 and PSD95 colocalization studies in figure 3, Braak stage 2 case NanoString protein data for intra-case regional comparisons of disease progression, and synaptosome Simoa total tau and pTau231 data in addition to western blot P4 PSD95 data that was used for generation of figures 4 and 6.
DOI: 10.5281/zenodo.7516448
title: Dataset for accumulation of pTau231 at the post synaptic density in Alzheimer's disease

description: Dataset included from Journal of Alzheimer's disease publication "Accumulation of pTau231 at postsynaptic density in Alzheimer's Disease. This includes validation of synaptosomal fraction richness in Figure 5, tTau and pTau231 Simoa Quanterix synaptosomal fraction data for Supplementary figure 2, entorhinal cortex PSD95 P4 adjusted total tau and pTau231 values from Simoa Quanterix for generation of model shown in figure 4, quantitative AMPA immunohistochemical staining data used for generating figure 2, NanoString protein panel data used to generate heatmap in figure 7, quantitative immunofluorescence staining data for pTau214 and PSD95 colocalization studies in figure 3, Braak stage 2 case NanoString protein data for intra-case regional comparisons of disease progression, and synaptosome Simoa total tau and pTau231 data in addition to western blot P4 PSD95 data that was used for generation of figures 4 and 6.