cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.976117
title: Visualizing Complex Data With Embedded Plots

description: This article describes a class of graphs, embedded plots, that are particularly useful for analyzing large and complex datasets. Embedded plots organize a collection of graphs into a larger graphic, which can display more complex relationships than would otherwise be possible. This arrangement provides additional axes, prevents overplotting, and allows for multiple levels of visual summarization. Embedded plots also preprocess complex data into a form suitable for the human cognitive system, which can facilitate comprehension. We illustrate the usefulness of embedded plots with a case study, discuss the practical and cognitive advantages of embedded plots, and demonstrate how to implement embedded plots as a general class within visualization software, something currently unavailable. This article has supplementary material online.
DOI: 10.6084/m9.figshare.976122.v1
title: Ocena pracy autorstwa Grzybek A.

description: This review relates to the work, which analyses how financial management in terms of financial planning on the example of a manufacturing enterprise in the wood industry. Review aims to assess the substantive content and conclusions.
DOI: 10.6084/m9.figshare.976116
title: Bayesian Semiparametric Density Deconvolution in the Presence of Conditionally Heteroscedastic Measurement Errors

description: We consider the problem of estimating the density of a random variable when precise measurements on the variable are not available, but replicated proxies contaminated with measurement error are available for sufficiently many subjects. Under the assumption of additive measurement errors this reduces to a problem of deconvolution of densities. Deconvolution methods often make restrictive and unrealistic assumptions about the density of interest and the distribution of measurement errors, for example, normality and homoscedasticity and thus independence from the variable of interest. This article relaxes these assumptions and introduces novel Bayesian semiparametric methodology based on Dirichlet process mixture models for robust deconvolution of densities in the presence of conditionally heteroscedastic measurement errors. In particular, the models can adapt to asymmetry, heavy tails, and multimodality. In simulation experiments, we show that our methods vastly outperform a recent Bayesian approach based on estimating the densities via mixtures of splines. We apply our methods to data from nutritional epidemiology. Even in the special case when the measurement errors are homoscedastic, our methodology is novel and dominates other methods that have been proposed previously. Additional simulation results, instructions on getting access to the dataset and R programs implementing our methods are included as part of online supplementary materials.
DOI: 10.6084/m9.figshare.976115
title: Stable Estimation in Dimension Reduction

description: We introduce stable estimation procedures for several aspects of a sufficient dimension-reduction matrix. We first propose a stable method for estimating structural dimension, which only selects the correct directions in the central subspace with no false positive selection. We then provide a Grassmann manifold sparse estimate for the central subspace. By using subsampling, we develop an ensemble method to obtain a stable nonsparse estimate for the central subspace. This ensemble idea is also used to stabilize the choice of the number of slices in sliced inverse methods. Theoretical results are established, and the efficacy of the proposed stable methods is demonstrated by simulation studies and the analysis of Hitters’ salary data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.976117.v1
title: Visualizing Complex Data with Embedded Plots

description: This paper describes a class of graphs, embedded plots, that are particularly useful for analyzing large and complex data sets. Embedded plots organize a collection of graphs into a larger graphic, which can display more complex relationships than would otherwise be possible. This arrangement provides additional axes, prevents overplotting, and allows for multiple levels of visual summarization. Embedded plots also preprocess complex data into a form suitable for the human cognitive system, which can facilitate comprehension. We illustrate the usefulness of embedded plots with a case study, discuss the practical and cognitive advantages of embedded plots, and demonstrate how to implement embedded plots as a general class within visualization software, something currently unavailable.
DOI: 10.6084/m9.figshare.976117.v3
title: Visualizing Complex Data With Embedded Plots

description: This article describes a class of graphs, embedded plots, that are particularly useful for analyzing large and complex datasets. Embedded plots organize a collection of graphs into a larger graphic, which can display more complex relationships than would otherwise be possible. This arrangement provides additional axes, prevents overplotting, and allows for multiple levels of visual summarization. Embedded plots also preprocess complex data into a form suitable for the human cognitive system, which can facilitate comprehension. We illustrate the usefulness of embedded plots with a case study, discuss the practical and cognitive advantages of embedded plots, and demonstrate how to implement embedded plots as a general class within visualization software, something currently unavailable. This article has supplementary material online.
DOI: 10.6084/m9.figshare.976113
title: Reweaving the Tapestry: A Supertree of Birds

description: Data and figures for "Reweaving the Tapestry: A Supertree of Birds".
DOI: 10.6084/m9.figshare.976117.v2
title: Visualizing Complex Data With Embedded Plots

description: This article describes a class of graphs, embedded plots, that are particularly useful for analyzing large and complex datasets. Embedded plots organize a collection of graphs into a larger graphic, which can display more complex relationships than would otherwise be possible. This arrangement provides additional axes, prevents overplotting, and allows for multiple levels of visual summarization. Embedded plots also preprocess complex data into a form suitable for the human cognitive system, which can facilitate comprehension. We illustrate the usefulness of embedded plots with a case study, discuss the practical and cognitive advantages of embedded plots, and demonstrate how to implement embedded plots as a general class within visualization software, something currently unavailable. This article has supplementary material online.
DOI: 10.6084/m9.figshare.976113.v1
title: Reweaving the Tapestry: A Supertree of Birds

description: Data and figures for "Reweaving the Tapestry: A Supertree of Birds".
DOI: 10.6084/m9.figshare.976115.v1
title: Stable Estimation in Dimension Reduction

description: We introduce stable estimation procedures for several aspects of a sufficient dimension reduction matrix. We first propose a stable method for estimating structural dimension, which only selects the correct directions in the central subspace with no false positive selection. We then provide a Grassmann Manifold sparse estimate for the central subspace. By using subsampling, we develop an ensemble method to obtain a stable nonsparse estimate for the central subspace. This ensemble idea is also used to stabilize the choice of the number of slices in sliced inverse methods. Theoretical results are established, and the efficacy of the proposed stable methods is demonstrated by simulation studies and the analysis of Hitters’ salary data. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.976116.v1
title: Bayesian Semiparametric Density Deconvolution in the Presence of Conditionally Heteroscedastic Measurement Errors

description: We consider the problem of estimating the density of a random variable when precise measurements on the variable are not available, but replicated proxies contaminated with measurement error are available for sufficiently many subjects. Under the assumption of additive measurement errors this reduces to a problem of deconvolution of densities. Deconvolution methods often make restrictive and unrealistic assumptions about the density of interest and the distribution of measurement errors, e.g., normality and homoscedasticity and thus independence from the variable of interest. This article relaxes these assumptions and introduces novel Bayesian semiparametric methodology based on Dirichlet process mixture models for robust deconvolution of densities in the presence of conditionally heteroscedastic measurement errors. In particular, the models can adapt to asymmetry, heavy tails and multimodality. In simulation experiments, we show that our methods vastly outperform a recent Bayesian approach based on estimating the densities via mixtures of splines. We apply our methods to data from nutritional epidemiology. Even in the special case when the measurement errors are homoscedastic, our methodology is novel and dominates other methods that have been proposed previously. Additional simulation results, instructions on getting access to the data set and R programs implementing our methods are included as part of online supplemental materials.
DOI: 10.6084/m9.figshare.976115.v3
title: Stable Estimation in Dimension Reduction

description: We introduce stable estimation procedures for several aspects of a sufficient dimension-reduction matrix. We first propose a stable method for estimating structural dimension, which only selects the correct directions in the central subspace with no false positive selection. We then provide a Grassmann manifold sparse estimate for the central subspace. By using subsampling, we develop an ensemble method to obtain a stable nonsparse estimate for the central subspace. This ensemble idea is also used to stabilize the choice of the number of slices in sliced inverse methods. Theoretical results are established, and the efficacy of the proposed stable methods is demonstrated by simulation studies and the analysis of Hitters’ salary data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.976097
title: De novo assembly of the Drosophila Melanogaster fruit fly genome by long-read sequencing

description: Revisiting the Drosophila melanogaster genome!
DOI: 10.6084/m9.figshare.976115.v2
title: Stable Estimation in Dimension Reduction

description: We introduce stable estimation procedures for several aspects of a sufficient dimension-reduction matrix. We first propose a stable method for estimating structural dimension, which only selects the correct directions in the central subspace with no false positive selection. We then provide a Grassmann manifold sparse estimate for the central subspace. By using subsampling, we develop an ensemble method to obtain a stable nonsparse estimate for the central subspace. This ensemble idea is also used to stabilize the choice of the number of slices in sliced inverse methods. Theoretical results are established, and the efficacy of the proposed stable methods is demonstrated by simulation studies and the analysis of Hitters’ salary data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.976116.v2
title: Bayesian Semiparametric Density Deconvolution in the Presence of Conditionally Heteroscedastic Measurement Errors

description: We consider the problem of estimating the density of a random variable when precise measurements on the variable are not available, but replicated proxies contaminated with measurement error are available for sufficiently many subjects. Under the assumption of additive measurement errors this reduces to a problem of deconvolution of densities. Deconvolution methods often make restrictive and unrealistic assumptions about the density of interest and the distribution of measurement errors, for example, normality and homoscedasticity and thus independence from the variable of interest. This article relaxes these assumptions and introduces novel Bayesian semiparametric methodology based on Dirichlet process mixture models for robust deconvolution of densities in the presence of conditionally heteroscedastic measurement errors. In particular, the models can adapt to asymmetry, heavy tails, and multimodality. In simulation experiments, we show that our methods vastly outperform a recent Bayesian approach based on estimating the densities via mixtures of splines. We apply our methods to data from nutritional epidemiology. Even in the special case when the measurement errors are homoscedastic, our methodology is novel and dominates other methods that have been proposed previously. Additional simulation results, instructions on getting access to the dataset and R programs implementing our methods are included as part of online supplementary materials.
DOI: 10.6084/m9.figshare.976116.v4
title: Bayesian Semiparametric Density Deconvolution in the Presence of Conditionally Heteroscedastic Measurement Errors

description: We consider the problem of estimating the density of a random variable when precise measurements on the variable are not available, but replicated proxies contaminated with measurement error are available for sufficiently many subjects. Under the assumption of additive measurement errors this reduces to a problem of deconvolution of densities. Deconvolution methods often make restrictive and unrealistic assumptions about the density of interest and the distribution of measurement errors, for example, normality and homoscedasticity and thus independence from the variable of interest. This article relaxes these assumptions and introduces novel Bayesian semiparametric methodology based on Dirichlet process mixture models for robust deconvolution of densities in the presence of conditionally heteroscedastic measurement errors. In particular, the models can adapt to asymmetry, heavy tails, and multimodality. In simulation experiments, we show that our methods vastly outperform a recent Bayesian approach based on estimating the densities via mixtures of splines. We apply our methods to data from nutritional epidemiology. Even in the special case when the measurement errors are homoscedastic, our methodology is novel and dominates other methods that have been proposed previously. Additional simulation results, instructions on getting access to the dataset and R programs implementing our methods are included as part of online supplementary materials.
DOI: 10.6084/m9.figshare.976097.v2
title: De novo assembly of the Drosophila Melanogaster fruit fly genome by long-read sequencing

description: Revisiting the Drosophila melanogaster genome!
DOI: 10.6084/m9.figshare.976097.v1
title: A better Drosophila Melanogaster genome by long-read sequencing

description: Revisiting the Drosophila melanogaster genome!
DOI: 10.6084/m9.figshare.976098.v1
title: REFLEKSJE NA TEMAT PRACY: „Wykorzystanie oceny zdrowia finansowego podmiotu do planowania finansowego.” Autorstwa: K. Baj, A. Malchrzycka, Ż. Protacka, D. Pytel, J. Staniewicz,

description: Review work "The use of the assessment of the financial health of the entity to financial planning" by K. Baj, A. Malchrzycka, Ż. Protacka, D. Pytel, J. Staniewicz. Were taken into account such elements as part of the substantive, structure, aesthetics and relevance of conclusions.
DOI: 10.6084/m9.figshare.976116.v3
title: Bayesian Semiparametric Density Deconvolution in the Presence of Conditionally Heteroscedastic Measurement Errors

description: We consider the problem of estimating the density of a random variable when precise measurements on the variable are not available, but replicated proxies contaminated with measurement error are available for sufficiently many subjects. Under the assumption of additive measurement errors this reduces to a problem of deconvolution of densities. Deconvolution methods often make restrictive and unrealistic assumptions about the density of interest and the distribution of measurement errors, for example, normality and homoscedasticity and thus independence from the variable of interest. This article relaxes these assumptions and introduces novel Bayesian semiparametric methodology based on Dirichlet process mixture models for robust deconvolution of densities in the presence of conditionally heteroscedastic measurement errors. In particular, the models can adapt to asymmetry, heavy tails, and multimodality. In simulation experiments, we show that our methods vastly outperform a recent Bayesian approach based on estimating the densities via mixtures of splines. We apply our methods to data from nutritional epidemiology. Even in the special case when the measurement errors are homoscedastic, our methodology is novel and dominates other methods that have been proposed previously. Additional simulation results, instructions on getting access to the dataset and R programs implementing our methods are included as part of online supplementary materials.
DOI: 10.6084/m9.figshare.976054
title: Molecular cloning, expression and polymorphism of goose <i>LRP8</i> gene

description: 1. The low-density lipoprotein receptor-related protein 8 (LRP8), a member of the low-density lipoprotein receptor (LDLR) gene family, participates in the supplying of lipid during follicular development. The objective of the study was to identify and characterise the <i>LRP8</i> gene in goose.2. A 2867 bp fragment that covered the complete coding region (CDS) of goose (<i>Anser cygnoides</i>) <i>LRP8</i> gene was cloned. It encoded a protein of 917 amino acid residues containing a 24-amino acid signal peptide and 5 functional domains. The goose LRP8 showed high nucleic acid and amino acid identities with those in other species.3. Similarly to duck <i>LRP8</i> gene, two splice variants of LRP8, LRP8-1 (containing 8 ligand-binding repeats) and LRP8-2 (containing 7 ligand-binding repeats), were identified in goose.4. Semi-quantitative RT-PCR analysis indicates that the <i>LRP8-1</i> transcript is expressed in heart, liver, spleen, lung, kidney, breast muscle, duodenum, hypothalamus, pituitary and ovary, negligible or absent in sebum and oviduct, and the <i>LRP8-2</i> transcript is widely expressed in all examined tissues.5. A total of 7 SNPs were identified in the coding region of the goose <i>LRP8</i> gene.
DOI: 10.6084/m9.figshare.976054.v2
title: Molecular cloning, expression and polymorphism of goose <i>LRP8</i> gene

description: 1. The low-density lipoprotein receptor-related protein 8 (LRP8), a member of the low-density lipoprotein receptor (LDLR) gene family, participates in the supplying of lipid during follicular development. The objective of the study was to identify and characterise the <i>LRP8</i> gene in goose.2. A 2867 bp fragment that covered the complete coding region (CDS) of goose (<i>Anser cygnoides</i>) <i>LRP8</i> gene was cloned. It encoded a protein of 917 amino acid residues containing a 24-amino acid signal peptide and 5 functional domains. The goose LRP8 showed high nucleic acid and amino acid identities with those in other species.3. Similarly to duck <i>LRP8</i> gene, two splice variants of LRP8, LRP8-1 (containing 8 ligand-binding repeats) and LRP8-2 (containing 7 ligand-binding repeats), were identified in goose.4. Semi-quantitative RT-PCR analysis indicates that the <i>LRP8-1</i> transcript is expressed in heart, liver, spleen, lung, kidney, breast muscle, duodenum, hypothalamus, pituitary and ovary, negligible or absent in sebum and oviduct, and the <i>LRP8-2</i> transcript is widely expressed in all examined tissues.5. A total of 7 SNPs were identified in the coding region of the goose <i>LRP8</i> gene.
DOI: 10.6084/m9.figshare.976054.v1
title: Molecular cloning, expression and polymorphism of goose <i>LRP8</i> gene

description: 1. The low density lipoprotein receptor-related protein 8 (LRP8), a member of the low density lipoprotein receptor (LDLR) gene family, participates in the supplying of lipid during follicular development. The objective of the study was to identify and characterise the <i>LRP8</i> gene in goose.2. A 2867 bp fragment that covered the complete coding region (CDS) of goose (<i>Anser cygnoides</i>) <i>LRP8</i> gene was cloned. It encoded a protein of 917 amino acid residues containing a 24-amino acid signal peptide and 5 functional domains. The goose LRP8 showed high nucleic acid and amino acid identities with those in other species.3. Similarly to duck <i>LRP8</i> gene, two splice variants of LRP8, LRP8-1 (containing eight ligand-binding repeats) and LRP8-2 (containing 7 ligand-binding repeats), were identified in goose.4. Semi-quantitative RT-PCR analysis indicates that the <i>LRP8-1</i> transcript is expressed in heart, liver, spleen, lung, kidney, breast muscle, duodenum, hypothalamus, pituitary and ovary, negligible or absent in sebum and oviduct, and the <i>LRP8-2</i> transcript is widely expressed in all examined tissues.5. A total of 7 SNPs were identified in the coding region of the goose <i>LRP8</i> gene.
DOI: 10.6084/m9.figshare.975970
title: Making air quality indices comparable – assessment of 10 years of air pollutant levels in western Europe

description: To address the incomparability of the large number of existing air quality indices, we propose a new normalization method that is suited to directly compare air quality indices based on the common European World Health Organization (WHO) air quality guidelines for NO<sub>2</sub>, O<sub>3</sub>, and PM<sub>10</sub>. Using this method, we compared three air quality indices based on the European guidelines, related them to another air quality index based on the relative risk concept, and used them to assess the air quality and its trends in northwest central Europe. The average air quality in the area of investigation is below the recommended European guidelines. The majority of index values exceeding this threshold are caused by PM<sub>10</sub>, which is also, in most cases, responsible for the degrading trends in air quality. Eleven out of 29 stations tested showed significant trends, of which eight indicated trends towards better air quality.
DOI: 10.6084/m9.figshare.975033.v1
title: Mutation analysis of IGH associated with different isotypes

description: Immunoglobulin heavy chain rearrangements were sequences from PBMCs of 8 individuals using IGHC primers for all isotypes bar IgE and IgD. Sequencing was carried out on the 454 pyrosequencing platform and rearranged immunoglobulin heavy chain sequences were partitioned against germline IGHV, IGHD and IGHJ using iHMMune-align. The datasets for each individual was filtered to remove non-productive rearrangements (frame shift of the IGHJ or stop codons), those with greater than 45 IGHV mutations, sequences which contained indels in the V or J, and those with ambiguities. Within each data only unique sequences were reatained (100% identical). Clonally-related sequences within each subject's dataset were also identified. Following germline reversion of all non-CDR3 nucleotide positions, sequence were clustered using the vmatch package (http://www.vmatch.de/) allowing up to 3 differences. A single representative for each cluster (representing a likely clonal set) was retained within the dataset. Representative sequences were selected based on the sequence with the highest copy number, or in cases where sequences shared the highest copy number, the least mutated sequence. If a cluster spanned multiple isotypes then a representative for each isotype was kept.