cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12134157
title: Additional file 2 of Prenatal and postnatal determinants in shaping offspring’s microbiome in the first 1000 days: study protocol and preliminary results at one month of life

description: Additional file 2: Figure S1a. Permutation analysis summarizing the genera with relative abundance &gt; 0.1%, statistically different (p-value 0.1%, statistically different (p-value 0.1%, statistically different (p-value 0.1%, statistically different (p-value
DOI: 10.6084/m9.figshare.12134160
title: Additional file 3 of Prenatal and postnatal determinants in shaping offspring’s microbiome in the first 1000 days: study protocol and preliminary results at one month of life

description: Additional file 3: Figure S2a. Permutation analysis summarizing the species with relative abundance&gt; 0.1%, statistically different (p-value 0.1%, statistically different (p-value 0.1%, statistically different (p-value 0.1%, statistically different (p-value
DOI: 10.6084/m9.figshare.12134157.v1
title: Additional file 2 of Prenatal and postnatal determinants in shaping offspring’s microbiome in the first 1000 days: study protocol and preliminary results at one month of life

description: Additional file 2: Figure S1a. Permutation analysis summarizing the genera with relative abundance &gt; 0.1%, statistically different (p-value 0.1%, statistically different (p-value 0.1%, statistically different (p-value 0.1%, statistically different (p-value
DOI: 10.6084/m9.figshare.12134160.v1
title: Additional file 3 of Prenatal and postnatal determinants in shaping offspring’s microbiome in the first 1000 days: study protocol and preliminary results at one month of life

description: Additional file 3: Figure S2a. Permutation analysis summarizing the species with relative abundance&gt; 0.1%, statistically different (p-value 0.1%, statistically different (p-value 0.1%, statistically different (p-value 0.1%, statistically different (p-value
DOI: 10.6084/m9.figshare.12134049
title: Additional file 9 of PCR-based detection and genetic characterization of porcine parvoviruses in South Korea in 2018

description: Additional file 9. Samples’ information.
DOI: 10.6084/m9.figshare.12134049.v1
title: Additional file 9 of PCR-based detection and genetic characterization of porcine parvoviruses in South Korea in 2018

description: Additional file 9. Samples’ information.
DOI: 10.6084/m9.figshare.12134025
title: Additional file 1 of Origin-dependence of variation in seed morphology, mineral composition and germination percentage in Gynandropsis gynandra (L.) Briq. accessions from Africa and Asia

description: Additional file 1. The file presents the list of accessions included in the study, their geographic origins and codification name. This file included also the mean and standard deviation for all morphological traits, mineral element contents and germination parameters of all Gynandropsis gynandra accessions used in the study.
DOI: 10.6084/m9.figshare.12134025.v1
title: Additional file 1 of Origin-dependence of variation in seed morphology, mineral composition and germination percentage in Gynandropsis gynandra (L.) Briq. accessions from Africa and Asia

description: Additional file 1. The file presents the list of accessions included in the study, their geographic origins and codification name. This file included also the mean and standard deviation for all morphological traits, mineral element contents and germination parameters of all Gynandropsis gynandra accessions used in the study.
DOI: 10.6084/m9.figshare.12133917
title: Additional file 8 of Sepsis-associated pathways segregate cancer groups

description: Additional file 8: Table S5. Viral integration in TCGA samples.
DOI: 10.6084/m9.figshare.12133908
title: Additional file 2 of Protein–protein interaction of Rv0148 with Htdy and its predicted role towards drug resistance in Mycobacterium tuberculosis

description: Additional file 2: Table S2. List of primers used in this study. Table S3. Data showing CT values for confirmation of gene knockout using real time PCR. Table S4. Data showing the CT values of htdy expression in gene knockout mutant.
DOI: 10.6084/m9.figshare.12133917.v1
title: Additional file 8 of Sepsis-associated pathways segregate cancer groups

description: Additional file 8: Table S5. Viral integration in TCGA samples.
DOI: 10.6084/m9.figshare.12133905
title: Additional file 7 of Sepsis-associated pathways segregate cancer groups

description: Additional file 7: Table S4. List of additional cancer data sets for machine learning-based prediction of cancer samples.
DOI: 10.6084/m9.figshare.12133902
title: Additional file 6 of Sepsis-associated pathways segregate cancer groups

description: Additional file 6: Table S3. Three SLC cancers were selected for survival analysis. The value in each cell corresponds to the p-value for association of the pathway with the cancer type. At a threshold of p
DOI: 10.6084/m9.figshare.12133899
title: Additional file 1 of Protein–protein interaction of Rv0148 with Htdy and its predicted role towards drug resistance in Mycobacterium tuberculosis

description: Additional file 1: Table S1. HMM analysis files showing 1270 significant sequences from Swissprot database.
DOI: 10.6084/m9.figshare.12133902.v1
title: Additional file 6 of Sepsis-associated pathways segregate cancer groups

description: Additional file 6: Table S3. Three SLC cancers were selected for survival analysis. The value in each cell corresponds to the p-value for association of the pathway with the cancer type. At a threshold of p
DOI: 10.6084/m9.figshare.12133905.v1
title: Additional file 7 of Sepsis-associated pathways segregate cancer groups

description: Additional file 7: Table S4. List of additional cancer data sets for machine learning-based prediction of cancer samples.
DOI: 10.6084/m9.figshare.12133908.v1
title: Additional file 2 of Protein–protein interaction of Rv0148 with Htdy and its predicted role towards drug resistance in Mycobacterium tuberculosis

description: Additional file 2: Table S2. List of primers used in this study. Table S3. Data showing CT values for confirmation of gene knockout using real time PCR. Table S4. Data showing the CT values of htdy expression in gene knockout mutant.
DOI: 10.6084/m9.figshare.12133896
title: Additional file 5 of Sepsis-associated pathways segregate cancer groups

description: Additional file 5: Table S2. This table contains the property of each node of the network. Degree of a node is the number of edges connected to a node. Betweennes centrality is a measure of how much information flows through a specific node, i.e., the average number of shortest paths passing through the node. Degree refers to the number of neighbours of a node while betweenness centrality refers to how important the node is for maintaining the overall structure of that network. The two nodes with highest betweenness centrality (Glycolysis/Gluconeogenesis, Lysosome) in the network are highlighted.
DOI: 10.6084/m9.figshare.12133899.v1
title: Additional file 1 of Protein–protein interaction of Rv0148 with Htdy and its predicted role towards drug resistance in Mycobacterium tuberculosis

description: Additional file 1: Table S1. HMM analysis files showing 1270 significant sequences from Swissprot database.
DOI: 10.6084/m9.figshare.12133893
title: Additional file 4 of Sepsis-associated pathways segregate cancer groups

description: Additional file 4: Table S1. Selected 66 pathways with their significance and direction of regulation in all the studies. Coloured cells are a representation of pathway scores. Red indicates significant up-regulation, green indicates significant down-regulation and grey indicates non-significant change in pathway. Number of up- and down-regulated pathways in each study is shown at the top. Group membership (to CA, SLC or SS) is shown as a label on top of the code of the data set. First three columns of the sheet are pathway name, annotation (functional class) and KEGG ID respectively.
DOI: 10.6084/m9.figshare.12133896.v1
title: Additional file 5 of Sepsis-associated pathways segregate cancer groups

description: Additional file 5: Table S2. This table contains the property of each node of the network. Degree of a node is the number of edges connected to a node. Betweennes centrality is a measure of how much information flows through a specific node, i.e., the average number of shortest paths passing through the node. Degree refers to the number of neighbours of a node while betweenness centrality refers to how important the node is for maintaining the overall structure of that network. The two nodes with highest betweenness centrality (Glycolysis/Gluconeogenesis, Lysosome) in the network are highlighted.
DOI: 10.6084/m9.figshare.12133893.v1
title: Additional file 4 of Sepsis-associated pathways segregate cancer groups

description: Additional file 4: Table S1. Selected 66 pathways with their significance and direction of regulation in all the studies. Coloured cells are a representation of pathway scores. Red indicates significant up-regulation, green indicates significant down-regulation and grey indicates non-significant change in pathway. Number of up- and down-regulated pathways in each study is shown at the top. Group membership (to CA, SLC or SS) is shown as a label on top of the code of the data set. First three columns of the sheet are pathway name, annotation (functional class) and KEGG ID respectively.
DOI: 10.6084/m9.figshare.12133782
title: Additional file 6 of Crystal structure of DRIK1, a stress-responsive receptor-like pseudokinase, reveals the molecular basis for the absence of ATP binding

description: Additional file 6: Table S1. Small molecule library thermal profiling for DRIK1 hits identification.
DOI: 10.6084/m9.figshare.12133782.v1
title: Additional file 6 of Crystal structure of DRIK1, a stress-responsive receptor-like pseudokinase, reveals the molecular basis for the absence of ATP binding

description: Additional file 6: Table S1. Small molecule library thermal profiling for DRIK1 hits identification.
DOI: 10.6084/m9.figshare.12133686
title: Additional file 1 of Undernutrition among Ethiopian adults living with HIV: a meta-analysis

description: Additional file 1. Data abstraction tool.
DOI: 10.6084/m9.figshare.12133686.v1
title: Additional file 1 of Undernutrition among Ethiopian adults living with HIV: a meta-analysis

description: Additional file 1. Data abstraction tool.
DOI: 10.6084/m9.figshare.12133647
title: The impact of national culture on IPO outflow and its influence mechanism: An empirical study. datasets

description: the data sets of the impact of national culture on IPO outflow and its influence mechanism: An empirical study
DOI: 10.6084/m9.figshare.12133647.v1
title: The impact of national culture on IPO outflow and its influence mechanism: An empirical study. datasets

description: the data sets of the impact of national culture on IPO outflow and its influence mechanism: An empirical study
DOI: 10.6084/m9.figshare.12133617
title: Data of “The impact of national culture on IPO outflow and its influence mechanism: An empirical study“

description: Data of “The impact of national culture on IPO outflow and its influence mechanism: An empirical study“
DOI: 10.6084/m9.figshare.12133617.v1
title: Data of “The impact of national culture on IPO outflow and its influence mechanism: An empirical study“

description: Data of “The impact of national culture on IPO outflow and its influence mechanism: An empirical study“
DOI: 10.6084/m9.figshare.12133575
title: Model and code for "The spatial structure of socioeconomic disadvantage: a Bayesian multivariate spatial factor analysis"

description: This repository contains the data, model code, and initial values for a spatial factor analysis model described in: The spatial structure of socioeconomic disadvantage: a Bayesian multivariate spatial factor analysis. <i>International Journal of Geographical Information Science.</i><i><br></i>The <i>International Journal of Geographical Information Science </i>is available at: https://www.tandfonline.com/toc/tgis20/current<i><br></i><i><br></i>Abstract:Neighborhood socioeconomic disadvantage is a measure of socio-spatial inequality that has been shown to be associated with a variety of social, economic, and health outcomes. Existing studies that explore the local patterning of disadvantage often construct composite indices that summarize the interactions between multiple dimensions of social status, but do not consider if, and how, disadvantage exhibits spatial structure. This study applies a Bayesian multivariate factor analytic modeling approach to examine the spatial structure of socioeconomic disadvantage in Toronto, Canada. Socioeconomic disadvantage is modeled as an area-based composite index associated with three variables measuring low income, low educational attainment, and low occupational status, and a series of models with different assumptions regarding the spatial structure of disadvantage are compared. The best-fitting model shows that the prevalence of low-income households has the strongest positive association with disadvantage and that spatial clustering is three times more important than spatial heterogeneity for explaining the spatial structure of disadvantage. The implications of this study for analyzing multivariate spatial data and for understanding the interactions amongst multiple dimensions of disadvantage are discussed.<br><i></i><br>
DOI: 10.6084/m9.figshare.12133578
title: Purohit_CommsBiol2020.txt

description: Western blot data
DOI: 10.6084/m9.figshare.12133575.v1
title: Model and code for "The spatial structure of socioeconomic disadvantage: a Bayesian multivariate spatial factor analysis"

description: This repository contains the data, model code, and initial values for a spatial factor analysis model described in: The spatial structure of socioeconomic disadvantage: a Bayesian multivariate spatial factor analysis. <i>International Journal of Geographical Information Science.</i><i><br></i>The <i>International Journal of Geographical Information Science </i>is available at: https://www.tandfonline.com/toc/tgis20/current<i><br></i><i><br></i>Abstract:Neighborhood socioeconomic disadvantage is a measure of socio-spatial inequality that has been shown to be associated with a variety of social, economic, and health outcomes. Existing studies that explore the local patterning of disadvantage often construct composite indices that summarize the interactions between multiple dimensions of social status, but do not consider if, and how, disadvantage exhibits spatial structure. This study applies a Bayesian multivariate factor analytic modeling approach to examine the spatial structure of socioeconomic disadvantage in Toronto, Canada. Socioeconomic disadvantage is modeled as an area-based composite index associated with three variables measuring low income, low educational attainment, and low occupational status, and a series of models with different assumptions regarding the spatial structure of disadvantage are compared. The best-fitting model shows that the prevalence of low-income households has the strongest positive association with disadvantage and that spatial clustering is three times more important than spatial heterogeneity for explaining the spatial structure of disadvantage. The implications of this study for analyzing multivariate spatial data and for understanding the interactions amongst multiple dimensions of disadvantage are discussed.<br><i></i><br>
DOI: 10.6084/m9.figshare.12133470
title: Try.R

description: test
DOI: 10.6084/m9.figshare.12133470.v1
title: Try.R

description: test
DOI: 10.23641/asha.12133380
title: Auditory-perceptual norms for speech development (Schölderle et al., 2020)

description: <b>Purpose: </b>The aim of this study was to collect auditory-perceptual data on established symptom categories of dysarthria from typically developing children between 3 and 9 years of age, for the purpose of creating age norms for dysarthria assessment.<b>Method: </b>One hundred forty-four typically developing children (3;0–9;11 [years;months], 72 girls and 72 boys) participated. We used a computer-based game specifically designed for this study to elicit sentence repetitions and spontaneous speech samples. Speech recordings were analyzed using the auditory-perceptual criteria of the Bogenhausen Dysarthria Scales, a standardized German assessment tool for dysarthria in adults. The Bogenhausen Dysarthria Scales (scales and features) cover clinically relevant dimensions of speech and allow for an evaluation of well-established symptom categories of dysarthria.<b>Results: </b>The typically developing children exhibited a number of speech characteristics overlapping with established symptom categories of dysarthria (e.g., breathy voice, frequent inspirations, reduced articulatory precision, decreased articulation rate). Substantial progress was observed between 3 and 9 years of age, but with different developmental trajectories across different dimensions. In several areas (e.g., respiration, voice quality), 9-year-olds still presented with salient developmental speech characteristics, while in other dimensions (e.g., prosodic modulation), features typically associated with dysarthria occurred only exceptionally, even in the 3-year-olds.<b>Conclusions: </b>The acquisition of speech motor functions is a prolonged process not yet completed with 9 years. Various developmental influences (e.g., anatomic–physiological changes) shape children’s speech specifically. Our findings are a first step toward establishing auditory-perceptual norms for dysarthria in children of kindergarten and elementary school age.<br><b>Supplemental Material S1. </b>Proposal for clinical use of the age norms (Bogenhausen Dysarthria Scales).<br>Schölderle, T., Haas, E., & Ziegler, W. (2020). Age norms for auditory-perceptual neurophonetic parameters: A prerequisite for the assessment of childhood dysarthria.<i> Journal of Speech, Language, and Hearing Research</i>. Advance online publication. https://doi.org/10.1044/2020_JSLHR-19-00114
DOI: 10.23641/asha.12133380.v1
title: Auditory-perceptual norms for speech development (Schölderle et al., 2020)

description: <b>Purpose: </b>The aim of this study was to collect auditory-perceptual data on established symptom categories of dysarthria from typically developing children between 3 and 9 years of age, for the purpose of creating age norms for dysarthria assessment.<b>Method: </b>One hundred forty-four typically developing children (3;0–9;11 [years;months], 72 girls and 72 boys) participated. We used a computer-based game specifically designed for this study to elicit sentence repetitions and spontaneous speech samples. Speech recordings were analyzed using the auditory-perceptual criteria of the Bogenhausen Dysarthria Scales, a standardized German assessment tool for dysarthria in adults. The Bogenhausen Dysarthria Scales (scales and features) cover clinically relevant dimensions of speech and allow for an evaluation of well-established symptom categories of dysarthria.<b>Results: </b>The typically developing children exhibited a number of speech characteristics overlapping with established symptom categories of dysarthria (e.g., breathy voice, frequent inspirations, reduced articulatory precision, decreased articulation rate). Substantial progress was observed between 3 and 9 years of age, but with different developmental trajectories across different dimensions. In several areas (e.g., respiration, voice quality), 9-year-olds still presented with salient developmental speech characteristics, while in other dimensions (e.g., prosodic modulation), features typically associated with dysarthria occurred only exceptionally, even in the 3-year-olds.<b>Conclusions: </b>The acquisition of speech motor functions is a prolonged process not yet completed with 9 years. Various developmental influences (e.g., anatomic–physiological changes) shape children’s speech specifically. Our findings are a first step toward establishing auditory-perceptual norms for dysarthria in children of kindergarten and elementary school age.<br><b>Supplemental Material S1. </b>Proposal for clinical use of the age norms (Bogenhausen Dysarthria Scales).<br>Schölderle, T., Haas, E., & Ziegler, W. (2020). Age norms for auditory-perceptual neurophonetic parameters: A prerequisite for the assessment of childhood dysarthria.<i> Journal of Speech, Language, and Hearing Research</i>. Advance online publication. https://doi.org/10.1044/2020_JSLHR-19-00114
DOI: 10.6084/m9.figshare.12133326
title: Figure 4

description: Raw illustrator file for ancestral reconstruction and model figure. Nexus files for unequal and equal and real branch lengths or branch lengths equal to one. Raw posterior probabilities are in the extended data table 2 and supplemental tables
DOI: 10.6084/m9.figshare.12133326.v1
title: Figure 4

description: Raw illustrator file for ancestral reconstruction and model figure. Nexus files for unequal and equal and real branch lengths or branch lengths equal to one. Raw posterior probabilities are in the extended data table 2 and supplemental tables
DOI: 10.6084/m9.figshare.12133155
title: Supplemental Information - miRNA PCR array results.xlsx

description: CVD PCR arrays (qiagen) using serum samples from habitually inactive and habitually highly active (endurance trained) groups.
DOI: 10.6084/m9.figshare.12133107
title: Family-specific gains and losses of protein domains in the legume and grass plant families

description: Raw data and associated result files for analyzing evolution of protein domains in legume and plant families<br><b>legume_data.tar.gz</b>: Contains legume and legume outgroup proteomes along with the associated data and result files<br><br><b>grasses_data.tar.gz</b>: Contains grass and grass outgroup proteomes along with the associated data and result files
DOI: 10.6084/m9.figshare.12133107.v1
title: Family-specific gains and losses of protein domains in the legume and grass plant families

description: Raw data and associated result files for analyzing evolution of protein domains in legume and plant families<br><b>legume_data.tar.gz</b>: Contains legume and legume outgroup proteomes along with the associated data and result files<br><br><b>grasses_data.tar.gz</b>: Contains grass and grass outgroup proteomes along with the associated data and result files
DOI: 10.6084/m9.figshare.12133155.v1
title: Supplemental Information - miRNA PCR array results.xlsx

description: CVD PCR arrays (qiagen) using serum samples from habitually inactive and habitually highly active (endurance trained) groups.
DOI: 10.6084/m9.figshare.12132903
title: Figure 5 Data Table

description: Data used to compare gene vs domain based methods found in figure 5
DOI: 10.6084/m9.figshare.12132903.v1
title: Figure 5 Data Table

description: Data used to compare gene vs domain based methods found in figure 5
DOI: 10.6084/m9.figshare.12132834
title: Tables S1 & S2 from Offspring reverse transcriptome responses to maternal deprivation when reared with pathogens in an insect with facultative family life

description: Table S1. Comparisons of different transcriptome assemblies. The table on the sheet "Transrate output" shows data obtained using Transrate v.1.03 (Smith-unna et al., 2016). We compared assemblies created using Trinity and CLC Assembly Cell, as well as a merged "hybrid" obtained using CAP3 (Huang and Madan, 1999). The assembly comparison was used to determine the best assembly for continued analyses. Table S2. Full list of all DEGs revealed by the LRTs testing for main and interaction effects, as well as the associated annotations and read counts per sample. Note that no DEGs depended on maternal presence alone. Table headers are default DEseq2 headers, while the Blast annotation was added by the authors. Note that the adjusted p-value was used for determining significance, and the Log2-Fold-Change values in the interaction sheet only represent a single pairwise comparison and are thus of limited usefulness (due to the nature of the LRT test).
DOI: 10.6084/m9.figshare.12132762
title: Figure 4 Data Table

description: Data used to create figure 4 comparing FAA based domain detection to genome based detection.