cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3761562.v1140
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.8174180
title: assessment data

description: data having reversal of KX with two reversing agents; istat analysis, blood chemistry, quality scoring
DOI: 10.6084/m9.figshare.8174180.v1
title: assessment data

description: data having reversal of KX with two reversing agents; istat analysis, blood chemistry, quality scoring
DOI: 10.6084/m9.figshare.8174180.v2
title: assessment data

description: data having reversal of KX with two reversing agents; istat analysis, blood chemistry, quality scoring
DOI: 10.25405/data.ncl.8325392
title: PREDICTED IMPACT OF CLIMATE CHANGE ON TRIHALOMETHANES FORMATION IN DRINKING WATER TREATMENT

description: This file contains raw and treated water quality parameters and THM formation measurements for 5 case study sites in Scotland.
DOI: 10.6084/m9.figshare.3761562.v1141
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.25386/genetics.7370168.v1
title: Supplemental Material for Schmidt et al., 2019

description: <b>Data used in Example 3 and Example 4. </b><br>The <b>phenotypic data </b>hast columns "loc", "gen", "rep", and "y", which denote the environment, gentoype, replicate (i.e. block) and response variable. The<b> marker data</b> contains the kinship matrix K with dimensions 89x89 - for the 89 genotypes in the phenotypic data.<br><br>Results based on this dataset were first published in Hadasch <i>et al. </i>(2016). See our article for more detailed information.
DOI: 10.25387/g3.7346714
title: Supplemental Material for Le Cam et al., 2018

description: The Venturia genus comprises fungal species that are pathogens on Rosaceae host plants, including V. inaequalis and V. asperata on apple, V. aucupariae on sorbus and V. pirina on pear. Genomic features underlying these subdivisions remain poorly understood. Here, we report whole genome sequencing of 87 Venturia strains that represent each species and each population within V. inaequalis. We present a PacBio genome assembly for the V. inaequalis EU-B04 reference isolate. The size of selected genomes was determined by flow cytometry, and varied from 45 to 93 Mb. Genome assemblies of V. inaequalis and V. aucupariae contain a high content of transposable elements, most of which belong to the Gypsy or Copia LTR superfamilies and have been inactivated by Repeat-Induced Point mutations. The reference assembly of V. inaequalis presents a mosaic structure of GC-equilibrated regions that mainly contain predicted genes and AT-rich regions, mainly composed of TEs. The availability of these genome sequences is expected to stimulate genetics and population genomics research of Venturia pathogens. Especially, it will help understanding the evolutionary history of Venturia species that are pathogenic on different hosts, a history that has probably been substantially influenced by TEs.<br>
DOI: 10.25387/g3.7346714.v1
title: Supplemental Material for Le Cam et al., 2018

description: The Venturia genus comprises fungal species that are pathogens on Rosaceae host plants, including V. inaequalis and V. asperata on apple, V. aucupariae on sorbus and V. pirina on pear. Genomic features underlying these subdivisions remain poorly understood. Here, we report whole genome sequencing of 87 Venturia strains that represent each species and each population within V. inaequalis. We present a PacBio genome assembly for the V. inaequalis EU-B04 reference isolate. The size of selected genomes was determined by flow cytometry, and varied from 45 to 93 Mb. Genome assemblies of V. inaequalis and V. aucupariae contain a high content of transposable elements, most of which belong to the Gypsy or Copia LTR superfamilies and have been inactivated by Repeat-Induced Point mutations. The reference assembly of V. inaequalis presents a mosaic structure of GC-equilibrated regions that mainly contain predicted genes and AT-rich regions, mainly composed of TEs. The availability of these genome sequences is expected to stimulate genetics and population genomics research of Venturia pathogens. Especially, it will help understanding the evolutionary history of Venturia species that are pathogenic on different hosts, a history that has probably been substantially influenced by TEs.<br>
DOI: 10.6084/m9.figshare.7749356.v2
title: FSE Submission 31 - A Statistics-based Performance Testing Methodology for Cloud Applications

description: There are the experiment result data sets for FSE submission 31<br>Including source code and dataset<br>Note that, these data set contains strings for identify the weeks, those needs to be processed before use.
DOI: 10.6084/m9.figshare.8342165.v1
title: Additional file 1: of Coordination of reach-to-grasp in physical and haptic-free virtual environments

description: Video 1. (ZIP 10700 kb)
DOI: 10.6084/m9.figshare.8342165
title: of Coordination of reach-to-grasp in physical and haptic-free virtual environments

description: of Coordination of reach-to-grasp in physical and haptic-free virtual environments
DOI: 10.25386/genetics.8343830
title: Supplemental Material for Aryaman et al., 2019

description: Figure S1, Phase portrait for an ODE representation of a network system with constant ratesFigure S2, Deterministic treatment of network systemFigure S3, Stochastic treatment of network systemFigure S4, Parameter sweeps of network fission-fusion ratesFigure S5, Stochastic treatment of high turnoverFigure S6, Exploration of analogous Moran processesFigure S7, Parameter sweep of selective fusion
DOI: 10.25386/genetics.8343830.v1
title: Supplemental Material for Aryaman et al., 2019

description: Figure S1, Phase portrait for an ODE representation of a network system with constant ratesFigure S2, Deterministic treatment of network systemFigure S3, Stochastic treatment of network systemFigure S4, Parameter sweeps of network fission-fusion ratesFigure S5, Stochastic treatment of high turnoverFigure S6, Exploration of analogous Moran processesFigure S7, Parameter sweep of selective fusion
DOI: 10.6084/m9.figshare.3761562.v1144
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.3761562.v1145
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.3761562.v1146
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.8203415.v4
title: Bat Recording Manager v8.n

description: This program provides a management system for your library of bat (or other) recordings. It can be flexible in its approach but is primarily designed to work with libraries stored and analysed according to a particular protocol which is described below.<br>The program has four main ‘views’. The default view lists all recording sessions, with details of the currently selected session. Sessions can be added or edited manually or deleted from the database. Similarly individual recordings within the session can be added, edited or deleted.<br>The program assumes that the recordings are of bat calls, and incorporates a reference list of bat species, which can also be modified by the user. This reference list also defines a set of ‘tags’ which can be used when analysing recordings to indicate the presence of each species.<br>Recordings may also be displayed ‘by bat species’. In this case the defined species are listed and the recording sessions and recordings which detected the selected species are shown.<br>An ‘Import’ page allows recording sessions which have been analysed using Audacity to be imported into the database. A single folder containing a set of recordings may be imported or a selection of folders can be imported sequentially.Analyse and Import mode allows interaction with Audacity or Kaleidoscope Light for rapid sequential analysis of recording files.The program also provides report generation in a number of formats, storage of screen capture images during analysis, and the ability to replay portions of recordings at reduced speed or in heterodyne (tuned bat detector) mode.This setup file incorporates .NET 4.7.2 and Audacity 2.3.2 which will be installed automatically if required. The Audacity installation will not affect any pre-existing installations. Kaleidoscope interaction requires that Kaleidoscope is installed to C:<br>v8.0 builds on version 7.3 (see other figshare project) but updates to .NET 4.72 and provides virtualized data access for faster loading in some situations.<br>v8.0.7111 completes the conversion to Visual Studio 2019 and .NET 4.72 with extensive refactoring and code tidying using Resharper. Source code is now available from GitHub.https://github.com/jathalls/BatRecordingManager_v8.n<br>v8.0.7115 Bugfixes for issues with identifying and extracting data from .txt files during import or analyse and import.<br>v8.0.7120 Bugfix causing a crash in 'analyse and import' when the filename doesn't include the date<br>
DOI: 10.25387/g3.8146097
title: Supplemental Material for Jacobsen et al., 2019

description: <b>Additional file 1. Supplementary tables 1-22. </b>1.<b> </b>Significantly differentially expressed genes in promyelocytes
treated with TNF-a, differentiated into granulocytes and macrophages, and granulocytes
treated with TNF-a. 2. RT-qPCR and RNA-seq of <i>CDK1</i>, <i>TNF</i>, and <i>VCAM1</i>. 3-21. Functional analysis summary
tables of GSEA, GO, KEGG, and hallmark terms enriched in subsets of
differentially expressed genes. 22. Primers and probes used for RT-qPCR validation.<br>




















<b>Supplementary figure 1. </b>PCA of VST-normalized transcript
counts show conditions cluster together in A) promyelocytes treated with TNF-a, B) granulocytes treated with TNF-a, and C) promyelocytes differentiated into
granulocytes and macrophages.

<i> </i>
DOI: 10.25387/g3.8146097.v1
title: Supplemental Material for Jacobsen et al., 2019

description: <b>Additional file 1. Supplementary tables 1-22. </b>1.<b> </b>Significantly differentially expressed genes in promyelocytes
treated with TNF-a, differentiated into granulocytes and macrophages, and granulocytes
treated with TNF-a. 2. RT-qPCR and RNA-seq of <i>CDK1</i>, <i>TNF</i>, and <i>VCAM1</i>. 3-21. Functional analysis summary
tables of GSEA, GO, KEGG, and hallmark terms enriched in subsets of
differentially expressed genes. 22. Primers and probes used for RT-qPCR validation.<br>




















<b>Supplementary figure 1. </b>PCA of VST-normalized transcript
counts show conditions cluster together in A) promyelocytes treated with TNF-a, B) granulocytes treated with TNF-a, and C) promyelocytes differentiated into
granulocytes and macrophages.

<i> </i>
DOI: 10.6084/m9.figshare.3761562.v1147
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.8426372
title: Additional file 1: of Sex differences in gray matter volume: how many and how large are they really?

description: Table S1. A. Descriptive statistics (mean and SD) and sex-based volumetric comparisons in the raw dataset). B. TIV-VOI linear regression analyses. Table S2. Descriptive statistics (mean and SD) and sex-based volumetric comparisons in the VBM8 “affine + non-linear” dataset. Table S3. A. Descriptive statistics (mean and SD) and sex-based volumetric comparisons in the VBM8- non-linear only TIV-adjusted dataset. B. TIV-VOI linear regression analyses. Table S4. A. Descriptive statistics (mean and SD) and sex-based volumetric comparisons in the proportions-adjusted dataset. Table S4. B. TIV-VOI linear regression analyses. Table S5. A. Sex and TIV contribution to local gray matter volumes as quantified by the covariate regression method. Table S6. A. Descriptive statistics (mean and SD) and sex-based volumetric comparisons in the power-corrected proportion (PCP) TIV-adjusted dataset B. TIV-VOI linear regression analyses. Table S7. A. Descriptive statistics (mean and SD) and sex-based volumetric comparisons in the residuals TIV-adjusted dataset. Table S8. A. Descriptive statistics (mean and SD) and sex-based volumetric comparisons in the TIV-matched subsample. Table S9. A. Descriptive statistics (mean and SD) and sex-based volumetric comparisons in the “only males” subsample. B. TIV-VOI linear regression analyses. Table S10. A. Descriptive statistics (mean and SD) and sex-based volumetric comparisons in the “only females” subsample B. TIV-VOI linear regression analyses. Additional file 1: Table S11. A. Replication score. B Cohen’s d averages (XLSX 581 kb)
DOI: 10.6084/m9.figshare.8426372.v1
title: Additional file 1: of Sex differences in gray matter volume: how many and how large are they really?

description: Table S1. A. Descriptive statistics (mean and SD) and sex-based volumetric comparisons in the raw dataset). B. TIV-VOI linear regression analyses. Table S2. Descriptive statistics (mean and SD) and sex-based volumetric comparisons in the VBM8 “affine + non-linear” dataset. Table S3. A. Descriptive statistics (mean and SD) and sex-based volumetric comparisons in the VBM8- non-linear only TIV-adjusted dataset. B. TIV-VOI linear regression analyses. Table S4. A. Descriptive statistics (mean and SD) and sex-based volumetric comparisons in the proportions-adjusted dataset. Table S4. B. TIV-VOI linear regression analyses. Table S5. A. Sex and TIV contribution to local gray matter volumes as quantified by the covariate regression method. Table S6. A. Descriptive statistics (mean and SD) and sex-based volumetric comparisons in the power-corrected proportion (PCP) TIV-adjusted dataset B. TIV-VOI linear regression analyses. Table S7. A. Descriptive statistics (mean and SD) and sex-based volumetric comparisons in the residuals TIV-adjusted dataset. Table S8. A. Descriptive statistics (mean and SD) and sex-based volumetric comparisons in the TIV-matched subsample. Table S9. A. Descriptive statistics (mean and SD) and sex-based volumetric comparisons in the “only males” subsample. B. TIV-VOI linear regression analyses. Table S10. A. Descriptive statistics (mean and SD) and sex-based volumetric comparisons in the “only females” subsample B. TIV-VOI linear regression analyses. Additional file 1: Table S11. A. Replication score. B Cohen’s d averages (XLSX 554 kb)
DOI: 10.25405/data.ncl.8325392.v1
title: PREDICTED IMPACT OF CLIMATE CHANGE ON TRIHALOMETHANES FORMATION IN DRINKING WATER TREATMENT

description: This file contains raw and treated water quality parameters and THM formation measurements for 5 case study sites in Scotland.
DOI: 10.25386/genetics.8085290.v1
title: Supplemental Material for Pianalto et al., 2019

description: File S1 is a List of supplemental files for the above submission. Supplemental Figure S1 displays localization of Fks1-GFP and GFP-Rho1 during caspofungin treatment. Supplemental Table S1 is a list of all strains used in this study.Supplemental Table S2 is a list of all primers used in this study. Supplemental Table S3 is a list of a plasmids used in this study.Supplemental Table S4 is a list of GO terms for mutant genes identified in this study. Supplemental Table S5 is a list of Cna1 target genes and their respective caspofungin MICs.Supplemental Table S6 is a list of the SNPs and indels identified in the genome of the msh1 mutant strain.
DOI: 10.25386/genetics.8085290
title: Supplemental Material for Pianalto et al., 2019

description: File S1 is a List of supplemental files for the above submission. Supplemental Figure S1 displays localization of Fks1-GFP and GFP-Rho1 during caspofungin treatment. Supplemental Table S1 is a list of all strains used in this study.Supplemental Table S2 is a list of all primers used in this study. Supplemental Table S3 is a list of a plasmids used in this study.Supplemental Table S4 is a list of GO terms for mutant genes identified in this study. Supplemental Table S5 is a list of Cna1 target genes and their respective caspofungin MICs.Supplemental Table S6 is a list of the SNPs and indels identified in the genome of the msh1 mutant strain.
DOI: 10.6084/m9.figshare.8160272.v2
title: A Semiparametric Bayesian Approach to Dropout in Longitudinal Studies With Auxiliary Covariates

description: We develop a semiparametric Bayesian approach to missing outcome data in longitudinal studies in the presence of auxiliary covariates. We consider a joint model for the full data response, missingness, and auxiliary covariates. We include auxiliary covariates to “move” the missingness “closer” to missing at random. In particular, we specify a semiparametric Bayesian model for the observed data via Gaussian process priors and Bayesian additive regression trees. These model specifications allow us to capture nonlinear and nonadditive effects, in contrast to existing parametric methods. We then separately specify the conditional distribution of the missing data response given the observed data response, missingness, and auxiliary covariates (i.e., the extrapolation distribution) using identifying restrictions. We introduce meaningful sensitivity parameters that allow for a simple sensitivity analysis. Informative priors on those sensitivity parameters can be elicited from subject-matter experts. We use Monte Carlo integration to compute the full data estimands. Performance of our approach is assessed using simulated datasets. Our methodology is motivated by, and applied to, data from a clinical trial on treatments for schizophrenia. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8188394
title: Synonymous Relationships in DBpedia

description: A sample of manually evaluted synonymous predicates in DBpedia-2016-10.
DOI: 10.25405/data.ncl.8429252
title: Cyclic voltammetries and DPV of porphyrins 1-3

description: cyclic voltammetry of porphyrins 1-3, in oxidation and reduction  varying the scan rate along with DPV of porphyrin 1-3 in oxidation and reduction.
DOI: 10.6084/m9.figshare.8188394.v1
title: Synonymous Relationships in DBpedia

description: A sample of manually evaluted synonymous predicates in DBpedia-2016-10.
DOI: 10.25405/data.ncl.8429252.v1
title: Cyclic voltammetries and DPV of porphyrins 1-3

description: cyclic voltammetry of porphyrins 1-3, in oxidation and reduction  varying the scan rate along with DPV of porphyrin 1-3 in oxidation and reduction.
DOI: 10.6084/m9.figshare.7749356.v3
title: FSE Submission 31 - A Statistics-based Performance Testing Methodology for Cloud Applications

description: There are the experiment result data sets for FSE submission 31<br>Including source code and dataset<br>Note that, these data set contains strings for identify the weeks, those needs to be processed before use.
DOI: 10.6084/m9.figshare.7749356.v5
title: FSE Submission 31 - A Statistics-based Performance Testing Methodology for Cloud Applications

description: There are the experiment result data sets for FSE submission 31<br>Including source code and dataset<br>Note that, these data set contains strings for identify the weeks, those needs to be processed before use.
DOI: 10.6084/m9.figshare.7749356.v4
title: FSE Submission 31 - A Statistics-based Performance Testing Methodology for Cloud Applications

description: There are the experiment result data sets for FSE submission 31<br>Including source code and dataset<br>Note that, these data set contains strings for identify the weeks, those needs to be processed before use.
DOI: 10.6084/m9.figshare.7749356.v6
title: FSE Submission 31 - A Statistics-based Performance Testing Methodology for Cloud Applications

description: There are the experiment result data sets for ESEC/FSE paper:<br>“<i>A Statistics-based Performance Testing Methodology for Cloud Applications</i>”<br><br>Including source code and dataset<br>For details please refer to Install and Readme
DOI: 10.6084/m9.figshare.7749356.v7
title: ESEC/FSE 2019 - A Statistics-based Performance Testing Methodology for Cloud Applications

description: There are the experiment result data sets for ESEC/FSE paper:<br>“<i>A Statistics-based Performance Testing Methodology for Cloud Applications</i>”<br><br>Including source code and dataset<br>For details please refer to Install and Readme
DOI: 10.5281/zenodo.3945108
title: Datasets used in "Fast phylogenetic inference from typing data"

description: Real datasets used in the paper "Fast phylogenetic inference from typing data", by João A Carriço, Maxime Crochemore, Alexandre P Francisco, Solon P Pissis, Bruno Ribeiro-Gonçalves, and Cátia Vaz, Algorithms for Molecular Biology 13 (1), 1-14, 2018. Check the paper for more information on the datasets.
DOI: 10.5281/zenodo.3945107
title: Datasets used in "Fast phylogenetic inference from typing data"

description: Real datasets used in the paper "Fast phylogenetic inference from typing data", by João A Carriço, Maxime Crochemore, Alexandre P Francisco, Solon P Pissis, Bruno Ribeiro-Gonçalves, and Cátia Vaz, Algorithms for Molecular Biology 13 (1), 1-14, 2018. Check the paper for more information on the datasets.
DOI: 10.25387/g3.8046995.v1
title: Supplemental Material for Gao et al., 2019

description: phenotypes for Group<sub>3x4 with </sub>Drop<sub>phe</sub>
DOI: 10.25387/g3.8046995
title: Supplemental Material for Gao et al., 2019

description: phenotypes for Group<sub>3x4 with </sub>Drop<sub>phe</sub>
DOI: 10.6084/m9.figshare.8791448.v1
title: hCINAP WT U2OS RNA-seq

description: hCINAP WT U2OS RNA-seq
DOI: 10.6084/m9.figshare.3761562.v1152
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.8791448.v2
title: hCINAP WT U2OS RNA-seq

description: hCINAP WT U2OS RNA-seq
DOI: 10.25386/genetics.8792594
title: Supplemental Material for Mehta et al., 2019

description: Figure S1 provides comparisons of observed FST to theoretical and null values of FST+, for both random SNP sets and SNP windows. Figure S2 displays the distribution of r^2 values from linear regressions for observed values of FST against theoretical or null values, for random SNP sets and for SNP windows. Figure S3 plots rescaled error as a function of the number of SNPs in the haplotype, for four quartiles of the sequence distance between the n-1st SNP and the nth SNP, n=2, 5, 10, 30. Figure S4 shows FST as a function of the number of SNPs in windows of neighboring SNPs, for pairs of geographic regions.
DOI: 10.25386/genetics.8792594.v1
title: Supplemental Material for Mehta et al., 2019

description: Figure S1 provides comparisons of observed FST to theoretical and null values of FST+, for both random SNP sets and SNP windows. Figure S2 displays the distribution of r^2 values from linear regressions for observed values of FST against theoretical or null values, for random SNP sets and for SNP windows. Figure S3 plots rescaled error as a function of the number of SNPs in the haplotype, for four quartiles of the sequence distance between the n-1st SNP and the nth SNP, n=2, 5, 10, 30. Figure S4 shows FST as a function of the number of SNPs in windows of neighboring SNPs, for pairs of geographic regions.