cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11423253.v1
title: MOESM4 of Synergistic effects of APOE and sex on the gut microbiome of young EFAD transgenic mice

description: Additional file 4: Table S2. Results of Mann-Whitney U tests at specific taxonomic levels in EFAD mice. Significantly different relative abundance of bacterial genera associated with APOE, sex, and APOE + sex, identified by Mann-Whitney U under the Monte Carlo Simulation corrected for false discovery rate (p
DOI: 10.6084/m9.figshare.11423247
title: MOESM3 of Synergistic effects of APOE and sex on the gut microbiome of young EFAD transgenic mice

description: Additional file 3: Table S1. Permutational multivariate analysis of variance (PERMANOVA) of EFAD mice stratified by APOE, sex, APOE + sex. (A) PERMANOVA was used to assess the effect of the interaction between universal biological variables on the microbiome composition at various taxonomic levels. P-values were obtained using 9999 permutations under a reduced model. Pseudo-F ratio is defined by the difference between cohorts over the difference within each cohort and the degrees of freedom. Each term is contributing a fixed component to the overall model. Estimated sizes of components of variation are multivariate analogs to the classical ANOVA unbiased estimators. Significance (bold) is defined by a p
DOI: 10.6084/m9.figshare.11423247.v1
title: MOESM3 of Synergistic effects of APOE and sex on the gut microbiome of young EFAD transgenic mice

description: Additional file 3: Table S1. Permutational multivariate analysis of variance (PERMANOVA) of EFAD mice stratified by APOE, sex, APOE + sex. (A) PERMANOVA was used to assess the effect of the interaction between universal biological variables on the microbiome composition at various taxonomic levels. P-values were obtained using 9999 permutations under a reduced model. Pseudo-F ratio is defined by the difference between cohorts over the difference within each cohort and the degrees of freedom. Each term is contributing a fixed component to the overall model. Estimated sizes of components of variation are multivariate analogs to the classical ANOVA unbiased estimators. Significance (bold) is defined by a p
DOI: 10.6084/m9.figshare.11423235
title: Additional file 2 of Phylogeography of higher Diptera in glacial and postglacial grasslands in western North America

description: Additional file 2. Coordinates for distribution maps of all 3 species. Each excel sheet contains the coordinates used to create the distribution map of one of the species along with the locality information and source.
DOI: 10.6084/m9.figshare.11423235.v1
title: MOESM2 of Phylogeography of higher Diptera in glacial and postglacial grasslands in western North America

description: Additional file 2. Coordinates for distribution maps of all 3 species. Each excel sheet contains the coordinates used to create the distribution map of one of the species along with the locality information and source.
DOI: 10.6084/m9.figshare.11423235.v2
title: Additional file 2 of Phylogeography of higher Diptera in glacial and postglacial grasslands in western North America

description: Additional file 2. Coordinates for distribution maps of all 3 species. Each excel sheet contains the coordinates used to create the distribution map of one of the species along with the locality information and source.
DOI: 10.6084/m9.figshare.11423229
title: MOESM8 of Investigating skewness to understand gene expression heterogeneity in large patient cohorts

description: Additional file 8 Supplemental File Table S5-S8. GOStats Terms in Each Category for Classification of Differential Skew. Specific GOStats terms and pathways included in each category used to summarize gene enrichment data. https://drive.google.com/open?id=0B57SQtVF1CxkUnRDU3A4Tm1DUkUhttps://drive.google.com/open?id=0B57SQtVF1CxkZlgwdmQwSWpUOTQhttps://drive.google.com/open?id=0B57SQtVF1CxkRzlnN2s5Y1pPNjghttps://drive.google.com/open?id=0B57SQtVF1CxkVEdiYktBbkc4YWc
DOI: 10.6084/m9.figshare.11423229.v1
title: MOESM8 of Investigating skewness to understand gene expression heterogeneity in large patient cohorts

description: Additional file 8 Supplemental File Table S5-S8. GOStats Terms in Each Category for Classification of Differential Skew. Specific GOStats terms and pathways included in each category used to summarize gene enrichment data. https://drive.google.com/open?id=0B57SQtVF1CxkUnRDU3A4Tm1DUkUhttps://drive.google.com/open?id=0B57SQtVF1CxkZlgwdmQwSWpUOTQhttps://drive.google.com/open?id=0B57SQtVF1CxkRzlnN2s5Y1pPNjghttps://drive.google.com/open?id=0B57SQtVF1CxkVEdiYktBbkc4YWc
DOI: 10.6084/m9.figshare.11423223
title: MOESM7 of Investigating skewness to understand gene expression heterogeneity in large patient cohorts

description: Additional file 7 Supplemental File Tables S4. RNA-Seq Cancer to Cancer Gene Enrichment Results. The ten most significant GOStats pathways for each subcategory comparing between RNA-Seq cancer cohorts. Results are separated by GOStat term function (Biological Process: BP, Cellular Component: CC, Molecular Function: MF, and KEGG), comparison group (“UP”, “DOWN”, “MIDDLE”), and by the two cancer cohort being compared. UP refers to larger skew in the first cohort indicated. https://drive.google.com/open?id=0B57SQtVF1Cxkd0VCUURhZnVoX00
DOI: 10.6084/m9.figshare.11423223.v1
title: MOESM7 of Investigating skewness to understand gene expression heterogeneity in large patient cohorts

description: Additional file 7 Supplemental File Tables S4. RNA-Seq Cancer to Cancer Gene Enrichment Results. The ten most significant GOStats pathways for each subcategory comparing between RNA-Seq cancer cohorts. Results are separated by GOStat term function (Biological Process: BP, Cellular Component: CC, Molecular Function: MF, and KEGG), comparison group (“UP”, “DOWN”, “MIDDLE”), and by the two cancer cohort being compared. UP refers to larger skew in the first cohort indicated. https://drive.google.com/open?id=0B57SQtVF1Cxkd0VCUURhZnVoX00
DOI: 10.6084/m9.figshare.11423217
title: MOESM6 of Investigating skewness to understand gene expression heterogeneity in large patient cohorts

description: Additional file 6 Supplemental File Table S3. RNA-Seq Cancer to Control Gene Enrichment Results. The ten most significant GOStats pathways for each subcategory comparing the RNA-Seq cancer cohorts to control. Results are separated by GOStat term function (Biological Process: BP, Cellular Component: CC, Molecular Function: MF, and KEGG), comparison group (“UP”, “DOWN”, “MIDDLE”), by cancer cohort. https://drive.google.com/open?id=0B57SQtVF1CxkUFBVQkxDcXJGWDg
DOI: 10.6084/m9.figshare.11423217.v1
title: MOESM6 of Investigating skewness to understand gene expression heterogeneity in large patient cohorts

description: Additional file 6 Supplemental File Table S3. RNA-Seq Cancer to Control Gene Enrichment Results. The ten most significant GOStats pathways for each subcategory comparing the RNA-Seq cancer cohorts to control. Results are separated by GOStat term function (Biological Process: BP, Cellular Component: CC, Molecular Function: MF, and KEGG), comparison group (“UP”, “DOWN”, “MIDDLE”), by cancer cohort. https://drive.google.com/open?id=0B57SQtVF1CxkUFBVQkxDcXJGWDg
DOI: 10.6084/m9.figshare.11423211
title: MOESM4 of Investigating skewness to understand gene expression heterogeneity in large patient cohorts

description: Additional file 4 Supplemental File Table S1. Microarray Cancer to Control Gene Enrichment Results. The ten most significant GOStats pathways for each subcategory comparing the microarray cancer cohorts to control. Results are separated by GOStat term function (Biological Process: BP, Cellular Component: CC, Molecular Function: MF, and KEGG), comparison group (“UP”, “DOWN”, “MIDDLE”), and by cancer cohort. https://drive.google.com/open?id=0B57SQtVF1CxkMkwzRHZpcTcwWWM
DOI: 10.6084/m9.figshare.11423214
title: MOESM5 of Investigating skewness to understand gene expression heterogeneity in large patient cohorts

description: Additional file 5 Supplemental File Table S2. Microarray Cancer to Cancer Gene Enrichment Results. The ten most significant GOStats pathways for each subcategory comparing between microarray cancer cohorts. Results are separated by GOStat term function (Biological Process: BP, Cellular Component: CC, Molecular Function: MF, and KEGG), comparison group (“UP”, “DOWN”, “MIDDLE”), and by the two cancer cohort being compared. UP refers to larger skew in the first cohort indicated. https://drive.google.com/open?id=0B57SQtVF1CxkemhMLWdBeFVZWVE
DOI: 10.6084/m9.figshare.11423211.v1
title: MOESM4 of Investigating skewness to understand gene expression heterogeneity in large patient cohorts

description: Additional file 4 Supplemental File Table S1. Microarray Cancer to Control Gene Enrichment Results. The ten most significant GOStats pathways for each subcategory comparing the microarray cancer cohorts to control. Results are separated by GOStat term function (Biological Process: BP, Cellular Component: CC, Molecular Function: MF, and KEGG), comparison group (“UP”, “DOWN”, “MIDDLE”), and by cancer cohort. https://drive.google.com/open?id=0B57SQtVF1CxkMkwzRHZpcTcwWWM
DOI: 10.6084/m9.figshare.11423214.v1
title: MOESM5 of Investigating skewness to understand gene expression heterogeneity in large patient cohorts

description: Additional file 5 Supplemental File Table S2. Microarray Cancer to Cancer Gene Enrichment Results. The ten most significant GOStats pathways for each subcategory comparing between microarray cancer cohorts. Results are separated by GOStat term function (Biological Process: BP, Cellular Component: CC, Molecular Function: MF, and KEGG), comparison group (“UP”, “DOWN”, “MIDDLE”), and by the two cancer cohort being compared. UP refers to larger skew in the first cohort indicated. https://drive.google.com/open?id=0B57SQtVF1CxkemhMLWdBeFVZWVE
DOI: 10.6084/m9.figshare.11423181
title: Arad Ancient Hebrew dataset

description: Reconstructions of Arad ostraca characters
DOI: 10.6084/m9.figshare.11423181.v1
title: Arad Ancient Hebrew dataset

description: Reconstructions of Arad ostraca characters
DOI: 10.6084/m9.figshare.11422743
title: Ice formation on lake surface in winter causes warm season bias of lacustrine brGDGT temperature estimates

description: ORIGINAL GEOCHEMISTRY DATA OF GONGHAI LAKE
DOI: 10.6084/m9.figshare.11422743.v1
title: Ice formation on lake surface in winter causes warm season bias of lacustrine brGDGT temperature estimates

description: ORIGINAL GEOCHEMISTRY DATA OF GONGHAI LAKE
DOI: 10.6084/m9.figshare.11422533
title: Does sea surface temperature affect solubility of iron in mineral dust? The Gulf of California as a case study.

description: This dataset was used in a manuscript recently submitted to Journal of Geophysical Research-Oceans: Armando Félix-Bermúdez, Francisco Delgadillo-Hinojosa, Eunice V. Torres-Delgado, and Albino Muñoz-Barbosa (2019), Does sea surface temperature affect solubility of iron in mineral dust? The Gulf of California as a case study.The dataset consists of:1) Experimental information (e.g., date, contact time, temperature, dust load) and results (e.g., dissolved Fe measured in control and dust-treatment bottles, fractional solubility of Fe) obteined in the Fe-dust solubility experiment performed at 28.9 ± 0.8 °C: summer (warm) conditions.2) Experimental information (e.g., date, contact time, temperature, dust load) and results (e.g., dissolved Fe measured in control and dust-treatment bottles, fractional solubility of Fe) obteined in the Fe-dust solubility experiment performed at 16.8 ± 0.6 °C: winter (cool) conditions.<br>
DOI: 10.6084/m9.figshare.11422329
title: Questionnaire (Myanmar version).pdf

description: Questionnaire (Myanmar version)<br>
DOI: 10.6084/m9.figshare.11422320
title: Proteome database of Pseudomonas syringae ATCC BAA-871

description: A known plant pathogen, <i>Pseudomonas syringae</i> is a Gram-negative bacterium that has attracted much research attention both from the plant biology community as well as those in biotechnology. Specifically, <i>P. syringae</i> uses an elaborate set of mechanisms to mediate infection of plant tissues, which naturally elicit research interest targeting at deactivating effectors in order to protect the vast diversity of plant species that could be infected by this bacterium. But, more recently, potential biotechnology uses have been found for the ice nucleation protein on the surface of this bacterium. This work sought to shed some light on the fundamental biology of <i>P. syringae</i> ATCC BAA-871 by parsing the UniProt proteome file of the bacterium. Using MATLAB built-in functions in an in-house proteome analysis programme, a proteome database comprising protein name, amino acid sequence, number of residues, molecular weight and nucleotide sequence was built. At the fundamental level, the proteome database of <i>P. syringae </i>ATCC BAA-871 should find use in guiding various gene deletion studies in uncovering mechanistic insights into, for example, how <i>P. syringae</i> avoids the plant immune system’s counteroffensive. But, greater use of the proteome information should lie in addressing systems-level understanding such as how an environmental microbe mixes plant pathogenesis with its ability to live off various environments. <br><br>
DOI: 10.6084/m9.figshare.11422320.v1
title: Proteome database of Pseudomonas syringae ATCC BAA-871

description: A known plant pathogen, <i>Pseudomonas syringae</i> is a Gram-negative bacterium that has attracted much research attention both from the plant biology community as well as those in biotechnology. Specifically, <i>P. syringae</i> uses an elaborate set of mechanisms to mediate infection of plant tissues, which naturally elicit research interest targeting at deactivating effectors in order to protect the vast diversity of plant species that could be infected by this bacterium. But, more recently, potential biotechnology uses have been found for the ice nucleation protein on the surface of this bacterium. This work sought to shed some light on the fundamental biology of <i>P. syringae</i> ATCC BAA-871 by parsing the UniProt proteome file of the bacterium. Using MATLAB built-in functions in an in-house proteome analysis programme, a proteome database comprising protein name, amino acid sequence, number of residues, molecular weight and nucleotide sequence was built. At the fundamental level, the proteome database of <i>P. syringae </i>ATCC BAA-871 should find use in guiding various gene deletion studies in uncovering mechanistic insights into, for example, how <i>P. syringae</i> avoids the plant immune system’s counteroffensive. But, greater use of the proteome information should lie in addressing systems-level understanding such as how an environmental microbe mixes plant pathogenesis with its ability to live off various environments. <br><br>
DOI: 10.6084/m9.figshare.11422329.v1
title: Questionnaire (Myanmar version).pdf

description: Questionnaire (Myanmar version)<br>
DOI: 10.6084/m9.figshare.11421885
title: V Jornadas Prevención y Diagnóstico Precoz de Cáncer de Cavidad Oral y Orofaringe y Primer Encuentro de la mención en Patología Oral

description: Certificados de Asistencia a las V Jornadas Prevención y Diagnóstico Precoz de Cáncer de Cavidad Oral y Orofaringe y Primer Encuentro de la mención en Patología Oral, organizadas por la Unidad de Patología y Medicina Oral de la Universidad de Talca, realizadas en el Auditorio del Colegio de Cirujano Dentistas Maule, los días 18 y 19 de noviembre de 2019, con una duración de 16 horas cronológicas.
DOI: 10.6084/m9.figshare.11421759
title: Mua.txt

description: Supplementary Material for the article titled: "Optical characterisation of porcine tissues from various organs in the 650 – 1100 nm range using time-domain diffuse spectroscopy".<br>The broadband (650-1100nm) absorption coefficient spectra of nine different porcine organs were obtained using time-domain diffuse optical spectroscopy. Measurements were performed on three different spatial locations of each organ, the mean value and the standard deviation over the three locations are presented here.
DOI: 10.6084/m9.figshare.11421777.v1
title: A systematic assessment of stable Sr isotopic compositions of vent fluids in arc/back-arc hydrothermal systems: effects of host rock type, phase separation, and overlying sediment

description: Data under review. <br>
DOI: 10.6084/m9.figshare.11421597
title: Figure 2

description: Data set to origin analysis
DOI: 10.6084/m9.figshare.11421597.v1
title: Figure 2

description: Data set to origin analysis
DOI: 10.6084/m9.figshare.11421759.v1
title: Mua.txt

description: Broadband optical properties (absorption and reduced scattering coefficient) of different porcine organs obtained using time-domain diffuse optical spectroscopy. Measurements were performed on three different spatial locations of each organ, the mean value and the standard deviation over the three values are presented here.
DOI: 10.6084/m9.figshare.11421579
title: Figure 1.opj

description: Data anlaysis software origin
DOI: 10.6084/m9.figshare.11421759.v2
title: Mua.txt

description: Supplementary Material for the article titled: "Optical characterisation of porcine tissues from various organs in the 650 – 1100 nm range using time-domain diffuse spectroscopy".<br>The broadband (650-1100nm) optical properties (absorption (Mua.txt) and reduced scattering (Musp.txt) coefficient) of nine different porcine organs were obtained using time-domain diffuse optical spectroscopy. Measurements were performed on three different spatial locations of each organ, the mean value and the standard deviation over the three locations are presented here.
DOI: 10.6084/m9.figshare.11421759.v3
title: Mua.txt

description: Supplementary Material for the article titled: "Optical characterisation of porcine tissues from various organs in the 650 – 1100 nm range using time-domain diffuse spectroscopy".<br>The broadband (650-1100nm) optical properties (absorption (Mua.txt) and reduced scattering (Musp.txt) coefficient) of nine different porcine organs were obtained using time-domain diffuse optical spectroscopy. Measurements were performed on three different spatial locations of each organ, the mean value and the standard deviation over the three locations are presented here.
DOI: 10.6084/m9.figshare.11421297
title: Hawaiian coral growth under ocean acidification to estimate heritability of calcification rates

description: Raw nubbin growth data in Excel format from Jury, Delano & Toonen study entitled "High heritability of coral calcification rates and evolutionary potential under ocean acidification."
DOI: 10.6084/m9.figshare.11420967
title: Raw data files for SI-KCTP @ IPhN2+ Layers

description: SEM, infrared, cyclic voltammetry, UV-vis, EDS, and XPS characterization of a set of conjugated thin films. Additional IR, voltammetric, XPS, and UV-vis data has been added (Version 2)<br><br>
DOI: 10.6084/m9.figshare.11420967.v1
title: Raw data files for SI-KCTP @ IPhN2+ Layers

description: SEM, infrared, cyclic voltammetry, UV-vis, EDS, and XPS characterization of a set of conjugated thin films. <br><br>
DOI: 10.6084/m9.figshare.11419368
title: Notes on Exact Power Calculations for <i>t</i> Tests and Analysis of Covariance

description: Tang derived the exact power formulas for <i>t</i> tests and analysis of covariance (ANCOVA) in superiority, noninferiority (NI), and equivalence trials. The power calculation in equivalence trials can be simplified by using Owen’s <i>Q</i> function, which is available in standard statistical software. We extend the exact power determination method for ANCOVA to unstratified and stratified multi-arm randomized trials. The method is applied to the design of multi-arm trials and gold standard NI trials. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.11419362
title: Hansen_et_al - Supplementary Figure 6, Nanobody-directed targeting of optogenetic tools to study signaling in the primary cilium

description: Data set presented in Supplementary Figure 6 of the publication "Nanobody-directed targeting of optogenetic tools to study signaling in the primary cilium", https://doi.org/10.7554/eLife.57907
DOI: 10.6084/m9.figshare.11419368.v2
title: Notes on Exact Power Calculations for <i>t</i> Tests and Analysis of Covariance

description: Tang derived the exact power formulas for <i>t</i> tests and analysis of covariance (ANCOVA) in superiority, noninferiority (NI), and equivalence trials. The power calculation in equivalence trials can be simplified by using Owen’s <i>Q</i> function, which is available in standard statistical software. We extend the exact power determination method for ANCOVA to unstratified and stratified multi-arm randomized trials. The method is applied to the design of multi-arm trials and gold standard NI trials. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.11419368.v1
title: “Exact and approximate power and sample size calculations for analysis of covariance in randomized clinical trials with or without stratification” by Yongqiang Tang

description: Tang (2018b,a) derived the exact power formulae for t tests and analysis of covariance (ANCOVA) in superiority, noninferiority and equivalence trials. The power calculation in equivalence trials can be simplified by using Owen’s Q function, which is available in standard statistical software. We extend the exact power determination method for ANCOVA to unstratified and stratified multi-arm randomized trials. The method is applied to the design of multi-arm trials and gold standard noninferiority trials
DOI: 10.6084/m9.figshare.11419242
title: Resting State/FTO Data and z-transformed maps

description: <b>Data supporting the paper:</b> <i>FTO</i> gene status in children is associated with variation in resting-state functional connectivity between nucleus accumbens and cerebellum <b></b><b><br></b><b>Abstract:</b>The fat-mass and obesity-associated gene, FTO, polymorphism has been consistently associated with increased body mass index (BMI). Furthermore, it has been associated with differences in nucleus accumbens (NAcc) volume and NAcc functional response to food cues. This study investigates whether functional connections from the NAcc to other brain regions vary as a function of obesity risk status, as determined by FTO, in a cohort of children. A seed-based whole-brain analyses revealed an inverse relationship between NAcc connectivity with the cerebellum and child FTO obesity risk status. This exploratory analysis elucidates a potential functional connection between the cerebellum and NAcc that varies by FTO genotype.The .sav file contains participant descriptive data. This data file also contains participants fisher's z-transformed value maps.
DOI: 10.6084/m9.figshare.11419242.v1
title: Resting State/FTO Data and z-transformed maps

description: <b>Data supporting the paper:</b> <i>FTO</i> gene status in children is associated with variation in resting-state functional connectivity between nucleus accumbens and cerebellum <b></b><b><br></b><b>Abstract:</b>The fat-mass and obesity-associated gene, FTO, polymorphism has been consistently associated with increased body mass index (BMI). Furthermore, it has been associated with differences in nucleus accumbens (NAcc) volume and NAcc functional response to food cues. This study investigates whether functional connections from the NAcc to other brain regions vary as a function of obesity risk status, as determined by FTO, in a cohort of children. A seed-based whole-brain analyses revealed an inverse relationship between NAcc connectivity with the cerebellum and child FTO obesity risk status. This exploratory analysis elucidates a potential functional connection between the cerebellum and NAcc that varies by FTO genotype.The .sav file contains participant descriptive data. This data file also contains participants fisher's z-transformed value maps.
DOI: 10.6084/m9.figshare.11419071
title: Hand ROM with Leap motion

description: This is a data base containing data for the paper:Automatic Wrist and Fingers Range of Motion with Leap motion