cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12505022
title: Metadata record for: A database of human gait performance on irregular and uneven surfaces collected by wearable sensors

description: This dataset contains key characteristics about the data described in the Data Descriptor A database of human gait performance on irregular and uneven surfaces collected by wearable sensors. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format <br> <br> <br>
DOI: 10.6084/m9.figshare.12504944
title: Raw Data for "On the Effect of Noise on Software Engineers’ Performance: Results from Two Replicated Experiments"

description: It contains the raw data for the paper entitled "On the Effect of Noise on Software Engineers’ Performance: Results from Two Replicated Experiments" by S. Romano, G. Scanniello, M.T. Baldassarre, and D. Fucci, in proc. of the 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2020.
DOI: 10.6084/m9.figshare.12504911
title: Metadata record for: A map of African humid tropical forest aboveground biomass derived from management inventories

description: This dataset contains key characteristics about the data described in the Data Descriptor A map of African humid tropical forest aboveground biomass derived from management inventories. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format <br> <br> <br>
DOI: 10.6084/m9.figshare.12504911.v1
title: Metadata record for: A map of African humid tropical forest aboveground biomass derived from management inventories

description: This dataset contains key characteristics about the data described in the Data Descriptor A map of African humid tropical forest aboveground biomass derived from management inventories. <br> Contents: <br> 1. human readable metadata summary table in CSV format 2. machine readable metadata file in JSON format <br> <br> <br>
DOI: 10.6084/m9.figshare.12504839
title: data.sav

description: These are coded data from a survey of university students in Saudi Arabia. The research explored the influence of various forms of motivation on individual's choice to work in the public sector.
DOI: 10.6084/m9.figshare.12504839.v1
title: data.sav

description: These are coded data from a survey of university students in Saudi Arabia. The research explored the influence of various forms of motivation on individual's choice to work in the public sector.
DOI: 10.15131/shef.data.12504815
title: Data for "Do Specific Types of Sleep Disturbances Represent Risk Factors for Poorer Health Related Quality of Life in Inflammatory Bowel Disease? A longitudinal Cohort Study".

description: This data set accompanies the open access article, "Do Specific Types of Sleep Disturbances Represent Risk Factors for Poorer Health Related Quality of Life in Inflammatory Bowel Disease? A longitudinal Cohort Study", published in the British Journal of Health Psychology (DOI:10.1111/bjhp.12457).<br>The data is longitudinal with two timepoints (baseline and 4-weeks later). All variable labels are stored within the datafile, and the accompanying published article contains more data information.
DOI: 10.15131/shef.data.12504815.v1
title: Data for "Do Specific Types of Sleep Disturbances Represent Risk Factors for Poorer Health Related Quality of Life in Inflammatory Bowel Disease? A longitudinal Cohort Study".

description: This data set accompanies the open access article, "Do Specific Types of Sleep Disturbances Represent Risk Factors for Poorer Health Related Quality of Life in Inflammatory Bowel Disease? A longitudinal Cohort Study", published in the British Journal of Health Psychology (DOI:10.1111/bjhp.12457).<br>The data is longitudinal with two timepoints (baseline and 4-weeks later). All variable labels are stored within the datafile, and the accompanying published article contains more data information.
DOI: 10.6084/m9.figshare.12504668
title: SSc-ILD NMA.xls

description: network meta-analysis raw data
DOI: 10.6084/m9.figshare.12504611
title: Additional file 1 of Tropomyosin 1 genetically constrains in vitro hematopoiesis

description: Additional file 1:Table S1. The 860 chromatin feature tracks included in our LASSO analysis. These data were obtained from ENCODE [68], ChromHMM [81], and analyses of primary human MK cells [26]. Table S2. Chromatin features and coefficients comprising our penalized regression-based platelet scoring model. Coefficients for background parameters are included at the bottom of this list, but were not included in subsequent genome-wide SNP scoring. Table S3. Gene Ontology pathways that were significantly enriched in the top 1% of SNPs, as defined by platelet model scores. Presented pathways had false discovery rate (FDR) &lt; 5%. Table S4. Gene Ontology pathways that were significantly enriched in the top 1% of SNPs, as defined by GWAVA scores. Presented pathways had false discovery rate (FDR) &lt; 5%. Table S5. Gene Ontology pathways that were significantly enriched in the top 1% of SNPs, as defined by CADD scores. Presented pathways had false discovery rate (FDR) &lt; 5%. Table S6. CRISPR/Cas9-edited Tropomyosin 1 knockout (KO) iPSC lines did not incur any additional CNVs compared to the parent line. Analyses of wild type CHOP14 and CHOP10 ‘parent’ lines, and derivative TPM1KO ‘child’ lines, are shown. Karyotype and copy number variation (CNV) analyses for all child lines were consistent with parental iPSC lines. Table S7. Dysregulated molecular pathways in TPM1KO MKs. FACS-sorted MKs were analyzed by microarray, and gene set enrichment was performed. Upregulated Gene Ontology [30] pathways with FDR&lt;25% are shown. There were no significantly downregulated pathways. GO, Gene Ontology. NES, nominal enrichment score. FDR, false discovery rate. Table S8. Chromatin features and coefficients comprising our penalized regression-based red cell scoring model. Coefficients for background parameters are included at the bottom of this list, but were not included in subsequent genome-wide SNP scoring. Table S9. Gene Ontology pathways that were significantly enriched in the top 1% of SNPs, as defined by red cell model scores. Presented pathways had false discovery rate (FDR) &lt; 5%. Table S10. Penalized regression-based fine-mapping identifies eQTLs in established platelet and/or red cell trait GWAS loci that overlie GATA binding sites. Listed SNPs are within platelet or red cell trait GWAS LD blocks (EUR r2&gt;0.7), scored in the top 5% by both our platelet trait and red cell models, overlap canonical or near-canonical GATA binding sites, and are eQTLs for at least 1 gene [41] (GTEx V7). Associated GWAVA [17] scores are present, if available. SNP rsIDs and locations refer to hg19 genome. Table S11. Semi-quantitative RT-PCR primers used in this study.
DOI: 10.6084/m9.figshare.12504611.v1
title: Additional file 1 of Tropomyosin 1 genetically constrains in vitro hematopoiesis

description: Additional file 1:Table S1. The 860 chromatin feature tracks included in our LASSO analysis. These data were obtained from ENCODE [68], ChromHMM [81], and analyses of primary human MK cells [26]. Table S2. Chromatin features and coefficients comprising our penalized regression-based platelet scoring model. Coefficients for background parameters are included at the bottom of this list, but were not included in subsequent genome-wide SNP scoring. Table S3. Gene Ontology pathways that were significantly enriched in the top 1% of SNPs, as defined by platelet model scores. Presented pathways had false discovery rate (FDR) &lt; 5%. Table S4. Gene Ontology pathways that were significantly enriched in the top 1% of SNPs, as defined by GWAVA scores. Presented pathways had false discovery rate (FDR) &lt; 5%. Table S5. Gene Ontology pathways that were significantly enriched in the top 1% of SNPs, as defined by CADD scores. Presented pathways had false discovery rate (FDR) &lt; 5%. Table S6. CRISPR/Cas9-edited Tropomyosin 1 knockout (KO) iPSC lines did not incur any additional CNVs compared to the parent line. Analyses of wild type CHOP14 and CHOP10 ‘parent’ lines, and derivative TPM1KO ‘child’ lines, are shown. Karyotype and copy number variation (CNV) analyses for all child lines were consistent with parental iPSC lines. Table S7. Dysregulated molecular pathways in TPM1KO MKs. FACS-sorted MKs were analyzed by microarray, and gene set enrichment was performed. Upregulated Gene Ontology [30] pathways with FDR&lt;25% are shown. There were no significantly downregulated pathways. GO, Gene Ontology. NES, nominal enrichment score. FDR, false discovery rate. Table S8. Chromatin features and coefficients comprising our penalized regression-based red cell scoring model. Coefficients for background parameters are included at the bottom of this list, but were not included in subsequent genome-wide SNP scoring. Table S9. Gene Ontology pathways that were significantly enriched in the top 1% of SNPs, as defined by red cell model scores. Presented pathways had false discovery rate (FDR) &lt; 5%. Table S10. Penalized regression-based fine-mapping identifies eQTLs in established platelet and/or red cell trait GWAS loci that overlie GATA binding sites. Listed SNPs are within platelet or red cell trait GWAS LD blocks (EUR r2&gt;0.7), scored in the top 5% by both our platelet trait and red cell models, overlap canonical or near-canonical GATA binding sites, and are eQTLs for at least 1 gene [41] (GTEx V7). Associated GWAVA [17] scores are present, if available. SNP rsIDs and locations refer to hg19 genome. Table S11. Semi-quantitative RT-PCR primers used in this study.
DOI: 10.6084/m9.figshare.12504245
title: Genome-wide-sgRNA-Selection-monkey.csv.zip

description: Genome-wide prioritized top10 high efficient sgRNAs for CRISPR/Cas9 protein knockouts in monkey using GuidePro method
DOI: 10.11583/dtu.12504500.v1
title: Corrigenda for the textbook: Turbulence in Coastal and Civil Engineering, by B. Mutlu Sumer and David R. Fuhrman

description: This document contains a list of known typographical errors for the textbook: <br>Sumer, B.M. & Fuhrman, D.R. (2020) Turbulence in Coastal and Civil Engineering. World Scientific. https://doi.org/10.1142/10829<br><br><br>
DOI: 10.6084/m9.figshare.12504203
title: Genome-wide-sgRNA-Selection-mouse.csv.zip

description: Genome-wide prioritized top10 high efficient sgRNAs for CRISPR/Cas9 protein knockouts in mouse using GuidePro method
DOI: 10.6084/m9.figshare.12504245.v1
title: Genome-wide-sgRNA-Selection-monkey.csv.zip

description: Genome-wide prioritized top10 high efficient sgRNAs for CRISPR/Cas9 protein knockouts in monkey using GuidePro method
DOI: 10.6084/m9.figshare.12504167
title: Genome-wide-sgRNA-Selection-human.csv.zip

description: Genome-wide prioritized top10 high efficient sgRNAs for CRISPR/Cas9 mediated protein knockouts in human using GuidePro method
DOI: 10.6084/m9.figshare.12504143
title: boykoDFE.tgz

description: This item contains all forward simulations of background selection under human-like parameters from Dan Schrider's paper comparing patterns of variation produced by hitchhiking to those produced by background selection. For more information see the manuscript (doi: https://doi.org/10.1101/2019.12.13.876136), and the code used to produce these simulations (https://github.com/SchriderLab/posSelVsBgs).<br><br>Note: the large-scale simulation output is not included here, but the windowed statistics and logs (which contain random seeds for reproducibility) from these simulations are present.
DOI: 10.6084/m9.figshare.12504203.v1
title: Genome-wide-sgRNA-Selection-mouse.csv.zip

description: Genome-wide prioritized top10 high efficient sgRNAs for CRISPR/Cas9 protein knockouts in mouse using GuidePro method
DOI: 10.6084/m9.figshare.12504143.v1
title: boykoDFE.tgz

description: This item contains all forward simulations of background selection under human-like parameters from Dan Schrider's paper comparing patterns of variation produced by hitchhiking to those produced by background selection. For more information see the manuscript (doi: https://doi.org/10.1101/2019.12.13.876136), and the code used to produce these simulations (https://github.com/SchriderLab/posSelVsBgs).<br><br>Note: the large-scale simulation output is not included here, but the windowed statistics and logs (which contain random seeds for reproducibility) from these simulations are present.
DOI: 10.6084/m9.figshare.12504098
title: First example of hybridisation between two Australian figs (Moraceae)

description: SNP data from research paper:First example of hybridisation between two Australian figs (Moraceae) <br><i>Brendan C Wilde</i><i> A</i><i>, </i><i>Susan Rutherford</i><i>,A,C</i><i>, </i><i>Marlien van der Merwe</i><i> A</i><i>, </i><i>Megan L. Murray</i><i> B</i><i>, Maurizio Rossetto</i><i> </i><i>A</i><i><br></i>A. National Herbarium of NSW, Royal Botanic Garden & Domain Trust, Mrs Macquaries Road, Sydney, NSW 2000, Australia.<br>B. Department of Environmental Sciences, University of Technology Sydney, PO Box 123, Broadway, NSW 2007, Australia.<br>C. Present address: Institute of Environment and Ecology, Academy of Environmental Health and Ecological Security, Jiangsu University, Zhenjiang 212013, P. R. China <br>Raw Genomic SNP data supplied by DArt (https://www.diversityarrays.com/) as a zipped .csv file.<br>Corresponding author: Brendan.Wilde@rbgsyd.nsw.gov.au<br>BrendanWilde@optusnet.com.au
DOI: 10.6084/m9.figshare.12504167.v2
title: Genome-wide-sgRNA-Selection-human.csv.zip

description: Genome-wide prioritized top10 high efficient sgRNAs for CRISPR/Cas9 mediated protein knockouts in human using GuidePro method
DOI: 10.6084/m9.figshare.12504167.v1
title: Genome-wide-sgRNA-Selection-human.csv.zip

description: Genome-wide prioritized sgRNAs for CRISPR/Cas9 mediated protein knockouts in human using GuidePro method
DOI: 10.17608/k6.auckland.12503810.v1
title: Machine learning identifies regulatory changes in the lung as key to the conversion of genetic risk to Type 1 diabetes

description: All datasets and supplementary information for this article.
DOI: 10.6084/m9.figshare.12503510
title: Impact of COVID-19 on the access to hearing health care services for children with cochlear implants: a survey of parents

description: Data was obtained from 24 participants in response to a questionnaire.parents of the CI child were asked to respond using likert scale
DOI: 10.17608/k6.auckland.12503810.v2
title: Machine learning identifies regulatory changes in the lung as key to the conversion of genetic risk to Type 1 diabetes

description: All datasets and supplementary information for this article.
DOI: 10.17608/k6.auckland.12503810.v3
title: Machine learning identifies regulatory changes in the lung as key to the conversion of genetic risk to Type 1 diabetes

description: All datasets and supplementary information for this article.
DOI: 10.6084/m9.figshare.12503510.v1
title: Impact of COVID-19 on the access to hearing health care services for children with cochlear implants

description: Data was obtained from 24 participants in response to a questionnaire.parents of the CI child were asked to respond using likert scale
DOI: 10.6084/m9.figshare.12503243
title: Additional file 1 of Level of quality of option B+PMTCT service provision in public health facilities in Mekelle zone, northern Ethiopia: cross-sectional study

description: Additional file 1. Excel files showing the detail scores for the three quality components.
DOI: 10.6084/m9.figshare.12503243.v1
title: Additional file 1 of Level of quality of option B+PMTCT service provision in public health facilities in Mekelle zone, northern Ethiopia: cross-sectional study

description: Additional file 1. Excel files showing the detail scores for the three quality components.
DOI: 10.6084/m9.figshare.12503228
title: Additional file 7 of Identification of therapeutic targets from genetic association studies using hierarchical component analysis

description: Additional file 7.
DOI: 10.6084/m9.figshare.12503222
title: Additional file 6 of Identification of therapeutic targets from genetic association studies using hierarchical component analysis

description: Additional file 6.
DOI: 10.6084/m9.figshare.12503228.v1
title: Additional file 7 of Identification of therapeutic targets from genetic association studies using hierarchical component analysis

description: Additional file 7.
DOI: 10.6084/m9.figshare.12503216
title: Additional file 5 of Identification of therapeutic targets from genetic association studies using hierarchical component analysis

description: Additional file 5.
DOI: 10.6084/m9.figshare.12503222.v1
title: Additional file 6 of Identification of therapeutic targets from genetic association studies using hierarchical component analysis

description: Additional file 6.
DOI: 10.6084/m9.figshare.12503210
title: Additional file 4 of Identification of therapeutic targets from genetic association studies using hierarchical component analysis

description: Additional file 4.
DOI: 10.6084/m9.figshare.12503216.v1
title: Additional file 5 of Identification of therapeutic targets from genetic association studies using hierarchical component analysis

description: Additional file 5.
DOI: 10.6084/m9.figshare.12503204
title: Additional file 3 of Identification of therapeutic targets from genetic association studies using hierarchical component analysis

description: Additional file 3.
DOI: 10.6084/m9.figshare.12503198
title: Additional file 2 of Identification of therapeutic targets from genetic association studies using hierarchical component analysis

description: Additional file 2.
DOI: 10.6084/m9.figshare.12503210.v1
title: Additional file 4 of Identification of therapeutic targets from genetic association studies using hierarchical component analysis

description: Additional file 4.
DOI: 10.6084/m9.figshare.12503204.v1
title: Additional file 3 of Identification of therapeutic targets from genetic association studies using hierarchical component analysis

description: Additional file 3.
DOI: 10.6084/m9.figshare.12503192
title: Additional file 1 of Identification of therapeutic targets from genetic association studies using hierarchical component analysis

description: Additional file 1.
DOI: 10.6084/m9.figshare.12503198.v1
title: Additional file 2 of Identification of therapeutic targets from genetic association studies using hierarchical component analysis

description: Additional file 2.
DOI: 10.6084/m9.figshare.12503192.v1
title: Additional file 1 of Identification of therapeutic targets from genetic association studies using hierarchical component analysis

description: Additional file 1.
DOI: 10.6084/m9.figshare.12503045
title: Additional file 1 of Exploiting collateral sensitivity controls growth of mixed culture of sensitive and resistant cells and decreases selection for resistant cells in a cell line model

description: Additional file 1: Table S1. Differentially expressed genes in CAMA-1 cells in response to 1 µM ribociclib treatment for 12 h. Detailed data from Fig. 1, Panel D. Table S2. Differentially expressed genes in CAMA-1_ribociclib_resistant cells in response to 1 µM ribociclib treatment for 12 h. Detailed data from Fig. 1, Panel C. Table S3. Spheroid area of spheroids plated in different composition under different treatments. Raw data of three replicates and statistical analysis in each time point of the summarized data in Fig. 2, Panel B are shown here. Table S4. Differentially expressed genes between CAMA-1 and CAMA-1_ribociclib_resistant cells under no treatment. Detailed data from Additional file 2: Figure S1. Table S5. Pathway enrichment scores for the Hallmark pathways in comparison of untreated CAMA-1 and untreated CAMA-1_ribociclib_resistant cells. Additional data to Fig. 3, Panel A. Table S6. Spheroid area of spheroids plated in different composition under different treatments. Raw data of three replicates and statistical analysis in each time point of the summarized data in Fig. 4, Panel B are shown here.
DOI: 10.6084/m9.figshare.12503045.v1
title: Additional file 1 of Exploiting collateral sensitivity controls growth of mixed culture of sensitive and resistant cells and decreases selection for resistant cells in a cell line model

description: Additional file 1: Table S1. Differentially expressed genes in CAMA-1 cells in response to 1 µM ribociclib treatment for 12 h. Detailed data from Fig. 1, Panel D. Table S2. Differentially expressed genes in CAMA-1_ribociclib_resistant cells in response to 1 µM ribociclib treatment for 12 h. Detailed data from Fig. 1, Panel C. Table S3. Spheroid area of spheroids plated in different composition under different treatments. Raw data of three replicates and statistical analysis in each time point of the summarized data in Fig. 2, Panel B are shown here. Table S4. Differentially expressed genes between CAMA-1 and CAMA-1_ribociclib_resistant cells under no treatment. Detailed data from Additional file 2: Figure S1. Table S5. Pathway enrichment scores for the Hallmark pathways in comparison of untreated CAMA-1 and untreated CAMA-1_ribociclib_resistant cells. Additional data to Fig. 3, Panel A. Table S6. Spheroid area of spheroids plated in different composition under different treatments. Raw data of three replicates and statistical analysis in each time point of the summarized data in Fig. 4, Panel B are shown here.
DOI: 10.6084/m9.figshare.12502904
title: Additional file 1 of Phase II trial evaluating the clinical efficacy of cefixime for treatment of active syphilis in non-pregnant women in Brazil (CeBra)

description: Additional file 1. Randomization of participants following the permuted block randomization 2:1 method.
DOI: 10.6084/m9.figshare.12502898
title: Additional file 3 of Laser capture microdissection coupled mass spectrometry (LCM-MS) for spatially resolved analysis of formalin-fixed and stained human lung tissues

description: Additional file 3. BayesENproteomic analysis of proteins expressed between morphologically normal alveoli and blood vessels.
DOI: 10.6084/m9.figshare.12502904.v1
title: Additional file 1 of Phase II trial evaluating the clinical efficacy of cefixime for treatment of active syphilis in non-pregnant women in Brazil (CeBra)

description: Additional file 1. Randomization of participants following the permuted block randomization 2:1 method.
DOI: 10.6084/m9.figshare.12502898.v1
title: Additional file 3 of Laser capture microdissection coupled mass spectrometry (LCM-MS) for spatially resolved analysis of formalin-fixed and stained human lung tissues

description: Additional file 3. BayesENproteomic analysis of proteins expressed between morphologically normal alveoli and blood vessels.
DOI: 10.6084/m9.figshare.12502847
title: Additional file 2 of Impacts of ambient air pollution on glucose metabolism in Korean adults: a Korea National Health and Nutrition Examination Survey study

description: Additional file 2: Figure S1. The location of the Korean Air Quality monitoring stations (AQMS) in South Korea with highlighted box of Seoul Metropolis. Figure S2. The results of cross-validation for daily mean concentration of PM10, PM2.5, and NO2 in South Korea during 2012 and 2013. x-axis: observed values. y-axis: predicted values.
DOI: 10.6084/m9.figshare.12502832
title: Additional file 2 of Implementing electronic decision-support tools to strengthen healthcare network data-driven decision-making

description: Additional file 2. Data Collection Tool and Database.
DOI: 10.6084/m9.figshare.12502847.v1
title: Additional file 2 of Impacts of ambient air pollution on glucose metabolism in Korean adults: a Korea National Health and Nutrition Examination Survey study

description: Additional file 2: Figure S1. The location of the Korean Air Quality monitoring stations (AQMS) in South Korea with highlighted box of Seoul Metropolis. Figure S2. The results of cross-validation for daily mean concentration of PM10, PM2.5, and NO2 in South Korea during 2012 and 2013. x-axis: observed values. y-axis: predicted values.