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How do table shape, group size, and gender affect on-task actions in computer education open-ended tasks_data
<p>The data set includes the coding scheme, results of intercoder reliability tests and results of statistical tests supporting the paper " How do table shape, group size, and gender affect on-task actions in computer education open-ended tasks "</p>
Figures and tables
<p><strong>Figure legends</strong></p> <p><strong>Figure 1. </strong>The figures of consecutive computed tomography slices for TCGA-50-5941 patient sample. TCGA, the cancer genome atlas. First row, the whole image contains region of interest. Second row, red color circle represents the region of interest.</p> <p><strong>Figure 2. </strong>Cluster analysis based on nonnegative matrix factorization. (A) cophenetic correlation coefficient at different k value. (B) dispersion correlation coefficient at different k value. (C) silhouette correlation coefficient at different k value. (D) consistency matrix of multi-omics clusters when k=3.</p> <p>Figure 3. The difference of survival analysis among subtypes. (A) subtype 1 versus subtype 2. (B) subtype 1 versus subtype 3. (C) subtype 2 versus subtype 3.</p> <p><strong>Figure 4.</strong> Construction of weighted gene co-expression network analysis. (A) consensus gene dendrogram and module colors are showed correspondingly. (B) the module-trait relationship is displayed by correlation values and p values (p value is in parenthesis). Each row represents the named module and each column represents the traits. Red color represents positive correlation and green color represents negative correlation.</p> <p><strong>Figure 5. </strong>Identification of special molecules for the three subtypes. (A) Venn diagram showing special molecules of each subtype. (B) Venn diagram showing the correlation of special molecules of each subtype and clinical trait such as region of interest integrate density (ROI_IntD). Different color areas represented different subtypes.</p> <p><strong>Figure S1. </strong>Flow-chart of the study. CT, computed tomography; DEG, differential express gene; GO, gene ontology; LUAD, lung adenocarcinoma; KEGG, kyoto encyclopedia of genes and genomes; NMF, non-negative matrix factorization; ROI, region of interest; TCGA, the cancer genome atlas; TCIA, the cancer imaging archive.</p> <p><strong>Figure S2. </strong>Parameter selection of weighted gene co-expression network analysis. (A) tumor samples dendrogram and clinical trait heatmap. (B) correlation of soft threshold (power) and scale independence. (C) correlation of soft threshold (power) and mean connectivity.</p> <p><strong>Figure S3. </strong>Identification of hub molecules in blue module and network heatmap plot of selected molecules. (A) correlation of module membership and gene significance in blue module of weighted gene co-expression network analysis, and the upper right region represents the hub molecules in blue module. (B) Visualizing the molecules network using a heatmap plot. The heatmap shows the topological overlap matrix among 2535 molecules in the analysis. Light color represents high overlap. Blocks of lighter color along the diagonal are the modules. The gene dendrogram and module assignment is also displayed along the left side and the top.</p>
PWB Muscle SR - Supplementary Table S2 (Reasons for Exclusion)
<p>Supplementary Material for the manuscript entitled 'Skeletal Muscle Deconditioning During Partial Weight-Bearing in Rodents – A Systematic Review and Meta-Analysis'</p>
dataset + tabled results
<p>1031 data points</p> <p><strong>meaning of each excel sheet:</strong></p> <p>IN - input variable values for each data point (each data point is one row) </p> <p>TARGET - target variable values for each data point (each data point is one row) </p> <p>VARS - presents the units used for each input (independent) and output/target (dependent) variables</p> <p>TARGET vs OUTPUT - presents the 1031 expected (experimental) values and the ones obtained by the proposed ANN</p> <p> </p> <p><strong>Check reference below</strong> (to be added when the paper is published)</p> <p> </p>
Table S2. Enhancer-gene correlation framework
<p>Outcome of all enhancer-gene correlations with FDR < 0.05</p>
Table S8.Co-expression of mRNAs with AD000684.2 and ASMTL-AS1 and their correlation coefficients
<p>The file consists of lncRNA AD000684.2 relevant 16 gene.ensembl,gene.symbol,r,FDR information,and lncRNA ASMTL-AS1 relevant 31 gene.ensembl,gene.symbol,r,FDR information.</p>
Supplemental tables for 'The impact of differential temperatures (30°C versus 45°C) on the methanogenic community in Philippine rice field soil'
<p>Supplemental tables for Xin Li's PhD thesis-'The impact of differential temperatures (30°C versus 45°C) on the methanogenic community in Philippine rice field soil'</p>
Table S1: Fetal phenotype and genotype
<p>Legend to supplementary Table 1</p> <p>This table contains the 28 fetal cases on whom WES was performed. For each case, the fetal phenotype and the organ system involved are described, as well as the sample type from which the DNA was extracted, the applied gene panel and the result. For the positive cases, the gestational age at ultrasound, the affected gene and variant, the inheritance mode, the associated syndrome, the fetal phenotype as described in the literature and the outcome of the pregnancy are listed as well.</p> <p>AC: amniocytes; AD: autosomal dominant; AR: autosomal recessive; CNS: central nervous system; CV: chorionic villi; hom: homozygous; IF: incidental finding; IUGR: intrauterine growth restriction; LB: live birth; norm: normal; NT: nuchal translucency; pat: paternal; path: pathogenic; TOP: termination of pregnancy; US: ultrasound; VSD: ventricular septal defect; WES: whole exome sequencing; WESSD: WES with skeletal dysplasia panel</p>
Table of Identified and Categorised Challenges for Implementing the EU Biodiversity Strategy for 2030
<p>Since the beginning of this century, the European Union (EU) has been committed to tackling the continuously accelerating biodiversity crisis. Yet, both its 2010 and 2020 Biodiversity Strategy failed to deliver their main targets of halting and reversing biodiversity loss. The design of the new Biodiversity Strategy for 2030 acknowledges lessons learned from the evaluation of these past strategies. However, improving on the ground implementation of biodiversity protection efforts requires a comprehensive understanding of the challenges occurring on the target-level. Horizon Europe project BioAgora aims to set up the scientific pillar of the European Commission to ratchet up the EU BDS implementation. As first task of BioAgora, this research investigates target-specific challenges of implementing past strategies. By applying a combined methods approach, we identified and categorised a variety of target-specific challenges which, if not addressed, are likely to remain barriers to achieving the 2030 objectives. By revealing those challenges, this typology fundamentally supports ratcheting up the Biodiversity Strategy 2030. We conclude by recommending further, ideally systematic, research on identifying challenges and continuously updating this work throughout the strategy’s timeframe to allow for the impact of adapted governance instruments to be reflected.</p>
ADAMTS12 as a stromal modulator in chronic liver disease - Supplementary Table S3
<p>Supplementary Table S3</p>
Tara Oceans (2009-2013) rDNA 16S V4V5 ASV table (dada2)
<p>This repository contains a rDNA 16S V4V5 ASV table (TARA-Oceans_16S-V4V5_dada2_table.tsv.gz) for <em>Tara</em> Oceans (2009-2013).</p> <p>In the file <strong>TARA-Oceans_16S-V4V5_dada2_table.tsv.gz</strong>, each ASV, one per row, is described by the following fields: <strong>amplicon</strong> = ASV identifier; <strong>taxonomy</strong> = taxonomic path assigned to the ASV using IDTAXA; <strong>confidence</strong> = IDTAXA confidence scores for each taxonomic rank; <strong>total</strong> = total number of reads for the entire dataset; <strong>spread</strong> = number of samples in which the ASV is detected; <strong>sequence</strong> = ASV nucleic acid sequence; <strong>TARA_XXXXXXXXXX</strong> = number of reads in each of the 1,134 <em>Tara</em> Oceans samples.</p> <p>Detailed information about DNA extraction, PCR amplification and Illumina sequencing of metabarcodes, as well as subsequent sequence data cleaning and taxonomic assignment can be found in (Alberti et al. 2017). Briefly, DNA samples were amplified by PCR targeting the hypervariable region V4V5 (primer pair: 15F-Y 5′- GTGYCAGCMGCCGCGGTAA-3′ and 926R 5′- CCGYCAATTYMTTTRAGTTT-3′ 16S primers; (Parada et al. 2016)) of the 16S rRNA marker gene followed by the high-throughput sequencing of the amplicons. The details of PCR mixes, thermocycling and sequencing conditions are provided in Alberti et al. (2017).</p> <p>Resulting paired-end reads were mixed-oriented meaning that both R1 and R2 files are composed by a mix of forward and reverse reads. Paired-end reads were trimmed to remove PCR primer sequences using Cutadapt v2.7 (Martin, 2011) and dispatched into four files, 2 files for the classical orientation (forward reads in R1 and reverse reads in R2) and 2 others for the other orientation (reverse reads in R1 and forward reads in R2). Paired-end reads without both primers were filtered out using the option --discard-untrimmed. Forward and reverse reads were trimmed at position 215 and 190 for R1 and R2 files respectively. Reads with ambiguous nucleotides or with a maximum number of expected errors (maxEE) superior to 2 were filtered out using the function filterAndTrim() from the R package dada2 (Callahan et al., 2016). For each run and read orientation, error rates were defined using the function learnErrors() and denoised using the dada() function with pool = TRUE before being merged using mergePairs() with default parameters. Mixed orientated reads from the same sample and sequencing replicates were summed together. Remaining chimeras were removed using the function removeBimeraDenovo(). Scripts producing the ASV table are publicly available here: https://gitlab.univ-nantes.fr/combi-ls2n/taradada.</p> <p>ASVs were taxonomically assigned using IDTAXA (50% confidence threshold) (Murali, Bhargava, and Wright 2018) with SILVA v138.</p> <p>Alberti, A., Poulain, J., Engelen, S., Labadie, K., Romac, S., Ferrera, I., Albini, G., Aury, J.-M., Belser, C., Bertrand, A., Cruaud, C., Da Silva, C., Dossat, C., Gavory, F., Gas, S., Guy, J., Haquelle, M., Jacoby, E., Jaillon, O., Lemainque, A., Pelletier, E., Samson, G., Wessner, M., Genoscope Technical Team, Bazire, P., Beluche, O., Bertrand, L., Besnard-Gonnet, M., Bordelais, I., Boutard, M., Dubois, M., Dumont, C., Ettedgui, E., Fernandez, P., Garcia, E., Aiach, N.G., Guerin, T., Hamon, C., Brun, E., Lebled, S., Lenoble, P., Louesse, C., Mahieu, E., Mairey, B., Martins, N., Megret, C., Milani, C., Muanga, J., Orvain, C., Payen, E., Perroud, P., Petit, E., Robert, D., Ronsin, M., Vacherie, B., Acinas, S.G., Royo-Llonch, M., Cornejo-Castillo, F.M., Logares, R., Fernández-Gómez, B., Bowler, C., Cochrane, G., Amid, C., Hoopen, P.T., De Vargas, C., Grimsley, N., Desgranges, E., Kandels-Lewis, S., Ogata, H., Poulton, N., Sieracki, M.E., Stepanauskas, R., Sullivan, M.B., Brum, J.R., Duhaime, M.B., Poulos, B.T., Hurwitz, B.L., Tara Oceans Consortium Coordinators, Acinas, S.G., Bork, P., Boss, E., Bowler, C., De Vargas, C., Follows, M., Gorsky, G., Grimsley, N., Hingamp, P., Iudicone, D., Jaillon, O., Kandels-Lewis, S., Karp-Boss, L., Karsenti, E., Not, F., Ogata, H., Pesant, S., Raes, J., Sardet, C., Sieracki, M.E., Speich, S., Stemmann, L., Sullivan, M.B., Sunagawa, S., Wincker, P., Pesant, S., Karsenti, E., Wincker, P., 2017. Viral to metazoan marine plankton nucleotide sequences from the Tara Oceans expedition. Sci Data 4, 170093. <a href="https://doi.org/10.1038/sdata.2017.93">https://doi.org/10.1038/sdata.2017.93</a></p> <p>Callahan, B.J., McMurdie, P.J., Rosen, M.J., Han, A.W., Johnson, A.J.A., Holmes, S.P., 2016. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13, 581–583. <a href="https://doi.org/10.1038/nmeth.3869">https://doi.org/10.1038/nmeth.3869</a></p> <p>Martin, M., 2011. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet j. 17, 10. <a href="https://doi.org/10.14806/ej.17.1.200">https://doi.org/10.14806/ej.17.1.200</a></p> <p>Murali, A., Bhargava, A., Wright, E.S., 2018. IDTAXA: a novel approach for accurate taxonomic classification of microbiome sequences. Microbiome 6, 140. <a href="https://doi.org/10.1186/s40168-018-0521-5">https://doi.org/10.1186/s40168-018-0521-5</a></p>
High-resolution gene expression atlases of two contrasting major Greek olive (Olea europaea L.) tree cultivars for oil and table olives production
<p>This dataset contains the supplementary data of the "<strong>High-resolution gene expression atlases of two contrasting major Greek olive (<em>Olea europaea </em>L.) tree cultivars for oil and table olives production</strong><strong>" </strong>research article submitted to the "<strong>Frontiers in Plant Science-Plant Bioinformatics</strong>" journal.</p>
Supplemental Tables S1-5
<p>Supplemental tables in Journal of Physiological Genomics on the Short Report titled "Changes in Fecal Gut Microbiome of Home Healthcare Patients with Disabilities through Constipation of Malted Rice Amazake"</p>
Supplementary Tables for Geomorphology of gullies at the Haughton Impact Structure, Devon Island, Canadian High Arctic
<p>Data describing the input and results from the FAMD and HCPC model for "Geomorphology of gullies at the Haughton Impact Structure, Devon Island, Canadian High Arctic"</p>
Supplementary Tables and Figures
<p>Supplementary tables and figures.</p>
Table S1. Extended Mass Spectrometry data table
<p>Table S1. Extended Mass Spectrometry data table </p>
Task 4.5 Table_4.5.3 Results of soil analysis of each fertility treatment main plot
<p>Results of soil samples.</p>
Task 4.3 Table 4.3.2_Progeny from cross GQ4X (nodulating x non-nodulating soybean) and nod_genotype status of progeny
<p>Soybean lines lacking N2-fixation.</p>
Table of the differentially expressed genes for "SP vs. BTS-embryos".
<p>Table of the differentially expressed genes for “SP vs. BTS-embryos”.</p>
Supplement figures and tables
Open the record for dataset details and reuse information.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.