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1,200 results for “Meta analysis”
Figure 3 in Does narrow row spacing suppress weeds and increase yields in corn and soybean? A meta-analysis
Figure 3. The overall effect of narrow row spacing (<76 cm) on weed density, weed biomass,weed control,weed seed production,and crop yield.The vertical black dashed line indicates zero effect. The black dots represent mean effect sizes (log of response ratios [lnðRRÞ]), and the black lines represent their respective 95% confidence intervals (CIs). The numbers in parentheses indicate the number of observations followed by the number of studies for each effect size. The effect sizes were considered significantly different when their 95% CIs did not overlap or contain zero.
Figure 6 in Does narrow row spacing suppress weeds and increase yields in corn and soybean? A meta-analysis
Figure 6. The effect of narrow row spacing (<76 cm) on crop yield as explained by subgroups of the crop, tillage, weed type, weed management method, herbicide application frequency, and time. The vertical black dashed line indicates zero effect. The black dots represent mean effect sizes (log of response ratios [lnðRRÞ]) for each subgroup, and the black lines represent their respective 99% confidence intervals (CIs). The numbers in parentheses indicate the number of observations followed by the number of studies for each effect size. The effect sizes were considered significantly different when their 99% CIs did not overlap or contain zero.
Figure 2. A in Does narrow row spacing suppress weeds and increase yields in corn and soybean? A meta-analysis
Figure 2. A map of the states in the midwestern and eastern United States showing experimental sites for the 35 corn and soybean narrow row spacing studies included in the meta-analysis.
Figure 5 in Does narrow row spacing suppress weeds and increase yields in corn and soybean? A meta-analysis
Figure 5. The individual effect sizes (natural log of response ratios [lnðRRÞ]) of (A) weed density, (B) weed biomass,(C) weed control, (D) weed seed production, and (E) crop yield as a function of crop row spacing. The green and red dots represent individual effect sizes for corn and soybean, respectively. The horizontal black dashed line represents zero effect,while the vertical black line represents 76-cm row spacing (control).The black bold line shows the relationship between individual effect sizes and crop row spacing,which is given as R (Pearson's correlation) with a P-value. The gray-shaded area represents 95% confidence intervals (CIs) of the linear relationship.
Figure 8 in Does narrow row spacing suppress weeds and increase yields in corn and soybean? A meta-analysis
Figure 8. Sensitivity analysis showing the variation in overall effect sizes (log of response ratios [ln(RR)]) (mean ± 95% confidence intervals [CIs]) of narrow row spacing effects on (A) weed density, (B) weed biomass, (C) weed control, (D) weed seed production, and (E) crop yield when any specific study was excluded from the analysis. The vertical red solid and dashed lines represent the mean ± 95% CIs, respectively, of overall effect sizes with all the studies included in the analysis.
Figure 1 in Does narrow row spacing suppress weeds and increase yields in corn and soybean? A meta-analysis
Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and MetaAnalyses; Page et al. 2021) flow diagram showing the stepwise procedure used for selecting 35 studies for meta-analysis.
Figure 4 in Does narrow row spacing suppress weeds and increase yields in corn and soybean? A meta-analysis
Figure 4. The effect of narrow row spacing (<76 cm) on (A) weed density, (B) weed biomass, (C) weed control, and (D) weed seed production as explained by the subgroups of crop,tillage,weed type,weed management method, herbicide application frequency, and time. The vertical black dashed line indicates zero effect.The black dots represent mean effect sizes (log of response ratios [lnðRRÞ]) for each subgroup, and the black lines represent their respective 99% confidence intervals (CIs). The numbers in parentheses indicate the number of observations followed by the number of studies for each effect size. The effect sizes were considered significantly different when their 99% CIs did not overlap or contain zero.
Figure 7 in Does narrow row spacing suppress weeds and increase yields in corn and soybean? A meta-analysis
Figure 7. Density plots show the distribution of individual effect sizes (log of response ratios [ln(RR)]) of weed density,biomass,control, weed seed production, and crop yield.
Figure 4 in Frequency of pyrethroid resistance in human head louse treatment: systematic review and meta-analysis
Figure 4. Forest plots of the proportion of heterozygote resistance and 95% confidence interval based on a random effect model in metaanalysis.
Figure 3 in Frequency of pyrethroid resistance in human head louse treatment: systematic review and meta-analysis
Figure 3. Forest plots of the proportion of homozygote resistant and 95% confidence interval based on a random effect model in metaanalysis.
Figure 2 in Frequency of pyrethroid resistance in human head louse treatment: systematic review and meta-analysis
Figure 2. Forest plots of the proportion of resistance in lice and 95% confidence interval based on a random effect model in meta-analysis.
Saccostrea glomerata (Sydney Rock Oyster) - Meta Analysis Dataset Repository
<h1>Saccostrea glomerata (Sydney Rock Oyster) - Meta Analysis Dataset Repository:</h1> <h2>Dataset used to do 8 separate meta-analyses on the following topics for <em>Saccostrea glomerata</em> (Sydney Rock Oyster):</h2> <p>1. Ocean acidification and size</p> <p>2. Ocean acidification and mortality</p> <p>3. Ocean warming and size</p> <p>4. Ocean warming and mortality</p> <p>5. Transgenerational exposure to ocean acidification and size</p> <p>6. Transgenerational exposure to ocean acidification and mortality</p> <p>7. Transgenerational exposure to ocean warming effect and size</p> <p>8. Transgenerational exposure to ocean warming and mortality</p> <h3><strong>Dataset contains:</strong></h3> <ul> <li>Original code and all associated data files in order to reproduce the work within the "Code and Formatted Data" Folder.</li> <li>Datasheets containing the raw data extracted from the papers identified during the literature searches </li> </ul> <h3>Description of the data and file structure</h3> <ul> <li>R-Markdown file contains the original code required to reproduce the results outlined in this paper</li> <li>All datasheets appended with "_Formatted_For_Effect_Sizes" were used to generate the effect sizes required for the meta-analysis</li> <li>All datasheets appended with "_Extracted_Raw_Data" are the raw data extracted from the papers identified during the literature searches</li> </ul> <h3><strong>Abbreviations</strong></h3> <ul> <li>OA - Ocean Acidification</li> <li>Temp - Temperature</li> <li>Transgen - Transgenerational Exposure</li> <li>Mort - Mortality</li> </ul> <h2>Abstract</h2> <div>Global oceans are warming and acidifying because of increasing greenhouse gas emissions which are anticipated to have cascading impacts on marine ecosystems and organisms, especially those essential for biodiversity and food security. Despite this concern, there remains some scepticism about the reproducibility and reliability of research done to predict the future climate change impacts on marine organisms. Here we present meta-analyses of over two decades of research on the climate change impacts on an ecologically and economically valuable Sydney rock oyster, <em>Saccostrea glomerata</em>. We confirm with high confidence that ocean acidification has a significant impact on mortality and size of <em>S. glomerata,</em> that ocean warming has a significant impact on mortality, but not size, and transgenerational exposure has positive benefits to offspring. These meta-analyses reveal impacts of climate change on an ecologically and economically significant oyster species and recommends solutions for experimental designs to ensure sustainability of this iconic oyster. </div>
A systematic meta-analysis of the role of perceptual and conceptual novelty in guiding infant looking behaviour
<p>These are the data, codebooks, and scripts required to reproduce the results of the paper Kunin, L., Piccolo, S., Saxe, R., & Liu, S. (under revision). A systematic meta-analysis of the role of perceptual and conceptual novelty in guiding infant looking behaviour.</p> <p>A previous version of the data and code can be found at https://zenodo.org/records/11084599.</p> <p> </p>
Fig. 2 in What drives population-level effects of parasites? Meta-analysis meets life-history
Fig. 2. Funnel plot of effect size (Hedges' g) by standard error (SE). The white circles represent studies included in the meta-analysis. The black circles represent missing imputed studies. The white diamond represents overall effect size as calculated in the meta-analysis, and the black diamond represents the corrected effect size.
Fig. 1 in What drives population-level effects of parasites? Meta-analysis meets life-history
Fig. 1. Forest plot of effect sizes (rectangles) and confidence intervals (bars) for each study and the effect averaged across all studies (diamond).
Fig. 3. A in What drives population-level effects of parasites? Meta-analysis meets life-history
Fig. 3. A meta-regression (random effects model) of effect size against host average lifespan using the complete dataset (n = 60). The slope of this regression is significant (p = 0.05).
The "social brain" is highly sensitive to the mere presence of social information: An automated meta-analysis and an independent study
<p><strong>Abstract</strong></p> <p>How the human brain process social information is an increasingly researched topic in psychology and neuroscience, advancing our understanding of basic human cognition and psychopathologies. Neuroimaging studies typically seek to isolate one specific aspect of social cognition when trying to map its neural substrates. It is unclear if brain activation elicited by different social cognitive processes and task instructions are also spontaneously elicited by general social information. In this study, we investigated whether these brain regions are evoked by the mere presence of social information using an automated meta-analysis and confirmatory data from an independent study of simple appraisal of social vs. non-social images. Results of 1,000 published fMRI studies containing the keyword of “social” were subject to an automated meta-analysis (neurosynth.org). To confirm that significant brain regions in the meta-analysis were driven by a social effect, these brain regions were used as regions of interest (ROIs) to extract and compare BOLD fMRI signals of social vs. non-social conditions in the independent study. The NeuroSynth results indicated that the dorsal and ventral medial prefrontal cortex, posterior cingulate cortex, bilateral amygdala, bilateral occipito-temporal junction, right fusiform gyrus, bilateral temporal pole, and right inferior frontal gyrus are commonly engaged in studies with a prominent social element. The social – non-social contrast in the independent study showed a strong resemblance of the NeuroSynth map. ROI analyses revealed that a social effect was credible in 8 out of the 11 NeuroSynth regions in the independent dataset. The findings support that the “social brain” is highly sensitive to the mere presence of social information. </p>
Data for: A systematic review and meta-analysis of Drosophila short-term-memory genetics: robust reproducibility, but little independent replication
<p>All the data, code, analyses, and figures used in the study entitled: "A systematic review and meta-analysis of Drosophila short-term-memory genetics: robust reproducibility, but little independent replication" <em>(doi: https://doi.org/<a href="http://bb2sz3ek3z.search.serialssolutions.com/?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&__char_set=utf8&rft_id=info:doi/10.1101/247650&rfr_id=info:sid/libx&rft.genre=article">10.1101/247650</a>)</em></p> <p><strong>Abstract</strong></p> <p>Geneticists have long used olfactory conditioning techniques in <em>Drosophila</em> to identify the neurons and genes that mediate learning. While this method has characterized an abundance of memory-related genes, little is known about how these genes induce short-term memory (STM) via signaling pathways; characterizing these networks will be essential to developing mechanistic models of memory formation. Here, we investigated why elucidating the STM pathways has been relatively slow. One possibility is that the STM evidence base is weak due to publication of poorly reproducible results, as has been observed in other fields. We examined this hypothesis by performing a systematic review and subsequent meta-analysis of the STM genetics field. Using several metrics to quantify the variation between discovery articles and follow-up studies, we found that seven genes were highly replicated, showed no publication bias, and had generally high reproducibility. However, the remaining ~80% memory genes have not been replicated since their initial discovery. Although we observed only a few studies that investigated gene interactions, the reviewed genes could together account for >1000% memory. This large summed effect size indicates either that some of the gene findings are not reproducible, that many memory genes participate in shared pathways, or that current protocols lack the specificity needed to identify core plasticity memory genes. Mechanistic theories of memory and cognition will require the convergence of evidence from system, circuit, cellular, molecular, and genetic experiments. As this study demonstrates, systematic data synthesis is an essential tool for this integrated brain science.</p>
QT-IGC Meta-Analysis Results
<p>Meta-analysis results from the QT-IGC meta-analysis of QT interval duration. The manuscript detailing the generation and analysis of these results can be found here (https://www.nature.com/articles/ng.3014, Nature Genetics) and here (<a href="https://paperpile.com/shared/JEVaXW">https://paperpile.com/shared/JEVaXW</a>).</p> <p>Column names:</p> <ol> <li>SNP - SNP identifier (note that these will be on a much older dbSNP build!)</li> <li>CHR - Chromosome</li> <li>POS - Position, hg18</li> <li>A1 - minor allele in HapMap 2</li> <li>A2 - other allele in HapMap 2</li> <li>HAPMAP_A1_FREQ - frequency of A1 in HapMap</li> <li>CODED_ALLELE - the coded allele in the meta. All stats refer to this allele.</li> <li>NONCODED_ALLELE - other allele</li> <li>CODED_ALLELE_FREQ - frequency of the coded allele</li> <li>N_EFF - effective sample size</li> <li>Z_SQRTN - Z-score of the SNP in sample size meta-analysis</li> <li>P_SQRTN - P-value of the SNP in sample size meta-analysis</li> <li>BETA_FIXED - Beta of coded allele in fixed effects meta-analysis</li> <li>SE_FIXED - standard error of coded allele in fixed effects meta-analysis</li> <li>Z_FIXED - z-score of coded allele in fixed effects meta-analysis</li> <li>P_FIXED - p-value in fixed effects meta-analysis</li> <li>BETA_LOWER_FIXED - lower estimate of the beta, fixed effects</li> <li>BETA_UPPER_FIXED - upper estimate of the beta, fixed effects</li> <li>BETA_RANDOM - beta of coded allele in random effects meta-analysis</li> <li>SE_RANDOM - standard error of the coded allele in random effects meta-analysis</li> <li>Z_RANDOM - z-score from random effects meta-analysis</li> <li>P_RANDOM - p-value from random effects meta-analysis</li> <li>BETA_LOWER_RANDOM - lower estimate of the beta, random effects meta</li> <li>BETA_UPPER_RANDOM - upper estimate of the beta, random effects meta</li> <li>COCHRANS_Q - Cochran's test of heterogeneity, Q statistic</li> <li>DF - degrees of freedom for Cochran's Q test</li> <li>P_COCHRANS_Q - p-value of Cochran's Q test</li> <li>I_SQUARED - I-squared stat, test of heterogeneity</li> <li>TAU_SQUARED - Tau-squared, test of heterogeneity</li> <li>DIRECTIONS - directions of the beta in each of the cohorts used in meta-analysis. Cohorts are in the same order as in the params file provided in the related GitHub repo (https://github.com/saralpulit/QTIGCmeta)</li> <li>GENES_1000KB - Genes within 1000kb of the SNP, according to RefSeq</li> <li>NEAREST_GENE - Gene closest to the SNP, according to RefSeq</li> <li>FUNCTION - function of the SNP, if relevant</li> <li>CAVEAT - any relevant caveats, e.g., A/T or C/G SNPs with >40% minor allele frequency</li> </ol>
Genome-wide association meta-analysis of 30,000 samples identifies seven novel loci for quantitative ECG traits
<p><strong>Introduction</strong></p> <p>These are the <em>Summary Level-data</em> as presented in:</p> <p>"Genome-wide association meta-analysis of 30,000 samples identifies seven novel loci for quantitative ECG traits". Eur J Hum Genet. 2019 Jan 24. doi: 10.1038/s41431-018-0295-z.<em> [Epub ahead of print]</em></p> <p>If you use these data please cite the corresponding manuscript, which can be downloaded here: <a href="http://em.rdcu.be/wf/click?upn=lMZy1lernSJ7apc5DgYM8eFz0euOx0-2B13Abimi4Sb0A-3D_2NNavOiAD9A7CPFnsa04dGla3sU002fLfkDtL-2FhGlad0GuoM-2B3OlDb0C5GiEhwIvtH7ba4KKF45ipTOFodx6CqvVvoP2GQ992sPGoV9ZPWIe04tUd8-2BGWey0In0TXPII5zK-2Bfp8Wk9TpEqEcSd-2BEmywqZc8o5TW4xGPXZqmchfUH8chy3P4SEtpzHXMG1LwsIYrKfwegqTXG85RAJPr-2B21Tk9SobtpvFs0frMkJ4ekKsl33ryoZfFPk1byjQunJYn4-2BB0iqMgGs6cXv0AOgAxg-3D-3D">https://rdcu.be/bh8mu</a>. When you have any questions or comments regarding this study or these files, please contact me via:</p> <p>Jessica van Setten, PhD | <em>Department of Cardiology, University Medical Center Utrecht, Utrecht University</em> | j.vansetten [at] umcutrecht [dot] nl</p> <p> </p> <p><strong>Files and description</strong></p> <p>There are four files available:</p> <ol> <li>RR_summary_Sept2018.txt.gz - gzipped file containing all the (unfiltered) meta-analysis results for RR interval</li> <li>PR_summary_Sept2018.txt.gz - gzipped file containing all the (unfiltered) meta-analysis results for PR interval</li> <li>QT_summary_Sept2018.txt.gz - gzipped file containing all the (unfiltered) meta-analysis results for QT interval</li> <li>QRS_summary_Sept2018.txt.gz - gzipped file containing all the (unfiltered) meta-analysis results for QRS duration</li> </ol> <p>All these files have the same lay-out and are gzipped. The reference used for meta-analysis of GWAS was Genome of the Netherlands v4. </p> <ul> <li><em>SNP</em> - variantID (rsID), please note that few hundred variants do not have an rsID, but are NA instead. These can still be identified by chromosome and position.</li> <li><em>CHR</em> - chromosome numbers [1-22 and X].</li> <li><em>POS</em> - base pair position, hg19 / build37.</li> <li><em>CODED_ALLELE</em> - coded allele, <em>i.e.</em> the effect allele, as represented (and harmonized) across cohorts. Note that this is not necessarily the minor allele.</li> <li><em>NON_CODED_ALLELE</em> - the other allele, <em>i.e.</em> the non-effect allele.</li> <li><em>CODED_ALLELE_FREQ</em> - coded allele frequency, <em>i.e.</em> the effect allele frequency. Note that this is not necessarily the minor allele frequency.</li> <li><em>BETA</em> - beta from the fixed-effects model.</li> <li><em>SE </em>- standard error from the fixed-effects model.</li> <li><em>P </em>- P-value from the fixed-effects model.</li> <li><em>NEAREST_GENE</em> - the gene closest to the respective variant.</li> </ul> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.