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32 results for “ANOVA”
Datasets for "Comparison of multivariate ANOVA-based approaches for the determination of relevant variables in experimentally designed metabolomic studies"
<p><em><strong>Raw files of the LC-MS designed experiments.</strong></em></p> <p> </p> <p>* <strong>YEAST GROWTH DATASET</strong></p> <blockquote> <p><strong>A) Phospholipids extraction</strong></p> <p>- FosfoB1.raw</p> <p>- FosfoB3.raw</p> <p>- FosfoC2.raw</p> <p>- FosfoD1.raw</p> <p>- FosfoE1.raw</p> <p>- FosfoE2.raw</p> <p>- FosfoE3.raw</p> </blockquote> <p> </p> <blockquote> <p><strong>B) Sphingolipids extraction</strong></p> <p>- EsfingoB1.raw</p> <p>- EsfingoB2.raw</p> <p>- EsfingoC1.raw</p> <p>- EsfingoC2.raw</p> <p>- EsfingoC3.raw</p> <p>- EsfingoD1.raw</p> <p>- EsfingoD2.raw</p> <p>- EsfingoD3.raw</p> </blockquote> <p> </p> <p><strong>* ZEBRAFISH DATASET</strong></p> <blockquote> <p><strong>A) BPA study</strong></p> <p>Low BPA exposure</p> <p>- Low_BPA_A.d</p> <p>- Low_BPA_B.d</p> <p>- Low_BPA_C.d</p> <p>High_BPA_exposure</p> <p>- High_BPA_A.d</p> <p>- High_BPA_B.d</p> <p>- High_BPA_C.d</p> <p>Controls</p> <p>- Control_BPA_A.d</p> <p>-Control_BPA_B.d</p> <p>- Control_BPA_C.d</p> </blockquote> <p> </p> <blockquote> <p><strong>B) Estradiol study</strong></p> <p>Low E2 exposure</p> <p>- Low_E2_A.d</p> <p>- Low_E2_B.d</p> <p>- Low_E2_C.d</p> <p>High_E2_exposure</p> <p>- High_E2_A.d</p> <p>- High_E2_B.d</p> <p>- High_E2_C.d</p> <p>Controls</p> <p>- Control_E2_A.d</p> <p>-Control_E2_B.d</p> <p>- Control_E2_C.d</p> </blockquote> <p> </p> <p><strong>Funding:</strong> This research was funded by the Spanish Ministry of Science and Innovation (MCI, Grant CTQ2017-82598-P) and Severo Ochoa Project CEX2018-000794-S (funded by MCIN/AEI/ 10.13039/501100011033), and supported from the Catalan Agency for Management of University and Research Grants (AGAUR, Grant 2017SGR753). MPC was funded by a predoctoral FPU 16/02640 scholarship from the Spanish Ministry of Education and Vocational Training (MEFP). The 1290 LC system and 6545 XT QTOF instrumentation were provided to DS as gifts by Agilent Technologies through their Thought Leader program.</p>
Table 4: ANOVA (b) for Grade Three
<p>The ANOVA table below (Table 4) tests the acceptability of the model from a statistical<br> perspective. The amount of regression sums of squares is122.385 indicating that about 33 percent of<br> the total variation is explained by the model and the amount of residual sums of squares is 281.134<br> showing that about 67 percent of the variation is due to some factors other than the average<br> vocabulary variable. The significance value of the F statistic is less than 0.05, which means that the<br> variation explained by the model is not due to chance.</p>
Nonmetric ANOVA
<p>Providing the novel non metric ANOVA method for testing the disparity of the network clusters with non metric dissimilarity measures. All the raw data, real examples and the source codes are deposited as outlined in the key resource tables in the article published in iScience. </p>
ANOVA analysis of lignin degradation by the different ligninolytic yeasts
<p>The ANOVA analysis shows us that, according to the type of treatment, that is, yeast, the amount of degraded lignin will be.</p>
Dental mesowear and microwear raw data for Cervus elaphus, Rupicapra pyrenaica and Sus scrofa from Balma del Gai; and the ANOVA - test for equal means
<p>Quantitative data for the dental microwear and mesowear analyses on <em>Cervus elaphus</em>, <em>Rupicapra pyrenaica </em>and <em>Sus scrofa</em> from the Epipalaeolithic sequence of Balma del Gai (Moià, Spain). And the ANOVA - Test for equal means.</p>
Table 5. Results of calculation of the percentage of wound healing analysis of variance (ANOVA) one way with SPSS 23.00
<p>Table 5. Results of calculation of the percentage of wound healing analysis of variance (ANOVA) one way with SPSS 23.00</p>
Table 11. Results of statistical calculation of hydroxyproline levels analysis of variance (ANOVA) two way spss 23.00
<p>Table 11. Results of statistical calculation of hydroxyproline levels analysis of variance (ANOVA) two way spss 23.00</p> <p> </p>
Deliverable D6.2 "TOOL FOR PERFORMANCE ASSESSMENT"- Anova Test Module 4 Annex 7
<p>This file will introduce the ANOVA test Module 4 (Annex 7) in deliverable D6.2 "TOOL FOR PERFORMANCE ASSESSMENT".</p>
Functional ANOVA for Carbon Flux Estimates: Supporting Data
<p>This archive provides input and supporting datasets for the examples presented in "Functional ANOVA for carbon flux estimates from remote sensing data". This version (v0.2.0) of the archive includes sub-directories for each of the three examples of the statistical model with a spatio-temporal error process. R code for running the examples can be found on <a href="https://github.com/co2anomaly/flux_fanova">Github</a>.</p>
Scripts, inputs and outputs of Module 4 ANOVA test regarding Deliverable D6.3 "Performance and Impact assessment" in the IP4MaaS project
<p>All the materials, inputs, models, and scripts that are used in the performance assessment toolbox (IP4MaaS project, Deliverable D6.3 Performance and Impact Assessment) are available in the attached folder. </p> <p>The attached folder contains the following:</p> <p>Module 4_Calculation of ANOVA test (the input, formulation, macros, and scripts of ANOVA test per each IP4MaaS demo site).</p> <p>The scripts have been designed for easy adoption in other projects with similar end goals. While the scripts operate on the codified representation of the traveller profiles, functionalities, and service providers, there is no restriction on the type of codification used. The only factor that the scripts assume is the order of the variables introduced for codification. That is the traveller profile variable (wherever applicable), followed by the Functionality variable, followed by the Service Provider variable. As long as the order is maintained, the scripts self-analyze the number of variables representing the traveller profiles, functionalities, and service providers, and perform calculations (USI, Effectiveness) and analysis (Regression and BN), Hence, in a similar framework, the scripts may be used for performing data analysis on a large data set with no restriction on the number of functionalities, service providers or subsets of traveller profiles. Documented Scripts can be found in a downloadable ZIP file here. </p>
Data from: Phylogenetic ANOVA: group-clade aggregation, biological challenges, and a refined permutation procedure
Phylogenetic regression is frequently utilized in macroevolutionary studies, and its statistical properties have been thoroughly investigated. By contrast, phylogenetic ANOVA has received relatively less attention, and the conditions leading to incorrect statistical and biological inferences when comparing multivariate phenotypes among groups remains under-explored. Here we propose a refined method of randomizing residuals in a permutation procedure (RRPP) for evaluating phenotypic differences among groups while conditioning the data on the phylogeny. We show that RRPP displays appropriate statistical properties for both phylogenetic ANOVA and regression models, and for univariate and multivariate datasets. For ANOVA, we find that RRPP exhibits higher statistical power than methods utilizing phylogenetic simulation. Additionally, we investigate how group dispersion across the phylogeny affects inferences, and reveal that highly aggregated groups generate strong and significant correlations with the phylogeny, which reduce statistical power and subsequently affect biological interpretations. We discuss the broader implications of this phylogenetic group aggregation, and its relation to challenges encountered with other comparative methods where one or a few transitions in discrete traits are observed on the phylogeny. Finally, we recommend that phylogenetic comparative studies of continuous trait data utilize RRPP for assessing the significance of indicator variables as sources of trait variation.
Dataset for ANOVA introduction
Open the record for dataset details and reuse information.
One-Way ANOVA Results on the Role of Family in Addressing Gender-Based Violence in Early Childhood in Jordan
<p><strong>One-Way ANOVA Results on the Role of Family in Addressing Gender-Based Violence in Early Childhood in Jordan </strong></p>
Open Datasets - available file for Design of Experimental (RSM) model, ANOVA table, SEM, VSM and Zeta potecial data for the nanocrystal clusters based on iron oxide.
<p>The open-access data from the statistical model for the design of experiment optimization and as well as the experimental data are available for the community. The data are based on an obtained results published in the article: https://www.mdpi.com/2079-4991/11/2/360</p>
Table 7. Results of calculation of epithelialization time statistics one-way analysis of variance (ANOVA) with SPSS 23.00
<p>Table 7. Results of calculation of epithelialization time statistics one-way analysis of variance (ANOVA) with SPSS 23.00</p>
Full results of the t test and/or ANOVA test for Figures.
<p>Complete results of t-tests and/or ANOVA tests for all figures, as well as tables with post hoc results where all P values are presented in numerical form.</p>
ANOVA test results for Figure 4, as well as tables with post hoc results where all P values are presented in numerical form.
<p>ANOVA test results for Figure 4, as well as tables with post hoc results where all P values are presented in numerical form.</p>
ANOVA test results for Figure 2, as well as tables with post hoc results where all P values are presented in numerical form.
<p>ANOVA test results for Figure 2, as well as tables with post hoc results where all P values are presented in numerical form.</p>
ANOVA test results for Figure 1, as well as tables with post hoc results where all P values were presented in numerical form.
<p>ANOVA test results for Figure 1, as well as tables with post hoc results where all P values were presented in numerical form.</p>
ANOVA test results for Figure 3, as well as tables with post hoc results where all P values are presented in numerical form.
<p>ANOVA test results for Figure 3, as well as tables with post hoc results where all P values are presented in numerical form.</p>
ScienceDex guides
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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.