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23 results for “analysis of variance”
Seasonal Soil Sampling of Grass-dominated, Mesquite-dominated, and Ecotone Sites at the Jornada Basin LTER site for the Analysis of Microbial Community Variance, 2022-2023
Fungal and bacterial soil communities were analyzed to assess the influence of woody shrub encroachment on soil microbial communities. Three study sites in the Jornada Long Term Ecological Research Site were selected to represent a grass-dominated site, a woody shrub dominated site, and an ecotone of woody shrubs and grass. The field sampling began in October 2022 and concluded in July 2023 with five sampling periods that aimed to capture seasonal variation: October 2022, January 2023, March 2023, May 2023, and July 2023. This dataset includes data pertaining to the soil microbial composition, environmental characteristics, microbial sequence processing, and documentation of the code utilized for data processing and statistical analyses. Data on soil microbial composition was collected from Phospholipid Fatty-Acid composition data from soil samples. Data on environmental characteristics were collected from on-site temperature probes, laboratory assessments of soil properties, and Jornada meteorological stations. Information pertaining to microbial sequence processing is included in the documented code as well as in the record of the primers utilized.
Analysis of variance for the effect of insecticides as a contact and systemic applications and Analysis of variance for the effect of insecticides tested under field condition
<p>Analysis of variance for the effect of insecticides as a contact and systemic applications and Analysis of variance for the effect of insecticides tested under field condition </p> <p>The mean number of <em>H. armigera</em> live larvae were transformed into square-root values before the statistical analysis. The one-way analysis of variance (ANOVA) was used for both transformed values under laboratory conditions. Means were compared using Fisher’s least significant differences (LSD) test at P< 0.05. Under field conditions, a two-way repeated measures analysis of variance (ANOVA) was used to determine the effects of insecticides and exposure time. The computations were carried out using GenStat (19th Edition, VSN International, UK). </p>
Рис. 6. Графики Зависимости оценок варианс (S2) от средней плотности (D) популЯций наЗемных моллюсков B. cylindrica (А) и M. cartusiana (В): 1 – участок № 1, 2010 г.; 2 – участок № 2, 2011 г.; 3 – участок № 4, 2012 г.; 4 – участок № 5, 2012 г. Fig. 6. Variance estimation (S2) and average density (D) of the land snail B. cylindrica (А) and M. cartusiana (В) population scatter plots: 1 – site 1, 2010; 2 – site 2, 2011; 3 – site 4, 2012; 4 – site 5, 2012. in Analysis of the spatial distribution patterns of the land snail populations: a geostatistic method approach
Рис. 6. Графики Зависимости оценок варианс (S2) от средней плотности (D) популЯций наЗемных моллюсков B. cylindrica (А) и M. cartusiana (В): 1 – участок № 1, 2010 г.; 2 – участок № 2, 2011 г.; 3 – участок № 4, 2012 г.; 4 – участок № 5, 2012 г. Fig. 6. Variance estimation (S2) and average density (D) of the land snail B. cylindrica (А) and M. cartusiana (В) population scatter plots: 1 – site 1, 2010; 2 – site 2, 2011; 3 – site 4, 2012; 4 – site 5, 2012.
Fig. 1 in Stable isotope analysis spills the beans about spatial variance in trophic structure in a fish host - parasite system from the Vaal River System, South Africa
Fig. 1. Map of the Vaal River showing the position of sampling sites (I: below Grootdraai Dam; II: Vaal Dam; III: below Vaal River Barrage; IV: Bloemhof Dam; V: below Vaalharts Weir; VI: Douglas Weir) along the Vaal River. The block (B) indicates the position of the Vaal River within South Africa and insert A indicates the position of South Africa shaded on the African continent.
Variance components of sex determination in the copepod Tigriopus californicus estimated from a pedigree analysis
<p>Extensive theory exists regarding population sex ratio evolution that predicts equal sex ratio (when parental investment is equal). In most animals, sex chromosomes determine the sex of offspring, and this fixed genotype for sex has made theory difficult to test since genotypic variance for the trait (sex) is lacking. It has long been argued that the genotype has become fixed in most animals due to the strong selection for equal sex ratios. The marine copepod <em>Tigriopus californicus</em> has no sex chromosomes, multiple genes affecting female brood sex ratio and a brood sex ratio that responds to selection. The species thus provides an opportune system in which to test established sex ratio theory. In this paper, we further our exploration of polygenic sex determination in <em>T. californicus</em> using an incomplete diallel crossing design for analysis of the variance components of sex determination in the species. Our data confirm the presence of extra-binomial variance for sex, further confirming that sex is not determined through simple Mendelian trait inheritance. In addition, our crosses and backcrosses of isofemale lines selected for biased brood sex ratios show intermediate phenotypic means, as expected if sex is a threshold trait determined by an underlying "liability" trait controlled by many genes of small effects. Furthermore, crosses between families from the same selection line had similar increases in phenotypic variance as crosses between families from different selection lines, suggesting families from artificial selection lines responded to selection pressure through different underlying genetic bases. Finally, we estimate heritability of an individual to be male or female on the observed binary scale as 0.09 (95% CI: 0.034-0.14). This work furthers our accumulating evidence for polygenic sex determination in <em>T. californicus</em> laying the foundation for this as a model species in future studies of sex ratio evolution theory.</p>
Variance components of sex determination in the copepod Tigriopus californicus estimated from a pedigree analysis
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Supplementary Table S1. Combined analysis of variance containing the degrees of freedom (DF), mean squares (MS), P value (P val.), mean, coefficient of experimental variation (CEV%) and selective accuracy (SA) for the traits of luminosity (L*), chromaticity a* (a*), chromaticity b* (b*), grain length (length, mm), grain width (width, mm), grain thickness (thickness, mm), mass of 100 grains (Mass, g), normal grains (Ng, %), water absorption (absorption, %), cooking time (Ct, min:s), and concentrations of potassium (K, g kg-1 dry matter - DM), phosphorus (P, g kg-1 DM), calcium (Ca, g kg-1 DM), magnesium (Mg, g kg-1 DM), iron (Fe, mg kg-1 DM), zinc (Zn, mg kg-1 DM), and copper (Cu, mg kg-1 DM) obtained in 25 common bean cultivars evaluated in four experiments carried out from 2019 to 2021
<p><strong><span>Table S1.</span></strong><span> Combined analysis of variance.</span></p> <p><strong><span>Indirect selection for multiple technological and nutritional traits in common bean cultivars under different degrees of multicollinearity</span></strong></p> <p><strong><span>Bragantia, 2024.</span></strong></p>
Data, sample sizes, and R code for analysis of: Variation in mutation (co)variances
<p>Because of pleiotropy, mutations affect the expression and inheritance of multiple traits and, together with selection, are expected to shape standing genetic covariances between traits and eventual phenotypic divergence between populations. It is therefore important to find if the M matrix, describing mutational variances of each trait and covariances between traits, varies between genotypes. We here estimate the M matrix for six locomotion behavior traits in lines of two genotypes of the nematode <em>Caenorhabditis elegans </em>that accumulated mutations in a nearly-neutral manner for 250 generations. We find significant mutational variance along at least one phenotypic dimension of the M matrices, but neither their size nor their orientation had detectable differences between genotypes. The number of generations of mutation accumulation, or the number of MA lines measured, was likely insufficient to sample enough mutations and detect potentially small differences between the two M matrices. We then tested if the M matrices were similar to one G matrix describing the standing genetic (co)variances of a population derived by the hybridization of several genotypes, including the two measured for M, and domesticated to a lab-defined environment for 140 generations. We found that the M and G were different because the genetic covariances caused by mutational pleiotropy in the two genotypes are smaller than those caused by linkage disequilibrium in the lab population. We further show that M matrices differed in their alignment with the lab population G matrix. If generalized to other founder genotypes of the lab population, these observations indicate that selection does not shape the evolution of the M matrix for locomotion behavior in the short-term of a few tens to hundreds of generations and suggests that the hybridization of <em>C. elegans </em>genotypes allows selection on new phenotypic dimensions of locomotion behavior.</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>
Data, sample sizes, and R code for analysis of: Variation in mutation (co)variances
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Performance Measurement Datasets of the HPC Benchmarks LAMMPS, MiniFE, LULESH for Hardware Counter Variance Analysis
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Age-by-maternal-age Markov chain with rewards analysis of variance in LRO in rotifers
<p>This set of data and code files is a supplement to the American Naturalist paper:<br> van Daalen et al. 2021 The contributions of maternal age heterogeneity to variance in lifetime reproductive output</p> <p>It provides the user the matrices (as first presented in Hernandez et al., 2020, PNAS) and the code to reproduce our analysis or apply the methods to their own data. Two multistate, age-by-maternal-age matrices with basic demographic information (survival and transitions, and fertility) are provided, as well as data to paramaterize a reward matrix. The code presents a Markov chain with rewards approach to calculating mean and variance in lifetime reproductive output from a multistate matrix model, and a method to decompose variance into contributions by individual heterogeneity (from maternal age) and stochasticity (due to inherent randomness in the outcomes of age-specific survival probabilities and age-specific reproductive output).</p>
zMAP toolset: model-based analysis of large-scale proteomic data via a variance stabilizing z-transformation
<p>Data and code used to generate the analyses and figures in paper "zMAP toolset: model-based analysis of large-scale proteomic data via a variance stabilizing z-transformation" are provided here.</p>
CRM Variance Transport validation in E3SM-MMF - analysis code and condensed data
<p>This tarball contains a collection of code and data used to performa a detailed analysis of the CRM variance transport feature added to E3SM-MMF. The data subset is condensed from the model output and observational datasets, so the ability to reproduce results directly is limited, but this archive mainly serves to document the methods used for our analysis. </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>
Age-by-maternal-age Markov chain with rewards analysis of variance in LRO in rotifers
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Data from: Environmental stress correlates with increases in both genetic and residual variances: a meta-analysis of animal studies
Adaptive evolutionary responses are determined by the strength of selection and the amount of genetic variation within traits, however, both are known to vary across environmental conditions. As selection is generally expected to be strongest under stressful conditions, understanding how the expression of genetic variation changes across stressful and benign environmental conditions is crucial for predicting the rate of adaptive change. While theory generally predicts increased genetic variation under stress, previous syntheses of the field has found limited support for this notion. These studies have focused on heritability, which is dependent on other environmentally sensitive, but non-genetic, sources of variation. Here, we aim to complement these studies with a meta-analysis where we examine changes in coefficient of variation (CV) in maternal, genetic, and residual variances across stressful and benign conditions. Confirming previous analyses, we did not find any clear direction in how heritability changes across stressful and benign conditions. However, when analyzing CV, we found higher genetic and residual variance under highly stressful conditions in life-history traits but not in morphological traits. Our findings are of broad significance to contemporary evolution suggesting that rapid evolutionary adaptive response may be mediated by increased evolutionary potential in stressed populations.
Table ¹: Comparison of analysis of variance results for skull (occlusal view) and mandible (side view) shape in Rhipidomys mastacalis from three vegetation classes in Brazil. Object asymmetry and correspondence methods were employed to assess asymmetry for skulls and mandibles, respectively. in Morphological symmetry of Rhipidomys mastacalis (Mammalia, Rodentia, Cricetidae) in fragmented habitats of the Atlantic Forest in Northeastern Brazil: a study on the influence of the environment on an endemic species
<p><b>Table ¹:</b> Comparison of analysis of variance results for skull (occlusal view) and mandible (side view) shape in <i>Rhipidomys mastacalis</i> from three vegetation classes in Brazil.Object asymmetry and correspondence methods were employed to assess asymmetry for skulls and mandibles,respectively.</p><table><tbody><tr><th><b>Shape procrustes ANOVA</b></th></tr></tbody><tbody><tr><th><b>Effect Sum of squares</b></th><td><b>Mean squares</b></td><td><b>Degrees of freedom</b></td><td><i>F statistic</i></td><td><i>p -Value</i></td><td><b>Pillai tr.</b></td><td><i>p -Value</i></td></tr><tr><th><b>Skulls</b></th></tr><tr><th><b>Forested vegetation</b></th></tr><tr><th>Individual</th><td>0.19908517</td><td>0.0004253957</td><td>468</td><td>22.36</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Side</th><td>0.00366522</td><td>0.0002036232</td><td>18</td><td>10.70</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Individual × side</th><td>0.00890443</td><td>0.0000190266</td><td>468</td><td>2.24</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Error 1</th><td>0.00825565</td><td>0.0000084935</td><td>972</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Occupancy mosaics in forested areas</b></th></tr><tr><th>Individual</th><td>0.37829478</td><td>0.0003965354</td><td>954</td><td>18.57</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Side</th><td>0.00547536</td><td>0.0003041869</td><td>18</td><td>14.25</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Individual × side</th><td>0.02037065</td><td>0.0000213529</td><td>954</td><td>1.89</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Error 1</th><td>0.02201359</td><td>0.0000113239</td><td>1944</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Cocoa plantations</b></th></tr><tr><th>Individual</th><td>0.0645902300</td><td>0.0001302222</td><td>496</td><td>5.18</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Side</th><td>0.0113531900</td><td>0.0007095741</td><td>16</td><td>28.23</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Individual × side</th><td>0.0124666800</td><td>0.0000251344</td><td>496</td><td>1.88</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Error 1</th><td>0.0136608800</td><td>0.0000133407</td><td>1024</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Mandibles</b></th></tr><tr><th><b>Forested vegetation</b></th></tr><tr><th>Individual</th><td>0.70443879</td><td>0.0012579264</td><td>560</td><td>8.10</td><td><0.0001</td><td>14.16</td><td><0.0001</td></tr><tr><th>Side</th><td>0.00549957</td><td>0.0002749783</td><td>20</td><td>1.77</td><td>0.0207</td><td>0.0207</td><td>0.0069</td></tr><tr><th>Individual × side</th><td>0.08696012</td><td>0.0001552859</td><td>560</td><td>2.46</td><td><0.0001</td><td>10.75</td><td><0.0001</td></tr><tr><th>Error 1</th><td>0.07312665</td><td>0.0000387718</td><td>1160</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Occupancy mosaics in forested areas</b></th></tr><tr><th>Individual</th><td>1.19843989</td><td>0.0011984399</td><td>1000</td><td>8.16</td><td><0.0001</td><td>14.70</td><td><0.0001</td></tr><tr><th>Side</th><td>0.01169771</td><td>0.0005848855</td><td>20</td><td>3.98</td><td><0.0001</td><td>0.74</td><td>0.0001</td></tr><tr><th>Individual × side</th><td>0.14685738</td><td>0.0001468574</td><td>1000</td><td>3.03</td><td><0.0001</td><td>11.21</td><td><0.0001</td></tr><tr><th>Error 1</th><td>0.09880745</td><td>0.0000484350</td><td>2040</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Cocoa plantations</b></th></tr><tr><th>Individual</th><td>0.3269927600</td><td>0.0004808717</td><td>680</td><td>4.52</td><td><0.0001</td><td>14.14</td><td><0.0001</td></tr><tr><th>Side</th><td>0.0143644400</td><td>0.0007182221</td><td>20</td><td>6.75</td><td><0.0001</td><td>0.86</td><td>0.0017</td></tr><tr><th>Individual × side</th><td>0.0723474900</td><td>0.0001063934</td><td>680</td><td>2.39</td><td><0.0001</td><td>10.41</td><td>0.0017</td></tr><tr><th>Error 1</th><td>0.0622041800</td><td>0.0000444316</td><td>1400</td><td>–</td><td>–</td><td>–</td><td>–</td></tr></tbody></table>
Table ²: Comparison of the results of analysis of variance on the shape of scapulae (occlusal view) and pelvis (side view) in Rhipidomys mastacalis from three vegetation classes in Brazil. Correspondence asymmetry was the only method used for asymmetry analysis. in Morphological symmetry of Rhipidomys mastacalis (Mammalia, Rodentia, Cricetidae) in fragmented habitats of the Atlantic Forest in Northeastern Brazil: a study on the influence of the environment on an endemic species
<p><b>Table ²:</b> Comparison of the results of analysis of variance on the shape of scapulae (occlusal view) and pelvis (side view) in <i>Rhipidomys mastacalis</i> from three vegetation classes in Brazil. Correspondence asymmetry was the only method used for asymmetry analysis.</p><table><tbody><tr><th><b>Shape procrustes ANOVA</b></th></tr></tbody><tbody><tr><th><b>Effect Sum of squares</b></th><td><b>Mean squares</b></td><td><b>Degrees of freedom</b></td><td><i>F statistic</i></td><td><i>p -Value</i></td><td><b>Pillai tr.</b></td><td><i>p -Value</i></td></tr><tr><th><b>Scapulae</b></th></tr><tr><th><b>Forested vegetation</b></th></tr><tr><th>Individual</th><td>0.0941373400</td><td>0.0010459705</td><td>90</td><td>3</td><td><0.0001</td><td>–</td><td>–</td></tr><tr><th>Side</th><td>0.0100439600</td><td>0.0010043960</td><td>2.88</td><td>0.0037</td><td>0.0003</td><td>–</td><td>–</td></tr><tr><th>Individual × side</th><td>0.0314069500</td><td>0.0003489662</td><td>90</td><td>5.89</td><td><0.0001</td><td>4.91</td><td><0.0001</td></tr><tr><th>Error 1</th><td>0.0118544100</td><td>0.0000592721</td><td>200</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Occupancy mosaics in forested areas</b></th></tr><tr><th>Individual</th><td>0.2064168200</td><td>0.0010320841</td><td>200</td><td>4.82</td><td><0.0001</td><td>7.15</td><td><0.0001</td></tr><tr><th>Side</th><td>0.0262808000</td><td>0.0026280796</td><td>10</td><td>12.28</td><td><0.0001</td><td>0.86</td><td>0.0022</td></tr><tr><th>Individual × side</th><td>0.0428160400</td><td>0.0002140802</td><td>200</td><td>2.68</td><td><0.0001</td><td>4.98</td><td><0.0001</td></tr><tr><th>Error 1</th><td>0.0335675700</td><td>0.0000799228</td><td>420</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Cocoa plantations</b></th></tr><tr><th>Individual</th><td>0.2508635400</td><td>0.0009291242</td><td>270</td><td>4.07</td><td><0.0001</td><td>7.11</td><td><0.0001</td></tr><tr><th>Side</th><td>0.0256608100</td><td>0.0025660812</td><td>10</td><td>11.24</td><td><0.0001</td><td>0.87</td><td><0.0001</td></tr><tr><th>Individual × side</th><td>0.0616394000</td><td>0.0002282941</td><td>270</td><td>3.10</td><td><0.0001</td><td>5.72</td><td><0.0001</td></tr><tr><th>Error 1</th><td>0.0412323300</td><td>0.0000736292</td><td>560</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Pelvis</b></th></tr><tr><th><b>Forested vegetation</b></th></tr><tr><th>Individual</th><td>0.0543411200</td><td>0.0004312787</td><td>126</td><td>4.63</td><td><0.0001</td><td></td><td></td></tr><tr><th>Side</th><td>0.0043155600</td><td>0.0003082544</td><td>14</td><td>3.31</td><td>0.0002</td><td></td><td></td></tr><tr><th>Individual × side</th><td>0.0117297800</td><td>0.0000930935</td><td>126</td><td>2.31</td><td><0.0001</td><td>6.07</td><td><0.0001</td></tr><tr><th>Error 1</th><td>0.0112943700</td><td>0.000040337</td><td>280</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Occupancy mosaics in forested areas</b></th></tr><tr><th>Individual</th><td>0.1059661700</td><td>0.0003440460</td><td>308</td><td>4.42</td><td><0.0001</td><td>9.69</td><td><0.0001</td></tr><tr><th>Side</th><td>0.0049395300</td><td>0.0003528236</td><td>14</td><td>4.53</td><td><0.0001</td><td>0.85</td><td>0.0311</td></tr><tr><th>Individual × side</th><td>0.0239852500</td><td>0.0000778742</td><td>308</td><td>2.00</td><td><0.0001</td><td>6.64</td><td><0.0001</td></tr><tr><th>Error 1</th><td>0.0251368400</td><td>0.0000390324</td><td>644</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><th><b>Cocoa plantations</b></th></tr><tr><th>Individual</th><td>0.1292837500</td><td>0.0003420205</td><td>378</td><td>5.68</td><td><0.0001</td><td>10.51</td><td><0.0001</td></tr><tr><th>Side</th><td>0.0043550500</td><td>0.0003110747</td><td>14</td><td>5.17</td><td><0.0001</td><td>0.84</td><td>0.0016</td></tr><tr><th>Individual × side</th><td>0.0227608400</td><td>0.0000602139</td><td>378</td><td>2.24</td><td><0.0001</td><td>6.17</td><td><0.0001</td></tr><tr><th>Error 1</th><td>0.0210413800</td><td>0.0000268385</td><td>714</td><td>–</td><td>–</td><td>–</td><td>–</td></tr></tbody></table>
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.