Impact on different areas of health. by Margherita Molaro (17803989)

Subjects:

Correlation coefficients and associated level of significance. by Ștefan Andrei Neştian (14755581)

Correlation coefficients and associated level of significance. by Ștefan Andrei Neştian (14755581)

Differences in mean performance results across all applied algorithms. by Quinlan D. Buchlak (13162874)

The trend of patient characteristics, risk factors, and risk scores. by Ayako Shoji (78037)

Characteristics by group. by Arisa Kobayashi (4379608)

Subjects:

Kaplan–Meier curve for mortality related to CVD. by Arisa Kobayashi (4379608)

Subjects:

ASVs with significant (adjusted p-value < 0.01) abundance differences between degraded and conserved status along all the study sites. by Daniel Esguerra-Rodríguez (19434367)

All of the quantification data and analysis of significance differences in Figs 1–9 and S1–S9 Figs are presented in the dataset (Quantification data.zip). by Zulin Wu (4451572)

Data_Sheet_1_One Does Not Fit All: European Study Shows Significant Differences in Value-Priorities in Clean Sport.docx by Toby Woolway (10860435)

Significantly different proteins in humans. by Nora Linscheid (10668543)

“…<p>Subset of hierarchical clustering analysis on protein intensities across species, showing all protein groups deemed significantly different based on multiple-sample ANOVA testing for differences between human against all other species at false discovery rate of 0.01.…”

Analysis of Significantly Different Bacteria. by Xu Jia (187142)

“…<p>(A, B) Heatmap and Principal Component Analysis (PCA) of significantly different bacteria abundances at various taxonomic levels in all samples: phylum (top), genera (middle), and species (bottom). …”

Examples of different stimuli for all subtasks of the task. by Marcel F. Hinss (14675457)

Risk of fracture with competing risk in denosumab users versus non-users. by Arisa Kobayashi (4379608)

Subjects:

Summary of basic characteristics of all participants, and differences between males and females. by Lotte van Dam (17347733)

Heatmap of all significant pairwise ERC values of glycosylation proteins (ERC > 3). by Holly J. Thorpe (17621369)

Fixed effects parameter estimates for all metrics of interest across both assessments and all load conditions [Estimate (95% CI)]. by Nicolas M. Philipp (13272198)

Pearson’s correlation matrix of BLUP means for all traits showing significant genotypic effects. by Herika Paula Pessoa (13254326)

Correlation matrix for all variables. by Yingjie Lu (283162)

“…Our findings show that there are significant differences in the demand for big data talent across different types of cities and industries. …”

Forest diagrams of all factors. by Na Wang (193263)

“…At the same time, we found that the obvious effect and recovery rate of GLM group was higher than that of PCM group, and the difference was statistically significant (p < 0.05).…”