<b>Supporting data for manuscript</b> "<b>Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins</b>" by Kira Shaw (18796168)

“…<p dir="ltr">The CSV file 'Eyreetal_DrainingVein_SourceData' contains the averaged time series traces and extracted metrics from individual experiments used across Figures 1-5 in the manuscript "Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins". The following acronyms included in the CSV file are defined as follows: Hbt is total hemoglobin, Art is artery region, DV is draining vein region, WV is whisker vein region, SEM is standard error mean, TS is time series, max peak is maximum peak, min peak is minima, AUC is area under the curve, WT is wild-type, AD is Alzheimer's disease, ATH is atherosclerosis and MIX is mixed AD/atherosclerosis. …”

The ROC curves and AUC values of the diagnostic model. by Zhe Wu (582498)

Column charts of WiAUC in therapy and control group at baseline and follow-up. by Felix L. Herr (21544362)

ROC Curve for the Best Model (AUC = 0.92). by Mudhafar Jalil Jassim Ghrabat (22177655)

Area under the curve (AUC) for external validation performance. by Junichi Kushioka (12236447)

AUC ROC curve. by Dieuwke Luijten (15282736)

Increase in AUC, MCC, and F1 between oversampling and undersampling. by Seongil Han (20490232)

Decreasing oxygen saturation with corresponding levels of NLR and D-dimer for understanding the interplay of inflammation. by Narendran Gopalan (110148)

Statistical analysis of the AUC measurement. by Esra Ayan (21156015)

“…<p>Analysis indicated that INSv application significantly changed spontaneous calcium activity (IAsp <i>p</i>-value: 0.1290, INSv <i>p</i>-value: 0.0002). The mean AUC decreased in both samples.</p> <p>(TIF)</p>…”

AUC of various models to predict imeglimin efficacy for liver function evaluated by ROC curves. by Naruyoshi Gyoutoku (22537831)

Performance comparison of AUC, MCC, and F1 across different IR on GMSC dataset. by Seongil Han (20490232)

The introduction of mutualisms into assembled communities increases their connectance and complexity while decreasing their richness. by Gui Araujo (22170819)

“…When they stop being introduced in further assembly events (i.e. introduced species do not carry any mutualistic interactions), their proportion slowly decreases with successive invasions. (B) Even though higher proportions of mutualism promote higher richness, introducing this type of interaction into already assembled large communities promotes a sudden drop in richness, while stopping mutualism promotes a slight boost in richness increase. …”

ROC curve and AUC value of all models. by Tiago de Oliveira Barreto (20485207)

“…As for Specificity (82.94%) and ROC-AUC (82.13%), the Multilayer Perceptron with SGD optimizer obtained the highest scores. …”

The AUCs of the GSRS and the established models in predicting the 1-, 2-, 3-, 4-, and 5-year OS in the TCGA-KIRC cohort. by Yu Zhang (12946)

Performance comparison of machine learning models across accuracy, AUC, MCC, and F1 score on GMSC dataset. by Seongil Han (20490232)

Performance comparison of machine learning models across accuracy, AUC, MCC, and F1 score on GMSC dataset. by Seongil Han (20490232)

AUC surface with respect to regularization parameters <i>λ</i> and . by Yangsong He (13909371)

AUC statistics comparing statistical trends in control and test populations. by Madeline Jarvis-Cross (22394247)

“…<p>To evaluate statistical trends, we calculated Kendall’s rank correlation coefficient during the pre-critical interval (here, days one to sixty), and compared control (constant temperature, non-epidemic) and warming (warming treatment, epidemic emergence) coefficients across simulations and experimental populations by calculating the area under the curve (AUC) statistic. Values less than 0.5 suggest that a decrease in the statistical metric indicates emergence, while values greater than 0.5 suggest that an increase in the statistical metric indicates emergence, with more extreme values indicating stronger trends. …”

Model assessment showing the AUC outcomes based on historical and projected bioclimatic variables (Esri ArcGIS Pro 2024). by Khalifa M. Al-Kindi (3719854)

Comparative Internal and External AUC Performance Across Single-Site Models. by Ioana Duta (18462981)

“…At Agincourt, the internal AUC is 0.96 (interquartile range (IQR) 0.95–0.97), while the external AUC is 0.88 (IQR 0.86–0.88), demonstrating a statistically significant difference with a decrease of approximately 0.08 in performance when models are externally validated. …”