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

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

“…After that, blank and inconclusive data were removed, as well as the outcomes that had values other than discharge and death, rendering a binary classification. …”

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

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

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

Area under the ROC Curve (AUC) analysis to predict AIS conversion at 6-months and 1-year following acute cervical tSCI. by Harshit Arora (22008280)

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Area under the ROC curve (AUC) analysis to predict neurological severity at 6-months and 1-year following acute cervical tSCI. by Harshit Arora (22008280)

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TITAN decreaser diatom heatmap. by Brent J. Bellinger (21156150)

Feature importance and beeswarm plot determined by mean absolute SHAP values for the DeepSurv model. by Theophilus Gyedu Baidoo (21162511)

“…<p>(A) A comprehensive view of the influence each variable has on model predictions, with N Stage Status ranked on top (B) Bar plot of mean absolute SHAP values by decreasing order of importance</p>…”

Analysis of tea output value distribution and growth- decrease rate of provinces. by Kun Qin (177235)

Daily number of adverse outcomes (bars) and area under the precision-recall curve (AUC-PRC) per day of the fullPIERS model. by Guiyou Yang (10259597)

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Daily number of adverse outcomes (bars) and area under the precision-recall curve (AUC-PRC) per day of the PIERS-ML model. by Guiyou Yang (10259597)

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Feature importance and beeswarm plot determined by mean absolute SHAP values for the random survival forest (RSF) model. by Theophilus Gyedu Baidoo (21162511)

“…(B) Bar plot of mean absolute SHAP values by decreasing order of importance.</p>…”

Missing value percentage for variables. by Xin Liu (43569)

<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. …”

Decrease in loss during the training process. by Muhammad Idrees (92773)

TITAN decreaser macroinvertebrate heatmap Heatmap of sensitive (i.e., <i>z-</i>) macroinvertebrate taxa (y-axis) to at least one of the five stressors, in decreasing order of numbe... by Brent J. Bellinger (21156150)

Maximum IFN concentrations are decreased in severe patients. by Sonia T. Gazeau (21512764)

Multivariate regression analysis to explain the decrease in visibility and palpability. by Miharu Matsumoto (21304390)

AUC statistics as calculated from simulated time series. Each statistical metric was calculated within sliding windows, throughout the pre-critical interval. We considered five-, fifteen-, and thirty-day sliding windows. Given that the temperature of the system increased to 12°C on day sixty, we also considered three pre-critical intervals: Days 1 to 60, Days 20 to 60, and Days 30 to 60. To evaluate trends in these metrics, we calculated Kendall’s rank correlation coefficient during the pre-critical interval, 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 tre by Madeline Jarvis-Cross (22394247)

“…To evaluate trends in these metrics, we calculated Kendall’s rank correlation coefficient during the pre-critical interval, 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 tre</p>…”