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

<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. …”

L* Value Increases with Increasing Zinc Oxide Percentage. by Alexandra M. Maldonado López (22127340)

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. …”

Association between the sum of health behaviors and decrease in physical health from before to during the pandemic. Results are from linear model analysis adjusted for age, gender... by Elie Mulhem (22537863)

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The Ka/Ks values of PtWRKY paralogous gene pairs. by Yuzhao Ma (12840632)

Subjects: “…electrical conductivity decreased…”

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. …”

Participant demographics. * P-values were determined using Fisher-Freeman-Halton test when contingency tables are larger than 2 × 2. by Elie Mulhem (22537863)

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Change in computation time for an increase in number of nodes <i>N</i> and number of people in system <i>K</i> (<i>C</i> = 3). by Shinya Mizuno (3689788)

Distribution of stable isotope values for consumers faceted by coastscape and feeding habit. by Nathan D. McTigue (10706460)

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Distribution of stable carbon and nitrogen values for pPOM and sPOM. by Nathan D. McTigue (10706460)

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ECoG timescales decrease during spatial attention. by Isabel Raposo (21615517)

Mathematical treatments applied to data to correct stable isotope values for lipid content. by Nathan D. McTigue (10706460)

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Linear regressions of consumer stable carbon and nitrogen isotope values and collection date. by Nathan D. McTigue (10706460)

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Association of each health behavior and the reported decrease in psychological well-being during the pandemic between individuals who often/very often compared to rarely/never enga... by Elie Mulhem (22537863)

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Effect of each health behavior on the reported decrease in physical health during the pandemic between individuals who often/very often compared to rarely/never engaged in health b... by Elie Mulhem (22537863)

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

Innovation and night light in large and small cities. by Saul Estrin (8629173)

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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>…”

Penalty conversions yield large practical decreases, but robustness and generalizability of result are limited by data quality and sample selection. by Ryan J. Treves (13527735)