Differences in mean number of papers (top) and co-authors (bottom) depending on (1) NI membership and (2) time joined NI. by William C. Barley (8915282)

The mean and standard deviation of outcome measures. by Soheila Qanbari (20455173)

Boxplot mean cfPWV comparisons. by Johannes Nairz (18010746)

Estimated mean values for light interception. by Torsten Schober (20485754)

Reducing Dscam1 diversity decreases adult lifespan in a cluster-specific manner, also see S6 and S7 Figs. by Haiyang Dong (4783608)

Volitional control frequency and intensity in VH (Kapsner-Smith et al., 2025) by Mara R. Kapsner-Smith (22139315)

“…Singers produced significantly smaller mean smallest changes of both <i>F</i>0 and intensity than nonsingers.…”

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

Fluctuation trend of the mean temperature index. by Chengyuan Hao (21615653)

“…Secondly, the daily minimum and maximum temperatures increased significantly, which were 0.395°C/10a and 0.200°C/10a respectively<b>—</b>less than the national mean. …”

Variation curve of the mean temperature index. by Chengyuan Hao (21615653)

“…Secondly, the daily minimum and maximum temperatures increased significantly, which were 0.395°C/10a and 0.200°C/10a respectively<b>—</b>less than the national mean. …”

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

Mann-Kendall test for the mean temperature index. by Chengyuan Hao (21615653)

“…Secondly, the daily minimum and maximum temperatures increased significantly, which were 0.395°C/10a and 0.200°C/10a respectively<b>—</b>less than the national mean. …”

Results from linear regression analysis for δ¹³C or δ¹⁵N and collection date. by Nathan D. McTigue (10706460)

Parameter estimates of linear mixed effects models for juvenile <i>M</i>. <i>mercenaria</i>, juvenile <i>M</i>. <i>arenaria</i> and juvenile <i>A</i>. <i>islandica</i> mean shell c... by Teagan McMahon (20108036)

Scatter plots of the linear regression for the relationship between the duration of the vegetative phase and the average mass of single inflorescences. by Torsten Schober (20485754)

Means scores of CAM and CAM-MYCS. by Senay Yildirim-Kahriman (22693464)

AZ10606120 treatment significantly increased cytotoxicity and reduced cell number in a dose-dependent manner. by Matthew Drill (22258391)

“…Cell numbers were significantly decreased in cells after treatment with AZ10606120 (50µM) compared to both temozolomide (TMZ; 50µM) treatment and untreated controls. …”

Structure diagram of ensemble model. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Fitting formula parameter table. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Test plan. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”

Fitting surface parameters. by Hongqi Wang (2208238)

“…By developing and validating both empirical and machine learning prediction models, we unravel the evolution of thermal conductivity in response to these factors: within the range of influencing variables, thermal conductivity exhibits an exponential or linear increase with rising water content and dry density, while it decreases exponentially with increasing freeze-thaw cycles. …”