The importance of entry criteria from the point of view of the SD method ‐ combinations B1 –B5. by Roman Vavrek (13200141)

S1 File - by Chantelle M. de Vet (16535257)

“…The student’s RV-GLS consistency was poor during the first 25 measurements, moderate-to good during the second 25 measurements and again poor-to-moderate during the final 50 measurements. Repeated measurements analysis showed a significant decrease in variability of the LV- and RV-GLS score differences between the expert and trainees over time (<i>p</i>_adj<0.001), which was not significantly different between trainees. …”

GLS analysis with 2D-STE software. by Chantelle M. de Vet (16535257)

“…The student’s RV-GLS consistency was poor during the first 25 measurements, moderate-to good during the second 25 measurements and again poor-to-moderate during the final 50 measurements. Repeated measurements analysis showed a significant decrease in variability of the LV- and RV-GLS score differences between the expert and trainees over time (<i>p</i>_adj<0.001), which was not significantly different between trainees. …”

S1 File - by Yonghui Zhang (279832)

“…This study aims to explore the quantitative influence of different combinations of the observed phenological stages on estimation of cultivar-specific parameters (CPSs). We used the CROPGRO-Soybean phenological model (CSPM) as a case study in combination with the Generalized Likelihood Uncertainty Estimation (GLUE) method. …”

Detailed information on field experiments. by Yonghui Zhang (279832)

“…This study aims to explore the quantitative influence of different combinations of the observed phenological stages on estimation of cultivar-specific parameters (CPSs). We used the CROPGRO-Soybean phenological model (CSPM) as a case study in combination with the Generalized Likelihood Uncertainty Estimation (GLUE) method. …”

List of symbols used in this study. by Yonghui Zhang (279832)

“…This study aims to explore the quantitative influence of different combinations of the observed phenological stages on estimation of cultivar-specific parameters (CPSs). We used the CROPGRO-Soybean phenological model (CSPM) as a case study in combination with the Generalized Likelihood Uncertainty Estimation (GLUE) method. …”

Data sources for calibration and evaluation. by Yonghui Zhang (279832)

“…This study aims to explore the quantitative influence of different combinations of the observed phenological stages on estimation of cultivar-specific parameters (CPSs). We used the CROPGRO-Soybean phenological model (CSPM) as a case study in combination with the Generalized Likelihood Uncertainty Estimation (GLUE) method. …”

Quantification of the locomotion of <i>C. elegans</i> exploring agar plates. by Masahiro Tomioka (430683)

Exposure to ketone and ether causes body shrinkage followed by relaxation. by Masahiro Tomioka (430683)

Effects of glucose exposure on motility and body length, and comparison among the effects of glucose and organic solvent exposure on motility. by Masahiro Tomioka (430683)

IC₅₀ of indole alkaloid against lipase [23]. by Atikarn Janthongkaw (17020498)

Selection of crossing point. by Yanchun Li (248761)

The Samn-Perelli Scale is a 7-point Likert scale used to assess fatigue [42, 45]. by Marcel F. Hinss (14675457)

S1 Data - by Jan Willem Koten (17743224)

“…The mean connectivity decreased to r = 0.09 (95% CI 0.03–0.16) when test-retest reliability and auto-correlations of single time courses were considered, indicating that less than a quarter of connectivity is detectable. …”

Connectivity statistics. by Jan Willem Koten (17743224)

“…The mean connectivity decreased to r = 0.09 (95% CI 0.03–0.16) when test-retest reliability and auto-correlations of single time courses were considered, indicating that less than a quarter of connectivity is detectable. …”

Reproducibility of response time and brain measures. by Jan Willem Koten (17743224)

Plot of mean compassion satisfaction by group and time. by Kunal Desai (18784208)

Effect of sample size on reproducibility. by Jan Willem Koten (17743224)

Decreased peritumoral myofibroblasts in the MC38 S.C. tumors of <i>S100a4-Cre; Ext1</i>^<i>f/f</i> mice. by Ayumi Niwa (6256790)

Advancing the science of NOWS research. by Sarah E. Maylott (14560785)

“…It is not known which infants will develop NOWS, therefore, the current hospital standard-of-care dictates a 96-hour observational hold. Understanding which infants will develop NOWS soon after birth could reduce hospital stays for infants who do not develop NOWS and decrease burdens on hospitals and clinicians. …”