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. …”

Prediction (a-d) and infusion deviation (e-f) results under different training sets and test sets. by Leijie Wang (21485157)

Subjects:

<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)

“…The locomotion values (traces and metrics) are in arbitrary units with larger integers representing a greater displacement of the spherical treadmill, the hemodynamic (Hbt) values (traces and metrics) are a percentage change from the normalised baseline (prior to stimulus presentation), and the corresponding time series vector is presented in seconds. …”

Values of <i>g</i>(<i>a</i>_<i>i</i>) satisfying Eq (2) for each action rule in <i>A</i> by Hitoshi Yamamoto (738861)

Graphs showing reproduction of selected South American annual case (a) and death (b) data using 1000 parameter sets drawn from post-burn-in distribution obtained from 4 Markov chai... by Keith Fraser (1868587)

Cells lacking Cbf11 and/or Mga2 display nutrient-responsive decrease in lipid droplet (LD) content. by Anna Marešová (14375187)

Increasing fractions of mobilized and static taxa in the microbial streamwater community, with a corresponding decrease in the fraction of diluted microbial taxa, detail the shift... by Dawn R. URycki (9659702)

Benchmarking DFT Accuracy in Predicting O 1s Binding Energies on Metals by Elizabeth E. Happel (22391811)

Subjects:

Benchmarking DFT Accuracy in Predicting O 1s Binding Energies on Metals by Elizabeth E. Happel (22391811)

Subjects:

Percentage of exhale flux penetrating through the mask fabric for different values of <i>c</i>_<i>k</i>; a–c and d–f decreasing porosity for outward and inward protection... by Akshay Anand (118579)

Data of the article "The physiological cost of being hot: High thermal stress and disturbance decrease energy reserves in dragonflies in the wild" by Eduardo Ulises Castillo-Pérez (20869904)

“…Despite this, individuals from disturbed sites were larger but had lower energy reserves than those from preserved sites. …”

Biases in larger populations. by Sander W. Keemink (21253563)

“…<p>(<b>A</b>) Maximum absolute bias vs the number of neurons in the population for the Bayesian decoder. …”

The MAE value of the model after STL decomposition. by Xiangjuan Liu (618000)

The MAE value of the model under raw data. by Xiangjuan Liu (618000)

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

Three error values under raw data. by Xiangjuan Liu (618000)

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)

Input parameter ranking for discrete measurements: Each column displays parameters (P) and their influence values (E) calculated using Eq 11, shown in decreasing order of influence. Results are presented for progressively expanding discrete measurement sets (from Set 1 to Set 3F). Only parameters with influence values greater than 0.01 are shown. by Harry Saxton (19165441)

“…Results are presented for progressively expanding discrete measurement sets (from Set 1 to Set 3F). Only parameters with influence values greater than 0.01 are shown.…”

Validation results for the different scenario parameter sets and the resulting KGE_mean value. by Fabian Mitze (21084251)

Experimental parameter information setting table. by Wenhui Wang (34265)