Data Sheet 2_Characterization of the salivary microbiome in healthy individuals under fatigue status.docx by Xianhui Peng (14551488)

“…A predictive model constructed using the Boruta-SHAP algorithm, based on 15 key genera, effectively discriminated between fatigue and non-fatigue states, achieving an area under the receiver operating characteristic curve (AUC) of 0.948. …”

Table 3_Characterization of the salivary microbiome in healthy individuals under fatigue status.xlsx by Xianhui Peng (14551488)

“…A predictive model constructed using the Boruta-SHAP algorithm, based on 15 key genera, effectively discriminated between fatigue and non-fatigue states, achieving an area under the receiver operating characteristic curve (AUC) of 0.948. …”

Data Sheet 1_Characterization of the salivary microbiome in healthy individuals under fatigue status.docx by Xianhui Peng (14551488)

“…A predictive model constructed using the Boruta-SHAP algorithm, based on 15 key genera, effectively discriminated between fatigue and non-fatigue states, achieving an area under the receiver operating characteristic curve (AUC) of 0.948. …”

Table 5_Characterization of the salivary microbiome in healthy individuals under fatigue status.xlsx by Xianhui Peng (14551488)

“…A predictive model constructed using the Boruta-SHAP algorithm, based on 15 key genera, effectively discriminated between fatigue and non-fatigue states, achieving an area under the receiver operating characteristic curve (AUC) of 0.948. …”

Table 4_Characterization of the salivary microbiome in healthy individuals under fatigue status.xlsx by Xianhui Peng (14551488)

“…A predictive model constructed using the Boruta-SHAP algorithm, based on 15 key genera, effectively discriminated between fatigue and non-fatigue states, achieving an area under the receiver operating characteristic curve (AUC) of 0.948. …”

Table 2_Characterization of the salivary microbiome in healthy individuals under fatigue status.xlsx by Xianhui Peng (14551488)

“…A predictive model constructed using the Boruta-SHAP algorithm, based on 15 key genera, effectively discriminated between fatigue and non-fatigue states, achieving an area under the receiver operating characteristic curve (AUC) of 0.948. …”

Table 1_Characterization of the salivary microbiome in healthy individuals under fatigue status.xlsx by Xianhui Peng (14551488)

“…A predictive model constructed using the Boruta-SHAP algorithm, based on 15 key genera, effectively discriminated between fatigue and non-fatigue states, achieving an area under the receiver operating characteristic curve (AUC) of 0.948. …”

JAG2025-cloud-top-properties by Chuanye Shi (18154549)

“…The meaning of each colomn of the LUTs can be found in this file. And the key code is: Xdata = Xdata[[0,1,5,6,19,21,22,23,28,30,32,33,36],:,:] and LUT[line] = np.hstack((data,doy,dem,sza, CTH,CTT,CTP))<br><br>ApplyLUT.py provides a way to apply the propsed LUT algorithm.…”

Structure of optimized model parameters in the high-dimensional cases. by Kevin J. Wischnewski (21354521)

“…The number and size of the clusters were determined with help of the -means clustering method. Both were set to zero if the absolute mean value of the off-diagonal elements in the correlation matrix (cf. …”

Feature selection, Random Forest, and SEM workflow for simulated stand dataset by yang guo (22176595)

“…The R script demonstrates the following steps:</p><ol><li>Feature selection with the Boruta algorithm</li><li>Random Forest modeling with cross-validation and stepwise variable elimination</li><li>Multicollinearity diagnostics using VIF</li><li>Structural Equation Modeling (SEM) and effect decomposition with <code>piecewiseSEM</code> and <code>semEff</code><b>Notes</b></li><li>The dataset is simulated for reproducibility and does not include raw inventory data.…”

BJSP-AGV.cs by shining chen (21391865)

“…<p dir="ltr">the source code of the algorithm, the benchmark data and the optimal solutions.…”

BJSP.xlsx by shining chen (21391865)

“…<p dir="ltr">the source code of the algorithm, the benchmark data and the optimal solutions.…”

Supplement Number 7 by Mees Flapper (21494918)

“…Code and sample data for the orientation tracking algorithm.…”

Single neurons in the human substantia nigra encode social learning signals by Arianna Davis (21370643)

“…Next, analyze the neural data by identifying putative units using the OSORT offline sorting algorithm (linked in related works and cited in the paper). …”

The Guardian Reading Dataset by Frans Van der Sluis (18365826)

“…</p><h3>Data File and Structure</h3><p dir="ltr">Data is stored in <code>.csv</code> format at the reading session level, with each row corresponding to a unique reading session by a participant. …”

A Multi-Pathology Ballistocardiogram Dataset for Cardiac Function Monitoring and Arrhythmia Assessment by Jing Zhan (20716673)

“…Additionally, the algorithm code for processing the signals in this dataset is also uploaded in the code.zip file.…”

DataSheet1_Enhancing slope stability prediction through integrated PCA-SSA-SVM modeling: a case study of LongLian expressway.docx by Jianxin Huang (11944014)

“…Traditional slope stability analysis methods, such as the limit equilibrium method, limit analysis method, and finite element method, often face limitations due to computational complexity and the need for extensive soil property data. …”

Synthetic dataset of 100 fundus auto fluorescence of inherited retinal disease by Nikolas Pontikos (6779939)

“…</p><h2>Code/software</h2><p dir="ltr">No software is needed to view the data. …”

Institutions in the Complexity-Relatedness Framework by İbrahim Tuğrul Çınar (12058328)

“…</p><p dir="ltr"><br></p><p dir="ltr">Files</p><ul><li><code>data_regional_performance models.csv</code> </li></ul><p dir="ltr">Geographic coverage<br>EU NUTS‑2 regions (excludes FRY overseas regions; Azores/Madeira and Spanish islands excluded for geographic/institutional consistency; countries where NUTS‑0 ≡ NUTS‑2—LV, EE, LU, CY, MT are omitted).…”

Brain-in-the-Loop Learning for Intelligent Vehicle Decision-Making by Xiaofei Zhang (16483224)

“…Both human subject studies, control groups and ablation studies data are included in this repository. Detailed description of file organization, data structures, requirements could be found in the README.md document.…”