Comparison of noise reduction algorithms. by Tianke Fang (22648035)

S2 station noise reduction result. by Yubo Zhao (3873457)

S3 station noise reduction result. by Yubo Zhao (3873457)

S1 station noise reduction result. by Yubo Zhao (3873457)

The source and details of the datasets. by Xingtao Wu (22139242)

The source of the fan datasets and details. by Xingtao Wu (22139242)

The source code of LazyAct. by Hongjie Zhang (136127)

Data Sheet 1_L-shaped nonlinear relationship between magnesium intake from diet and supplements and the risk of diabetic nephropathy: a cross-sectional study.docx by Jia Du (3363635)

“…</p>Methods<p>Data were from the National Health and Nutrition Examination Survey 2007–2018. A multi-step analytical strategy was adopted: (1) confounders were selected using variance inflation factor and Boruta feature selection algorithm; (2) weighted multivariable logistic regression assessed the association between magnesium intake and DN; (3) restricted cubic splines (RCS), generalized additive models (GAM), and curve fitting were used to evaluate nonlinear dose–response trends; (4) piecewise regression identified potential thresholds; (5) subgroup analyses examined interactions across age, gender, BMI, hypertension, and cardiovascular disease.…”

Mapping rubber monoculture and jungle rubber across Kalimantan using multisource satellite data by Guerric le Maire (2671198)

“…We developed a method based on a Random Forest machine-learning classification algorithm calibrated on multi-source imagery from Sentinel-1 (radar) and Sentinel-2 (optical) satellites, applying a Principal Component Analysis for feature reduction. …”

Table 1_Remote sensing-based assessment of four decades of land use/land cover change in the Sigi River Watershed, East Usambara Mountains, Tanzania.docx by Simon Chidodo (21572894)

“…This study analyzed LULC dynamics from 1983 to 2022 in the Sigi River watershed (887 km²) in the East Usambara Mountains, a biodiversity hotspot and critical water source in northeastern Tanzania.</p>Methods:<p>Multi-temporal Landsat satellite images were classified using the Random Forest algorithm to assess LULC transitions across elevation and slope gradients.…”

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

“…Bioinformatics analyses encompassed assessment of alpha and beta diversity, identification of differential taxa using Linear discriminant analysis Effect Size (LEfSe) with multi-method cross-validation, construction of microbial co-occurrence networks, and screening of fatigue-associated biomarker genera via the Boruta-SHAP algorithm. …”

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

“…Bioinformatics analyses encompassed assessment of alpha and beta diversity, identification of differential taxa using Linear discriminant analysis Effect Size (LEfSe) with multi-method cross-validation, construction of microbial co-occurrence networks, and screening of fatigue-associated biomarker genera via the Boruta-SHAP algorithm. …”

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

“…Bioinformatics analyses encompassed assessment of alpha and beta diversity, identification of differential taxa using Linear discriminant analysis Effect Size (LEfSe) with multi-method cross-validation, construction of microbial co-occurrence networks, and screening of fatigue-associated biomarker genera via the Boruta-SHAP algorithm. …”

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

“…Bioinformatics analyses encompassed assessment of alpha and beta diversity, identification of differential taxa using Linear discriminant analysis Effect Size (LEfSe) with multi-method cross-validation, construction of microbial co-occurrence networks, and screening of fatigue-associated biomarker genera via the Boruta-SHAP algorithm. …”

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

“…Bioinformatics analyses encompassed assessment of alpha and beta diversity, identification of differential taxa using Linear discriminant analysis Effect Size (LEfSe) with multi-method cross-validation, construction of microbial co-occurrence networks, and screening of fatigue-associated biomarker genera via the Boruta-SHAP algorithm. …”

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

“…Bioinformatics analyses encompassed assessment of alpha and beta diversity, identification of differential taxa using Linear discriminant analysis Effect Size (LEfSe) with multi-method cross-validation, construction of microbial co-occurrence networks, and screening of fatigue-associated biomarker genera via the Boruta-SHAP algorithm. …”

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

“…Bioinformatics analyses encompassed assessment of alpha and beta diversity, identification of differential taxa using Linear discriminant analysis Effect Size (LEfSe) with multi-method cross-validation, construction of microbial co-occurrence networks, and screening of fatigue-associated biomarker genera via the Boruta-SHAP algorithm. …”

Processing of Published Data and Construction of the Core UVmap Reference by David Adams (10283936)

“…</p><p dir="ltr">5. Gautam, P. et al. Multi-species single-cell transcriptomic analysis of ocular compartment regulons. …”

Supplementary Data: Biodiversity and Energy System Planning - Queensland 2025 by Andrew Rogers (17623239)

“…</li><li>Core Tool: CPAs are generated using a custom fork of the source code (released with this Article) supplied by the Multi-criteria Analysis for Planning Renewable Energy (MapRE) initiative.…”

Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles by Soham Savarkar (21811825)

“…</p><p dir="ltr"><b>Applications and Model Compatibility:</b></p><p dir="ltr">The dataset is optimized for use in supervised learning workflows and has been tested with algorithms such as:</p><p dir="ltr">Gradient Boosting Machines (GBM),</p><p dir="ltr">Support Vector Machines (SVM-RBF),</p><p dir="ltr">Random Forests, and</p><p dir="ltr">Principal Component Analysis (PCA) for feature reduction.…”