Image 1_Identification and validation of biomarkers, construction of diagnostic models, and investigation of immunological infiltration characteristics for idiopathic frozen should... by Han-tao Jiang (21724118)

“…At the outset, we conducted differential expression analysis, weighted gene co-expression network analysis (WGCNA), and utilized the cytoHubba plugin, complemented by two machine learning algorithms, receiver operating characteristic (ROC) analysis, and expression level evaluation to identify biomarkers for FS. …”

Table 3_Identification and validation of biomarkers, construction of diagnostic models, and investigation of immunological infiltration characteristics for idiopathic frozen should... by Han-tao Jiang (21724118)

“…At the outset, we conducted differential expression analysis, weighted gene co-expression network analysis (WGCNA), and utilized the cytoHubba plugin, complemented by two machine learning algorithms, receiver operating characteristic (ROC) analysis, and expression level evaluation to identify biomarkers for FS. …”

Table 1_Identification and validation of biomarkers, construction of diagnostic models, and investigation of immunological infiltration characteristics for idiopathic frozen should... by Han-tao Jiang (21724118)

“…At the outset, we conducted differential expression analysis, weighted gene co-expression network analysis (WGCNA), and utilized the cytoHubba plugin, complemented by two machine learning algorithms, receiver operating characteristic (ROC) analysis, and expression level evaluation to identify biomarkers for FS. …”

Table 2_Identification and validation of biomarkers, construction of diagnostic models, and investigation of immunological infiltration characteristics for idiopathic frozen should... by Han-tao Jiang (21724118)

“…At the outset, we conducted differential expression analysis, weighted gene co-expression network analysis (WGCNA), and utilized the cytoHubba plugin, complemented by two machine learning algorithms, receiver operating characteristic (ROC) analysis, and expression level evaluation to identify biomarkers for FS. …”

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

“…</li></ul><h3>Analysis Scripts</h3><p dir="ltr">Complete set of R scripts for reproducing all analyses:</p><ul><li><b>percent cost increase_line plot.R</b>: Creates visualizations of energy cost impacts under different conservation scenarios</li><li><b>Zonation curves.R</b>: Generates conservation performance curves and coverage statistics</li><li><b>NPV_bar_plot.R</b>: Produces economic analysis plots with Net Present Value breakdowns</li><li><b>domestic_export_map_iterations.R</b>: Creates spatial maps of renewable energy infrastructure for domestic and export scenarios</li></ul><h2>Technical Specifications</h2><h3>Data Formats</h3><ul><li><b>Spatial Data</b>: ESRI Geodatabase (.gdb), Shapefile (.shp), GeoTIFF (.tif)</li><li><b>Tabular Data</b>: CSV, Microsoft Excel (.xlsx, .xls)</li><li><b>Analysis Code</b>: R scripts (.R)</li></ul><h3>Software Requirements</h3><ul><li><b>R</b> (≥4.0.0) with packages: sf, dplyr, ggplot2, readr, readxl, tidyr, furrr, ozmaps, ggpattern</li><li><b>ESRI ArcGIS</b> or <b>QGIS</b> for geodatabase access and spatial analysis</li><li><b>Zonation</b> conservation planning software (for methodology understanding)</li></ul><h3>Hardware Recommendations</h3><ul><li><b>RAM</b>: 16GB minimum (32GB recommended for full spatial analysis)</li><li><b>Storage</b>: 15GB free space for data extraction and processing</li><li><b>CPU</b>: Multi-core processor recommended for parallel processing scripts</li></ul><h2>Detailed Description of the VRE Siting and Cost-Minimization Model</h2><p><br></p><p dir="ltr">This section provides an in-depth description of the Variable Renewable Energy (VRE) siting model, including the software, the core algorithm, and the optimisation process used to determine the least-cost, spatially constrained development trajectory for VRE infrastructure in Queensland, Australia.…”

Supplementary file 1_A real-world disproportionality analysis of FDA adverse event reporting system (FAERS) events for lecanemab.docx by Linlin Yan (4480570)

“…Using the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS) algorithms, we conducted a comprehensive analysis of lecanemab-related AEs, restricting the analysis to AEs with the role code of primary suspect (PS).…”

Data Sheet 1_Forestry climate adaptation with HarvesterSeasons service—a gradient boosting model to forecast soil water index SWI from a comprehensive set of predictors in Destinat... by Mikko Strahlendorff (20458865)

“…<p>Soil wetness forecasts on a local level are needed to ensure sustainable forestry operations during summer when the soil is neither frozen nor covered with snow. …”

AP-2α 相关研究 by Ya-Hong Wang (21080642)

“…(C) Disease index and severity grading of cotton seedlings infected with the wild-type, VdAP-2α mutant, and complemented strains. Disease severity was graded based on the number of wilted leaves: 0 (no wilted leaves), 1 (one wilted leaf), 2 (two wilted leaves), and 3 (more than three wilted leaves or plant death). …”

Table 1_Using machine learning to predict the rupture risk of multiple intracranial aneurysms.xlsx by Junqiang Feng (10300150)

“…Therefore, we constructed a risk prediction model for the rupture of MIAs by machine learning algorithms.</p>…”

Table 1_Assessing the diagnostic value of qPCR for Trichuris trichiura: sub-analysis of a multi-country clinical trial to determine the efficacy of albendazole compared to an alben... by Pedro E. Fleitas (8704023)

“…While not interchangeable, qPCR complements KK and enhances the precision of drug efficacy evaluation in helminth clinical trials.…”

Quantitatively linking morphology and optical response of individual silver nanohedra - data by Zoltan Sztranyovszky (19655521)

“…</p><p>This dataset complements the article cited above and the associated supplementary information. …”

<b>Rethinking neighbourhood boundaries for urban planning: A data-driven framework for perception-based delineation</b> by Shubham Pawar (22471285)

“…</p><p dir="ltr"><b>Reference:</b> <code>cluster_perceptions.py</code> script logic</p><p dir="ltr">Stages:</p><ul><li>(a) Data input</li><li>(b) K-nearest neighbours spatial weights matrix</li><li>(c) Initial agglomerative clustering</li><li>(d) Merging small clusters</li><li>(e-f) Iterative splitting of largest clusters</li></ul><h4>Figure 7: Predicted Urban Perception Scores</h4><p dir="ltr">ET cell-level perception scores across the study area.…”

Integrating urinary metabolomics and clinical datasets for multi-cancer detection by Dongyong Lee (18786694)

“…</p><p dir="ltr">- `group_code`: short group code </p><p dir="ltr"> - e.g., `Normal`, `HTN`, `DM`, `HTN+DM`, `CRC`, `LungCA`, `PancreasCA`.…”

Data Sheet 2_Interplay between tumor mutation burden and the tumor microenvironment predicts the prognosis of pan-cancer anti-PD-1/PD-L1 therapy.pdf by Wuyuan Liao (21776432)

“…Additionally, we employed algorithms such as CIBERSORT and ESTIMATE to assess the correlation between the risk score and TME components. …”

Image 6_Interplay between tumor mutation burden and the tumor microenvironment predicts the prognosis of pan-cancer anti-PD-1/PD-L1 therapy.tif by Wuyuan Liao (21776432)

“…Additionally, we employed algorithms such as CIBERSORT and ESTIMATE to assess the correlation between the risk score and TME components. …”

Table 1_Interplay between tumor mutation burden and the tumor microenvironment predicts the prognosis of pan-cancer anti-PD-1/PD-L1 therapy.xlsx by Wuyuan Liao (21776432)

“…Additionally, we employed algorithms such as CIBERSORT and ESTIMATE to assess the correlation between the risk score and TME components. …”

Image 4_Interplay between tumor mutation burden and the tumor microenvironment predicts the prognosis of pan-cancer anti-PD-1/PD-L1 therapy.tif by Wuyuan Liao (21776432)

“…Additionally, we employed algorithms such as CIBERSORT and ESTIMATE to assess the correlation between the risk score and TME components. …”

Image 7_Interplay between tumor mutation burden and the tumor microenvironment predicts the prognosis of pan-cancer anti-PD-1/PD-L1 therapy.tif by Wuyuan Liao (21776432)

“…Additionally, we employed algorithms such as CIBERSORT and ESTIMATE to assess the correlation between the risk score and TME components. …”

Data Sheet 4_Interplay between tumor mutation burden and the tumor microenvironment predicts the prognosis of pan-cancer anti-PD-1/PD-L1 therapy.pdf by Wuyuan Liao (21776432)

“…Additionally, we employed algorithms such as CIBERSORT and ESTIMATE to assess the correlation between the risk score and TME components. …”

Data Sheet 1_Interplay between tumor mutation burden and the tumor microenvironment predicts the prognosis of pan-cancer anti-PD-1/PD-L1 therapy.docx by Wuyuan Liao (21776432)

“…Additionally, we employed algorithms such as CIBERSORT and ESTIMATE to assess the correlation between the risk score and TME components. …”