Data Sheet 1_Triglyceride-glucose index and mortality in congestive heart failure with diabetes: a machine learning predictive model.doc by Lin Yu (221619)

“…Subgroup analyses were conducted based on age, gender, chronic pulmonary disease, atrial fibrillation, hypertension, and mechanical ventilation to assess the robustness of our findings. Feature selection was performed using LASSO regression, and predictive modeling was carried out using machine learning algorithms.…”

Image 1_A real-world pharmacovigilance study of Sorafenib based on the FDA Adverse Event Reporting System.tif by Dongdong Zhang (644246)

“…Disproportionality analysis was performed using robust algorithms for effective data mining to quantify the signals associated with Sorafenib-related AEs.…”

Table 2_A real-world pharmacovigilance study of Sorafenib based on the FDA Adverse Event Reporting System.docx by Dongdong Zhang (644246)

“…Disproportionality analysis was performed using robust algorithms for effective data mining to quantify the signals associated with Sorafenib-related AEs.…”

Table 5_A real-world pharmacovigilance study of Sorafenib based on the FDA Adverse Event Reporting System.docx by Dongdong Zhang (644246)

“…Disproportionality analysis was performed using robust algorithms for effective data mining to quantify the signals associated with Sorafenib-related AEs.…”

Table 3_A real-world pharmacovigilance study of Sorafenib based on the FDA Adverse Event Reporting System.docx by Dongdong Zhang (644246)

“…Disproportionality analysis was performed using robust algorithms for effective data mining to quantify the signals associated with Sorafenib-related AEs.…”

Table 1_A real-world pharmacovigilance study of Sorafenib based on the FDA Adverse Event Reporting System.doc by Dongdong Zhang (644246)

“…Disproportionality analysis was performed using robust algorithms for effective data mining to quantify the signals associated with Sorafenib-related AEs.…”

Table 4_A real-world pharmacovigilance study of Sorafenib based on the FDA Adverse Event Reporting System.docx by Dongdong Zhang (644246)

“…Disproportionality analysis was performed using robust algorithms for effective data mining to quantify the signals associated with Sorafenib-related AEs.…”

Table 6_A real-world pharmacovigilance study of Sorafenib based on the FDA Adverse Event Reporting System.docx by Dongdong Zhang (644246)

“…Disproportionality analysis was performed using robust algorithms for effective data mining to quantify the signals associated with Sorafenib-related AEs.…”

Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method by Tao Huang (110613)

“…Among the GRS models, the extreme gradient boosting (XGBoost) model demonstrated superior discriminative performance (AUC = 0.837). The XGBoost’s optimal robustness was also validated through five-fold cross-validation (mean ROC-AUC = 0.706). …”

Data Sheet 1_Machine learning based assessment of hoarseness severity: a multi-sensor approach centered on high-speed videoendoscopy.docx by Tobias Schraut (21488108)

“…A videoendoscopic model was developed by selecting a suitable classification algorithm and a minimal-optimal subset of glottal parameters. …”

DataSheet_1_DNA Damage Response Evaluation Provides Novel Insights for Personalized Immunotherapy in Glioma.pdf by Mu Chen (290262)

“…Immunohistochemistry was used to determine the protein levels of PD-L1 and TGFβ in glioma specimens. …”

Table 1_The future of critical care: AI-powered mortality prediction for acute variceal gastrointestinal bleeding and acute non-variceal gastrointestinal bleeding patients.docx by Zhou Liu (1506679)

“…The model performance was evaluated using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). …”

IUTF Dataset(Enhanced): Enabling Cross-Border Resource for Analysing the Impact of Rainfall on Urban Transportation Systems by Xuhui Lin (19505503)

“…</p><h2>Data Structure</h2><p dir="ltr">The dataset is organized into four primary components:</p><ol><li><b>Road Network Data</b>: Topological representations including spatial geometry, functional classification, and connectivity information</li><li><b>Traffic Sensor Data</b>: Sensor metadata, locations, and measurements at both 5-minute and hourly resolutions</li><li><b>Precipitation Data</b>: Hourly meteorological information with spatial grid cell metadata</li><li><b>Derived Analytical Matrices</b>: Pre-computed structures for advanced spatial-temporal modelling and network analyses</li></ol><h2>File Formats</h2><ul><li><b>Tabular Data</b>: Apache Parquet format for optimal compression and fast query performance</li><li><b>Numerical Matrices</b>: NumPy NPZ format for efficient scientific computing</li><li><b>Total Size</b>: Approximately 2 GB uncompressed</li></ul><h2>Applications</h2><p dir="ltr">The IUTF dataset enables diverse analytical applications including:</p><ul><li><b>Traffic Flow Prediction</b>: Developing weather-aware traffic forecasting models</li><li><b>Infrastructure Planning</b>: Identifying vulnerable network components and prioritizing investments</li><li><b>Resilience Assessment</b>: Quantifying system recovery curves, robustness metrics, and adaptive capacity</li><li><b>Climate Adaptation</b>: Supporting evidence-based transportation planning under changing precipitation patterns</li><li><b>Emergency Management</b>: Improving response strategies for weather-related traffic disruptions</li></ul><h2>Methodology</h2><p dir="ltr">The dataset creation involved three main stages:</p><ol><li><b>Data Collection</b>: Sourcing traffic data from UTD19, road networks from OpenStreetMap, and precipitation data from ERA5 reanalysis</li><li><b>Spatio-Temporal Harmonization</b>: Comprehensive integration using novel algorithms for spatial alignment and temporal synchronization</li><li><b>Quality Assurance</b>: Rigorous validation and technical verification across all cities and data components</li></ol><h2>Code Availability</h2><p dir="ltr">Processing code is available at: https://github.com/viviRG2024/IUTDF_processing</p>…”