The Pseudo-Code of the IRBMO Algorithm. by Chenyi Zhu (9383370)

S1 Data - by Haoqiang Liu (193675)

Pseudo Code of RBMO. by Chenyi Zhu (9383370)

Microsoft excel file containing exhaustive list of Abbreviated-Injury Scale codes used to generate binary variables encoding the presence or absence of injury to various body regio... by Michael D. Cobler-Lichter (22492930)

Calibration plots for all models on the Derivation cohort. by Agni Orfanoudaki (8878097)

Selecting an optimal machine learning classifier. by Ramu Anandakrishnan (294535)

Surround Suppression mechanism. by Elnaz Nemati (21402730)

Manhattan plot of the univariate <i>p</i>-values for body mass index. by Junyang Qian (9558026)

Manhattan plot of the univariate <i>p</i>-values for asthma. by Junyang Qian (9558026)

Manhattan plot of the univariate <i>p</i>-values for height. by Junyang Qian (9558026)

Manhattan plot of the univariate <i>p</i>-values for high cholesterol. by Junyang Qian (9558026)

Dual UHPLC-HRMS Metabolomics and Lipidomics and Automated Data Processing Workflow for Comprehensive High-Throughput Gut Phenotyping by P. Vangeenderhuysen (15854812)

“…The latter was benchmarked toward vendor-specific targeted and untargeted software and our isotopologue parameter optimization/XCMS-based untargeted pipeline in LifeLines Deep cohort samples (<i>n</i> = 97). …”

Algoritmo de clasificación de expresiones de odio por tipos en español (Algorithm for classifying hate expressions by type in Spanish) by Daniel Pérez Palau (11097348)

“…</li></ul><p dir="ltr"><b>File Structure</b></p><p dir="ltr">The code generates and saves:</p><ul><li>Weights of the trained model (.h5)</li><li>Configured tokenizer</li><li>Training history in CSV</li><li>Requirements file</li></ul><p dir="ltr"><b>Important Notes</b></p><ul><li>The model excludes category 2 during training</li><li>Implements transfer learning from a pre-trained model for binary hate detection</li><li>Includes early stopping callbacks to prevent overfitting</li><li>Uses class weighting to handle category imbalances</li></ul><p dir="ltr">The process of creating this algorithm is explained in the technical report located at: Blanco-Valencia, X., De Gregorio-Vicente, O., Ruiz Iniesta, A., & Said-Hung, E. (2025). …”

A. Top – 14 days of continuous glucose monitor (CGM) data for a patient that spends all of their time within an optimal glucose range. by Jamison H. Burks (21029607)

Establishment of MPM. by Amir Abbas Navidinia (21656136)

Validation of image processing steps and metric results using images of a microfluidic device. by Gavrielle R. Untracht (11820311)

tUbenet Foundation Model Weights by Natalie Holroyd (7099391)

DataSheet2_Concordance of the treatment patterns for major depressive disorders between the Canadian Network for Mood and Anxiety Treatments (CANMAT) algorithm and real-world pract... by Lu Yang (30847)

“…</p><p>Conclusion: Gap existed between clinical practice and AD algorithm. Improved access to evidence-based treatment is required, especially for optimized strategies during outpatient follow-up.…”

DataSheet1_Concordance of the treatment patterns for major depressive disorders between the Canadian Network for Mood and Anxiety Treatments (CANMAT) algorithm and real-world pract... by Lu Yang (30847)

“…</p><p>Conclusion: Gap existed between clinical practice and AD algorithm. Improved access to evidence-based treatment is required, especially for optimized strategies during outpatient follow-up.…”

Methodology block diagram. by Gahao Chen (21688843)

“…Six machine learning algorithms - Random Forest (RF), AdaBoost, Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Tabular Prior-data Fitted Network version 2.0 (TabPFN-V2) - were implemented with five-fold cross-validation to optimize model hyperparameters. …”