Data Sheet 1_Novel deep learning-based prediction of HER2 expression in breast cancer using multimodal MRI, nomogram, and decision curve analysis.docx by Shi Qiu (425335)

“…Nomogram construction, ROC analysis, and DCA evaluation were performed to assess model performance. Statistical analyses were conducted using Python and R, with significance set at p < 0.05.…”

face recognation with Flask by Muammar, SST, M.Kom (21435692)

“…Built using the <b>Flask</b> web framework (Python), this system provides a lightweight and scalable solution for implementing facial recognition capabilities in real-time or on-demand through a browser interface.…”

Single Cell DNA methylation data for Human Brain altas MajorType allc files (CG+CH) by Wubin Ding (11823941)

Single Cell DNA methylation data for Human Brain altas (MajorType+Region CG allc files) by Wubin Ding (11823941)

Replication Package by Fatemeh Hadadi (14651705)

Image 1_Differential diagnosis of pneumoconiosis mass shadows and peripheral lung cancer using CT radiomics and the AdaBoost machine learning model.tif by Xiaobing Li (291454)

“…LR, SVM, and AdaBoost algorithms were implemented using Python to build the models. In the training set, the accuracies of the LR, SVM, and AdaBoost models were 79.4, 84.0, and 80.9%, respectively; the sensitivities were 74.1, 74.1, and 81.0%, respectively; the specificities were 83.6, 91.8, and 80.8%, respectively; and the AUC values were 0.837, 0.886, and 0.900, respectively. …”

Image 2_Differential diagnosis of pneumoconiosis mass shadows and peripheral lung cancer using CT radiomics and the AdaBoost machine learning model.tif by Xiaobing Li (291454)

“…LR, SVM, and AdaBoost algorithms were implemented using Python to build the models. In the training set, the accuracies of the LR, SVM, and AdaBoost models were 79.4, 84.0, and 80.9%, respectively; the sensitivities were 74.1, 74.1, and 81.0%, respectively; the specificities were 83.6, 91.8, and 80.8%, respectively; and the AUC values were 0.837, 0.886, and 0.900, respectively. …”

Collaborative Research: Framework: Improving the Understanding and Representation of Atmospheric Gravity Waves using High-Resolution Observations and Machine Learning by Joan Alexander (17047170)

“…Establishing a framework for implementing and testing ML-based parameterizations in atmospheric models. …”

Data&Codes.zip by Zer0 Star (20545655)

Genosophus: A Dynamical-Systems Diagnostic Engine for Neural Representation Analysis by Alan Glanz (22109698)

“…</b><code><strong>GenosophusV2.py</strong></code></h3><p dir="ltr">Executable Python implementation of the Genosophus Engine.</p><h3><b>2. …”

<b>Code and derived data for</b><b>Training Sample Location Matters: Accuracy Impacts in LULC Classification</b> by Pajtim Zariqi (22155799)

“…<p dir="ltr">This repository contains the analysis code and derived outputs for the study <i>“Training Sample Location Matters: Accuracy Impacts in LULC Classification”</i>. The workflow was implemented in Google Earth Engine (JavaScript API) and replicated in Python notebooks (Jupyter/Kaggle) for reproducibility.…”

Missing Value Imputation in Relational Data Using Variational Inference by Simon Fontaine (7046618)

“…Additional results, implementation details, a Python implementation, and the code reproducing the results are available online. …”

DeepB3Pred - code by Saleh Musleh (22263718)

“…MCTD is the implementation of composition-transition and distribution, and GSFE is the implementation of graphical and statistical-based feature engineering.…”

SpatialKNifeY analysis landscape. by Shunsuke A. Sakai (13789939)

“…<p>(A) The concept of the extension from spatial omics data and spatial domain to the microenvironment. (B) Implementation of SpatialKNifeY (SKNY). A Python library of SKNY depends on stlearn [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012854#pcbi.1012854.ref023" target="_blank">23</a>] and scanpy [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012854#pcbi.1012854.ref009" target="_blank">9</a>] functions (see “Methods”) and AnnData object programming [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1012854#pcbi.1012854.ref010" target="_blank">10</a>]. …”

Parallel Sampling of Decomposable Graphs Using Markov Chains on Junction Trees by Mohamad Elmasri (19421498)

“…We find that our parallel sampler yields improved mixing properties in comparison to the single-move variate, and outperforms current state-of-the-art methods in terms of accuracy and computational efficiency. The implementation of our work is available in the Python package parallelDG. …”

<b>DeepB3Pred</b> by Saleh Musleh (22263718)

“…MCTD is the implementation of composition-transition and distribution, and GSFE is the implementation of graphical and statistical-based feature engineering.…”

<b>Altered cognitive processes shape tactile perception in autism.</b> (data) by Ourania Semelidou (19178362)

“…The perceptual decision-making setup was controlled by Bpod (Sanworks) through scripts in Python (PyBpod, https://pybpod.readthedocs.io/en/latest/). …”

Supporting data for "Software library to quantify the value of forecasts for decision-making: Case study on sensitivity to damages" by Laugesen et al. (2025) by Richard Laugesen (6480371)

“…<br></p><p dir="ltr">Journal paper introduces RUVPY, a Python software library which implements the Relative Utility Value (RUV) method. …”

DeepB3Pred_source_code by Muhammad Arif (17012472)

“…Testing dataset TS_BB includes BBB_pos and BBB_neg testing samples</p><p dir="ltr">**Feature_Extraction: CPSR is the implementation of component protein sequence representation. mctd is the implementation of composition-transition and distribution, GSFE is the implementation of Graphical and statistical-based feature engineering.…”

Global Aridity Index and Potential Evapotranspiration (ET0) Database: Version 3.1 by Robert Zomer (12796235)

“…The database also includes three averaged multi-model ensembles produced for each of the four emission scenarios:</p><p>**************************************************************************************************************************</p><p dir="ltr">The Global Aridity Index (Global-AI) and Global Reference Evapo-Transpiration (Global-ET0) datasets provided in Version 3.1 of the Global Aridity Index and Potential Evapo-Transpiration (ET0) Database (Global-AI_PET_v3.x1) provide high-resolution (30 arc-seconds) global raster data for the 1970-2000 period, related to evapotranspiration processes and rainfall deficit for potential vegetative growth, based upon implementation of the FAO-56 Penman-Monteith Reference Evapotranspiration (ET₀) equation.…”