<b>MSLU-100K: A multi-source land use dataset of Chinese major cities</b> حسب Yao Yao (7903457)

"…<p dir="ltr">The project includes the code of a deep learning model related to the paper "MSLU-100K: A Multi-Source Land Use Dataset for Major Cities in China". …"

Supervised Classification of Burned Areas Using Spectral Reflectance and Machine Learning حسب Baptista Boanha (22424668)

"…<p dir="ltr">This dataset and code package presents a modular framework for supervised classification of burned and unburned land surfaces using satellite-derived spectral reflectance. …"

Moulin distributions during 2016-2021 on the southwest Greenland Ice Sheet حسب Kang Yang (7323734)

"…</p><p><br></p><ul><li>00_Satellite-derived moulins: Moulins directly mapped from Sentinel-2 imagery, representing actual moulin positions;</li><li>01_Snapped moulins: Moulins snapped to DEM-modeled supraglacial drainage networks, primarily used for analyses;</li><li>02_Moulin recurrences: Recurring moulins determined from the snapped moulins;</li><li>03_Internally drained catchments: Internally drained catchment (IDC) associated with each moulin;</li><li>04_Surface meltwater runoff: surface meltwater runoff calculated from MAR for the study area, elevation bins, and IDCs; </li><li>05_DEM-derived: Topographic features modeled from ArcticDEM, including elevation bins, depressions and drainage networks;</li><li>06_GWR: Variables for conducting geographically weighted regression (GWR) analysis;</li></ul><p><br></p><ul><li>Code_01_Mapping moulins on the southwestern GrIS.ipynb: A Jupyter Notebook to analyze moulin distributions, reproducing most of the analyses and figures presented in the manuscript using the provided datasets;</li><li>Code_02_pre1_calculate Strain Rate from XY ice velocity.py: A preprocessing Python script to calculate strain rate for the GWR analysis;</li><li>Code_02_pre2_calculate Driving Stress from ice thickness and surface slope.py: A preprocessing Python script to calculate driving stress for the GWR analysis;</li><li>Code_02_GWR analysis.ipynb: A Jupyter Notebook to conduct the GWR analysis using the provided datasets.…"

Unfiltered TCR beta chain calls for 463 cancer samples and 587 control subjects حسب Yilong Li (20428445)

"…The columns are as follows.</p><ul><li><code>v_gene</code>: V gene of each TCR clonotype</li><li><code>j_gene</code>: J gene of each TCR clonotype</li><li><code>cdr3_nt</code>: Nucleotide sequence over the CDR3 region</li><li><code>cdr3</code>: Amino acid sequence over the CDR3 region</li><li><code>templates</code>: Number of UMIs supporting the clonotype</li><li><code>sample_name</code>: Sample that the clonotype derived from.…"

<b>Alpha-Synuclein Degradome Foundation Atlas</b> حسب Axel Petzold (7076261)

"…</p><p dir="ltr">Whether your work involves biomarker development, precision neurology, or machine learning, this dataset provides structured, labelled inputs that are ideal for:</p><ul><li>Training supervised models to detect or predict cleavage sites</li><li>Feature extraction from protein sequences</li><li>Clustering or classification of fragment types by mutation or disease context</li><li>Integrating with omics data for multimodal prediction tasks</li></ul><p dir="ltr">Dataset Features:</p><ul><li>Annotated α-synuclein proteolytic fragments</li><li>Includes wild-type and clinically relevant variants</li><li>Tab-delimited ASCII format for compatibility with Python, R, and ML frameworks</li><li>Linked SAS and Python scripts for pipeline reproducibility and updates</li><li>Ready-to-use for computational modelling, AI training, and bioinformatics workflows</li></ul><p dir="ltr">The dataset was generated using a reproducible codes involving Python, BLAST, and SAS. …"

Bayesian Changepoint Detection via Logistic Regression and the Topological Analysis of Image Series حسب Andrew M. Thomas (712104)

"…<p>We present a Bayesian method for multivariate changepoint detection that allows for simultaneous inference on the location of a changepoint and the coefficients of a logistic regression model for distinguishing pre-changepoint data from post-changepoint data. …"

Trustworthy and Ethical AI for Intrusion Detection in Healthcare IoT (IoMT) Systems: An Agentic Decision Loop Framework حسب ibrahim adabara (22107287)

"…</p><h2>️ Repository Structure</h2><pre><pre>agentic-ethical-ids-healthcare/<br>│<br>├── src/ # Source code for model, rule engine, and agent<br>│ ├── train_agent.py<br>│ ├── ethical_engine.py<br>│ ├── detector_model.py<br>│ └── utils/<br>│<br>├── data/ # Links or sample data subsets<br>│ ├── CIC-IoMT-2024/ <br>│ └── CSE-CIC-IDS2018/<br>│<br>├── notebooks/ # Jupyter notebooks for training and analysis<br>│<br>├── models/ # Pretrained model checkpoints (.pth, .pkl)<br>│<br>├── results/ # Evaluation outputs and figures<br>│<br>├── requirements.txt # Python dependencies<br>├── LICENSE # MIT License for open research use<br>└── README.md # Project documentation<br></pre></pre><h2>⚙️ Setup and Installation</h2><p dir="ltr">Clone the repository and set up your environment:</p><pre><pre>git clone https://github.com/ibrahimadabara01/agentic-ethical-ids-healthcare.git<br>cd agentic-ethical-ids-healthcare<br>python -m venv venv<br>source venv/bin/activate # On Windows: venv\Scripts\activate<br>pip install -r requirements.txt<br></pre></pre><h2> Datasets</h2><p dir="ltr">This project uses three datasets:</p><table><tr><th><p dir="ltr">Dataset</p></th><th><p dir="ltr">Purpose</p></th><th><p dir="ltr">Source</p></th></tr><tr><td><b>CIC-IoMT 2024</b></td><td><p dir="ltr">Primary IoMT intrusion detection dataset</p></td><td><a href="https://www.unb.ca/cic/datasets/index.html" rel="noopener" target="_new">Canadian Institute for Cybersecurity</a></td></tr><tr><td><b>CSE-CIC-IDS2018</b></td><td><p dir="ltr">Domain-shift evaluation</p></td><td><a href="https://www.unb.ca/cic/datasets/ids-2018.html" rel="noopener" target="_new">CIC Dataset Portal</a></td></tr><tr><td><b>MIMIC-IV (Demo)</b></td><td><p dir="ltr">Clinical context signals</p></td><td><a href="https://physionet.org/content/mimic-iv-demo/2.2/" rel="noopener" target="_new">PhysioNet</a></td></tr></table><blockquote><p dir="ltr">⚠️ Note: All datasets are publicly available. …"

Ambient Air Pollutant Dynamics (2010–2025) and the Exceptional Winter 2016–17 Pollution Episode: Implications for a Uranium/Arsenic Exposure Event حسب Thomas Clemens Carmine (19756929)

"…Includes imputation statistics, data dictionary, and the Python imputation code (Imputation_Air_Pollutants_NABEL.py). …"

entity-poster.pdf حسب Evgeny Gorbunov (21771560)

"…<p dir="ltr">We update on status of development Entity Toolkit, a next-generation Particle-in-Cell (PIC) code designed to model plasmas in extreme astrophysical environments, such as black hole accretion disks and jets, neutron star magnetospheres, pulsar winds, and intracluster media. …"

Vector-to-Image Converted Building Footprints or Building Change Detection حسب anonymous (20480480)

"…And we explore a more explainable multi-stage workflow of deep models.</p><p dir="ltr">1．<b>Python environment</b>: requirements.txt</p><p dir="ltr">2．…"

Influence of vibrational motion and temperature on Interatomic Coulombic electron capture - Dataset حسب Elena M. Jahr (16313754)

"…The python libraries <code>matplotlib</code>, <code>numpy</code> and <code>scipy</code> are needed.…"

LNP drug delivery image data حسب Philip Harrison (8899735)

"…</div><div><br></div><div><u>Python code:</u></div><div><a href="https://github.com/pharmbio/phil_LNP_modelling">https://github.com/pharmbio/phil_LNP_modelling</a><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div><br></div><p></p>…"

Supplementary Material for review (<b>Revealing the co-occurrence patterns of public emotions from social media data</b>) حسب Yang Hua (21399140)

"…</p><p dir="ltr">This document provides a detailed explanation of how to reproduce all experimental results, figures and tables presented in the paper, and the key indicators in the abstract by using the shared datasets and source code. …"

<b>EEG dataset for multi-class Chinese character stroke and pinyin vowel handwriting imagery (16 subjects, CCS-HI & SV-HI)</b> حسب Fan Wang (22125886)

"…Preprocessing & Trial Integrity</h3><p dir="ltr">The publicly released dataset contains raw EEG data (no preprocessing); preprocessing (via MNE-Python, code in code folder) was only conducted for model training/testing: 1–40 Hz Butterworth bandpass filtering + 50 Hz notch filtering for noise reduction, manual bad channel labeling (EEGLAB) and spherical spline interpolation (per BIDS _channels.tsv), downsampling from 1000 Hz to 250 Hz, z-score normalization per trial, and epoch extraction of the 0–4 s imagery period (for both tasks). …"

Supplementary material for "Euler inversion: Locating sources of potential-field data through inversion of Euler's homogeneity equation" حسب Leonardo Uieda (97471)

"…<p dir="ltr">This repository contains the data and source code used to produce the results presented in:</p><blockquote><p dir="ltr">Uieda, L., Souza-Junior, G. …"

SRL OF TIM حسب Jefferson Rodrigo Speck (21510347)

"…</li><li><code><strong>plot_scripts/</strong></code>: Includes data files and Python scripts used to generate the visualizations presented in the review (e.g., bar charts, pie charts, distribution graphs).…"

Void-Center Galaxies and the Gravity of Probability Framework: Pre-DESI Consistency with VGS 12 and NGC 6789 حسب Jordan Waters (21620558)

"…<br><br><br><b>ORCID ID: https://orcid.org/0009-0009-0793-8089</b><br></p><p dir="ltr"><b>Code Availability:</b></p><p dir="ltr"><b>All Python tools used for GoP simulations and predictions are available at:</b></p><p dir="ltr"><b>https://github.com/Jwaters290/GoP-Probabilistic-Curvature</b><br><br>The Gravity of Probability framework is implemented in this public Python codebase that reproduces all published GoP predictions from preexisting DESI data, using a single fixed set of global parameters. …"

Supplementary Material to '<i>Mechanical instabilities and snapping phenomena in helical rods with perversion</i>' حسب Émilien Dilly (20415464)

"…</p><p dir="ltr"><b>Shooting calculation</b>: Shooting method, the Python code.</p><p dir="ltr"><b>Supplemental video 1</b>: Video illustrating a chirality inversion in the biphasic model, with plots in both the <i>(z,n)</i> and <i>(kappa,tau)</i> planes.…"

Supplementary materials to 'Critical phenomena in helical rods with perversion' حسب Émilien Dilly (20415464)

"…</p><p dir="ltr"><b>Shooting calculation</b>: Shooting method, the Python code.</p><p dir="ltr"><b>Supplemental video 1</b>: Video illustrating a chirality inversion in the biphasic model, with plots in both the <i>(z,n)</i> and <i>(kappa,tau)</i> planes.…"

<b>Myelin oligodendrocyte glycoprotein (MOG) Degradome Foundation Atlas</b> حسب Axel Petzold (7076261)

"…</li></ul><h3>Reproducibility and Code Availability</h3><p dir="ltr">Dataset generation is fully reproducible using open-source tools:</p><ul><li>Python</li><li>SAS</li></ul><p dir="ltr">All required scripts are included in the repository and are well documented to support local replication and custom adaptations. …"