Wolframin Degradome Foundation Atlas by Axel Petzold (7076261)

“…To extract, use the following command in a bash terminal:</p><pre><pre>tar -xvJf Wolframin_Degradome_Foundation_Atlas_v3.tar.gz<br></pre></pre><p dir="ltr"><b>Codes</b><br>Dataset generation is fully reproducible using three open-source tools: <b>Python, BLAST, and SAS</b>. …”

Supervised Classification of Burned Areas Using Spectral Reflectance and Machine Learning by 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. …”

Knowledge Graph validation using SHACL Shapes by Ángel Iglesias Préstamo (19745767)

“…Leveraging Rust’s performance and safety features, rudof provides efficient validation tools and Python bindings for integration with data science workflows. …”

Endothelial cells stably infected with recombinant Kaposi’s sarcoma-associated herpesvirus display distinct viscoelastic and morphological properties by Majahonkhe Shabangu (17340538)

“…<p dir="ltr">Data and code supporting work presented in article.</p><p dir="ltr">File: 01_preprocessing_macro.ijm</p><p dir="ltr">Description: A simplified macro outlining time-lapse image pre-processing prior to mitochondria-tracking analysis in TrackMate (Image J). …”

Learning States by Rui Varandas (11900993)

“…<br></p><p dir="ltr"><b>HCI features</b> encompass keyboard, mouse, and screenshot data. Below is a Python code snippet for extracting screenshot files from the screenshots CSV file.​…”

Trustworthy and Ethical AI for Intrusion Detection in Healthcare IoT (IoMT) Systems: An Agentic Decision Loop Framework by 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. …”

<b>InterHub: A Naturalistic Trajectory Dataset with Dense Interaction for Autonomous Driving</b> by Xiyan Jiang (20320758)

“…</b></li></ul><p dir="ltr">The Python codes used to process and analyze the dataset can be found at <a href="https://github.com/zxc-tju/InterHub" rel="noreferrer" target="_blank">https://github.com/zxc-tju/InterHub</a>. …”

Horuss Research: methodology for validating unstructured data using large language models by Fabiano Castello (6803171)

Replication Package: "The SBOM Gap: Adoption and Compliance in Open Source Software" by anonymous author (14191683)

“…<p dir="ltr">Replication Package Structure:</p><p dir="ltr">The replication package contains all data and scripts necessary to reproduce the analyses and results presented in this study.</p><p><br></p><p dir="ltr">replication_package/</p><p>│</p><p dir="ltr">├── data/</p><p dir="ltr">│ ├── sbom_repo_paths.csv # Repository paths and metadata for analyzed projects</p><p dir="ltr">│ ├── sbom_project_features.csv # Extracted features for SBOM projects</p><p dir="ltr">│ ├── non_sbom_project_features.csv # Extracted features for non-SBOM projects</p><p dir="ltr">│ └── SBOM_files/ # Raw SBOM files collected from selected repositories</p><p>│</p><p dir="ltr">└── code/</p><p dir="ltr"> ├── RQ1_regression/ # Scripts for regression analysis (RQ1)</p><p dir="ltr"> │ ├── regression.R # Main regression analysis script</p><p dir="ltr"> │ └── common.R # Shared functions for data filtering and formatting</p><p> │</p><p dir="ltr"> └── RQ2_compliance/ # Scripts for compliance and coverage checks (RQ2)</p><p dir="ltr"> ├── check_component_name.py</p><p dir="ltr"> ├── check_component_version.py</p><p dir="ltr"> ├── check_supplier.py</p><p dir="ltr"> ├── check_unique_identifiers.py</p><p dir="ltr"> ├── check_sbom_author.py</p><p dir="ltr"> ├── check_timestamp.py</p><p dir="ltr"> ├── check_dependency.py</p><p dir="ltr"> ├── check_hash.py</p><p dir="ltr"> ├── check_lifecycle_phase.py</p><p dir="ltr"> ├── check_license.py</p><p dir="ltr"> ├── check_vex.py</p><p dir="ltr"> ├── check_transitive_dependency.py</p><p dir="ltr"> ├── check_circular_dep.py</p><p dir="ltr"> └── check_all_7_min_req_files.py</p><p dir="ltr"><br></p><p dir="ltr"><br></p><p><br></p><p dir="ltr">Folder Descriptions:</p><p><br></p><p dir="ltr">data/: Contains datasets and raw SBOM files used in the analysis.…”

<b>Challenges and Strategies for the Management of Quality-Oriented Education Bases in Universities under Informatization Background</b> by Yang Yanxi (21571568)

“…Final codes, together with basic demographic attributes supplied by the institutions’ HR offices, were exported to Excel and cleaned in Python 3.10 using pandas 2.2.1 and numpy 1.26. …”

Table 1_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.docx by A. L. Back (20719049)

“…A total of 51 descriptors, including elongation and Fourier amplitudes, were extracted, compiled, and computed using Python. The descriptor computation code is provided as a library with this article. …”

Data Sheet 5_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…A total of 51 descriptors, including elongation and Fourier amplitudes, were extracted, compiled, and computed using Python. The descriptor computation code is provided as a library with this article. …”

Data Sheet 8_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…A total of 51 descriptors, including elongation and Fourier amplitudes, were extracted, compiled, and computed using Python. The descriptor computation code is provided as a library with this article. …”

Data Sheet 4_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…A total of 51 descriptors, including elongation and Fourier amplitudes, were extracted, compiled, and computed using Python. The descriptor computation code is provided as a library with this article. …”

Data Sheet 9_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…A total of 51 descriptors, including elongation and Fourier amplitudes, were extracted, compiled, and computed using Python. The descriptor computation code is provided as a library with this article. …”

Supplementary Material to '<i>Mechanical instabilities and snapping phenomena in helical rods with perversion</i>' by 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.…”

Data Sheet 15_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…A total of 51 descriptors, including elongation and Fourier amplitudes, were extracted, compiled, and computed using Python. The descriptor computation code is provided as a library with this article. …”

Data Sheet 13_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…A total of 51 descriptors, including elongation and Fourier amplitudes, were extracted, compiled, and computed using Python. The descriptor computation code is provided as a library with this article. …”

Data Sheet 2_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv by A. L. Back (20719049)

“…A total of 51 descriptors, including elongation and Fourier amplitudes, were extracted, compiled, and computed using Python. The descriptor computation code is provided as a library with this article. …”

Supplementary materials to 'Critical phenomena in helical rods with perversion' by 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.…”