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 حسب 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 حسب 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 15_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv حسب 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 حسب 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 حسب 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 3_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv حسب 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 10_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv حسب 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 14_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv حسب 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. …"

Table 2_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.xlsx حسب 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 11_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv حسب 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 1_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv حسب 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 12_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv حسب 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 7_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv حسب 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 6_From rocks to pixels: a comprehensive framework for grain shape characterization through the image analysis of size, orientation, and form descriptors.csv حسب 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. …"

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.…"

ML model for prediction of postpartum rehospitalization in pregnant women/new mothers using readily obtainable pre-pregnancy or early pregnancy sociodemographic and health determin... حسب Martin Frasch (5754731)

"…</li><li>This model delivers a 3,492% ROI over 5 years with $325,080 annual net benefit per 10,000 deliveries in the U.S.A.</li><li>Here, we present an open-access Python code including the ML model for inference to facilitate prospective utilization of the developed model and further study of the nuMoM2b and similar datasets with machine learning approaches.…"

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. …"

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

"…</p><p dir="ltr">1．<b>Python environment</b>: requirements.txt</p><p dir="ltr">2．…"

Leveraging Large Language Models as Requirements Elicitation Interview Bots-all data حسب Samuel Gorsch (19947618)

"…<p dir="ltr"><b>Title:</b> Code and Supplementary Files for "Leveraging Large Language Models as Requirements Elicitation Interview Bots"</p><p dir="ltr"><b>Description:</b><br>This repository contains all code, supplementary plots, and select data files used in the master’s thesis, "Leveraging Large Language Models (LLMs) as Requirements Elicitation Interview Bots." …"

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. …"