Comparative analysis of WordNet replacement impact on data distribution. by Enock Niyonkuru (21246879)

PyGMT – Accessing and Integrating GMT with Python and the Scientific Python Ecosystem (AGU24, U12B-05) by Yvonne Fröhlich (20442683)

“…PyGMT integrates smoothly within the Scientific Python ecosystem. In addition to standard file formats such as ASCII and NetCDF files, common Pythonic data structures for tabular and grid data such as numpy.ndarray, pandas.DataFrame, geopandas.GeoDataFrame, and xarray.DataArray are supported. …”

Table 1_Net2Brain: a toolbox to compare artificial vision models with human brain responses.pdf by Domenic Bersch (21248975)

“…To address these challenges, we present Net2Brain, a Python-based toolbox that provides an end-to-end pipeline for incorporating DNNs into neuroscience research, encompassing dataset download, a large selection of models, feature extraction, evaluation, and visualization. …”

ML model for prediction of postpartum rehospitalization in pregnant women/new mothers using readily obtainable pre-pregnancy or early pregnancy sociodemographic and health determin... by 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.…”

<b>VISION — an open-source software for automated multi-dimensional image analysis of cellular biophysics</b> by Erdinc Sezgin (10714689)

“…However, the few open-source packages available for processing of spectral images are limited in scope. Here, we present VISION, a stand-alone software based on Python for spectral analysis with improved applicability. …”

Comparison of MODIS and SGLI Albedo Retrievals Over the Sea of Okhotsk (January-May 2021) by Yingzhen Zhou (19723405)

“…The mask uses the same classification scheme as the MODIS mask:<br>• 0: Water<br>• 1: Sea-ice<br>• 2: Sea-covered ice</p><p><br></p><h2>Time Periods Covered:</h2><p dir="ltr"><br>The dataset spans eight weekly time intervals, corresponding to the following periods:</p><p>• 2021-01-08 ~ 2021-01-13<br>• 2021-01-31 ~ 2021-02-06<br>• 2021-02-11 ~ 2021-02-17<br>• 2021-03-05 ~ 2021-03-12<br>• 2021-03-15 ~ 2021-03-20<br>• 2021-04-01 ~ 2021-04-07<br>• 2021-04-22 ~ 2021-04-30<br>• 2021-05-07 ~ 2021-05-12</p><p dir="ltr">Each period represents a weekly aggregate of albedo and surface classification data from both sensors, averaged where valid sea-ice pixels were present.</p><h2>Spatial Coverage:</h2><p dir="ltr">• Latitude: 40° to 64° N<br>• Longitude: 135° to 165° E</p><p><br></p><h2>Format:</h2><p dir="ltr"><br>The dataset is provided in NetCDF format (.nc), which can be read and analyzed using common geospatial and scientific data tools such as Python’s xarray or Panoply.…”

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. …”

Neural-Signal Tokenization and Real-Time Contextual Foundation Modelling for Sovereign-Scale AGI Systems by Lakshit Mathur (20894549)

Fast, FAIR, and Scalable: Managing Big Data in HPC with Zarr by Alfonso Ladino (21447002)

The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation" by FirstName LastName (20554465)

“…</p><p dir="ltr"><i>cd 1point2dem/CIPrediction</i></p><p dir="ltr"><i>python -u point_prediction.py --model [GCN|ChebNet|GATNet]</i></p><h3>step 4: Parallel computation</h3><p dir="ltr">This step uses the trained models to optimize parallel computation. …”

The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation" by FirstName LastName (20554465)

“…</p><p dir="ltr"><i>cd 1point2dem/CIPrediction</i></p><p dir="ltr"><i>python -u point_prediction.py --model [GCN|ChebNet|GATNet]</i></p><h3>step 4: Parallel computation</h3><p dir="ltr">This step uses the trained models to optimize parallel computation. …”

Online Test-time Adaptation for Interatomic Potentials by Cui Taoyong (18076012)

“…The MD simulations for evaluating the performance of TAIP are conducted using the Atomic Simulation Environment (ASE) Python library. SchNet and PaiNN are used, respectively, as the baseline models to produce the potential energy and interatomic forces. …”

Supplementary Data: Biodiversity and Energy System Planning - Queensland 2025 by Andrew Rogers (17623239)

“…</li><li><b>eplus_Domestic_NPV_2025.xlsx</b>: Net Present Value calculations for domestic renewable energy projects across different protection thresholds and projection years (2030, 2040, 2050).…”

Datasets and Trajectories for Online Test-time Adaptation for Better Generalization of Interatomic Potentials to Out-of-distribution Data by Cui Taoyong (18076012)

“…The MD simulations for evaluating the performance of TAIP are conducted using the Atomic Simulation Environment (ASE) Python library. SchNet and PaiNN are used, respectively, as the baseline models to produce the potential energy and interatomic forces. …”

Source Data for Figures in Spatial colocalization and molecular crosstalk of myofibroblastic CAFs and tumor cells shape lymph node metastasis in oral squamous cell carcinoma by Ken Furudate (13018584)

“…</li><li>Specific data formats for certain analyses, such as NetCDF (.nc) files for trace plot data (e.g., Supporting Figure S4R).…”