MCCN Case Study 3 - Select optimal survey locality by Donald Hobern (21435904)

“…</p><p dir="ltr">This is a simple implementation that uses four environmental attributes imported for all Australia (or a subset like NSW) at a moderate grid scale:</p><ol><li>Digital soil maps for key soil properties over New South Wales, version 2.0 - SEED - see <a href="https://esoil.io/TERNLandscapes/Public/Pages/SLGA/ProductDetails-SoilAttributes.html" target="_blank">https://esoil.io/TERNLandscapes/Public/Pages/SLGA/ProductDetails-SoilAttributes.html</a></li><li>ANUCLIM Annual Mean Rainfall raster layer - SEED - see <a href="https://datasets.seed.nsw.gov.au/dataset/anuclim-annual-mean-rainfall-raster-layer" target="_blank">https://datasets.seed.nsw.gov.au/dataset/anuclim-annual-mean-rainfall-raster-layer</a></li><li>ANUCLIM Annual Mean Temperature raster layer - SEED - see <a href="https://datasets.seed.nsw.gov.au/dataset/anuclim-annual-mean-temperature-raster-layer" target="_blank">https://datasets.seed.nsw.gov.au/dataset/anuclim-annual-mean-temperature-raster-layer</a></li></ol><h4><b>Dependencies</b></h4><ul><li>This notebook requires Python 3.10 or higher</li><li>Install relevant Python libraries with: <b>pip install mccn-engine rocrate</b></li><li>Installing mccn-engine will install other dependencies</li></ul><h4><b>Overview</b></h4><ol><li>Generate STAC metadata for layers from predefined configuratiion</li><li>Load data cube and exclude nodata values</li><li>Scale all variables to a 0.0-1.0 range</li><li>Select four layers for comparison (soil organic carbon 0-30 cm, soil pH 0-30 cm, mean annual rainfall, mean annual temperature)</li><li>Select 10 random points within NSW</li><li>Generate 10 new layers representing standardised environmental distance between one of the selected points and all other points in NSW</li><li>For every point in NSW, find the lowest environmental distance to any of the selected points</li><li>Select the point in NSW that has the highest value for the lowest environmental distance to any selected point - this is the most different point</li><li>Clean up and save results to RO-Crate</li></ol><p><br></p>…”

RabbitSketch by tong zhang (20852432)

“…RabbitSketch achieves significant speedups compared to existing implementations, ranging from 2.30x to 49.55x.In addition, we provide flexible and easy-to-use interfaces for both Python and C++. …”

A Hybrid Ensemble-Based Parallel Learning Framework for Multi-Omics Data Integration and Cancer Subtype Classification by Mohammed Nasser Al-Andoli (21431681)

“…<p dir="ltr">The code supports replication of results on TCGA Pan-cancer and BRCA datasets and includes data preprocessing, model training, and evaluation scripts:<br>Python scripts for data preprocessing and integration</p><ul><li>Autoencoder implementation for multimodal feature learning</li><li>Hybrid ensemble training code (DL/ML models and meta-learner)</li><li>PSO and backpropagation hybrid optimization code</li><li>Parallel execution scripts</li><li>Instructions for replicating results on TCGA Pan-cancer and BRCA datasets</li></ul><p></p>…”

Keyhole Imagery Global Coverage Dataset (1960–1984) by Hao Li (20223762)

“…</li><li><b>Code</b>: Python scripts implementing the three-step workflow (imagery classification, global grid generation, and point-based property calculation).…”

Recursive generation of substructures using point data by Jackie R (18359715)

“…<p dir="ltr">The dataset contains generated substructure using POI in China, the pseudo code for the algorithm and python implement of the algorithm. …”

<b>Algorithm Pseudocode</b> by Yibin Zhao (22425801)

“…The model generates point forecasts and forecast interval boundaries for short-term loads, providing important support for risk quantification and decision-making in power systems. The pseudo-code follows standard Python syntax specifications for functions and loops and is easy to understand and implement. …”

<b>Anonymous, runnable artifact for </b><b>Testing AI Applications Under Nondeterminism, Drift, and Resource Constraints: A Problem‑Driven Multi‑Layer Approach</b> by Nariman Mani (21380459)

“…</b> The anonymized archive includes a dependency‑free Python implementation of all five layers (oracle, coverage, drift mapping, prioritization, resource scheduling), an orchestrator, and synthetic datasets with 50 test cases per sub‑application (LLM assistant, retrieval with citation, vision calories, notification/social). …”

<b>Testing AI Applications Under Nondeterminism, Drift, and Resource Constraints</b> by Nariman Mani (21380459)

“…<ul><li>A <b>Python repo</b> with minimal implementations of all five layers<br>(<b>COL</b>, <b>SCL</b>, <b>CDM</b>, <b>RPE</b>, <b>RAS</b>) plus an <b>orchestrator</b> and utilities.…”

Curvature-Adaptive Embedding of Geographic Knowledge Graphs in Hyperbolic Space by chenchen Guo (21327470)

“…</p><h3>Requirements</h3><ul><li>Python 3.7</li><li>PyTorch 1.10.0 & CUDA 11.8</li></ul><h3>Main Result Running commands:</h3><p dir="ltr">Execute <code>.sh: bash .…”

adnus by Mehmet Keçeci (14301782)

“…<p dir="ltr">adnus (AdNuS): Advanced Number Systems</p><p dir="ltr">adnus is a Python library that provides an implementation of various advanced number systems. …”

Leveraging explainable causal artificial intelligence to study forest gross primary productivity dynamics in China's protected areas by Pedro Cabral (18947566)

“…<p dir="ltr">A Python script used for modeling forest GPP in China´s Protected Areas, including mean encoding of the categorical variable climate zone (CZ), multicollinearity testing using Variance Inflation Factor (VIF), implementation of four machine learning models to predict forest GPP, XAI and causality analysis.…”

Accompanying data files (Melbourne, Washington DC, Singapore, and NYC-Manhattan) by Winston Yap (13771969)

“…</p><p dir="ltr">Each zipped folder consists the following files:</p><ul><li>Graph data - City object nodes (.parquet) and COO format edges (.txt)</li><li>predictions.txt (model predictions from GraphSAGE model)</li><li>final_energy.parquet (Compiled training and validation building energy data)</li></ul><p dir="ltr">The provided files are supplementary to the code repository which provides Python notebooks stepping through the data preprocessing, GNN training, and satellite imagery download processes. …”

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.…”

kececilayout by Mehmet Keçeci (14301782)

“…<p dir="ltr"><b>Kececi Layout (Keçeci Yerleşimi)</b>: A deterministic graph layout algorithm designed for visualizing linear or sequential structures with a characteristic "zig-zag" or "serpentine" pattern.</p><p dir="ltr"><i>Python implementation of the Keçeci layout algorithm for graph visualization.…”

Spotted owl habitat quality maps and disturbance attribution analysis by Josh Barry (7573823)

“…<p dir="ltr">This dataset includes annual spatial maps of spotted owl nesting habitat quality in Southern California and an accompanying ArcPython script used to attribute negative annual habitat change to wildfire (Barry et al., 2025). …”

Automatic data reduction for the typical astronomer by Bradford Holden (21789524)

“…PypeIt has been developed by a small team of astronomers with two leading philosophies: (1) build instrument-agnostic code to serve nearly any spectrograph; (2) implement algorithms that achieve Poisson-level sky-subtraction with minimal systematics to yield precisely calibrated spectra with a meaningful noise model. …”

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

“…</p><p dir="ltr">The Python programming source code used to run the calculation of ET0 and AI is provided and available online on Figshare at:</p><p dir="ltr">https://figshare.com/articles/software/Global_Aridity_Index_and_Potential_Evapotranspiration_Climate_Database_v3_-_Algorithm_Code_Python_/20005589</p><p dir="ltr">Peer-Review Reference and Proper Citation:</p><p dir="ltr">Zomer, R.J.; Xu, J.; Trabuco, A. 2022. …”

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

“…</p><p dir="ltr"><b>Availability</b> — The repository includes LaTeX sources, trained model checkpoints, Python/PyTorch code, and synthetic datasets. Data are released under a Creative Commons Attribution-NonCommercial-4.0 (CC BY-NC 4.0) license; code under MIT License.…”

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

“…Six Python scripts are provided, each implementing a distinct machine learning algorithm—Random Forest, k-Nearest Neighbors (k-NN), Multi-Layer Perceptron (MLP), Decision Tree, Naïve Bayes, and Logistic Regression. …”

Numerical analysis and modeling of water quality indicators in the Ribeirão João Leite reservoir (Goiás, Brazil) by Amanda Bueno de Moraes (22559249)

“…The code implements a statistical–computational workflow for parameter selection (VIF, Bartlett and KMO tests, PCA and FA with <i>varimax</i>) and then trains and evaluates machine-learning models to predict three key physico-chemical indicators: turbidity, true color, and total iron. …”