Machine Learning-Driven Discovery and Database of Cyanobacteria Bioactive Compounds: A Resource for Therapeutics and Bioremediation by Renato Soares (20348202)

“…This resource, adhering to the findability, accessibility, interoperability, and reuse of digital assets (FAIR) principles, is an excellent tool for pharmaceutical and bioremediation researchers. …”

Map of the Iberian Peninsula, including the Canary and Balearic Islands, showing the location of ICENET stations. by Celia Herrero del Barrio (20479635)

Tracking when the number of individuals in the video frame changes. by Hirotsugu Azechi (20700528)

“…The yellow box in the schematic represents the processes using multi-animal tracking tools, while the green box represents the processes using single-animal tracking tools. …”

CpG Signature Profiling and Heatmap Visualization of SARS-CoV Genomes: Tracing the Genomic Divergence From SARS-CoV (2003) to SARS-CoV-2 (2019) by Tahir Bhatti (20961974)

“…Heatmap Generation</p><p dir="ltr">Tools :</p><p dir="ltr">Heatmaps were generated using Python's seaborn library (sns.heatmap) and matplotlib for visualization.…”

End-to-end example-based sim-to-real RL policy transfer based on neural stylisation with application to robotic cutting by Jamie Hathaway (10285367)

“…</p><h3>policy/</h3><p dir="ltr">This folder contains pickled trajectories, in the form of a Python list.</p><p dir="ltr">The list's elements are TrajWithRew dataclass objects from the Imitation Python library (https://imitation.readthedocs.io/en/latest/)</p><p dir="ltr">TrajWithRew contains 4 main fields</p><ul><li> obs - the (unnormalised) observations, in the form of a [WINDOW_LENGTH * NUM_CHANNELS] array</li><li> acts - the actions in the form of a [WINDOW_LENGTH - 1 * NUM_ACTS] array</li><li> infos - the info values at each timestep, as a [WINDOW_LENGTH - 1] array of dicts</li><li> terminals - boolean indicating if that trajectory segment is a terminal segment</li><li> rews - the rewards as a [WINDOW_LENGTH - 1] array</li></ul><p dir="ltr">Each TrajWithRew represents not a full episodic trajectory, as is usually the case with Imitiation - rather they represent segments of a full episodic trajectory, of length WINDOW_LENGTH. …”

Missing and Unaccounted-for People in Mexico (1960s–2025) by Montserrat Mora (20430644)

“…</li><li><b>Requirements File:</b> A <code>requirements.txt</code> file listing the necessary Python libraries to run the script seamlessly.</li></ol><p dir="ltr">This dataset and accompanying tools aim to support researchers, policymakers, and journalists in analyzing and addressing the issue of missing persons in Mexico.…”

Genomic Surveillance of Pemivibart (VYD2311) Escape-Associated Mutations in SARS-CoV-2: December 2025 BioSamples (n=2) by Tahir Bhatti (20961974)

“…</p><p dir="ltr"><b>Note:</b></p><p dir="ltr">Analysis was performed using a custom Python-based bioinformatics pipeline developed for <b>high-throughput surveillance of pemivibart (VYD2311) escape mutations in SARS-CoV-2</b>. …”

Scope of our collection of pathogen models of metabolism. by Emma M. Glass (13102872)

“…This cladogram was created using the GraPhlAn python tool. (b) Our collection of GENREs represents 9 phyla, 17 classes, 36 orders, 94 genera, and 345 species of pathogens. …”

Social media images of China's terraces by Song Chen (19488280)

“…Geo-tagged images were collected using Weibo cookies and Python-based scraping tools (available at: https://github.com/dataabc/weibo-search). …”

PepENS by Abel Chandra (16854753)

“…To run the tool, execute the script PepENS_user.py (located in the Dataset1 directory). …”

Environmental Census: Modeling Synthetic Biology Ecological Risk with Metagenomic Enzymatic Data and High-Performance Computing by John Docter (22772100)

“…Improved predictive computational tools are necessary to assess the potential establishment risk and environmental location of these escaped engineered microorganisms, assisting their design and management. …”

Exploring post-wildfire hydrologic response in central Colorado using field observations and the Landlab modeling framework by Jordan Adams (595056)

“…Landlab, an open-source, componentized model written in Python, can be used to explore landscape evolution across both short and long time scales. …”

<b>Dataset for manuscript: </b><b>Phylogenetic and genomic insights into the evolution of terpenoid biosynthesis genes in diverse plant lineages</b> by Puguang Zhao (19023065)

“…It is run directly in the command: python3 cdhit.py </p><p dir="ltr"> This script is designed to remove redundant sequences using the CD-HIT tool.…”

Advancing Solar Magnetic Field Modeling by Carlos António (21257432)

“…<br><br>We developed a significantly faster Python code built upon a functional optimization framework previously proposed and implemented by our team. …”

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>…”

Collocation and Contextual Analysis of 'Anglais' in Algerian and French Press Corpora by Bouri Hadj (17214271)

“…</p><p dir="ltr">Usage Notes: The file is in CSV format, suitable for analysis with tools like R, Python (pandas), or spreadsheet software. …”

Code by Baoqiang Chen (21099509)

“…</p><p><br></p><p dir="ltr">For the 5′ UTR library, we developed a Python script to extract sequences and Unique Molecular Identifiers (UMIs) from the FASTQ files. …”

Core data by Baoqiang Chen (21099509)

“…</p><p><br></p><p dir="ltr">For the 5′ UTR library, we developed a Python script to extract sequences and Unique Molecular Identifiers (UMIs) from the FASTQ files. …”

<b>Rethinking neighbourhood boundaries for urban planning: A data-driven framework for perception-based delineation</b> by Shubham Pawar (22471285)

“…</p><p dir="ltr"><b>Input:</b></p><ul><li><code>svi_module/svi_data/svi_info.csv</code> - Image metadata from Step 1</li><li><code>perception_module/trained_models/</code> - Pre-trained models</li></ul><p dir="ltr"><b>Command:</b></p><pre><pre>python -m perception_module.pred \<br> --model-weights .…”

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

“…</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.</p>…”