<i>In vivo</i> identification of <i>Toxoplasma gondii</i> antigenic proteins and <i>in silico</i> study of their polymorphism. by Julie DENIS (19696753)

“…The filtered data is presented in the <b>Filtered_Variants</b> document.…”

<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>. …”

Pteredactyl: Patient Clinical Free-Text Redaction Software by Matt Stammers (14037525)

“…</p><p dir="ltr">This is why we created <a href="https://pypi.org/project/pteredactyl/" rel="noopener noreferrer" target="_blank">Pteredactyl</a> - a python module to help with redaction of clinical free text.…”

Streamlining a National Flood Assessment using a Flow Scheduler by eRNZ Admin (6438486)

Continental-scale impact of bomb radiocarbon affects historical fossil fuel carbon dioxide reconstruction by Jing Li (21564146)

“…</p><p dir="ltr"><b>Source CO2 data (Mauna Loa).xlsx:</b> CO2 data from Mauna Loa (MLO) which belong to Global Greenhouse Gas Reference Network were used in this study as the background CO2 levels, which available from 1970 to 2020 (https://gml.noaa.gov/ccgg/trends/).14</p><p dir="ltr"><b>Statistical analysis code and data (SI table 1-2,4) folder: </b>It contains the python code, source data and results that conducted the statistical 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. …”

<b>AutoMated tool for Antimicrobial resistance Surveillance System version 3.1 (AMASS3.1)</b> by Chalida Rangsiwutisak (10501496)

“…;</li><li><i>Enterococcus</i> <i>faecalis</i> and <i>E. faecium</i> have been explicitly included in the pathogens under the survey (while <i>Enterococcus</i> spp. are used in the AMASS version 2.0);</li><li>We have added a few antibiotics in the list of antibiotics for a few pathogens under the survey;</li></ol><p dir="ltr">Technical aspects</p><ol><li>We have added a configuration for “Annex C: Cluster signals” in Configuration.xlsx;</li><li>We have improved the algorithm to support more several date formats;</li><li>We have improved the algorithm to translate data files;</li><li>We have improved Data_verification_logfile report to present local languages of the variable names and values (according to how they were recorded in the data files) in the report;</li><li>We have improved Annex B: Data indicators to support a larger data set;</li><li>We have used only Python rather than R + Python (as used in the AMASSv2.0);</li><li>We have set a default config for infection origin stratification by allowing a specimen collected two calendar days before the hospital admission date and one day after the hospital discharge date into consideration. …”

MCCN Case Study 1 - Evaluate impact from environmental events/pressures by Donald Hobern (21435904)

“…filters=eyJTVEFURSI6WyJBQ1QiXX0=&location=-35.437128,149.203518,11.00</a></li><li><b>caladenia_act.csv</b> - Distribution records for orchids in the genus <i>Caladenia</i> between 1990 and present from the ALA in CSV format: <a href="https://doi.org/10.26197/ala.1e501311-7077-403b-a743-59e096068fa0" target="_blank">https://doi.org/10.26197/ala.1e501311-7077-403b-a743-59e096068fa0</a></li></ul><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>Group orchid species records by species</li><li>Prepare STAC metadata records for each data source (separate records for the distribution data for each orchid species)</li><li>Load data cube</li><li>Mask orchid distribution records to boundaries of ACT</li><li>Calculate the proportion of distribution records for each species occurring inside and outside protected areas</li><li>Calculate the proportion of distribution records for each species occurring in areas with each class of vegetation cover</li><li>Report the apparent affinity between each species and protected areas and between each species and different classes of vegetation cover</li></ol><h4><b>Notes</b></h4><ul><li>No attempt is made here to compensate for underlying bias in the areas where observers have spent time recording orchids. …”

MSc Personalised Medicine at Ulster University by Steven Watterson (100045)

“…</b> Introducing computational approaches to studying genes, proteins or metabolites, this module teaches Python coding, data analysis and how to work with the databases that support data analysis.…”

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>. …”

<b>The abundance of ground-level atmospheric ice-nucleating particles and aerosol properties </b><b>in the North Slope of Alaska</b> by Aidan Pantoya (19416546)

“…An individual method-oriented data abstract is available in metadata for each output sub-folder. All Python codes used for processing input data and generating output data are paired with the readme files and archived in folders. …”

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

“…</li></ul><h3>Analysis Scripts</h3><p dir="ltr">Complete set of R scripts for reproducing all analyses:</p><ul><li><b>percent cost increase_line plot.R</b>: Creates visualizations of energy cost impacts under different conservation scenarios</li><li><b>Zonation curves.R</b>: Generates conservation performance curves and coverage statistics</li><li><b>NPV_bar_plot.R</b>: Produces economic analysis plots with Net Present Value breakdowns</li><li><b>domestic_export_map_iterations.R</b>: Creates spatial maps of renewable energy infrastructure for domestic and export scenarios</li></ul><h2>Technical Specifications</h2><h3>Data Formats</h3><ul><li><b>Spatial Data</b>: ESRI Geodatabase (.gdb), Shapefile (.shp), GeoTIFF (.tif)</li><li><b>Tabular Data</b>: CSV, Microsoft Excel (.xlsx, .xls)</li><li><b>Analysis Code</b>: R scripts (.R)</li></ul><h3>Software Requirements</h3><ul><li><b>R</b> (≥4.0.0) with packages: sf, dplyr, ggplot2, readr, readxl, tidyr, furrr, ozmaps, ggpattern</li><li><b>ESRI ArcGIS</b> or <b>QGIS</b> for geodatabase access and spatial analysis</li><li><b>Zonation</b> conservation planning software (for methodology understanding)</li></ul><h3>Hardware Recommendations</h3><ul><li><b>RAM</b>: 16GB minimum (32GB recommended for full spatial analysis)</li><li><b>Storage</b>: 15GB free space for data extraction and processing</li><li><b>CPU</b>: Multi-core processor recommended for parallel processing scripts</li></ul><h2>Detailed Description of the VRE Siting and Cost-Minimization Model</h2><p><br></p><p dir="ltr">This section provides an in-depth description of the Variable Renewable Energy (VRE) siting model, including the software, the core algorithm, and the optimisation process used to determine the least-cost, spatially constrained development trajectory for VRE infrastructure in Queensland, Australia.…”