Data sets and coding scripts for research on sensory processing in ADHD and ASD by Vesko Varbanov (9687029)

“…</p><h4>Contents</h4><p dir="ltr">The repository includes:</p><ul><li>Questionnaire data (ASRS, BAPQ)</li><li>Visual orientation discrimination thresholds (vertical and oblique)</li><li>Demographic variables (age, gender)</li><li>Clinical vs. non-clinical group labels</li><li>Propensity score matching files and reproducible Python code</li><li>JASP analysis files and outputs</li><li>Study documentation and methodological details</li></ul><p dir="ltr">These data support the study’s finding that ADHD and ASD show distinct sensory signatures: clinical ADHD was associated with reduced oblique sensitivity, while clinical ASD showed enhanced vertical discrimination relative to matched non-clinical controls. …”

Supporting data for "Software library to quantify the value of forecasts for decision-making: Case study on sensitivity to damages" by Laugesen et al. (2025) by Richard Laugesen (6480371)

“…</p><p dir="ltr">Dataset includes compressed Python Pickle files containing Dictionaries of NumPy arrays and metadata for each figure. …”

Data and software for "Social networks affect redistribution decisions and polarization" by Milena Tsvetkova (11217969)

“…</p><p dir="ltr">The repository contains data in .csv and .xlsx format, model code in .nlogox Netlogo format, analysis code in .ipynb Jupyter notebooks, and helping code in .py Python files.</p><p dir="ltr"><br></p>…”

CNG-ARCO-RADAR.pdf by Alfonso Ladino (21447002)

“…This approach uses a suite of Python libraries, including Xarray (Xarray-Datatree), Xradar, and Zarr, to implement a hierarchical tree-like data model. …”

World Heritage documents reveal persistent gaps between climate awareness and local action by Yang Chen (20756166)

“…Some components require non-standard Python libraries such as pdfminer.six and pingouin.…”

Data and some code used in the paper:<b>Expansion quantization network: A micro-emotion detection and annotation framework</b> by Zhou (20184816)

“…Create a directory named "28pd" to place the .csv format data files to be labeled or predicted.</p>…”

Demonstration of Isosteric Heat of Adsorption Calculation using AIFs and pyGAPs by Jack Evans (11275386)

“…</p><p dir="ltr">The calculation is performed using the Clausius-Clapeyron method as implemented in the <code><strong>pyGAPS</strong></code> Python library for adsorption science. …”

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

“…This multiplier has been used to increase the precision of the variable values without using decimals. The Readme File is provided with a detailed description of the dataset files. …”

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>…”

RealBench: A Repo-Level Code Generation Benchmark Aligned with Real-World Software Development Practices by RealBench RealBench (22275393)

“…<br>└── README.md # This file.<br>```<br><br>---<br><br>## ⚙️ Environment<br><br>* Windows/Linux system (Windows 10/11 or Ubuntu 20.04+ recommended)<br>* Python 3.9<br>* Conda environment management<br>* Understand API (for UML analysis)<br><br>---<br><br><br></p><p><br></p>…”

Data from: Circadian activity predicts breeding phenology in the Asian burying beetle <i>Nicrophorus nepalensis</i> by Hao Chen (20313552)

“…</p><p dir="ltr">The dataset includes:</p><ol><li>Raw locomotor activity measurements (.txt files) with 1-minute resolution</li><li>Breeding experiment data (Pair_breeding.csv) documenting nest IDs, population sources, photoperiod treatments, and breeding success</li><li>Activity measurement metadata (Loc_metadataset.csv) containing detailed experimental parameters and daily activity metrics extracted using tsfresh</li></ol><p dir="ltr">The repository also includes complete analysis pipelines implemented in both Python (3.8.8) and R (4.3.1), featuring:</p><ul><li>Data preprocessing and machine learning model development</li><li>Statistical analyses</li><li>Visualization scripts for generating Shapley plots, activity pattern plots, and other figures</li></ul><p></p>…”

High-Throughput Mass Spectral Library Searching of Small Molecules in R with NIST MSPepSearch by Andrey Samokhin (20282728)

“…High-level programming languages such as Python and R are widely used in mass spectrometry data processing, where library searching is a standard step. …”

OHID-FF dataset for forest fire detection and classification by xin chen (20496938)

“…</p><p dir="ltr"> - labels/ — YOLO-format label files (xywh, normalized) matching each sliced image. …”

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).…”

Code for High-quality Human Activity Intensity Maps in China from 2000-2020 by Wenqi Xie (18273238)

“…<p dir="ltr">Code and remote sensing images and interpretation results of the samples for uncertainty analysis for "High-quality Human Activity Intensity Maps in China from 2000-2020"</p><p dir="ltr">“Mapping_HAI.py”：We generated the HAI maps using ArcGIS 10.8, and the geoprocessing tasks were implemented using Python 2.7 with the ArcPy library (ArcGIS 10.8 + Python 2.7 environment). …”

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>. …”

Code and data for reproducing the results in the original paper of DML-Geo by Pengfei CHEN (8059976)

“…<p dir="ltr">This asset provides all the code and data for reproducing the results (figures and statistics) in the original paper of DML-Geo</p><h2>Main Files:</h2><p dir="ltr"><b>main.ipynb</b>: the main notebook to generate all the figures and data presented in the paper</p><p dir="ltr"><b>data_generator.py</b>: used for generating synthetic datasets to validate the performance of different models</p><p dir="ltr"><b>dml_models.py</b>: Contains implementations of different Double Machine Learning variants used in this study.…”

Elements: Streaming Molecular Dynamics Simulation Trajectories for Direct Analysis – Applications to Sub-Picosecond Dynamics in Microsecond Simulations by Matthias Heyden (17087794)

“…This eliminates the need for intermediate storage and allows immediate access to high-frequency fluctuations and vibrational signatures that would otherwise be inaccessible. We have implemented this streaming interface in the MD engines NAMD, LAMMPS, and GROMACS</p><p dir="ltr">On the client side, we developed the IMDClient Python package which receives the streamed data, stores into a custom buffer, and provides it to external tools as NumPy arrays, facilitating integration with scientific computing workflows. …”

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

“…(NEXRAD), using open-source tools from the Python ecosystem such as Xarray, Xradar, and Dask to enable efficient parallel processing and scalable analysis. …”

HCC Evaluation Dataset and Results by Jens-Rene Giesen (18461928)

“…</p><h3>Report Script</h3><p dir="ltr">On the top-level directory you find a <code>report.py</code> file, which is an executable Python script. The only requirement for running this script is a Python 3.6+ interpreter as well as an installation of the <code>numpy</code> package. …”