Moulin distributions during 2016-2021 on the southwest Greenland Ice Sheet by Kang Yang (7323734)

“…</p><p><br></p><ul><li>00_Satellite-derived moulins: Moulins directly mapped from Sentinel-2 imagery, representing actual moulin positions;</li><li>01_Snapped moulins: Moulins snapped to DEM-modeled supraglacial drainage networks, primarily used for analyses;</li><li>02_Moulin recurrences: Recurring moulins determined from the snapped moulins;</li><li>03_Internally drained catchments: Internally drained catchment (IDC) associated with each moulin;</li><li>04_Surface meltwater runoff: surface meltwater runoff calculated from MAR for the study area, elevation bins, and IDCs; </li><li>05_DEM-derived: Topographic features modeled from ArcticDEM, including elevation bins, depressions and drainage networks;</li><li>06_GWR: Variables for conducting geographically weighted regression (GWR) analysis;</li></ul><p><br></p><ul><li>Code_01_Mapping moulins on the southwestern GrIS.ipynb: A Jupyter Notebook to analyze moulin distributions, reproducing most of the analyses and figures presented in the manuscript using the provided datasets;</li><li>Code_02_pre1_calculate Strain Rate from XY ice velocity.py: A preprocessing Python script to calculate strain rate for the GWR analysis;</li><li>Code_02_pre2_calculate Driving Stress from ice thickness and surface slope.py: A preprocessing Python script to calculate driving stress for the GWR analysis;</li><li>Code_02_GWR analysis.ipynb: A Jupyter Notebook to conduct the GWR analysis using the provided datasets.…”

Supporting data for "Optimisation of Trust in Collaborative Human-Machine Intelligence in Construction" by Hao Chen (9696848)

“…The first folder contains Scopus-derived data alongside analytical results that substantiate the figures presented in Chapter 1. The second folder mirrors the structure of the first, encompassing Scopus data and Python source code used to generate the visualizations featured in Chapter 2. …”

<b>Beyond absolute space: Modeling disease dispersion and reactive actions from a multi-spatialization perspective</b> by Shiran Zhong (14518376)

“…</p><h3>Running the Code</h3><p dir="ltr">· To run the Python code (preferably in Jupyter Notebook), ensure that all dependencies are installed by running: <i>pip install pandas pgmpy</i>. …”

dataset by Jingyi Chang (20404166)

“…<p dir="ltr">The R and Python code used to perform the analysis and generate the results and visualizations presented in the forest canopy height, and the related data and results produced in the research analyses.…”

<b>EEG dataset for multi-class Chinese character stroke and pinyin vowel handwriting imagery (16 subjects, CCS-HI & SV-HI)</b> by Fan Wang (22125886)

“…Preprocessing & Trial Integrity</h3><p dir="ltr">The publicly released dataset contains raw EEG data (no preprocessing); preprocessing (via MNE-Python, code in code folder) was only conducted for model training/testing: 1–40 Hz Butterworth bandpass filtering + 50 Hz notch filtering for noise reduction, manual bad channel labeling (EEGLAB) and spherical spline interpolation (per BIDS _channels.tsv), downsampling from 1000 Hz to 250 Hz, z-score normalization per trial, and epoch extraction of the 0–4 s imagery period (for both tasks). …”

Olson et al Muscle spindles provide flexible sensory feedback for movement sequences by William Olson (21703679)

“…</li></ul><h4><b>Code</b></h4><p dir="ltr">Accompanying Python scripts and Jupyter notebooks are included for:</p><ul><li>Data preprocessing and formatting.…”

Supplementary material for "Euler inversion: Locating sources of potential-field data through inversion of Euler's homogeneity equation" by Leonardo Uieda (97471)

“…<p dir="ltr">This repository contains the data and source code used to produce the results presented in:</p><blockquote><p dir="ltr">Uieda, L., Souza-Junior, G. …”

Void-Center Galaxies and the Gravity of Probability Framework: Pre-DESI Consistency with VGS 12 and NGC 6789 by Jordan Waters (21620558)

“…<br><br><br><b>ORCID ID: https://orcid.org/0009-0009-0793-8089</b><br></p><p dir="ltr"><b>Code Availability:</b></p><p dir="ltr"><b>All Python tools used for GoP simulations and predictions are available at:</b></p><p dir="ltr"><b>https://github.com/Jwaters290/GoP-Probabilistic-Curvature</b><br><br>The Gravity of Probability framework is implemented in this public Python codebase that reproduces all published GoP predictions from preexisting DESI data, using a single fixed set of global parameters. …”

Supplementary Materials for the article: ”Damped sectorial oscillations of an acoustically levitated droplet". by Taisiia Nagorskaia (22557095)

“…</li><li><b>Supplementary Video S3 (</b><i>S3_Video_Damped_oscillations.avi</i><b>):</b> High-speed footage of the damped oscillations used for the quantitative analysis presented in the paper.</li><li><b>Supplementary Code S4 (</b><i>S4_Code_Data_processing.ipynb</i><b>):</b> Python analysis code used for automated processing and for analyzing manually extracted data from the damped oscillations (S3).…”

Unfiltered TCR beta chain calls for 463 cancer samples and 587 control subjects by Yilong Li (20428445)

“…The columns are as follows.</p><ul><li><code>v_gene</code>: V gene of each TCR clonotype</li><li><code>j_gene</code>: J gene of each TCR clonotype</li><li><code>cdr3_nt</code>: Nucleotide sequence over the CDR3 region</li><li><code>cdr3</code>: Amino acid sequence over the CDR3 region</li><li><code>templates</code>: Number of UMIs supporting the clonotype</li><li><code>sample_name</code>: Sample that the clonotype derived from.…”

<b>Alpha-Synuclein Degradome Foundation Atlas</b> by Axel Petzold (7076261)

“…</p><p dir="ltr">Whether your work involves biomarker development, precision neurology, or machine learning, this dataset provides structured, labelled inputs that are ideal for:</p><ul><li>Training supervised models to detect or predict cleavage sites</li><li>Feature extraction from protein sequences</li><li>Clustering or classification of fragment types by mutation or disease context</li><li>Integrating with omics data for multimodal prediction tasks</li></ul><p dir="ltr">Dataset Features:</p><ul><li>Annotated α-synuclein proteolytic fragments</li><li>Includes wild-type and clinically relevant variants</li><li>Tab-delimited ASCII format for compatibility with Python, R, and ML frameworks</li><li>Linked SAS and Python scripts for pipeline reproducibility and updates</li><li>Ready-to-use for computational modelling, AI training, and bioinformatics workflows</li></ul><p dir="ltr">The dataset was generated using a reproducible codes involving Python, BLAST, and SAS. …”

A Quick Guide to Prompt Engineering: Using Generative AI Wisely in Learning and Work by Uce Indahyanti (21324434)

Probabilistic-QSR-GeoQA by Mohammad Kazemi (19442467)

“…</p><p><br></p><p><br></p><p dir="ltr"><b>Perquisites</b></p><p dir="ltr">Two spatial reasoning tools, SparQ for conventional reasoning and Probcog for probabilistic reasoning need to be installed:</p><p><br></p><p dir="ltr">- Probcog ( Follow the their github repo in https://github.com/opcode81/ProbCog)</p><p dir="ltr">- SparQ (Follow their manual in https://www.uni-bamberg.de/fileadmin/sme/SparQ/SparQ-Manual.pdf)</p><p><br></p><p><br></p><p dir="ltr"><b>Materials</b></p><p dir="ltr">This includes codes, data, evidence sets, and mln folders for two experiments:</p><p dir="ltr">- Code: This folder includes questionGenerator.py and answerExtraction.py for generating synthetic questions and post-processing of inferences from Probcog and SparQ reasoners. …”

<b>MSLU-100K: A multi-source land use dataset of Chinese major cities</b> by Yao Yao (7903457)

“…</li><li>The Manual Filtering.py-Based Multilevel Model Classification Method includes code to perform multilevel model predictions.</li></ul><h3>5.requirements.txt</h3><ul><li>Lists environment configurations and version specifications, including Python 3.7 and Pytorch 2.2.…”

Data for "Are pseudo first-order kinetic constants properly calculated for catalytic membranes?" by Timothy Warner (20222838)

“…</li><li><b>catalytic_membrane_meta-analysis-0.1.3.zip</b>: zip folder containing figures from the publication and python codes and instructions for generating PFO results and individual figures for every research article listed in database.xlsx</li></ol><h2>Data Contents</h2><h3>database.xlsx</h3><h4>Porous Catalytic Membranes (Tab 1):</h4><ul><li>Index - number associated with analysis and figures generated by python codes in catalytic_membrane_meta-analysis-0.1.3.zip</li><li>Author full names</li><li>Title</li><li>Year</li><li>Source title</li><li>DOI</li><li>Abstract</li><li>Keywords</li></ul><h4>Data Table (Tab 2):</h4><ul><li>Index - number associated with analysis and figures generated by python codes in catalytic_membrane_meta-analysis-0.1.3.zip</li><li>Title</li><li>Comments on C/C0 Graph - description on what data was extracted, the format of the data and any modification required for analysis (if any).…”

Supplementary Material for review (<b>Revealing the co-occurrence patterns of public emotions from social media data</b>) by Yang Hua (21399140)

“…</p><p dir="ltr">This document provides a detailed explanation of how to reproduce all experimental results, figures and tables presented in the paper, and the key indicators in the abstract by using the shared datasets and source code. …”

Western Oregon Wet Dry (WOWTDR) annual predictions of late summer streamflow status for western Oregon, 2019-2021 by Jonathan D. Burnett (19659601)

“…Also included is all R code and Python code needed to run and process this model.…”

Leveraging Large Language Models as Requirements Elicitation Interview Bots-all data by Samuel Gorsch (19947618)

“…<p dir="ltr"><b>Title:</b> Code and Supplementary Files for "Leveraging Large Language Models as Requirements Elicitation Interview Bots"</p><p dir="ltr"><b>Description:</b><br>This repository contains all code, supplementary plots, and select data files used in the master’s thesis, "Leveraging Large Language Models (LLMs) as Requirements Elicitation Interview Bots." …”

<b>GFAP Degradome Foundation Atlas</b> by Axel Petzold (7076261)

“…To extract you can use the bash terminal command: <br><b><i>tar -xvJf GFAP_Degradome_Foundation_Atlas_v3.tar.gz</i></b></p><p dir="ltr"><br></p><h3>Codes</h3><p dir="ltr">Dataset generation is reproducible using three open-source tools:<br><b>Python</b>, <b>BLAST</b>, and <b>SAS</b>.…”

Vector-to-Image Converted Building Footprints or Building Change Detection by anonymous (20480480)

“…</p><p dir="ltr">1．<b>Python environment</b>: requirements.txt</p><p dir="ltr">2．…”