Anisotropic Skyrmion and Multi-<i>q </i>Spin Dynamics in Centrosymmetric Gd₂PdSi₃: Data and Code by Matjaž Gomilšek (14278739)

“…Python script file<br>*.dat, [no extension] ... ASCII data file. …”

Data and analysis code for manuscript "Preparations for ultra-high dose rate 25-90 MeV electron irradiation experiments with a compact, high-peak-current, X-band linear accelerator... by Haytham Effarah (11979509)

“…</p><p dir="ltr">The environment.yml file can create a conda virtual environment named "prep4vhee" with the required Python version and dependencies using the following conda command:</p><pre>conda env create -f environment.yml<br></pre><p dir="ltr">TOPAS input decks are also included in some folders with seeds set to reproduce Monte Carlo simulation results presented in the manuscript. …”

Workflow of T cell activation analysis. by Yitong Ma (8144697)

Methodological Approach Based on Structural Parameters, Vibrational Frequencies, and MMFF94 Bond Charge Increments for Platinum-Based Compounds by Gloria Castañeda-Valencia (20758502)

“…The developed bci optimization tool, based on MMFF94, was implemented using a Python code made available at https://github.com/molmodcs/bci_solver. …”

SRL OF TIM by Jefferson Rodrigo Speck (21510347)

“…</li><li><code><strong>plot_scripts/</strong></code>: Includes data files and Python scripts used to generate the visualizations presented in the review (e.g., bar charts, pie charts, distribution graphs).…”

<b>China’s naturally regenerated forests currently have greater aboveground carbon accumulation rates than newly planted forests</b> by Kai Cheng (18367101)

“…As well as, the Google earth engine code for detecting their ages and extents, python code for modelling the carbon accumulation rate of China’s PYF and NYF, python code for evaluating the influence of various factors on the patterns and differences in AGC accumulation rates between NYF and PYF in China.…”

MEG Dataset and Analysis Scripts for “The Effects of Task Similarity During Representation Learning in Brains and Neural Networks” by Nicholas Menghi (22426174)

“…</p><h3><b>Contents</b></h3><ul><li><b>MEG data</b> (results of the correlation between empirical and model matrices at different dimensionalities and domains)</li><li><b>Behavioral data</b> (behavioural accuracy performance: "Spatual Source Data")</li><li><b>Analysis script</b></li><li><b>Python package </b>developed to help with retrieving and computing simple operations</li></ul><h3><b>Data format</b></h3><p dir="ltr">Data are organized according to a structured folder layout (see <code>README.md</code> in the repository) and include:</p><ul><li><code>npy</code> MEG files (numpy)</li><li><code>.csv</code> behavioral files</li><li>Python scripts using MNE-Python for statistical analysis and visualization</li></ul><h3><b>Usage</b></h3><p dir="ltr">The provided scripts reproduce the statistical tests and figures presented in the manuscript. …”

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.…”

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.…”

M-SGWR model by M. Naser Lessani (17817475)

“…The repo contains all the necessary information, including the python code "M-SGWR", datasets and the instruction of how to reproduce the results presented in the article. …”

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. …”

Attention and Cognitive Workload by Rui Varandas (11900993)

“…</p><p dir="ltr">The data for subject 2 do not include the 2nd part of the acquisition (python task) because the equipment stopped acquiring; subject 3 has the 1st (N-Back task and mental subtraction) and the 2nd part (python tutorial) together in the <code>First part</code> folder (file <code>D1_S3_PB_description.json</code> indicates the start and end of each task); subject 4 only has the mental subtraction task in the 1st part acquisition and in subject 8, the subtraction task data is included in the 2nd part acquisition, along with python task.…”

Global blue carbon losses from salt marshes exceed restoration gains by Yuhan Zheng (21610220)

“…<h4>This repository contains the main code used to generate the figures and results presented in the manuscript.…”

<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>. …”

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).…”

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.…”

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.…”

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. …”

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. …”

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." …”