Comparison data 2 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

“…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”

Comparison data 5 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

“…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”

Comparison data 6 for <i>Lamprologus ocellatus</i>. by Nicolai Kraus (19949667)

“…TIBA accepts data outputs from popular logging software and is implemented in Python and JavaScript, with all current browsers supported. …”

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. …”

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

“…The pipeline integrates established open-source tools (fastp, BWA-MEM, samtools, iVar, bcftools) and implements <b>codon-aware mutation calling</b> at five canonical RBD positions (R346, S371, K444, F456, F486) relative to NC_045512.2. …”

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. …”

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. …”

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.…”

<b>Myelin oligodendrocyte glycoprotein (MOG) Degradome Foundation Atlas</b> by Axel Petzold (7076261)

“…</li></ul><h3>Reproducibility and Code Availability</h3><p dir="ltr">Dataset generation is fully reproducible using open-source tools:</p><ul><li>Python</li><li>SAS</li></ul><p dir="ltr">All required scripts are included in the repository and are well documented to support local replication and custom adaptations. …”

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. …”

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.…”

<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>. …”

Supervised Classification of Burned Areas Using Spectral Reflectance and Machine Learning by Baptista Boanha (22424668)

“…<p dir="ltr">This dataset and code package presents a modular framework for supervised classification of burned and unburned land surfaces using satellite-derived spectral reflectance. …”

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>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.…”

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.…”

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.…”

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>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>.…”