Raw Data EEG.pdfBrainwaves and Higher-Order Thinking: An EEG Study of Cognitive Engagement in Mathematics Tasks by NORLIZA BINTI MOHAMED (20739875)

“…Code and Algorithms (if applicable)</p> <p><br></p> <p>Scripts for EEG signal processing and analysis</p> <p><br></p> <p>Machine learning or statistical modeling scripts</p> <p><br></p> <p>Any software implementation used to analyze brainwave patterns</p> <p><br></p> <p><br></p> <p><br></p> <p>4. …”

DA-Faster-RCNN code by Seunghyeon Wang (16500132)

“…<p dir="ltr">This repository provides the code used in the paper “Domain-Adaptive Faster R-CNN for Non-PPE Identification on Construction Sites from Body-Worn and General Images.” The implementation is written in Python using PyTorch and Detectron2.…”

Dataset for: Phylotranscriptomics reveals the phylogeny of Asparagales and the evolution of allium flavor biosynthesis, Nature Communications，DOI：10.1038/s41467-024-53943-6 by Xiao-Xiao Wang (2447920)

“…Extract homologs for all the 501 samples</p><p dir="ltr"><i>python2 blast_to_mcl.py all.rawblast 0.25 >mcl_all_rawblast_out_nohup_out_0.25</i></p><p dir="ltr"><i>mcl mcl_all_rawblast_out_nohup_out_0.25 --abc -te 80 -tf 'gq(5)' -I 1.5 -o hit-frac0.25_I1.5_e5</i></p><p dir="ltr"><i>python2 write_fasta_files_from_mcl.py all.fa hit-frac0.25_I1.5_e5 minimal_taxa outDIR</i></p><p><br></p><p dir="ltr"><b>Step 5: Build Maximum likelihood tree for each homolog group</b></p><p dir="ltr"><i>python2 fasta_to_tree_pxclsq.py fasta_dir number_cores dna bootstrap(y)</i></p><p><br></p><p dir="ltr"><b>Step 6: Extract ortholog groups</b></p><p dir="ltr">Instead of 1to1, MI, RT, or MO methods in Ya et al. (2014), we used a revised version of DISCO (https://github.com/JSdoubleL/DISCO) to infer homologs. …”

adnus by Mehmet Keçeci (14301782)

“…<p dir="ltr">adnus (AdNuS): Advanced Number Systems</p><p dir="ltr">adnus is a Python library that provides an implementation of various advanced number systems. …”

Reinforcement Learning based traffic steering inOpen Radio Access Network (ORAN)- oran-ts GitHub Repository by Aaradhy Sharma (21503465)

“…It features a modular Python framework implementing various RL agents (Q-Learning, SARSA, N-Step SARSA, DQN) and a traditional baseline evaluated in a realistic cellular network environment. …”

Evaluation and Statistical Analysis Code for "Multi-Task Learning for Joint Fisheye Compression and Perception for Autonomous Driving" by Basem Ahmed (18127861)

“…</li></ul><p dir="ltr">These scripts are implemented in Python using the PyTorch framework and are provided to ensure the reproducibility of the experimental results presented in the manuscript.…”

Landscape Change Monitoring System (LCMS) Conterminous United States Cause of Change (Image Service) by U.S. Forest Service (17476914)

“…The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades.Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. …”

Code by Baoqiang Chen (21099509)

“…</p><p><br></p><p dir="ltr">For the 5′ UTR library, we developed a Python script to extract sequences and Unique Molecular Identifiers (UMIs) from the FASTQ files. …”

Core data by Baoqiang Chen (21099509)

“…</p><p><br></p><p dir="ltr">For the 5′ UTR library, we developed a Python script to extract sequences and Unique Molecular Identifiers (UMIs) from the FASTQ files. …”

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. Six Python scripts are provided, each implementing a distinct machine learning algorithm—Random Forest, k-Nearest Neighbors (k-NN), Multi-Layer Perceptron (MLP), Decision Tree, Naïve Bayes, and Logistic Regression. …”

A Structured Attempt at a Polynomial-Time Solution to the Subset Sum Problem and Its Implications for P vs NP by MInakshi Aggarwal (21677633)

“…The manuscript includes theoretical formulation, Python implementation, verified output snapshots, and detailed analysis — aimed at opening fresh discourse on the P vs NP question. …”

Gene Editing using Transformer Architecture by Rishabh Garg (5261744)

“…Implementation requires Python and deep learning frameworks like TensorFlow or PyTorch, with optional use of Biopython for genetic sequence handling. …”

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

“…Informed consent was obtained from all participants, and all data have been anonymised following institutional and GDPR requirements.…”

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

“…The data corresponds to carbon dioxide (CO2) adsorption on the metal-organic framework CALF-20 at six different temperatures ranging from 253 K to 373 K.</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. …”

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

“…The samples (SRR36268464, SRR36225071) were retrieved from the NCBI Sequence Read Archive (SRA) and represent publicly available, real-world viral specimens collected during the final month of 2025, <b>the most recent temporal window available at the time of analysis.…”

Contrast enhancement of digital images using dragonfly algorithm by Soumyajit Saha (19726163)

“…The Python implementation of the proposed approach is available in this <a href="https://github.com/somnath796/DA_contrast_enhancement" target="_blank">Github repository</a>.…”

The artifacts and data for the paper "DD4AV: Detecting Atomicity Violations in Interrupt-Driven Programs with Guided Concolic Execution and Filtering" (OOPSLA 2025) by zixuan yuan (17602152)

“…</p><pre><pre>sudo apt-get install -y wget git build-essential python3 python python-pip python3-pip tmux cmake libtool libtool-bin automake autoconf autotools-dev m4 autopoint libboost-dev help2man gnulib bison flex texinfo zlib1g-dev libexpat1-dev libfreetype6 libfreetype6-dev libbz2-dev liblzo2-dev libtinfo-dev libssl-dev pkg-config libswscale-dev libarchive-dev liblzma-dev liblz4-dev doxygen libncurses5 vim intltool gcc-multilib sudo --fix-missing<br></pre></pre><pre><pre>pip install numpy && pip3 install numpy && pip3 install sysv_ipc<br></pre></pre><h4><b>Download the Code</b></h4><p dir="ltr">Download <b>DD4AV</b> from the Figshare website to your local machine and navigate to the project directory:</p><pre><pre>cd DD4AV<br></pre></pre><h4><b>Configure Environment and Install the Tool</b></h4><p dir="ltr">For convenience, we provide shell scripts to automate the installation process. …”

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. …”

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. …”

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

“…<p dir="ltr">This archive contains the evaluation data we produced during the evaluation of our prototype implementation of HCC from our paper <i>HCC: A Language-Independent Hardening Contract Compiler for Smart Contracts</i>.…”