<b>Force-Position-Speed Planning and Roughness rediction for Robotic Polishing</b> by Ma Haohao (19780875)

“…The improved dung beetle optimization algorithm, back propagation neural network, finite element analysis and response surface method provide a strong guarantee for the selection of robotic polishing process parameters. …”

<b>SAFE: </b><b>s</b><b>ensitive </b><b>a</b><b>nnotation </b><b>f</b><b>inding and </b><b>e</b><b>xtraction from multi-type Chinese maps via hybrid intelligence and knowledge grap... by jiaxin ren (20482655)

“…Experiments validate the effectiveness of SAFE: in detection tasks, SAFE achieves an Hmean of 96.44%, approximately ten percentage points higher than the baseline model; in recognition tasks, SAFE attains an accuracy of 96.73%, which is 15.59% higher than the original algorithm. …”

Identification of neutrophil extracellular trap-related genes in Alzheimer’s disease based on comprehensive bioinformatics analysis by Nana Qiao (19730190)

“…Protein–protein interaction (PPI) network, Minutia Cylinder-Code (MCC) algorithm, and molecular complex detection (MCODE) algorithm in the CytoHubba plug-in were employed to identify five hub genes (NFKBIA, SOCS3, CCL2, TIMP1, ACTB). …”

Overtuning in Hyperparameter Optimization - Artifacts by Lennart Schneider (20131899)

“…<br></p><p dir="ltr">This data contains the following columns (and some additional ones not explained here which are self-explanatory) where the validation and test performance of each proposed hyperparameter configuration are tracked over time in the form of trajectories</p><ul><li>iteration (iteration of an HPO run)</li><li>valid (validation performance)</li><li>test_retrained (test performance after retraining)</li><li>seed (replication id)</li><li>classifier (learning algorithm)</li><li>data_id (data set id)</li><li>train_valid_size (size of the set used for training and validation)</li><li>resampling (resampling method)</li><li>metric (performance metric)</li><li>method (post selection method and resampling method)</li><li>optimizer (HPO algorithm)</li></ul><p><br></p>…”

A Gentle Introduction and Application of Feature-Based Clustering with Psychological Time Series by Jannis Kreienkamp (10641803)

“…We also provide practical algorithm overviews and readily available code for data preparation, analysis, and interpretation.…”

Identification of Neuronal Activity from Extracelullar Recordings with Ground-truth Patch Clamp by Carina Germer (18692413)

“…<p dir="ltr">Automated algorithm for the identification of neuronal activity from multichannel electrode arrays available on an online <a href="https://zenodo.org/records/1205233" rel="noreferrer" target="_blank">database</a>¹</p><p dir="ltr"><i>Decomposition of an initial segment of the recording:</i><br>main code: NeuroNella_DecomposeGroundTruth<br><br>How to run (in MATLAB):</p><p dir="ltr"><br>1: Download the main code (NeuroNella_DecomposeGroundTruth) and zip folder containing the subcodes<br>2: Download recording files: Download one or more files from the <a href="https://zenodo.org/records/1205233" rel="noreferrer" target="_blank">database</a> and save in one folder<br>3: Set Up the Main Directory: Locate the MainDir variable in the code and edit it to include the path to the folder containing the code files.…”

Supplementary file 1_Construction of an oligometastatic prediction model for nasopharyngeal carcinoma patients based on pathomics features and dynamic multi-swarm particle swarm op... by Yunfei Li (652635)

“…A demo of the DMS-PSO-SVM modeling algorithm code used in this study can be found on Github (https://github.com/Edward-E-S-Wang/DMS-PSO-SVM).…”

Research on Olympic medal prediction based on GA-BP and logistic regression model Extended data by Sanglin Zhao (20599835)

“…</p><p dir="ltr">Including data and code (Matlab)</p>…”

Back to the Roots: Assessing Mining Techniques for Java Vulnerability-Contributing Commits by Torge Hinrichs (17121607)

“…<p dir="ltr">Vulnerability-contributing commits (VCCs) are changes in code repositories that contribute to the insertion of vulnerabilities. …”

Inferring Regional Commuting Systems from Network Signaling Data by Alexe Vlad (20556224)

“…Researchers can adapt the code to similar mobility datasets to investigate commuting trends and urban mobility structures.…”

<b>A virtual tracer experiment to assess the temporal origin of root water uptake, evaporation, and </b><b>drainage</b> by Paolo Nasta (19710883)

“…</p><p dir="ltr"><a href="" target="_blank">Two open-source Matlab scripts are available in the zip-files. The PT.m Matlab code determines the drainage transit time based on the particle tracking algorithm, while the VTE.m Matlab code determines the drainage and RWU transit times and relative rainfall contributions to actual evaporation, actual transpiration, and drainage using isotope transport simulations in HYDRUS-1D</a>. …”

Data Sheet 1_Toward a unified gait freeze index: a standardized benchmark for clinical and regulatory evaluations.pdf by Alessandro Schaer (14234780)

“…Our method demonstrates improved performance compared to existing approaches while effectively mitigating the risk of divergent outcomes, which could otherwise lead to unforeseen and potentially hazardous consequences in real-world applications. Our algorithm is made available as open-source Python code, promoting accessibility and reproducibility.…”

<b>From street view imagery to the countryside: large-scale perception of rural China using deep learning</b> by Yao Yao (7903457)

“…The project includes both the data and code that support the Pair-CNN model.</p><h3>1. "data" folder</h3><ul><li>Picture.zip—Rural street view imagery data, containing 100 randomly selected rural imagery in JPG format.…”

Genosophus: A Dynamical-Systems Diagnostic Engine for Neural Representation Analysis by Alan Glanz (22109698)

“…</p><h2><b>Intended Applications</b></h2><ul><li>Diagnosis of representational collapse or instability</li><li>Detection of emergent structure in transformers</li><li>Monitoring internal geometry during training or RLHF</li><li>Studying drift and novelty in sequential reasoning tasks</li><li>Complementing mechanistic interpretability efforts</li><li>Model evaluation and safety diagnostics</li></ul><h2><b>Intellectual Property Notice</b></h2><p dir="ltr">This diagnostic framework and its algorithmic components are covered under a U.S. …”

Peer Review Fundamentals: Enhancing Quality and Integrity in Scholarly Publishing by Thaer Al-Jadir (12237572)

“…</li><li><b>Engineering/Robotics</b>: emphasis on reproducibility of algorithms, code, and simulations.</li></ul><p></p>…”

<b>Rethinking neighbourhood boundaries for urban planning: A data-driven framework for perception-based delineation</b> by Shubham Pawar (22471285)

“…</p><p dir="ltr"><b>Input:</b></p><ul><li><code>svi_module/svi_data/svi_info.csv</code> - Image metadata from Step 1</li><li><code>perception_module/trained_models/</code> - Pre-trained models</li></ul><p dir="ltr"><b>Command:</b></p><pre><pre>python -m perception_module.pred \<br> --model-weights .…”

Dataset for Partial Parallelism Plot Analysis in Neurodegeneration Biomarker Assays (2010–2024) by Axel Petzold (7076261)

“…<br></p><p dir="ltr">Each dataset entry is annotated with:</p><ul><li>Sample type (serum, plasma, cerebrospinal fluid)</li><li>Assay platform and dilution steps</li><li>Classification of outcome (partial parallelism achieved or not)</li></ul><p dir="ltr"><b>Use cases:</b><br>This dataset is designed to help researchers, assay developers, and meta-analysts to:</p><ul><li>Reproduce figures and analyses from the published review</li><li>Benchmark or validate new assay performance pipelines</li><li>Train algorithms for automated detection of dilutional non-parallelism</li></ul><p dir="ltr"><b>Files included:</b></p><ul><li><code>.csv</code> files containing dilution–response data</li><li>Metadata spreadsheets with assay and sample annotations</li></ul><p></p>…”

Multi-Offset Synthetic GPR Data by Giacomo Roncoroni (16630053)

“…</li><li>- <code>data</code> Directory containing the synthetic GPR data files.…”

Data Sheet 1_ARGContextProfiler: extracting and scoring the genomic contexts of antibiotic resistance genes using assembly graphs.pdf by Nazifa Ahmed Moumi (7434359)

“…Several tools, databases, and algorithms are now available to facilitate the identification of ARGs in metagenomic sequencing data; however, direct annotation of short-read data provides limited contextual information. …”

ImproBR Replication Package by Anonymus (18533633)

“…<br><br>**Import Errors:**<br>Make sure you're in the replication package directory:<br>```bash<br>cd ImproBR-Replication<br>python improbr_pipeline.py --help<br>```<br><br>## Research Results & Evaluation Data<br>### RQ1: Bug Report Improvement Evaluation (139 reports)<br>**Manual Evaluation Results:**<br>- [`RQ1-RQ2/RQ1/manual_evaluation/Author 1 Responses.csv`](<u>RQ1-RQ2/RQ1/manual_evaluation/Author 1 Responses.csv</u>) - First evaluator assessments<br>- [`RQ1-RQ2/RQ1/manual_evaluation/Author 2 Responses.csv`](<u>RQ1-RQ2/RQ1/manual_evaluation/Author 2 Responses.csv</u>) - Second evaluator assessments <br>- [`RQ1-RQ2/RQ1/manual_evaluation/Final Results.csv`](<u>RQ1-RQ2/RQ1/manual_evaluation/Final Results.csv</u>) - Consolidated evaluation results<br><br>**Inter-Rater Agreement (Cohen's Kappa):**<br>- [`RQ1-RQ2/RQ1/cohen's_cappa_coefficient_matrices/confusion_matrix_s2r_label.png`](<u>RQ1-RQ2/RQ1/cohen's_cappa_coefficient_matrices/confusion_matrix_s2r_label.png</u>) - Steps to Reproduce κ scores<br>- [`RQ1-RQ2/RQ1/cohen's_cappa_coefficient_matrices/confusion_matrix_ob_label.png`](<u>RQ1-RQ2/RQ1/cohen's_cappa_coefficient_matrices/confusion_matrix_ob_label.png</u>) - Observed Behavior κ scores<br>- [`RQ1-RQ2/RQ1/cohen's_cappa_coefficient_matrices/confusion_matrix_eb_label.png`](<u>RQ1-RQ2/RQ1/cohen's_cappa_coefficient_matrices/confusion_matrix_eb_label.png</u>) - Expected Behavior κ scores<br><br>**Algorithm Results:**<br>- [`RQ1-RQ2/RQ1/algorithm_results/improbr_outputs/`](<u>RQ1-RQ2/RQ1/algorithm_results/improbr_outputs/</u>) - ImproBR improved reports<br>- [`RQ1-RQ2/RQ1/algorithm_results/chatbr_outputs/`](<u>RQ1-RQ2/RQ1/algorithm_results/chatbr_outputs/</u>) - ChatBR baseline results<br>- [`RQ1-RQ2/RQ1/algorithm_results/bee_analysis/`](<u>RQ1-RQ2/RQ1/algorithm_results/bee_analysis/</u>) - BEE tool structural analysis<br><br>### RQ2: Comparative Analysis vs ChatBR (37 pairs)<br>**Similarity Score Results:**<br>- [`RQ1-RQ2/RQ2/algorithm_results/similarity_scores/overall_tfidf.csv`](<u>RQ1-RQ2/RQ2/algorithm_results/similarity_scores/overall_tfidf.csv</u>) - TF-IDF similarity scores<br>- [`RQ1-RQ2/RQ2/algorithm_results/similarity_scores/overall_word2vec.csv`](<u>RQ1-RQ2/RQ2/algorithm_results/similarity_scores/overall_word2vec.csv</u>) - Word2Vec similarity scores<br>- [`RQ1-RQ2/RQ2/algorithm_results/similarity_scores/exact_string_comparisons.json`](<u>RQ1-RQ2/RQ2/algorithm_results/similarity_scores/exact_string_comparisons.json</u>) - Complete TF-IDF comparison with scores for each comparison unit (full debugging)<br>- [`RQ1-RQ2/RQ2/algorithm_results/similarity_scores/word2vec_comparisons.json`](<u>RQ1-RQ2/RQ2/algorithm_results/similarity_scores/word2vec_comparisons.json</u>) - Complete Word2Vec comparison with scores for each comparison unit (full debugging)<br><br>**Algorithm Outputs:**<br>- [`RQ1-RQ2/RQ2/algorithm_results/ImproBR_outputs/`](<u>RQ1-RQ2/RQ2/algorithm_results/ImproBR_outputs/</u>) - ImproBR enhanced reports<br>- [`RQ1-RQ2/RQ2/algorithm_results/ChatBR_outputs/`](<u>RQ1-RQ2/RQ2/algorithm_results/ChatBR_outputs/</u>) - ChatBR baseline outputs<br>- [`RQ1-RQ2/RQ2/dataset/ground_truth/`](<u>RQ1-RQ2/RQ2/dataset/ground_truth/</u>) - High-quality reference reports<br>## Important Notes<br><br>1. …”