Supplementary Material for review——Revealing the co-occurrence patterns of the group emotions from social media data by Yang Hua (21399140)

“…</p><p dir="ltr">研究区域边界数据文件位于：“data/420000.geojson”</p><p><br></p><p dir="ltr">2.2 数据处理流程</p><p dir="ltr">python code/clear_duplicate.py # 原始数据清洗；获得清洗后的数据，文件位置：“data/wh_data_cleaned.csv”</p><p dir="ltr">三、结果复现说明</p><p dir="ltr">请依次运行以下脚本以生成对应图表/表格。…”

Numerical analysis and modeling of water quality indicators in the Ribeirão João Leite reservoir (Goiás, Brazil) by Amanda Bueno de Moraes (22559249)

“…The code implements a statistical–computational workflow for parameter selection (VIF, Bartlett and KMO tests, PCA and FA with <i>varimax</i>) and then trains and evaluates machine-learning models to predict three key physico-chemical indicators: turbidity, true color, and total iron. …”

HMRLBA_V1.0 by Zhenyu Huang (20153082)

“…<h2><b>HMRLBA</b></h2><p dir="ltr">This is a repository to deposit the data and code for HMRLBA model. HMRLBA is a hierarchical multi-scale representation learning model for predicting protein-ligand binding affinity.…”

DCPR_V1.0 by xiao han (21405770)

“…</p><p dir="ltr"><b>DCPR_codes</b>: Source code for DCPR model.</p><p dir="ltr"><b>Visualization</b>: Scripts for data visualization, including circadian curves (cosine model fitting), sample plots (predicted and real time), model performance (CDF curves, and accuracy plots).…”

Judge the Votes: A System to Classify Bug Reports and Give Suggestions by Emre Dinç (21720032)

“…The data folder contains 2 files: training.csv, and bug_reports.csv. training.csv contains only 2 columns text and label, and it is the version we used when training the models. bug_reports.csv contains the columns and ids we have retrieved from Bugzilla. The code folder contains the .ipynb files we used when creating the dataset, training ML models, fine-tuning BERT-based models, and getting predictions from LLMs with both zero-shot and few-shot settings. …”

ML for anomalous diffusion model by Yuehua Zhao (8498052)

“…<p dir="ltr"><b>ML for anomalous diffusion model</b></p><p dir="ltr">Dapeng Wang</p><p>7.24.2025</p><p dir="ltr">This repository contains the necessary codes written in Python 3 to train the classifiers.…”

Critical Planck Spin Dynamics (CPSD): A Geometric Quantum Spacetime with Zero Free Parameters by Daniel Zunzunegui (22583093)

“…Complete Python verification code included.</p><p><br></p><p dir="ltr">This work solves the hierarchy problem, explains dark energy as geometric anisotropy, and predicts the universe's ultimate fate as "Big Silence" - eternal expansion at c/φ ≈ 0.618c.…”

Multi-Task Learning in Analyzing the Working capacity of MOFs by Junhui Kou (20327073)

“…</p><ul><li><b>CIF files</b>: CIF files for 252,352 MOFs;</li><li><b>Geometric descriptors</b>: 14 geometric descriptors;</li><li><b>Chemical descriptors</b>: 176 chemical descriptors;</li><li><b>Methane_v, Methane_g</b>: Volumetric and gravimetric working capacities for methane adsorption, including methane adsorption data under six pressures across three application scenarios (landfill gas treatment, methane purification, and methane storage);</li><li><b>MTL4MOFsWC</b>: Python code for training the MTL models to predict the working capacity of methane adsorption in MOFs;</li><li><b>best_model_v_full, best_model_v_sim, best_model_g_full, best_model_g_sim</b>: Pre-trained MTL models.…”

TB PROSPECT - Reference-based chemical-genetic interaction profiling to elucidate small molecule mechanism of action in Mycobacterium tuberculosis by Austin Bond (20723383)

“…GCTx format is a binary file used to store the scores in matrix format with annotated row and column metadata in a compressed, memory-efficient manner. Code libraries in Matlab (cmapM), Python (cmapPy), and R (cmapR) are publicly available on Github to work with it. …”

LGF v11 Final Archive: EWL, GMMI, LGF-EWI Metrics and Language Spacetime Specification (Dec 2025) by heojeongbeom heo (22544705)

“…This was confirmed by the final $\mathbf{\Psi}$ Operator simulation: the Python code returned <b>$\text{nan}$</b><b> (Not a Number)</b> due to the runaway divergence of the Language Potential ($\mathbf{\Phi_L}$), serving as the <b>computational proof of the </b><b>$\mathbf{T=0}$</b><b> structural failure.…”

The Kidmose CANid Dataset (KCID) by Brooke Elizabeth Kidmose (13626754)

“…</p><h2>FILE TYPES</h2><p dir="ltr">The dataset provides data in three formats to support different use cases:</p><p dir="ltr"><b>.mf4 (MDF4) Format:</b> Measurement Data Format version 4 (MDF4)</p><ul><li>Binary format standardized by the Association for Standardization of Automation (ASAM)</li><li><b>Advantages:</b> Compact size, popular with automotive/CAN tools</li><li><b>Use case:</b> Native format from CSS Electronics CANEdge2</li><li><b>Reference:</b> <a href="https://www.csselectronics.com/pages/mf4-mdf4-measurement-data-format" rel="noreferrer" target="_blank">https://www.csselectronics.com/pages/mf4-mdf4-measurement-data-format</a></li></ul><p dir="ltr"><b>.log Format:</b> Text-based log format</p><ul><li><b>Compatibility:</b> Linux SocketCAN can-utils</li><li><b>Advantages:</b> Compatibility with SocketCAN can-utils; if a .log file is replayed, then data can be captured and monitored using Python's python-can library</li><li><b>References:</b> <a href="https://github.com/linux-can/can-utils" rel="noreferrer" target="_blank">https://github.com/linux-can/can-utils</a>, <a href="https://packages.debian.org/sid/can-utils" rel="noreferrer" target="_blank">https://packages.debian.org/sid/can-utils</a>, <a href="https://python-can.readthedocs.io/en/stable/" rel="noreferrer" target="_blank">https://python-can.readthedocs.io/en/stable/</a></li></ul><p dir="ltr"><b>.csv Format:</b> Text-based comma-separated values (CSV) format</p><ul><li><b>Advantages:</b> Easy to load with Python using the pandas library; easy to use with Python-based machine learning frameworks (e.g., scikit-learn, Keras, TensorFlow, PyTorch)</li><li><b>Usage:</b> Load with Python pandas: pd.read_csv()</li><li><b>Reference:</b> <a href="https://pandas.pydata.org/" rel="noreferrer" target="_blank">https://pandas.pydata.org/</a></li></ul><h2>SPECIALIZED EXPERIMENTS</h2><p dir="ltr">The KCID Dataset includes five specialized experiments:</p><p dir="ltr"><b>Fixed Routes Experiment</b></p><ul><li><b>Vehicles:</b> 2011 Chevrolet Traverse, 2017 Subaru Forester</li><li><b>Drivers:</b> male-30-55-3, male-30-55-4, male-over55-1, female-all-ages-1, female-all-ages-2, female-all-ages-5</li><li><b>Location:</b> Florida, USA (specific routes)</li><li><b>Data Collection Methods:</b> CSS Electronics CANEdge2, Kvaser Hybrid CAN-LIN</li><li><b>Purpose:</b> Capture CAN traces for specific, mappable routes; eliminate route-based variations in driver authentication data (e.g., low-speed local routes vs. high-speed long-distance routes)</li></ul><p dir="ltr"><b>OBD Requests and Responses Experiment</b></p><ul><li><b>Vehicle:</b> 2011 Chevrolet Traverse</li><li><b>Driver:</b> female-all-ages-5</li><li><b>Location:</b> Florida, USA</li><li><b>Data Collection Method:</b> CSS Electronics CANEdge2</li><li><b>Purpose:</b> Capture OBD requests and responses Arbitration IDs: <i>Requests:</i> 0x7DF, <i>Responses:</i> 0x7E8</li></ul><p dir="ltr"><b>Tire Pressure Experiment</b></p><ul><li><b>Vehicle:</b> 2011 Chevrolet Traverse</li><li><b>Driver:</b> female-all-ages-5</li><li><b>Location:</b> Florida, USA</li><li><b>Data Collection Method:</b> Kvaser Hybrid CAN-LIN</li><li><b>Purpose:</b> Capture normal and low tire pressure scenarios</li><li><b>Applications:</b> Detect tire pressure issues via CAN bus analysis; develop predictive maintenance strategies</li></ul><p dir="ltr"><b>Driving Modes and Features Experiment</b></p><ul><li><b>Vehicle:</b> 2017 Ford Focus</li><li><b>Driver:</b> male-30-55-1</li><li><b>Location:</b> Denmark</li><li><b>Data Collection Method:</b> Korlan USB2CAN</li><li><b>Purpose:</b> Capture different driving (and non-driving) modes and features</li><li><b>Examples:</b> gear (park, reverse, neutral, drive, sport); headlights on/off</li></ul><p dir="ltr"><b>Stationary Vehicles Experiment</b></p><ul><li><b>Vehicles:</b> 2024 Chevrolet Malibu, 2025 Toyota Corolla</li><li><b>Driver:</b> N/A (vehicles remained stationary)</li><li><b>Location:</b> Florida, USA</li><li><b>Data Collection Method:</b> Kvaser Hybrid CAN-LIN</li><li><b>Purpose:</b> Capture CAN bus traffic from very new, very modern vehicles; identify differences between an older vehicle's CAN bus (e.g., 2011 Chevrolet Traverse), and a newer vehicle's CAN bus (e.g., 2024 Chevrolet Malibu)</li></ul><h2>ADDITIONAL DOCUMENTATION</h2><p dir="ltr">Each "specialized experiment" directory contains a detailed README.md file with specific information about the experiment and the data collected.…”

Tracking when the number of individuals in the video frame changes. by Hirotsugu Azechi (20700528)

“…The removal of unnecessary keypoint data is achieved through a Python code that allows specified ranges of tracking data obtained from DeepLabCut to be rewritten as NaN (no data) (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.3003002#pbio.3003002.s019" target="_blank">S1 Protocol</a> and <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.3003002#pbio.3003002.s010" target="_blank">S10C Fig</a>). …”

Dataset for:Exploring the Pharmacological Properties and Mechanism of Action of Lithocarpus litseifolius (Hance) Chun. in Treating Diabetic Neuropathy Based on SwissADME, Network P... by Xiaoyong Wang (22361497)

“…</p><p dir="ltr">Software & code</p><p dir="ltr">R 4.4.2 scripts for GO/KEGG (clusterProfiler, enrichplot, ggplot2) and Python notebooks for data cleaning are provided in the Code/ folder.…”

Ambient Air Pollutant Dynamics (2010–2025) and the Exceptional Winter 2016–17 Pollution Episode: Implications for a Uranium/Arsenic Exposure Event by Thomas Clemens Carmine (19756929)

“…Includes imputation statistics, data dictionary, and the Python imputation code (Imputation_Air_Pollutants_NABEL.py). …”

Murakami et al. Supplemental Data for "Microstructural Analysis of Li-Ion Conductors with Deep Learning and SEM Images" by Kento Murakami (21260244)

“…</p><h2><b>SEM_images.zip</b>: </h2><p dir="ltr">These files consists of SEM images and numerical datasets (descriptors and objective variables) of composition, sintering temperature, and ionic conductivities for 52 samples (1-3 SEM images are included per 1 sample, total 130 images)</p><h2><b>python_codes.zip</b>: </h2><p dir="ltr">Python codes for four convolutional neural network (CNN) models used to investigate the relationship between these image data and ionic conductivity are provided.…”

PepENS by Abel Chandra (16854753)

“…<br><br>Download and Use</p><p dir="ltr">The codes for Datasets 1 and 2 are found in the respective folders of this repository.…”

<b>Engineered Muscle-Derived Extracellular Vesicles Boost Insulin Sensitivity and Glucose Regulation</b> by Hagit Shoyhet (21090650)

“…</p><p dir="ltr"><b>miR_path_target_enrichment.csv</b></p><p dir="ltr"><b>Description:</b> KEGG pathway enrichment analysis results of shared mRNA targets of miRNAs miR-16-5p, miR-122-5p and miR-486-5p ranked by their interaction score defined in our paper. this includes the pathway name, the enrichment p-value, number of genes found in the term and number of miRNAs targeting these genes</p><p dir="ltr"><b>Code/software</b></p><p dir="ltr">Data were analyzed using R-V4.0.4, Python-V3.9.2 and GraphPad software. miRNA analyses were run in R-V4.0.4 Differential expression analysis was conducted using the “DEseq2” package and corrected for multiple hypotheses by FDR. …”