ncRNA.zip by Chongyi Jiang (20987126)

“…<h3><b>ncRNA (non-coding RNA) </b><b>predicted by</b><b> CPC2 (v 0.1) and PINC for 25 </b><b>grassland plant species.…”

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.…”

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.…”

Image 1_An explainable analysis of depression status and influencing factors among nursing students.png by Yingying Li (50341)

“…According to the random forest model, the order of depression predicted by this study from high to low is Sleep Condition, Social anxiety, Mother's Educational Level, Sexual Orientation, Smoking, and Household composition.…”

Galaxy Zoo: Cosmic Dawn -- morphological classifications for over 41,000 galaxies in the Euclid Deep Field North from the Hawaii Two-0 Cosmic Dawn survey by James Pearson (554629)

“…The data consists of classifications made by volunteers through the Galaxy Zoo project on the Zooniverse citizen science platform, as well as predicted classifications made by a deep learning model, Zoobot. …”

Supplementary Material by Aris Shahbazian (22464325)

“…It consists of three modules: 1.COMSOL Data Generator: A script that automates 500 high-fidelity COMSOL simulations using Latin Hypercube Sampling and extracts key plasma parameters such as electron density, uniformity, and absorbed power. 2.DNN Surrogate Model: A complete training pipeline using TensorFlow/Keras, including data preprocessing, model architecture, training, evaluation, and visualization of prediction accuracy. 3.Genetic Algorithm Optimization: A DEAP-based evolutionary optimization script that identifies optimal RF power and gas pressure values to maximize electron density while maintaining plasma uniformity above 90%. …”

Hierarchical Deep Learning Framework for Automated Marine Vegetation and Fauna Analysis Using ROV Video Data by Bjørn Christian Weinbach (16918707)

“…</li><li>confidence: Confidence score for the prediction.</li><li>predicted_species: Predicted species label.…”

PRCC elasticity analysis of . by Yousef AbuHour (17536686)

“…Additionally, we analyze the reproduction number’s sensitivity and explore the proposed discrete system’s local and global stability. The model was simulated and analyzed using Python packages, providing practical solutions to improve cybersecurity in IoT networks. …”

PRCC of analysis. by Yousef AbuHour (17536686)

“…Additionally, we analyze the reproduction number’s sensitivity and explore the proposed discrete system’s local and global stability. The model was simulated and analyzed using Python packages, providing practical solutions to improve cybersecurity in IoT networks. …”

Simulated network for 2000 devices. by Yousef AbuHour (17536686)

“…Additionally, we analyze the reproduction number’s sensitivity and explore the proposed discrete system’s local and global stability. The model was simulated and analyzed using Python packages, providing practical solutions to improve cybersecurity in IoT networks. …”

Tools used in this study. by Yousef AbuHour (17536686)

“…Additionally, we analyze the reproduction number’s sensitivity and explore the proposed discrete system’s local and global stability. The model was simulated and analyzed using Python packages, providing practical solutions to improve cybersecurity in IoT networks. …”

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. …”

Mushroom Classification Using Support Vector Machines (SVM) Focusing on Cap Features. by Gabriel Minato (22462099)

“…Methods: This is a</p><p dir="ltr">quantitative predictive modeling study, conducted through a computational experiment. …”

Enhancing Healthcare Transparency: Leveraging Machine Learning, GIS Mapping and Power BI for Private Hospital Insurance Claims Analysis by Maryam Binti Haji Abdul Halim (20249544)

“…</p><p dir="ltr">Key Features and Tools:</p><ul><li><b>Machine Learning Algorithms:</b> Leveraging <b>Python (pandas, numpy, scikit-learn)</b> for predictive modeling to assess claim validity and treatment outcomes.…”

Scripts_scParadise_article by Vadim Chechekhin (20569496)

“…ScAdam outperforms existing annotation methods by providing enhanced consistency and offers a model hub of over 30 pre-trained models. ScEve enables accurate imputation of cell surface protein markers, facilitating cell type annotation and in-depth multi-omic analyses in different tissues and species. …”

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>). …”

Supplementary file 1_Sociodemographic predictors and usability perceptions explaining academic use intention of ChatGPT among university students in Ecuador.pdf by Edgar Rolando Morales Caluña (22085753)

“…Descriptive statistics, exploratory factor analysis, binary logistic regression, and k-means clustering were performed using Python in Google Colab.</p>Results<p>The logistic regression model revealed that perceived usefulness (OR = 2.37) and compatibility with learning style (OR = 1.87) were the most significant predictors of high academic use intention. …”

<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. …”

Machine Learning-Driven Discovery and Database of Cyanobacteria Bioactive Compounds: A Resource for Therapeutics and Bioremediation by Renato Soares (20348202)

“…In this study, a searchable, updated, curated, and downloadable database of cyanobacteria bioactive compounds was designed, along with a machine-learning model to predict the compounds’ targets of newly discovered molecules. …”

Fast, FAIR, and Scalable: Managing Big Data in HPC with Zarr by Alfonso Ladino (21447002)

“…Nesbitt1, Deepack Cheerian2 </p><ol><li>University of Illinois at Urbana-Champaign </li></ol><ol><li>Earthmover PBC </li></ol><p dir="ltr">The explosive growth of high-resolution, multidimensional data — from radar and satellite imagery to weather prediction, climate modeling, MRI and microscopy in medicine and biology, CFD simulations in engineering, and large-scale machine learning datasets — is pushing the limits of traditional data storage and processing methods. …”