Albumin Degradome Foundation Atlas حسب Axel Petzold (7076261)

"…</p><h2><b>Data Format and Access</b></h2><ul><li><b>Primary file:</b> <code>Albumin_Degradome_Foundation_Atlas_v1.tar.xz</code><br>(contains all peptide tables in standard CSV format)</li><li><b>File Type:</b> ASCII comma-separated values (CSV)</li><li><b>Compression:</b> <code>xz -9 -T0</code> for maximal CPU-parallelised compression</li><li><b>Compatibility:</b></li><li><ul><li>R, Python, MATLAB, SAS</li><li>Excel, LibreOffice</li><li>Any proteomics workflow (e.g., Skyline, MaxQuant preprocessing, MS/MS spectral libraries)</li></ul></li></ul><h2><b>FAIR Principles</b></h2><p dir="ltr">This dataset is fully aligned with FAIR data standards:</p><ul><li><b>Findable:</b> Rich metadata, stable DOI, search-optimised description</li><li><b>Accessible:</b> Open-access Figshare repository</li><li><b>Interoperable:</b> Standard numeric and CSV formats</li><li><b>Reusable:</b> Transparent, reproducible Python source code included</li></ul><h2><b>Applications</b></h2><p dir="ltr">The Albumin Degradome Foundation Atlas supports research across multiple biomedical domains:</p><ul><li>Biomarker development in liver disease, kidney dysfunction, inflammation, and systemic disorders</li><li>Mass-spectrometry method development</li><li>Computational proteomics and peptide modelling</li><li>Autoimmunity and neo-epitope analysis</li><li>Protein–peptide interaction studies</li><li>Proteolytic pathway mapping and degradomics</li></ul><h2><b>Versioning and Future Work</b></h2><p dir="ltr">This is <b>Version 1</b> of the Albumin Degradome Foundation Atlas. …"

<b>Antibiotics in the Global River System Arising from Human Consumption</b> حسب Heloisa Ehalt Macedo (10319300)

"…Python code: python project repository including the structure necessary for the model to run.…"

Synthetic Mental Health Survey Dataset حسب Shakil Hossain (22546658)

"…Data were generated using Python code to simulate realistic patterns and scoring based on psychological constructs.This dataset was to develop a machine learning model for predicting severity of multiple mental health disorders & overall Mental Health Status based on survey responses and psychological features.…"

LNP drug delivery image data حسب Philip Harrison (8899735)

"…</div><div><br></div><div><u>Python code:</u></div><div><a href="https://github.com/pharmbio/phil_LNP_modelling">https://github.com/pharmbio/phil_LNP_modelling</a><br></div><div><br></div><div><br></div><div><br></div><div><br></div><div><br></div><p></p>…"

Seattle Demo Accompanying Files حسب Winston Yap (13771969)

"…<p dir="ltr">We release the data, code, and prepared city graph objects to facilitate city scale building operating energy prediction with Seattle as a case study. …"

<b>Effects of Lifestyle and GLP-1RA based Interventions on Waist Circumference: A Systematic Review and Meta-Analysis</b> حسب Samit Ghosal (22024556)

"…</li><li><b>R scripts (00–06)</b> — reproducible code for primary, sensitivity, subgroup, and meta-regression analyses, forest plots, funnel plots, ROB2 templates, and prediction intervals.…"

<b>MSLU-100K: A multi-source land use dataset of Chinese major cities</b> حسب Yao Yao (7903457)

"…</li><li>The Manual Filtering.py-Based Multilevel Model Classification Method includes code to perform multilevel model predictions.…"

Leveraging explainable causal artificial intelligence to study forest gross primary productivity dynamics in China's protected areas حسب Pedro Cabral (18947566)

"…<p dir="ltr">A Python script used for modeling forest GPP in China´s Protected Areas, including mean encoding of the categorical variable climate zone (CZ), multicollinearity testing using Variance Inflation Factor (VIF), implementation of four machine learning models to predict forest GPP, XAI and causality analysis.…"

MsipNet حسب Nan Song (22280815)

"…<h2><b>MsipNet</b></h2><p dir="ltr">This is a directory for storing the **MsipNet** model code and data. **MsipNet** is a multi-scale representation learning framework for predicting protein-RNA interactions.…"

<b>Alpha-Synuclein Degradome Foundation Atlas</b> حسب Axel Petzold (7076261)

"…</p><p dir="ltr">Whether your work involves biomarker development, precision neurology, or machine learning, this dataset provides structured, labelled inputs that are ideal for:</p><ul><li>Training supervised models to detect or predict cleavage sites</li><li>Feature extraction from protein sequences</li><li>Clustering or classification of fragment types by mutation or disease context</li><li>Integrating with omics data for multimodal prediction tasks</li></ul><p dir="ltr">Dataset Features:</p><ul><li>Annotated α-synuclein proteolytic fragments</li><li>Includes wild-type and clinically relevant variants</li><li>Tab-delimited ASCII format for compatibility with Python, R, and ML frameworks</li><li>Linked SAS and Python scripts for pipeline reproducibility and updates</li><li>Ready-to-use for computational modelling, AI training, and bioinformatics workflows</li></ul><p dir="ltr">The dataset was generated using a reproducible codes involving Python, BLAST, and SAS. …"

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

"…</p><p dir="ltr"><b>Input:</b></p><ul><li><code>raw_data/glasgow_open_built/glasgow_open_built_areas.shp</code> - Grid defining sampling points</li></ul><p dir="ltr"><b>Command:</b></p><pre><pre>python svi_module/get_svi_data.py<br></pre></pre><p dir="ltr"><b>Output:</b></p><ul><li><code>svi_module/svi_data/svi_info.csv</code> - Image metadata (IDs, coordinates)</li><li><code>svi_module/svi_data/images/</code> - Downloaded street view images</li></ul><h3>Step 2: Predict Perceptions</h3><p dir="ltr">Use pre-trained deep learning models to predict perceptual qualities (safety, beauty, liveliness, etc.) from street view images.…"

DCPR_V1.0 حسب 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).…"

IGD-cyberbullying-detection-AI حسب Bryan James (19921044)

"…</li></ul></li><li><b>Internet Gaming Disorder Prediction</b>:</li><li><ul><li>Open the <code>Gamestudy.ipynb</code> notebook and run the cells to analyze IGD data using models like LSTM and CNN to detect patterns in gaming behavior.…"

Multi-Task Learning in Analyzing the Working capacity of MOFs حسب 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.…"

HMRLBA_V1.0 حسب 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.…"

Critical Planck Spin Dynamics (CPSD): A Geometric Quantum Spacetime with Zero Free Parameters حسب 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.…"

The Kidmose CANid Dataset (KCID) حسب 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.…"

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

"…</p><p dir="ltr">The main.py file can be run directly to automate the computation and output of the model.</p><p dir="ltr">表格部分</p><p dir="ltr">1.Table 3：Model accuracy assessment</p><p dir="ltr">脚本路径：’code/bert.py’</p><p dir="ltr">输入数据：’data/wh_data_cleaned.csv’</p><p dir="ltr">输出位置：’data/emotion_prediction_wh.csv’</p><p dir="ltr">说明：Output Precision, Recall, F1 for each emotion, and calculate the weighted average of Precision, Recall, F1</p><p dir="ltr">2.Table 4： Examples of different types of emotional structures</p><p dir="ltr">脚本路径：’code/countnum.py’</p><p dir="ltr">输入数据：’data/emotion_prediction_wh.csv’</p><p dir="ltr">输出位置：’data/emotion_prediction_wh.csv’</p><p dir="ltr">说明：Determine whether an emotion is of a single type, a dominant subsidiary type, or one of the composite types by using emotion probabilities and entropy values</p><p dir="ltr">3.Table 5-6： Examples of different types of emotional structures</p><p dir="ltr">①脚本路径：’code/lat_lon.py’</p><p dir="ltr">输入数据：’data/emotion_prediction_wh.csv’</p><p dir="ltr">输出位置：’result/bert/wh/128/grid_lat_lon.csv’</p><p dir="ltr">说明：The study area can be gridded by running the file.…"

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

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

<b>Tau Degradome Foundation Atlas</b> حسب Axel Petzold (7076261)

"…</li><li><b>Thermodynamic stability</b> is predicted for each peptide, with stable candidates labelled (<code>stable=1</code>), highlighting their suitability for biomarker assay development.…"