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Virtual Reality in Nursing Education: A Systematic Review and Meta-Analysis of Its Impact on Stress Reduction and Retention in the Philippines, India, Indonesia, and Israel (2000–2025)

This dataset contains de-identified clinical, imaging, and molecular data from 14 patients with extremity giant cell tumor of bone (GCTB) treated at a single tertiary center between January 2020 and December 2024. The dataset was generated to investigate the relationship between PD-L1 mRNA expression and MRI-based volumetric tumor growth rate. The Excel file includes two sheets: Clinical_Volumetric_Data – patient-level demographics, tumor site, preoperative denosumab use, volumetric MRI data (volumes, voxel parameters), and qPCR-derived ΔCt, ΔΔCt, and fold-change values. PCR_Triplicate_Data – raw qPCR cycle threshold (Ct) values for PD-L1 and β-actin measured in triplicate for each sample. ΔCt and ΔΔCt values were computed according to the 2^−ΔΔCt method, using β-actin as the endogenous reference and normal skeletal muscle as the control. Tumor growth rate was defined as the change in MRI-measured tumor volume between two preoperative scans divided by the interval in months. All patient identifiers have been removed, and no protected health information is included. This dataset supports the findings reported in the corresponding manuscript, providing transparency and enabling further exploration of molecular–imaging correlations in GCTB.

The Bangladesh IoT Environmental Dataset (BIED) is a curated collection of realistic IoT sensor readings representing diverse environmental conditions across Bangladesh’s urban, rural, coastal, agricultural, and industrial regions. It simulates continuous monitoring of critical environmental parameters such as temperature, humidity, air quality, CO₂ levels, soil moisture, and rainfall, following realistic diurnal, seasonal, and regional variations. BIED aims to support AI, IoT, and environmental research by providing Bangladesh-contextualized sensor data suitable for analysis, prediction, and system validation. Features: 1. Environmental Variables: Temperature (°C), Humidity (%), Air Quality Index (AQI), CO₂ (ppm), Soil Moisture (%), Rainfall (mm), Light Intensity (lux). 2. Temporal Resolution: Hourly readings over several simulated days. 3. Regional Coverage: Urban (Dhaka), Rural (Rajshahi), Coastal (Khulna), Industrial (Narayanganj), Agricultural (Bogura). 4. Sensor Behavior Modeling: Includes realistic drift, noise, and fluctuation patterns mimicking real-world sensor data. Potential Applications: 1. Smart Agriculture: Predict soil moisture and rainfall patterns for irrigation planning. 2. Smart City Systems: Model urban air quality and temperature control. 3. Climate Research: Analyze coastal zone trends and early warning modeling. 4. Anomaly Detection: Detect faulty sensor behaviors in IoT systems. 5. AI & TinyML Training: Build and deploy embedded ML models for environmental monitoring. 6. IoT Platform Testing: Validate IoT dashboards, edge computing systems, and cloud integration pipelines. 7. Data Structure: CSV format with timestamps, location tags, and sensor values. 8. Data Size: ~10,000+ records (customizable for research needs).

The dataset is downloaded and selected sets used for the journal paper titled "Bridging the Gap: A Comprehensive Analytical Study of Traditional, Hybrid, and Explainable AI for Bridge Condition Prediction" by the same authors.

Supplementary Table. Demographic and laboratory characteristics of all patients included with nutritional deficiency dermatoses (NDD).

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This dataset contains a subset of raw images from the published work titled "Imaging the time course of DNA damage response at a nonrepetitive endogenous locus" in Cell Reports Methods. The raw images are organized into zip folders according to the figure they correspond to in the manuscript. All images are in .nd2 format and consist of z-stack acquisitions with a step size of 0.35 μm. Most images feature U2OS cells. If RPE1 cells were used (Figure 4), this is indicated in the file name. HEK293T cells were used in Figure 2 to showcase fixation conditions with H2B staining. U2OS cells were used in Figure 2 with etoposide treatment. Each file represents a specific fixation time point following either vfCRISPR activation or electroporation with CRISPR-Cas9, denoted by labels such as 5m (5 minutes), 3h (3 hours), NL (No Light, indicating no vfCRISPR activation), NoEP (No Electroporation), or EP (Electroporation with CRISPR-Cas9). Labeling details include: ACTB and MUC4 loci - Cy5; 53BP1 and H2B - Alexa 555 (A555); γH2AX and BRCA1 - Alexa 488 (A488); Hoechst (DAPI). Fixation conditions in the Figure 2 folder are labeled as: M100 - Methanol; MAA - Methanol:Acetic Acid; M100toMAA - Methanol followed by Methanol:Acetic Acid. Etoposide treatment conditions are denoted as Control (DMSO) or concentrations ranging from 1-100 μM Etoposide. G1 and G2 synchronized U2OS cells are labeled accordingly in the folder for Figure 4.

This dataset provides the complete code, data structure, and environment requirements necessary to reproduce the findings of the manuscript: "Deep learning identifies optimal single spectral bands for non-destructive fresh weight estimation in cabbage" (submitted to Computers and Electronics in Agriculture). The repository is organized to support two distinct deep learning workflows, both of which are iterated over 150 separate spectral bands (band_1 to band_150): CNN Regression: A 1D Convolutional Neural Network (CNN) to perform the main regression task (fresh weight estimation) using HDF5 (.h5) data. Attention U-Net Segmentation: An Attention U-Net model to perform semantic segmentation on image-mask pairs, likely for plant area identification or related preprocessing. All scripts were developed and tested using Python 3.10. Data and Directory Structure The project requires a BASIS_FOLDER (root directory) containing the Python scripts and a series of band_X subfolders. The data for both workflows is co-located within these band-specific folders. 1. CNN Workflow (Fresh Weight Estimation) This workflow uses the CNN_MODEL_TRAIN_TEST_EVALUATION.py script. BASIS_FOLDER/ band_X/ INPUT_H5/: Contains NOR_Train.h5 (H5 file with training patches and labels). TEST_H5/: Contains NOR_Test.h5 (H5 file with test patches and labels). MODEL_SAVE/: [Output] Directory where the trained CNN models, weights, and history logs (.csv) are saved. RESULTS/: [Output] Directory where the final predictions (Predict_CNN_model.csv) are saved. CNN_MODEL_TRAIN_TEST_EVALUATION.py: The main script to run this workflow. 2. Attention U-Net Workflow (Segmentation) This workflow uses the three ATT_UNET scripts in sequence. BASIS_FOLDER/ band_X/ input_frames/: [Input] Folder containing training images (e.g., .png, .tif). input_masks/: [Input] Folder containing corresponding ground-truth masks. SAVE/: [Output] Directory where the trained U-Net models, weights, and history logs (.csv) are saved. 01_ATT_UNET_MODEL.py: Script to train the U-Net model. 02_ATT_UNET_POSTPROCESSING.py: Script for post-processing and prediction. 03_ATT_UNET_EVALUATION.py: Script to evaluate the segmentation results. Code and Reproducibility The root folder contains the following Python scripts: CNN_MODEL_TRAIN_TEST_EVALUATION.py: The primary script for the paper. It iterates through all bands, loads the H5 data, trains the CNN regression model, and saves all models and prediction results. 01_ATT_UNET_MODEL.py: Part 1 of the segmentation workflow. Iterates through all bands, loads image/mask pairs, and trains the Attention U-Net model. 02_ATT_UNET_POSTPROCESSING.py: Part 2 of the segmentation workflow. Loads trained models to perform prediction and post-processing tasks. 03_ATT_UNET_EVALUATION.py: Part 3 of the segmentation workflow. Evaluates the segmentation model's performance (e.g., IoU, Dice).

Data collection was conducted through offline questionnaire surveys administered to high school students in Guangzhou. Using a stratified sampling approach, the study selected three classes from each grade level across four key high schools (two provincial-level and two municipal-level key schools). A total of 1,800 questionnaires were distributed between September 9 and October 25, 2023. After data cleaning, 1,546 valid responses were retained, resulting in a valid response rate of 85.89%. The survey instruments measured motivations for digital reading social behavior (MSBDR) and digital reading literacy (DRL) using Likert five-point scales, with items adapted from established questionnaires to fit the research context. The dataset titled “High School Students’ Digital Reading Social Behavior Motivation and Digital Reading Literacy in Guangzhou” is organized into two main components: the raw survey response dataset and the processed analytical dataset. A total of 1,546 valid student questionnaires were collected and compiled into a structured Excel file and Word file.

A sample dataset, named HerbFusion-8, has been prepared to illustrate the NIR spectral characteristics of the eight Chinese herbal materials investigated in this study. The dataset contains a total of 40 representative NIR spectra, obtained by randomly selecting five samples per class from the full experimental dataset. The data are stored in a MATLAB .mat file and organized as follows: nir_data (40 × 248) – Matrix containing NIR absorbance spectra for all samples. labels (40 × 1) – Integer class labels corresponding to the eight herbal categories (1–8). class_names (1 × 8) – Names of the eight Chinese herbal materials used in the study. wl (248 × 1) – Wavelength vector (900–1700 nm) representing the spectral axis. All spectra were preprocessed to remove noise and normalized to the same wavelength range.