Mendeley Data Showcase

Title: Burnout as a Predictor of Turnover Intention Among Nurses in the Philippines: A Cross-Sectional Study Creators: Fernan Torreno Frincess Flores Study Description: This dataset contains anonymized cross-sectional survey data from 300 registered nurses in Region I, Philippines (2026). The study examined burnout levels and their association with turnover intention, including demographic, organizational, and well-being factors. Data Structure: One row represents one participant. Total participants: 300. Anonymization: No direct or indirect personal identifiers are included. Data were collected anonymously. Scale Scoring: Burnout Score = Mean of Burnout_Q1–Burnout_Q6 Job Satisfaction Score = Mean of JobSat_Q1–JobSat_Q5 Turnover Intention Score = Mean of Intent_Q1–Intent_Q3 Binary Variable: Intent_Leave_Binary 0 = No 1 = Yes

This dataset was collected to support the empirical investigation of the research titled "Factors Influencing the Development of K–12 Teachers’ Practical Knowledge in a GenAI-Supported Environment: A Knowledge Creation Theory Perspective." The primary objective of data collection was to capture the processes, interactions, and contextual factors that influence how K–12 teachers develop their practical knowledge when using Generative AI (GenAI) tools in their professional practice. The data is analyzed through the lens of Knowledge Creation Theory (specifically the SECI model – Socialization, Externalization, Combination, Internalization), allowing for a structured examination of knowledge conversion modes within a technology-enhanced environment.

This file contains the complete genome sequences of 150 human gut lysogenic bacteria, assembled using next-generation sequencing.

Data shared for the article of "Modeling and Optimization of Aluminum Electrolysis for Power Grid Interaction: A Full-Process Dispatch Approach"

The dataset contains 444 physiological recordings and 148 reference values (simultaneous acquisition of dual-wavelength PPG signals with 1-leadECG signals,cuffed blood pressure values) from 148 subjects in three different exercise states.The dataset is designed to support cuffless blood pressure estimation, PPG signal analysis, PPG signal reconstruction of ECG signals, and wearable health technology robustness in wearable devices,development of cardiovascular monitoring technologies. All data were collected with informed consent and institutional medical ethics approval was obtained." information.csv", records basic information for all subjects, including unique identification IDs age, gender, height, weight, SBP, DBP, and HR.The The dataset consists of two folders. The Raw_Data folder contains the original data files collected in csv format. Each file includes four signal segments. The first column, labeled ECG_I, stores the original ECG signal. The second column, labeled PPG_RED, stores the original PPG signal measured in red light. The second column, labeled PPG_RED, stores the original PPG signal measured in red light. The third column, labeled PPG_IR, stores the original PPG signal measured in infrared light. Fourth column is a string labeled {'Hz': 250}; it does not contain signal data and only records the sampling rate. Note that files in the Raw_Data folder span more than 180 s of recording and exceed 45,000 samples. The files in the Filtered_Data folder are csvs exported by the self-developed host computer software. Each file contains five signal segments. Column 1, ECG_I_Filtered, stores the filtered ECG signal. Column 2, ECG_I_mv, stores the ECG signal converted to millivolts and is of floating-point type. Column 3, PPG_RED_Filtered, stores the filtered red PPG signal and is floating-point. Column 4, PPG_IR_Filtered, stores the filtered infrared PPG signal and is floating-point. Column 5 stores the sampling rate and the ECG voltage conversion formula.Note that some subjects in the Filtered_Data folder have less than 180 s of filtered data (fewer than 45,000 samples) because of the equipment used during measurement. In both the Raw_Data and Filtered_Data folders, file names follow the format 000_x, where "000" denotes the subject ID and "x" denotes the subject's state during acquisition; "x" takes the values 1, 2, or 3, which correspond to three different states. Thus, each ID has three csv waveform files, for example 001_1.csv, 001_2.csv, and 001_3.csv, representing synchronized PPG and ECG signals recorded under three different conditions. Note that blood pressure was measured only in the resting state and not after exercise; therefore, blood pressure values appear only in the csv file ending with "_1" for each ID.

Data shared for the article of "An Edge-Enhanced Graph Attention Network with Global Virtual Node for Real-Time High-Fidelity Exergy-Flow Mapping in Integrated Energy Systems"

This dataset contains cross-sectional survey data collected from adult recreational exercise participants. The data were gathered through an anonymous self-report questionnaire and include measures related to exercise participation and psychological experiences. The dataset has been de-identified and does not contain any personally identifiable information. It is provided to support transparency and reproducibility of research findings.

Survey Data

This dataset contains raw data from a study on integrating Appreciative Education with AI-assisted oral training (FIF platform) in EFL contexts. It includes: FLSAQ responses (n=1,044), AEDS responses (n=209), Pre/post intervention data: IELTS speaking scores and FLSAQ anxiety scores (n=13). Codebook for all variables, all data are anonymized.