Mendeley Data Showcase

This is counting of pollen and fungal spore of the soil analyzed samples.

This dataset includes both single-angle and multi-angle IQ ultrasound data acquired from the hand and forearm regions of human subjects using a Verasonics system. Each .mat file contains either single-angle (s) or multi-angle (m) acquisitions, denoted in the filename. Multi-angle files include 4800 frames across varying insonification angles, stored in shape (400, 2, 256, 256), capturing rich directional information useful for deep learning-based image enhancement and reconstruction. Single-angle files contain a single insonification view with the same spatial resolution, suitable for baseline comparison or conventional image formation. The dataset captures both in-phase (I) and quadrature (Q) components, enabling raw signal-level analysis prior to B-mode conversion. All data collection was approved by the IRB of Pusan National University (Protocol No. PNU-2025-001), and participants provided written informed consent.

The dataset used in this study consists of 716 sagittal lumbar spine X-ray images: 208 images from a proprietary dataset of Honduran patients diagnosed with spondylolisthesis. 508 images from the publicly available BUU-LSPINE dataset, filtered for sagittal views only. Each image was manually annotated to identify vertebral landmarks (corners of each vertebral body from L3 to S1). These annotations include: Bounding boxes for vertebrae. Four anatomical corner keypoints per vertebra. Annotations were formatted in JSON formats compatible with PyTorch's Keypoint R-CNN. The dataset was split as follows: 69% (494 images) for training. 29% (206 images) for validation. 16 images reserved for clinical evaluation by experts. The curated dataset of 698 annotated images can serve as a public benchmark for future research in vertebra detection, spinal alignment analysis, or degenerative disorder classification. The consistent performance across variable image qualities and patient anatomies enhances its potential for deployment in real-world radiology workflows.

Maximum index finger force in a simulated trigger pull of a handgun measured for male and female subjects. The effect of wrist angle was investigated by having subjects pull the trigger for 7 wrist angles varying between 60 deg extension and 60 deg flexion.

Ultrasound B-Mode High-Resolution and Low-Resolution Hand/Arm Tissue Image Data. This dataset comprises grayscale B-mode ultrasound images of soft tissues in the human hand and forearm, captured under both high-resolution (HR) and low-resolution (LR) imaging conditions. The data is designed to support image enhancement, super-resolution, and tissue structure analysis tasks. High-Resolution (HR) Images: Acquired using wide-angle apertures (e.g., 75°) or high-frequency probes (>15 MHz). Provide superior spatial resolution with clear tissue boundaries, muscle layers, and skin-fat interfaces. Represent the target domain in image enhancement models. Low-Resolution (LR) Images: Captured using narrow-angle apertures (e.g., 1° to 15°) or low-frequency probes, or synthetically downsampled from HR images. Exhibit blurring, loss of anatomical detail, and reduced contrast. Serve as input for reconstruction or learning-based enhancement pipelines (e.g., TransCycleGAN, U-Net). The total has 42 .mat files. Each file has 100 Frames.

!!!!! IMPORTANT !!!!! This file is compressed into separate files (total 7 files; AC.z01, AC.z02, AC.z03, AC.z04, AC.z05, AC.z06, AC.zip), so you must extract them after securing all of the separate files below. Gather all of the data from the link below, store them in one folder, and then extract "AC.zip" them. A comprehensive fault dataset was constructed using a testbed composed of AC motors with different capacities and fault types. This dataset includes vibration, current, and torque measurements under both constant(50Hz) and variable speed conditions(50~52Hz, 50~58Hz). In this case study, four motor fault types were simulated: misalignment, bearing fault, winding fault, and journal bearing clearance fault. Current (R-, S-, T-phase; sampling rate=100kHz), Vibration (z-axis; sampling rate=25.6kHz), Torque (sampling rate=25.6kHz) are included. For more informations, please visit open-access journal "Data in Brief". Title: Wonho Jung et al., "Experimental Dataset for Multiple Fault Conditions in Industrial-Scale Electric Motors under Randomized Speed and Load Variations," Data in Brief, (2025).

This dataset supports a meta-analytic structural equation modelling (MASEM) study investigating the factors influencing students’ behavioural intention to use educational AI (EAI) technologies. The research integrates constructs from the Technology Acceptance Model (TAM), Theory of Planned Behaviour (TPB), and Artificial Intelligence Literacy (AIL), aiming to resolve inconsistencies in previous studies and improve theoretical understanding of EAI technology adoption. Research Hypotheses The study hypothesized that: Students’ behavioural intention (INT) to use EAI technologies is influenced by perceived usefulness (PU), perceived ease of use (PEU), attitude (ATT), subjective norm (SN), and perceived behavioural control (PBC), as described in TAM and TPB. AI literacy (AIL) directly and indirectly predicts PU, PEU, ATT, and INT. These relationships are moderated by contextual factors such as academic level (K–12 vs. higher education) and regional economic development (developed vs. developing countries). What the Data Shows The meta-analytic dataset comprises 166 empirical studies involving over 69,000 participants. It includes pairwise Pearson correlations among seven constructs (PU, PEU, ATT, SN, PBC, INT, AIL) and is used to compute a pooled correlation matrix. This matrix was then used to test three models via MASEM: A baseline TAM-TPB model, An internal-extended model with additional TPB internal paths, An AIL-integrated extended model. The AIL-integrated model achieved the best fit (CFI = 0.997, RMSEA = 0.053) and explained 62.3% of the variance in behavioural intention. Notable Findings AI literacy (AIL) is the strongest predictor of intention to use EAI technologies (Total Effect = 0.408). PU, ATT, and SN also significantly influence intention. The effect of PEU on intention is fully mediated by PU and ATT. Moderation analysis showed that the relationships differ between developed and developing countries and between K–12 and higher education populations. How the Data Can Be Interpreted and Used The dataset includes bivariate correlations between variables, publication metadata, sample sizes, coding information, and reliability values (e.g., CR scores). Suitable for replication of MASEM procedures, moderation analysis, and meta-regression. Researchers may use it to test additional theoretical models or assess the influence of new moderators (e.g., AI tool type). Educators and policymakers can leverage insights from the meta-analytic results to inform AI literacy training and technology adoption strategies.

The data in the file correspond to responses from potential electric vehicle customers in Ecuador. The Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) served as the theoretical basis for this study. The participants ranged in age from 18 to 65 years and resided in the cities of Quito and Guayaquil. The first variables in the file represent the demographic information of the individuals. The constructions utilized in the model are shortened and assigned a numerical identifier.

Research data related to the ​​​Department of Materials Science of the University of Milano - Bicocca

Output of a parallel discontinuous Galerkin (DG) C++ code that models 3D nonlinear, lossy wave propagation using MPI. Output is in the form of HDF5 files. Each file has the format "variable_processor_timestep.h" where variable is the value being output (either the scalar excess density "dRho", or the 3d vector of particle velocity "vel", or one of the scalar physical constants B/A "ba", diffusion constant "diff", ambient density "rho0", or sound speed "c0"). The second argument in the filename is the MPI core rank that is outputting, and the third argument is the number of the timestep being output. Runtime data (in seconds) is in a seperate text file. This data was used to create the figures in the corresponding paper "Scalable 3D Simulations of Attenuating Nonlinear Acoustic Waves with MPI and the Discontinuous Galerkin Method" by Drew Murray and Robert J. McGough.