Share your research data

In this review, we analyze the current applications of AI in cosmetic dermatology.

As artificial intelligence increasingly permeates healthcare, it remains unclear whether clinical medicine students and professionals are adequately prepared for this trans-formation. This study aimed to investigate AI awareness, usage patterns, teaching needs, and curriculum expectations among this population to inform evidence-based curricular reform. A cross-sectional online survey was administered to 930 students and professionals from a medical and pharmaceutical college and its affiliated hospi-tals

The proposed BDFE-6 (Bangladeshi Facial Expression Dataset) is a six-class facial expression image dataset developed to facilitate research in facial expression recognition, affective computing, human–computer interaction, and deep learning-based computer vision applications. The dataset comprises 1,236 original RGB facial images collected from 277 unique participants affiliated with Daffodil International University (DIU), Bangladesh. The images are categorized into six emotion classes: Angry (152), Fear (135), Happy (276), Normal (277), Sad (221), and Surprise (175). To ensure consistency across experiments, all images were preprocessed and resized to a uniform resolution of 512 × 512 pixels. To improve the diversity of the training data and enhance the generalization capability of deep learning models, various data augmentation techniques, including horizontal flipping, rotation, scaling, translation, brightness adjustment, and other geometric transformations, were applied. Consequently, the dataset size was expanded from 1,236 original images to 4,944 augmented images while preserving the semantic integrity of each emotion class. All facial images were collected exclusively for research purposes from volunteers at Daffodil International University after obtaining written informed consent from every participant. The dataset collection protocol adhered to ethical research standards and received approval from the Institutional Review Board (IRB)/Ethics Committee of Daffodil International University, Reference No.: REC/FSIT/DIU/2026/2015. The dataset is intended to serve as a valuable benchmark for developing and evaluating machine learning, deep learning, transfer learning, and explainable AI (XAI)-based facial expression recognition systems.

Data were collected on-site between November 2025 and March 2026 from employees working in the hospitality sector in Northern Vietnam. The dataset is intended to facilitate research on the relationships among AI adoption, digital transformation, work engagement, and customer-oriented behavior among hospitality employees in Vietnam

This dataset supports the manuscript entitled “Dolomite-Derived Grain-Oriented Silicon Steel-Grade Magnesium Oxide: Process Optimization and Machine-Learning-Assisted Purity Prediction”. The dataset includes experimental process data, characterization data, machine-learning modeling files, and validation data related to the preparation and purity prediction of dolomite-derived grain-oriented silicon steel-grade magnesium oxide. The main experimental dataset contains 305 samples and 28 process parameters, including calcination, hydration, carbonation, complexation-assisted purification, pyrolysis, and precursor calcination conditions. MgO purity was used as the target variable for machine-learning modeling. The deposited files also include variable definitions, source data for the figures, new-process validation data, Python scripts for feature engineering and model training, SHAP and permutation-importance analysis, error analysis, and GUI-based MgO purity prediction. This file provides the variable dictionary for the experimental and machine-learning datasets used in this study. It includes definitions, units, process stages, data types, model roles, and descriptions of the raw process variables, engineered features, target variable, and prediction-output variables related to MgO purity prediction. These data and code can be used to reproduce the main machine-learning workflow, evaluate the final ExtraTrees–LightGBM–KNN non-negative weighted ensemble model, and support model-guided screening of new MgO preparation conditions.

La violencia contra las mujeres constituye una vulneración de derechos humanos y un problema persistente de salud pública, justicia social y acceso efectivo a la protección. Este artículo analiza la evidencia científica y normativa sobre el uso de inteligencia artificial para la prevención, con atención a la valoración predictiva del riesgo, la detección textual, el apoyo digital, la estimación del subregistro y la gobernanza institucional. La revisión siguió las directrices PRISMA 2020 y cubrió publicaciones aparecidas entre enero de 2015 y junio de 2026. La búsqueda inicial recuperó 1 512 registros; tras la depuración, el cribado y la evaluación de textos completos, el corpus quedó integrado por 52 documentos: 42 estudios empíricos, cinco revisiones o protocolos y cinco marcos normativos. Los resultados muestran mayor madurez en los modelos clínicos y policiales. Varias investigaciones reportaron exactitudes cercanas o superiores al 85 %, aunque la validación externa, la calibración y las pruebas de equidad aún presentan una presencia limitada. El procesamiento del lenguaje natural alcanzó valores F1 próximos a .80 en distintas muestras, mientras los modelos multimodales obtuvieron los mejores resultados descriptivos. También se identificaron aplicaciones para asistentes conversacionales, derivación hacia servicios, moderación de violencia digital y análisis de respuestas ausentes. Los principales riesgos corresponden al sesgo de selección, la filtración de datos, los falsos negativos, los falsos positivos, la opacidad y la ausencia de servicios después de una alerta. Se concluye que la inteligencia artificial puede ampliar la identificación temprana y ordenar información compleja, pero su utilidad depende de supervisión profesional, validación local, protección de datos, auditoría independiente y rutas efectivas de atención.

Violence against women constitutes a human rights violation and a persistent problem of public health, social justice, and effective access to protection. This article analyzes scientific and normative evidence on the use of artificial intelligence for prevention, with attention to predictive risk assessment, text-based detection, digital support, estimation of underreporting, and institutional governance. The review followed the PRISMA 2020 guidelines and covered publications released between January 2015 and June 2026. The initial search retrieved 1,512 records; after deduplication, screening, and full-text assessment, the corpus comprised 52 documents: 42 empirical studies, five reviews or protocols, and five normative frameworks. The results show greater maturity in clinical and police models. Several studies reported accuracy near or above 85%, although external validation, calibration, and fairness testing remain limited. Natural language processing achieved F1 values close to .80 across different samples, while multimodal models obtained the highest descriptive performance. Applications also included conversational assistants, referral to services, moderation of digital violence, and analysis of missing responses. The main risks concerned selection bias, data leakage, false negatives, false positives, opacity, and the absence of services after an alert. The article concludes that artificial intelligence can expand early identification and organize complex information, but its usefulness depends on professional oversight, local validation, data protection, independent audits, and effective pathways to care.

This dataset contains raw and uncropped SDS-PAGE gel images used to assess the expression, purification, and molecular weight of proteins reported in this study. The gels were performed to evaluate protein purity and integrity following purification steps. Molecular weight markers are shown on the left side of each gel image. All images are original, unprocessed data used for the quantitative and qualitative assessment of protein samples described in the manuscript.

Scopus database as of 30 June 2026 (Artificial Intelligence –Supported Personalised Learning in Education)

Lithium iron phosphate (LiFePO4, LFP) remains one of the most commercially important cathode materials for lithium-ion batteries, and aluminum (Al) doping combined with carbon coating is a widely adopted strategy to improve its electrochemical performance. However, the combined effect of different Al sources and sintering processes on the final structural and electrochemical properties of LFP/C composites has not been systematically compared in a controlled experimental framework, and the lack of open, standardized datasets for this system hinders reproducible research and the development of data-driven material optimization. This dataset presents a complete set of systematically collected structural and electrochemical data for Al-doped LFP/C materials, generated from a controlled experimental design with two variables: Al source (Al2O3, Al(OH)3, AlPO4) and sintering process (static sintering in a tube furnace, dynamic sintering in a fluidized bed). The dataset contains all core quantitative data supporting the conclusions of our associated research: it includes Rietveld refinement results of X-ray diffraction (XRD) patterns that confirm Al doping into the LFP lattice, resulting in varied cell parameters across different samples; the ID/IG ratio calculated from Raman spectra that reflects the graphitization degree of the carbon coating; full first-cycle charge-discharge data, rate performance data, and long-term cycling capacity data for all samples; and raw Nyquist plot data from electrochemical impedance spectroscopy (EIS) along with calculated apparent lithium-ion diffusion coefficients. Notably, the dataset shows that Al source type and sintering process jointly influence the degree of Al incorporation, interfacial impedance, and final electrochemical performance, with Al(OH)3 as the Al source and dynamic fluidized bed sintering delivering the best overall rate and cycling performance. This dataset can be used directly for: (1) cross-study comparison of Al-doped LFP performance to verify process-structure-property relationships; (2) training and validation of machine learning models for LFP material performance prediction; (3) benchmarking new characterization or data analysis methods for battery cathode materials; and (4) reproducing our original results to enable further meta-analysis of LFP modification strategies. All data files are provided in open, editable XLSX format with clear sheet and column labels to facilitate direct use by other researchers.