Mendeley Data Showcase

Purpose: In healthcare, research has documented that emotional intelligence (EI) is known to influence and impact leadership effectiveness. The present study undertakes a scoping review on the role of EI in healthcare leadership to determine the extent of impact, the association of EI with certain leadership styles, the methodologies used, and the associated outcomes with workplace and team performance, patient care and satisfaction, and reducing stress and burnout. Design: A total of 30 publications (2000–2024) were selected from PubMed, Google Scholar, ResearchGate, DOAJ, and SpringerLink using defined keywords and criteria. Findings: The key recurring themes observed revolve around the dimensions of EI, key effective leadership indicators, primary subjects of research of EI in healthcare, and research environment for the same. Empathy and social skills are the most crucial EI dimensions for effective leadership. Job performance, stress reduction, and patient satisfaction are noted to be key leadership effectiveness indicators. Healthcare managers and physicians are the primary subjects of research, and hospital settings are the most common research environment. Originality: While the quality of studies is generally high, future research should explore EI-based leadership beyond hospitals, including primary care and long-term care settings, while also examining its impact on nurses and allied health professionals. Expanding the scope will provide a more comprehensive understanding of EI in healthcare leadership. Integrating EI training into leadership programs can enhance workplace dynamics, reduce stress, and improve both staff well-being and patient care.

Test datasets for the four studies in the research paper named above. The table has been converted between Chinese and English and the paper' title has been revised.

Data File for paper

This dataset contains real-world images of Yemeni banknotes collected for research in currency recognition and computer vision. The images were captured using smartphone cameras under diverse conditions, including variations in illumination, background, orientation, scale, and partial occlusions. The dataset includes six Yemeni Riyal denominations: 50, 100, 200 (old), 250, 500, and 1000 YER. Each denomination is organized in a separate folder. The dataset consists of a total of 1,691 RGB images. This dataset is intended to support tasks such as image classification, object detection, and evaluation of currency recognition systems, including assistive applications for visually impaired users. It is designed to facilitate reproducible research and testing of deep learning models under realistic acquisition conditions.

This dataset is a comprehensive collection of Bottle Gourd, Zucchini, and Papaya leaf disease images, developed at Daffodil International University, Dhaka, Bangladesh, to support research in machine learning, deep learning, and computer vision–based plant disease detection. A total of 2,144 original leaf images were collected from local agricultural fields and university research plots under natural lighting conditions between January 13, 2024, and October 22, 2024. All images were captured using smartphone and DSLR cameras and standardized to a resolution of 512 × 512 pixels in RGB format. The dataset covers multiple disease categories across three crops: Bottle Gourd (Alternaria Leaf Blight, Angular Leaf Spot, Anthracnose, Downy Mildew, Early Alternaria Leaf Blight, Fungal Damage, Healthy, and Mosaic Virus), Zucchini (Angular Leaf Spot, Anthracnose, Downy Mildew, Dry Leaf, Healthy, Insect Damage, Iron Chlorosis Damage, Xanthomonas Leaf Spot, and Yellow Mosaic Virus), and Papaya (Bacterial Blight, Carica Insect Hole, Curled Yellow Spot, Healthy Leaf, Pathogen Symptoms, and Yellow Necrotic Spots). To improve class balance and model generalization, extensive data augmentation techniques—such as rotation, flipping, brightness and contrast adjustment, zooming, and cropping—were applied, expanding the dataset to 23,000 augmented images. Each image preserves distinct disease features, making it highly suitable for training and testing machine learning and deep learning models for plant disease classification, detection, and recognition. This dataset serves as a valuable resource for advancing research in precision agriculture, smart farming, and AI-based crop health monitoring, and is freely available for academic and research purposes with proper acknowledgment to the creators and Daffodil International University.

This dataset contains data from 209 university students (93 males, 116 females; age 22.79 ± 2.23 years) from Jiangsu Province, China. Hair biomarkers of neuroendocrine function (cortisol, cortisone, testosterone, progesterone) and psychological resilience subdimensions were collected. The dataset also includes R scripts for moderated network analysis.

Product reviews help the sellers to understand their customers' expectations and sentiment towards the product and based on those reviews they take measures accordingly to heighten the satisfaction level of their customers. Beauty products are unique because various factors can influence a customer's purchase decision. With the help of machine learning techniques, the product reviews can be utilized to achieve insights and patterns to understand customers sentiment and recommend products according to their purchase records. To maintain the confidentiality of user, real dataset was not used. A synthetic dataset can heighten the efficiency of machine learning techniques. This dataset was generated by AI, packs a vast number of reviews of various products, sentiment towards those and elaborate exploratory analysis. A total of 50,000 reviews were generated from 200 different products and 1,000 unique users. A series of processing steps were performed on the raw dataset, including content addition. Another aspect of this work is that there are still not many datasets available that contains user CTR (Click-Through Rate) alongside their Spent time on a product interface which can help the researchers exploit this dataset to develop recommender systems, and natural language processing algorithms for analytical purposes.

Meadows are valuable foraging habitats for many bird species during the breeding season. Certain management practices, such as hay harvesting, can enhance foraging opportunities. Our study, conducted in eastern Poland, compared bird occurrence on two types of meadows: meadows during mowing and the same meadows several days after mowing. The total number of species was similar on both meadow types; however, more than twice as many individuals were recorded on meadows during mowing. Mowing activity attracted higher numbers of White Storks and corvids, whereas it did not increase the occurrence of raptors. The main factors significantly affecting both the mean species number and the mean number of individuals were meadow type, meadow area and distance to nearest forest. Both indices were higher on meadows being mown than on meadows cleared of hay. Larger meadow areas supported higher species richness and greater bird abundance. Among habitat characteristics, only increasing distance from the nearest forest had a positive effect on bird occurrence. These findings confirm that meadows during mowning provide short-term but highly attractive foraging conditions, and that extensive management in a fragmented landscape may play an important role in the conservation of meadow bird communities.

This dataset contains attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectra of natural and man-made textile fibres assembled to support research in forensic, analytical, and environmental science. The dataset was developed under the hypothesis that textile fibres exhibit characteristic and reproducible infrared signatures that enable differentiation between fibre classes. By capturing a broad representation of commonly encountered textile materials, the dataset provides a reference framework for fibre identification, spectral comparison, and the development and evaluation of classification approaches. A total of 160 spectra were obtained from 137 verified textile samples sourced from industry reference collections and academic textile archives. Only pure (non-blended) fibres were included. Samples were provided as fibre tufts, yarns, or fabric swatches, and natural variation in manufacturing treatments was retained to reflect the diversity typically encountered in real-world materials. The dataset currently covers 26 fibre subtypes, spanning both natural and man-made categories. The data are organised into six components: raw instrument files, tabular matrices of raw transmission spectra, baseline-corrected spectra, averaged spectra grouped by fibre subtype, three alternative fully pre-processed feature matrices prepared using different spectral preprocessing pipelines, and a metadata file describing sample-level attributes. These datasets allow users to explore spectral variability within and between fibre categories, build custom reference libraries, benchmark spectral matching algorithms, examine discriminating spectral regions, and train or evaluate chemometric or machine-learning models using a consistent, well-structured foundation.

This dataset contains data from 209 university students (93 males, 116 females; age 22.79 ± 2.23 years) from Jiangsu Province, China. Hair biomarkers of neuroendocrine function (cortisol, cortisone, testosterone, progesterone) and psychological resilience subdimensions were collected. The dataset also includes R scripts for moderated network analysis.