Mendeley Data Showcase

This is the raw data of the title paper: Beyond Adoption: How TOE Factors Shape the Use of Social Media Technologies and Performance in B2B Companies

The raw numerical data supporting the results presented in Figures 1, 3, 7–13 and all supplementary appendix content are archived in a permanent, open-access data repository, consolidated as a single compressed file (Data.zip).

A randomized controlled trial was conducted in Foundation University College of Physical Therapy, Foundation University Islamabad, Pakistan from July 2022 to July 2023 on 26 cervicogenic headache patients of either gender, aged 18-45 years. The dataset includes the demographic data as well as pre and post treatment data of Numeric Pain Rating Scale, cervical rotation, and neck disability.

A survey on the use of bio-pesticides by Chinese farmers

Alcohol-associated liver disease (ALD) is a leading cause of chronic liver morbidity and mortality worldwide, yet effective clinical therapeutic strategies remain scarce. Ginsenoside Rb1 (Rb1), a primary bioactive constituent of Panax ginseng, has exhibited promising hepatoprotective activity against liver injury. Here, we investigated the hepatoprotective effects and underlying mechanisms of Rb1 and its probiotic-fermented counterpart (F-Rb1) in ALD, with a focus on the gut-microbiota-liver axis. Using the Gao-Binge model (NIAAA model), we demonstrated that both Rb1 and F-Rb1 significantly attenuated alcohol-induced hepatic steatosis, inflammation, and injury, as evidenced by reduced AST, ALT, TG, TBIL and inflammatory cytokines. 16S rRNA sequencing revealed that Rb1 and F-Rb1 distinctly modulated the gut microbiota: Rb1 selectively enriched Bacteroides uniformis, whereas F-Rb1 primarily increased Prevotella copri. Metabolomics analysis and functional assays further showed that Rb1 treatment enhanced intestinal glutamine levels and restored hepatic urea cycle function by upregulating the mitochondrial ornithine transporter SLC25A2 and key enzymes (CPS1, OTC, ASS1). Mechanistically, Rb1-driven B. uniformis and its derived metabolite glutamine, which suppressed the hepatic amino acid starvation response (GCN2–eIF2α–ATF4) and upregulated SLC25A2, thereby restoring urea cycle function and reducing systemic ammonia. These effects were abrogated by antibiotic-induced microbiota depletion and recapitulated by fecal microbiota transplantation from Rb1-treated donors. Furthermore, direct administration of live B. uniformis mimicked the protective effects of Rb1, while heat-killed B. uniformis had no effect. Notably, Rb1 was metabolized by gut bacteria into rare ginsenosides (Rg3, Rg5, PPD), but these metabolites were not detected in liver tissue, supporting a gut-restricted mechanism of action. Collectively, this study uncovers a discrete mechanism by which Rb1 alleviates ALD through the B. uniformis–glutamine–GCN2-eIF2α-ATF4-SLC25A2–urea cycle axis. These findings provide new insights into microbiota-dependent actions of ginsenosides and highlight potential microbial and metabolic targets for precision therapy in ALD.

Data

This dataset presents a comprehensive collection of medicinal plant leaf images developed for research in plant disease detection, computer vision, and machine learning applications. The data were collected between 11 September 2025 and 27 February 2026 from multiple locations in Bangladesh, including Rajbari (Dhaka), Ashulia (Dhaka), Mirpur (Dhaka), and Hajigonj (Chandpur), using three smartphone devices: iPhone 16 Pro Max, OnePlus Nord CE 4 Lite, and OnePlus 7T. During collection, detached leaves were placed on relatively uniform backgrounds to enhance the visibility of leaf morphology and disease symptoms. Importantly, the original dataset consists entirely of unique leaf samples, where each image corresponds to a different leaf, and no multiple images of the same leaf were captured; additionally, the leaves were collected from different individual plants/trees to ensure high diversity and minimize redundancy. The dataset includes images from seven medicinal plant species—Aloe Vera, Azadirachta Indica (Neem), Centella Asiatica, Hibiscus Rosa Sinensis, Kalanchoe Pinnata, Mikania Micrantha, and Piper Betle—covering multiple leaf condition classes such as healthy, diseased, chlorotic, dried, distorted, insect-affected, mild disease, and different growth stages (young and mature). In total, the dataset contains 1,981 original images and 20,019 augmented images, resulting in 22,000 images. The original images were captured in high resolution (including 3072×4096, 4096×3072, and 3024×4032 pixels, along with other variations recorded in metadata). During preprocessing, background removal techniques were applied to isolate the leaf region and reduce irrelevant visual noise, and all background-removed images were resized to a standardized resolution of 1440 × 1080 pixels, converted to RGB format, normalized, and stored in JPG format. For the augmentation pipeline, images were further resized to 512 × 512 pixels to make them more suitable for deep learning model training. Data augmentation techniques—including rotation, horizontal and vertical flipping, brightness and contrast adjustment, Gaussian noise addition, and image sharpening—were applied to increase dataset diversity and improve class balance, with all augmented images maintained at 512 × 512 resolution. The dataset is organized into three main directories: Original Dataset, Background Remove Dataset (1440 × 1080), and Augmented Dataset (512 × 512), and is accompanied by a CSV metadata file containing structured information such as plant species names, leaf condition labels, image counts, and collection locations, facilitating efficient dataset management and reproducible research.

The dataset includes microscopic images documenting the growth of Verrucodesmus verrucosus in a bubble column photobioreactor under controlled conditions in a smart systems laboratory at TecNM/Instituto Tecnológico de Tuxtla Gutiérrez, Mexico. A total of 368 images with varying algal densities and operating environments are included to improve training robustness. Each image includes its corresponding label file to enable automated algal counting and biomass growth modeling. Labeling has been validated with YOLOv8 and YOLOv11; however, the dataset is designed to be compatible with any neural network platform.

The results of the tests were analyzed to determine whether knowledge serves as a predictive factor of skills.