Mendeley Data Showcase

The dataset consists of UAV-based hyperspectral imagery collected over Zhejiang Shuren University, comprising seven flight strips.

This dataset contains annotated images of dental surgical instruments designed for object detection and computer vision research. The dataset aims to support the development and evaluation of deep learning models for automatic detection and recognition of dental instruments used in dental and oral surgical procedures. The dataset consists of 847 images covering 22 different types of dental instruments. The instrument categories included are: Aji Bone Cutter, Andrew Tongue Depressor, Angled Artery Forceps, Cross Bar Elevator, Curved Gum Scissors, Dental Aspirating Syringe, Dental Elevator, Frazier Suction Tube, Mandibular Anterior Extraction Forceps, Mandibular Cowhorn Forceps, Mandibular Lower Incisors Forceps, Mandibular Lower Molar Forceps, Maxillary Anterior Canine Forceps, Maxillary Molar Dental Extraction Forceps, Maxillary Upper Incisors Forceps, Maxillary Upper Molar Forceps, Maxillary Upper Molars Extraction Forceps, Mayo Hegar Needle Holder, Orthodontic Ligature Cutter, Straight Artery Forceps, Straight Tweezers, and Wire Orthopedic Cutter. All images were captured using a GoPro camera from a top-view perspective. During data collection, the instruments were placed on white paper sheets on a table surface to create a clean and consistent background suitable for object detection tasks. To enhance dataset diversity and improve model robustness, images were captured under different illumination conditions including: Normal lighting, Bluish lighting conditions, Warm/orange lighting conditions. These variations simulate different real-world imaging environments commonly encountered in clinical or laboratory settings. Additionally, each instrument was photographed from multiple orientations and angles by slightly rotating and repositioning the tools during image acquisition. This approach ensures that the dataset contains diverse perspectives of each instrument, enabling machine learning models to learn more robust visual features. The annotation process was conducted using the Roboflow platform. Each instrument was manually labeled using bounding box annotations following the standard YOLO object detection format. The dataset is compatible with several YOLO-based object detection frameworks, including: YOLOv5 (PyTorch), YOLOv7 (PyTorch), YOLOv8, YOLOv11, This dataset can be used for training, validation, and testing machine learning models in areas such as: computer vision, medical image analysis, intelligent healthcare systems, automated dental instrument detection for robotic surgery. The dataset contains only surgical instruments and no patient data, therefore no ethical approval or patient consent was required.

ESI ToF MS data of organic mixture leached from irradiated and hydrolysed (under alkaline conditions) cellulosic tissues. This dataset contains the raw data obtained for 13 different samples, the background (alkaline) solution and the background solvent solution. For data treatment and interpretation, the reader is referred to the corresponding scientific paper: Nushi et al (2026). Identification of Radiolytic and Hydrolytic Degradation Products from Cellulosic Materials in Radioactive Waste Disposal Environments. Polysaccharides. DOI: 10.3390/polysaccharides7010031. See file 'Explanation of data files ESI ToF MS.xlsx' for information on the data files and the corresponding samples. The analyses have been performed by Enida Nushi at SCK CEN (Institute Nuclear Medical Applications, Mol, Belgium) under supervision of Felice Mastroleo (for analyses and interpretation), Katrien Hendrix (interpretation and review) and Nele Bleyen (interpretation and review)

Supplementary source data and annotated R code for manuscript titled "Artificial oyster reefs can facilitate the recovery of lost ecosystem function in fragmented seagrass habitat", currently under consideration at Ecosphere.

Multi-omics Analysis of Ephedra sinica at the Early Seed Germination Stage Under Aspergillus novofumigatus Inoculation

Supplemental data for REGRESSION STATISTICS FOR HEAD LOSS-DISCHARGE LINEARITY and APPARENT CONDUCTIVITY – GRADIENT PLOTS

Research Hypothesis The central hypothesis of this study is that the chloroplast genomes of 3 Ulmus pumila cultivars—' 'Xu Ri', 'Yang Gang', and 'Zhao Yang'—harbor sufficient genetic variation to distinguish them at the molecular level. Data Description The dataset consists of raw Illumina paired-end sequencing data (FASTQ format) from the chloroplast genomes of 3 Ulmus pumila cultivars. Fresh leaf tissue was collected for each cultivar, and total genomic DNA was extracted. Chloroplast DNA was enriched through a combination of whole genome sequencing and subsequent bioinformatic filtering, or via long-range PCR amplification. Sequencing was performed on an Illumina platform (e.g., NovaSeq 6000) with 2 × 150 bp read lengths. Each sample generated approximately 2–4 Gb of raw data, ensuring deep coverage for de novo assembly. The raw FASTQ files are provided per cultivar and contain unprocessed reads with quality scores suitable for downstream analysis. Data Interpretation and Usage The data can be interpreted and utilized in several ways: Cultivar identification: Identified SNPs and SSRs can be developed into molecular markers for rapid and accurate discrimination of U. pumila cultivars, aiding in nursery quality control and intellectual property protection. Breeding and conservation: Insights into genetic diversity and relatedness among cultivars can inform cross-breeding strategies for improved traits and guide conservation of elite germplasm. Comparative genomics: Alignments among cultivars will reveal mutation hotspots and structural variants, contributing to understanding chloroplast genome evolution under domestication and selection. Raw data serves as a valuable resource for testing assembly algorithms and teaching plant organellar genomics. Researchers should process raw reads using standard QC tools (FastQC, Trimmomatic) followed by assembly with GetOrganelle or NOVOPlasty. Assemblies should be validated by read mapping and coverage analysis. The data are shared to promote reproducibility and enable broader comparative studies within Ulmus.

"Distinct distributed neural dynamics predict pallium-dependent social approach" Imri Lifshitz (1), Asia Prag (1), Netta Livneh (1), Maayan Moshkovitz (1), Abeer Karmi(1), Lilach Avitan (1*) (1) Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, 9190401, Israel. *Corresponding author. Email:lilach.avitan@mail.huji.ac.il

MedLeaf-16 is a multiclass image dataset containing 18,595 labeled images of medicinal plant leaves representing 16 plant species commonly used in herbal and traditional medicine. The dataset was created to support research in computer vision, machine learning, and deep learning–based plant identification systems. Each image corresponds to a single leaf sample and is organized into class-specific directories according to plant species. All images were captured using consumer smartphone cameras, including a Samsung Galaxy M12 (48 MP) and a Mi 11X smartphone equipped with an 8 MP wide camera (f/1.8 aperture, 26 mm focal length) based on the Sony IMX582 sensor. Data acquisition was conducted in a controlled indoor environment, where leaves were placed on a white paper background under artificial lighting conditions to maintain consistent image quality. Images were resized to a uniform resolution of 960 × 1280 pixels. Leaf samples were collected from four geographic locations in Bangladesh: Jashore, Faridpur, Savar, and Naogaon. The dataset includes variations in leaf orientation, shape, color, and texture while maintaining consistent capture conditions. Plant species identification was verified by an expert from Sher-e-Bangla Agricultural University, Dhaka, Bangladesh. The MedLeaf-16 dataset can be used for applications such as image classification, feature extraction, transfer learning, and automated medicinal plant recognition in agricultural and computer vision research.

we carried out a double-blind, placebo-controlled clinical trial to evaluate the protective effects and underlying mechanisms of a compound probiotic product on the gut health of elderly individuals with functional constipation. This study primarily focuses on the changes in intestinal barrier indicators potentially associated with intestinal aging. Simultaneously, constipation-specific and ancillary questionnaires were adopted to evaluate bowel movements and the symptoms induced by constipation. To deeper understand the relationship between gut microbiota and intestinal aging, we carried out the 16S rRNA gene sequencing and ungtageted metabolome at different phases. This research was funded by Amway (Shanghai) Innovation & Science Co., Ltd. , China