Scientific Data最新文献

The Egyptian Plant Leaf Image Dataset (EgyPLI) is the first comprehensive collection of plant leaf images curated in Egypt to support research in automated plant identification. It addresses the lack of locally representative datasets and the broader need for geographically diverse data to enable the development of generalized models. EgyPLI contains real-world leaf images captured under varying viewpoints, lighting conditions, and background clutter, reflecting realistic agricultural environments. Unlike laboratory-controlled datasets, it includes natural noise and variability, supporting the training of robust deep learning models suitable for real deployment. The dataset is carefully annotated and preprocessed to establish a consistent standard for plant identification tasks. EgyPLI comprises 3,588 images covering eight widely cultivated plant species: apple, berry, fig, guava, orange, plum, persimmon, and tomato, including both healthy and diseased leaves. This diversity supports classification, diagnosis, and health assessment applications. To demonstrate its effectiveness, the dataset was evaluated using ResNet50, VGG16, and a custom CNN, achieving accuracies of 61.67%, 96.81%, and 99.22%, respectively. As an available resource, EgyPLI fills a critical gap.

Accessing high-quality, open-access dermatopathology image datasets for learning and cross-referencing is a common challenge for clinicians and trainees. To establish a comprehensive open-access dermatopathology dataset for educational, cross-referencing, and machine-learning purposes, we employed a hybrid workflow to curate and categorize images from the PubMed Central (PMC) repository. We used specific keywords to extract relevant images, and classified them using a novel hybrid method that combined deep learning-based image modality classification with figure caption analyses. Validation on 651 manually annotated images demonstrated the robustness of our workflow, with an F-score of 89.6% for the deep learning approach, 61.0% for the keyword-based retrieval method, and 90.4% for the hybrid approach. We retrieved over 7,772 images across 166 diagnoses and released this fully annotated dataset, reviewed by board-certified dermatopathologists. Using our dataset as a challenging task, we found the current image analysis algorithm from OpenAI inadequate for analyzing dermatopathology images. In conclusion, we have developed a large, peer-reviewed, open-access dermatopathology image dataset, DermpathNet, which features a semi-automated curation workflow.

Breast ultrasound (BUS) is an essential tool for diagnosing breast lesions, with millions of examinations per year. However, publicly available high-quality BUS benchmarks for AI development are limited in data scale and annotation richness. In this work, we present BUS-CoT, a BUS dataset for chain-of-thought (CoT) reasoning analysis, which contains 11,439 ultrasound images from 11,850 lesions and 4,838 patients, covering all 99 WHO-defined histopathology categories. For model training and evaluation, we provide a curated high-quality subset of 5,163 lesion-focused images annotated by experienced radiologists. To facilitate research on incentivizing CoT reasoning, we construct the reasoning processes based on observation, feature, diagnosis and pathology labels, annotated and verified by experienced experts. Moreover, by covering lesions of all histopathology types, we aim to facilitate robust AI systems in rare cases, which can be error-prone in clinical practice. The data and code are publicly available at https://doi.org/10.6084/m9.figshare.30838715.

While microbiota plays a crucial role in maintaining overall health, archaea, a component of microbiota, remain relatively unexplored. Here, we present a newly assembled set of archaeal metagenome-assembled genomes (MAGs) from 1,878 fecal microbiome samples. These MAGs were reconstructed from metagenomic reads of the Estonian Microbiome Deep (EstMB-deep) cohort, which were reused here specifically for archaeal MAG reconstruction. We identified 273 archaeal MAGs, representing 21 species and 144 strains which we curated into the "EstMB MAGdb Archaea-273" MAGs collection.

Oral Squamous Cell Carcinoma (OSCC) is a prevalent and aggressive malignancy where deep learning-based computer-aided diagnosis and prognosis can enhance clinical assessments. However, existing publicly available OSCC datasets often suffer from limited patient cohorts and a restricted focus on either diagnostic or prognostic tasks, limiting the development of comprehensive and generalizable models. To bridge this gap, we introduce Multi-OSCC, a new histopathology image dataset comprising 1,325 OSCC patients, integrating both diagnostic and prognostic information to expand existing public resources. Each patient is represented by six high resolution histopathology images captured at ×200, ×400, and ×1000-two per magnification-covering both the core and edge tumor regions. The Multi-OSCC dataset is richly annotated for six critical clinical tasks: recurrence prediction (REC), lymph node metastasis (LNM), tumor differentiation (TD), tumor invasion (TI), cancer embolus (CE), and perineural invasion (PI). We systematically evaluate the impact of different visual encoders, multi-image fusion techniques, stain normalization, and multi-task learning frameworks to benchmark this dataset. To accelerate future research, we publicly release the Multi-OSCC dataset at: https://github.com/guanjinquan/OSCC-PathologyImageDataset.

Jakyakgamcho-tang (JGT), the simplest form of herbal medicine, comprises Paeoniae Radix (PR) and Glycyrrhizae Radix et Rhizoma (GR). It has been used to treat muscle-related diseases and inflammation. However, its pharmacological effects may vary with the proportions of ingredients and preparatory factors such as the extraction method. Nevertheless, gene expression datasets systematically reflecting these variables are lacking. A total of 513 transcriptome profiles were created with three concentrations and three replicates of RNA-seq data. This dataset structure will enable multidimensional analysis of the effects of various JGT preparation factors on gene expression; these factors include the PR to GR proportional ratio (2:1, 1:1, and 1:2), solvent (water or 70% ethanol), and extraction method (combined or individual extraction method). The HepG2, C2C12, and PC12 cell lines were targeted. All raw and preprocessed data are available through GEO. Standardized metadata and ingredient data are also provided. This dataset provides a foundation for exploring traditional herbal formulations effects on cellular transcriptomic responses and can facilitate the scientific optimization of herbal medicines.