Mendeley Data Showcase

The dataset contains de-identified clinical and laboratory data from a retrospective study of 290 hospitalized thrombocytopenic patients who received at least one platelet (PLT) transfusion between April 2020 and December 2025 at our institution. Among them, 145 patients required repeat PLT transfusions (≥2 transfusions), while 145 patients received only a single transfusion. The dataset includes patient demographics (age, sex, BMI, smoking and alcohol history), comorbidities (e.g., hypertension, diabetes, cardiovascular and renal diseases), primary disease types (hematologic malignancies, bone marrow failure syndromes, solid tumors, liver disease, immune thrombocytopenia, and infectious diseases), clinical parameters (PLT count, hemoglobin, white blood cell count, coagulation profile, albumin), bleeding severity, splenomegaly, fever and infection status, prior use of thrombopoietic agents, type and timing of transfusion, and outcome of repeat transfusion. This dataset was used to identify independent risk factors for repeat PLT transfusion, develop a predictive nomogram, and construct a simplified scoring system for risk stratification. All data have been de-identified according to the Safe Harbor Method to protect patient privacy.

The dataset includes physical, chemical, and chronological analyses from sediment cores collected in mangroves northwest of the Amazon River mouth. The files contain data on bulk density, sedimentation rate, age models, mass accumulation, carbon and nitrogen content, stable isotope ratios, carbon and nitrogen accumulation rates, and 210Pb-excess (in Bq/kg).

This dataset provides a longitudinal, multi-dimensional perspective on the global socioeconomic landscape, spanning a 50-year period from 1975 to 2024. By integrating 50 diverse nations across five distinct geographic regions and various income brackets, the data offers a robust framework for analyzing the complex interplay between macroeconomic expansion, demographic shifts, and the evolving nature of wealth distribution. The primary objective of this compilation is to facilitate research into how industrial transitions and employment structures influence national prosperity and social equity over a half-century of globalization. Data Composition and Indicators The dataset is meticulously structured into five core thematic domains, derived from the World Bank Open Data API. This ensures a high degree of reliability and standardization for cross-country comparisons. 1. Demographic and Spatial Dynamics To capture the changing human footprint, the dataset tracks Total Population alongside the spatial distribution of residents via Urban and Rural Population Percentages. Furthermore, Population Density provides insights into the intensity of land use and urbanization trends. 2. Macroeconomic Performance Economic health is monitored through Gross Domestic Product (GDP) and GDP per capita, providing both absolute and relative measures of national wealth. To account for economic stability and labor market efficiency, the data includes annual Inflation Rates and Unemployment Rates. 3. Employment Structure and Gender Stratification A distinctive feature of this dataset is the granular breakdown of labor markets. It tracks the percentage of the workforce in Agriculture, Industry, and Services. Crucially, each sector is further disaggregated by gender (e.g., Emp_Agri_F_Percent vs. Emp_Agri_M_Percent), enabling researchers to explore gender-specific shifts in labor as economies modernize from agrarian to service-oriented models. 4. Income Distribution and Poverty Metrics To address the "Inequality" aspect of the title, the dataset includes the Poverty Headcount Ratio and Mean Income (GNI per capita). It also provides a detailed look at wealth concentration by tracking the income shares held by the Top 10% and 20%, contrasted against the shares of the Bottom 10% and 20%. This allows for the calculation of inequality gaps and the study of middle-class erosion or expansion. This dataset is ideally suited for researchers investigating: - The correlation between industrialization (shift from Agriculture to Industry) and the Gini coefficient. - The impact of gender-based employment shifts on national GDP growth. - Long-term poverty reduction trends in relation to urban-rural migration. - The resilience of various income levels against global inflationary periods.

This study is based on the hypothesis that personalized protective equipment can be improved by linking dynamic biomechanical loading data with a programmable multi-scale spring structure library. The data include dynamic pressure distribution maps collected from multi-angle impact experiments using thin-film force sensors, together with mechanical performance data for six spring structures obtained from quasi-static compression, high-strain-rate impact, drop-hammer, rebound, and surface morphology tests. The results show that graded structures provide clear advantages in energy absorption and load management, including a 54% increase in plateau stress for the 1.3mm–1.6mm graded structure and a 13.3% reduction in peak force for the 3.2mm–4.0mm graded structure under dynamic impact . These data can be interpreted as a quantitative mapping between local biomechanical demands and structural performance, providing a foundation for designing customized protective devices with region-specific mechanical functions.

Data from experiments for the paper Phytotoxic activity and chemical characterization of food byproduct extracts

Raw MS lipidomics and flow cytometry data from the project entitled "Comprehensive Lipidomic Profiling Reveals Distinct Metabolic Remodeling during Differentiation of Human Monocyte-Derived Macrophages" . JPR Manuscript ID, pr-2025-01060h.

Raw data of weight per fruit, fruit diameter, color (L*, a*, b*, C, hue), total carotenoids, beta carotene, lycopene, water content, firmness, total soluble solid, fructose, sucrose, glucose, total sweetness index, total phenolics, total flavonoids, antioxidant activity (IC50), antioxidant capacity (AEAC), vitamin C of beef tomatoes.

The CheoFaMo dataset is a segmented image sequence resource designed for computational mood recognition in Vietnamese traditional Chèo, consisting of 5,844 labeled segments extracted from seven plays. The dataset captures challenging conditions such as theatrical makeup, dynamic lighting, and motion, making it a suitable benchmark for deep learning models in facial expression and mood analysis. Its integration with the CheoGoogle Ontology further enables structured applications in emotion recognition, psychological study, and educational tools for digitizing the art of Chèo

This supporting information provides the tables about water/sediment physicochemical properties, functional genes involved in N2O production, sediment N2O production potential, dissolved N2O concentrations and flux.

This dataset was developed to investigate the topographic–hydrodynamic controls on vegetation responses to mountain flood disturbances in arid and semi-arid environments. The underlying research hypothesis is that terrain conditions modulate hydrological processes during flood events, which in turn drive spatial heterogeneity in vegetation dynamics. Specifically, terrain-derived hydrological indices (e.g., flow accumulation, topographic wetness) are expected to influence vegetation resistance and recovery patterns following flood disturbances. The dataset integrates multi-source geospatial data, including satellite-derived vegetation indices, digital elevation model (DEM)-derived topographic variables, and land cover information. Vegetation conditions were primarily characterized using the Normalized Difference Vegetation Index (NDVI), derived from Sentinel-2 imagery. Topographic and hydrological variables (e.g., elevation, slope, aspect, topographic wetness index) were extracted from DEM data. Land cover data were used to classify surface types and assist in stratified analysis. All data were preprocessed following standard procedures, including atmospheric correction, cloud masking, geometric correction, and spatial resampling to a consistent resolution. Terrain variables were calculated using GIS-based spatial analysis methods. The study area was further divided into terrain zones using a classification approach (e.g., Jenks natural breaks), and all raster pixels were assigned corresponding zone labels to facilitate statistical comparison. The dataset reveals clear spatial differentiation in vegetation response patterns under varying terrain conditions. Areas characterized by higher moisture accumulation potential (e.g., valley bottoms and concave slopes) tend to exhibit stronger vegetation recovery, whereas steep slopes and well-drained areas show weaker or delayed responses. These findings support the hypothesis that terrain-driven hydrological processes play a critical role in regulating vegetation dynamics in flood-affected arid mountain regions. Users of this dataset should interpret the variables in a spatially explicit context. Each raster layer represents a specific environmental factor, and pixel values correspond to measured or derived quantities at a given spatial resolution. The terrain zone classification layer can be used as a categorical variable for comparative or statistical analysis. The dataset is suitable for applications such as ecological modeling, hazard assessment, vegetation resilience analysis, and machine learning-based environmental prediction. To ensure reproducibility and correct usage, users are advised to consider the spatial resolution, temporal coverage, and preprocessing steps applied. The dataset can be directly used in GIS or remote sensing software and supports further analysis such as regression modeling, classification, or spatial statistics.