Mendeley Data Showcase

The open-hole plate dataset is obtained under a quasi-static axial tension test. (1) “The full-field simulation data of the open-hole plate under various load levels.xlsx” provides the full-field simulation results for each load level, including 54,013 nodes with their spatial coordinates (X, Y, Z in mm) and the corresponding von Mises stress ( in MPa). (2) “The sensor data corresponding to the 24 strain rosettes of the open-hole plate under various load levels.xlsx” provides the data at the 24 right-angle strain rosettes for each load level, includingthe corresponding sheet contains the sensor ID (Number), spatial coordinates of each measurement point (X, Y, Z in mm), the three strain components from the strain rosette (LE_0, LE_45, LE_90 in με), and the von Mises stress ( in MPa) . (3) “The-quasi-static-axial-tension-test-simulation-model-of-the-open-hole-plate.inp” is an Abaqus input file defining the finite element model of the open-hole plate under quasi-static axial tension. It can be directly executed in Abaqus to reproduce or extend the simulations used to generate the dataset. This dataset can be used for research on digital twin modeling, multi-source data fusion, and validation of full-field structural strength prediction methods.

Western Blot source data for "A fin loop–like structure in GPX4 underlies neuroprotection from ferroptosis".

Dataset for the manuscript "Tri-Functional CRISPR Screen Reveals Overexpression of QDR2 and QDR3 Transporters Increase Fumaric Acid Production in Kluyveromyces marxianus "

This dataset contains tweets about Mitsubishi automotive products in Bahasa Indonesia from 2014 to 2024.

This replication package contains synthetic data and Stata code that allow an independent researcher to reproduce the empirical results and main figures reported in the manuscript. The package follows the replication standards of Economic Modelling. The data files included here are synthetic versions of the original analysis data. They preserve the structure, variable names, and approximate moments of the real data but do not contain confidential information.

README: Replication Package Paper Title The Economics of Resilience: How Firms Overcome Perceived Policy Shocks in a Dynamic Environment Authors: Chengming Huang (Hangzhou City University) Dr. Sultan Sikandar Mirza (University of Waikato) Chengwei Zhang (Johns Hopkins University) Date: November 24, 2025 This This replication package provides all the data, code, and instructions required to reproduce the main empirical results of the above-mentioned research paper. The package is designed to facilitate transparency and enable independent verification of all statistical findings reported in the study.

This dataset is a result of the "Integrating national open databases for a comprehensive view on food, environment and health in Brazil", an initiative that integrates diverse national open databases to provide insights into food production, consumption, environmental impact, and public health in Brazil from 1974 to 2022. The primary hypothesis behind the dataset is that analyzing food systems, nutrition, and agricultural practices over time will reveal patterns that can inform sustainable food policies and health interventions. We hypothesize that by aggregating and harmonizing data from various national sources, it will be possible to identify critical relationships between food production, nutrient availability, and environmental sustainability. This integrated dataset allows us to evaluate how agricultural practices and food consumption patterns have evolved in response to changing environmental, economic, and societal conditions in Brazil. Food Production and Nutrient Data: The dataset includes detailed information on harvested areas, food production (in tons), and nutrient availability (e.g., energy, protein, fiber, vitamins, and minerals) for various food groups across different Brazilian regions and states. Water and Environmental Data: Water usage, deficit, and environmental impact data are linked to agricultural activities, allowing the assessment of water efficiency in food production. Pesticides and Residue Monitoring: Information on pesticide usage, maximum residue levels (MRL), and residue percentage in food items is provided, with additional details on authorized pesticide types, toxicity classifications, and environmental risks. Food Consumption: Data on food acquisition and consumption patterns, broken down by food groups (e.g., beans, vegetables, fruits), are presented, highlighting dietary trends across various population groups. Data Collection and Methodology: The dataset was constructed by linking several government datasets, including agricultural statistics, water use reports, and residue monitoring programs. Each dataset was meticulously harmonized to ensure compatibility and completeness, following Extract, Transform, Load (ETL) processes. The data was collected over several decades, representing longitudinal trends in food systems and health. The harmonization process ensures that all data fields are consistent and comparable across time and regions. Interpretation and Use: This dataset can be used to explore the connections between food systems, health, and the environment. It supports studies in agrifood systems, sustainability, public health, and climate change. Researchers can investigate the impacts of food production on nutrient availability, water consumption, and pesticide use over time. It can also be used to model future food system sustainability under different climate scenarios and dietary patterns, contributing to the development of food policies that balance human and planetary health.

The fishing occupation exposes workers to multiple dermatological risk factors including chronic seawater contact, elevated environmental humidity, mechanical trauma from fishing implements, and continuous ultraviolet radiation. These occupational exposures result in increased susceptibility to various cutaneous disorders. This investigation sought to determine the frequency and spectrum of cutaneous diseases within Pondicherry's fishing population, evaluate their understanding and implementation of skin protection measures, and examine the association between occupational duration and dermatological disease burden. Cross-sectional observational research has been held including 150 fishermen between 18 and 65 years of age from coastal communities in Pondicherry. Participants received complete dermatological assessments, and structured questionnaires captured demographic data, occupational duration, and skin protection knowledge which was done at a clinic located nearby to the coastal area.Fungal infections represented the predominant dermatological finding in this population. Dermatophytosis was diagnosed in 30% of participants, with pityriasis versicolor identified in 18.0%. The study cohort consisted of 66.7% males and 33.3% females. The mean occupational duration was 18.5 years and 36.6 percent of the subjects had worked more than 20 years in fishing. A major dose effect trend was observed.The most significant dermatological issue of the fishing population in Pondicherry is that of mycological infections and the disease is more prevalent in proportion to the working experience. Certain preventive measures and education is crucial.

Table S1. The relationship between the differential bacteria taxa, SCFAs, oxidative stress and inflammatory factors and osteoporosis parameters after CYC intervention.

This dataset contains 7,196 clinically validated complete blood count (CBC) samples collected from patients at Noakhali 3814, Bangladesh. The data include standard hematological parameters such as hemoglobin, red blood cells, white blood cells, platelets, lymphocyte percentage, monocyte percentage, hematocrit percentage, mean corpuscular volume, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, and red cell distribution width percentage, along with patient age and gender. All samples have been anonymized to protect patient privacy. The dataset is intended for research on hematological disorders, machine learning modeling, and biomedical data analysis. It can be used for classification, prediction, and other computational studies in hematology. Ethical approval: The data collection and use were approved by the Ethics Committee of Noakhali Science and Technology University (Reference no. NSTU/SCI/EC/2025/420).