Mendeley Data Showcase

Mycosis fungoides. Flow cytometry gating strategy. (A) Lymphocytes were gated on the CD45 versus side scatter plot. (B) CD3+CD4+ T cells were selected from the lymphocyte gate. (C) CD7 and CD26 expression was evaluated on CD3+CD4+ cells (red box). (D) CD4 and CD8 expression on lymphocytes for CD4/CD8 ratio calculation.

Project in C++ with a template and different examples to codify in an ESP32 and STM32 a Grafcet that follows the specifications of IEC 60848. It is ready to be included in a project created in Visual Studio Code + PlatformIO, or STM32CubeIDE

We provide automated knowledge extractions from scientific literature to support data-driven materials discovery. This corpus, FC-CoMIcs, contains semantic annotations automatically extracted from approximately 1,000 research articles focused on Oxygen Reduction Reaction (ORR) catalysts for fuel cells. The data was generated using a specialized DyGIE++ model fine-tuned on MatSciBERT, specifically trained to recognize complex materials, properties, and relationships in the fuel cell domain. FC-CoMIcs contributes to the advancement of Materials Informatics (MI), particularly for polymer electrolyte fuel cells, by providing structured data ready for Knowledge Graph construction and statistical analysis. The dataset consists of: 1. DOI Index: A mapping file linking our internal IDs to the original article Digital Object Identifiers (DOIs). 2. Structured JSON Extractions: Machine-generated files containing recognized entities (e.g., catalysts, precursors, operating conditions) and their semantic relationships. 3. Interactive Knowledge Maps: Visual representations of the extracted relations for each article, generated via the Pyvis library for rapid human exploration. Note on Accessibility: To comply with publisher copyright and funding guidelines, the original full-text articles are not redistributed. Researchers can use the provided DOIs to link these high-fidelity semantic extractions back to the source literature.

This dataset includes the code used to scrape social media comments from Weibo and Xiaohongshu, LLM-based sentiment analysis scripts, text semantic analysis scripts for extracting waterscape feature keywords and topic mapping, as well as the complete analytical dataset.

Chemical compositions of samples before and after diffusion experiments and original data of diffusion profiles

Supplemental material for "Food Restriction Patterns and Their Impact on Growth and Nutrition in Chinese Pediatric Atopic Dermatitis"

This study aimed to quantify eight volatile secondary lipid oxidation products in the fruit juice of five industrial potato cultivars and to assess whether significant differences exist among them. Of the eight target compounds, five were successfully detected. The data file is organized into three sections. In section 1 (yellow 1)), the five potato cultivars are listed along with the measured concentrations of detected volatiles (ng/0.5 mL potato fruit juice, PFJ). Data are based on three biological replicates (R1, R2, and R3), and the mean and standard deviation (SD) have been calculated for each compound. Section 2 (yellow 2)) presents the mean concentrations of the detected volatiles expressed as ng/mL PFJ, while section 3 (yellow 3)) shows the corresponding standard deviations. The results indicate significant differences among potato cultivars in the levels of volatile oxidation products. Notably, 2-pentylfuran and 2-nonenal are present at relatively high concentrations in PFJ, whereas the other detected compounds occur at lower levels. This pattern provides insight into the predominant lipid oxidation pathways in potato. Further details on data acquisition are provided in the “Steps to reproduce” section. Overall, the data demonstrate clear cultivar-dependent variation in the concentration of some key volatile lipid oxidation products.

Data from the research questionnaire analyzing the influence of the Trusting Beliefs Theory on the use of Artificial Intelligence (AI) on WhatsApp

This dataset comprises the numerical simulation results and corresponding code from the study titled “Artificial Intelligence Empowers Port Pollution Control to Achieve Sustainable Development.” The research investigates how artificial intelligence technologies reshape the strategic interactions among governments, port authorities, and shipping enterprises to facilitate the green transition of the maritime sector. The core methodology is built on a tripartite evolutionary game model, which captures the dynamic strategy adjustments of these three stakeholders under various parameter settings reflecting the influence of AI-enabled supervision, cost structures, and incentive mechanisms. Data supporting the model were sourced from authoritative environmental reports, policy documents, and empirical case studies on port pollution governance, enabling the calibration of key parameters such as regulatory costs, penalty intensity, AI recognition accuracy, and emission reduction efficiency. The simulation codes—developed and executed manually in MATLAB—include time-series plots illustrating the evolution of strategy probabilities, phase diagrams of system stability, Sobol global sensitivity analysis, and scripts for equilibrium stability analysis, all designed to ensure reproducibility of the model’s findings. The dataset is systematically structured and comprehensive, offering a valuable reference and practical toolkit for researchers in the fields of evolutionary game theory, green port governance, and AI-driven environmental regulation.

Date of Geochemical Behavior of Iodate During the Ferrihydrite Aging Affected by Adsorbed/Coprecipitated Organic Matter