Mendeley Data Showcase

This dataset subset was created from the public LSUI dataset sourced from https://drive.google.com/file/d/10gD4s12uJxCHcuFdX9Khkv37zzBwNFbL/view?usp=sharing. The homepage for the LSUI dataset is: https://lintaopeng.github.io/_pages/UIE%20Project%20Page.html and the paper for the LSUI dataset is: https://arxiv.org/abs/2111.11843.

The dataset provides projected monthly and annual changes in electricity production for various technologies across Iran's primary climate zones through 2050. The projections are based on integrating climate data from the SSP5-8.5 scenario, mathematical relationships, and calculations linking climatic variables to technology capacity factors and electricity production, along with baseline production levels in 2021. The dataset includes both raw data and user-friendly pivot tables and charts to facilitate analysis.

This dataset subset was created from the public EUVP dataset sourced from https://www.kaggle.com/datasets/pamuduranasinghe/euvp-dataset. The homepage for the EUVP dataset is: https://irvlab.cs.umn.edu/resources/euvp-dataset and the paper for the EUVP dataset is: https://ieeexplore.ieee.org/document/9001231.

The dataset contains monthly averages of temperature (tas, tasmax, tasmin), precipitation (mm), wind speed (km/h), evaporation (mm), and solar radiation (W/m²) for the main climate regions (Cold, Dry, Temperate-Humid, Hot-Humid) of Iran up to 2050. These projections were extracted from CMIP6 under the SSP5-8.5 climate change scenario. The dataset includes both raw data and user-friendly pivot tables and visualizations to facilitate analysis.

This dataset subset was created from the public UIEBD dataset sourced from https://drive.google.com/file/d/12W_kkblc2Vryb9zHQ6BfGQ_NKUfXYk13/view and https://drive.google.com/file/d/1cA-8CzajnVEL4feBRKdBxjEe6hwql6Z7/view as raw and reference images, respectively. The homepage for the UIEBD dataset is: https://li-chongyi.github.io/proj_benchmark.html and the paper for the UIEBD dataset is: https://arxiv.org/pdf/1901.05495.pdf.

This dataset supports an empirical investigation into national systemic readiness for sustainable development beyond conventional outcome-based indicators such as Gross Domestic Product (GDP). The underlying research hypothesis is that development readiness is not adequately captured by isolated measures of income, logistics performance, or sustainability outcomes, but instead emerges from the structural coherence among logistics capability, governance quality, and environmental and social sustainability conditions. The dataset operationalizes this concept through the construction of the LESG (Logistics–Environmental, Social and Governance) index using country-level data for a balanced cross-sectional sample of 123 countries. Four widely used international indicators are included: the World Bank’s Logistics Performance Index (LPI), the Worldwide Governance Indicators (WGI), the Environmental Performance Index (EPI), and the Sustainable Development Goals (SDG) Index. All variables are derived from publicly available sources and represent relatively stable structural characteristics rather than short-term economic fluctuations. Prior to analysis, indicators were harmonized to a common scale to ensure cross-country comparability, and an alternative equal-weight index was constructed to assess sensitivity to aggregation choices. The analytical framework underlying the dataset is diagnostic rather than causal. Principal Component Analysis (PCA) is employed to identify the latent structure underlying the four dimensions and to derive variance-based weights for the LESG index. The results consistently indicate a single dominant component explaining more than 80% of total variance, with all dimensions loading strongly and positively, suggesting that logistics performance, governance quality, and sustainability outcomes form a coherent readiness construct. External validation is conducted through regression analysis against GDP per capita, interpreted as an assessment of coherence rather than causality. To explore structural heterogeneity, hierarchical and k-means clustering techniques are applied to classify countries into distinct systemic readiness regimes. The dataset enables full replication of these procedures and supports comparative analysis of development readiness across countries. It is intended to be used as a transparent diagnostic tool for research and policy analysis, allowing users to examine how different structural configurations shape development capacity while remaining explicit about methodological choices and limitations.

Coupled Biofilm Spatial Structure and Redox Gradients Enhance Chalcopyrite Dissolution

We present a multimodal exercise dataset collected to support autonomous feedback systems for training in spatially constrained environments. Twenty healthy adults performed a structured set of whole-body exercises in limited space. The dataset includes surface EMG from four muscles, IMU kinematics (quaternions), wrist heart rate, and expert annotations of movement quality against predefined biomechanical criteria. Raw signals were filtered, segmented, synchronized, and EMG envelopes were derived. This resource enables development and validation of machine-learning models for automated assessment of exercise quality when professional supervision is not available.

This dataset provides a comprehensive scientometric analysis of research on jenipapo (Genipa americana) published between 2000 and 2025. It includes bibliometric indicators such as annual publication counts, citation metrics, authorship patterns, institutional affiliations, journal distribution, keyword co-occurrence, and collaboration networks. The dataset highlights research trends in chemistry, food technology, bioactive compounds, agro-industrial residues, and resource reutilization. The dataset contains the following files: scrip.txt: R script used in RStudio for data processing, cleaning, and analysis. Wos.txt: Raw data retrieved from Web of Science. dadosbiblio-limpo-csv.csv: Data obtained from Scopus and refined using OpenRefine. dadosbibliosel.csv: Main dataset merging bibliometric information in RStudio, including authors, papers, DOIs, and publication years. main information about data.txt: Results from RStudio using Bibliometrix, summarizing key bibliometric indicators and analyses. Data were collected from Scopus and Web of Science using standardized search terms for “jenipapo” and related synonyms. Data cleaning and refinement were performed with OpenRefine and R scripts. Bibliometric analyses, including network construction, keyword co-occurrence, and trend mapping, were conducted in RStudio using Bibliometrix and BiblioShiny, with additional visualizations generated in VOSviewer. This dataset can be used for scientometric analyses, meta-research, and mapping trends in chemistry, food technology, bioactive compounds, agro-industrial residues, and resource reutilization related to jenipapo. Users are encouraged to cite this dataset using the provided DOI.

Supplementary material for a clinical review article for the Journal of the American Academy of Dermatology (JAAD).