Mendeley Data Showcase

This dataset has been used for PLS-SEM in "From Dynamic Capabilities to Resilience: The Role of Organisational Learning in Equipment-Intensive Infrastructure Organisations"

Data from the article "Long-term organic no-tillage onion system enhances soil enzyme activity and fertility attributes in southern Brazil".

This systematic review synthesizes and critically appraises the current evidence on the role of proteolytic enzymes in dentin erosion, with a specific focus on matrix metalloproteinases (MMPs) and cathepsins, and evaluates the effectiveness of synthetic and natural inhibitors in modulating their activity. Dentin erosion is a multifactorial process in which acid-mediated demineralization is exacerbated by endogenous enzymatic degradation of the exposed organic matrix, compromising structural integrity and accelerating tissue loss. Evidence indicates that MMPs and cysteine cathepsins act synergistically under erosive conditions, amplifying collagen breakdown and hindering remineralization. Following PRISMA guidelines, electronic searches were conducted across major databases to identify in vitro, ex vivo, and clinical studies assessing proteolytic activity in eroded dentin and the impact of inhibitory strategies. The review summarizes the mechanisms of enzyme activation, spatial distribution, and interaction, and compares the efficacy of commonly investigated inhibitors, including chlorhexidine, metal ions, polyphenols, and other naturally derived compounds. Overall, the findings highlight that targeted inhibition of proteolytic enzymes—particularly combined or dual-inhibition approaches—represents a promising adjunctive strategy for controlling dentin erosion. The review also identifies methodological limitations and research gaps, providing directions for future translational and clinical studies.

The dataset comprises groundwater quality data collected during a post-monsoon sampling campaign from the Jajpur district, Odisha, India. Twelve hydrochemical parameters were used as predictors for Water Quality Index (WQI) modelling. The WQI served as the response variable. The dataset was analysed using Bayesian hyperparameter–optimized artificial neural network (ANN), random forest (RF), and multiple linear regression (MLR) models. Model evaluation included cross-validation, resilience testing, predictor importance analysis This submission includes the compiled groundwater dataset, optimized model architectures, and source codes used for analysis and graphical representation.

README: Replication Package Paper Title From drill to chill: The effect of military service on subjective well-being in rural China Authors: Chunkai Zhao (South China Agricultural University) Boou Chen (Nanjing University of Finance and Economics) Huilin Xie (Sun Yat-sen University) Date: Jan 27, 2026 This folder contains the programs and materials required to reproduce the results of the paper "From drill to chill: The effect of military service on subjective well-being in rural China."

This study aimed to investigate the efficacy and safety of obinutuzumab in treating various subtypes of PMN.

The files contained in this folder allow replication of the analysis presented in “Cultural Industry Meets Tourism: Productive Intersectoral Spillovers and Cluster Synergies” (C. Bernini, S. Emili, F. Galli). Although the original data are confidential, following the procedures described in the README and the accompanying scripts allows reconstruction of the final dataset used for model estimation. For transparency and replication purposes, the folder also includes an example dataset with randomly generated (perturbed) data.

Spreadsheet of the research project “Effect of electronic cigarette smoking on the levels of macro- and trace elements in saliva: a cross-sectional study”.

Material de pesquisa da Revisão de Literatura sobre inteligência artificial em plataformas de audiolivros. Relação das strings utilizadas nas bases de dados e documentos recuperados e selecionados para leitura na íntegra para a revisão de literatura sobre inteligência artificial em plataformas de audiolivros no contexto da Ciência da Informação, realizada em 2025.