Mendeley Data Showcase

The data from the Bottoms-Up project can be requested at https://www.bottoms-up.eu/en/contribute-request-data/propose-a-project.html by performing the selection of the DataID and Taxonomic group as indicated in the data table. Further details on the BOTTOMS-UP database are available at https://doi.org/10.5281/zenodo.7886698.

Results dataset of the evaluation with bertscore on llms.txt.

DFI Europe

The dataset is derived from the publicly available Internet Firewall Data dataset provided by the UCI Machine Learning Repository (DOI: 10.24432/C5131M). The original data were collected from internet traffic records on a university firewall and comprise 65,532 multi-class labeled flow records. - FW-Flow provides a curated and feature-engineered version of the data, including the original 12 raw firewall attributes (ports, NAT mappings, bytes, packets, elapsed time) together with 30 additional flow-based, behavioral, and statistical derived features, resulting in 42 structured attributes. - The dataset supports multi-class classification (allow, deny, drop, reset-both), anomaly detection, and traffic behavior modeling research. - Approximately 43% of flows exhibit zero-second duration due to logging granularity in the original records. A small epsilon value (1e-6) was applied during rate-based feature computation to ensure numerical stability. All preprocessing and feature engineering procedures are fully reproducible via the accompanying scripts. [1] Internet Firewall Data [Dataset], UCI Machine Learning Repository, 2019. DOI: 10.24432/C5131M.

This dataset contains over 1,000,000 industrial and scientific products managed by QTE Technologies (Vietnam). Official Distributor: AS ONE Corporation Japan. Organization ID: ROR (đang cập nhật), Wikidata: Q138411149. Files included: > 1. product-E-14-02-2026.csv: English version for global MRO markets. 2. product-V-14-02-2026(x1.1).csv: Vietnamese version for local industrial supply chains.

The computational data supporting the paper "Theoretical Study of TiO2 Anatase Surfaces via a CO IR Probe".

This dataset contains survey data examining the influence of online user-generated content (UGC) characteristics on tourist engagement, self-congruity, revisit intention, and destination brand evangelism in Vietnam. The study investigates how perceived informativeness, authenticity, vividness, and relevance of UGC affect engagement behavior and subsequent loyalty-related outcomes, while also incorporating travel constraints as a contextual factor. Data were collected from domestic and international tourists at major tourism destinations across Northern, Central, and Southern Vietnam between December 2025 and February 2026. A structured questionnaire using five-point Likert scales was administered on-site with the support of trained research assistants. A total of 1,500 tourists were approached, resulting in 1,347 valid responses after screening. The dataset includes raw survey responses, recoded variables, and processed outputs used for Partial Least Squares Structural Equation Modeling (PLS-SEM). The data can be used for replication studies, comparative tourism research, and further investigation into digital engagement, destination branding, and behavioral intention models in emerging tourism markets

This dataset contains anonymized cross-sectional survey data collected from organisational representatives across multiple industries in Ghana (2026). The study examined the relationships between green taxation, tax compliance, employee behaviour, corporate social responsibility (CSR), and environmental sustainability. The objective was to assess how corporate social responsibility mediates the effects of environmental fiscal policies and organisational behavioural mechanisms on sustainability outcomes. One row represents one organisational respondent. Total participants: 400 organisations (as reported in manuscript). Data collected using a structured Likert-scale questionnaire (5-point scale: 1 = Strongly disagree to 5 = Strongly agree). Variables and Scale Scoring Environmental Sustainability (ES) Measured using ES1–ES5 Environmental Sustainability Score = Mean of ES1–ES5 Green Taxation (GT) Measured using GT1–GT5 Green Taxation Score = Mean of GT1–GT5 Tax Compliance (TC) Measured using TC1–TC5 Tax Compliance Score = Mean of TC1–TC5 Employee Behaviour (EB) Measured using EB1–EB7 Employee Behaviour Score = Mean of EB1–EB7 Corporate Social Responsibility (CSR) Measured using CSR1–CSR5 CSR Score = Mean of CSR1–CSR5 Measurement Sources Scales adapted from: Dilchert et al. (2012) – Environmental Sustainability Uddin et al. (2023) – Green Taxation Obaid et al. (2020) – Tax Compliance Reed et al. (2018) – Employee Behaviour Khan et al. (2021) – CSR Demographic Variables Gender (Male/Female) Age group (4 categories) Educational level (5 categories) Job role/position (5 categories) Industry type (5 categories) Organisation size (5 categories) Location (Urban/Suburban/Rural) Years of experience (3 categories) Anonymization No direct personal identifiers were collected. No organisation names were recorded. Data were collected anonymously. Results are reported in aggregate form only.

This is the data associated with the manuscript "Effect of Lithium Doping on MgO Hydroxylation and Carbonation" which is submitted for review in ACS Earth and Space Chemistry.