Mendeley Data Showcase

Dataset for article titled "The Causal effect of atopic dermatitis on osteoporosis and osteopenia: A Mendelian randomization study"

This repository contains the supplementary datasets used in the study “Evaluating the Mainstream Influence of Cardiothoracic Surgical Research: An Analysis of Google Trends Data from 2004 to 2024.” The data include Google Trends Relative Search Volume (RSV) values and PubMed publication counts for 26 cardiothoracic conditions and procedures analyzed between 2004 and 2024. Supplementary File 1 contains the compiled dataset used for statistical analysis, including annual RSV values and publication counts. Supplementary Table 1 details the PubMed search strategies used to identify publications for each condition. These materials enable replication of the analyses performed in the study, including correlation, ANOVA, and regression models evaluating the relationship between public search interest and cardiothoracic research output.

This dataset is the raw data from the paper "Enhancing the cooling effects of urban small-scale parks from a day-night balance perspective: A case study of the Beijing-Tianjin region". By processing and analyzing it according to the materials and methods section of the paper, the results presented in the paper can be reproduced.

Supplemental materials for the JAAD brief report titled "No significant difference in disease-specific survival between Mohs micrographic surgery and wide local excision in the treatment of cutaneous leiomyosarcoma".

Supplemental Materials for <Effects of Urbanization-Related Environmental Exposures on Atopic Dermatitis: A UK Biobank Prospective Cohort Study Stratified by Genetic Risk>

This dataset contains network traffic samples focused on DNS Data Exfiltration (DNS Tunneling), designed for training and validating Machine Learning models in Cybersecurity. The data was collected in a controlled environment, simulating both legitimate DNS traffic and malicious exfiltration activities using well-known tools such as dnscat2. This dataset is intended for researchers working on Intrusion Detection Systems (IDS), network traffic analysis, and the development of robust models against modern traffic mimicry techniques used by malware. This dataset consists of 109.359 records, categorized into two classes: Benign (99,995 samples) and Exfiltration (9.364 samples).

Multiphysics Modeling of Molten Pool Dynamics in Wire-Arc Additive Manufacturing of TC4 Titanium Alloy

Drivers of high salinity groundwater: quantifying the impacts of evaporation in-tensity and terrain complexity

A panel dataset containing: - GHG emissions in Norwegian municipalities from 2009-2022 (retrieved from the Norwegian Environmental Agency (Miljødirektoratet)) - Seat share of political parties at the executive comittee in each municipality from 2007 until 2022 (retrieved from Statistics Norway (SSB)) - Control variables (also retrieved from Statistics Norway (SSB))

This dataset accompanies a study submitted to "Transportation Research Part E: Logistics and Transportation Review", focusing on a modular, demand-responsive transport system. The system uses passive, modular containers, or “pods,” that are transported across multiple vehicle types like trucks, trains, and boats to fulfill door-to-door customer requests. The study's main objective is to improve customer satisfaction and reduce operator costs, thus providing a more flexible alternative to traditional public transportation systems. The unique challenge lies in synchronizing the movements of both the pods and the vehicles they rely on, which creates interdependencies between routes. To address this, a mixed-integer linear program (MILP) and an adaptive large neighborhood search (ALNS) heuristic were developed, alongside a clustering approach to accelerate solution times for real-world applications. This dataset validates the performance of these optimization methods, providing a practical framework for testing advanced multimodal routing solutions. Since no standard benchmark data existed for this novel system, the dataset consists of five custom instance sets - A to E - with increasing levels of complexity and sizes, containing 30 instances each. The instances include randomly generated hubs in a two-dimensional space, with minimum pairwise distances maintained for spatial realism. The transportation network is constructed by assigning edges randomly within each modality (e.g., road, rail) while ensuring full connectivity. Additionally, each base and pod is assigned a capacity, and time windows for customer requests vary, with each subsequent set having a longer time window range. Travel times are computed as the Euclidean distance between hubs divided by a constant speed factor, with adjustments for loading and boarding times to reflect real-world operational delays.