Mendeley Data Showcase

This is a dataset and Stata do files for the research paper titled "The Economics of Longevity: Efficiency of Health Investments, Institutional Quality, and Carbon Emissions Across Income Groups in Sub-Saharan Africa – Implications for SDG 3" that I submitted to Discover Public Health journal for publication.

Data on elderly care facilities in Hangzhou.

Este conjunto de dados reúne dados de pacientes diagnosticados com tuberculose disponibilizados pelo Ministério da Saúde através do SINAN (Sistema de Informação de Agravos de Notificação). O conjunto abrange notificações de pacientes com tuberculose no Brasil entre 2001 e 2024, com informações de dados clínicos, laboratoriais e sociodemográficos. Os dados foram pré-processados para construir um conjunto de dados focados no desfecho dos tratamentos da tuberculose, atráves de uma classificação binária dos desfechos pela variável SITUA_ENCE, sendo dividida entre casos Favoráveis e Desfavoráveis. O dataset possui 74 variáveis e 2.188.043 registros, sendo 1.465.479 de casos Favoráveis e 722.564 de casos Desfavoráveis. Os dados desse repositório incluem: •⁠ ⁠Base de dados pré-processada para análise exploratória •⁠ ⁠Dicionário de dados do SINAN •⁠ ⁠Scripts de pré-processamento e plotagens de gráficos para análise

This dataset contains the bibliographic records and thematic coding underlying a systematic literature review on ultra-processed foods, nutrition labeling, and consumer response. The dataset includes 297 scientific articles published between 2000 and 2026 and retrieved from Web of Science and Scopus. It contains bibliographic metadata, thematic classification, summaries, and supporting tables used for the descriptive and qualitative analysis. Pubblica il dataset e copia il DOI.

This replication package contains the Stata code necessary to reproduce all tables and figures in the paper Digital Infrastructure and Entrepreneurship: Evidence from China's Broadband Expansion.

Data for the Chironomid-based Holocene summer temperature variability in Lake Prokoško, Vranica Mountains, Bosnia Hercegovina (Western-Balkan region)

This is the code to replicate the results in the paper by Case and Stankov. You need STATA to run the code. You may need to install packages as you go if the code throws an error. If so, repeat from line 1 after installing the packages. Before you begin, place the code and data in the same folder. You also need to create a folder: draftresults. Then create a subfolder: plots. Then run the attached code.

1. Overview This replication package provides all the publicly available data, code, and instructions required to reproduce the main empirical results of the above-mentioned research paper. The package is designed to facilitate transparency and enable independent verification of all statistical findings reported in the study.

Dataset used in the empirical analysis.

This repository presents the Joint False Alarm Dataset (JFAD). The dataset is designed to support the training, evaluation, and validation of deep learning models for thermal anomaly detection and false alarm reduction in terrestrial remote sensing scenarios. JFAD provides a comprehensive representation of thermal interference sources that commonly trigger false detections, making it a strong benchmark for developing robust cascade-based detection architectures. JFAD was constructed by consolidating two complementary repositories: the Addressing False Alarm Situations (AFAS) dataset and the Thermal Anomaly (TA) dataset. Together, these collections characterize a broad spectrum of challenging thermal conditions across both the near-infrared (NIR) and long-wave infrared (LWIR) domains. The dataset includes diverse environments such as urban infrastructure monitoring, rural surveillance, and wilderness fire-like anomaly scenarios. A key contribution of this dataset is the re-annotation of the TA subset, extending traditional bounding-box labels into pixel-level instance masks. This enables models to better distinguish filament-like structures such as powerlines from irregular thermal signatures caused by incipient fires, solar glint, or exhaust emissions, an essential requirement for minimizing false alarms in real-world deployments. Dataset Composition The JFAD dataset comprises a total of 6098 thermal images, aggregated from multiple measurement campaigns and established open benchmarks, including: 1) FLIR Advanced Driver Assistance Systems (ADAS) 2) TarDAL M3FD 3) Powerline Image Dataset (PID) 4) In-house thermal acquisitions across urban, rural, and wilderness settings The dataset reflects substantial cross-sensor variability, incorporating different platforms, resolutions, and monitoring conditions. For experimental evaluation, JFAD is divided into: 4308 training samples and 1790 validation samples Technical Specifications File Formats: JPEG, TXT, NPY Spectral Bands: NIR and LWIR Resolution Range: 160×120 up to 640×512 Cameras used: FLIR Tau 2, FLIR A615, FLIR A35, Seek Mosaic, and FLIR Lepton This repository contains the dataset used in our work. If you wish to replicate our experiments or access the full implementation, please refer to the link provided in the Related Links section, which directs you to the official GitHub repository associated with the paper.