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The dataset is a relational, analytics-oriented panel designed to support research on global development patterns across 217 World Bank economies from 1976 through 2025. It consolidates wide-range of indicators from the World Bank and the United Nations into a consistent country-year structure, making it suitable for econometric modeling, trend analysis, policy benchmarking, and cross-domain feature engineering. Most indicator tables use a composite key of Country_ISO3_Code + Observation_Year, which enables clean joins across domains without reshaping source files each time. Core indicator domains include: - Macroeconomics: GDP, GDP per capita PPP, inflation, unemployment - Demographics: population, life expectancy, age distribution, urbanization - Investment & Infrastructure: capital formation, electricity and internet access, broadband/mobile penetration, energy use, transport proxies - Foreign Investment & External Finance: FDI inflows/outflows, remittances, ODA, external debt - Education Investment & Outcomes: education spending, completion/literacy metrics, enrollment levels, and gender parity indicators To support interpretability and governance, the dataset separates descriptive reference entities from measurements via dimension-style tables such as continent, lending category, and income group. A dedicated historical classification table tracks how each economy’s income group changes over time, allowing analysts to study structural transitions (for example, low-income to middle-income movement) while preserving period context. Although the time window is broad and standardized, users should treat it as a bounded panel rather than a perfectly complete matrix: some countries or indicators may have missing values in specific years depending on source availability. Overall, the dataset is built to be SQL-friendly, reproducible, and directly usable for downstream BI dashboards, forecasting workflows, and academic-grade comparative analysis. Overview of the included files: - The “/data” folder includes original datasets. - The “/Final_Dataset_20260411_144349” folder contains processed datasets. - “transform.py” convert raw datasets into clean, relational datasets. - “ER_Diagram.png” shows an overview and relationships between entities. - “create_tables.sql” and “ER_model.mwb” are used to model the final data structures in mySQL Workbench.

This dataset contains the final documentation for the "TERRA–RES" framework, a multi-layer Digital Twin architecture designed for climate-resilient urban infrastructure and sovereign operational continuity. The framework is built upon the UIP-100 Modular Infrastructure Continuity System, ensuring high-level resilience for critical services.The dataset includes:The Core Research Paper: "TERRA–RES: A Multi-Layer Digital Twin Framework for Climate-Resilient Urban Infrastructure Simulation and Multi-Risk Assessment." This document details the integration of energy, water, data, and safety layers, validated through Monte Carlo simulations ($N > 10^3$ runs) on a virtual case study (Pianoro della Civita).Technical Illustration Specifications: A camera-ready guide for Figures 1-10, providing the visual and topological evidence of the system’s architecture, including the AQUAFORTIS water management and FIRETOWER safety protocols.The study demonstrates how distributed underground redundancy and coupled infrastructure modeling can maintain energy service thresholds (MST-E) above 85% and ensure emergency evacuation within a 120-second safety window under extreme multi-hazard conditions (blackouts, drought, and wildfires).

This study investigated the effect of poles and KT applied to the abdomen on the LD and RA activity, LS angle, pelvic symmetry and forelimb and hindlimb protraction and retraction angles. It was hypothesised that (1) poles would increase RA and LD activation and improve kinematics; (2) kinesiotaping would independently increase RA and LD activation and kinematics parameters would be improved; and (3) combined application would produce summative effects.

This collection features a variety of chessboard states captured under different conditions. To ensure a robust model, the dataset provides a comprehensive variety of lighting, angles, and piece positioning.

This dataset represents a normalized relational database designed to analyze global economic, demographic, and investment indicators across multiple countries over a defined period. The primary objective of this dataset is to provide a structured and scalable data model that supports analytical queries and demonstrates best practices in database design and normalization. The database is composed of four main entities: country, economic_indicators, demographic_indicators, and investment_indicators. The country table stores static information such as country name, ISO code, region, development status, and capital city. The other three tables store time-dependent data linked to each country through foreign key relationships, enabling the representation of longitudinal data across multiple years. The economic_indicators table includes key financial metrics such as GDP, GDP per capita, inflation rate, unemployment rate, and industrial contribution to the economy. The demographic_indicators table focuses on population-related data, including total population, growth rate, life expectancy, median age, and fertility rate. The investment_indicators table captures government and private sector investment metrics, such as education expenditure, infrastructure investment, foreign direct investment, and construction investment. This dataset was designed following normalization principles up to Third Normal Form (3NF), ensuring minimal redundancy, improved data consistency, and efficient storage. Each table represents a distinct domain, and all relationships are enforced through primary and foreign key constraints to maintain referential integrity. Although the dataset uses synthetically generated data, its structure is inspired by real-world sources such as the World Bank and United Nations datasets. It is suitable for academic purposes, including data analysis, database management, and machine learning preprocessing. The dataset enables users to explore relationships between economic performance, population dynamics, and investment strategies across different countries and years, making it a valuable resource for both educational and analytical applications.

This dataset presents a normalized multi-table relational database containing socioeconomic, demographic, educational, and investment indicators for 50 selected countries for 2018, 2019, and 2020. The dataset is organized into five interconnected tables: Country, Economic_Indicators, Population_Statistics, Education_Statistics, and Investment_Indicators. Each table is linked through a common CountryID key, ensuring referential integrity and consistent relationships across the database. The Economic_Indicators table includes key macroeconomic variables such as Gross Domestic Product (GDP), GDP per capita, inflation rate, and unemployment rate. The Population_Statistics table provides demographic data, including the total population and the percentage of the population living in urban areas. The Education_Statistics table contains government expenditure on education as a percentage of GDP. The Investment_Indicators table includes foreign direct investment (FDI) inflows as a percentage of GDP. Together, these tables provide a comprehensive view of economic performance, demographic trends, education investment, and international capital flows. The data used in this dataset were collected from publicly available international sources, primarily the World Bank’s World Development Indicators. The datasets were carefully cleaned, filtered, and normalized to support efficient querying, maintain data integrity, and enable meaningful analysis. This dataset is suitable for comparative analysis of economic development, demographic changes, and policy-related indicators across multiple countries and time periods.

Accompanying data and code for Chau, Conesa Martinez, Kim, and Spray (2026): "Global Value Chains and Inflation Dynamics".

This dataset supports a large-scale LoRa measurement campaign conducted at 868 MHz across urban and suburban environments in the city of Funchal, Madeira Island, Portugal. The research hypothesis is that inconsistencies in measurement methodology, particularly the use of mobile platforms, uncalibrated hardware, and insufficient sampling per location, introduce systematic bias in path-loss estimation for LoRa-based Internet of Things deployments, and that a fixed-position, calibrated, multi-sample approach yields more accurate and reproducible path-loss characterization. The dataset contains received signal strength indicator (RSSI), signal-to-noise ratio (SNR), and estimated effective signal power (ESP) values for both uplink and downlink transmissions, collected at 1,203 fixed locations across an area of approximately 8 km², from February to November 2025. Three custom gateways, designated Gateway A, Gateway B, and Gateway C, operating at carrier frequencies of 868.1 MHz, 868.3 MHz, and 868.5 MHz respectively, received transmissions from four end devices arranged in a spatial diversity structure. Eight samples were acquired per measurement location, resulting in a total of 28,872 uplink packets transmitted. Each record includes GPS-verified geographic coordinates, timestamp, and per-packet RSSI and SNR values for each gateway link. The data reveal that small-scale fading induces signal fluctuations of up to 35 dB, with per-location standard deviations reaching approximately 12 dB, underscoring the need for multiple samples when estimating path loss. Uplink and downlink path-loss values were found to be statistically similar, with a mean difference of 0.9 dB and a standard deviation of 1.5 dB. Near the receiver sensitivity limit, packet loss reduces the number of available samples and degrades path-loss estimation accuracy; an order-statistics-based correction technique is described in the companion paper to address this effect. The data also demonstrate that conventional single-slope log-distance path-loss models achieve a root mean square error of approximately 12 dB across the full measurement area, indicating that more refined modeling approaches incorporating environmental parameters are necessary for accurate propagation prediction in complex urban scenarios.

This dataset supports the systematic mapping review "Towards persistent coverage in wildfire monitoring: A systematic mapping review". It includes search results from multiple digital libraries, the consolidated bibliographic library (RIS), the mapping file, and supporting materials such as search strings and a thesaurus. The dataset ensures transparency and reproducibility of the review process.

This dataset presents a comprehensive, fully normalized relational database capturing critical economic, investment, and demographic indicators for 50 diverse countries spanning a 50-year period from 1974 to 2023. Designed specifically for relational database management systems (RDBMS), the dataset is structured in Third Normal Form (3NF) to eliminate data redundancy and ensure referential integrity. Instead of raw flat files, this dataset is provided as a complete, self-contained SQL dump (.sql file) that includes the Data Definition Language (DDL) to create the schema and the Data Manipulation Language (DML) to populate the records. The database is divided into four interconnected tables: a central 'Country' parent table, and three child tables ('Economic_Record', 'Investment_Record', and 'Demographic_Record'). The Country table establishes the primary entity with unique identifiers (Country_ID), ISO Alpha-3 codes, continent classifications, and development statuses (Developed vs. Developing). The Economic_Record table details annual Gross Domestic Product (GDP in current USD), GDP per capita, inflation rates (consumer prices), and unemployment rates. The Investment_Record table tracks Foreign Direct Investment (FDI) net inflows, gross capital formation (total investment as a percentage of GDP), and gross national savings. Finally, the Demographic_Record table incorporates United Nations data on total population, life expectancy at birth, and annual population growth rates. By synthesizing authoritative data from the World Bank and the UN, this dataset offers an integrated resource for researchers and students looking to perform complex SQL querying and cross-country comparisons using clean, relational data architectures.