| --- |
| license: mit |
| task_categories: |
| - tabular-classification |
| - tabular-regression |
| language: |
| - en |
| tags: |
| - economics |
| - development |
| - world-bank |
| - global |
| - climate |
| - healthcare |
| - dataverse-2026 |
| pretty_name: DataVerse — Global Development Indicators Dataset |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # 🌐 DataVerse — Global Development Indicators Dataset |
|
|
| This dataset was compiled for **DataVerse-2026**, a data analytics competition organized by the Departmental Statistics Association (DSA), Department of Statistics, Faculty of Science, **The Maharaja Sayajirao University of Baroda**. The competition challenges participants to solve real-world problems through visualization, machine learning, and analytical storytelling. |
|
|
| > 📅 Competition Dates: Feb 26–28, 2026 · 📍 Venue: Dept. of Statistics, MSU Baroda · 👥 Team Size: 1–4 Members |
|
|
| --- |
|
|
| ## 📋 Dataset Overview |
|
|
| A longitudinal panel dataset covering **global development indicators** across world regions and country groups from **2000 to 2020**. Each row represents a region-year observation and spans economic, environmental, health, digital, and governance dimensions. |
|
|
| The dataset is structured around aggregated World Bank-style regional groupings (e.g., Africa Eastern and Southern, Arab World, Central Europe and the Baltics, etc.) rather than individual countries, making it well-suited for comparative regional analysis. |
|
|
| --- |
|
|
| ## 📊 Features |
|
|
| The dataset contains **47 columns** organized into the following thematic groups: |
|
|
| ### 🆔 Identifiers |
| | Column | Description | |
| |---|---| |
| | `year` | Year of observation (2000–2020) | |
| | `country_code` | World Bank region/group code (e.g., `AFE`, `ARB`, `CEB`) | |
| | `country_name` | Full name of the region or country group | |
| | `region` | Sub-region classification *(often null for aggregated groups)* | |
| | `income_group` | World Bank income classification *(often null)* | |
| | `currency_unit` | Primary currency *(often null for regional aggregates)* | |
|
|
| ### 💰 Economic Indicators |
| | Column | Description | |
| |---|---| |
| | `gdp_usd` | GDP in current USD | |
| | `population` | Total population | |
| | `gdp_per_capita` | GDP per capita (USD) | |
| | `inflation_rate` | Annual inflation rate (%) | |
| | `unemployment_rate` | Unemployment rate (%) | |
| | `fdi_pct_gdp` | Foreign direct investment as % of GDP | |
|
|
| ### 🌱 Environmental Indicators |
| | Column | Description | |
| |---|---| |
| | `co2_emissions_kt` | CO₂ emissions in kilotons | |
| | `energy_use_per_capita` | Energy use per capita (kg oil equivalent) | |
| | `renewable_energy_pct` | Renewable energy as % of total | |
| | `forest_area_pct` | Forest area as % of land area | |
|
|
| ### ❤️ Health & Social Indicators |
| | Column | Description | |
| |---|---| |
| | `life_expectancy` | Average life expectancy at birth | |
| | `child_mortality` | Under-5 mortality rate (per 1,000 live births) | |
| | `health_expenditure_pct_gdp` | Health expenditure as % of GDP | |
| | `hospital_beds_per_1000` | Hospital beds per 1,000 people | |
| | `physicians_per_1000` | Physicians per 1,000 people | |
| | `electricity_access_pct` | Population with access to electricity (%) | |
| | `school_enrollment_secondary` | Secondary school enrollment rate | |
|
|
| ### 📡 Digital & Connectivity Indicators |
| | Column | Description | |
| |---|---| |
| | `internet_usage_pct` | Internet users as % of population | |
| | `mobile_subscriptions_per_100` | Mobile subscriptions per 100 people | |
|
|
| ### 📐 Derived / Composite Indices |
| | Column | Description | |
| |---|---| |
| | `calculated_gdp_per_capita` | Recalculated GDP per capita | |
| | `real_economic_growth_indicator` | Derived economic growth metric | |
| | `econ_opportunity_index` | Composite economic opportunity score | |
| | `co2_emissions_per_capita_tons` | Per capita CO₂ emissions (metric tons) | |
| | `co2_intensity_per_million_gdp` | CO₂ intensity per million USD of GDP | |
| | `green_transition_score` | Score reflecting transition to green energy | |
| | `ecological_preservation_index` | Index measuring ecological health | |
| | `renewable_energy_efficiency` | Efficiency ratio of renewable energy use | |
| | `human_development_composite` | Composite HDI-style score | |
| | `healthcare_capacity_index` | Derived healthcare system capacity score | |
| | `digital_connectivity_index` | Composite digital access/readiness score | |
| | `health_development_ratio` | Ratio of health indicators to development level | |
| | `education_health_ratio` | Ratio of education to health indicators | |
| | `human_development_index` | HDI estimate | |
| | `climate_vulnerability_index` | Climate risk and vulnerability score | |
| | `digital_readiness_score` | Digital readiness composite | |
| | `governance_quality_index` | Governance quality composite | |
| | `global_resilience_score` | Overall resilience composite | |
| | `global_development_resilience_index` | Master composite development index | |
|
|
| ### 🗓️ Temporal Features |
| | Column | Description | |
| |---|---| |
| | `years_since_2000` | Years elapsed since 2000 | |
| | `years_since_century` | Same as above (century baseline) | |
| | `is_pandemic_period` | Binary flag — `1` for year 2020 (COVID-19) | |
|
|
| --- |
|
|
| ## 🔍 Potential Use Cases |
|
|
| This dataset is particularly well-suited for: |
|
|
| - **Exploratory Data Analysis (EDA)** — regional trends, missing data patterns, distributions |
| - **Time-series forecasting** — predict GDP, HDI, or emissions over time |
| - **Clustering / Unsupervised learning** — group regions by development profile |
| - **Regression modeling** — predict composite indices from base indicators |
| - **Dashboard / Visualization** — build interactive development dashboards |
| - **Analytical storytelling** — narrate global development trends |
|
|
| --- |
|
|
| ## ⚠️ Data Notes |
|
|
| - Many columns contain `null` values, especially for later years (2016–2020) where source data was unavailable. |
| - The dataset uses **regional aggregates** (World Bank group codes), not individual country data. |
| - The `is_pandemic_period` flag marks 2020 as a notable anomaly year — many indicators show sharp discontinuities. |
| - Some derived indices may have been computed by the dataset author and are not direct World Bank outputs. |
|
|
| --- |
|
|
| ## 🏆 About DataVerse-2026 |
|
|
| DataVerse-2026 is a data analytics hackathon organized by the **Departmental Statistics Association (DSA)**, Department of Statistics, Faculty of Science, MSU Baroda (Est. 1949). The competition runs over 2 rounds and spans 3 domains, bringing together students to demonstrate data-driven thinking, creativity, and collaboration. |
|
|
| - 🌐 Website: [threed2y.github.io/DataVerse-2026](https://threed2y.github.io/DataVerse-2026) |
| - 📧 Contact: [dataversestats@gmail.com](mailto:dataversestats@gmail.com) |
| - 🏛️ Organizer:Departmental Statistics Association, Dept. of Statistics, Faculty of Science, The Maharaja Sayajirao University of Baroda |
|
|
| --- |
|
|
| ## 📄 License |
|
|
| This dataset is released under the **MIT License**. |
