Brainstorming Possible Domains

[Adamx090]

Hi guys! Lets write here in this discussion our favorite domains with potential research questions, and a potential data source

[Husayn01]

Hi guys! Lets write here in this discussion our favorite domains with potential research questions, and a potential data source

Domain: Urban Heat Islands (Environment & Urban Planning)
Research Focus: Impact of Urban Heat on Schools and Healthcare Facilities
Research Questions:

• What is the spatial pattern of land surface temperature in [City Name]?
• Which schools and healthcare facilities are located in heat-prone zones?
• What is the relationship between vegetation cover and temperature distribution?
• What urban planning measures can help reduce heat stress in these areas?

Data Source(s):

• Climate Change: Earth Surface Temperature Data (Kaggle)
• Landsat 8/9 Satellite Data (Google Earth Engine)
• OpenStreetMap (for school and health facility locations)

[salihakalender]

Domain: Global Air Pollution
Global air pollution remains a pressing environmental and health crisis, responsible for around 7 million premature deaths annually and driven by pollutants such as PM2.5, NO₂, O₃, and CO. This dataset enables city-level analysis of air quality, allowing researchers to identify pollution hotspots, compare regions, investigate pollutant-AQI correlations, and build predictive models to support targeted interventions and policy-making.
Research Questions

1. Which cities had the highest and lowest AQI levels globally in 2024?
2. What is the dominant pollutant (PM2.5, NO₂, CO, or O₃) in the top 10 most polluted cities?
3. Can AQI be accurately predicted using pollutant concentrations with machine learning models?
4. What are the geographical patterns of air quality across continents in 2024?
5. Which pollutant contributes most significantly to poor air quality in different climate regions?
   Data Sources:

• https://www.kaggle.com/datasets/sazidthe1/global-air-pollution-data/code
• https://openaq.org
• CAMS Global Reanalysis EAC4
• https://databank.worldbank.org

References:
• World Health Organization (WHO). (2021). Ambient Air Pollution: A Global Assessment of Exposure and Burden of Disease. https://www.who.int/publications/i/item/9789241511353
• Brunekreef, B., & Holgate, S. T. (2002). Air pollution and health. The Lancet, 360(9341), 1233–1242. https://doi.org/10.1016/S0140-6736(02)11274-8
• Lelieveld, J., Evans, J. S., Fnais, M., Giannadaki, D., & Pozzer, A. (2015). The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature, 525(7569), 367–371. https://doi.org/10.1038/nature15371
• Pope, C. A., Burnett, R. T., Thun, M. J., et al. (2002). Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. JAMA, 287(9), 1132–1141. https://doi.org/10.1001/jama.287.9.1132
• Cohen, A. J., Brauer, M., Burnett, R., et al. (2017). Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: An analysis of data from the Global Burden of Diseases Study 2015. The Lancet, 389(10082), 1907–1918. https://doi.org/10.1016/S0140-6736(17)30505-6
• World Bank. (2016). The Cost of Air Pollution: Strengthening the Economic Case for Action. World Bank Group. https://openknowledge.worldbank.org/handle/10986/25013

[linahKhayri]

🌞 Domain: Solar Energy in Socioeconomic Context

🎯 Research Question (Possible):

How do cost factors and socioeconomic development influence the adoption of solar energy in developed vs. developing countries?

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📊 Key Data Factors & Sources

1. Installed Solar Energy Capacity (MW)

• What it tells us: Annual solar installation capacity by country.
• Source: IRENA Renewable Capacity Statistics

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2. Access to Electricity (% of Population)

• What it tells us: Percentage of the population with electricity access in each country.
• Source: World Bank – Electricity Access Indicator

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3. GDP per Capita (US$)

• What it tells us: Average income per person — helps distinguish between developed and developing countries.
• Source: World Bank – GDP per Capita

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🧹 Data Preparation Notes

• Each dataset focuses on a single topic.
• To explore relationships, we must:
  • Match data by country and year
  • Merge into one master file
  • Clean inconsistencies (e.g., mismatched country names, missing data)
• ⚠️ We may need to search for additional factors (e.g., solar radiation, policy incentives, installation costs).

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✅ Pros

• Easy to access and clean (CSV available from IRENA & World Bank)
• Standardized global data with broad temporal and geographic coverage
• Enables socioeconomic comparisons (developed vs. developing countries)
• Aligns with sustainability and energy justice themes (e.g., UN SDGs)
• Suitable for correlation and trend analysis
• Feasible within project timeline

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⚠️ Cons

• Shows correlation, not causation
• Limited to country-level data (no regional or household granularity)
• Still requires data cleaning
• May lack key context (e.g., government policies, sunlight availability, installation cost)

[Adamx090]

1. Domain: inflation

Inflation, the sustained increase in the general price level of goods and services in an economy over time, remains one of the most critical and complex indicators of economic stability and performance. As both a macroeconomic phenomenon and a household-level concern, inflation has far-reaching implications, affecting purchasing power, interest rates, employment, business investment, and government fiscal policy

Research Questions Examples:

• What is the relationship between interest-based loan volumes and inflation rates across countries from 2000 to 2022?
• How has the relationship between interest-based lending and inflation changed before and after the 2008 global financial crisis?

List of Potential Data sources:

• World Bank Open Data
• International Monetary Fund (IMF)
• OECD Statistics
• Federal Reserve Economic Data (FRED)
• Global Financial Development Database (World Bank)

2. Domain: Birthrate Decline

In recent decades, many countries around the world have experienced a steady decline in birthrates, raising concerns about long-term economic stability, population aging, and labor force sustainability. This demographic shift is particularly pronounced in high-income and rapidly developing nations, where fertility rates have fallen below the replacement level of 2.1 births per woman. Factors contributing to this trend include increased urbanization, higher educational attainment, delayed marriage, economic uncertainty, and changing social norms around family and gender roles.

Research Questions Examples:

• Can female labor force participation and average marriage age predict birthrate decline in OECD countries?
• How do fertility trends differ between urban and rural regions in East Asia, and what factors explain the gap?

List of Potential Data sources:

• World Bank Open Data
• United Nations – World Population Prospects
• OECD Family Database
• Human Fertility Database (HFD)

[salihakalender]

Domain: Renewable Energy
The global shift to renewable energy is essential for reducing emissions and addressing climate change, with solar and wind energy expected to nearly triple in capacity by 2030. Data science and AI techniques like forecasting and clustering are helping optimize this transition by analyzing historical trends, predicting future growth, and identifying regional patterns. Using these datasets, researchers can explore energy trends from 1990–2020, compare country-level progress, and forecast the future of clean energy adoption.
Research Questions:

1. How has global renewable energy (solar, wind, hydro, bioenergy) consumption evolved from 1990 to 2020?
2. Which countries have shown the fastest and slowest growth in renewable energy adoption since 1990?
3. What is the relationship between renewable energy growth and reductions in CO₂ emissions?

Data Source:

• https://www.kaggle.com/code/mehmetisik/02-the-evolution-of-modern-renewable-energy/notebook
• https://www.iea.org/
• https://www.irena.org/Statistics
• https://ourworldindata.org/energy

[FalaqMajeed]

Do introverts or extroverts acheive more?

• Exploring whether introverts or extroverts are more likely to successfully complete long-term goals using existing personality and goal achievement data.
• Research question: Are introverts more likely to finish long-term goals than extroverts?
• Data
• Personality Data: Big Five – Including Extraversion
  Dataset: IPIP–Big Five Factor Markers (Cleaned)

Available in cleaned form on Kaggle: “IPIP-Big-Five Factor Markers_Cleaned”
Also available in larger versions (120- and 300-item) via GitHub/Kaggle
What it includes:
Scores for all Big Five traits (including extraversion)
Often comes with age, gender, and other demographics

2️⃣ Goal Completion Data
Option A: MOOC/Digital Course Completion
Example dataset: Predict Online Course Engagement by Rabie El Kharoua on Kaggle
Tracks user behavior and completion outcomes in online courses
Can be used as a proxy for long-term goal completion
Another MOOC dataset (student behavior & performance) on Kaggle

Option B: College Completion Rates
Kaggle dataset tracking college graduation rates across demographics
Graduation count (yes/no) acts as a clear long-term goal completion instance

How to Use Them Together
Download both datasets:

• Big Five personality scores dataset with extraversion
• A goal-related dataset (MOOC or college completion)
• Analyze separately, then merge results using demographic fields (e.g., age, gender, location).
• Compare extroversion between those who completed (learners/graduates) and those who didn’t.
• Use statistical tests and visualizations to uncover meaningful patterns.

System Thinking Aspect
This project applies systemic thinking by examining how personality traits (a complex human factor) interact with goal completion outcomes, considering multiple influencing variables (motivation, behavior patterns, personality) as parts of an interconnected system rather than isolated factors.

Constraits
Data Matching Across Sources
Challenge: The personality and goal-completion data come from different studies.
Impact: They don’t belong to the same individuals, which makes direct correlation impossible.
Workaround: You can only compare group-level trends, not individual-level predictions.
2. No Causal Inference
Challenge: This project is observational, not experimental.
Impact: You can’t prove that extroversion causes higher or lower completion rates—only that there’s a pattern or correlation.
Workaround: Frame your conclusion carefully (e.g., “This suggests...” rather than “This proves…”).
3. Limited Demographic Overlap
Challenge: The two datasets may not have matching age, region, or gender distributions.
Impact: It may be hard to control for external factors (like age or education).
Workaround: You may need to filter the datasets to focus on comparable subgroups.
4. Indirect Goal Proxies
Challenge: "Goal completion" is interpreted via proxies (like course completion or graduation).
Impact: These are not perfect representations of all long-term goals.
Workaround: Be explicit in defining what kind of “goals” you're analyzing.
5. Assumption of Stable Traits
Challenge: You assume that extraversion is stable and accurately captured.
Impact: Real-life personality is complex and may shift over time or across contexts.
Workaround: Rely on the validated IPIP dataset, and acknowledge this in limitations.
6. Ethical Use of Public Data
Challenge: You must ensure the datasets are openly licensed for analysis.
Impact: Any restrictions in the data license could affect your ability to share results.
Workaround: Only use datasets from platforms like Kaggle that provide explicit data use terms.
7. No Experimental Validation
Challenge: Without primary data or simulations, you can't “test” your hypothesis directly.
Impact: You’ll rely heavily on statistical interpretation and literature review.
Workaround: Emphasize the strength of patterns and back your insights with theory from OB or psychology research.

[salihakalender]

Domain: Global Warming / Climate Change
Global warming refers to the rise in Earth’s average temperature due to greenhouse gas emissions, while climate change includes broader shifts like extreme weather and sea-level rise. The last decade was the warmest on record, and data shows increasing temperature trends and climate-related disasters. Using datasets and tools like ARIMA, clustering, and regression, researchers analyze patterns, forecast future warming, and compare regional impacts to guide climate resilience efforts.
Research Questions:

1. How have global mean temperatures evolved year by year over the past century?
2. What is the correlation between atmospheric CO₂ levels and global temperature anomalies?
3. Which countries have experienced the fastest rate of warming between 1900 and 2024?

Data Source:

• https://www.kaggle.com/datasets/kkhandekar/climate-change-vs-global-warming
• https://climate.nasa.gov/
• https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions
• https://climateknowledgeportal.worldbank.org/

[ObayCipher]

🌍 Domain Suggestion

🌡️ Domain: Global Warming & Climate Change

Global warming refers to the increase in Earth's average surface temperature due
to the buildup of greenhouse gases like carbon dioxide (CO₂), methane (CH₄), and
nitrous oxide (N₂O). Climate change includes not only rising temperatures but
also extreme weather events, shifts in precipitation patterns, melting glaciers,
and sea-level rise. The last decade has been the hottest in recorded history.

📊 Data science plays a critical role in tracking, analyzing, and forecasting
the impacts of global warming. Techniques such as time series forecasting
(e.g., ARIMA), clustering, regression modeling, and spatial analysis are
commonly used to interpret patterns and build predictive models.

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🔍 Research Focus Areas & Questions

🏭 Carbon Emissions & Carbon Capture

🧠 Understanding sources of emissions and exploring solutions for capture and reduction.

Research Questions

• What are the trends in CO₂ emissions globally and by
  sector (transport, energy, industry) from 1980–2024?
• Which countries have been most successful in reducing their carbon intensity
  (emissions per GDP)?
• How effective are carbon capture and storage (CCS) strategies in reducing net emissions?

📚 Data Sources

• IEA Carbon Emissions Data
• Global Carbon Atlas
• World Bank CO₂ Indicators

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🔋 Energy Transition & Climate Impact

🧠 Analyzing the shift from fossil fuels to renewable energy and its
environmental implications.

Research Questions

• How is the global energy mix evolving, and what impact does this have on emissions?
• What is the relationship between the adoption of renewable energy and the
  reduction of local air pollutants?
• How does renewable energy adoption correlate with changes in national CO₂
  emissions over time?

📚 Data Sources

• Our World in Data – Energy Mix
• IRENA Statistics
• BP Statistical Review of World Energy

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🌋 Geothermal Energy from Depleted Oil Wells

🧠 Exploring the dual role of geothermal energy in clean energy production
and CO₂ mitigation.

Research Questions

• Can depleted oil and gas wells be repurposed for geothermal energy production?
• What is the estimated reduction in CO₂ emissions when replacing fossil fuel
  energy with geothermal power?
• What are the technical and economic barriers to large-scale deployment of
  this approach?

📚 Data Sources

• U.S. DOE Geothermal Technologies Office
• International Geothermal Association (IGA)
• SPE Technical Papers & GeoScienceWorld

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🌐 Global Warming & CO₂ Trends

🧠 Studying how temperature and CO₂ concentrations have evolved and how they correlate.

Research Questions

• How have global mean surface temperatures changed annually since 1900?
• What is the statistical correlation between atmospheric CO₂ levels and
  temperature anomalies?
• Which countries or regions have experienced the most rapid warming trends?

📚 Data Sources

• NASA GISTEMP
• Our World in Data – CO₂ and Temperature
• World Bank Climate Data

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🛢️ Petroleum Geoscience & Climate Change

🧠 Evaluating the environmental footprint of petroleum exploration and
strategies for mitigation.

Research Questions

• What role does petroleum exploration play in regional CO₂ and methane emissions?
• How can subsurface data and basin models be used to identify potential CCS
  storage sites?
• What is the carbon footprint of upstream petroleum operations compared to
  renewable alternatives?

📚 Data Sources

• IEA Methane Tracker
• SPE Reservoir & GeoScienceWorld
• OGCI Reports – Oil & Gas Climate Initiative