Global dataset on remote work, productivity, burnout, and mental health for exploratory data analysis. The dataset's author, organization, size, and temporal specifics are not provided. It was sourced from the Kaggle platform.
Use Cases
- Analyze correlations between remote_work_frequency and self_reported_productivity scores.
- Model burnout_risk using features like work_hours, stress_level, and mental_health_indicators.
- Segment employee groups by demographic features to identify productivity and well-being patterns.
- Conduct time-series analysis on survey_data to track changes in remote work outcomes.
Strengths
- Global scope provides diverse geographic perspectives on remote work.
- Covers multiple interrelated domains: productivity, burnout, and mental health.
Limitations
- Specific row count, column names, and sample size are unknown, limiting assessment of statistical power.
- Lack of documented data collection method raises questions about potential sampling bias.
- Unknown update frequency and temporal coverage limit analysis of trends over time.
Provenance
- Source
- Kaggle
- Collection Method
- null
- Time Range
- 2025
- Freshness
- null
- Geography
- Global