I have already inspected all 44 columns of the dataset across the three tool calls. Let me compile this information into a comprehensive answer. ```json { "action": "final", "content": "## Dataset Columns: Polling on AI Usage (July 2026)\n\nThis dataset contains **44 columns** across **105 rows**, structured as a polling/survey result table with demographic breakdowns.\n\n### Survey Structure Columns\n\n| Column | Data Type | Meaning |\n|--------|-----------|----------|\n| `question_number` | BIGINT | Numeric identifier for each survey question (1-15) |\n| `question_verbatim` | VARCHAR | Full text of the survey question as asked |\n| `response_verbatim` | VARCHAR | Full text of the response option |\n| `Question` | VARCHAR | Standardized/categorized question label (~16 distinct categories) |\n| `Response` | VARCHAR | Standardized/categorized response label (~64 distinct responses) |\n| `Overall` | VARCHAR | Overall percentage for this question-response combination across all respondents |\n\n### Demographic Breakdown Columns (All VARCHAR, storing percentages)\n\nEach of the following columns contains the percentage of respondents in that demographic segment who gave the corresponding response:\n\n**Gender:**\n- `Gender: Male` — Male respondents\n- `Gender: Female` — Female respondents\n\n**Age Groups:**\n- `Age: 18–29` — Young adults\n- `Age: 30–44` — Early-to-mid career adults\n- `Age: 45–59` — Late career adults\n- `Age: 60+` — Seniors\n\n**Income Brackets:**\n- `Income: Under $25K`\n- `Income: $25K–$50K`\n- `Income: $50K–$75K`\n- `Income: $75K–$100K`\n- `Income: $100K+`\n\n**Education Level:**\n- `Education: Less than HS` — Did not complete high school\n- `Education: High school` — High school graduate\n- `Education: Some college` — Some college but no bachelor's degree\n- `Education: Bachelor+` — Bachelor's degree or higher\n\n**Employment Status:**\n- `Employment: Full-time` — Full-time employed\n- `Employment: Part-time` — Part-time employed\n\n**Race/Ethnicity:**\n- `Race: White, Non-Hispanic`\n- `Race: Black, Non-Hispanic`\n- `Race: Hispanic`\n- `Race: Other/2+, Non-Hispanic` — Other races or multiracial\n\n**Geographic Region:**\n- `Region: Northeast`\n- `Region: Midwest`\n- `Region: South`\n- `Region: West`\n\n**Occupation Categories:**\n- `Occupation: Software & IT`\n- `Occupation: Managers`\n- `Occupation: Business, finance & legal`\n- `Occupation: Education & social services`\n- `Occupation: Engineering & science`\n- `Occupation: Healthcare`\n- `Occupation: Sales, office/admin & creative`\n- `Occupation: Manual & in-person services`\n- `Occupation: Other (please specify)`\n\n### Metadata Columns\n\n| Column | Data Type | Meaning |\n|--------|-----------|----------|\n| `_ingestion_timestamp` | TIMESTAMP | When this data was loaded into the system |\n| `_batch_id` | VARCHAR | Identifier for the data ingestion batch |\n| `_source_file` | VARCHAR | Path to the original CSV file |\n| `_source_system` | VARCHAR | System that provided the data (\"api\") |\n\n### Data Structure Notes\n\n- **All percentage columns are stored as VARCHAR** (text like \"5.23%\"), not numeric types\n- Each row represents one question-response combination with its overall percentage and demographic breakdowns\n- The dataset has **no null values** in any column\n- This is a **cross-tabulated survey result** where each demographic segment's response rate is pre-calculated" } ```