**Short answer:** the table has one identifier, about eight categorical dimensions, eight numeric measures, and no true business date or time column. The only real timestamp is ingestion metadata. This comes from the column metadata result (16 columns) and the dataset card's column types. **Identifier (1)** - `Student_ID` (VARCHAR) is the primary key, one row per student. **Categorical dimensions (8)** - Text categories: - `Gender` - `Academic_Level` - `Primary_Platform` - `Device_Type` - `Social_Comparison_Frequency` - `Overall_Impact` - Boolean flag: `Late_Night_Usage`. - `Age` (BIGINT, 15–26) is stored as a number. The profiler labelled it a "measure", but with about 10 distinct values it works better as a grouping dimension (age band). It is also a classifier on the card. **Numeric metrics (8)** - Outcome measures: - `Academic_Performance_GPA` (1.9–4.0, 85 nulls) - `Mental_Health_Index` (32–98) - `Perceived_Stress_Score` (0–40, 46 nulls) - `Sleep_Quality_Score` (1–5) - `Sleep_Duration_Hours` (3.0–10.5) - Usage measures: - `Daily_Usage_Hours` (0.9–14.0) - `Weekend_Extra_Hours` (0.0–4.5) - `Age` could also be treated as a numeric measure if you want averages or correlations. **Dates/times** - `Daily_Usage_Hours` and `Weekend_Extra_Hours` were tagged "temporal" by the profiler. They are durations in hours, not dates, so you can't build a time series from them. Treat them as numeric measures. - The only true timestamp is `_ingestion_timestamp` (TIMESTAMP). It records when rows were loaded, not when anything happened to the student, so it is not useful for analysis. - This is a cross-sectional snapshot, so trend-over-time questions can't be answered. **Other columns** - `_batch_id`, `_source_file` and `_source_system` are ingestion lineage fields. They are not analytical. **Quality note:** only `Perceived_Stress_Score` (46) and `Academic_Performance_GPA` (85) have nulls. Every other profiled column is complete.