**This dataset has no real time dimension, so there are no time-based spikes, dips, or breaks to find.** - **Ingestion is a single event.** The only timestamp column is `_ingestion_timestamp`. The query I ran shows all 4,500 rows loaded in one batch (`batch_2899399bad41`) on 2026-10-01, from one source file. This records when the file was loaded, not when anything happened to a student, so it can't show a trend. - **The "temporal axes" on the card are not dates.** `Daily_Usage_Hours` (0.9–14.0) and `Weekend_Extra_Hours` (0.0–4.5) are hours of social media use per student. They are usage measures, not a calendar or sequence. - **Each student appears once.** The grain is one row per `Student_ID`, so there are no repeated observations of the same student over time. The data is a cross-sectional snapshot. If you want to look for breaks, the natural option is to treat `Daily_Usage_Hours` as an ordinal axis. I could bucket it and check where sleep, stress, mental-health score, or GPA shift sharply. That would show thresholds in usage, not changes over time. I haven't run that analysis yet.