This document explains the two reporting layers added for cross-disciplinary teams.
In many projects, the bottleneck is not computing a metric. The bottleneck is explaining to another person:
- what kind of time-series dataset this is
- what the main structural issues are
- why those issues matter for the project
- what the team should do next
The summary card and the narrative report are built for that step.
Use the summary card when someone wants a short, practical readout.
Typical readers:
- clinicians
- product managers
- study coordinators
- operations partners
- collaborators outside the method team
What it emphasizes:
- executive summary
- top structure axes in plain language
- main watchouts
- analysis opportunities
- recommended next actions
API:
profile.to_summary_card_markdown()
profile.to_summary_card_json()CLI:
EchoTime data.npy --format summary-cardUse the narrative report when someone wants a full prose explanation.
Typical readers:
- domain experts reviewing a dataset handoff
- coauthors writing methods-light sections
- lab members onboarding to a dataset
- project teams deciding validation strategy
What it emphasizes:
- what the dataset is in everyday language
- what stands out structurally
- why that matters for common tasks
- what could go wrong if structure is ignored
- practical next steps
- reliability and interpretation guardrails
API:
profile.to_narrative_report()CLI:
EchoTime data.npy --format narrativeA good pattern is:
- Run the full profile for technical inspection.
- Export a dataset card for reproducibility.
- Export a summary card for the broader team.
- Export a narrative report when decisions need explanation.
Use the default audience.
Use audience="clinical" when you want the surrounding workflow and examples to stay clinically framed.
Use audience="product" or audience="operations" if the dataset is closer to traffic, demand, or operational telemetry.
Use audience="neuroscience" when you want the surrounding examples to match neural time-series work.