Analysis
Multimodal annotation
Code text, page and image regions, table cells, and audiovisual time ranges with overlap and multiple codes.
Implemented task
Public task
Open a review item or select uncoded evidence.
Expected outcome: A human-authored or human-reviewed evidence decision is persisted with exact revision history.
Declared product contract
Inputs and task boundary
Inputs
- Text, page or image region, table range, or audiovisual interval
- One or more project codes
Input constraints
- Typed evidence locations must remain within their source and revision boundaries.
User actions
- Open a supported source reader.
- Select an exact typed region or interval.
- Apply one or more codes and inspect overlapping annotations.
Outputs
- One or more idempotent coding annotations bound to typed evidence locations.
Decision boundary
Where it fits
- Research evidence that needs overlapping or multi-code annotations across supported media.
Outside the boundary
Where it does not fit
- Unsupported locator types or evidence outside the authorized project.
Verification
Product proof
journey
proof.m2-analysis-kernel
- Verified
- Review due
- Expires
- All five locator kinds, overlap, multiple codes, and exact evidence return are covered.
- Codebook, memo, history, research connections, and withdrawal child journeys pass.
- Every child artifact is hashed and rerun from a fresh database.
Failure boundary: The proof covers the completed M2 kernel, not M3 cases, matrices, or later collaboration rounds.
OpenVerbatim is an open-source (Apache-2.0) qualitative data analysis platform for coding and analyzing interview transcripts. AI-suggested codes stay marked as suggestions until a human reviewer confirms or rejects them, and every decision is kept in an audit trail. The full feature set is available when self-hosted; there is no paid feature wall.