Analysis

Exact versioned evidence navigation

Return from codes, memos, logs, questions, shares, and history to the exact evidence revision and location.

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.

ApplicationRequires Sign-in, Existing project

Declared product contract

Inputs and task boundary

Inputs

  • A code, memo, event, answer citation, share item, or history link with evidence identity

Input constraints

  • The requested revision and source must remain readable to the current identity.

User actions

  1. Open an evidence-bearing result.
  2. Follow its exact return action.
  3. Inspect the current or historical source location.

Outputs

  • A focused source reader at the exact versioned evidence location or a clear stale/withdrawn boundary.

Decision boundary

Where it fits

  • Auditing where a finding, note, answer, share, or operation came from.

Outside the boundary

Where it does not fit

  • Bypassing source withdrawal or project authorization.

Verification

Product proof

journey

proof.m2-analysis-kernel

Open artifact
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.

journey

proof.m2-evidence-history

Open artifact
Verified
Review due
Expires
  • History, memos, shares, and events return to the exact revision and evidence.
  • Reversible operations append compensation while governance actions use dedicated recovery.
  • Published evidence remains frozen until an explicit new share.

Failure boundary: The proof covers reviewed excerpts and operation history, not complete reports.

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.