JBI Qualitative Data Extraction

Purpose and user goal: provide a structured workflow for extracting qualitative data from studies, aligned with JBI synthesis methods. It supports consistent data capture across studies, enabling transparent audit trails and high-quality evidence summaries. Users include systematic reviewers, methodologists, and research teams who need reproducible qualitative coding. The tool focuses on data integrity and traceability throughout the extraction process.
Core logic: enforce a predefined schema with required fields and optional academic fields, support multiple coders, and maintain versioned histories. Outputs can be organized by study or by theme and exported in CSV, JSON, or Excel-friendly formats for downstream synthesis. It integrates with reference management systems to streamline literature workflows.
Inputs & outputs: accept study metadata and extraction templates; generate structured extraction records, coding maps, theme labels, and synthesis-ready summaries. Validation ensures required fields exist and fields types are consistent; outputs retain source provenance for audit purposes.
Algorithms & calculations: apply rule-based validation, field-type checks, and cross-study coding consistency. Optional lightweight theme clustering suggests candidate themes via frequency analysis, with explicit links to source items for traceability. No quantitative synthesis is performed.
Error & edge cases: handle missing identifiers, conflicting coder inputs, and import failures by flagging warnings and preserving partial results; fallback to local schemas when external data is unavailable.
Industry localization: supports international characters and multilingual metadata; aligns with JBI qualitative guidance for reports and summaries; suitable for global academic teams.
Assumptions & exclusions: assumes identifiable, citable study metadata; excludes quantitative extraction, meta-analysis, and UI styling considerations.