Structured Data Extraction
The process of systematically pulling specific, predefined data fields from a set of documents — in research contexts, used to populate tables comparing outcomes, methods, or sample characteristics across papers.
Structured data extraction is the process of systematically identifying and recording specific pieces of information from a document set according to predefined fields. In systematic reviews, this means pulling items like sample size, effect size, methodology type, and population characteristics from every included paper, often into a comparison table.
AI tools like Elicit can automate this by scanning papers and populating extraction fields automatically, with sentence-level citation links back to where each value was found. This reduces the time required for manual extraction, though spot-checking extracted values against source PDFs remains important — especially for papers with multiple reported outcomes.
Related tools: Elicit Related tutorial: Running a Systematic Literature Review with AI