Data Extraction Methodology
How data extraction works, what can be extracted, and important limitations
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Overview
Data extraction is a feature for systematic reviews and meta-analyses. Instead of analyzing themes, it extracts specific data points from papers according to fields you define.
For example, you could extract "model architecture," "dataset used," "performance metrics," or "limitations" from all papers about a specific topic.
How it works
- Define your fields: Specify what data you want to extract (e.g., "primary methodology," "sample size," "key findings")
- Provide field descriptions: Help the AI understand what each field means and what to look for
- Search for papers: Use the same semantic search as trend analysis to find relevant papers
- AI extraction: Claude Sonnet 4.6 reads the full PDF of each paper and extracts the requested information
- Export results: Download extracted data as CSV for further analysis in Excel, R, or other tools
Key feature: Each extracted field includes a confidence score (high, medium, low) and a supporting quote from the paper, letting you verify the AI's interpretation.
What can be extracted
The AI can extract any information that appears in the full PDF of papers:
Technical details
- • Model architectures
- • Datasets used
- • Evaluation metrics
- • Performance results
Research context
- • Research questions
- • Methodologies
- • Key contributions
- • Stated limitations
Best use cases
- Systematic reviews: Extract standardized information from many papers for comparison
- Meta-analyses: Collect quantitative results and study characteristics for statistical analysis
- Literature surveys: Track how specific methodologies or approaches vary across studies
- Competitive analysis: Compare technical approaches, datasets, or results across research groups
Data extraction vs. trend analysis
| Feature | Trend Analysis | Data Extraction |
|---|---|---|
| Purpose | Discover themes and patterns | Extract specific data points |
| Output | Narrative analysis and visualizations | Structured data table (CSV) |
| Customization | Choose review type and parameters | Define custom extraction fields |
| Best for | Exploratory research, landscape mapping | Systematic reviews, meta-analyses |
Want to learn more about trend analysis? View trend analysis methodology →
Important limitations
- AI interpretation: The AI interprets paper text, which may miss nuances. Always verify critical data points by reading the original papers.
- Not all papers have all fields: Some papers may not mention the specific information you're looking for.
- Confidence scores are estimates: Even "high confidence" results should be spot-checked against the original papers when precision matters.