Scientific PDF Data Extraction

About

Transform large volumes of scientific PDF literature into structured, validated databases ready for statistical analysis in Python, R, or SQLite. This skill provides a comprehensive pipeline that manages the entire research lifecycle, from metadata organization and abstract filtering to vision-based extraction using customizable schemas. It features automated JSON repair, enrichment via external databases like GBIF and NCBI, and a robust validation framework to calculate precision and recall metrics, ensuring high-quality, reproducible outputs for research and publication.

Key Features

• Multi-step pipeline for metadata organization and abstract filtering
• Support for cost-optimized processing using cloud and local models
• Vision-based structured data extraction using customizable JSON schemas
• Built-in quality assurance with precision, recall, and F1 metric calculation
• Automated JSON repair and enrichment via external research APIs
• 3 GitHub stars

Use Cases

• Validating and benchmarking the accuracy of AI extraction against manually annotated ground-truth data
• Building structured research databases from scientific publications for meta-analysis
• Conducting systematic literature reviews that require standardized data extraction from PDF collections