ScienceCLAW Information Extraction

Extract structured information from scientific texts including named entities, relations, data tables, methods, results, and experimental parameters.

When to Use

Use this skill when the user:

Needs to parse a scientific paper for key data points (measurements, parameters, outcomes)
Wants to extract experimental methods, conditions, or protocols from a paper
Needs named entity recognition (NER) on scientific documents (chemicals, genes, diseases, etc.)
Wants to build a knowledge graph or structured database from literature
Needs to extract relations between scientific entities (drug-target, gene-disease, cause-effect)
Wants structured tables from unstructured scientific text
Needs to compare methods or results across multiple papers in a structured format
Wants to extract metadata from papers (authors, affiliations, funding, conflicts of interest)