causalizeR

causalizeR extracts causal relationships from unstructured scientific literature using grammatical rule-based text mining to synthesize complex ecological interactions.

Key Features:

• Text processing and extraction: Applies grammatical rules based on the relative positioning of nouns around specific keywords to identify cause-and-effect statements in text.
• Database creation: Exports extracted causal links into a structured database of relationships for downstream analysis.
• Network analysis integration: Interfaces with network analysis tools to estimate comprehensive impacts of abiotic and biotic drivers and support hypothesis generation.

Scientific Applications:

• Ecological synthesis: Synthesizes literature-derived causal links to map interactions among abiotic and biotic drivers.
• Ecosystem change prediction: Supports prediction of ecosystem responses and potential abrupt shifts by aggregating direct and indirect effects reported in the literature.
• Case study — tundra ecosystems: Has been applied to extract causal relationships relevant to tundra ecosystems to inform predictions of ecological change.

Methodology:

Implements grammatical, keyword-based extraction that analyzes the relative positioning of nouns around specific keywords to identify causal statements and organizes the extracted relationships into a structured database for subsequent network analysis.