Cross-Disciplinary Research Collaboration Finder

from scripts.interdisciplinary import CollaborationFinder

finder = CollaborationFinder()

# Find collaborators in different field
collaborators = finder.find_experts(
    my_expertise="machine_learning",
    target_field="immunology",
    collaboration_type="co_authorship",
    min_publications=10,
    h_index_threshold=15
)

if not collaborators:
    print("No collaborators found — try lowering min_publications or h_index_threshold.")
else:
    # Validate quality before proceeding: only consider complementarity_score > 0.7
    qualified = [e for e in collaborators if e.complementarity_score > 0.7]
    print(f"Found {len(collaborators)} candidates; {len(qualified)} meet quality threshold (score > 0.7):")
    for expert in qualified[:5]:
        print(f"  - {expert.name} ({expert.institution})")
        print(f"    Research: {expert.research_focus}")
        print(f"    Complementarity score: {expert.complementarity_score}")

# Identify transferable methods
methods = finder.identify_transferable_methods(
    from_field="physics",
    to_field="biology",
    application_area="systems_modeling"
)

if not methods:
    print("No transferable methods found — consider broadening the application_area.")
else:
    # Validate applicability before proceeding: review transfer_potential
    for method in methods:
        print(f"Method: {method.name}")
        print(f"  Success in source field: {method.success_rate}")
        print(f"  Application potential: {method.transfer_potential}")
        if method.transfer_potential < 0.6:
            print(f"  ⚠ Low transfer potential — consider a different application_area.")

# Find interdisciplinary funding
grants = finder.find_interdisciplinary_funding(
    fields=["AI", "medicine", "ethics"],
    funder_types=["NIH", "NSF", "private_foundation"],
    deadline_within_months=6
)

if not grants:
    print("No grants found — try extending deadline_within_months or broadening funder_types.")

# Generate collaboration proposal outline
proposal_outline = finder.generate_collaboration_proposal(
    partner_expertise="clinical_trial_design",
    my_expertise="data_science",
    research_question="precision_medicine"
)

python scripts/main.py --my-field machine_learning --target-field immunology --find-collaborators --output matches.json