Cross Disciplinary Bridge Finder
by @aipoch-ai
Use when identifying collaboration opportunities across fields, finding experts in complementary disciplines, translating methodologies between scientific do...
clawhub install cross-disciplinary-bridge-finderπ About This Skill
name: cross-disciplinary-bridge-finder description: Use when identifying collaboration opportunities across fields, finding experts in complementary disciplines, translating methodologies between scientific domains, or building interdisciplinary research teams. Identifies synergies between scientific disciplines, matches researchers with complementary expertise, and facilitates cross-domain collaborations. Supports interdisciplinary grant applications and innovative research team formation. allowed-tools: "Read Write Bash Edit" license: MIT metadata: skill-author: AIPOCH version: "1.0"
Cross-Disciplinary Research Collaboration Finder
When to Use This Skill
Quick Start
from scripts.interdisciplinary import CollaborationFinderfinder = 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 β 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 β 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" β Low transfer potential β 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 β 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"
)
Command Line Usage
python scripts/main.py --my-field machine_learning --target-field immunology --find-collaborators --output matches.json
Handling Poor Results
min_publications or h_index_threshold; broaden collaboration_type.application_area to a higher-level domain (e.g., "modeling" instead of "systems_modeling").deadline_within_months or add more entries to funder_types.research_question is a descriptive string rather than a short keyword.References
references/guide.md - Comprehensive user guidereferences/examples/ - Working code examplesreferences/api-docs/ - Complete API documentationπ‘ Examples
from scripts.interdisciplinary import CollaborationFinderfinder = 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 β 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 β 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" β Low transfer potential β 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 β 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"
)