Plot-Logic-Pipeline#

Systematically deconstruct scientific papers by following the figure-discussion logical backbone.

When to Use#

• Analyzing a research paper's argument structure
• Reviewing manuscripts before submission
• Understanding how figures support claims in technical papers
• Mapping evidence sources (literature vs. new measurements)
• Identifying logical gaps or unsupported claims

Core Principle#

Figures are the bare bones of a paper's logic flow. Each figure corresponds to a discussion that either:

• Sets up the next key finding (preparation)
• States the key finding (conclusion)

Complete understanding requires analyzing every figure-discussion pair and tracking evidence sources.

Analysis Framework#

Step 1: Figure Inventory#

Create a complete inventory of all figures in the paper:

text

Figure 1: [Brief description]
Figure 2: [Brief description]
...
Figure N: [Brief description]

Step 2: Figure-Discussion Mapping#

For each figure, identify its corresponding discussion section and analyze:

text

Figure X: [Description]
├── Location: [Section/page where discussed]
├── Discussion Type: [Setup / Statement]
├── Main Claim: [Key finding or point]
└── Evidence Source:
    ├── Previous Study: [Citation(s) if supported by literature]
    ├── This Paper: [Analysis method if new measurement/calculation]
    └── Support Level: [Strong / Partial / Contradictory / Missing]

Step 3: Logic Flow Reconstruction#

Map how figures build upon each other:

text

Paper Logic Flow:
Figure 1 → Figure 2 → Figure 3 → ... → Conclusion
  ↓            ↓            ↓
[Setup]   [Key Finding 1]  [Key Finding 2]

Step 4: Evidence Assessment#

Evaluate the strength of the paper's argument:

• Are all major claims supported by figures?
• Are evidence sources properly attributed?
• Are there logical gaps between figures?
• Do setup discussions adequately prepare for key findings?

Evidence Classification#

Previous Study Support#

• Direct citation: Specific reference supporting the claim
• Literature consensus: Multiple citations building consensus
• Comparative reference: Contrasting with previous work

This Paper's Contributions#

• New experimental data: Novel measurements with method specified
• Novel calculations: Computational work or modeling
• Reanalysis: New interpretation of existing data

Combined Evidence#

• Validation: New data confirms previous studies
• Extension: New data builds upon previous work
• Contradiction: New data challenges previous findings

Analysis Templates#

See TEMPLATES.md for detailed templates including:

• Basic figure-discussion analysis
• Complete paper analysis workflow
• Materials science specific templates
• Quality assurance checklist

Quality Checks#

Before concluding analysis:

• ✅ All figures mapped to discussions
• ✅ Evidence sources identified for major claims
• ✅ Logic flow clearly traced from introduction to conclusion
• ✅ Setup vs. statement discussions distinguished
• ✅ Contradictions or gaps noted and flagged

Common Pitfalls#

• Skipping "obvious" figures: Even simple schematics contribute to logic flow
• Missing evidence attribution: Always identify if claims come from citations or new work
• Ignoring setup discussions: These are crucial for understanding logical progression
• Overlooking figure details: Axis labels, error bars, and annotations often contain key information
• Conflating correlation with causation: Note when figures show correlation vs. when claims assert causation

Rules#

1. Every figure gets analyzed — no skipping, even if it seems straightforward
2. Always classify evidence — distinguish previous work from new contributions
3. Trace the logic chain — show how each figure builds on the previous one
4. Flag gaps honestly — note missing evidence or weak logical connections
5. Separate observation from interpretation — what the figure shows vs. what the authors claim