gh-k-dense-ai-claude-scientific-writer-claude-scientific-writer/skills/latex-posters/references/poster_content_guide.md

Research Poster Content Guide

Overview

Content is king in research posters. This guide covers writing strategies, section-specific guidance, visual-text balance, and best practices for communicating research effectively in poster format.

Core Content Principles

1. The 3-5 Minute Rule

Reality: Most viewers spend 3-5 minutes at your poster

• 1 minute: Scanning from distance (title, figures)
• 2-4 minutes: Reading key points up close
• 5+ minutes: Engaged conversation (if interested)

Design Implication: Poster must work at three levels:

1. Distance view (6-10 feet): Title and main figure visible
2. Browse view (3-6 feet): Section headers and key results readable
3. Detail view (1-3 feet): Full content accessible

2. Tell a Story, Not a Paper

Poster ≠ Condensed Paper

Paper approach (❌):

• Comprehensive literature review
• Detailed methodology
• All results presented
• Lengthy discussion
• 50+ references

Poster approach (✅):

• One sentence background
• Visual methods diagram
• 3-5 key results
• 3-4 bullet point conclusions
• 5-10 key references

Story Arc for Posters:

Hook (Problem) → Approach → Discovery → Impact

Example:

• Hook: "Antibiotic resistance threatens millions of lives annually"
• Approach: "We developed an AI system to predict resistance patterns"
• Discovery: "Our model achieves 87% accuracy, 20% better than existing methods"
• Impact: "Could reduce treatment failures by identifying resistance earlier"

3. The 800-Word Maximum

Word Count Guidelines:

• Ideal: 300-500 words
• Maximum: 800 words
• Hard limit: 1000 words (beyond this, poster is unreadable)

Word Budget by Section:

Section	Word Count	% of Total
Introduction/Background	50-100	15%
Methods	100-150	25%
Results (text)	100-200	25%
Discussion/Conclusions	100-150	25%
References/Acknowledgments	50-100	10%

Counting Tool:

% Add word count to poster (remove for final)
\usepackage{texcount}
% Compile with: texcount -inc poster.tex

4. Visual-to-Text Ratio

Optimal Balance: 40-50% visual content, 50-60% text+white space

Visual Content Includes:

• Figures and graphs
• Photos and images
• Diagrams and flowcharts
• Icons and symbols
• Color blocks and design elements

Too Text-Heavy (❌):

• Wall of text
• Small figures
• Intimidating to viewers
• Low engagement

Well-Balanced (✅):

• Clear figures dominate
• Text supports visuals
• Easy to scan
• Inviting appearance

Section-Specific Content Guidance

Title

Purpose: Capture attention, convey topic, establish credibility

Characteristics of Effective Titles:

• Concise: 10-15 words maximum
• Descriptive: Clearly states research topic
• Active: Uses strong verbs when possible
• Specific: Avoids vague terms
• Jargon-aware: Balances field-specific terms with accessibility

Title Formulas:

1. Descriptive:

[Method/Approach] for [Problem/Application]

Example: "Deep Learning for Early Detection of Alzheimer's Disease"

2. Question:

[Research Question]?

Example: "Can Microbiome Diversity Predict Treatment Response?"

3. Assertion:

[Finding] in [Context]

Example: "Novel Mechanism Identified in Drug Resistance Pathways"

4. Colon Format:

[Topic]: [Specific Approach/Finding]

Example: "Urban Heat Islands: A Machine Learning Framework for Mitigation"

Avoid:

• ❌ Generic titles: "A Study of X"
• ❌ Overly cute or clever wordplay (confuses message)
• ❌ Excessive jargon: "Utilization of CRISPR-Cas9..."
• ❌ Unnecessarily long: "Investigation of the potential role of..."

LaTeX Title Formatting:

% Emphasize key words with bold
\title{Deep Learning for \textbf{Early Detection} of Alzheimer's Disease}

% Two-line titles for long names
\title{Machine Learning Framework for\\Urban Heat Island Mitigation}

% Avoid ALL CAPS (harder to read)

Authors and Affiliations

Best Practices:

• Presenting author: Bold, underline, or asterisk
• Corresponding author: Include email
• Affiliations: Superscript numbers or symbols
• Institutional logos: 2-4 maximum

Format Examples:

% Simple format
\author{\textbf{Jane Smith}\textsuperscript{1}, John Doe\textsuperscript{2}}
\institute{
  \textsuperscript{1}University of Example, 
  \textsuperscript{2}Research Institute
}

% With contact
\author{Jane Smith\textsuperscript{1,*}}
\institute{
  \textsuperscript{1}Department, University\\
  \textsuperscript{*}jane.smith@university.edu
}

Introduction/Background

Purpose: Establish context, motivate research, state objective

Structure (50-100 words):

1. Problem statement (1-2 sentences): What's the issue?
2. Knowledge gap (1-2 sentences): What's unknown/unsolved?
3. Research objective (1 sentence): What did you do?

Example (95 words):

Antibiotic resistance causes 700,000 deaths annually, projected to reach 
10 million by 2050. Current diagnostic methods require 48-72 hours, 
delaying appropriate treatment. Machine learning offers potential for 
rapid resistance prediction, but existing models lack generalizability 
across bacterial species. 

We developed a transformer-based deep learning model to predict antibiotic 
resistance from genomic sequences across multiple pathogen species. Our 
approach integrates evolutionary information and protein structure to 
improve cross-species accuracy.

Visual Support:

• Conceptual diagram showing problem
• Infographic with statistics
• Image of application context

Common Mistakes:

• ❌ Extensive literature review
• ❌ Too much background detail
• ❌ Undefined acronyms at first use
• ❌ Missing clear objective statement

Methods

Purpose: Describe approach sufficiently for understanding (not replication)

Key Question: "How did you do it?" not "How could someone else replicate it?"

Content Strategy:

• Prioritize: Visual methods diagram > text description
• Include: Study design, key procedures, analysis approach
• Omit: Detailed protocols, routine procedures, specific reagent details

Visual Methods (Highly Recommended):

% Flowchart of study design
\begin{tikzpicture}[node distance=2cm]
  \node (start) [box] {Data Collection\\n=1,000 samples};
  \node (process) [box, below of=start] {Preprocessing\\Quality Control};
  \node (analysis) [box, below of=process] {Statistical Analysis\\Mixed Models};
  \node (end) [box, below of=analysis] {Validation\\Independent Cohort};
  
  \draw [arrow] (start) -- (process);
  \draw [arrow] (process) -- (analysis);
  \draw [arrow] (analysis) -- (end);
\end{tikzpicture}

Text Methods (50-150 words):

For Experimental Studies:

Methods
• Study design: Randomized controlled trial (n=200)
• Participants: Adults aged 18-65 with Type 2 diabetes
• Intervention: 12-week exercise program vs. standard care
• Outcomes: HbA1c (primary), insulin sensitivity (secondary)
• Analysis: Linear mixed models, intention-to-treat

For Computational Studies:

Methods
• Dataset: 10,000 labeled images from ImageNet
• Architecture: ResNet-50 with custom attention mechanism
• Training: 100 epochs, Adam optimizer, learning rate 0.001
• Validation: 5-fold cross-validation
• Comparison: Baseline CNN, VGG-16, Inception-v3

Format Options:

• Bullet points: Quick scanning (recommended)
• Numbered list: Sequential procedures
• Diagram + brief text: Ideal combination
• Table: Multiple conditions or parameters

Results

Purpose: Present key findings visually and clearly

Golden Rule: Show, don't tell

Content Allocation:

• Figures: 70-80% of Results section
• Text: 20-30% (brief descriptions, statistics)

How Many Results:

• Ideal: 3-5 main findings
• Maximum: 6-7 distinct results
• Focus: Primary outcomes, most impactful findings

Figure Selection Criteria:

1. Does it support the main message?
2. Is it self-explanatory with caption?
3. Can it be understood in 10 seconds?
4. Does it add information beyond text?

Figure Captions:

• Descriptive: Explain what is shown
• Standalone: Understandable without reading full poster
• Statistical: Include significance indicators, sample sizes
• Concise: 1-3 sentences

Example Caption:

\caption{Treatment significantly improved outcomes. 
Mean±SD shown for control (blue, n=45) and treatment (orange, n=47) groups. 
**p<0.01, ***p<0.001 (two-tailed t-test).}

Text Support for Results (100-200 words):

• State main finding per figure
• Include key statistics
• Note trends or patterns
• Avoid detailed interpretation (save for Discussion)

Example Results Text:

Key Findings
• Model achieved 87% accuracy on test set (vs. 73% baseline)
• Performance consistent across 5 bacterial species (p<0.001)
• Prediction speed: <30 seconds per isolate
• Feature importance: protein structure (42%), sequence (35%), 
  evolutionary conservation (23%)

Data Presentation Formats:

1. Bar Charts: Comparing categories

\begin{tikzpicture}
  \begin{axis}[
    ybar,
    ylabel=Accuracy (\%),
    symbolic x coords={Baseline, Model A, Our Method},
    xtick=data,
    nodes near coords
  ]
  \addplot coordinates {(Baseline,73) (Model A,81) (Our Method,87)};
  \end{axis}
\end{tikzpicture}

2. Line Graphs: Trends over time 3. Scatter Plots: Correlations 4. Heatmaps: Matrix data, clustering 5. Box Plots: Distributions, comparisons 6. ROC Curves: Classification performance

Discussion/Conclusions

Purpose: Interpret findings, state implications, acknowledge limitations

Structure (100-150 words):

1. Main Conclusions (50-75 words):

• 3-5 bullet points
• Clear, specific takeaways
• Linked to research objectives

Example:

Conclusions
• First cross-species model for antibiotic resistance prediction 
  achieving >85% accuracy
• Protein structure integration critical for generalizability 
  (improved accuracy by 14%)
• Prediction speed enables clinical decision support within 
  consultation timeframe
• Potential to reduce inappropriate antibiotic use by 20-30%

2. Limitations (25-50 words, optional but recommended):

• Acknowledge key constraints
• Brief, honest
• Shows scientific rigor

Example:

Limitations
• Training data limited to 5 bacterial species
• Requires genomic sequencing (not widely available)
• Validation needed in prospective clinical trials

3. Future Directions (25-50 words, optional):

• Next steps
• Broader implications
• Call to action

Example:

Next Steps
• Expand to 20+ additional species
• Develop point-of-care sequencing integration
• Launch multi-center clinical validation study (2025)

Avoid:

• ❌ Overstating findings: "This revolutionary breakthrough..."
• ❌ Extensive comparison to other work
• ❌ New results in Discussion
• ❌ Vague conclusions: "Further research is needed"

References

How Many: 5-10 key citations

Selection Criteria:

• Include seminal work in the field
• Recent relevant studies (last 5 years)
• Methods cited in your poster
• Controversial claims that need support

Format: Abbreviated, consistent style

Examples:

Numbered (Vancouver):

References
1. Smith et al. (2023). Nature. 615:234-240.
2. Jones & Lee (2024). Science. 383:112-118.
3. Chen et al. (2022). Cell. 185:456-470.

Author-Year (APA):

References
Smith, J. et al. (2023). Title. Nature, 615, 234-240.
Jones, A., & Lee, B. (2024). Title. Science, 383, 112-118.

Minimal (For Space Constraints):

Key References: Smith (Nature 2023), Jones (Science 2024), 
Chen (Cell 2022). Full bibliography: [QR Code]

Alternative: QR code linking to full reference list

Acknowledgments

Include:

• Funding sources (with grant numbers)
• Major collaborators
• Core facilities used
• Dataset sources

Format (25-50 words):

Acknowledgments
Funded by NIH Grant R01-123456 and NSF Award 7890123. 
We thank Dr. X for data access, the Y Core Facility for 
sequencing, and Z for helpful discussions.

Contact Information

Essential Elements:

• Name of presenting/corresponding author
• Email address
• Optional: Lab website, Twitter/X, LinkedIn, ORCID

Format:

Contact: Jane Smith, jane.smith@university.edu
Lab: smithlab.university.edu | Twitter: @smithlab

QR Code Alternative:

• Link to personal/lab website
• Link to paper preprint/publication
• Link to code repository (GitHub)
• Link to supplementary materials

Writing Style for Posters

Active vs. Passive Voice

Prefer Active Voice (more engaging, clearer):

• ✅ "We developed a model..."
• ✅ "The treatment reduced symptoms..."

Passive Voice (when appropriate):

• ✅ "Samples were collected from..."
• ✅ "Data were analyzed using..."

Sentence Length

Keep Sentences Short:

• Ideal: 10-15 words per sentence
• Maximum: 20-25 words
• Avoid: >30 words (hard to follow)

Example Revision:

• ❌ Long: "We performed a comprehensive analysis of gene expression data from 500 patients with colorectal cancer using RNA sequencing and identified 47 differentially expressed genes associated with treatment response." (31 words)
• ✅ Short: "We analyzed RNA sequencing data from 500 colorectal cancer patients. We identified 47 genes associated with treatment response." (19 words total, two sentences)

Bullet Points vs. Paragraphs

Use Bullet Points For:

• ✅ Lists of items or findings
• ✅ Key conclusions
• ✅ Methods steps
• ✅ Study characteristics

Use Short Paragraphs For:

• ✅ Narrative flow (Introduction)
• ✅ Complex explanations
• ✅ Connected ideas

Bullet Point Best Practices:

• Start with action verbs or nouns
• Parallel structure throughout list
• 3-7 bullets per list (not too many)
• Brief (1-2 lines each)

Example:

Methods
• Participants: 200 adults (18-65 years)
• Design: Double-blind RCT (12 weeks)
• Intervention: Daily 30-min exercise
• Control: Standard care
• Analysis: Mixed models (SPSS v.28)

Acronyms and Jargon

First Use Rule: Define at first appearance

We used machine learning (ML) to analyze... Later, ML predicted...

Common Acronyms: May not need definition if universal to field

• DNA, RNA, MRI, CT, PCR (in biomedical context)
• AI, ML, CNN (in computer science context)

Avoid Excessive Jargon:

• ❌ "Utilized" → ✅ "Used"
• ❌ "Implement utilization of" → ✅ "Use"
• ❌ "A majority of" → ✅ "Most"

Numbers and Statistics

Present Statistics Clearly:

• Always include measure of variability (SD, SE, CI)
• Report sample sizes: n=50
• Indicate significance: p<0.05, p<0.01, p<0.001
• Use symbols consistently: * for p<0.05, ** for p<0.01

Format Numbers:

• Round appropriately (avoid false precision)
• Use consistent decimal places
• Include units: 25 mg/dL, 37°C
• Large numbers: 1,000 or 1000 (be consistent)

Example:

Treatment increased response by 23.5% (95% CI: 18.2-28.8%, p<0.001, n=150)

Visual-Text Integration

Figure-Text Relationship

Figure First, Text Second:

1. Design poster around key figures
2. Add text to support and explain visuals
3. Ensure figures can stand alone

Text Placement Relative to Figures:

• Above: Context, "What you're about to see"
• Below: Explanation, statistics, caption
• Beside: Comparison, interpretation

Callouts and Annotations

On-Figure Annotations:

\begin{tikzpicture}
  \node[inner sep=0] (img) {\includegraphics[width=10cm]{figure.pdf}};
  \draw[->, thick, red] (8,5) -- (6,3) node[left] {Key region};
  \draw[red, thick] (3,2) circle (1cm) node[above=1.2cm] {Anomaly};
\end{tikzpicture}

Callout Boxes:

\begin{tcolorbox}[colback=yellow!10, colframe=orange!80, 
                  title=Key Finding]
Our method reduces errors by 34\% compared to state-of-the-art.
\end{tcolorbox}

Icons for Section Headers

Visual Section Markers:

\usepackage{fontawesome5}

\block{\faFlask~Introduction}{...}
\block{\faCog~Methods}{...}
\block{\faChartBar~Results}{...}
\block{\faLightbulb~Conclusions}{...}

Content Adaptation Strategies

From Paper to Poster

Condensation Process:

1. Identify Core Message (The Elevator Pitch):

• What's the one thing you want people to remember?
• If you had 30 seconds, what would you say?

2. Select Key Results:

• Choose 3-5 most impactful findings
• Omit supporting/secondary results
• Focus on figures with strong visual impact

3. Simplify Methods:

• Visual flowchart > text description
• Omit routine procedures
• Include only essential parameters

4. Trim Literature Review:

• One sentence background
• One sentence gap/motivation
• One sentence your contribution

5. Condense Discussion:

• Main conclusions only
• Brief limitations
• One sentence future direction

For Different Audiences

Specialist Audience (Same Field):

• Can use field-specific jargon
• Less background needed
• Focus on novel methodology
• Emphasize nuanced findings

General Scientific Audience:

• Define key terms
• More context/background
• Broader implications
• Visual metaphors helpful

Public/Lay Audience:

• Minimal jargon, all defined
• Extensive context
• Real-world applications
• Analogies and simple language

Example Adaptation:

Specialist: "CRISPR-Cas9 knockout of BRCA1 induced synthetic lethality with PARP inhibitors"

General: "We used gene editing to make cancer cells vulnerable to existing drugs"

Public: "We found a way to make cancer treatments work better by targeting specific genetic weaknesses"

Quality Control Checklist

Content Review

Clarity:

• Main message immediately clear
• All acronyms defined
• Sentences short and direct
• No unnecessary jargon

Completeness:

• Research question/objective stated
• Methods sufficiently described
• Key results presented
• Conclusions drawn
• Limitations acknowledged

Accuracy:

• All statistics correct
• Figure captions accurate
• References properly cited
• No overstated claims

Engagement:

• Compelling title
• Visual interest
• Clear take-home message
• Conversation starters

Readability Testing

Distance Test:

• Print at 25% scale
• View from 2-3 feet (simulates 8-12 feet for full poster)
• Can you read: Title? Section headers? Body text?

Scan Test:

• Give poster to colleague for 30 seconds
• Ask: "What is this poster about?"
• They should identify: Topic, approach, main finding

Detail Test:

• Ask colleague to read poster thoroughly (5 min)
• Ask: "What are the key conclusions?"
• Verify understanding matches your intent

Common Content Mistakes

1. Too Much Text

• ❌ >1000 words
• ❌ Long paragraphs
• ❌ Full paper condensed
• ✅ 300-800 words, bullet points, key findings only

2. Unclear Message

• ❌ Multiple unrelated findings
• ❌ No clear conclusion
• ❌ Vague implications
• ✅ 1-3 main points, explicit conclusions

3. Methods Overkill

• ❌ Detailed protocols
• ❌ All parameters listed
• ❌ Routine procedures described
• ✅ Visual flowchart, key details only

4. Poor Figure Integration

• ❌ Figures without context
• ❌ Unclear captions
• ❌ Text doesn't reference figures
• ✅ Figures central, well-captioned, text integrated

5. Missing Context

• ❌ No background
• ❌ Undefined acronyms
• ❌ Assumes expert knowledge
• ✅ Brief context, definitions, accessible to broader audience

Conclusion

Effective poster content:

• Concise: 300-800 words maximum
• Visual: 40-50% figures and graphics
• Clear: One main message, 3-5 key findings
• Engaging: Compelling story, not just facts
• Accessible: Appropriate for target audience
• Actionable: Clear implications and next steps

Remember: Your poster is a conversation starter, not a comprehensive treatise. Design content to intrigue, engage, and invite discussion.