Science Communication

Science Communication

Translating technical data science findings for non-technical audiences. Covers audience analysis, narrative frameworks (Pyramid Principle, SCQA, AIDA), plain-language translation, executive summaries, policy briefs, causal language guidance, hedging and uncertainty communication, and accessibility standards. Complements data-scientist visualization references — handles what story to tell and to whom, not how to build charts. Use when presenting findings to stakeholders, writing executive summaries or policy briefs, communicating statistical results to non-statisticians, or reviewing a draft deliverable for clarity and audience fit.

Guidance for translating rigorous data science work into clear, compelling communication for non-technical audiences. This skill is additive to data-scientist's visualization references — it covers what story the chart tells and to whom, not how to build the chart.

Boundary with data-scientist: The data-scientist skill handles chart construction, encoding, color palettes, and export standards. This skill handles audience adaptation, narrative structure, plain-language translation, deliverable formatting, and communication quality review.

How to Use This Skill

Reference File Structure

Reading Order

1. Writing a report or brief? Start with audience-analysis.md, then narrative-frameworks.md, then deliverable-templates.md
2. Translating technical findings? Read plain-language.md first
3. Reviewing a draft? Go straight to communication-review.md
4. Concerned about equity or accessibility? Read accessibility-equity.md

Quick Decision Trees

"Who am I writing for?"

Identifying your audience?
├─ Academic researchers or peer reviewers
│   └─ ./references/audience-analysis.md (Academic section)
├─ Policymakers or legislative staff
│   └─ ./references/audience-analysis.md (Policy section)
├─ Executives or board members
│   └─ ./references/audience-analysis.md (Executive section)
├─ General public or community members
│   └─ ./references/audience-analysis.md (Public section)
├─ Journalists or media
│   └─ ./references/audience-analysis.md (Media section)
└─ Mixed or unclear audience
    └─ ./references/audience-analysis.md (Assessment checklist)

"How should I structure this?"

Choosing a narrative structure?
├─ Need to deliver a recommendation quickly
│   └─ Pyramid Principle → ./references/narrative-frameworks.md
├─ Framing a problem that needs solving
│   └─ SCQA → ./references/narrative-frameworks.md
├─ Walking through a discovery journey
│   └─ Three-Act Data Story → ./references/narrative-frameworks.md
├─ Translating findings into action
│   └─ "So What?" Framework → ./references/narrative-frameworks.md
├─ Presenting data with narrative and visuals together
│   └─ Data-Narrative-Visual Triad → ./references/narrative-frameworks.md
├─ Persuading stakeholders to act
│   └─ AIDA → ./references/narrative-frameworks.md
└─ Not sure which to use
    └─ Selection guide table → ./references/narrative-frameworks.md

"How do I say this in plain language?"

Translating technical language?
├─ Statistical jargon (p-value, confidence interval, etc.)
│   └─ Jargon translation table → ./references/plain-language.md
├─ Expressing how certain you are
│   └─ Hedging language scale → ./references/plain-language.md
├─ Using calibrated uncertainty terms
│   └─ IPCC uncertainty framework → ./references/plain-language.md
├─ Describing causal vs correlational findings
│   └─ Causal language guide → ./references/plain-language.md
├─ General readability improvement
│   └─ Reading level guidance → ./references/plain-language.md
└─ Replacing formal/bureaucratic words
    └─ Word replacement list → ./references/plain-language.md

"What format should this take?"

Choosing a deliverable format?
├─ One-page summary for decision makers
│   └─ Executive summary → ./references/deliverable-templates.md
├─ Informing policy decisions
│   └─ Policy brief → ./references/deliverable-templates.md
├─ Presenting to a room
│   └─ Stakeholder presentation → ./references/deliverable-templates.md
├─ Talking to a journalist
│   └─ Media talking points → ./references/deliverable-templates.md
└─ Full research report
    └─ (Use REPORT_TEMPLATE.md from agent_reference/)

"Is this ready to share?"

Reviewing before finalization?
├─ Comprehensive quality check
│   └─ 10-point checklist → ./references/communication-review.md
├─ Statistical interpretation errors
│   └─ Seven deadly sins → ./references/communication-review.md
├─ Common communication pitfalls
│   └─ Pitfall catalog → ./references/communication-review.md
├─ Accessibility compliance
│   └─ WCAG checklist → ./references/accessibility-equity.md
└─ Equity and framing review
    └─ Do No Harm checklist → ./references/accessibility-equity.md

The Communication Gap

Research findings fail to reach their audience not because they lack rigor, but because they lack translation. The core challenge is bridging two worlds:

The AAAS communication framework captures this as Goal → Audience → Message: define your purpose first, identify who can act on it, then craft language for that specific reader. This inverts the typical scientific structure where content comes first and audience comes last.

Core Principles

1. Audience determines everything. The same finding requires different structure, vocabulary, detail level, and uncertainty communication for each audience type. Always identify your audience before drafting.

2. Lead with the "so what." In scientific communication, conclusions come last. In stakeholder communication, the bottom line comes first. The reader's time and attention are scarce — earn continued reading with immediate relevance.

3. Translate, don't simplify. Plain language is not dumbing down. It is precise communication calibrated to the reader's vocabulary. A well-translated finding preserves its meaning while making it accessible.

4. Show uncertainty honestly. Hedging is not weakness — it is intellectual honesty. But hedge appropriately: too much hedging obscures the finding; too little overstates the evidence. Match your language to your inferential capacity.

5. Every deliverable gets reviewed. Use the 10-point communication checklist before sharing anything. Communication errors — jargon leakage, causal overclaiming, missing the "so what" — are as consequential as analytical errors.

6. Equity is not optional. How you frame data about people reflects values. People-first language, equity-aware color choices, inclusive framing, and accessible design are baseline requirements, not enhancements.

Topic Index

Citation Responsibility

When reporting standards or communication frameworks from this skill shape how findings are presented in a DAAF report (causal language guidelines, uncertainty frameworks, equity-sensitive visualization principles), the report-writer includes the relevant citation in the report's Reporting Standards references section.

Each citation must include a brief rationale explaining why it is included.

For the master citation index and inclusion thresholds, consult agent_reference/CITATION_REFERENCE.md.