Scientific Graphical Abstract Caption Writer

Write concise graphical abstract captions, figure callouts, and plain-language summaries for research papers, posters, and journal submissions.

Prompt Template

You are a scientific editor helping researchers write clear graphical abstract captions and figure callouts without overstating results. Draft caption options for:

Research field: [biology, chemistry, medicine, climate science, engineering, social science, materials science]
Study title or working title: [title]
Research question: [main question or hypothesis]
Method summary: [experiment, dataset, model, trial, review, simulation]
Main finding: [specific result with measured effect if available]
Graphical abstract elements: [icons, workflow steps, organisms, devices, charts, molecules, patient groups, environment]
Target publication or audience: [journal, conference, interdisciplinary readers, funders, public summary]
Word limit: [25, 50, 75, 100 words]
Required terminology: [terms that must appear]
Terms to avoid: [jargon, claims, acronyms, brand names, unsupported causal language]
Tone: [technical, plain language, clinical, policy-facing, interdisciplinary]
Compliance needs: [no medical advice, no efficacy claims, preregistration limits, conflict disclosures]

Create:
1. Three caption versions at different levels of technicality.
2. One plain-language caption for non-specialist readers.
3. Five short callout labels for the visual elements.
4. A one-sentence takeaway that avoids hype.
5. A journal-style figure legend starter if more detail is needed.
6. Suggested alt text for accessibility.
7. A claims check table separating observed results, interpretation, and speculation.
8. Acronym and jargon cleanup suggestions.
9. Questions the author should verify before submission.

Do not invent findings, sample sizes, p-values, clinical claims, or mechanisms. Flag any statement that needs author confirmation.

Example Output

Caption Options

Technical, 52 words

Single-cell transcriptomic profiling identified two injury-associated fibroblast states that expanded after treatment exposure and were associated with delayed tissue repair markers. The graphical workflow summarizes sample collection, clustering, marker validation, and pathway enrichment used to connect cellular state shifts with repair outcomes.

Plain Language, 38 words

The study maps how certain support cells changed after treatment and links those changes with slower signs of tissue repair. The visual shows the path from collected samples to cell grouping, marker checks, and final interpretation.

Callout Labels

Sample collection, cell clustering, marker validation, pathway analysis, repair signal comparison.

Claims Check

Observed: fibroblast states expanded in the dataset. Interpretation: expansion may relate to repair markers. Avoid: claiming the cell states caused delayed repair unless the study proves causality.

Tips for Best Results

  • 💡Provide the actual result and method so the caption does not become vague science-flavored copy.
  • 💡Ask for a claims check when the research has clinical, policy, or product implications.
  • 💡Include alt text early because graphical abstracts are often reused in accessible PDFs and web pages.
  • 💡Set a word limit that matches the journal or conference instructions.