The Celsus data visualisation service turns the findings in your thesis or article into publication-grade, journal-standard figures. We produce forest plots, path diagrams, interaction plots and journal-specific composite figures as reproducible ggplot2 and matplotlib scripts. It is designed for researchers who want figures that are easy to assess under peer review, that meet resolution and colour requirements, and that communicate the result clearly.
What's included
- Forest plots and funnel plots for meta-analysis, with pooled estimates and confidence intervals.
- Path diagrams for structural equation models, with standardised coefficients and fit indices.
- Interaction plots and marginal-effect curves for regression and ANOVA.
- Journal-specific composite figures: multi-panel layouts, shared axes and panel labels (A, B, C).
- Compliance with journal figure requirements: 300+ dpi resolution, TIFF/EPS/PDF outputs, embedded fonts.
- Colour-blind safe palettes and greyscale legibility checks.
- Commented R/Python scripts and data that reproduce every figure.
How we work
- Scope: We discuss the figures you need, the target journal's style guide and your data, then send a scoped fixed quote within one working day.
- Preparation: We reshape the data for plotting, draft palette and layout options, and agree a preview sample with you.
- Production: We generate the figures script-side in ggplot2 or matplotlib and tune resolution and format to the journal's requirements.
- Reporting and aftercare: We deliver the figures, their captions and the reproducible code, and apply any reviewer-requested revisions quickly.
Figure types and when they are used
| Figure type | When used | Tool |
|---|---|---|
| Forest plot | Pooled effect and study contributions in meta-analysis | R (ggplot2, metafor) |
| Path diagram | Structural equation models and mediation paths | R (lavaan/semPlot) |
| Interaction plot | Moderator effects in regression/ANOVA | R (ggplot2), matplotlib |
| Box/violin plot | Comparing distributions across groups | ggplot2, matplotlib |
| Heatmap | Correlation matrices and pattern display | ggplot2, seaborn |
| Survival curve | Kaplan-Meier survival analysis | R (survminer) |
| Composite figure | Multi-panel, submission-ready combined figure | patchwork, matplotlib |
Most-requested figure types
Related guides
The method behind forest plots and evidence-synthesis figures is set out in our meta-analysis and PRISMA guide. For the models that path diagrams depict, see our structural equation modelling guide, and for reporting figure captions and values consult our APA 7 statistics reporting checklist.
Frequently Asked Questions
How quickly are the figures ready?
It depends on the number of figures and their complexity. A single forest plot is quick, while multi-panel composite figures take longer. Once the scope is clear we share a firm delivery timeline.
How is the price determined?
We base the price on scope factors such as the type and number of figures and the journal's formatting requirements. You receive a scoped fixed quote within one working day.
Is my data kept safe?
All data and figures are kept confidential. We sign a non-disclosure agreement on request and our work is aligned with UK GDPR and KVKK. Your data is deleted on completion if you ask us to.
How do I get started?
Send the figures you need and your target journal through the contact form or via WhatsApp. We share a preview sample along with a scoped fixed quote within one working day.