Describe a distribution
Use: histogram-density, histogram, ECDF, raincloud or strip-and-summary plot.
Scientific figure maker for researchers
Create publication-ready plots for research
Choose the figure from a grouped research-objective menu, not from a generic chart list. Build clear statistical plots with effect estimates, confidence intervals, p-values, typed variable guidance, academic typography and journal-friendly export.
19 plot workflows
SVG + 300 DPI PNG
Black-and-white themes
CIs, p-values and effect sizes
Publication-Ready Plots require Pro ($89/year) or Edu Pro ($49/year). Explore the plot catalogue before choosing a plan.
Last updated:
Raincloud comparison
Kaplan-Meier survival
ROC diagnostic accuracy
Factorial interaction
What is the best way to make a publication-quality graph?
Start with the study objective and statistical design. Then show the observations or model estimates, display uncertainty, label axes in scientific units, use legible print-safe styling, and export in a vector or high-resolution format. DataStatPro applies this workflow in one objective-driven plot builder.
Research plot catalogue
Choose a plot that matches the analysis
Each objective provides meaningful alternatives instead of forcing every analysis into a histogram or bar chart. Common publication plots appear first in the builder, with specialist clinical, diagnostic, multivariate and summary-estimate figures grouped below.
Compare independent groups
Use: raincloud, estimation, box-and-point, mean-with-CI, patterned summary bars or jittered points.
Compare paired measurements
Use: paired profile, within-pair change or paired mean-and-CI plot with paired inference.
Compare repeated conditions
Use: individual profiles plus marginal means or a clean marginal-means plot, with optional grouping.
Assess a continuous association
Use: scatter with fitted relationship, points-only scatter or binned trend with correlation and slope.
Assess a categorical association
Use: Pearson residual heatmap, grouped proportions or mosaic plot with chi-square inference.
Display regression effects
Use: coefficient forest or exponentiated odds-ratio and hazard-ratio forest plots.
Display longitudinal trends
Use: individual trajectories with estimated means or a clean group mean profile over time.
Analyze survival outcomes
Use: Kaplan-Meier survival or cumulative-event curves with censor marks and number at risk.
Evaluate diagnostic accuracy
Use: ROC curve with AUC and Youden threshold or precision-recall curve with prevalence baseline.
Assess measurement agreement
Use: absolute or percentage Bland-Altman plot with mean bias and 95% limits of agreement.
Display subgroup effects
Use: subgroup forest plot with overall and subgroup estimates, confidence intervals and interaction p-value.
Display a factorial interaction
Use: cell means joined by factor level with confidence intervals and two-way interaction inference.
Plot summary estimates
Use: a forest plot for any estimate plus confidence interval and optional p-value, without raw data.
Screen correlation structure
Use: Pearson or Spearman correlation matrix heatmaps with readable labels and pairwise-complete sample sizes.
Show composition
Use: absolute or relative stacked bars and stacked area plots for shares, categories and component totals.
Summarize a time course
Use: grouped mean lines with CI or SEM ribbons for repeated experimental measurements without mixed-model setup.
Explore multivariate separation
Use: PCA or ordination score plots with group ellipses, sample labels and black-and-white-safe markers.
Screen high-dimensional effects
Use: volcano plots for effect size versus adjusted p-value, with labeled hits and threshold guides.
Built for manuscripts
Readable figures, meaningful statistics
A publication-ready figure is more than a high-resolution screenshot. The statistical display and the visual hierarchy must both support interpretation. Researchers can also explore the broader DataStatPro visualization toolkit.
Statistical annotation
Effect estimates, CIs and p-values
Show the inferential result appropriate to the design, including omnibus tests, selected comparisons, significance stars, regression estimates, AUCs, limits of agreement and interaction tests. Review the statistical methods guide when selecting an analysis.
Academic styling
Readable typography and stronger lines
Use Times-style serif text at practical sizes, dark axis labels, thicker axes, visible data lines and point markers that remain clear in multi-panel figures.
Print-safe design
Pure black-and-white plots
Choose manuscript, compact journal, presentation, grayscale, colorblind-safe, pure black-and-white or dark-preview themes. Academic hatch patterns distinguish bars without relying on color.
Research control
Customize the details that matter
Set confidence level, titles, labels, annotation visibility, patterns and display settings. Survival plots also support optional confidence bands and researcher-defined time cutoffs; see the dedicated survival analysis tools.
Accurate plot grammar
Alternative plots for every objective
Switch between raw-data, distribution, estimation, composition, matrix, time-course and model-based views while preserving the same scientific question and variable assignments.
Cleaner variable mapping
Separate dataset and variable selection
Pick the dataset first, then assign variables in a focused panel with visual type indicators for numerical, categorical, ordinal, boolean and text variables.
Publication export
SVG and 300 DPI PNG
Download an editable vector SVG or a high-resolution PNG. The preview and export use the same geometry, reducing surprises when the figure enters a manuscript. Combine figures with other publication-ready outputs.
Flexible forest plot maker
Turn regression or summary results into a clear forest plot
Forest plots are available for model effects and for estimates calculated elsewhere. Use coefficients, mean differences, odds ratios, hazard ratios, risk ratios or another estimate with confidence limits, and pair them with publication-ready regression tables.
- Linear-regression coefficient forest plots with a zero reference
- Logistic and Cox regression forests with a no-effect reference at 1
- Subgroup estimates with an overall interaction p-value
- Manual entry or dataset mapping for summary estimates
- Sort rows by estimate magnitude or p-value
Four-step workflow
From research question to manuscript figure
The interface separates dataset choice, variable mapping, statistical method, visual alternative and export settings without consuming the plot canvas.
Choose the objective
Select from a grouped menu that puts common publication plots first, then specialist clinical, diagnostic, multivariate and summary-result figures.
Select data and assign variables
Choose the dataset separately, then map variables using type indicators for numerical, categorical, ordinal, boolean and text fields.
Select a plot variant
Compare appropriate alternatives while retaining the same analytical objective and statistical interpretation.
Style and export
Refine annotations, typography, line strength, confidence level and theme, then download SVG or 300 DPI PNG.
Publication quality checklist
What makes a scientific plot publication-ready?
| Figure requirement | How the plot builder addresses it |
|---|---|
| Appropriate chart choice | Plot alternatives are organized by grouped research objectives and statistical design rather than by a generic chart gallery. |
| Visible uncertainty | Confidence intervals, confidence bands, error bars, limits of agreement and model uncertainty are shown where meaningful. |
| Statistical context | Relevant effects, tests, p-values and significance annotations can be displayed without adding tests to purely descriptive figures. |
| Readable at print size | Academic serif typography, dark labels, stronger axis lines, thicker plot lines and visible points support manuscript and panel layouts. |
| Color-independent meaning | Grayscale and pure black-and-white themes, varied markers and hatch patterns preserve distinctions in print. |
| Suitable export | Vector SVG supports editing and scaling, while 300 DPI PNG supports common journal submission workflows. |
| Correct variable mapping | Dataset selection is separated from variable specification, and variable type indicators help users choose valid roles for each plot. |
Questions researchers ask
Publication-ready plot FAQ
What is a publication-ready plot?
A publication-ready plot is a scientific figure designed for clear interpretation in a journal article, thesis, dissertation, conference paper or research report. It uses readable typography, meaningful axis labels, appropriate statistical geometry, visible uncertainty and export quality suitable for publication.
Which plot should I use to compare independent groups?
Use a raincloud, estimation, box-and-point or mean-with-confidence-interval plot. Raincloud and estimation plots reveal the observations and effect size more clearly than a bar chart, while patterned summary bars remain available when a journal or field expects that format.
Can DataStatPro create forest plots?
Yes. DataStatPro creates coefficient forest plots for linear regression, odds-ratio forest plots for logistic regression, hazard-ratio forest plots for Cox regression, subgroup forest plots with interaction p-values, and forest plots from manually entered or dataset-based summary estimates.
Can I make a Kaplan-Meier plot with a risk table?
Yes. The survival figure builder creates Kaplan-Meier or cumulative-event curves with censor marks, an optional confidence band, a number-at-risk table, custom time cutoffs and a log-rank comparison for grouped survival data.
Does the plot builder add confidence intervals and p-values?
Yes, when they are appropriate for the selected analysis. Figures can display effect estimates, confidence intervals, p-values, significance stars, omnibus tests, adjusted pairwise comparisons and interaction p-values. Descriptive distribution plots do not add an unnecessary significance test.
Can I create black-and-white plots for journal submission?
Yes. DataStatPro includes pure black-and-white and grayscale themes, academic hatch patterns, readable Times-style typography, stronger axis lines, thicker plot lines and visible point markers for print-friendly scientific figures.
What file formats can I export?
The publication-ready plot builder exports editable vector SVG files and high-resolution 300 DPI PNG files. The same accessible SVG geometry is used for the on-screen preview and export.
Can I build a forest plot without raw data?
Yes. Enter a label, estimate, lower confidence limit, upper confidence limit and optional p-value for each row, or map those columns from a dataset. The resulting forest plot can be sorted by estimate magnitude or p-value.
How does DataStatPro help me choose valid variables?
The plot builder separates dataset selection from variable mapping and shows variable type indicators for numerical, categorical, ordinal, boolean and text fields, so each plot role is easier to assign correctly.
Unlock Publication-Ready Plots
Sign in to choose Pro or Edu Pro and unlock the objective-driven plot builder, publication styling, statistical annotations, and SVG or 300 DPI PNG export.