MMF.
• 8 min read

Plot and Figure Generating Tools for Researchers and Scientists

A practical guide to the tools researchers use for workflow diagrams, statistical plots, vector illustrations, and paper figures.

Scientific Figures, Technical Plots, Vector Curves, and Workflow Diagrams

Plots, process flows, vector curves, and diagrams for academic research.

Plots and figures are an integral part of research and analytics. A single figure can describe more than 2-3 pages of essays. However, researchers around the world use different tools for different purposes.

Here is a breakdown of the most useful tools for methodology flowcharts, statistical plots, vector illustrations, and study designs.

1. Overview Table

Quick comparison of tools, primary strengths, learning curves, and export formats.

Category Tool Best For Learning Curve Export Formats
Visual Editors draw.io Flowcharts, system architecture, workflows, study designs Low SVG, PDF, PNG, XML
Excalidraw Hand-drawn sketches, brainstorming, whiteboard figures Very Low SVG, PNG, JSON
Inkscape Custom vector illustrations, multi-panel figure assembly Medium SVG, EPS, PDF
Diagrams as Code Mermaid Flowcharts, sequence diagrams, Gantt charts Low SVG, PNG
Graphviz Networks, DAGs, hierarchical layouts Medium SVG, PDF, PostScript
PlantUML UML, component, and sequence diagrams Medium SVG, PNG, LaTeX
TikZ / PGFPlots Mathematical diagrams matching paper typography High Native PDF vector
Statistical Plotting Matplotlib + Seaborn Publication plots, training curves, statistical graphics Medium PDF, SVG, EPS, PNG
ggplot2 (R) Grammar-of-graphics statistical visualizations Medium PDF, SVG, EPS, TIFF
gnuplot Scriptable, command-line scientific plotting Medium PostScript, PDF, SVG
Plotly Interactive exploratory plots and static exports Low–Medium HTML, SVG, PDF, PNG
Icons & AI Tools Open Icon Libraries Reusable scientific vectors (bio, organisms, medical) None SVG, PNG
Automated / AI Tools PRISMA flowcharts, paper schematics, knowledge graphs Low SVG, Code

2. Diagram & Flowchart Tools (GUI / Visual Editors)

These tools give you an interactive canvas to draw, arrange, and export figures without writing code.

draw.io (diagrams.net)

Author's Pick

Best For: Flowcharts, architecture diagrams, workflows, study designs.

Notes: Completely free, open-source, and works both in the browser and as an offline desktop app. It has rich shape libraries, supports transparent background rendering, and exports SVG, PNG, and PDF. You can also re-edit exported SVGs because the diagram data is stored inside the file.

Excalidraw

Hand-Drawn Style

Best For: Hand-drawn sketches, quick brainstorming, collaborative diagrams.

Notes: Open-source, browser-based, and end-to-end encrypted. It supports Mermaid syntax import and is great for informal, whiteboard-style figures.

Inkscape

Vector Illustration

Best For: Complex custom illustrations, multi-panel figures, vector polishing.

Notes: Full-featured free alternative to Illustrator (SVG-native). Ideal for assembling multi-panel publication figures (Panel A, B, C) with exact column widths and 300+ DPI.

3. Diagrams as Code (Text → Diagram)

Write markup or code to define nodes and connections. Diagrams are generated automatically, making them easy to version control in Git and modify quickly.

Mermaid

Best For: Flowcharts, sequence diagrams, Gantt charts, class/state diagrams, mind maps.

Notes: Uses Markdown-like syntax. Renders directly in GitHub, GitLab, Notion, Obsidian, and docs. Fast for reproducible diagrams.

graph TD
  A[Data Ingestion] --> B{Validation}
  B -- Pass --> C[Feature Extraction]
  B -- Fail --> D[Log Error]
  C --> E[Model Training]
mermaid.js.org →

Graphviz

Best For: Graphs, networks, hierarchical layouts, complex node-edge diagrams.

Notes: Automatic layout engine. Excellent for large or structured graphs without manual positioning.

digraph G {
  rankdir=LR;
  "Input" -> "Filter" -> "Classifier" -> "Output";
}
graphviz.org →

PlantUML

Best For: UML, sequence, activity, and component diagrams.

Notes: Text-based and highly flexible. Integrates with IDEs and exports SVG, PNG, or LaTeX.

plantuml.com →

TikZ / PGFPlots (LaTeX)

LaTeX Native

Best For: High-precision methodology diagrams, flowcharts, plots that match paper fonts.

Notes: Steeper learning curve, but unmatched quality and reproducibility in LaTeX papers. Fonts and math symbols match your paper body text exactly. Part of any modern LaTeX distribution.

\begin{tikzpicture}
  \node [draw] (a) {Raw Data};
  \node [draw, right of=a, node distance=2.5cm] (b) {$\int f(x)dx$};
  \draw [->] (a) -- (b);
\end{tikzpicture}

4. Data Plots & Statistical Figures

For empirical data, benchmark comparisons, and statistical distributions, scripted plotting libraries give you full reproducibility.

Matplotlib + Seaborn

Best For: Publication plots, training curves, statistical graphics.

Notes: Python standard. Full control over every axis, spine, and label, highly customizable.

pip install matplotlib seaborn

ggplot2 (R)

Best For: Grammar-of-graphics statistical visualizations.

Notes: Reproducible, elegant plots. Pairs well with cowplot, ggpubr, and patchwork for multi-panel publication figures.

install.packages("ggplot2")

gnuplot

Best For: Command-line scientific plotting.

Notes: Lightweight, scriptable, high-quality vector output (PostScript, PDF, SVG). Great for fast terminal workflows.

Plotly (Open-Source Core)

Best For: Interactive + static scientific plots.

Notes: Good for exploratory analysis and web dashboards, with static publication exports via Kaleido.

5. Scientific Icon & Illustration Libraries (Free / Open)

These provide reusable vector icons and drawings you can drop directly into Inkscape, draw.io, or other tools:

Bioicons

Open Source

Open-source scientific icons (especially life sciences, molecular biology, and biochemistry).

bioicons.com →

PhyloPic

Free Silhouette

Silhouette vector icons of organisms across the tree of life.

phylopic.org →

SciDraw

Community Drawings

Community-contributed scientific drawings for research rigs, apparatuses, and setups.

scidraw.io →

NIH BioArt

Biomedical

Free biomedical illustrations provided by the National Institute of Allergy and Infectious Diseases (NIAID / NIH).

bioart.niaid.nih.gov →

Servier Medical Art (SMART)

CC BY 4.0

High-quality medical illustrations (free for academic and educational use).

smart.servier.com →

Health Icons

Public Domain

Open health-related icons for clinical, healthcare, and public health projects.

healthicons.org →

6. Emerging Open-Source AI / Automated Tools

Automated tools for generating diagrams, knowledge graphs, and standardized flows from text:

SciSketch (Yale NLP)

Framework for generating schematic diagrams from scientific papers (open-source).

GitHub: yale-nlp/SciSketch →

OpenDiagram

AI diagram generator (Excalidraw-based, self-hostable) for architecture, sequence, and ER diagrams.

GitHub: Itz-Agasta/OpenDiagram →

Napkin AI

Excellent knowledge graph generator (free version available, with paid options).

napkin.ai →

SchemaTex

Text DSL → industry-standard diagrams (including PRISMA flow diagrams) as pure SVG.

GitHub: SchemaTex/SchemaTex →

7. What I Personally Use & Key Checklist

Why I Personally Use draw.io

However, I personally use draw.io. It’s an amazing tool from where you can render a lot of types of images whether it's a simple transparent background, besides vector images also.

A major advantage is that when you export an SVG with embedded XML data, you can drag the exported file right back into draw.io at any time to edit the diagram without starting over.

Checklist for Publication-Ready Figures

01
Vector First (SVG / PDF / EPS): Always use vector formats for diagrams, line charts, and flowcharts so lines remain sharp when zoomed.
02
300 to 600 DPI for Raster Images: If your figure includes photos, microscopy, or 3D renderings, export at minimum 300 DPI (600 DPI for fine line bitmaps).
03
Font Legibility: Axis labels and annotations should remain readable (at least 7–8 pt) at printed column width.
04
Colorblind-Safe Palettes: Use viridis or ColorBrewer palettes, and use different line styles (solid, dashed, dotted) so curves are clear in grayscale.
05
Units and Labels: Always state physical units on every axis and keep figures self-explanatory with a clear caption.
06
Multi-Panel Alignment: When combining sub-figures (A, B, C), align margins and axes consistently using Inkscape or Matplotlib GridSpec.
07
Script Reproducibility: Keep raw data and plotting scripts in your paper's Git repository for quick adjustments during revisions.

About the Author

Md. Muqtadir Fuad is an Industrial & Production Engineering researcher and software engineer at BUET specializing in applied machine learning, computer-aided engineering design (CAD/FEA), and full-stack computational tools.