figure-quality-standards

name	figure-quality-standards
description	Use whenever generating figures or tables for a research paper — enforces publication-quality visual standards including style consistency, readability, accessibility, and venue-appropriate formatting

Figure & Table Quality Standards

Overview

Figures are the first thing reviewers look at. A sloppy figure signals sloppy science. This skill defines mandatory quality standards for every figure and table produced during Phase 4 (experiment execution) and Phase 5 (results integration), and enforced again during Phase 6 (paper writing).

Invoke this skill before generating any figure intended for a paper.

Universal Figure Standards

Resolution and Format

Property	Requirement
Format	Vector (PDF or SVG) for plots; PNG at ≥300 DPI only for raster images (photos, heatmaps)
Minimum DPI	300 for raster, vector preferred for all line/bar/scatter plots
File format for LaTeX	PDF (first choice) or EPS; avoid PNG/JPG for plots
Size	Match column width of target venue (typically 3.25" single column, 6.875" double column for IEEE/ACM)

Typography

Property	Requirement
Font family	Match venue profile (see Venue-Specific Styles below) — sans-serif for CNS, serif for CS/IEEE
Axis label size	≥ 8pt after scaling to final print size
Tick label size	≥ 7pt after scaling
Legend text size	≥ 7pt after scaling
Figure title	OMIT — do NOT put a title on the figure. The LaTeX \caption{} serves as the title.
Panel labels	Bold lowercase for CNS-style (a, b, c); uppercase for CS/IEEE (A, B, C) — see venue profile

Test: After generating a figure, mentally scale it to its final column width. If any text becomes unreadable at that size, increase the font.

Color

Property	Requirement
Color palette	Match venue profile — Nature palette for CNS, Tableau 10 for CS, see below
Consistency	ALL figures in the same paper must use the SAME color → method mapping
Grayscale fallback	Figures must be distinguishable in grayscale (some venues print in B&W). Use markers/hatching in addition to color
Maximum colors	≤ 8 distinct colors per figure; beyond that, use subplots

Layout and Readability

Property	Requirement
Axis labels	Present on every axis; include units (e.g., "Accuracy (%)", "Time (s)")
Grid lines	Depends on venue profile: subtle for CS, often absent for CNS
Legend placement	Inside the plot area if space allows, otherwise outside. Never overlap data points.
White space	Tight layout (bbox_inches='tight' in matplotlib); no excessive margins
Aspect ratio	Standard ratios (4:3, 16:9, 1:1). Never stretched or squished.
Subplot spacing	Consistent spacing; shared axes where appropriate to save space

What Goes ON the Figure vs. IN LaTeX

Figures and captions are SEPARATE things. The figure is an image file (PDF/SVG). The caption is LaTeX text in `\caption{}`. Do NOT confuse them.

ON the figure (in the image file itself):

• Axis labels with units
• Tick labels
• Legend (if multiple series)
• Panel labels (a, b, c — positioned top-left of each subplot)
• Annotations (arrows, text callouts if needed)
• NO figure title (the caption replaces it)
• NO caption text

IN LaTeX \caption{}:

• What the figure shows (one sentence)
• Key takeaway / main observation
• Per-panel descriptions for multi-panel figures: "(a) Method comparison on Dataset X. (b) Ablation study..."
• Define abbreviations not defined in main text
• Statistical details if relevant ("Error bars indicate ± 1 std over 5 seeds")
• Must be self-contained: reader should understand the figure from caption alone

LaTeX pattern:

\begin{figure}[t]
\centering
\includegraphics[width=\columnwidth]{figures/main_comparison.pdf}
\caption{Comparison of methods on three benchmarks.
(\textbf{a}) Accuracy on Dataset X. Our method (blue) outperforms all baselines.
(\textbf{b}) Training efficiency. Our method converges 2$\times$ faster.
Error bars indicate $\pm$ 1 std over 5 random seeds.}
\label{fig:main}
\end{figure}

---

Venue-Specific Figure Styles

Read target_venue from research-anchor.yaml and select the matching profile. If unsure which profile to use, ask the user.

Profile: CNS (Nature, Science, Cell and their sub-journals)

Nature/Science/Cell have a distinctive, recognizable figure aesthetic. Matching it signals professionalism.

Property	CNS Standard
Font	Helvetica / Arial (sans-serif). Nature explicitly requires this.
Font size	5–7pt for figure text (Nature allows small text because figures are high-resolution)
Panel labels	Bold lowercase: a, b, c, d — top-left of each panel, outside plot area
Color palette	Nature palette: ['#E64B35', '#4DBBD5', '#00A087', '#3C5488', '#F39B7F', '#8491B4', '#91D1C2', '#DC0000', '#7E6148', '#B09C85']
Background	White. No gray background.
Grid lines	None or extremely subtle. CNS figures are clean and minimal.
Spines	Usually left + bottom only. No top/right spines.
Line width	0.5–1pt for data lines, 0.25–0.5pt for axes
Multi-panel	Very common. 4–8 panels per figure. Use plt.subplot_mosaic() for complex layouts.
Figure width	Single column: 89mm. Double column: 183mm. Full page: 183mm × 247mm.
Annotations	Clean arrows, minimal text. Let the data speak.
Bar plots	Thin bars, often with individual data points overlaid (strip/swarm plot on top of bars)
Statistical markers	Brackets with asterisks: *, **, ***, ns

CNS_STYLE = {
    'font.family': 'sans-serif',
    'font.sans-serif': ['Helvetica', 'Arial', 'DejaVu Sans'],
    'font.size': 7,
    'axes.titlesize': 8,
    'axes.labelsize': 7,
    'xtick.labelsize': 6,
    'ytick.labelsize': 6,
    'legend.fontsize': 6,
    'axes.linewidth': 0.5,
    'xtick.major.width': 0.5,
    'ytick.major.width': 0.5,
    'lines.linewidth': 1.0,
    'lines.markersize': 4,
    'axes.spines.top': False,
    'axes.spines.right': False,
    'axes.grid': False,
    'figure.dpi': 300,
    'savefig.dpi': 300,
    'savefig.bbox': 'tight',
}

NATURE_COLORS = ['#E64B35', '#4DBBD5', '#00A087', '#3C5488',
                 '#F39B7F', '#8491B4', '#91D1C2', '#DC0000',
                 '#7E6148', '#B09C85']

Profile: CS Conferences (NeurIPS, ICML, ICLR, CVPR, AAAI, ACL, EMNLP)

CS conferences prioritize clarity and information density over aesthetics.

Property	CS Conference Standard
Font	Serif (Times, Computer Modern) to match paper body, OR sans-serif if consistent
Font size	8–10pt (larger than CNS because columns are wider)
Panel labels	Uppercase or "(a) (b) (c)" in caption text; less common as on-figure labels
Color palette	Tableau 10, ColorBrewer, or custom — must be colorblind-safe
Background	White
Grid lines	Light gray dashed — acceptable and often helpful for reading values
Spines	Left + bottom preferred; all four acceptable
Line width	1.5–2pt for data lines (thick enough to see in projected slides too)
Multi-panel	2–4 panels typical. Subfigures common.
Figure width	Single column: ~3.25". Double column: ~6.875" (LaTeX \textwidth).
Error bands	Shaded regions (alpha=0.2) with mean line. Very standard for learning curves.
Tables > figures	CS values tables highly; main results are often a table, not a figure

CS_STYLE = {
    'font.family': 'serif',
    'font.serif': ['Times New Roman', 'DejaVu Serif', 'Computer Modern Roman'],
    'font.size': 10,
    'axes.titlesize': 11,
    'axes.labelsize': 10,
    'xtick.labelsize': 9,
    'ytick.labelsize': 9,
    'legend.fontsize': 9,
    'axes.linewidth': 0.8,
    'lines.linewidth': 1.5,
    'lines.markersize': 6,
    'axes.spines.top': False,
    'axes.spines.right': False,
    'axes.grid': True,
    'grid.alpha': 0.3,
    'grid.linestyle': '--',
    'figure.dpi': 300,
    'savefig.dpi': 300,
    'savefig.bbox': 'tight',
}

CS_COLORS = ['#4e79a7', '#f28e2b', '#e15759', '#76b7b2',
             '#59a14f', '#edc948', '#b07aa1', '#ff9da7',
             '#9c755f', '#bab0ac']  # Tableau 10

Profile: IEEE (Transactions, Conference Proceedings)

IEEE has strict formatting requirements documented in their author guidelines.

Property	IEEE Standard
Font	Times New Roman (mandatory)
Font size	8–10pt in figures
Column width	Single: 3.5". Double: 7.16".
Color	Allowed but paper may be printed B&W — MUST be readable in grayscale
Captions	"Fig. 1." format (not "Figure 1")
Line markers	Essential — distinguish lines by marker shape, not just color
Grid lines	Optional, light

IEEE_STYLE = {
    'font.family': 'serif',
    'font.serif': ['Times New Roman'],
    'font.size': 9,
    'axes.titlesize': 10,
    'axes.labelsize': 9,
    'xtick.labelsize': 8,
    'ytick.labelsize': 8,
    'legend.fontsize': 8,
    'axes.linewidth': 0.6,
    'lines.linewidth': 1.2,
    'lines.markersize': 5,
    'axes.spines.top': False,
    'axes.spines.right': False,
    'axes.grid': True,
    'grid.alpha': 0.2,
    'grid.linestyle': ':',
    'figure.dpi': 300,
    'savefig.dpi': 300,
    'savefig.bbox': 'tight',
}

Profile: Bioinformatics / Life Sciences (Genome Research, Bioinformatics, PNAS, eLife)

Life science journals generally follow CNS aesthetics with domain-specific plot types.

Property	Life Science Standard
Font	Helvetica / Arial (following Nature/CNS tradition)
Style	Very close to CNS profile above
Domain-specific plots	Volcano plots, MA plots, heatmaps with dendrograms, Kaplan-Meier survival curves, Manhattan plots, circos plots
Heatmap conventions	Row/column clustering dendrograms, diverging colormap (red-white-blue for expression), annotated color bars
Statistical notation	Brackets with */**/***/ns between groups, Bonferroni-corrected p-values
Bar plots	Individual data points overlaid (strip/swarm), NOT just bars with error bars

Use CNS_STYLE and NATURE_COLORS from the CNS profile.

Profile: Physical Sciences (APS/Physical Review, ACS, RSC)

Property	Physical Sciences Standard
Font	Computer Modern or Helvetica depending on journal
Figure width	APS single column: 3.375". Double: 6.75".
Conventions	SI units on all axes, scientific notation for large/small numbers, insets common for zoomed regions
Color	Conservative — fewer colors, more line style variation

Use CS_STYLE as base, adjust font to Computer Modern.

How to Select a Profile

At project start (Phase 0/1), when target_venue is set in research-anchor.yaml:

1. Read the venue name
2. Map to profile:
   • Nature, Science, Cell, Nature *, Science *, Cell *, PNAS, eLife → CNS profile
   • NeurIPS, ICML, ICLR, CVPR, ECCV, AAAI, ACL, EMNLP, KDD, WWW → CS profile
   • IEEE *, any IEEE transaction or conference → IEEE profile
   • Bioinformatics, Genome Research, Nucleic Acids Research → Life Science profile
   • Physical Review *, ACS *, RSC *, J. Chem. Phys. → Physical Sciences profile
3. If venue is unclear or not listed, ask user: "Which figure style matches your target venue? (1) CNS/Nature style (2) CS conference style (3) IEEE style (4) Other — please describe"
4. Write the selected profile to src/plot_style.py and use it for ALL figures

Figure Type Selection Guide

Data type	Recommended figure	Avoid
Method A vs B vs C on multiple datasets	Grouped bar chart or table	Pie chart
Performance vs hyperparameter	Line plot with error bands	Scatter without connection
Ablation (component contribution)	Grouped bar chart or stacked bar	Line plot (components aren't ordered)
Training dynamics	Line plot (x: epoch, y: metric) with shaded std	Bar chart
Feature importance / attention	Heatmap with annotated values	3D plots
Distribution comparison	Violin plot or box plot	Overlapping histograms
Embedding visualization	t-SNE/UMAP scatter with class colors	PCA (usually uninformative for high-dim)
Qualitative examples	Grid of input→output pairs	Random cherry-picked singles
Architecture diagram	Clean schematic (tikz, draw.io, or programmatic)	Hand-drawn or overly complex
Confusion matrix	Annotated heatmap with numbers in cells	Plain matrix without annotations

Style Template

At project start, create src/plot_style.py based on the selected venue profile. This file is imported by every plotting script.

import matplotlib.pyplot as plt
import matplotlib as mpl

# ──────────────────────────────────────────────
# SELECT ONE profile based on target venue.
# See Venue-Specific Figure Styles section above.
# Copy the matching STYLE dict and COLORS list here.
# ──────────────────────────────────────────────

# Example: CS conference profile (NeurIPS, ICML, etc.)
STYLE_CONFIG = CS_STYLE   # Replace with CNS_STYLE, IEEE_STYLE, etc.
COLORS = CS_COLORS         # Replace with NATURE_COLORS, etc.

mpl.rcParams.update(STYLE_CONFIG)

Save as src/plot_style.py and import in every plotting script. This ensures ALL figures have consistent, venue-appropriate style.

Method-Color Mapping

At the start of the project, define a global color mapping and use it everywhere:

METHOD_COLORS = {
    'Ours': COLORS[0],       # Always blue
    'Baseline A': COLORS[1], # Always orange
    'Baseline B': COLORS[2], # Always red
    'Baseline C': COLORS[3], # Always teal
    'Ablation': COLORS[4],   # Always green
}

Store this mapping in src/plot_style.py and update it as methods are added. Never assign colors ad-hoc per figure.

Table Standards

Property	Requirement
Format	booktabs style in LaTeX (\toprule, \midrule, \bottomrule); no vertical lines
Best result	Bold the best value in each column/metric
Second best	Underline the second best (if comparing ≥4 methods)
Uncertainty	Always report mean ± std (or CI); bare numbers without variance are unacceptable
Alignment	Decimal-aligned numbers; consistent decimal places per column
Significance	Mark statistically significant improvements (e.g., † or * with p-value in caption)
Our method highlight	Use light gray row shading or clear label; never bury it in the middle

LaTeX Table Template

\begin{table}[t]
\centering
\caption{Main results on [datasets]. Best in \textbf{bold}, second best \underline{underlined}.
$\dagger$: statistically significant improvement over best baseline ($p < 0.05$, paired t-test).}
\label{tab:main}
\begin{tabular}{@{}lccc@{}}
\toprule
Method & Dataset A & Dataset B & Dataset C \\
\midrule
Baseline 1 & $83.2 \pm 0.4$ & $76.1 \pm 0.8$ & $91.3 \pm 0.2$ \\
Baseline 2 & $\underline{85.1 \pm 0.3}$ & $77.4 \pm 0.6$ & $\underline{92.0 \pm 0.3}$ \\
Baseline 3 & $84.7 \pm 0.5$ & $\underline{78.2 \pm 0.5}$ & $91.8 \pm 0.4$ \\
\midrule
Ours & $\mathbf{87.3 \pm 0.2}^\dagger$ & $\mathbf{80.1 \pm 0.4}^\dagger$ & $\mathbf{93.5 \pm 0.2}^\dagger$ \\
\bottomrule
\end{tabular}
\end{table}

Per-Figure Quality Checklist

Before including ANY figure in the paper, verify every item.

Figure file (the image itself):

• Venue profile applied — using the correct style from src/plot_style.py?
• Readable at print size — scale to final column width; all text ≥ 7pt (CNS: ≥ 5pt)?
• Axis labels present — with units (e.g., "Accuracy (%)", "Time (s)")?
• No figure title — title belongs in LaTeX \caption{}, not on the figure
• Legend present — if multiple series; not overlapping data?
• Panel labels — if multi-panel: bold a, b, c (CNS) or (A), (B), (C) (CS/IEEE) top-left?
• Color consistent — same method = same color as all other figures?
• Colorblind safe — distinguishable without color (markers, line styles, hatching)?
• Error bars / variance — shown where applicable (shaded region or error bars)?
• Vector format — PDF/SVG for plots (not PNG/JPG)?
• No chartjunk — no 3D effects, no excessive decoration, no rainbow gradients?
• White space optimized — tight layout, no giant margins?

LaTeX side (in the .tex file):

• \caption{} self-contained — reader understands figure from caption alone, without reading body text?
• \caption{} describes each panel — for multi-panel: "(a) ... (b) ..."?
• \caption{} states key takeaway — not just "Results on Dataset X" but what the results show?
• \caption{} notes statistical details — "Error bars: ± 1 std over 5 seeds" or similar?
• \label{fig:xxx} present and descriptive?
• \includegraphics width — matches venue column width (\columnwidth or \textwidth)?
• Referenced in text — every figure is \ref{}'d in the body text; no orphaned figures?

Anti-Patterns — NEVER Do These

Anti-pattern	Why it's bad	What to do instead
Default matplotlib style (white bg, thin lines, small fonts)	Unreadable at print size	Apply the style template above
Rainbow colormap for categorical data	Perceptually nonlinear, colorblind-hostile	Use qualitative palette (Tableau 10, ColorBrewer)
3D bar charts or pie charts	Distort proportions, waste ink	2D grouped bar chart
Inconsistent colors across figures	Reader can't track methods	Global method-color mapping
Screenshots of terminal output	Unreadable, unprofessional	Proper table or formatted code block
Figures without error bars	Results look unreliable	Always show variance (std, CI, min-max)
Tiny axis labels that need zooming	Will be illegible in print	≥ 8pt at final size
Cherry-picked qualitative examples	Misleading	Show representative range (good + average + failure)

Integration with Workflow

This skill should be invoked:

1. Phase 4 (experiment-execution): When generating any experimental figure — apply style template, use method-color mapping
2. Phase 5 (results-integration): When producing the content outline — verify all planned figures meet standards; run the per-figure checklist
3. Phase 6 (paper-writing): Before including each figure in a .tex file — final quality check; verify LaTeX \includegraphics path and caption

Rationalization Prevention

Excuse	Reality
"I'll fix the figures later"	You won't. Style issues compound. Apply the template from the first plot.
"Default matplotlib looks fine"	On screen at 100%, maybe. At conference poster or PDF zoom, it's unreadable.
"Color doesn't matter"	8% of men are colorblind. Reviewers print in B&W. Color always matters.
"Error bars clutter the plot"	Error bars ARE the data. Without them, your plot is a lie.
"One quick plot is fine for now"	Quick plots become final figures 90% of the time. Do it right the first time.