Figure Quality Standards

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

Typography

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

Layout and Readability

What Goes ON the Figure vs. IN LaTeX

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.

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.

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.

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.

Use CNS_STYLE and NATURE_COLORS from the CNS profile.

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

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

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

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

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