Scientific figure conventions

Contract

When you write matplotlib/seaborn code for a journal-submission figure, emit code that already encodes the journal's house style: the Okabe-Ito categorical palette, the exact per-journal column width in inches, the per-journal panel-label case, perceptually-uniform colormaps, vector export, and sans-serif fonts. Apply this whenever the task names a journal (Nature / Science / Cell) or says "publication", "manuscript", "submission", or "journal figure". Do not substitute library defaults.

Rules (the complete spec)

R1 — Categorical colors: the Okabe-Ito palette, in this order

For every set of categorical series (lines, bars, groups, hues), assign colors from this exact 8-value list, in order, cycling if there are more than 8 series:

#E69F00  #56B4E9  #009E73  #F0E442  #0072B2  #D55E00  #CC79A7  #000000

(orange, sky blue, bluish green, yellow, blue, vermillion, reddish purple, black.) Never fall back to matplotlib's default cycle (C0/C1/C2 = blue/orange/green), tab10, or seaborn's colorblind theme. Set it explicitly per-artist (color=okabe_ito[i]) or globally (plt.rcParams['axes.prop_cycle'] = plt.cycler(color=okabe_ito)).

R2 — Figure width: the journal's exact column width (1 in = 25.4 mm)

Set the figsize width to the target journal's column width. Height is free (choose by aspect); width is fixed:

| Journal | Single column | Double column | |---|---|---| | Nature | 89 mm = 3.50 in | 183 mm = 7.20 in | | Science | 55 mm = 2.17 in | 175 mm = 6.89 in | | Cell | 85 mm = 3.35 in | 178 mm = 7.01 in |

Never use the matplotlib default (6.4 in) or a round number like 6, 8, or 10. A "single-column" or unspecified-width request defaults to that journal's single-column value.

R3 — Panel labels: case depends on the journal

Multi-panel figures get bold per-panel letters. The case is journal-specific:

• Nature → lowercase a, b, c, … (bold)
• Science and Cell → uppercase A, B, C, … (bold)

Render with fontweight='bold' (or weight='bold'), typically via ax.text(-0.15, 1.05, 'a', transform=ax.transAxes, fontweight='bold').

R4 — Continuous colormaps

• Sequential (one-directional magnitude): viridis or cividis. (plasma is also acceptable

perceptually-uniform.) Never jet or rainbow.

• Diverging (data spanning negative and positive, e.g. log2 fold-change, correlation): RdBu_r

or PuOr, centered at 0 (center=0 for seaborn, or symmetric vmin=-vmax for matplotlib).

• Always include a labeled colorbar for image/heatmap plots.

R5 — Export format

• Plots / line art (graphs): vector PDF or EPS. If rasterized, 600+ DPI.
• Images (microscopy, photos): TIFF or PNG at ≥ 300 DPI.
• Never JPEG for any scientific figure — it introduces compression artifacts.

R6 — Fonts

Sans-serif, specifically Arial or Helvetica. Sizes tuned for final print size: axis labels 7–9 pt, tick labels 6–8 pt, panel labels 8–12 pt bold. Set via rcParams['font.family'] = 'sans-serif' and rcParams['font.sans-serif'] = ['Arial', 'Helvetica'].

R7 — Spines

Remove the top and right spines (ax.spines['top'].set_visible(False) and same for 'right', or sns.despine()). Keep left and bottom.

R8 — Statistical rigor (when the plot summarizes a distribution)

Show error bars and state their meaning (SD, SEM, or 95% CI) in the caption; report sample size n; mark significance with */**/***; overlay individual data points where feasible rather than showing summary statistics alone.

Worked examples (BEFORE = base default → AFTER = conforming)

R1 — categorical colors

python
# BEFORE: matplotlib default cycle (C0 blue, C1 orange, C2 green)
ax.plot(x, a, label='Control')
ax.plot(x, b, label='Treated')

# AFTER: Okabe-Ito, in order
okabe_ito = ['#E69F00', '#56B4E9', '#009E73', '#F0E442',
             '#0072B2', '#D55E00', '#CC79A7', '#000000']
ax.plot(x, a, color=okabe_ito[0], label='Control')   # #E69F00
ax.plot(x, b, color=okabe_ito[1], label='Treated')   # #56B4E9

R2 — figure width (Nature single column)

python
# BEFORE: round, arbitrary size
fig, ax = plt.subplots(figsize=(8, 5))

# AFTER: Nature single column = 89 mm = 3.50 in
fig, ax = plt.subplots(figsize=(3.5, 2.5))

R3 — panel labels (Nature vs Cell)

python
# BEFORE: uppercase used for everything
ax1.text(-0.15, 1.05, 'A', transform=ax1.transAxes, fontweight='bold')  # wrong for Nature

# AFTER (Nature → lowercase, bold)
ax1.text(-0.15, 1.05, 'a', transform=ax1.transAxes, fontweight='bold')
ax2.text(-0.15, 1.05, 'b', transform=ax2.transAxes, fontweight='bold')

# AFTER (Cell or Science → uppercase, bold)
ax1.text(-0.15, 1.05, 'A', transform=ax1.transAxes, fontweight='bold')

R4 — colormaps

python
# BEFORE (sequential): jet
im = ax.imshow(M, cmap='jet')

# AFTER (sequential): perceptually uniform
im = ax.imshow(M, cmap='viridis')
fig.colorbar(im, ax=ax, label='Expression (AU)')

# BEFORE (diverging): default, not centered
sns.heatmap(corr, cmap='coolwarm')

# AFTER (diverging): RdBu_r centered at 0
sns.heatmap(corr, cmap='RdBu_r', center=0)

R5 — export

python
# BEFORE: JPEG at low DPI
fig.savefig('figure1.jpg', dpi=150)

# AFTER: vector for a plot
fig.savefig('figure1.pdf')                 # or .eps
# AFTER: raster image panel
fig.savefig('micrograph.tiff', dpi=300)    # TIFF/PNG, ≥300 DPI, never JPEG

R6 / R7 — fonts and spines

python
# BEFORE: default DejaVu Sans, all four spines
# (no font set; box stays closed)

# AFTER
import matplotlib as mpl
mpl.rcParams['font.family'] = 'sans-serif'
mpl.rcParams['font.sans-serif'] = ['Arial', 'Helvetica']
mpl.rcParams['axes.labelsize'] = 9
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)

R8 — statistics

python
# BEFORE: bare bars, no uncertainty
ax.bar(groups, means)

# AFTER: error bars + individual points + significance
ax.bar(groups, means, yerr=sems, capsize=3, color=okabe_ito[:len(groups)])
ax.scatter(jittered_x, individual_points, color='black', alpha=0.3, s=8)
ax.text(1.5, ymax * 1.05, '***', ha='center')   # state "mean ± SEM, n=…" in caption

Edge cases & exceptions

• More than 8 categorical series: cycle the Okabe-Ito list back to the start, and add a redundant

encoding (line style or marker) so series stay distinguishable beyond color.

• Width given but no journal: if the task says "single column" with no journal, default to Nature

89 mm (3.50 in); if it says "full page" / "full width", use that journal's double-column value.

• Diverging vs sequential: the deciding factor is whether the data spans both signs (correlations

−1..1, log2 fold-changes) → diverging centered at 0; one-directional magnitude → sequential.

• Bar charts: start the y-axis at 0 unless there is a scientific reason not to (truncated bar

axes mislead).

• Grayscale safety: because reviewers may print in grayscale, pair color with redundant encoding

(markers/line styles) for line plots.

• Plotly/other libs: the same K applies — Okabe-Ito hexes, exact width, never-JPEG export

(scale=3 ≈ 300 DPI for raster export).

Do / Don't

• Never use cmap='jet' or 'rainbow'. Always use viridis/cividis (sequential) or

RdBu_r/PuOr centered at 0 (diverging).

• Never save a figure as .jpg/.jpeg. Always PDF/EPS for plots, TIFF/PNG (≥300 DPI) for

images.

• Never leave the matplotlib default color cycle. Always set Okabe-Ito explicitly.
• Never use uppercase panel labels for Nature. Always lowercase for Nature, uppercase for

Science/Cell, both bold.

• Never use figsize=(6.4, …) or a round 8/10. Always the journal's exact column width in

inches.

• Never leave the top/right spines on. Always remove them.

Common mistakes (the base's wrong defaults)

1. Uses matplotlib's blue/orange/green default cycle instead of Okabe-Ito.
2. Picks a round figsize like (8, 6) or (10, 6) instead of the journal column width.
3. Uses jet/coolwarm/rainbow for heatmaps; forgets to center diverging maps at 0.
4. Saves as .png or .jpg for a line plot instead of vector PDF/EPS.
5. Uses uppercase A, B, C panel labels for Nature (Nature wants lowercase).
6. Leaves all four spines; never sets a sans-serif font.
7. Plots bare summary bars with no error bars, n, or individual points.

Quick checklist

• ] Categorical colors = Okabe-Ito 8-hex, in order
• ] figsize width = journal column width (Nature 3.50/7.20, Science 2.17/6.89, Cell 3.35/7.01 in)
• ] Panel labels: Nature lowercase, Science/Cell uppercase, bold
• ] Sequential cmap viridis/cividis; diverging RdBu_r/PuOr centered at 0; never jet/rainbow
• ] Export vector PDF/EPS for plots, TIFF/PNG ≥300 DPI for images, never JPEG
• ] Sans-serif Arial/Helvetica; top & right spines removed
• ] Error bars + n + significance + individual points when summarizing distributions