Lab 34: Scientific Poster Preparation Workshop

BI 255 · Bethune-Cookman University · Fall 2026

Author

Dr. Rosie Stanbrook-Buyer

Published

November 23, 2026

Communicating Science Visually

A scientific poster is not a printed version of a paper. It is a visual conversation starter — designed to be understood in 3 minutes by someone standing a metre away, and to invite the reader to ask you questions. This lab covers the principles of poster design, how to produce publication-quality figures in R, and how to use a peer review process to improve your work before the final presentation.

Learning Objectives

By the end of this lab you will be able to:

Identify the key sections of a scientific poster and what goes in each
Apply design principles: visual hierarchy, colour, whitespace, and font size
Produce publication-quality R figures at 300 dpi with appropriate dimensions
Write a poster title, summary, and conclusion that communicate to a non-specialist
Give and receive constructive peer feedback using a structured rubric
Export figures from R in formats suitable for PowerPoint and printing (PNG, PDF, SVG)

Part 1: Anatomy of a Scientific Poster

Standard Poster Sections

Title bar — spans the full width. Contains: title (largest text on poster), your name, course/institution, date.

Introduction / Background — 3–5 sentences max. What question are you answering and why does it matter? No citations needed at this level.

Methods — brief description of your data and analysis approach. Can use a numbered list or a simple flowchart.

Results — this is the heart of the poster. 2–4 figures, each self-contained with a caption. The figures should tell the story without extensive text.

Discussion / Conclusion — 3–5 bullet points or 2 short paragraphs. What do your results mean? Any limitations?

References — 3–5 key references if appropriate.

Acknowledgements — brief (1–2 lines).


POSTER LAYOUT GUIDE (A0 landscape = 1189 x 841 mm)
====================================================

[          TITLE / NAME / INSTITUTION               ]
[                                                    ]
[ Intro  ] [   Results Fig 1  ] [  Results Fig 3   ]
[        ] [                  ] [                  ]
[ Methods] [   Results Fig 2  ] [  Discussion      ]
[        ] [                  ] [                  ]
[        ] [   (main figure)  ] [  Conclusion      ]

DESIGN RULES:
  - Title: 80–100pt font
  - Body text: minimum 24pt (readable from 1 metre)
  - Maximum 500 words of text on entire poster
  - 30–40% whitespace (empty space is not wasted space)
  - Maximum 4 colours (including black/white)
  - Every figure must have a self-contained caption

Part 2: Producing Publication-Quality Figures

The key difference between exploratory figures (for your own analysis) and publication/poster figures is:

Font size large enough to read when printed
Clean theme (no grey background)
Appropriate dimensions for the intended space
Saved at 300 dpi (print quality) or as a vector format (PDF/SVG)

Example: Polishing a Figure for a Poster

library(ggplot2)
library(dplyr)

# Use a dataset that might appear in a BI 255 poster
# Enzyme activity across three treatment groups
set.seed(42)
poster_df <- data.frame(
  treatment = rep(c("Control", "Low Dose", "High Dose"), each = 20),
  activity  = c(rnorm(20, 50, 8), rnorm(20, 72, 10), rnorm(20, 45, 12))
)
poster_df$treatment <- factor(poster_df$treatment,
                               levels = c("Control", "Low Dose", "High Dose"))

# Version 1: Default ggplot (NOT suitable for poster)
ggplot(poster_df, aes(x = treatment, y = activity, fill = treatment)) +
  geom_boxplot() +
  labs(title = "Enzyme Activity by Treatment")

# Version 2: Publication-quality poster figure
poster_fig <- ggplot(poster_df, aes(x = treatment, y = activity, fill = treatment)) +
  geom_violin(trim = FALSE, alpha = 0.55) +
  geom_boxplot(width = 0.12, fill = "white", outlier.shape = NA, linewidth = 0.8) +
  geom_jitter(width = 0.08, size = 2, alpha = 0.6, colour = "grey30") +
  scale_fill_manual(values = c("Control"   = "#A8C4D9",
                               "Low Dose"  = "#2C5F8A",
                               "High Dose" = "#C8102E")) +
  labs(
    title   = "Enzyme Activity Is Significantly Altered by Treatment",
    x       = NULL,
    y       = "Enzyme Activity (nmol/min/mg protein)",
    caption = "n = 20 per group; one-way ANOVA, Tukey post-hoc (not shown)"
  ) +
  theme_classic(base_size = 18) +   # Large base font for poster
  theme(
    legend.position  = "none",
    plot.title       = element_text(size = 16, face = "bold"),
    axis.text        = element_text(size = 14),
    axis.title.y     = element_text(size = 14),
    plot.caption     = element_text(size = 10, colour = "grey50")
  )

poster_fig

# Save for printing (300 dpi PNG) and for importing into PowerPoint
ggsave("enzyme_poster_figure.png",
       plot   = poster_fig,
       width  = 18,    # cm — size in the poster layout
       height = 14,
       units  = "cm",
       dpi    = 300)

# Vector format (PDF or SVG — infinitely scalable, ideal for posters)
ggsave("enzyme_poster_figure.pdf",
       plot   = poster_fig,
       width  = 18, height = 14, units = "cm")

Figure Size and Resolution

Format	When to use
PNG at 300 dpi	Standard for posters and papers; fixed resolution
PDF	Vector — perfectly sharp at any size; for InDesign or Illustrator
SVG	Vector — works in PowerPoint and web; editable in Inkscape
TIFF at 300+ dpi	Some journals require this for submission

For a PowerPoint poster: use PNG at 300 dpi. For a professionally printed poster: use PDF or high-resolution PNG (300 dpi minimum; 600 dpi preferred for text-heavy figures).

Part 3: Writing Poster Text


EXAMPLE: TRANSFORMING RESULTS TEXT FOR A POSTER
================================================

PAPER VERSION (too dense for a poster):
  "Enzyme activity was significantly elevated in the low-dose group
   (72.3 ± 10.1 nmol/min/mg) compared to both the control group
   (50.4 ± 8.3 nmol/min/mg; p = 0.003) and the high-dose group
   (45.2 ± 11.8 nmol/min/mg; p < 0.001), as determined by one-way
   ANOVA (F(2,57) = 18.4, p < 0.001) followed by Tukey-Kramer
   post-hoc comparisons."

POSTER VERSION (concise and visual):
  Title of figure: "Low-Dose Treatment Maximises Enzyme Activity"
  Caption: "n = 20 per group. One-way ANOVA: F(2,57) = 18.4, p < 0.001.
            Letters indicate Tukey-Kramer post-hoc groupings."
  The figure shows the result. The text reinforces it in one sentence.

KEY RULE: If the figure caption already explains the result,
the body text does not need to repeat it. Point to the figure.

Titles That Tell the Story


WEAK TITLE (describes what you did):
  "Enzyme Activity Results Under Three Concentrations"

STRONG TITLE (tells the main finding):
  "Low-Dose Treatment Maximises Enzyme Activity — High Dose Is Inhibitory"

A strong poster title is a headline — it gives away the answer.
The reader should know your main finding before reading anything else.

Part 4: Creating a Figure Panel

Many posters include a multi-panel figure. Here is how to produce a clean panel layout in R:

# install.packages("patchwork")
library(patchwork)

# Panel A: histogram of control data
fig_A <- ggplot(poster_df |> filter(treatment == "Control"),
                aes(x = activity)) +
  geom_histogram(fill = "#A8C4D9", colour = "white", bins = 10) +
  labs(title = "A. Control Distribution",
       x = "Enzyme Activity", y = "Count") +
  theme_classic(base_size = 14)

# Panel B: scatter-style comparison
fig_B <- ggplot(poster_df, aes(x = treatment, y = activity, colour = treatment)) +
  geom_jitter(width = 0.15, size = 2.5, alpha = 0.8) +
  stat_summary(fun = mean, geom = "crossbar", width = 0.3, colour = "black") +
  scale_colour_manual(values = c("Control" = "#A8C4D9",
                                 "Low Dose" = "#2C5F8A",
                                 "High Dose" = "#C8102E")) +
  labs(title = "B. Group Comparison",
       x = NULL, y = "Enzyme Activity") +
  theme_classic(base_size = 14) +
  theme(legend.position = "none")

# Combine with patchwork
combined_panel <- fig_A | fig_B
combined_panel + plot_annotation(
  title = "Figure 1. Enzyme Activity Under Three Treatment Conditions",
  theme = theme(plot.title = element_text(size = 13, face = "bold"))
)

# Save the panel at poster quality
ggsave("figure1_panel.png",
       combined_panel,
       width = 28, height = 14, units = "cm", dpi = 300)

Part 5: Peer Review Rubric

Use this rubric to give structured, constructive feedback on a classmate’s poster draft.


BI 255 POSTER PEER REVIEW RUBRIC
==================================

VISUAL DESIGN (20 pts)
  [ ] Title is readable from 1 metre and states the main finding
  [ ] Font size is minimum 24pt for body text
  [ ] Figures are clearly labelled with axes and units
  [ ] Colour palette is consistent and accessible
  [ ] Whitespace is used well (not overcrowded)

SCIENTIFIC CONTENT (40 pts)
  [ ] Research question is clearly stated in Introduction
  [ ] Methods are described concisely (enough to replicate the analysis)
  [ ] Results section contains 2-4 clear, well-captioned figures
  [ ] Statistical tests are named and results reported (F, t, p, or CI)
  [ ] Conclusions are supported by the data shown

COMMUNICATION (20 pts)
  [ ] Poster tells a clear story from top to bottom
  [ ] Non-specialist could understand the main finding
  [ ] Technical jargon is defined or avoided
  [ ] Conclusions are appropriately qualified (limitations noted)

R FIGURE QUALITY (20 pts)
  [ ] Figures were produced in R (ggplot2 or equivalent)
  [ ] Figures show raw data (not just means/bars)
  [ ] Font size in figures is large enough to read when printed
  [ ] Colour scheme is consistent with poster colour palette

TOTAL: ___ / 100

ONE STRENGTH: _________________________________

ONE SUGGESTION: ________________________________

Part 6: Presentation Tips

The 2-Minute Elevator Pitch

When someone stops at your poster, you have approximately 2 minutes before they decide to engage or move on. Practice this structure:

The question (15 seconds): “My project asked whether…”
The approach (20 seconds): “I used data from… and analysed it with…”
The main finding (30 seconds): “The key result was… [point to the main figure]”
Why it matters (15 seconds): “This is important because…”
Invite a question (40 seconds): “I’d love to hear your thoughts — especially about…”

Practice this out loud at least five times before the presentation.


COMMON MISTAKES TO AVOID
=========================

1. Too much text — aim for a maximum of 500 words total
2. Too many figures — 2 to 4 clear figures beat 8 cluttered ones
3. Bar charts without raw data — show the distribution (violin, jitter)
4. Missing units on axes — always label with units in parentheses
5. Printing at too low resolution — minimum 300 dpi for poster printing
6. Reading the poster out loud during presentation — talk to the person, 
   not the poster
7. Unexplained statistical notation — define p-values, CIs, and test names
8. Inconsistent fonts and colours — pick a palette and stick to it

3-Minute Knowledge Check

Close your notes. Answer these on your own — you have 3 minutes. We’ll go through the answers together after.

Knowledge Check Questions

1. What is the minimum font size recommended for poster body text, and why?

2. You have a beautiful regression plot saved at 72 dpi. The printed poster is A0 size. What problem will occur, and how do you fix it?

3. Which ggplot2 theme argument produces figures with the clean appearance most appropriate for a scientific poster?

a) `theme_grey()`    b) `theme_classic()`    c) `theme_dark()`    d) `theme_void()`

4. A strong poster figure caption should include which three elements?

5. True or False: Saving a figure as PDF from R means it will look blurry if scaled up on a large poster.

Answers (Only reveal if you have answered the above questions)

1. The minimum is 24pt for body text on an A0 poster. The rule of thumb is that text should be readable from approximately 1 metre away. Titles should be 80–100pt, section headings 40–48pt, body text 24–28pt, and figure labels/captions at least 18–20pt. Text smaller than 24pt requires the reader to step forward, disrupting the natural flow of the poster session.

2. A 72 dpi image will appear pixelated / blurry when printed at poster size. At 72 dpi, each pixel is large and visible at print resolution. Fix it by regenerating the figure with ggsave(..., dpi = 300) and re-exporting. Alternatively, save as PDF (vector) which is infinitely scalable with no quality loss. Never submit 72 dpi (screen resolution) images for print.

3. b) theme_classic() — removes the grey background and grid lines, producing a clean white background with axis lines only. This is the standard for published biology figures. theme_grey() (the default) has a grey background inappropriate for print; theme_dark() is unsuitable; theme_void() removes all axes which is only appropriate for maps.

4. A strong figure caption should include: (1) A title sentence stating the main finding (not just what the figure shows), (2) Sample size and key experimental details (n = X per group, conditions, etc.), (3) Statistical results (test name, test statistic, degrees of freedom, and p-value) — enough for the reader to judge the conclusion. Example: “Enzyme activity was significantly higher in the low-dose group. n = 20 per group; one-way ANOVA: F(2,57) = 18.4, p < 0.001; Tukey-Kramer post-hoc results indicated by letters.”

5. False — PDFs saved from R are vector graphics — they are mathematically defined curves and shapes with no pixel resolution. Vector images are perfectly sharp at any size, from a business card to a billboard. This is why PDF (or SVG) is the ideal format for poster figures. Raster formats (PNG, JPEG, TIFF) are resolution-dependent and must be saved at 300 dpi or higher for print.

Before the Final Presentation

Review all figure code from Labs 12–13 (ggplot2), 19 (ANOVA), 22 (regression), and whichever labs relate to your project
Ensure your Posit Cloud project is saved and backed up
Prepare questions you can ask the audience to engage them
Check Canvas for the final poster submission deadline and presentation schedule

--- title: "Lab 34: Scientific Poster Preparation Workshop" subtitle: "BI 255 · Bethune-Cookman University · Fall 2026" author: "Dr. Rosie Stanbrook-Buyer" date: "November 23, 2026" format: html: theme: cosmo toc: true toc-location: left toc-title: "In This Lab" toc-depth: 3 number-sections: false code-fold: false code-tools: true highlight-style: github smooth-scroll: true embed-resources: true callout-appearance: default execute: warning: false message: false echo: true --- ```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE) ``` ------------------------------------------------------------------------ ## Communicating Science Visually A scientific poster is not a printed version of a paper. It is a **visual conversation starter** — designed to be understood in 3 minutes by someone standing a metre away, and to invite the reader to ask you questions. This lab covers the principles of poster design, how to produce publication-quality figures in R, and how to use a peer review process to improve your work before the final presentation. ::: callout-note ## Learning Objectives By the end of this lab you will be able to: 1. Identify the key sections of a scientific poster and what goes in each 2. Apply design principles: visual hierarchy, colour, whitespace, and font size 3. Produce publication-quality R figures at 300 dpi with appropriate dimensions 4. Write a poster title, summary, and conclusion that communicate to a non-specialist 5. Give and receive constructive peer feedback using a structured rubric 6. Export figures from R in formats suitable for PowerPoint and printing (PNG, PDF, SVG) ::: ------------------------------------------------------------------------ ## Part 1: Anatomy of a Scientific Poster ::: callout-important ## Standard Poster Sections **Title bar** — spans the full width. Contains: title (largest text on poster), your name, course/institution, date. **Introduction / Background** — 3–5 sentences max. What question are you answering and why does it matter? No citations needed at this level. **Methods** — brief description of your data and analysis approach. Can use a numbered list or a simple flowchart. **Results** — this is the heart of the poster. 2–4 figures, each self-contained with a caption. The figures should tell the story without extensive text. **Discussion / Conclusion** — 3–5 bullet points or 2 short paragraphs. What do your results mean? Any limitations? **References** — 3–5 key references if appropriate. **Acknowledgements** — brief (1–2 lines). ::: ```{r, echo=FALSE} cat(' POSTER LAYOUT GUIDE (A0 landscape = 1189 x 841 mm) ==================================================== [ TITLE / NAME / INSTITUTION ] [ ] [ Intro ] [ Results Fig 1 ] [ Results Fig 3 ] [ ] [ ] [ ] [ Methods] [ Results Fig 2 ] [ Discussion ] [ ] [ ] [ ] [ ] [ (main figure) ] [ Conclusion ] DESIGN RULES: - Title: 80–100pt font - Body text: minimum 24pt (readable from 1 metre) - Maximum 500 words of text on entire poster - 30–40% whitespace (empty space is not wasted space) - Maximum 4 colours (including black/white) - Every figure must have a self-contained caption ') ``` ------------------------------------------------------------------------ ## Part 2: Producing Publication-Quality Figures The key difference between exploratory figures (for your own analysis) and publication/poster figures is: - Font size large enough to read when printed - Clean theme (no grey background) - Appropriate dimensions for the intended space - Saved at 300 dpi (print quality) or as a vector format (PDF/SVG) ### Example: Polishing a Figure for a Poster ```{r} library(ggplot2) library(dplyr) # Use a dataset that might appear in a BI 255 poster # Enzyme activity across three treatment groups set.seed(42) poster_df <- data.frame( treatment = rep(c("Control", "Low Dose", "High Dose"), each = 20), activity = c(rnorm(20, 50, 8), rnorm(20, 72, 10), rnorm(20, 45, 12)) ) poster_df$treatment <- factor(poster_df$treatment, levels = c("Control", "Low Dose", "High Dose")) # Version 1: Default ggplot (NOT suitable for poster) ggplot(poster_df, aes(x = treatment, y = activity, fill = treatment)) + geom_boxplot() + labs(title = "Enzyme Activity by Treatment") ``` ```{r} # Version 2: Publication-quality poster figure poster_fig <- ggplot(poster_df, aes(x = treatment, y = activity, fill = treatment)) + geom_violin(trim = FALSE, alpha = 0.55) + geom_boxplot(width = 0.12, fill = "white", outlier.shape = NA, linewidth = 0.8) + geom_jitter(width = 0.08, size = 2, alpha = 0.6, colour = "grey30") + scale_fill_manual(values = c("Control" = "#A8C4D9", "Low Dose" = "#2C5F8A", "High Dose" = "#C8102E")) + labs( title = "Enzyme Activity Is Significantly Altered by Treatment", x = NULL, y = "Enzyme Activity (nmol/min/mg protein)", caption = "n = 20 per group; one-way ANOVA, Tukey post-hoc (not shown)" ) + theme_classic(base_size = 18) + # Large base font for poster theme( legend.position = "none", plot.title = element_text(size = 16, face = "bold"), axis.text = element_text(size = 14), axis.title.y = element_text(size = 14), plot.caption = element_text(size = 10, colour = "grey50") ) poster_fig ``` ```{r, eval=FALSE} # Save for printing (300 dpi PNG) and for importing into PowerPoint ggsave("enzyme_poster_figure.png", plot = poster_fig, width = 18, # cm — size in the poster layout height = 14, units = "cm", dpi = 300) # Vector format (PDF or SVG — infinitely scalable, ideal for posters) ggsave("enzyme_poster_figure.pdf", plot = poster_fig, width = 18, height = 14, units = "cm") ``` ::: callout-tip ## Figure Size and Resolution | Format | When to use | |--------|-------------| | PNG at 300 dpi | Standard for posters and papers; fixed resolution | | PDF | Vector — perfectly sharp at any size; for InDesign or Illustrator | | SVG | Vector — works in PowerPoint and web; editable in Inkscape | | TIFF at 300+ dpi | Some journals require this for submission | For a PowerPoint poster: use PNG at 300 dpi. For a professionally printed poster: use PDF or high-resolution PNG (300 dpi minimum; 600 dpi preferred for text-heavy figures). ::: ------------------------------------------------------------------------ ## Part 3: Writing Poster Text ```{r, echo=FALSE} cat(' EXAMPLE: TRANSFORMING RESULTS TEXT FOR A POSTER ================================================ PAPER VERSION (too dense for a poster): "Enzyme activity was significantly elevated in the low-dose group (72.3 ± 10.1 nmol/min/mg) compared to both the control group (50.4 ± 8.3 nmol/min/mg; p = 0.003) and the high-dose group (45.2 ± 11.8 nmol/min/mg; p < 0.001), as determined by one-way ANOVA (F(2,57) = 18.4, p < 0.001) followed by Tukey-Kramer post-hoc comparisons." POSTER VERSION (concise and visual): Title of figure: "Low-Dose Treatment Maximises Enzyme Activity" Caption: "n = 20 per group. One-way ANOVA: F(2,57) = 18.4, p < 0.001. Letters indicate Tukey-Kramer post-hoc groupings." The figure shows the result. The text reinforces it in one sentence. KEY RULE: If the figure caption already explains the result, the body text does not need to repeat it. Point to the figure. ') ``` ### Titles That Tell the Story ```{r, echo=FALSE} cat(' WEAK TITLE (describes what you did): "Enzyme Activity Results Under Three Concentrations" STRONG TITLE (tells the main finding): "Low-Dose Treatment Maximises Enzyme Activity — High Dose Is Inhibitory" A strong poster title is a headline — it gives away the answer. The reader should know your main finding before reading anything else. ') ``` ------------------------------------------------------------------------ ## Part 4: Creating a Figure Panel Many posters include a multi-panel figure. Here is how to produce a clean panel layout in R: ```{r} # install.packages("patchwork") library(patchwork) # Panel A: histogram of control data fig_A <- ggplot(poster_df |> filter(treatment == "Control"), aes(x = activity)) + geom_histogram(fill = "#A8C4D9", colour = "white", bins = 10) + labs(title = "A. Control Distribution", x = "Enzyme Activity", y = "Count") + theme_classic(base_size = 14) # Panel B: scatter-style comparison fig_B <- ggplot(poster_df, aes(x = treatment, y = activity, colour = treatment)) + geom_jitter(width = 0.15, size = 2.5, alpha = 0.8) + stat_summary(fun = mean, geom = "crossbar", width = 0.3, colour = "black") + scale_colour_manual(values = c("Control" = "#A8C4D9", "Low Dose" = "#2C5F8A", "High Dose" = "#C8102E")) + labs(title = "B. Group Comparison", x = NULL, y = "Enzyme Activity") + theme_classic(base_size = 14) + theme(legend.position = "none") # Combine with patchwork combined_panel <- fig_A | fig_B combined_panel + plot_annotation( title = "Figure 1. Enzyme Activity Under Three Treatment Conditions", theme = theme(plot.title = element_text(size = 13, face = "bold")) ) ``` ```{r, eval=FALSE} # Save the panel at poster quality ggsave("figure1_panel.png", combined_panel, width = 28, height = 14, units = "cm", dpi = 300) ``` ------------------------------------------------------------------------ ## Part 5: Peer Review Rubric Use this rubric to give structured, constructive feedback on a classmate's poster draft. ```{r, echo=FALSE} cat(' BI 255 POSTER PEER REVIEW RUBRIC ================================== VISUAL DESIGN (20 pts) [ ] Title is readable from 1 metre and states the main finding [ ] Font size is minimum 24pt for body text [ ] Figures are clearly labelled with axes and units [ ] Colour palette is consistent and accessible [ ] Whitespace is used well (not overcrowded) SCIENTIFIC CONTENT (40 pts) [ ] Research question is clearly stated in Introduction [ ] Methods are described concisely (enough to replicate the analysis) [ ] Results section contains 2-4 clear, well-captioned figures [ ] Statistical tests are named and results reported (F, t, p, or CI) [ ] Conclusions are supported by the data shown COMMUNICATION (20 pts) [ ] Poster tells a clear story from top to bottom [ ] Non-specialist could understand the main finding [ ] Technical jargon is defined or avoided [ ] Conclusions are appropriately qualified (limitations noted) R FIGURE QUALITY (20 pts) [ ] Figures were produced in R (ggplot2 or equivalent) [ ] Figures show raw data (not just means/bars) [ ] Font size in figures is large enough to read when printed [ ] Colour scheme is consistent with poster colour palette TOTAL: ___ / 100 ONE STRENGTH: _________________________________ ONE SUGGESTION: ________________________________ ') ``` ------------------------------------------------------------------------ ## Part 6: Presentation Tips ::: callout-note ## The 2-Minute Elevator Pitch When someone stops at your poster, you have approximately 2 minutes before they decide to engage or move on. Practice this structure: 1. **The question** (15 seconds): "My project asked whether..." 2. **The approach** (20 seconds): "I used data from... and analysed it with..." 3. **The main finding** (30 seconds): "The key result was... [point to the main figure]" 4. **Why it matters** (15 seconds): "This is important because..." 5. **Invite a question** (40 seconds): "I'd love to hear your thoughts — especially about..." Practice this out loud at least five times before the presentation. ::: ```{r, echo=FALSE} cat(' COMMON MISTAKES TO AVOID ========================= 1. Too much text — aim for a maximum of 500 words total 2. Too many figures — 2 to 4 clear figures beat 8 cluttered ones 3. Bar charts without raw data — show the distribution (violin, jitter) 4. Missing units on axes — always label with units in parentheses 5. Printing at too low resolution — minimum 300 dpi for poster printing 6. Reading the poster out loud during presentation — talk to the person, not the poster 7. Unexplained statistical notation — define p-values, CIs, and test names 8. Inconsistent fonts and colours — pick a palette and stick to it ') ``` ------------------------------------------------------------------------ ## 3-Minute Knowledge Check *Close your notes. Answer these on your own — you have 3 minutes. We'll go through the answers together after.* ::: callout-caution ## Knowledge Check Questions **1.** What is the minimum font size recommended for poster body text, and why? **2.** You have a beautiful regression plot saved at 72 dpi. The printed poster is A0 size. What problem will occur, and how do you fix it? **3.** Which ggplot2 theme argument produces figures with the clean appearance most appropriate for a scientific poster? a) `theme_grey()` b) `theme_classic()` c) `theme_dark()` d) `theme_void()` **4.** A strong poster figure caption should include which three elements? **5.** True or False: Saving a figure as PDF from R means it will look blurry if scaled up on a large poster. ::: ::: {.callout-caution collapse="true"} ## Answers (Only reveal if you have answered the above questions) **1.** The minimum is **24pt** for body text on an A0 poster. The rule of thumb is that text should be readable from approximately 1 metre away. Titles should be 80–100pt, section headings 40–48pt, body text 24–28pt, and figure labels/captions at least 18–20pt. Text smaller than 24pt requires the reader to step forward, disrupting the natural flow of the poster session. **2.** A 72 dpi image will appear **pixelated / blurry** when printed at poster size. At 72 dpi, each pixel is large and visible at print resolution. Fix it by regenerating the figure with `ggsave(..., dpi = 300)` and re-exporting. Alternatively, save as PDF (vector) which is infinitely scalable with no quality loss. Never submit 72 dpi (screen resolution) images for print. **3.** b) `theme_classic()` — removes the grey background and grid lines, producing a clean white background with axis lines only. This is the standard for published biology figures. `theme_grey()` (the default) has a grey background inappropriate for print; `theme_dark()` is unsuitable; `theme_void()` removes all axes which is only appropriate for maps. **4.** A strong figure caption should include: (1) **A title sentence** stating the main finding (not just what the figure shows), (2) **Sample size and key experimental details** (n = X per group, conditions, etc.), (3) **Statistical results** (test name, test statistic, degrees of freedom, and p-value) — enough for the reader to judge the conclusion. Example: "Enzyme activity was significantly higher in the low-dose group. n = 20 per group; one-way ANOVA: F(2,57) = 18.4, p < 0.001; Tukey-Kramer post-hoc results indicated by letters." **5.** False — PDFs saved from R are **vector graphics** — they are mathematically defined curves and shapes with no pixel resolution. Vector images are perfectly sharp at any size, from a business card to a billboard. This is why PDF (or SVG) is the ideal format for poster figures. Raster formats (PNG, JPEG, TIFF) are resolution-dependent and must be saved at 300 dpi or higher for print. ::: ------------------------------------------------------------------------ ## Lab 34 Checklist Before you leave, make sure you can: - [ ] List the required sections of a scientific poster and what each contains - [ ] Apply `theme_classic(base_size = 18)` and appropriate colour palettes for poster figures - [ ] Save a figure at 300 dpi PNG and as PDF using `ggsave()` - [ ] Create a multi-panel figure with the `patchwork` package - [ ] Write a poster title that states the main finding (not just the topic) - [ ] Give structured peer feedback using the rubric - [ ] Deliver a 2-minute verbal summary of your research ::: callout-tip ## Final Poster Checklist (Take This to Your Printing Appointment) Before submitting your poster file for printing: - [ ] All text is at least 24pt - [ ] All figures are 300 dpi or vector (PDF/SVG) - [ ] Axes have units in parentheses - [ ] Statistical results are reported in standard format - [ ] No more than 4 colours in the whole poster - [ ] The title states your main finding - [ ] You have a 2-minute pitch practiced and timed - [ ] Poster dimensions match the required size (check Canvas) ::: ------------------------------------------------------------------------ ## Before the Final Presentation - Review all figure code from Labs 12–13 (ggplot2), 19 (ANOVA), 22 (regression), and whichever labs relate to your project - Ensure your Posit Cloud project is saved and backed up - Prepare questions you can ask the audience to engage them - Check Canvas for the final poster submission deadline and presentation schedule ------------------------------------------------------------------------