Examples¶

Real-world examples to learn from and use as references for your scientific manuscripts.

Official Example Manuscript¶

The Rxiv-Maker Paper¶

The best way to learn Rxiv-Maker is to explore the official example: the manuscript describing Rxiv-Maker itself.

Quick Start

Clone the example with one command

rxiv get-rxiv-preprint
cd manuscript-rxiv-maker/MANUSCRIPT
rxiv pdf

View on GitHub

Published Version

See the final PDF output

arXiv:2508.00836

Example Gallery¶

Basic Examples¶

Simple Article¶

# 00_CONFIG.yml
title: "My First Scientific Paper"
authors:
  - name: "Your Name"
    affiliation: "1"

affiliations:
  - id: "1"
    name: "Your Institution"

# 01_MAIN.md

## Abstract
This is a simple example demonstrating basic Rxiv-Maker features.

## Introduction
Scientific writing doesn't have to be complicated [@smith2023].

## Methods
We analyzed data using Python scripts (Figure @fig:results).

![Analysis results](FIGURES/simple_plot.py)
{#fig:results}

## Conclusion
Rxiv-Maker simplifies manuscript preparation.

## References

Static Figure Example¶

![System architecture diagram](FIGURES/architecture.png)
{#fig:architecture width="0.8\\linewidth"}

As shown in Figure @fig:architecture, the system has three components.

Intermediate Examples¶

Python Figure with Data¶

# FIGURES/experiment_results.py
import matplotlib.pyplot as plt
import pandas as pd

# Load experimental data
df = pd.read_csv("DATA/experiment.csv")

# Create publication-quality figure
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

# Plot 1: Time series
ax1.plot(df['time'], df['signal'], 'b-', linewidth=2)
ax1.set_xlabel('Time (s)')
ax1.set_ylabel('Signal (a.u.)')
ax1.set_title('Time Series Analysis')
ax1.grid(True, alpha=0.3)

# Plot 2: Distribution
ax2.hist(df['signal'], bins=30, alpha=0.7, color='green', edgecolor='black')
ax2.set_xlabel('Signal Intensity')
ax2.set_ylabel('Frequency')
ax2.set_title('Signal Distribution')

plt.tight_layout()
plt.show()

R Figure with ggplot2¶

# FIGURES/statistical_analysis.R
library(ggplot2)
library(dplyr)

# Load and process data
data <- read.csv("DATA/experiment.csv") %>%
  mutate(group = as.factor(group))

# Create publication-quality plot
p <- ggplot(data, aes(x = treatment, y = response, fill = group)) +
  geom_boxplot(alpha = 0.7) +
  geom_jitter(width = 0.2, alpha = 0.5) +
  theme_minimal() +
  theme(
    text = element_text(size = 12),
    plot.title = element_text(hjust = 0.5, face = "bold")
  ) +
  labs(
    title = "Treatment Response by Group",
    x = "Treatment",
    y = "Response (μM)",
    fill = "Group"
  ) +
  scale_fill_brewer(palette = "Set2")

# Save high-resolution figure
ggsave("statistical_analysis.png", p,
       width = 10, height = 6, dpi = 300)

Advanced Examples¶

Multi-Panel Figure¶

![**Comprehensive Analysis.** **(A)** Time series showing signal evolution.
**(B)** Frequency distribution of signals. **(C)** Correlation matrix between
experimental variables. **(D)** Principal component analysis revealing distinct
clusters. All panels use data from the same experimental run (n=1000 measurements).](FIGURES/multi_panel_figure.py)
{#fig:comprehensive width="\\textwidth" tex_position="t"}

Dynamic Statistical Reporting¶

{{py:exec
import pandas as pd
import numpy as np
from scipy import stats

# Load experimental data
df = pd.read_csv("DATA/experiment.csv")

# Calculate statistics
control_mean = df[df['group']=='control']['value'].mean()
treatment_mean = df[df['group']=='treatment']['value'].mean()
t_stat, p_value = stats.ttest_ind(
    df[df['group']=='control']['value'],
    df[df['group']=='treatment']['value']
)
effect_size = (treatment_mean - control_mean) / df['value'].std()
}}

Our analysis of {{py:get len(df)}} samples revealed a significant difference
between control (M={{py:get control_mean:.2f}}) and treatment
(M={{py:get treatment_mean:.2f}}) groups (t={{py:get t_stat:.2f}},
p={{py:get p_value:.4f}}, Cohen's d={{py:get effect_size:.2f}}).

Example Use Cases¶

Computational Biology¶

## Results

We analyzed {{py:get num_cells}} single cells using our pipeline
(Figure @fig:pipeline). The analysis revealed {{py:get num_clusters}}
distinct cell populations with expression patterns shown in Figure @fig:heatmap.

![Analysis pipeline](FIGURES/pipeline_diagram.py)
{#fig:pipeline}

![Expression heatmap](FIGURES/expression_heatmap.py)
{#fig:heatmap}

Physics and Engineering¶

## Experimental Results

The resonance frequency was measured as {{py:get freq_resonance:.3f}} GHz
with Q-factor of {{py:get q_factor:.1f}} (Figure @fig:spectrum).

$$
Q = \frac{f_0}{\Delta f}
$$
{#eq:q_factor}

where $f_0$ is the resonance frequency and $\Delta f$ is the bandwidth.

Data Science and Machine Learning¶

## Model Performance

The trained model achieved {{py:get accuracy:.2%}} accuracy on the test set
(n={{py:get test_size}} samples). Confusion matrix and ROC curves are shown
in Figure @fig:performance.

![Model performance metrics](FIGURES/model_performance.py)
{#fig:performance width="\\textwidth"}

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