Getting Started¶

Welcome to Rxiv-Maker! This guide will help you get up and running with professional scientific manuscript preparation in minutes.

What is Rxiv-Maker?¶

Rxiv-Maker transforms scientific preprint writing by converting enhanced Markdown into professional PDFs with automated figure generation, citation management, and LaTeX typesetting - no LaTeX knowledge required.

🎯 Perfect For¶

• Researchers writing preprints for arXiv, bioRxiv, or other preprint servers
• Teams collaborating on preprint manuscripts with version control
• Scientists creating reproducible preprints with live data integration

✨ Key Benefits¶

Quick Start Path¶

Choose your journey based on your experience level:

Installation Preview¶

# Universal installation (works everywhere)
pipx install rxiv-maker

# Create your first manuscript
rxiv init my-paper
cd my-paper

# Generate PDF
rxiv pdf

That's it! You now have a publication-ready PDF.

What You'll Learn¶

This getting started section covers:

1. Installation - Set up Rxiv-Maker on your system
2. First Manuscript - Create your first PDF in minutes
3. Quick Reference - Essential commands and syntax

System Requirements¶

Minimum Requirements: - Python 3.10 or later - 1 GB free disk space - LaTeX distribution (automatically guided installation)

Supported Platforms: - ✅ Linux (Ubuntu, Debian, CentOS, Fedora) - ✅ macOS (Intel and Apple Silicon) - ✅ Windows (Native and WSL2)

Optional Enhancements: - VS Code with Rxiv-Maker extension for enhanced editing - Git for version control and collaboration - Python scientific stack for advanced data analysis

Common Use Cases¶

Research Papers¶

# My Research Paper

## Abstract
This study investigates...

{{py:exec
import pandas as pd
df = pd.read_csv("FIGURES/DATA/results.csv")
sample_size = len(df)
}}

We analyzed {{py:get sample_size}} samples and found...

![Results](FIGURES/generate_plot.py)
{#fig:results}

As shown in @fig:results, our method outperforms [@baseline2023].

Computational Preprints¶

# Machine Learning Model Performance Analysis

{{py:exec
# Live data analysis for scientific results
import pandas as pd
import numpy as np
from sklearn.metrics import accuracy_score
results_data = pd.read_csv("FIGURES/DATA/model_results.csv")
mean_accuracy = np.mean(results_data['accuracy'])
}}

Our model achieved {{py:get mean_accuracy:.1%}} accuracy on the test set.

Physics Preprints¶

# Novel Quantum Computing Approach

## Experimental Setup

The experiment was conducted at {{tex: \SI{273.15}{\kelvin}}}
with precision of {{tex: \SI{+-0.1}{\kelvin}}}.

Results show quantum coherence at {{tex: \SI{10}{\nano\second}}}.

Next Steps¶

Ready to start? Choose your path:

New to Rxiv-Maker? → Installation Guide

Want to see it in action first? → First Manuscript

Prefer to browse examples? → Examples Gallery

Need help with a specific feature? → User Guides