Reproducible Research in Practice

Scientific writing is more than just putting words on paper. It is about presenting your data, methods, and results in a clear, transparent, and reproducible way. In this course, you’ll learn how to streamline your research workflow by integrating statistical analysis, narrative writing, and figure generation into one coherent process.

We focus on replacing the traditional fragmented and error-prone workflow (storing data in e.g. Excel, creating figures in Excel/R/SPSS/etc, writing in Word and endlessly copy-pasting everything together) with a modern, reproducible approach using Quarto and R/RStudio.

Quarto allows you to combine the writing of your manuscript and your statistical analysis into a single dynamic document that can be rendered into HTML, PDF, or Word. The result: more efficient writing, fewer mistakes, and documents that you (and others) can update and re-run at any time. Adding or removing data no longer means starting over again.

Learning outcomes

You will learn how to:

    Integrate data analysis, results, and your manuscript text using Quarto

    Render your work into publishable formats (PDF, HTML, Word)

    Reuse or adapt content and figures without copy-paste chaos

    Manage references and citations using a bibliography file

    Create well-formatted tables and mathematical equations

    Combine multiple manuscripts into a single document, such as a PhD thesis

    Build workflows that your future self will still understand

Who is this course for?

This course is designed for researchers, PhD candidates, and teachers who:

    write scientific reports, papers, or teaching materials

    use R for data analysis (basic knowledge is required)

    want to improve the reproducibility, transparency, and reusability of their work

Requirements

Familiarity with R and basic statistical concepts is expected. For example, participants are recommended to have completed the VLAG course “Introduction to R”, and either “Applied Statistics”, “Chemometrics”, or an equivalent course.

Lecturer

Dr Jos Hageman, Mathematical and statistical methods - Biometris

Date and venue:

To be announced

Study load:

The study load of this course is 0.7 ECTS credits.

Registration & Costs:

PhD candidates affiliated with VLAG/WUR *	175 €
All other PhD candidates	350 €
Postdoc / staff from VLAG	350 €
Postdoc / non-profit staff not affiliated with VLAG	525 €
Industry / Non academics	800 €

Costs includes material, tea/coffee and lunches.
* VLAG/EPS/PE&RC/WASS/WIAS/WIMEK PhD candidates with an approved TSP.

Cancellation conditions: see External knowledge - VLAG Cancellation Conditions