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Analyse your Data

chartFor your research to be transparent and reproducible, a vital part is to provide data cleaning instructions and analysis code (ideally by using nonproprietary software). See the PRO Initiative's basic guidelines for making your analyses public for a few information. This collection of resources aims at providing some helpful links to facilitate and improving your analysis sharing practices.


Analysing your data with free software such as R enhances reproducibility without the limitations of proprietary software. A common way to use R is by writing analysis code and graphics code in the RStudio interface. It further implements R Markdown, with can be used to create documents, reports, and presentations that are fully reproducible.

Here are some helpful links for researchers who want to learn R:

Reproducible Research

The PRO Initiative gives a few basic guidelines for authors on how to facilitate reproducibility when sharing your analyses:

For a hands-on example of reproducible research, Lars Vilhuber created a Replication Tutorial in which he generates a fully reproducible example. More specific collections of useful advice on this topic can be found in the following sources:



"Works on my Machine" Error

In his paper (available here), Nicholas Eubank makes the case for increasing reproducibility by testing files on a different computer. Testing or even sharing code via cloud-based platforms prevents deficits in reproducibility that occur when code runs on the researcher's local platform but not on others'. Avoid the so called WOMME by using tools like:

Workflow Documentation

  • Rouder, Haaf, and Snyder (2018) wrote a helpful tutorial on how to organize a lab in the face of Open Science practices:
  • R Markdown: Create fully reproducible documents that combine code execution and documentation. A big advantage is the variety of output format: documents (e.g. Word, PDF, interactive R notebook, HTML), presentation slides, shiny apps, websites and more. Multiple languages including R, python, and SQL can be used.
  • GitHub serves as data repository and active research workflow tool. Tracking of contributions of others enables version control on your files. This tool is especially useful for a research team that collaborates on developing code.
  • As one of multiple features, Open Science Framework's version control system and its live-editing mode facilitate collaboration within the research team.



The Jupyter Notebook is an open source web-application for interactive computing. Virtual notebooks support over 40 programming languages, can be shared, collaboratively edited and can return interactive output. Uses vary from data cleaning, transformation and visualization to machine learning, statistical modeling and more.

A tutorial on the Jupyter Notebook by Karlijn Willems is a first place-to-go for trying out the Jupyter Notebook. Also, help can be seeked at the Jupyter community (e.g. on GitHub or StackOverflow)

Responsible for content: Lutz Heil