No more “works on my machine”! - Tools and Practices for Reproducible Research

This is a past event

202 people went


Target audience: Data analysts, Data Scientists, BI experts, Algorithms Developers, Algorithms Engineers


F5’s Tel-Aviv R&D center: 30th floor, building #8, Kiryat Atidim, Tel-Aviv (


1. 17:00-17:30: Arrival and mingling

2. 17:30-17:45: Shlomo Yona ( Introduction and overview: What’s Reproducible Research, motivation and scope

3. 17:50-18:50: Shlomi Lifshits ( Reproducible Research in R [Rstudio, RMarkdown, Knitr, …]

4. 19:00-20:00: Maydan Wienreb ( Reproducible Research in Python [Anaconda, IPython, …]

5. 20:10-21:10: Eliran Bivas ( Reproducible machines and setups [VirtualBox, Vagrant, Docker, …]

We suggest to use Kahoot! ( your smartphones in order to engage during sessions (will be fun!)


You will learn how to document our datasets, code, thoughts, attempts and results (intermediate and final) such that we have clear research documentation (as a research notebook, for example) as well as means to reproduce the research in full with a click of a button. To further allow reproducibility, we will also show how you can box your environment such that it can be reconstructed elsewhere so you won’t suffer from the “works on my machine syndrome”.

We will show concepts and tools behind reporting modern data analyses in a reproducible manner. This meetup will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results.

What is reproducible research?

Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available.