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Make your research reproducible – a hands-on course (2024)

Do you know that feeling, when you work on your research project after not having looked at it for a while (for example after receiving reviews) and you can’t remember how the different parts fit together?

And when you finally figure it out again, the results are somehow different. Oh no!

In this course we will set you up to never run into these kinds of issues again.

Let’s get your research project organized, under version control, stable, and published so that you can confidently say that your research is reproducible.

Topics

  • The goal of producing FAIR and reproducible research outputs
  • Working on research projects as a team
  • Get your project organized: organized folders, naming, documentation
  • Set up reproducible workflows: version control with git, stable computing environments, and automation
  • Make your project reproducible and FAIR for all: publication platforms, licensing, and more

Format

This is an online course. Each week you will:

  • watch the 📺weekly videos (~45 min),
  • review one of the 📖 booklet chapters,
  • have a short session with your 👤 accountability buddy* (15-20 minutes), 
  • implement the ✍️ tasks of the week, and
  • discuss your progress in the 👩‍🏫 weekly online meeting with the instructor and fellow course participants (1-1.5 hours).

* You will choose your accountability buddy during the course. You and your buddy will help each other in implementing the tasks of each week.

Weekly Meetings

The course includes 5 Online Meetings, in which you will discuss the week’s contents with the instructor and fellow participants:

Meeting 1: Sep 25, 3:00 – 4:30 pm CEST
Meeting 2: Oct 02, 3:00 – 4:30 pm CEST
Meeting 3: Oct 09, 3:00 – 4:30 pm CEST
Meeting 4: Oct 16, 3:00 – 4:30 pm CEST
Meeting 5: Oct 23, 3:00 – 4:30 pm CEST

Prerequisites

  • Basic programming knowledge (R, python, …)
  • Willingness to learn new technical skills

About the Instructor

Heidi Seibold holds a PhD in Biostatistics with a focus on Machine Learning. Her research has been in the intersection of Data Science, Reproducibility and Medicine. She is a solopreneur in Open and Reproducible Research and an independent researcher at IGDORE. Heidi is the co-founder of the Digital Research Academy and Open Science Freelancers.

Data Challenge: Mobile Phone Data

Mobile phone data are ubiquitous today, as almost everyone carries a mobile device in their pocket. This generates huge data trails that provide almost unbiased information about people’s mobility and location. These data have been used, for example, in the COVID-19 years to assess the impact of non-pharmaceutical interventions on mobility patterns.

The workshop will focus on the analysis of mobile phone data. This includes the pre-processing of massive data, the restructuring of data into mobility data such as flows, or the detection of local clusters, to name just few possible applications.

Under the umbrella of the BERD Academy, the workshop aims to bring together data scientists who work with or on mobile phone data and/or are interested in flow and mobility data to address applied research questions from sociology, economics or related fields. The workshop provides an open forum for current research and new ideas on data analysis.

:spiral_calendar_pad: Nov 12, 2024
:round_pushpin: LMU Munich
⏰Starting Midday

About the Instructor

Göran Kauermann has been a full professor of Statistics at LMU Munich since 2011 and heads the Chair of Statistics for Economics, Business, and Social Sciences there. Additionally, he is the chairman of the German Data Science Society (GDS). His research interests focus on semi- and nonparametric analysis, generalized linear and mixed models, and network data analysis.