Projects submitted to the Data Journalism Awards competition

Right here you will find a list of all the projects submitted to the Data Journalism Awards competition.  

Individual portfolio: Timo Grossenbacher
Country: Switzerland
Organisation: SRF Data
Best individual portfolio
Artificial intelligenceInteractiveCrowdsourcingMapsVisualisationPersonalisationEnvironment
Team Members
Project Description
I\'ve been working in the field of data journalism since early 2014, when I finished my studies of Geography. Since then, I have become a jack of all trades: From the uncovering of hidden data sources over tedious preprocessing and analysis in R to frontend development with Javascript, writing articles and giving radio statements – all of that is part of my daily business. In 2016 and 2017, I have co-published over 30 stories for SRF Data, the Swiss Public Broadcast\'s data journalism outlet. These stories range from fast scoops over data investigations to interactive explainers. The stories I\'d like to include as my personal portfolio are the following seven, by order of publication date (youngest first): "Switzerland\'s dual use exports" / "Identifying a large number of fake followers on Instagram" / "Pendlerland: Swiss commuting patterns revealed" / "Mapping urban sprawl in Switzerland" / "Here’s how 670’000 people speak German" / "Vested interests of Swiss universities" / "A template for bootstrapping reproducible RMarkdown documents for data journalistic purposes". See the description below and in the respective links.
What makes this project innovative?
The seven portfolio projects cover a wide range of topics and approaches: From making Swiss arms trade deals better visible ("Switzerland\'s dual use exports") over investigating the intransparent links of Swiss universities to the private sector ("Vested interests of Swiss universities") over applying machine learning to predict whether an Instagram account is fake or not ("Identifying a large number of fake followers on Instagram") to producing maps showing the linguo-cultural diversity of German-speaking Europe ("Here’s how 670’000 people speak German"). What these projects have in common is that I was responsible for the whole data journalism process: From collecting data to preprocessing it in R to visualizing it with Javascript. Also, most of the code and data used for these projects was later published on GitHub for others to be re-used, re-mixed and re-published (see for details). The last project I\'d like to submit is not a journalistic publication but a software template empowering data journalists to work with the R software. It has a special focus on reproducibility and I came up with it because I saw that many scripts published by data journalists are indeed not really reproducible. My template fixes this.
What was the impact of your project? How did you measure it?
The impact of these projects differs widely. Probably the biggest impact had our publication on vested interests of Swiss universities, which led to a transparency initiative among several universities and a broad media uptake.
Source and methodology
In most of the published projects, I follow the principles of a transparent and reproducible methodology. The details for the respective methodologies are always linked to in the articles, more often than not they lead to, where I publish the R code behind my stories.
Technologies Used
I work with the R software on an almost daily basis and try to give away my learnings and best practices to other data journalists, 1) via published and reproducible scripts (, 2) blog posts (, 3) my reproducible R template ( In terms of frontend development, I normally use ReactJS together with a range of third party Javascript libraries.