Project description

I am a 23 year old freelance data journalist working at Deutsche Welle, Germany’s international public broadcaster. In the past, I have worked on data-driven projects with the regional networks rbb and BR as well as the Guardian, the London Times and others.

At DW, our data team aims to create projects that bring to light new information on relevant issues. This can apply to any topic: Even before working at Deutsche Welle, I have published stories on politics (e.g. analyzing the social network of the German far-right group Pegida or comparing minimum wages in Europe), media (e.g. illustrating the languages of the Eurovision Song Contest or exploring Hollywood stereotypes about ethnic groups), environmental issues (e.g. an interactive piece on the return of the wolf) and more. I started publishing data-driven stories during my studies at TU Dortmund, comparing right-wing parties throughout Europe for our campus newspaper (see additional links).

Starting during my journalism studies a few years back, I’ve also been part of Journocode. Then a study group teaching ourselves and fellow students about data-driven work, we are now a 7-person data journalism initiative dedicated to helping journalists work with data. We organize workshops and publish tutorials and resources online. Through Journocode, I have worked with a number of different newsrooms and journalism networks in Germany and beyond. Last November, for example, we got the chance to work with DW Academy and the West Africa Media Foundation to offer data journalism trainings to journalists in Ghana.

From studying physics to working as an art mediator, I have found that there are stories worth being told in every field – and data-driven reporting remains an underrated way to contribute new perspectives to the conversation. I am glad to be able to not only work on data-driven stories myself, but to also help other journalists do the same.

What makes this project innovative?

The way we work at DW, every person in our team is a generalist, or what we would call an “Eierlegende Wollmilchsau” in German: We work on our stories from start to finish, sometimes in collaboration with partners from the data team or another editorial department. As a result, I have skills in every part of the data journalism workflow. Working at DW also gives me the chance to work on a broad range of international topics, covering issues relevant to many different groups of readers. Our data stories often get adapted into some of the 30 languages DW offers content in. It is important for me to be able to combine my journalistic work at DW with the educational, collaborative approach we employ at Journocode. I feel fortunate to be part of a young generation of journalists that are passionate about working with data and willing to teach themselves and each other the skills they need to do it. I myself benefited greatly from this attitude of knowledge-sharing and am happy to pass on the favour. Especially among young data journalists, the share of women in our field continues to grow. Working in two female-majority teams at DW Data and Journocode, and offering data journalism workshops for women through Journocode, I hope to be able to support my fellow female data journalists as best I can.

What was the impact of your project? How did you measure it?

At DW Data, we mainly focus on average reading time to get a sense of how well our audience engages with a story. It is usually well above the DW-wide level in general, but at more than 5 minutes, the most recent story I published there had an above-average reading time even for our data projects. With Journocode, the fact that we get more and more requests for workshops and resources from newsrooms as well as individual journalists encourages us to keep going, even while most of us work other full-time jobs.

Source and methodology

I work on my stories from idea via research and analysis to text and visualization, instead of handing parts of the work off to specialists. This means that I have skills in every part of the data journalism workflow. When Open Data is not available, I have often used web scraping to gather the data I needed for a story. My background in statistics has also come in handy for past projects. It helps me check the validity of routine analyses as well as employ more complex methods to gain new insight. Transparency in journalism is an issue close to my heart. At DW data, we publish the methodology, as well as the data and code behind our projects, on our GitHub page to give audiences, as well as fellow journalists, a chance to check our work and build on it.

Technologies Used

I mainly use R for data gathering (e.g. scraping), cleaning and analysis, but also know some basic Python, and use Open Refine and Spreadsheet software as well. I have some web development skills (HTML, CSS, JavaScript, with some useful visualization libraries) and create visualizations with Illustrator as well as a variety of programming and non-programming tools.

Project members

Kira Schacht


Additional links


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