In a special guest post Anders Eriksen from the #bord4editorial development and data journalism team at Norwegian news website Bergens Tidende talks about how they manage large data projects.
Do you really know how you ended up with those results after analyzing the data from Public Source?
Well, often we did not. This is what we knew:
We had downloaded some data in Excel format.
We did some magic cleaning of the data in Excel.
We did some manual alterations of wrong or wrongly formatted data.
We sorted, grouped, pivoted, and eureka! We had a story!
Then we got a new and updated batch of the same data. Or the editor wanted to check how we ended up with those numbers, that story.
…And so the problems start to appear.
How could we do the exact same analysis over and over again on different batches of data?
And how could we explain to curious readers and editors exactly how we ended up with those numbers or that graph?
We needed a way to structure our data analysis and make it traceable, reusable and documented. This post will show you how. We will not teach you how to code, but maybe inspire you to learn that in the process. Continue reading →
Using a combination of contact-led information and FOI requests, they uncovered the extent of the ambitions to dig deep into Scottish soil.
It was part of a steady flow of fracking stories from the Ferret team, ensuring those involved in making decisions were in no doubt of their responsibilities and recognised that every step would be scrutinised. Continue reading →
In a guest post for OJB, Steve Carufel interviews Dutch data journalist Thomas de Beusabout visualisation, storytelling — and useful new tools for data journalists.
Data journalism is, among other things, the art of resisting the temptation to show spectacular visualisations that fail to highlight the data behind a story.
Insights and relevant statistics can get lost in visual translation, so Thomas de Beus’ Colourful Facts is a great place to start thinking more about clarity and your audience — and less about spectacular graphic design (although you do not want to forego attractiveness entirely). Continue reading →
As the first group of MA Data Journalism students prepare to start their course this month, I’ve been recommending a number of email newsletters in the field that they should be following — and I thought I should share it here too.
Here, then, are 9 email newsletters about data — if I’ve missed any please let me know. Continue reading →
Given that in two weeks I’ll be doing exactly the opposite (my first intake of MA students begin a new module in Narrative at the end of the month) I thought I should add my own reaction. Continue reading →
In this second extract from a forthcoming book chapter I look at the role that computational thinking is likely to play in the next wave of data journalism — and the need to problematise that. You can read the first part of this series here.
Computational thinking is the process of logical problem solving that allows us to break down challenges into manageable chunks. It is ‘computational’ not only because it is logical in the same way that a computer is, but also because this allows us to turn to computer power to solve it.
“To reading, writing, and arithmetic, we should add computational thinking to every child’s analytical ability. Just as the printing press facilitated the spread of the three Rs, what is appropriately incestuous about this vision is that computing and computers facilitate the spread of computational thinking.”
This process is at the heart of a data journalist’s work: it is what allows the data journalist to solve the problems that make up so much of modern journalism, and to be able to do so with the speed and accuracy that news processes demand. Continue reading →