Rui Barros – Público

Country/area: Portugal

Organisation: Público

Organisation size: Big

Cover letter:

My name is Rui Barros, a 26 years old data journalist/journocoder based in Portugal currently working at Público. 2020 was a weird year for everyone, but it was a year of change for me. After four years at Rádio Renascença, where I bootstrapped their data practice and team, I decided to accept the offer to work at Público – one of the biggest newspapers in the country. A hard decision to make, because I was leaving all the work I had built there and starting everything from scratch. And then covid-19 came.

Hired to start up, just by myself, a data journalism practice on the most read online newspaper in Portugal was quite the challenge, especially in a country where you can count with one hand how many data journalists there are. Most colleagues don’t really gasp what you really do and creating an understanding of the role in a new newsroom seemed impossible to achieve in only one year. But covid-19 helped to bridge the gap. There were numbers, so many numbers to report on. And mostly importantly, we needed to add context to those numbers.

That’s where my love for content personalization came in. I knew that I could not only help report on those numbers, but also offer the reader tools that provided personalized information about those numbers, while allowing them to further explore the data without any statistical knowledge. During 2020, I developed several of those news apps: related to covid-19 numbers by municipality, to measure the levels of confinement or allow people to check how the government was spending public money to fight the pandemic.

Even though developing news applications is one of the things that makes me love this job, I didn’t stop there. I worked on two international investigations, with european publishers, and produced several news pieces that allowed the public to understand how the portuguese NHS was coping with the pandemic. I  also worked in some education related pieces, since it was one of the areas that was most affected by the pandemic.

When I was not working on such big projects, I collaborated with specialized journalists of the newsroom – who lacked data knowledge but compensated with their field knowledge when it was the time to contextualize the numbers.

Sigma Awards provides me with an opportunity to look back and reflect on my work of the previous year. Working all alone and doing everything from data analysis to coding the interactive feature surely is hard. And even though this year I was able to further develop my skillset (working for the first time with Machine Learning, for example), that’s not what matters: I truly believe I was able to contribute a bit more to a more informed and clarified discussion on the range of topics I worked on.

I believe the projects showcased below are representative of the quality of my efforts and work, making me eligible for this award.

Description of portfolio:

These projects are some of the works that I’m most proud of and that I believe hilight what my data journalism skills can do. In all of them, I was the first responsible for them, doing everything from the data collection, analysis, interactive features and interactive data visualization.

[Project link 1]

It was my first week in the newsroom when the health reporter said that a source told her that the NHS health emergency line was “chaotic”. I quickly added: wait, there’s public data on that, giving my first front page headline. Colleagues who didn’t really understood what a data journalist could do got a glimpse of what I could add to the newsroom.

[Project link 2]

Using covid data and contextual information about the municipalities, I built a news application that generated a personalized news story about every one of the 308 municipalities in Portugal. Everyone was reporting on the national numbers or just talking about the outliers. But I felt that people wanted to know a bit more about where they actually lived.

[Project link 3]

A national lockdown was something new for everyone. Everyone felt that the country was changing. But what exactly was changing? We turned to data to figure it out. We ended up combing about 20 different data sources that showed us expectable things – such as the fact that pollution levels dropped. But also some really surprising stuff – like the fact that people seemed to be listening to happier songs.

[Project link 4]

A collaborative effort between Público and other 15 european news organizations, coordinated by OCCRP, found out that the european market was being flooded with fake and faulty FFP masks. Using a data-driven approach, I found out that Portugal had bought at least 627 thousands of FFP masks that had fake or dubious certification.

[Project link 5]

Using the data from the national exams and the percentage of poor students by municipality, I calculated the euclidean distance to an hypothetical place where all students were poor and yet had 20 out 20 on their national exams. That led us to four municipalities that contradicted the trend of a poorer environment meaning worst marks.

[Project link 6]

At the start of the school year, and amid the covid-19 pandemic, the portuguese government ruled that every coffee shop 300 meters from any school would have a limit of four people per table. But how many businesses were affected by this rule? No one seemed to know – not even the government. I turned into to the Google Maps API to get an answer.

[Project link 7]

With the covid-19 pandemic, governments across the world increased their spending on several medical equipment and services to fight the spread of the vírus. Portugal was no exception. But how much was spent? And which companies profited the most with the pandemic? No one seemed to know. Using the Portuguese Public tenders database and Machine Learning, we were able to provide some answers.

[Project link 8]

The problem with covid-19 data by county is that it gives the impression that the risk gets higher or lower everytime you change from one county to another. To tackle this issue, a team from a Portuguese university divided the country into a 2km X 2km grid and calculated the risk associated with that area and the uncertainty of that forecast. After talking with some members of this team, I developed an interactive map that allowed people to check the risk of getting the virus where they lived.

[Project link 9]

Portuguese started listening to Christmas songs 11 days earlier.

Project links: