I am a coder-journalist who has left the newsroom behind in order to focus on different practices of data analysis and visualisation.
Update Nov. 2020: I am looking for freelance/contract work in February 2021 and onwards!
+49 172 253 9671 (Signal/Telegram)
@basilesimon on some social platforms
My journalism career started in the UK, where I worked for large broadcast, newspaper, and agency organisations.
I also co-founded Airwars, a non-profit monitoring organisation exposing the harm done to civilians by air conflicts.
Long-term monitoring of Iran's nuclear commitments, including their often misunderstood space race efforts. Automation of the parsing of the nuclear agency's reports.
Collaboration with the Data team to bring this multi-week exclusive analysis of police data to life in just over two days.
Quick turnaround (one afternoon) of a summary ahead of an extraordinary parliamentary session, building on visualisation templates we established.
An election results analysis of socio-economic factors that may have swung the Italian vote in 2018. Modelled in R, prepared ahead of time, and published the following day, on deadline.
Since the beginning of western airstrikes against Isis, Airwars.org has held militaries across the world to account for the harm they have caused to civilians. This work was made possible through the creation of a dataset collating military reports and OSINT investigations into claims of civilian harm. This project and stories have spanned years and led to meaningful change, not only raising the bar for public accountability of their governments but holding those in power in check.
During my time at Reuters we published several pieces about the unraveling of the nuclear deal between Iran and the so-called P5+1. I created a dataset that tracked the progress of Iran’s commitments to the deal in terms of its efforts to enrich Uranium which underpinned much of the reporting.
I’ve covered the (never-ending) Brexit saga for a number of years and devised bespoke maps of the British political spectrum on the topic. This has involved manipulating the raw voting records and feeding the results to a simple machine learning clustering algorithm.
For several newsrooms I have covered (more than enough) elections. They are the bread and butter of news nerds’ work and involve standing up both a back end and front end which picks up results as they come in and redraws charts and analysis live (as well as without forcing the reader to refresh).
Think of big dashboards of live data, for lack of a better image.
I am also familiar with building projects which involve readers directly engaging with content, like our survey of knowledge of modern slavery in Britain, our analysis of changing attitudes to divorce, or our 2017 Budget calculator.
Fortunately I was able to open-source some of the work I have done:
I had the privilege of teaching the Advanced Data and Coding module on the MA in Interactive Journalism course at City University. We ran through an introduction to programming, statistical methods, R data analysis and modelling with ggplot.
All my teaching notes are available on Github.
I have done some studying myself:
Kantar Information is Beautiful 2019, long list for: