Dataharvest 2026 - the European Investigative Journalism Conference

Reporting on transgender people can be a minefield: official data is scarce, public understanding is patchy, and with their rights increasingly contested by politicians and commentators, the community itself is often wary of journalists.

Drawing on Investigate Europe’s project The Cruel Condition -published with Arte, New Lines, Taz, and others- this session explores how laws across Europe pushed trans people toward sterilising surgeries over decades. It will outline how we compiled first-of-its-kind data to produce an unprecedented cross-border estimate, and share practical insights on building trust with trans sources.

We will also look at other investigations using data to cover LGBT issues (e.g., on money flows to conservative groups), a topic quite often overlooked by data journalists, to consider possible pathways for future investigations amid an intensifying backlash against LGBT rights in many parts of the world. We will look at other examples and invite you to discuss how to be creative with unusual, and sometimes even non-existent, datasets.

Large language models can do more than generate text – they can help clean and structure messy data files as well as enrich datasets. As LLMs increasingly become a useful tool for data journalists, the Ellmer package is a useful resource for R users to easily work with LLMs. The Guardian data team has used the Ellmer R package to clean and organise thousands of emails from the Epstein files, to investigate private equity firms in the United Kingdom, and to classify recipients of climate finance.

Using some of these examples, attendees will learn when this package can be the perfect tool for your investigation, which are the good practices when using LLMs, how to connect to an API of an LLM, how to write an efficient prompt, how to submit the prompts in bulk using the batch function for structured data and how to evaluate your results and iterate for improvements.

This is an advanced R session and we will assume that attendees have some prior knowledge of R.

Learn how the investigative team at the Danish Altinget used scraped data from inspection reports to map structural inequality in Copenhagen’s nurseries and kindergartens, and how the method can be applied to other local areas and welfare institutions.

We scraped data from +350 inspection reports and created our own color coded ranking system to investigate the quality of daycare centers across Copenhagen.

Our investigation found that 14 daycare centers have serious problems, 3 had illegal practice and most of the problematic institutions located in a very small area. It lead to one daycare center being closed down (with the funds transferred somewhere better), an extensive political debate in the local elections and in the local government of Copenhagen and the most selling story in Altinget Privat’s history.

In this session we will go through the hands-on methods behind our investigation, and discuss how it can be applied to other countries, local areas or institutions.

We will focus on scraping data from websites and PDF's with a minimum of coding, of structuring data found in a large amount of documents and of maximizing impact of your investigation. Also, our talk will feature a warrior princess, a shield with a unicorn and a flaming sword!

Looking forward to seeing you.