Dataharvest 2026 - the European Investigative Journalism Conference

The European Union promises openness in its decision-making. But, despite this commitment, the European Commission and the Member States recently blocked transparency around key decisions on public spending and legislative proposals. Moreover, the Commission tightened its internal rules to limit freedom of information. These obstacles to transparency have hampered reporting on how the EU spent billions of euros of its post-COVID recovery fund and military aid for Ukraine.

The official veil of secrecy also restricts investigations into topics such as the EU's fight against Big Tech, the rollback of environmental laws, or human rights violations at the EU's external borders.

How can journalists push back against this reduction in transparency? This panel will discuss the mounting challenges for journalists covering the European Union, and how to fight back against the rising tide of opaqueness.

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.

Do you need help drafting an FOI request, writing a complaint about your FOI request being rejected, or understanding EU legislation? Are you looking for specific EU databases, experts, and sources? Come to the "FOI Cafe" for advice!

Please note: you need to book a spot in advance. You can do it HERE.

Have you ever come across a striking data visual and wondered: "How the hell did they make that?" Your follow-up question may be: "Can I achieve that too with the resources I have?" In this session, we start with a deep dive into the (technical) production of two data visuals. Then, for the third visual, it's your turn to figure out how it might have been made. Finally, we launch our online inventory and subjective guide of data visualization tools - a work in progress you can contribute to!

Heads up: we won't teach any tool in depth, but we'll highlight a range of options so you can find an approach or tool that best fits your needs and constraints (e.g., limited budget, small team, learning curve…).

For more than eight years, we have been covering the housing crisis – locally and in a European-wide comparison. We investigated big corporate landlords, calculated rent affordability for key workers, explained how real estate speculation works, and investigated the complicated relationship between the climate crisis, energy costs, and housing unaffordability. We collected numerous datasets, interviewed scientists, politicians, and investors, and found ways to translate the data and abstract financial schemes into engaging and compelling stories for readers.

Now we want to share this knowledge with other journalists.

Come to our hands-on session where we will show you the most interesting datasets and modes of visualisations you can use for your own local reporting. We will explain different data-driven ways and methodologies for approaching the housing issue, which can be replicated in any European city, discuss different themes within the housing topic and what pitfalls to avoid, as well as what issues the current reporting is lacking and could be dug deeper.

Do you need help drafting an FOI request, writing a complaint about your FOI request being rejected, or understanding EU legislation? Are you looking for specific EU databases, experts, and sources? Come to the "Eu Cafe" for advice!

Please note: you need to book a spot in advance. You can do it HERE.

You don't necessarily have to be a data journalist to report on climate change using data. As data journalists, we will explain options available to non-coders. This session is intended to give you a feeling for what you can do yourself with temperature measurement data, emissions figures, and tables showing the damage caused by extreme weather events, and to overcome your fear that working with data is “too complicated.”

In the session, we will discuss existing databases, some of which already have built-in filtering and visualization options. Where they don't, we will explain what the first steps might look like if you can't or don't want to code yourself. We also will address the questions that are important for assessing the quality of a data set.

This session is explicitly not aimed at data journalists, but at climate journalists who have little or no experience with data but want to know what is possible and what they can do themselves.

Learn how to build reusable report templates in Quarto that generate multiple outputs (PDF, HTML, Word documents) from a single source document. By defining parameters — such as a region, time period, or data source — you can produce dozens or even hundreds of tailored reports without duplicating code or copy-pasting results.

This is especially useful for cross-border investigations, where partners share a common dataset, but each team needs a report focused on its own country. Build the analysis once, then render a customized version for each partner with only their slice of the data.

To follow along, participants should have basic familiarity with Quarto, R Markdown, or Jupyter notebooks, and some experience writing code in R or Python.

Data journalism has always relied on clean, structured data; but cleaning messy datasets remains one of the most time-consuming parts of the workflow. Enter OpenRefine, our old buddy for data wrangling, now enhanced by Large Language Models (LLMs).

In this 20-minute session, we explore how combining OpenRefine’s powerful transformation capabilities with modern AI unlocks new possibilities for journalists. Using the open-source LLM extension for OpenRefine, we’ll demonstrate practical workflows for:
- Automated Enrichment: Extracting entities, categorizing content, and enriching records using natural language prompts.
- Smart Disambiguation: Resolving inconsistencies and matching fuzzy data with AI-assisted reconciliation.
- Rapid Prototyping: Turning raw, unstructured text into structured datasets ready for investigation

Why This Matters Now: Journalists are increasingly working with large, messy datasets, from leaked documents to public records.

While LLMs offer powerful analysis, they often lack precision on structured data. OpenRefine provides that precision. Together, they create a workflow that is both scalable and auditable; critical for investigative reporting where accuracy is non-negotiable.

What Attendees Will Take Away:
- A clear understanding of how to integrate LLMs into existing OpenRefine workflows.
- Practical examples relevant to journalistic investigations (entity extraction, classification, enrichment).

To attend this session, participants should have experience with data cleaning