Dataharvest 2026 - the European Investigative Journalism Conference

A separate ticket is required to attend this masterclass. If you already have a conference ticket and would like to attend but haven't yet purchased a masterclass ticket, please contact us at [email protected]

Heat waves in Europe are increasing in frequency and intensity. People and economies are under pressure: extreme heat is costly for agriculture and deadly for people. At the same time, floods are among the most frequent and damaging natural disasters in Europe – yet understanding their true impact remains difficult.

In this session, participants will learn the skills necessary to make use of satellite images to analyse extreme heat or to systematically track flood damage. After a morning introduction to the topic, tools, and data/satellite imagery sources, participants will spend the afternoon working on one of two hands-on tracks:

Track 1: Flooding

Participants will learn how to use Copernicus Emergency Management Service (EMS) to retrieve flood data manually and via the Copernicus EMS API, clean and structure the data, and calculate flood extent and impact across agriculture, infrastructure, ecosystems, and population areas. They will learn how to link impacted areas to the EU’s statistical regions (using NUTS classifications).

Track 2: Extreme heat

Participants will learn to navigate USGS Earth Explorer to find and download imagery for land surface temperature (LST) analysis. Using R for spatial analysis, they’ll identify which neighborhoods in their region are most affected by heat. They will also use auxiliary data to examine the impact of different land types on heat. Participants are welcome to bring their own socioeconomic or location data (e.g., nursing homes, kindergartens) for investigation.

We will assume you have some experience with data in spreadsheets, but you do not need any prior knowledge of coding in R or Python or satellite imagery. You will leave with the skills (and the data!) needed to work on a hyper-local or national stories about the effects of extreme heat and flooding. These methodologies will also help you create a blueprint for other investigations, which would make good use of satellite imagery.

Key skills learned:

Learn the basics of R (navigating RStudio, importing data, tidyverse, ggplot) and Python (using the pandas library to load, filter, and analyze data, combine datasets, and export your results);

Basics of geodata (file types, projections, NUTS system);

Where to access free, high-quality satellite imagery, and common limitations of using it in investigations

Navigating satellite imagery portals and databases for natural disasters (depends on the choice of Copernicus EMS or Landsat).

A separate ticket is required to attend this masterclass. If you already have a conference ticket and would like to attend but haven't yet purchased a masterclass ticket, please contact us at [email protected]

Heat waves in Europe are increasing in frequency and intensity. People and economies are under pressure: extreme heat is costly for agriculture and deadly for people. At the same time, floods are among the most frequent and damaging natural disasters in Europe – yet understanding their true impact remains difficult.

In this session, participants will learn the skills necessary to make use of satellite images to analyse extreme heat or to systematically track flood damage. After a morning introduction to the topic, tools, and data/satellite imagery sources, participants will spend the afternoon working on one of two hands-on tracks:

Track 1: Flooding

Participants will learn how to use Copernicus Emergency Management Service (EMS) to retrieve flood data manually and via the Copernicus EMS API, clean and structure the data, and calculate flood extent and impact across agriculture, infrastructure, ecosystems, and population areas. They will learn how to link impacted areas to the EU’s statistical regions (using NUTS classifications).

Track 2: Extreme heat

Participants will learn to navigate USGS Earth Explorer to find and download imagery for land surface temperature (LST) analysis. Using R for spatial analysis, they’ll identify which neighborhoods in their region are most affected by heat. They will also use auxiliary data to examine the impact of different land types on heat. Participants are welcome to bring their own socioeconomic or location data (e.g., nursing homes, kindergartens) for investigation.

We will assume you have some experience with data in spreadsheets, but you do not need any prior knowledge of coding in R or Python or satellite imagery. You will leave with the skills (and the data!) needed to work on a hyper-local or national stories about the effects of extreme heat and flooding. These methodologies will also help you create a blueprint for other investigations, which would make good use of satellite imagery.

Key skills learned:

Learn the basics of R (navigating RStudio, importing data, tidyverse, ggplot) and Python (using the pandas library to load, filter, and analyze data, combine datasets, and export your results);

Basics of geodata (file types, projections, NUTS system);

Where to access free, high-quality satellite imagery, and common limitations of using it in investigations

Navigating satellite imagery portals and databases for natural disasters (depends on the choice of Copernicus EMS or Landsat).

A separate ticket is required to attend this masterclass. If you already have a conference ticket and would like to attend but haven't yet purchased a masterclass ticket, please contact us at [email protected]

Heat waves in Europe are increasing in frequency and intensity. People and economies are under pressure: extreme heat is costly for agriculture and deadly for people. At the same time, floods are among the most frequent and damaging natural disasters in Europe – yet understanding their true impact remains difficult.

In this session, participants will learn the skills necessary to make use of satellite images to analyse extreme heat or to systematically track flood damage. After a morning introduction to the topic, tools, and data/satellite imagery sources, participants will spend the afternoon working on one of two hands-on tracks:

Track 1: Flooding

Participants will learn how to use Copernicus Emergency Management Service (EMS) to retrieve flood data manually and via the Copernicus EMS API, clean and structure the data, and calculate flood extent and impact across agriculture, infrastructure, ecosystems, and population areas. They will learn how to link impacted areas to the EU’s statistical regions (using NUTS classifications).

Track 2: Extreme heat

Participants will learn to navigate USGS Earth Explorer to find and download imagery for land surface temperature (LST) analysis. Using R for spatial analysis, they’ll identify which neighborhoods in their region are most affected by heat. They will also use auxiliary data to examine the impact of different land types on heat. Participants are welcome to bring their own socioeconomic or location data (e.g., nursing homes, kindergartens) for investigation.

We will assume you have some experience with data in spreadsheets, but you do not need any prior knowledge of coding in R or Python or satellite imagery. You will leave with the skills (and the data!) needed to work on a hyper-local or national stories about the effects of extreme heat and flooding. These methodologies will also help you create a blueprint for other investigations, which would make good use of satellite imagery.

Key skills learned:

Learn the basics of R (navigating RStudio, importing data, tidyverse, ggplot) and Python (using the pandas library to load, filter, and analyze data, combine datasets, and export your results);

Basics of geodata (file types, projections, NUTS system);

Where to access free, high-quality satellite imagery, and common limitations of using it in investigations

Navigating satellite imagery portals and databases for natural disasters (depends on the choice of Copernicus EMS or Landsat).

Every investigative journalist has faced the same bottleneck. What would I find if I could check all of them: all the company registrations, all the addresses, all the permits? Until recently, answering that question required weeks of scripting. In this session, we introduce a faster way: directing an AI coding agent to build investigative data pipelines on demand. Participants will direct an agent to pull data from a public source, clean it, and turn it into an interactive visualization, all without writing code manually. The approach is applicable to a range of investigative beats, from financial crime and corruption to environmental accountability and lobbying networks.

To follow along, participants should have a basic understanding of web technologies, but no programming experience is needed. After attending this session, participants will be able to direct an AI coding agent to build a data pipeline, from raw data to interactive visualization, and apply this methodology to their own investigative questions. Participants should have a laptop with a modern web browser. We will provide API keys and access credentials during the session. Detailed setup instructions will be shared via a GitHub repository before the workshop.

In this session, participants will take an archive of podcast episodes and other documents, and set up some cloud infrastructure to analyse the files using open source transcription, text extraction and generative AI tooling. The aim is to equip attendees with the skills to rapidly perform bulk operations on large troves of data by leveraging cloud platforms. By the end of the workshop participants will be have a pipeline that can answer questions like 'which podcast episodes have instances of greenwashing in them'. At The Guardian, we have used these techniques in two recent investigations. When investigating the Free Birth Society we needed to perform analysis on hundreds of hours of audio files. When the Epstein files were released we had to try and extract meaning out of millions of unstructured text documents. By making use of simple cloud tools (queues and instances) we were able to process hundreds of files in parallel whilst retaining control of the data.

Participants should have some experience of using the command line. All cloud accounts will be provided. After attending this session, participants will be able to use the cloud to quickly analyse large numbers of documents and media files. Participants using Windows could save some time by setting up WSL https://learn.microsoft.com/en-us/windows/wsl/install

Learn approaches to tooling and infrastructure that allow every member of your newsroom to participate in your AI experiments, along with how to test and track both improvements and disappointments along the way!

In this workshop, we'll look at: Python libraries that can turn tiny snippets of code or prompts into shareable web apps (Gradio, Streamlit), platforms that allow non-technical users to build evaluations and experiment on their own (Braintrust, n8n), and approaches to models and tooling that provide long-term value and flexibility when selecting services and providers (Pydantic, OpenRouter).

Whether you're looking to use AI for investigative work or to ease the copy-editing burden, increasing participation across the newsroom can help discover limitations and inspiration, along with easing anxieties over automation. To get the most out of this session, participants should have a working knowledge of Python.

After attending this session, participants will have a suite of approaches to bring non-technical members of their newsroom into their AI processes. Participants should have Jupyter installed or a Google account to work in the cloud.

Most investigative workflows still rely on manually juggling dozens of tools. In this session, we'll walk through a live demo of a semi-automated pipeline built for real casework: web search and archiving with Playwright, face extraction, reverse image search, database cross-referencing with Telegram bots, social media analysis, and structured reporting via Obsidian mcp. All of this is orchestrated by Claude, an AI layer you can teach your own investigative methodology. At the end, participants will work through a simplified case using a workflow of their own.

Before the session, please install: Python, Claude Code. This session will teach participants to combine several smaller OSINT tools so they work together efficiently without requiring much manual effort. No special tools needed

"Agentic AI" is all the rage, but what does it offer beyond traditional LLM workflows? In this hands-on session we'll answer this question (and more) while leveraging Python's Pydantic AI library to build a start-to-finish agentic AI workflow.

Participants will learn how agents work, when they're useful, how to build custom tools, and options for tracing and evaluation. You'll leave able to write agentic workflows to extract information from texts, do semi-autonomous research, and deliver clean, structured results.

Basic experience with Python/LLMs is helpful but not required. After attending this session, participants will be able to understand when and how to apply agentic approaches to problems. Participants should have Python/Jupyter installed or a Google account for working in the cloud.

Projects involving FOI requests to multiple bodies often create significant challenges, from different file formats and data trapped in PDFs, to organisations providing data in different structures and different levels of detail. To get the big picture often requires data extraction, cleaning, reshaping, and checking.

In this session, we will share a series of tips and tools used to manage one project — including vibe coding with AI — which can be used to make any multi-response FOI project more efficient and accurate. No prior knowledge is required. By the end of this session, attendees should be able to design a data structure for an FOI project, use a range of tools, including AI, to extract, reshape, clean, and combine data from FOI responses, and design a data validation process to check AI outputs.

You will need a laptop with Google Drive and an account with an AI tool such as ChatGPT, Gemini, Claude, or Copilot. Installing Tabula and Open Refine will help you get more out of the session.

Tampermonkey is an age-old browser extension that allows you to inject scripts and stylesheets into any web page, turning the web into your personal playground. We'll look at how to customize your CMS with DIY features, add "Download all" buttons to paginated websites, automate tedious processes like filling out forms and redesign websites however you'd like. Best of all, Tampermonkey scripts are saveable and sharable, allowing you to give other members of your newsroom superpowers without fiddling with distributing extensions or asking them to run Python scripts. To follow along, participants should be able to install extensions in their web browser of choice.

Journalists often run into data that is visible on a website but impossible to download directly: a table buried in a government page, a list of public records, or search results that change with every query. Writing a full scraper can be time-consuming and technically demanding for what is often a one-time task.

This session introduces three lightweight approaches that cover most of these cases: reading a table directly from a page using pandas, downloading raw HTML and parsing it into a dataframe and pulling data through network requests. These techniques are practical tools for everyday newsroom situations. Participants will take home a GitHub repository with a working notebook to try on their own data, though some adaptation will be needed to apply it to different websites.

The three approaches vary in complexity. Basic Python knowledge is enough to follow along, but participants with more experience will be able to go further, and the code can be adapted with the help of an LLM.

You have a folder of documents and you want to extract data points from each one. And the data isn't in a structured table with neat rows and columns either. Here's where string functions and regular expressions can help. The demonstration will be in R but the skills are generic to all languages.

Say hello to Natural PDF, a new Python library for wrangling PDFs that's focused on usability and feature-completeness. Process PDFs with scraping-like selectors and spatially-aware queries, asking for "the red alphanumeric string" or "the content below the big Summary header." Beyond the basics, Natural PDF is also full of modern conveniences like table detection, multiple OCR engines, and citation-aware LLM data extraction.

To get the most out of this session, participants should have experience with Python and struggling with terrible PDFs.

Google search is all the same since 1996? No, Google does change over time, but so slow that most people will not notice. The session will give you an update about recent changes (i.e. in the last 4-5 years), will point at workarounds where necessary and will show you what is really new and useful. Towards the end of the session it will give you some advanced Google dorks for immediate journalistic use, but also inspire you to build your own dorks and how to combine LLMs and Google searches.

To follow along, the participants should have used google operators before. After attending the session, you will have an up to date knowledge of Googles web search and other tools for journalistic use.

A Google account can be useful, but is not a must-have.

What really happens behind the glossy surface of OnlyFans — and how do you investigate an industry built on secrecy, money, and blurred consent? After more than a year of reporting on the hidden economy around OnlyFans across five countries, I will take participants inside a world of agencies, chatters, fake identities, reseller servers, and leaked-content markets that platforms never talk about.

This session reveals how intimacy becomes a trap: how creators are manipulated by intermediaries, how their content is redistributed through underground Discord and Telegram networks, and how money flows through a web of crypto, burner accounts, and anonymous middlemen. Using findings from the cross-border investigation, From OnlyFans to OnlyScams, we will show how to uncover what the platform hides.

What attendees will take away:
- Investigating online sex-work ecosystems ethically and safely
- Techniques for undercover reporting, digital tracing, scraping, and mapping hidden networks
- Following payment trails and linking them to real actors
- Interviewing victims and insiders without causing additional harm
- Turning a sensitive, high-risk digital investigation into a powerful cross-border story

This is a practical session for journalists who want to explore one of the fastest-growing — and least understood — shadow economies on the internet.