This project uses state-of-the-art machine learning techniques to study conceptual change over time. It builds on the seminal BERT infrastructure that has, in recent years, caused a breakthrough in the computational understanding of language. With the help of the Dutch National Library’s massive archive of historical newspapers, magazines and books, it is possible to show how Dutch words have changed their meaning and connotation in public discourse from the Second World War until the present day. The project aims to study the conceptual history of one of the most urgent issues of today: global sustainability. Together with the NL eScience Center Amsterdam, this project will re-train the base model to create multiple, chronologically ordered models based on historical Dutch textual data. This technique will help to trace continuities and breaks in this discourse to, ultimately, gain insights into the forces at play when it comes to sustainability. 

• Martijn Kleppe (National Library The Hague)

This project experiments with replication of historical research to achieve a more open and reliable knowledge production in the humanities. Although the data repositories that historians use are often open, the way historians actually select and interpret their data that in turn validate their findings remains opaque. By replicating cornerstone studies from three historical subdisciplines, we attempt to make this process more transparent and publicly accountable and thus in line with the principles of Open Science. The envisioned results are (i) replications, (ii) a methodology for doing replication in history and (iii) recommendations to ensure replicability in the humanities.

• dr. Auke Rijpma
• dr. Pieter Huistra

This project aims to use the sentiment pipeline to trace historical shifts in awareness. Many debates - whether about climate change, genetically modified foodstuffs, or #metoo - hint at a high form of awareness in our current global society. It is, however, far less evident where these sentiments are rooted in and how they have evolved over time. We aim to investigate this for the Dutch case by focusing on a genealogical study of central modifiers of awareness - (un)healthy, (not) harmful, etc. We are particularly interested in the roles of multinationals like Shell and Unilever as agents of change in these debates.

To do so, this project develops a historical sentiment analysis pipeline that is based on machine learning. The use of this text mining approach in historical scholarship has been hampered by the manner in which "sentiments" are usually implemented in thesauri and binary systems of sentiment qualifications (positive - negative; based on static lists). With this project we aim to make sentiment analysis more historically dynamic and context-specific.

• dr. Gertjan Plets
• prof. dr. Jaap Verheul

The Netherlands is a “petrostate”: since the colonial period oil and gas has defined both economy and politics. Today, remnants of fossil fuel extraction are disappearing. Therefore, it is urgent to decide how we want to remember gas and oil as cultural heritage. However, to date the history of gas and oil extraction has written by the industry itself. In this project students and historians of the UU will rewrite Dutch fossil fuel history. Open science mechanisms such as podcasts, online platforms and community meetings will ensure that this contested past is explored in collaboration with the wider public.

• dr. Gertjan Plets

This project engages a group of BA and MA history students with the aim of teaming up with local residents of Utrecht to investigate some of the untold stories of this city. Under professional supervision, the students will present their investigation in the form of a (Dutch) podcast: www.domcast.nl. The reason for this is threefold: first, podcasts are able to reach a much larger public than conventional forms of scholarly output can. Second, they give listeners new and highly promising opportunities to actively engage with ongoing investigations. Third, they provide students with useful and transferrable skills.