Data science and EU politics – My on-going work using data science approaches to study EU politics in collaboration with colleagues from the Connected_Politics Lab and the Insight Centre for Data Analytics at UCD demonstrates that combining machine learning, text analysis, and network analysis is a fruitful approach to examine speeches in the European Parliament, tweets, press releases from the Commission, and ECB speeches (Cross & Greene 2019; 2017a; 2017b; Cross et al. 2019; Greene & Cross, 2017). This on-going work demonstrates the usefulness of the proposed methods for extracting reliable and valid measures of concepts of theoretical interest from different text corpora. Tracking legislative amendments – In a related and complementary project, I completed two studies demonstrating that minimum edit distances can capture legislative amendments in a replicable and reliable fashion (Cross et al. 2019; Cross & Hermansson 2017). The results demonstrate that the new method replicates and significantly extends existing hand-coding methods in terms of measurement reliably and efficiently, and provides new insights into the drivers of legislative amendments. EU Transparency – In my work on EU transparency (Cross 2013b; 2014; Cross & Boelstad 2015), I utilise the EU’s online databases extensively to provide insight into the manner in which transparency and censorship influences actor behaviour and outcomes. This has provided me with a strong understanding of the promises and pitfalls of using online databases as a basis for analysing political institutions, and the challenges associated with creating large-scale text corpora amenable to further analysis. Decision-making in the European Union (DEU) II – I was involved in the extension of the highly successful Decision-making in the EU study (Thomson et al. 2012) and have used these data to publish work on the role of policy interventions in negotiations, and their impact upon legislative outcomes and bargaining success (Cross 2014; 2013a; 2013b; 2012).