The growing importance of public innovation has been manifested through the creation of policy labs: spaces for policy experimentation and innovation that work for or within a government entity. The rise of this phenomenon in Europe was evidenced by the creation of a policy lab by the European Commission (EC) in 2016 and the publication by the EC of a report identifying policy labs and their influencers in Europe. Public innovation is increasingly based on national and international networks, giving rise to complex ecosystems involving participation by multiple actors from countries with different administrative approaches. Our study uses social network analysis of these labs’ Twitter profile data to map the European Union’s (EU) public innovation ecosystem and identify the major influencers. Policy labs and their influencers are analyzed by administration style by using a large geographical database. The results reveal a complex global network of influencers and a strong predominance of the Anglo-Saxon administration style. From an EU perspective, our systematic analysis of influence is particularly important in the post-Brexit context, helping to foster a genuine public innovation ecosystem that is both autonomous and interconnected with the aim of facing challenges such as the Sustainable Development Agenda and COVID-19 crisis recovery.

Wikipedia is one of the most visited websites in the world and is also a frequent subject of scientific research. However, the analytical possibilities of Wikipedia information have not yet been analyzed considering at the same time both a large volume of pages and attributes. The main objective of this work is to offer a methodological framework and an open knowledge graph for the informetric large-scale study of Wikipedia. Features of Wikipedia pages are compared with those of scientific publications to highlight the (dis)similarities between the two types of documents. Based on this comparison, different analytical possibilities that Wikipedia and its various data sources offer are explored, ultimately offering a set of metrics meant to study Wikipedia from different analytical dimensions. In parallel, a complete dedicated data set of the English Wikipedia was built (and shared) following a relational model. Finally, a descriptive case study is carried out on the English Wikipedia data set to illustrate the analytical potential of the knowledge graph and its metrics.