This study seeks to understand how the map of science has evolved and to identify the forces driving that evolution. By examining changes in these maps over time, we track the development of science through the growing interdisciplinarity of subject categories and the way they cluster together.

We integrate multiple classification schemes from Web of Science products to build a multilevel framework that connects journals, categories, groups, and broad domains. Using Journal Citation Reports (JCR) data from 2011, 2016, and 2024, we construct two types of maps of science: one based on citation relationships and another based on the sharing of journals across categories. We then examine how these maps evolve, identify factors influencing their development, and analyze how knowledge percolates through a multilevel structure.

The map of science has evolved from a bipolar structure into a more interconnected, rounded triangular configuration. In 2016, Arts & Humanities and the Social Sciences comprised a single cluster; by 2024, they had separated, while Biological Sciences and Medical Sciences, once distinct, had merged into a unified cluster. At the same time, categories such as education, special education, and applied psychology shifted toward hearing and speech pathology, forming a new special education cluster at the intersection of the social sciences, biomedicine, and technology. The expansion of Arts & Humanities categories, along with the addition of new categories in the Journal Citation Reports (JCR), and the resulting growth in interdisciplinarity across categories and journals, has played a key role in reshaping the overall map of science. Although categories within a cluster may disperse across multiple groups and broader domains through knowledge percolation in a multilevel system, they nonetheless remain relatively concentrated.

As with any empirical investigation, this study has some limitations. Most notably, our analysis is based on only three points in time and relies on a single data source, namely the Web of Science (WoS). Yet, we described our results in far more detail than is usually done.

Maps of science serve as tools for navigating the research landscape, helping to inform strategic investments and shape future research directions. Examining how these maps evolve over time and how such changes influence research trajectories is a central concern of the science of science.

Mapping clusters to their respective groups and broad categories reveals a hierarchical classification system in which clusters extend beyond disciplinary boundaries. Overlaps among categories, groups and broad categories indicate that scientific knowledge percolates through traditional field divisions.

This study explores how EU integration, globalisation, and geopolitical disruptions have influenced scientific collaboration among European countries at different stages of EU membership. Specifically, it distinguishes between the EU-14 (long-standing Western and Southern European member states prior to the 2004 enlargement), the EU-13 (the Central and Eastern European countries that joined the EU in 2004 or later), and EU candidate countries, reflecting differing historical trajectories, institutional capacities, and levels of integration into European and global research networks.

Using articles from the Scopus database, the study analyses Relative Intensity of Collaboration (RIC) among three distinct groups of countries: EU-14, EU-13, and EU candidate countries, as well as with China, Latin America, the UK, the USA and Russia.

Findings indicate increasing integration within European groups and with global partners, yet persistent hierarchical structures remain. EU-14 countries form the core of the network, exhibiting stable and cohesive collaboration, including with the UK despite Brexit. EU-13 countries occupy an intermediate position, showing moderate collaboration with EU-14 but stronger collaboration within their own group, with EU candidate countries and Russia. EU candidate countries demonstrate even weaker integration with EU-14, focusing on intra-group ties and links with EU-13 and Russia. RIC peaks in 2012 and 2018 for EU-13 and EU candidate countries correspond to Horizon 2020 and Horizon Europe cycles, highlighting the role of EU Framework Programmes. Collaboration with Russia increased following 2014 and only marginally declined after 2022. For EU-14, it exceeds collaboration with the USA. Collaboration with China remains limited due to network and cultural constraints, with similar intensity across all three groups. Overall, funding and policy initiatives are critical for stable international collaboration.

The analysis is limited by the Scopus database coverage.

Findings suggest that to strengthen the EU’s scientific position, policymakers should prioritise targeted funding and strategic initiatives that bridge collaboration gaps between EU-13, EU-14, EU candidate countries, and global partners.

This study provides a comprehensive, longitudinal analysis of European scientific collaboration, highlighting hierarchical structures, the differential roles of EU-14, EU-13, and candidate countries, and the resilience of networks with global partners such as the UK and Russia, while linking collaboration dynamics to EU Framework Programmes.

Identifying technological opportunities in the field of sixth-generation mobile communications (6G) is crucial for research institutions and enterprises seeking to anticipate technological trajectories, and for policymakers formulating forward-looking innovation strategies.

Grounded in the notion that scientific knowledge drives technological R&D, this study integrates scientific publications and patent data to develop a framework for identifying technological opportunities in the 6G domain. We first employ a pretrained SBERT model to generate vector representations of mixed paper–patent data, followed by dimensionality reduction and visualization to characterize the structural features of science and technology. We then apply the HDBSCAN algorithm to identify thematic clusters across both corpora. Based on the clustering results and the semantic relationships between scientific themes and patented technologies, we construct two indicators – scientific knowledge reserve rate and technological invention competitiveness – to capture scientific and technological dynamics, respectively. A portfolio map is used to systematically identify potential technological opportunities within 6G.

The results demonstrate the feasibility and effectiveness of the proposed framework: eight potential technological opportunities are identified and validated to possess strong technological promise, confirming the robustness of our approach.

The analysis relies on scientific publications from WOS and patent data from DII, which may not fully capture the entire landscape of emerging 6G knowledge.

The framework provides actionable insights for 6G technology planning, enabling R&D institutions, enterprises, and policymakers to anticipate emerging trajectories and allocate innovation resources more effectively.

This study advances technology opportunity identification by bridging scientific research and technological development through semantic analysis. It offers a novel integrative framework that uncovers deep science–technology linkages and supports the cultivation of a global 6G innovation ecosystem and next-generation intelligent communication infrastructures.