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AI Watch

R: Research and Development

Elaborates on the EU’s capabilities and strengths in research, covering various aspects of R&D activity and network of collaborations. 

This dimension of the AI Watch Index investigates those activities that contribute to the development of the technological domain.

From a socio-economic point of view these activities are crucial in order to make progress happen, and so to improve technology maturity, and to reach (or maintain) economic competitiveness.

Research and development (R&D) activities considered in this work are of three types:

  • frontier research activities (i.e., scientific publications in top international conferences),
  • filing of patent applications,
  • participation in EC-funded projects.

To avoid a biased vision of AI R&D, the latter are considered only when addressing the EU Member States. This dimension considers several network indicators, as information exchange and interactions between multiple actors are essential for the blooming of innovation.

R&D activity


R1: AI players in AI R&D

The R1 – AI players in AI R&D indicator shows the presence of players active in AI R&D activities, to assess how distinct geographical areas are involved in the technical development of AI.


R2: AI R&D activity score

This indicator assesses the level of involvement of different geographical areas in AI R&D activities, by considering the weight of the activities developed by AI economic players and therefore taking into account the relative importance of players.

Network of collaborations


R4: Peer-to-peer collaborations

This indicator measures how many collaborations are developed by AI players from each geographical area (or Member State) by type of R&D activity: patent applications, frontier research publications and, when analysing the EU, EC-funded projects.


R5: Strategic position in the network of collaborations

This indicator provides a metric for players’ capacity to act as connecting bridges between other players, aggregated at the geographical level, to assess the influence that a geographical area can exert on other areas thanks to the structure of collaborations in which they are involved.