The innovative dimension of the research training programmes under H2020-MSCA-ITN1: a methodological approach to track, measure and analyse innovative aspects and provide policy-feedback conclusions. [version 1; peer review: 3 approved]

• Published in: F1000 research Vol. 12; p. 1020
• Main Authors: Bitsios, Ioannis, Martone, Fabrizio, Ricci, Riccardo, Arfi, Audrey
• Format: Journal Article
• Language: English
• Published: London Faculty of 1000 Ltd 01.01.2023; F1000 Research Limited; F1000 Research Ltd
• Subjects: Alumni associations; Bibliometrics; Curie, Marie (1867-1934); eng; Feedback; H2020; Indicators; Innovation; Innovations; Marie Sklodowska-Curie Actions -Innovative Training Networks (MSCA-ITN); Methodology; Policy-feedback; Researchers; Science; Success; Europe; Innovation; H2020; Policy-feedback; Methodology; Indicators; Marie Sklodowska-Curie Actions -Innovative Training Networks (MSCA-ITN)
• ISSN: 2046-1402; 2046-1402
• DOI: 10.12688/f1000research.138482.1

Background: Innovative research training programmes funded by the European Union are essential for the forging of highly skilled researchers to tackle, via breakthrough ideas and solutions, the challenges of our society. Being able to track, measure and analyse innovative aspects of the Marie Sklodowska-Curie Actions, Innovative Training Networks under the Horizon2020 funding scheme enables the impact assessment of such programmes, while filtering best practices and the generated knowledge that could ultimately breed and create further innovation. In parallel, it helps the identification of areas for improvement, the understanding of new needs to be accommodated and the co-design and implementation of EU funding policy activities to further promote innovation and excellence for researchers across Europe and beyond. Methods: In this study, a novel methodological approach is proposed for tracking and analysing innovation, using a representative sample of projects. Basic innovation indicators are examined and considered from the existing literature and from the applicable Multi-Annual Framework Programme Horizon2020. Additional ones are defined, complemented by questionnaires/surveys findings, to capture innovative aspects for which the standard indicators do not apply. Data mining and data visualization tools are used for the collection and processing of data. Innovation Radar2 (IR) reports and HorizonResultsBooster3 services are also engaged for the cross-validation of the identified innovative aspects. Results/Conclusions: The study provides first-level input for policy-feedback activities, by identifying scientific domains and EU countries that may potentially require more attention for innovation generation. It highlights domains that are front-runners and can be used as examples or best practices for under-represented domains in terms of innovative outputs. Collaboration with organisations, defined as medium/high innovators, can increase innovation generation and success in future projects. Best practices are collected to serve as references for designing impactful future training programmes. The excellence of the H2020-MSCA-ITN actions is confirmed via the generated innovations.